From b47cf426e29cbd9274aee7662d48b67cec4a47b0 Mon Sep 17 00:00:00 2001 From: Halina Do-Linh <88209419+hdolinh@users.noreply.github.com> Date: Mon, 31 Jul 2023 16:20:11 -0700 Subject: [PATCH 001/104] new GHA (#325) * refactor GHA main.yml (#317) * refactor main.yml; test using branch as an input * test new script triggers build in RRC main.yml * return to previous code cleaned up main.yml * test branch variable; expect gha to break main.yml * small change to test branch variable in gha * remove pull request details step main.yml * small change to test branch var gha * correct format main.yml * small change to test branch var gha * extract branch name from PR main.yml * small text change to test branch var gha * match branch var names main.yml * use client_payload to reference develop branch in RRC main.yml * small text change to text branch var gha * test change * use $GITHUB_HEAD_REF to get branch name main.yml * change on from push to PR main.yml * type closed for PR main.yml * small change to test branch var gha * test rm content type main.yml * test credentials for nceas-training gha * test bearer as auth main.yml * change to test bad credentials * revert to old code to test bad credentials main.yml * small change to test bad credentials * test connection main.yml * small change to test connection * small change to test connection * small change to test connection * revert to old code to test main.yml * small change to test * test w push main.yml * rm pr main.yml * test branch var * Update main.yml * Update main.yml * Update README.md * update github api request main.yml * Update README.md * refactor http dispatch request main.yml * Update README.md * change from PR to push main.yml * test client payload main.yml * change format main.yml * Update main.yml * change format main.yml * change syntax main.yml * Update main.yml * Update main.yml * fix syntax main.yml * Update main.yml * use Repository Dispatch GHA (#324) * add content-type to curl main.yml * revert to working state main.yml * rm quotes main.yml * syntax for echo client payload main.yml * check client payload main.yml * syntax main.yml * test syntax main.yml * test built repo dispatch GHA main.yml * fix syntax main.yml * test syntax main.yml * syntax main.yml * syntax to call branch main.yml * syntax main.yml * toJSON() for format main.yml * rm $ main.yml * syntax for gh var main.yml * test client payload echo main.yml * syntax main.yml * Update version main.yml * rm check main.yml --- .github/workflows/main.yml | 27 +++++++++------------------ materials/sections/text-analysis.Rmd | 3 ++- 2 files changed, 11 insertions(+), 19 deletions(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 2501e0de..6e14057c 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -1,26 +1,17 @@ name: Dispatcher on: - pull_request: + push: branches: ['**'] - types: - - closed jobs: dispatch: runs-on: ubuntu-latest - steps: - - name: Check name of branch - run: | - echo $GITHUB_HEAD_REF - - - name: Dispatch to RRC - run: | - curl -L \ - -X POST \ - -H "Accept: application/vnd.github+json" \ - -H "Authorization: token ${{ secrets.RRCOURSE }}" \ - https://api.github.com/repos/nceas/repro-research-course/dispatches \ - -d '{"event_type": "quarto", "client_payload": {"branch": "$GITHUB_HEAD_REF"}}' - - + steps: + - name: Repository Dispatch + uses: peter-evans/repository-dispatch@v2.1.1 + with: + token: ${{ secrets.RRCOURSE }} + repository: nceas/repro-research-course + event-type: quarto + client-payload: '{"branch": "${{ github.ref_name }}"}' \ No newline at end of file diff --git a/materials/sections/text-analysis.Rmd b/materials/sections/text-analysis.Rmd index 06325299..9f84d24d 100644 --- a/materials/sections/text-analysis.Rmd +++ b/materials/sections/text-analysis.Rmd @@ -1,7 +1,8 @@ --- author: "Jeanette Clark" --- -## Extracting Data for Text Analysis + +## Extracting Data for Text Analysis ### Learning Objectives From f7d1d74003353bd99871e58a3c34fcfb49d448c3 Mon Sep 17 00:00:00 2001 From: hdolinh Date: Mon, 31 Jul 2023 16:27:29 -0700 Subject: [PATCH 002/104] text change to test gha --- materials/sections/text-analysis.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/materials/sections/text-analysis.Rmd b/materials/sections/text-analysis.Rmd index 9f84d24d..f3d22047 100644 --- a/materials/sections/text-analysis.Rmd +++ b/materials/sections/text-analysis.Rmd @@ -2,7 +2,7 @@ author: "Jeanette Clark" --- -## Extracting Data for Text Analysis +## Extracting Data for Text Analysis ### Learning Objectives From e7bced664c63a6ca8c4e9a98e1322865078d6de2 Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Wed, 2 Aug 2023 12:01:39 -0700 Subject: [PATCH 003/104] starting new qmd for data vis lesson for delta --- materials/sections/visualization-delta.qmd | 642 ++++++++++++++++++ .../sections/visualization-ggplot-leaflet.qmd | 12 +- materials/session_06.qmd | 2 +- 3 files changed, 649 insertions(+), 7 deletions(-) create mode 100644 materials/sections/visualization-delta.qmd diff --git a/materials/sections/visualization-delta.qmd b/materials/sections/visualization-delta.qmd new file mode 100644 index 00000000..b72258ac --- /dev/null +++ b/materials/sections/visualization-delta.qmd @@ -0,0 +1,642 @@ +## Learning Objectives {.unnumbered} + +- The basics of the `ggplot2` package to create static plots +- How to use `ggplot2`'s `theme` abilities to create publication-grade graphics +- The basics of the `leaflet` package to create interactive maps + +## Overview + +`ggplot2` is a popular package for visualizing data in R. +From the [home page](http://ggplot2.tidyverse.org/): + +> `ggplot2` is a system for declaratively creating graphics, based on The Grammar of Graphics. You provide the data, tell `ggplot2` how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details. + + +It's been around for years and has pretty good documentation and tons of example code around the web (like on [StackOverflow](https://stackoverflow.com/questions/tagged/ggplot2)). The goal of this lesson is to introduce you to the basic components of working with `ggplot2` and inspire you to go and explore this awesome resource for visualizing your data. + +::: callout-important + +## `ggplot2` vs base graphics in R vs others +There are many different ways to plot your data in R. All of them work! However, `ggplot2` excels at making complicated plots easy and easy plots simple enough + +Base R graphics (`plot()`, `hist()`, etc) can be helpful for simple, quick and dirty plots. `ggplot2` can be used for almost everything else. + +::: + +Let's dive into creating and customizing plots with `ggplot2`. + +::: callout-tip +## Setup + +0. Make sure you’re in the right project (`training_{USERNAME}`) and use the `Git` workflow by `Pull`ing to check for any changes. Then, create a new Quarto document, delete the default text, and save this document. + +1. Load the packages we'll need: + +```{r load_packages} +#| message: false +#| warning: false + +library(readr) +library(dplyr) +library(tidyr) +library(forcats) # makes working with factors easier +library(ggplot2) +library(leaflet) # interactive maps +library(DT) # interactive tables +library(scales) # scale functions for visualization +``` + + +2. Load the data directly from the [KNB Data Repository: Daily salmon escapement counts from the OceanAK database, Alaska, 1921-2017](https://knb.ecoinformatics.org/view/doi%3A10.5063%2FP26WJJ). Navegate to the link above, hover over the "Download" button for the `ADFG_fisrtAttempt_reformatted.csv`, right click, and select "Copy Link". + +```{r} +#| message: false +#| warning: false + +escape <- read_csv("https://knb.ecoinformatics.org/knb/d1/mn/v2/object/urn%3Auuid%3Af119a05b-bbe7-4aea-93c6-85434dcb1c5e") +``` + +3. Learn about the data. For this session we are going to be working with data on **daily salmon escapement counts in Alaska**. Check out [the documentation](https://knb.ecoinformatics.org/view/doi%3A10.5063%2FP26WJJ). + +4. Finally, let's explore the data we just read into our working environment. + +```{r} +#| eval: false +## Check out column names +colnames(escape) + +## Peak at each column and class +glimpse(escape) + +## From when to when +range(escape$sampleDate) + +## How frequent? +head(escape$sampleDate) +tail(escape$sampleDate) + +## Which species? +unique(escape$Species) +``` + +::: + + + + + + +## Getting the data ready + +It is more frequent than not, that we need to do some wrangling before we can plot our data the way we want to. Now that we have read out data and have done some exploration, we'll put our data wrangling skills to practice to get our data in the desired format. + +::: callout-note +### Exercise + +- Calculate the annual escapement by `Species` and `SASAP.Region`, +- Filter the main 5 salmon species (Chinook, Sockeye, Chum, Coho and Pink) + +::: + +```{r mean_esc} +#| message: false +annual_esc <- escape %>% + separate(sampleDate, c("Year", "Month", "Day"), sep = "-") %>% + mutate(Year = as.numeric(Year)) %>% + group_by(Species, SASAP.Region, Year) %>% + summarize(escapement = sum(DailyCount)) %>% + filter(Species %in% c("Chinook", "Sockeye", "Chum", "Coho", "Pink")) + +head(annual_esc) +``` + +The chunk above used a lot of the dplyr commands that we've used, and some that are new. The `separate()` function is used to divide the sampleDate column up into Year, Month, and Day columns, and then we use `group_by()` to indicate that we want to calculate our results for the unique combinations of species, region, and year. We next use `summarize()` to calculate an escapement value for each of these groups. Finally, we use a filter and the `%in%` operator to select only the salmon species. + +## Plotting with `ggplot2` + +### Essentials components +First, we’ll cover some `ggplot2` basics to create the foundation of our plot. Then, we’ll add on to make our great customized data visualization. + +::: {.callout-caution icon=false} + +### The basics + +1. Indicate we are using `ggplot()` (call the `ggplot2::ggplot()` function) +2. What data do we want to plot? (`data = my_data`) +3. What is my mapping aesthetics? What variables do we want to plot? (define using`aes()` function) +4. Define the geometry of our plot. This specifies the type of plot we're making (use `geom_*()` to indicate the type of plot e.g: point, bar, etc.) + +**Note** +To add layers to our plot, for example, additional geometries/aesthetics and theme elements or any `ggplot` object we use `+`. + +::: + + +For example, let's plot total escapement by species. We will show this by creating the same plot in 3 slightly different ways. Each of the options below have the essential pieces of a `ggplot`. + + +```{r basic_esc_plot} +#| eval: false + +## Option 1 - data and mapping called in the ggplot() function +ggplot(data = annual_esc, + aes(x = Species, y = escapement)) + + geom_col() + +## Option 2 - data called in ggplot function; mapping called in geom +ggplot(data = annual_esc) + + geom_col(aes(x = Species, y = escapement)) + + +## Option 3 - data and mapping called in geom +ggplot() + + geom_col(data = annual_esc, + aes(x = Species, y = escapement)) + +``` + +They all will create the same plot: + +```{r esc_plot} +#| echo: false + +ggplot(data = annual_esc, + aes(x = Species, y = escapement)) + + geom_col() +``` + +### Looking at different geoms + +Having the basic structure with the essential components in mind, we can easily change the type of graph by updating the `geom_*()`. + + +::: callout-important +### `ggplot2` and the pipe operator + +Just like in `dplyr` and `tidyr`, we can also pipe a `data.frame` directly into the first argument of the `ggplot` function using the `%>%` operator. + +This can certainly be convenient, but use it carefully! Combining too many data-tidying or subsetting operations with your `ggplot` call can make your code more difficult to debug and understand. +::: + +Next, we will use the pipe operator to pass into `ggplot()` a filtered version of `annual_esc`, and make a plot with different geometries. + + +**Boxplot** + +```{r boxplot_ex} +annual_esc %>% + filter(Year == 1974, + Species %in% c("Chum", "Pink")) %>% + ggplot(aes(x = Species, y = escapement)) + + geom_boxplot() +``` + + +**Violin plot** + +```{r violin_plot} +annual_esc %>% + filter(Year == 1974, + Species %in% c("Chum", "Pink")) %>% + ggplot(aes(x = Species, y = escapement)) + + geom_violin() +``` + + +**Line and point** + +```{r line_poin_plot} +annual_esc %>% + filter(Species == "Sockeye", + SASAP.Region == "Bristol Bay") %>% + ggplot(aes(x = Year, y = escapement)) + + geom_line() + + geom_point() +``` + + +### Customizing our plot + +Let's go back to our base bar graph. What if we want our bars to be blue instead of gray? You might think we could run this: + +```{r fill_blue} +ggplot(annual_esc, + aes(x = Species, y = escapement, + fill = "blue")) + + geom_col() +``` + +Why did that happen? + +Notice that we tried to set the fill color of the plot inside the mapping aesthetic call. What we have done, behind the scenes, is create a column filled with the word "blue" in our data frame, and then mapped it to the `fill` aesthetic, which then chose the default fill color of red. + +What we really wanted to do was just change the color of the bars. If we want do do that, we can call the color option in the `geom_col()` function, **outside** of the mapping aesthetics function call. + +```{r fill_blue_geom} +ggplot(annual_esc, + aes(x = Species, y = escapement)) + + geom_col(fill = "blue") +``` + +What if we did want to map the color of the bars to a variable, such as region. `ggplot()` is really powerful because we can easily get this plot to visualize more aspects of our data. + +```{r} +ggplot(annual_esc, + aes(x = Species, y = escapement, + fill = SASAP.Region)) + + geom_col() +``` + +::: {.callout-caution icon=false} + +#### Keep in mind + +- If you want to map a variable onto a graph aesthetic (e.g., point color should be based on a specific region), put it within `aes()`. + +- If you want to update your plot base on a constant (e.g. “Make ALL the points BLUE”), you can add the information directly to the relevant geom_ layer. + +::: + + +#### Creating multiple plots + +We know that in the graph we just plotted, each bar includes escapements for multiple years. Let's leverage the power of `ggplot` to plot more aspects of our data in one plot. + +We are going to plot escapement by species over time, from 2000 to 2016, for each region. + +An easy way to plot another aspect of your data is using the function `facet_wrap()`. This function takes a mapping to a variable using the syntax `~{variable_name}`. The `~` (tilde) is a model operator which tells `facet_wrap()` to model each unique value within `variable_name` to a facet in the plot. + +The default behavior of facet wrap is to put all facets on the same x and y scale. You can use the `scales` argument to specify whether to allow different scales between facet plots (e.g `scales = "free_y"` to free the y axis scale). You can also specify the number of columns using the `ncol = ` argument or number of rows using `nrow =`. + + +```{r facet_base_plot} +## Subset with data from years 2000 to 2016 + +annual_esc_2000s <- annual_esc %>% + filter(Year %in% c(2000:2016)) + +## Quick check +unique(annual_esc_2000s$Year) + + +## Plot with facets +ggplot(annual_esc_2000s, + aes(x = Year, + y = escapement, + color = Species)) + + geom_line() + + geom_point() + + facet_wrap( ~ SASAP.Region, + scales = "free_y") + +``` + + +#### Setting ggplot themes + +Now let's work on making this plot look a bit nicer. We are going to" + +- Add a title using `ggtitle()` +- Adjust labels using `ylab()` +- Include a built in theme using `theme_bw()` + +There are a wide variety of built in themes in `ggplot` that help quickly set the look of the plot. Use the RStudio autocomplete `theme_` `` to view a list of theme functions. + + +```{r theme_bw_plot} +ggplot(annual_esc_2000s, + aes(x = Year, + y = escapement, + color = Species)) + + geom_line() + + geom_point() + + facet_wrap( ~ SASAP.Region, + scales = "free_y") + + ylab("Escapement") + + ggtitle("Annual Salmon Escapement by Region") + + theme_bw() +``` + +You can see that the `theme_bw()` function changed a lot of the aspects of our plot! The background is white, the grid is a different color, etc. There are lots of other built in themes like this that come with the `ggplot2` package. + +::: callout-note + +#### Exercise + +Use the RStudio auto complete, the `ggplot2` documentation, a cheat sheet, or good old Google to find other built in themes. Pick out your favorite one and add it to your plot. + +::: + + +```{r} +#| code-fold: true +#| code-summary: "Themes" +#| eval: false + +## Useful baseline themes are +theme_minimal() +theme_light() +theme_classic() +``` + + + +The built in theme functions (`theme_*()`) change the default settings for many elements that can also be changed individually using the`theme()` function. The `theme()` function is a way to further fine-tune the look of your plot. This function takes MANY arguments (just have a look at `?theme`). Luckily there are many great `ggplot` resources online so we don't have to remember all of these, just Google ["ggplot cheat sheet"](https://www.google.com/search?q=ggplot+cheat+sheet&rlz=1C5CHFA_enUS989US989&oq=ggplot+chea&aqs=chrome.0.0i433i512j69i57j0i512l2j0i22i30i395l2.1681j1j7&sourceid=chrome&ie=UTF-8) and find one you like. + +Let's look at an example of a `theme()` call, where we change the position of the legend from the right side to the bottom, and remove its title. + +```{r} +ggplot(annual_esc_2000s, + aes(x = Year, + y = escapement, + color = Species)) + + geom_line() + + geom_point() + + facet_wrap( ~ SASAP.Region, + scales = "free_y") + + ylab("Escapement") + + ggtitle("Annual Salmon Escapement by Region") + + theme_light() + + theme(legend.position = "bottom", + legend.title = element_blank()) +``` + + +Note that the `theme()` call needs to come after any built-in themes like `theme_bw()` are used. Otherwise, `theme_bw()` will likely override any theme elements that you changed using `theme()`. + +You can also save the result of a series of `theme()` function calls to an object to use on multiple plots. This prevents needing to copy paste the same lines over and over again! + +```{r save_theme} +my_theme <- theme_light() + + theme(legend.position = "bottom", + legend.title = element_blank()) +``` + +So now our code will look like this: + + +```{r} +ggplot(annual_esc_2000s, + aes(x = Year, + y = escapement, + color = Species)) + + geom_line() + + geom_point() + + facet_wrap( ~ SASAP.Region, + scales = "free_y") + + ylab("Escapement") + + ggtitle("Annual Salmon Escapement by Region") + + my_theme +``` + + +::: callout-note +#### Exercise + + 1. Using whatever method you like, figure out how to rotate the x-axis tick labels to a 45 degree angle. + +**Hint:** You can start by looking at the documentation of the function by typing `?theme()` in the console. And googling is a great way to figure out how to do the modifications you want to your plot. + +2. What changes do you expect to see in your plot by adding the following line of code? Discuss with your neighbor and then try it out! + +`scale_x_continuous(breaks = seq(2000,2016, 2))` + +::: + + +```{r} +#| code-fold: true +#| code-summary: "Answer" +#| eval: false + +## Useful baseline themes are +ggplot(annual_esc_2000s, + aes(x = Year, + y = escapement, + color = Species)) + + geom_line() + + geom_point() + + scale_x_continuous(breaks = seq(2000, 2016, 2)) + + facet_wrap( ~ SASAP.Region, + scales = "free_y") + + ylab("Escapement") + + ggtitle("Annual Salmon Escapement by Region") + + my_theme + + theme(axis.text.x = element_text(angle = 45, + vjust = 0.5)) + +``` + + +#### Smarter tick labels using `scales` + +Fixing tick labels in `ggplot` can be super annoying. The y-axis labels in the plot above don't look great. We could manually fix them, but it would likely be tedious and error prone. + +The `scales` package provides some nice helper functions to easily rescale and relabel your plots. Here, we use `scale_y_continuous()` from `ggplot2`, with the argument `labels`, which is assigned to the function name `comma`, from the `scales` package. This will format all of the labels on the y-axis of our plot with comma-formatted numbers. + +```{r} + +ggplot(annual_esc_2000s, + aes(x = Year, + y = escapement, + color = Species)) + + geom_line() + + geom_point() + + scale_x_continuous(breaks = seq(2000, 2016, 2)) + + scale_y_continuous(labels = comma) + + facet_wrap( ~ SASAP.Region, + scales = "free_y") + + ylab("Escapement") + + ggtitle("Annual Salmon Escapement by Region") + + my_theme + + theme(axis.text.x = element_text(angle = 45, + vjust = 0.5)) + +``` + + +You can also save all your code into an object in your working environment by assigning a name to the `ggplot()` code. + + +```{r} +annual_region_plot <- ggplot(annual_esc_2000s, + aes(x = Year, + y = escapement, + color = Species)) + + geom_line() + + geom_point() + + scale_x_continuous(breaks = seq(2000, 2016, 2)) + + scale_y_continuous(labels = comma) + + facet_wrap( ~ SASAP.Region, + scales = "free_y") + + ylab("Escapement") + + xlab("\nYear") + + ggtitle("Annual Salmon Escapement by Region") + + my_theme + + theme(axis.text.x = element_text(angle = 45, + vjust = 0.5)) +``` + +And then call your object to see your plot. + +```{r} +annual_region_plot +``` + + +#### Reordering things + +`ggplot()` loves putting things in alphabetical order. But more frequent than not, that's not the order you actually want things to be plotted if you have categorical groups. Let’s find some total years of data by species for Kuskokwim. + +```{r n_year_data} +#| warning: false +#| message: false + +## Number Years of data for each salmon species at Kuskokwim +n_years <- annual_esc %>% + group_by(SASAP.Region, Species) %>% + summarize(n = n()) %>% + filter(SASAP.Region == "Kuskokwim") +``` + +Now let's plot this using `geom_bar()`. + +```{r} +## base plot +ggplot(n_years, + aes(x = Species, + y = n)) + + geom_bar(aes(fill = Species), + stat = "identity") +``` + +Now, let's apply some of the customizations we have seen so far and learn some new ones. + +```{r} + +## Reordering, flipping coords and other customization +ggplot(n_years, + aes( + x = fct_reorder(Species, n), + y = n, + fill = Species + )) + + geom_bar(stat = "identity") + + coord_flip() + + theme_minimal() + + ## another way to customize labels + labs(x = "Species", + y = "Number of years of data", + title = "Number of years of escapement data for salmon species in Kuskokwim") + + theme(legend.position = "none") + +``` + + +#### Saving plots + +Saving plots using `ggplot` is easy! The `ggsave()` function will save either the last plot you created, or any plot that you have saved to a variable. You can specify what output format you want, size, resolution, etc. See `?ggsave()` for documentation. + +```{r} +#| eval: false +ggsave("figures/nyears_data_kus.jpg", width = 8, height = 6, units = "in") +``` + + +We can also save our facet plot showing annual escapements by region calling the plot's object. + +```{r} +#| eval: false +ggsave(annual_region_plot, "figures/annual_esc_region.png", width = 12, height = 8, units = "in") +``` + + +## Interactive visualization + +### Tables with `DT` + +Now that we know how to make great static visualizations, let's introduce two other packages that allow us to display our data in interactive ways. These packages really shine when used with GitHub Pages, so at the end of this lesson we will publish our figures to the website we created earlier. + +First let's show an interactive table of unique sampling locations using `DT`. +Write a `data.frame` containing unique sampling locations with no missing values using two new functions from `dplyr` and `tidyr`: `distinct()` and `drop_na()`. + +```{r uniq_locations} +locations <- escape %>% + distinct(Location, Latitude, Longitude) %>% + drop_na() +``` + +And display it as an interactive table using `datatable()` from the `DT` package. + +```{r display_locations} +datatable(locations) +``` + +### Maps with `leaflet` + +Similar to `ggplot2`, you can make a basic `leaflet` map using just a couple lines of code. Note that unlike `ggplot2`, the `leaflet` package uses pipe operators (`%>%`) and not the additive operator (`+`). + +The `addTiles()` function without arguments will add base tiles to your map from [OpenStreetMap](https://www.openstreetmap.org/). `addMarkers()` will add a marker at each location specified by the latitude and longitude arguments. Note that the `~` symbol is used here to model the coordinates to the map (similar to `facet_wrap()` in `ggplot`). + + +```{r simple_map} +leaflet(locations) %>% + addTiles() %>% + addMarkers( + lng = ~ Longitude, + lat = ~ Latitude, + popup = ~ Location + ) +``` + +
+ +You can also use `leaflet` to import Web Map Service (WMS) tiles. Here is an example that utilizes the General Bathymetric Map of the Oceans [(GEBCO)](https://www.gebco.net/) WMS tiles. In this example, we also demonstrate how to create a more simple circle marker, the look of which is explicitly set using a series of style-related arguments. + + +```{r} +leaflet(locations) %>% + addWMSTiles( + "https://www.gebco.net/data_and_products/gebco_web_services/web_map_service/mapserv?request=getmap&service=wms&BBOX=-90,-180,90,360&crs=EPSG:4326&format=image/jpeg&layers=gebco_latest&width=1200&height=600&version=1.3.0", + layers = 'GEBCO_LATEST', + attribution = "Imagery reproduced from the GEBCO_2022 Grid, WMS 1.3.0 GetMap, www.gebco.net" + ) %>% + addCircleMarkers( + lng = ~ Longitude, + lat = ~ Latitude, + popup = ~ Location, + radius = 5, + # set fill properties + fillColor = "salmon", + fillOpacity = 1, + # set stroke properties + stroke = T, + weight = 0.5, + color = "white", + opacity = 1 + ) +``` + +
+ +Leaflet has a ton of functionality that can enable you to create some beautiful, functional maps with relative ease. [Here](https://pages.github.nceas.ucsb.edu/NCEAS/sasap-data/language_vis.html) is an example of some we created as part of the [State of Alaskan Salmon and People (SASAP) project](https://www.nceas.ucsb.edu/workinggroups/state-alaskan-salmon-and-people-sasap), created using the same tools we showed you here. This map hopefully gives you an idea of how powerful the combination of R Markdown and GitHub Pages can be. + + +## Publish the Data Visualization lesson to your webpage + +::: callout-tip +### Steps + +1. Save the `Rmd` you have been working on for this lesson. +2. "Knit" the `Rmd`. This is a good way to test if everything in your code is working. +3. Go to your `index.Rmd` and the link to the `html` file with this lesson's content. +4. Save and render `index.Rmd` to an `html`. +5. Use the `Git` workflow: ```Stage > Commit > Pull > Push``` + +::: + +## `ggplot2` Resources + +- [Why not to use two axes, and what to use instead: The case against dual axis charts](https://blog.datawrapper.de/dualaxis/) by Lisa Charlotte Rost. +- [Customized Data Visualization in `ggplot2`](https://allisonhorst.github.io/eds221-day8-comp/data_viz.html#3_ggplot2_Basics_Review) by Allison Horst. +- [A `ggplot2` tutorial for beautiful plotting in R](https://www.cedricscherer.com/2019/08/05/a-ggplot2-tutorial-for-beautiful-plotting-in-r/#legends) by Cedric Scherer. diff --git a/materials/sections/visualization-ggplot-leaflet.qmd b/materials/sections/visualization-ggplot-leaflet.qmd index 8d0f7d94..54ab02da 100644 --- a/materials/sections/visualization-ggplot-leaflet.qmd +++ b/materials/sections/visualization-ggplot-leaflet.qmd @@ -29,7 +29,7 @@ Let's dive into creating and customizing plots with `ggplot2`. ::: callout-tip ## Setup -0. Make sure you’re in the right project (`training_{USERNAME}`) and use the `Git` workflow by `Pull`ing to check for any changes. Then, create a new R Markdown document and remove the default text. +0. Make sure you’re in the right project (`training_{USERNAME}`) and use the `Git` workflow by `Pull`ing to check for any changes. Then, create a new Quarto document, delete the default text, and save this document. 1. Load the packages we'll need: @@ -40,15 +40,15 @@ Let's dive into creating and customizing plots with `ggplot2`. library(readr) library(dplyr) library(tidyr) -library(forcats) +library(forcats) # makes working with factors easier library(ggplot2) -library(leaflet) -library(DT) -library(scales) +library(leaflet) # interactive maps +library(DT) # interactive tables +library(scales) # scale functions for visualization ``` -2. Load the data table directly from the [KNB Data Repository: Daily salmon escapement counts from the OceanAK database, Alaska, 1921-2017](https://knb.ecoinformatics.org/view/doi%3A10.5063%2FP26WJJ). Navegate to the link above, hover over the "Download" button for the `ADFG_fisrtAttempt_reformatted.csv`, right click, and select "Copy Link". +2. Load the data directly from the [KNB Data Repository: Daily salmon escapement counts from the OceanAK database, Alaska, 1921-2017](https://knb.ecoinformatics.org/view/doi%3A10.5063%2FP26WJJ). Navegate to the link above, hover over the "Download" button for the `ADFG_fisrtAttempt_reformatted.csv`, right click, and select "Copy Link". ```{r} #| message: false diff --git a/materials/session_06.qmd b/materials/session_06.qmd index 5b747ec1..e458fa0a 100644 --- a/materials/session_06.qmd +++ b/materials/session_06.qmd @@ -3,4 +3,4 @@ title: "Data Visualization in R" title-block-banner: true --- -{{< include /sections/visualization-ggplot-leaflet.qmd >}} \ No newline at end of file +{{< include /sections/visualization-delta.qmd >}} \ No newline at end of file From 470f3f1b102e8f6a842a60a8ccbc60a62a0596e4 Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Thu, 3 Aug 2023 10:32:33 -0700 Subject: [PATCH 004/104] data link to data viz lesson --- materials/sections/visualization-delta.qmd | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/materials/sections/visualization-delta.qmd b/materials/sections/visualization-delta.qmd index b72258ac..e70c0869 100644 --- a/materials/sections/visualization-delta.qmd +++ b/materials/sections/visualization-delta.qmd @@ -1,5 +1,7 @@ ## Learning Objectives {.unnumbered} +**REVISE** + - The basics of the `ggplot2` package to create static plots - How to use `ggplot2`'s `theme` abilities to create publication-grade graphics - The basics of the `leaflet` package to create interactive maps @@ -12,7 +14,7 @@ From the [home page](http://ggplot2.tidyverse.org/): > `ggplot2` is a system for declaratively creating graphics, based on The Grammar of Graphics. You provide the data, tell `ggplot2` how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details. -It's been around for years and has pretty good documentation and tons of example code around the web (like on [StackOverflow](https://stackoverflow.com/questions/tagged/ggplot2)). The goal of this lesson is to introduce you to the basic components of working with `ggplot2` and inspire you to go and explore this awesome resource for visualizing your data. +It's been around for years and has pretty good documentation and tons of example code around the web (like on [StackOverflow](https://stackoverflow.com/questions/tagged/ggplot2)). The goal of this lesson is to explain the fundamentals of how `ggplot2` work, introduce useful functions for customizing your plots and inspire you to go and explore this awesome resource for visualizing your data. ::: callout-important @@ -47,13 +49,14 @@ library(scales) # scale functions for visualization ``` -2. Load the data directly from the [KNB Data Repository: Daily salmon escapement counts from the OceanAK database, Alaska, 1921-2017](https://knb.ecoinformatics.org/view/doi%3A10.5063%2FP26WJJ). Navegate to the link above, hover over the "Download" button for the `ADFG_fisrtAttempt_reformatted.csv`, right click, and select "Copy Link". +2. Load the data directly from the [EDI Data Repository: Sacramento-San Joaquin Delta Socioecological Monitoring ](https://portal.edirepository.org/nis/mapbrowse?packageid=edi.587.1). Navegate to the link above, scroll down and under *Resources*, hover over the "Download" button for the "Socioecological monitoring data", right click, and select "Copy Link Address". ```{r} #| message: false #| warning: false -escape <- read_csv("https://knb.ecoinformatics.org/knb/d1/mn/v2/object/urn%3Auuid%3Af119a05b-bbe7-4aea-93c6-85434dcb1c5e") +delta_visits <- read_csv("https://portal.edirepository.org/nis/dataviewer?packageid=edi.587.1&entityid=cda8c1384af0089b506d51ad8507641f") + ``` 3. Learn about the data. For this session we are going to be working with data on **daily salmon escapement counts in Alaska**. Check out [the documentation](https://knb.ecoinformatics.org/view/doi%3A10.5063%2FP26WJJ). From e147ed0cf3ccf50e6f3d6c128ab23b73ff26fc7f Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Thu, 3 Aug 2023 12:14:32 -0700 Subject: [PATCH 005/104] adding janitor, updating initial plots to delta data varaibles --- materials/sections/visualization-delta.qmd | 177 +++++++++++---------- 1 file changed, 95 insertions(+), 82 deletions(-) diff --git a/materials/sections/visualization-delta.qmd b/materials/sections/visualization-delta.qmd index e70c0869..e1be198a 100644 --- a/materials/sections/visualization-delta.qmd +++ b/materials/sections/visualization-delta.qmd @@ -46,6 +46,7 @@ library(ggplot2) library(leaflet) # interactive maps library(DT) # interactive tables library(scales) # scale functions for visualization +library(janitor) # expedite cleaning and exploring data ``` @@ -55,65 +56,81 @@ library(scales) # scale functions for visualization #| message: false #| warning: false -delta_visits <- read_csv("https://portal.edirepository.org/nis/dataviewer?packageid=edi.587.1&entityid=cda8c1384af0089b506d51ad8507641f") +delta_visits <- read_csv("https://portal.edirepository.org/nis/dataviewer?packageid=edi.587.1&entityid=cda8c1384af0089b506d51ad8507641f") %>% + clean_names() ``` -3. Learn about the data. For this session we are going to be working with data on **daily salmon escapement counts in Alaska**. Check out [the documentation](https://knb.ecoinformatics.org/view/doi%3A10.5063%2FP26WJJ). +3. Learn about the data. For this session we are going to be working with data on **Socioecological Monitoring on the Sacramento-San Joaquin Delta**. Check out [the documentation](https://portal.edirepository.org/nis/mapbrowse?packageid=edi.587.1). 4. Finally, let's explore the data we just read into our working environment. ```{r} #| eval: false ## Check out column names -colnames(escape) +colnames(delta_visits) ## Peak at each column and class -glimpse(escape) +glimpse(delta_visits) ## From when to when -range(escape$sampleDate) +range(delta_visits$date) -## How frequent? -head(escape$sampleDate) -tail(escape$sampleDate) +## First and last rows +head(delta_visits$date) +tail(delta_visits$date) -## Which species? -unique(escape$Species) +## Which time of day? +unique(delta_visits$time_of_day) ``` ::: +## Getting the data ready +It is more frequent than not, that we need to do some wrangling before we can plot our data the way we want to. Now that we have read out data and have done some exploration, we'll put our data wrangling skills to practice to get our data in the desired format. +With the [tidy data principles](https://learning.nceas.ucsb.edu/2023-06-delta/session_09.html#tidy-data) in mind. Is this data tidy? + +1. Every column is a variable. +2. Every row is an observation. +3. Every cell is a single value. + +`ggplot2` for the most part likes data input to be in a long format (aka "tidy"). So let go ahead and make this data frame long instead of wide. Do you remember the name of the function we can use? + +```{r} + +visits_long <- delta_visits %>% + pivot_longer(cols = c("sm_boat", "med_boat", "lrg_boat", "bank_angler", "scientist", "cars"), + names_to = "visitor_type", + values_to = "quantity") %>% + rename(restore_loc = eco_restore_approximate_location) %>% + select(-notes) + +``` -## Getting the data ready -It is more frequent than not, that we need to do some wrangling before we can plot our data the way we want to. Now that we have read out data and have done some exploration, we'll put our data wrangling skills to practice to get our data in the desired format. ::: callout-note ### Exercise -- Calculate the annual escapement by `Species` and `SASAP.Region`, -- Filter the main 5 salmon species (Chinook, Sockeye, Chum, Coho and Pink) +- Calculate the daily visits by `visit_type` and `restore_loc``, ::: ```{r mean_esc} #| message: false -annual_esc <- escape %>% - separate(sampleDate, c("Year", "Month", "Day"), sep = "-") %>% - mutate(Year = as.numeric(Year)) %>% - group_by(Species, SASAP.Region, Year) %>% - summarize(escapement = sum(DailyCount)) %>% - filter(Species %in% c("Chinook", "Sockeye", "Chum", "Coho", "Pink")) +daily_visits_loc <- visits_long %>% + group_by(restore_loc, date, visitor_type) %>% + summarise(daily_visits = sum(quantity)) + +head(daily_visits_loc) -head(annual_esc) ``` -The chunk above used a lot of the dplyr commands that we've used, and some that are new. The `separate()` function is used to divide the sampleDate column up into Year, Month, and Day columns, and then we use `group_by()` to indicate that we want to calculate our results for the unique combinations of species, region, and year. We next use `summarize()` to calculate an escapement value for each of these groups. Finally, we use a filter and the `%in%` operator to select only the salmon species. +The chunk above usees some of the dplyr functions that we've used in the past. We use `group_by()` to indicate that we want to calculate our results for the unique combinations of type of visit, restoration location proximity, and day. We next use `summarize()` to calculate an daily visit value for each of these groups. Note we use the `%in%` operator to pipe in the result of one command as an argument to the next one. ## Plotting with `ggplot2` @@ -135,26 +152,27 @@ To add layers to our plot, for example, additional geometries/aesthetics and the ::: -For example, let's plot total escapement by species. We will show this by creating the same plot in 3 slightly different ways. Each of the options below have the essential pieces of a `ggplot`. +For example, let's plot total daily visits by restoration location. We will show this by creating the same plot in 3 slightly different ways. Each of the options below have the essential pieces of a `ggplot`. ```{r basic_esc_plot} #| eval: false ## Option 1 - data and mapping called in the ggplot() function -ggplot(data = annual_esc, - aes(x = Species, y = escapement)) + +ggplot(data = daily_visits_loc, + aes(x = restore_loc, y = daily_visits))+ geom_col() + ## Option 2 - data called in ggplot function; mapping called in geom -ggplot(data = annual_esc) + - geom_col(aes(x = Species, y = escapement)) +ggplot(data = daily_visits_loc) + + geom_col(aes(x = restore_loc, y = daily_visits)) ## Option 3 - data and mapping called in geom ggplot() + - geom_col(data = annual_esc, - aes(x = Species, y = escapement)) + geom_col(data = daily_visits_loc, + aes(x = restore_loc, y = daily_visits)) ``` @@ -163,8 +181,8 @@ They all will create the same plot: ```{r esc_plot} #| echo: false -ggplot(data = annual_esc, - aes(x = Species, y = escapement)) + +ggplot(data = daily_visits_loc, + aes(x = restore_loc, y = daily_visits)) + geom_col() ``` @@ -181,16 +199,18 @@ Just like in `dplyr` and `tidyr`, we can also pipe a `data.frame` directly into This can certainly be convenient, but use it carefully! Combining too many data-tidying or subsetting operations with your `ggplot` call can make your code more difficult to debug and understand. ::: -Next, we will use the pipe operator to pass into `ggplot()` a filtered version of `annual_esc`, and make a plot with different geometries. +We will use the pipe operator to pass into `ggplot()` a filtered version of `daily_visit_loc`, and make a plot with different geometries. **Boxplot** +_Note: These examples are to demonstrate case uses of wrangling function proir to plotting. They are not necessarily plotting best practices._ ```{r boxplot_ex} -annual_esc %>% - filter(Year == 1974, - Species %in% c("Chum", "Pink")) %>% - ggplot(aes(x = Species, y = escapement)) + +daily_visits_loc %>% + separate(date, c("year", "month", "day"), sep = "-") %>% + filter(daily_visits < 30, + visitor_type %in% c("sm_boat", "med_boat", "lrg_boat")) %>% + ggplot(aes(x = visitor_type, y = daily_visits)) + geom_boxplot() ``` @@ -198,25 +218,27 @@ annual_esc %>% **Violin plot** ```{r violin_plot} -annual_esc %>% - filter(Year == 1974, - Species %in% c("Chum", "Pink")) %>% - ggplot(aes(x = Species, y = escapement)) + +daily_visits_loc %>% + separate(date, c("year", "month", "day"), sep = "-") %>% + filter(daily_visits < 30, + visitor_type %in% c("sm_boat", "med_boat", "lrg_boat")) %>% + ggplot(aes(x = visitor_type, y = daily_visits)) + geom_violin() ``` -**Line and point** + ### Customizing our plot @@ -224,7 +246,7 @@ Let's go back to our base bar graph. What if we want our bars to be blue instead ```{r fill_blue} ggplot(annual_esc, - aes(x = Species, y = escapement, + aes(x = Species, y = delta_visitsment, fill = "blue")) + geom_col() ``` @@ -237,7 +259,7 @@ What we really wanted to do was just change the color of the bars. If we want do ```{r fill_blue_geom} ggplot(annual_esc, - aes(x = Species, y = escapement)) + + aes(x = Species, y = delta_visitsment)) + geom_col(fill = "blue") ``` @@ -245,7 +267,7 @@ What if we did want to map the color of the bars to a variable, such as region. ```{r} ggplot(annual_esc, - aes(x = Species, y = escapement, + aes(x = Species, y = delta_visitsment, fill = SASAP.Region)) + geom_col() ``` @@ -263,9 +285,9 @@ ggplot(annual_esc, #### Creating multiple plots -We know that in the graph we just plotted, each bar includes escapements for multiple years. Let's leverage the power of `ggplot` to plot more aspects of our data in one plot. +We know that in the graph we just plotted, each bar includes delta_visitsments for multiple years. Let's leverage the power of `ggplot` to plot more aspects of our data in one plot. -We are going to plot escapement by species over time, from 2000 to 2016, for each region. +We are going to plot delta_visitsment by species over time, from 2000 to 2016, for each region. An easy way to plot another aspect of your data is using the function `facet_wrap()`. This function takes a mapping to a variable using the syntax `~{variable_name}`. The `~` (tilde) is a model operator which tells `facet_wrap()` to model each unique value within `variable_name` to a facet in the plot. @@ -285,7 +307,7 @@ unique(annual_esc_2000s$Year) ## Plot with facets ggplot(annual_esc_2000s, aes(x = Year, - y = escapement, + y = delta_visitsment, color = Species)) + geom_line() + geom_point() + @@ -309,30 +331,21 @@ There are a wide variety of built in themes in `ggplot` that help quickly set th ```{r theme_bw_plot} ggplot(annual_esc_2000s, aes(x = Year, - y = escapement, + y = delta_visitsment, color = Species)) + geom_line() + geom_point() + facet_wrap( ~ SASAP.Region, scales = "free_y") + - ylab("Escapement") + - ggtitle("Annual Salmon Escapement by Region") + + ylab("delta_visitsment") + + ggtitle("Annual Salmon delta_visitsment by Region") + theme_bw() ``` You can see that the `theme_bw()` function changed a lot of the aspects of our plot! The background is white, the grid is a different color, etc. There are lots of other built in themes like this that come with the `ggplot2` package. -::: callout-note - -#### Exercise - -Use the RStudio auto complete, the `ggplot2` documentation, a cheat sheet, or good old Google to find other built in themes. Pick out your favorite one and add it to your plot. - -::: - ```{r} -#| code-fold: true #| code-summary: "Themes" #| eval: false @@ -351,14 +364,14 @@ Let's look at an example of a `theme()` call, where we change the position of th ```{r} ggplot(annual_esc_2000s, aes(x = Year, - y = escapement, + y = delta_visitsment, color = Species)) + geom_line() + geom_point() + facet_wrap( ~ SASAP.Region, scales = "free_y") + - ylab("Escapement") + - ggtitle("Annual Salmon Escapement by Region") + + ylab("delta_visitsment") + + ggtitle("Annual Salmon delta_visitsment by Region") + theme_light() + theme(legend.position = "bottom", legend.title = element_blank()) @@ -381,14 +394,14 @@ So now our code will look like this: ```{r} ggplot(annual_esc_2000s, aes(x = Year, - y = escapement, + y = delta_visitsment, color = Species)) + geom_line() + geom_point() + facet_wrap( ~ SASAP.Region, scales = "free_y") + - ylab("Escapement") + - ggtitle("Annual Salmon Escapement by Region") + + ylab("delta_visitsment") + + ggtitle("Annual Salmon delta_visitsment by Region") + my_theme ``` @@ -415,15 +428,15 @@ ggplot(annual_esc_2000s, ## Useful baseline themes are ggplot(annual_esc_2000s, aes(x = Year, - y = escapement, + y = delta_visitsment, color = Species)) + geom_line() + geom_point() + scale_x_continuous(breaks = seq(2000, 2016, 2)) + facet_wrap( ~ SASAP.Region, scales = "free_y") + - ylab("Escapement") + - ggtitle("Annual Salmon Escapement by Region") + + ylab("delta_visitsment") + + ggtitle("Annual Salmon delta_visitsment by Region") + my_theme + theme(axis.text.x = element_text(angle = 45, vjust = 0.5)) @@ -441,7 +454,7 @@ The `scales` package provides some nice helper functions to easily rescale and r ggplot(annual_esc_2000s, aes(x = Year, - y = escapement, + y = delta_visitsment, color = Species)) + geom_line() + geom_point() + @@ -449,8 +462,8 @@ ggplot(annual_esc_2000s, scale_y_continuous(labels = comma) + facet_wrap( ~ SASAP.Region, scales = "free_y") + - ylab("Escapement") + - ggtitle("Annual Salmon Escapement by Region") + + ylab("delta_visitsment") + + ggtitle("Annual Salmon delta_visitsment by Region") + my_theme + theme(axis.text.x = element_text(angle = 45, vjust = 0.5)) @@ -464,7 +477,7 @@ You can also save all your code into an object in your working environment by as ```{r} annual_region_plot <- ggplot(annual_esc_2000s, aes(x = Year, - y = escapement, + y = delta_visitsment, color = Species)) + geom_line() + geom_point() + @@ -472,9 +485,9 @@ annual_region_plot <- ggplot(annual_esc_2000s, scale_y_continuous(labels = comma) + facet_wrap( ~ SASAP.Region, scales = "free_y") + - ylab("Escapement") + + ylab("delta_visitsment") + xlab("\nYear") + - ggtitle("Annual Salmon Escapement by Region") + + ggtitle("Annual Salmon delta_visitsment by Region") + my_theme + theme(axis.text.x = element_text(angle = 45, vjust = 0.5)) @@ -530,7 +543,7 @@ ggplot(n_years, ## another way to customize labels labs(x = "Species", y = "Number of years of data", - title = "Number of years of escapement data for salmon species in Kuskokwim") + + title = "Number of years of delta_visitsment data for salmon species in Kuskokwim") + theme(legend.position = "none") ``` @@ -546,7 +559,7 @@ ggsave("figures/nyears_data_kus.jpg", width = 8, height = 6, units = "in") ``` -We can also save our facet plot showing annual escapements by region calling the plot's object. +We can also save our facet plot showing annual delta_visitsments by region calling the plot's object. ```{r} #| eval: false @@ -564,7 +577,7 @@ First let's show an interactive table of unique sampling locations using `DT`. Write a `data.frame` containing unique sampling locations with no missing values using two new functions from `dplyr` and `tidyr`: `distinct()` and `drop_na()`. ```{r uniq_locations} -locations <- escape %>% +locations <- delta_visits %>% distinct(Location, Latitude, Longitude) %>% drop_na() ``` From b08cf8d1fa2dfbd1c1d2f803de000eee9ca11991 Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Thu, 3 Aug 2023 12:23:05 -0700 Subject: [PATCH 006/104] setting codechucks to eval = false to avoid issues --- materials/sections/visualization-delta.qmd | 28 +++++++++++++++------- 1 file changed, 20 insertions(+), 8 deletions(-) diff --git a/materials/sections/visualization-delta.qmd b/materials/sections/visualization-delta.qmd index e1be198a..814f2924 100644 --- a/materials/sections/visualization-delta.qmd +++ b/materials/sections/visualization-delta.qmd @@ -245,6 +245,7 @@ daily_visits_loc %>% Let's go back to our base bar graph. What if we want our bars to be blue instead of gray? You might think we could run this: ```{r fill_blue} +#| eval: false ggplot(annual_esc, aes(x = Species, y = delta_visitsment, fill = "blue")) + @@ -266,6 +267,7 @@ ggplot(annual_esc, What if we did want to map the color of the bars to a variable, such as region. `ggplot()` is really powerful because we can easily get this plot to visualize more aspects of our data. ```{r} +#| eval: false ggplot(annual_esc, aes(x = Species, y = delta_visitsment, fill = SASAP.Region)) + @@ -295,6 +297,7 @@ The default behavior of facet wrap is to put all facets on the same x and y scal ```{r facet_base_plot} +#| eval: false ## Subset with data from years 2000 to 2016 annual_esc_2000s <- annual_esc %>% @@ -329,6 +332,7 @@ There are a wide variety of built in themes in `ggplot` that help quickly set th ```{r theme_bw_plot} +#| eval: false ggplot(annual_esc_2000s, aes(x = Year, y = delta_visitsment, @@ -362,6 +366,7 @@ The built in theme functions (`theme_*()`) change the default settings for many Let's look at an example of a `theme()` call, where we change the position of the legend from the right side to the bottom, and remove its title. ```{r} +#| eval: false ggplot(annual_esc_2000s, aes(x = Year, y = delta_visitsment, @@ -392,6 +397,7 @@ So now our code will look like this: ```{r} +#| eval: false ggplot(annual_esc_2000s, aes(x = Year, y = delta_visitsment, @@ -451,7 +457,7 @@ Fixing tick labels in `ggplot` can be super annoying. The y-axis labels in the p The `scales` package provides some nice helper functions to easily rescale and relabel your plots. Here, we use `scale_y_continuous()` from `ggplot2`, with the argument `labels`, which is assigned to the function name `comma`, from the `scales` package. This will format all of the labels on the y-axis of our plot with comma-formatted numbers. ```{r} - +#| eval: false ggplot(annual_esc_2000s, aes(x = Year, y = delta_visitsment, @@ -475,6 +481,7 @@ You can also save all your code into an object in your working environment by as ```{r} +#| eval: false annual_region_plot <- ggplot(annual_esc_2000s, aes(x = Year, y = delta_visitsment, @@ -496,6 +503,7 @@ annual_region_plot <- ggplot(annual_esc_2000s, And then call your object to see your plot. ```{r} +#| eval: false annual_region_plot ``` @@ -505,6 +513,7 @@ annual_region_plot `ggplot()` loves putting things in alphabetical order. But more frequent than not, that's not the order you actually want things to be plotted if you have categorical groups. Let’s find some total years of data by species for Kuskokwim. ```{r n_year_data} +#| eval: false #| warning: false #| message: false @@ -518,6 +527,7 @@ n_years <- annual_esc %>% Now let's plot this using `geom_bar()`. ```{r} +#| eval: false ## base plot ggplot(n_years, aes(x = Species, @@ -529,7 +539,7 @@ ggplot(n_years, Now, let's apply some of the customizations we have seen so far and learn some new ones. ```{r} - +#| eval: false ## Reordering, flipping coords and other customization ggplot(n_years, aes( @@ -577,9 +587,10 @@ First let's show an interactive table of unique sampling locations using `DT`. Write a `data.frame` containing unique sampling locations with no missing values using two new functions from `dplyr` and `tidyr`: `distinct()` and `drop_na()`. ```{r uniq_locations} -locations <- delta_visits %>% - distinct(Location, Latitude, Longitude) %>% - drop_na() +locations <- visits_long %>% + distinct(restore_loc, .keep_all = T) %>% + select(restore_loc, latitude, longitude) + ``` And display it as an interactive table using `datatable()` from the `DT` package. @@ -599,9 +610,9 @@ The `addTiles()` function without arguments will add base tiles to your map from leaflet(locations) %>% addTiles() %>% addMarkers( - lng = ~ Longitude, - lat = ~ Latitude, - popup = ~ Location + lng = ~ longitude, + lat = ~ latitude, + popup = ~ resore_loc ) ``` @@ -611,6 +622,7 @@ You can also use `leaflet` to import Web Map Service (WMS) tiles. Here is an exa ```{r} +#| eval: false leaflet(locations) %>% addWMSTiles( "https://www.gebco.net/data_and_products/gebco_web_services/web_map_service/mapserv?request=getmap&service=wms&BBOX=-90,-180,90,360&crs=EPSG:4326&format=image/jpeg&layers=gebco_latest&width=1200&height=600&version=1.3.0", From 6b55afc655cf0c92166bd917bde16962d195629d Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Thu, 3 Aug 2023 12:49:32 -0700 Subject: [PATCH 007/104] addin eval: false to fill_blue_geom chunck --- materials/sections/visualization-delta.qmd | 2 ++ 1 file changed, 2 insertions(+) diff --git a/materials/sections/visualization-delta.qmd b/materials/sections/visualization-delta.qmd index 814f2924..782a48c0 100644 --- a/materials/sections/visualization-delta.qmd +++ b/materials/sections/visualization-delta.qmd @@ -230,6 +230,7 @@ daily_visits_loc %>% From 516c40b42430d7e443335c1559ff0c412431781d Mon Sep 17 00:00:00 2001 From: Halina Do-Linh <88209419+hdolinh@users.noreply.github.com> Date: Tue, 8 Aug 2023 08:50:06 -0700 Subject: [PATCH 010/104] 2023 08 delta (#334) * add gutenbergr package to DESCRIPTION --- materials/DESCRIPTION | 1 + 1 file changed, 1 insertion(+) diff --git a/materials/DESCRIPTION b/materials/DESCRIPTION index 3cdd8dc7..260bdfc0 100755 --- a/materials/DESCRIPTION +++ b/materials/DESCRIPTION @@ -21,6 +21,7 @@ Imports: ggmap, ggpmisc, googlesheets4, + gutenbergr, htmlwidgets, httr, janitor, From 41c53b5dd016d57a1bbdc22761b664eb2097de60 Mon Sep 17 00:00:00 2001 From: Halina Do-Linh <88209419+hdolinh@users.noreply.github.com> Date: Tue, 8 Aug 2023 09:25:56 -0700 Subject: [PATCH 011/104] 2023 08 delta (#335) * refactor GHA main.yml (#317) * refactor main.yml; test using branch as an input * test new script triggers build in RRC main.yml * return to previous code cleaned up main.yml * test branch variable; expect gha to break main.yml * small change to test branch variable in gha * remove pull request details step main.yml * small change to test branch var gha * correct format main.yml * small change to test branch var gha * extract branch name from PR main.yml * small text change to test branch var gha * match branch var names main.yml * use client_payload to reference develop branch in RRC main.yml * small text change to text branch var gha * test change * use $GITHUB_HEAD_REF to get branch name main.yml * change on from push to PR main.yml * type closed for PR main.yml * small change to test branch var gha * test rm content type main.yml * test credentials for nceas-training gha * test bearer as auth main.yml * change to test bad credentials * revert to old code to test bad credentials main.yml * small change to test bad credentials * test connection main.yml * small change to test connection * small change to test connection * small change to test connection * revert to old code to test main.yml * small change to test * test w push main.yml * rm pr main.yml * test branch var * Update main.yml * Update main.yml * Update README.md * update github api request main.yml * Update README.md * refactor http dispatch request main.yml * Update README.md * change from PR to push main.yml * test client payload main.yml * change format main.yml * Update main.yml * change format main.yml * change syntax main.yml * Update main.yml * Update main.yml * fix syntax main.yml * Update main.yml * use Repository Dispatch GHA (#324) * add content-type to curl main.yml * revert to working state main.yml * rm quotes main.yml * syntax for echo client payload main.yml * check client payload main.yml * syntax main.yml * test syntax main.yml * test built repo dispatch GHA main.yml * fix syntax main.yml * test syntax main.yml * syntax main.yml * syntax to call branch main.yml * syntax main.yml * toJSON() for format main.yml * rm $ main.yml * syntax for gh var main.yml * test client payload echo main.yml * syntax main.yml * Update version main.yml * rm check main.yml * add gutenbergr package to DESCRIPTION --- materials/sections/text-analysis.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/materials/sections/text-analysis.Rmd b/materials/sections/text-analysis.Rmd index f3d22047..9f84d24d 100644 --- a/materials/sections/text-analysis.Rmd +++ b/materials/sections/text-analysis.Rmd @@ -2,7 +2,7 @@ author: "Jeanette Clark" --- -## Extracting Data for Text Analysis +## Extracting Data for Text Analysis ### Learning Objectives From f42606068bbf74fd4781ca881e82db718b6cf9cd Mon Sep 17 00:00:00 2001 From: Halina Do-Linh <88209419+hdolinh@users.noreply.github.com> Date: Mon, 31 Jul 2023 11:09:12 -0700 Subject: [PATCH 012/104] refactor GHA main.yml (#317) * refactor main.yml; test using branch as an input * test new script triggers build in RRC main.yml * return to previous code cleaned up main.yml * test branch variable; expect gha to break main.yml * small change to test branch variable in gha * remove pull request details step main.yml * small change to test branch var gha * correct format main.yml * small change to test branch var gha * extract branch name from PR main.yml * small text change to test branch var gha * match branch var names main.yml * use client_payload to reference develop branch in RRC main.yml * small text change to text branch var gha * test change * use $GITHUB_HEAD_REF to get branch name main.yml * change on from push to PR main.yml * type closed for PR main.yml * small change to test branch var gha * test rm content type main.yml * test credentials for nceas-training gha * test bearer as auth main.yml * change to test bad credentials * revert to old code to test bad credentials main.yml * small change to test bad credentials * test connection main.yml * small change to test connection * small change to test connection * small change to test connection * revert to old code to test main.yml * small change to test * test w push main.yml * rm pr main.yml * test branch var * Update main.yml * Update main.yml * Update README.md * update github api request main.yml * Update README.md * refactor http dispatch request main.yml * Update README.md --- .github/workflows/main.yml | 2 +- materials/sections/text-analysis.Rmd | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 6e14057c..7daad3e8 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -14,4 +14,4 @@ jobs: token: ${{ secrets.RRCOURSE }} repository: nceas/repro-research-course event-type: quarto - client-payload: '{"branch": "${{ github.ref_name }}"}' \ No newline at end of file + client-payload: '{"branch": "${{ github.ref_name }}"}' diff --git a/materials/sections/text-analysis.Rmd b/materials/sections/text-analysis.Rmd index 9f84d24d..6c3d3c22 100644 --- a/materials/sections/text-analysis.Rmd +++ b/materials/sections/text-analysis.Rmd @@ -1,7 +1,7 @@ --- author: "Jeanette Clark" --- - + ## Extracting Data for Text Analysis ### Learning Objectives From 41218501ee398f9ceb691404e6a5d3381f504076 Mon Sep 17 00:00:00 2001 From: Halina Do-Linh <88209419+hdolinh@users.noreply.github.com> Date: Mon, 31 Jul 2023 13:46:44 -0700 Subject: [PATCH 013/104] change from PR to push main.yml --- .github/workflows/main.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 7daad3e8..7a85ea2c 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -15,3 +15,4 @@ jobs: repository: nceas/repro-research-course event-type: quarto client-payload: '{"branch": "${{ github.ref_name }}"}' + From e5533807c9adbcc22ad7dd377eafad12254db3e6 Mon Sep 17 00:00:00 2001 From: Halina Do-Linh <88209419+hdolinh@users.noreply.github.com> Date: Mon, 31 Jul 2023 13:58:19 -0700 Subject: [PATCH 014/104] test client payload main.yml --- .github/workflows/main.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 7a85ea2c..7daad3e8 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -15,4 +15,3 @@ jobs: repository: nceas/repro-research-course event-type: quarto client-payload: '{"branch": "${{ github.ref_name }}"}' - From 47716feb2eaa530b16e1ce82c95912c291efb3bd Mon Sep 17 00:00:00 2001 From: Halina Do-Linh <88209419+hdolinh@users.noreply.github.com> Date: Mon, 31 Jul 2023 14:02:48 -0700 Subject: [PATCH 015/104] change format main.yml --- .github/workflows/main.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 7daad3e8..fbe95e57 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -15,3 +15,5 @@ jobs: repository: nceas/repro-research-course event-type: quarto client-payload: '{"branch": "${{ github.ref_name }}"}' + + From f0ed59d8de67088c5e78d3915e814667dc08224b Mon Sep 17 00:00:00 2001 From: Halina Do-Linh <88209419+hdolinh@users.noreply.github.com> Date: Mon, 31 Jul 2023 14:04:34 -0700 Subject: [PATCH 016/104] Update main.yml --- .github/workflows/main.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index fbe95e57..b1805c5a 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -7,6 +7,7 @@ on: jobs: dispatch: runs-on: ubuntu-latest + steps: - name: Repository Dispatch uses: peter-evans/repository-dispatch@v2.1.1 @@ -17,3 +18,4 @@ jobs: client-payload: '{"branch": "${{ github.ref_name }}"}' + From afe1c0493a902df5b2ca1691f8f908b3a7db66e9 Mon Sep 17 00:00:00 2001 From: Halina Do-Linh <88209419+hdolinh@users.noreply.github.com> Date: Mon, 31 Jul 2023 14:08:41 -0700 Subject: [PATCH 017/104] change format main.yml --- .github/workflows/main.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index b1805c5a..b4163982 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -8,6 +8,7 @@ jobs: dispatch: runs-on: ubuntu-latest + steps: - name: Repository Dispatch uses: peter-evans/repository-dispatch@v2.1.1 From 4b1df18c672b91b159f79bd196d3c5d4e8b33a79 Mon Sep 17 00:00:00 2001 From: Halina Do-Linh <88209419+hdolinh@users.noreply.github.com> Date: Mon, 31 Jul 2023 14:12:53 -0700 Subject: [PATCH 018/104] change syntax main.yml --- .github/workflows/main.yml | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index b4163982..fef8c47f 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -8,7 +8,6 @@ jobs: dispatch: runs-on: ubuntu-latest - steps: - name: Repository Dispatch uses: peter-evans/repository-dispatch@v2.1.1 @@ -16,7 +15,4 @@ jobs: token: ${{ secrets.RRCOURSE }} repository: nceas/repro-research-course event-type: quarto - client-payload: '{"branch": "${{ github.ref_name }}"}' - - - + client-payload: '{"branch": "${{ github.ref_name }}"}' \ No newline at end of file From f8c0ad8b469d325f5d44002dcd70a734e318b3c4 Mon Sep 17 00:00:00 2001 From: Halina Do-Linh <88209419+hdolinh@users.noreply.github.com> Date: Mon, 31 Jul 2023 14:15:13 -0700 Subject: [PATCH 019/104] Update main.yml --- .github/workflows/main.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index fef8c47f..cf6e39c8 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -15,4 +15,5 @@ jobs: token: ${{ secrets.RRCOURSE }} repository: nceas/repro-research-course event-type: quarto - client-payload: '{"branch": "${{ github.ref_name }}"}' \ No newline at end of file + client-payload: '{"branch": "${{ github.ref_name }}"}' + From 16ab7cfb4e9612f0892c281b8c1236a01eabf971 Mon Sep 17 00:00:00 2001 From: Halina Do-Linh <88209419+hdolinh@users.noreply.github.com> Date: Mon, 31 Jul 2023 14:20:43 -0700 Subject: [PATCH 020/104] fix syntax main.yml --- .github/workflows/main.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index cf6e39c8..fe35fa37 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -16,4 +16,3 @@ jobs: repository: nceas/repro-research-course event-type: quarto client-payload: '{"branch": "${{ github.ref_name }}"}' - From 613b96a0e1ec42bb8b7ef4563bb99dd01eec5e49 Mon Sep 17 00:00:00 2001 From: Halina Do-Linh <88209419+hdolinh@users.noreply.github.com> Date: Mon, 31 Jul 2023 14:21:52 -0700 Subject: [PATCH 021/104] Update main.yml --- .github/workflows/main.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index fe35fa37..28daf60a 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -8,6 +8,7 @@ jobs: dispatch: runs-on: ubuntu-latest + steps: - name: Repository Dispatch uses: peter-evans/repository-dispatch@v2.1.1 From 3de4eda3f3acc636a1657ac47b7ee148cac6ad32 Mon Sep 17 00:00:00 2001 From: Halina Do-Linh <88209419+hdolinh@users.noreply.github.com> Date: Mon, 31 Jul 2023 16:16:40 -0700 Subject: [PATCH 022/104] use Repository Dispatch GHA (#324) * add content-type to curl main.yml * revert to working state main.yml * rm quotes main.yml * syntax for echo client payload main.yml * check client payload main.yml * syntax main.yml * test syntax main.yml * test built repo dispatch GHA main.yml * fix syntax main.yml * test syntax main.yml * syntax main.yml * syntax to call branch main.yml * syntax main.yml * toJSON() for format main.yml * rm $ main.yml * syntax for gh var main.yml * test client payload echo main.yml * syntax main.yml * Update version main.yml * rm check main.yml --- .github/workflows/main.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 28daf60a..fe35fa37 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -8,7 +8,6 @@ jobs: dispatch: runs-on: ubuntu-latest - steps: - name: Repository Dispatch uses: peter-evans/repository-dispatch@v2.1.1 From 85580b67d89bf9180beea3f64e3cfa4ac82680fe Mon Sep 17 00:00:00 2001 From: hdolinh Date: Tue, 8 Aug 2023 09:28:46 -0700 Subject: [PATCH 023/104] rm tidyverse package and add specific packages --- materials/sections/intro-tidy-text-data.qmd | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/materials/sections/intro-tidy-text-data.qmd b/materials/sections/intro-tidy-text-data.qmd index 8d207cbd..8754690b 100644 --- a/materials/sections/intro-tidy-text-data.qmd +++ b/materials/sections/intro-tidy-text-data.qmd @@ -60,9 +60,11 @@ By using tidy data principles, we can apply many "tidy" R packages including dpy Text analysis workflow using tidy data principles ```{r} -library(gutenbergr) # add to description and ask Nick to add to included crab -library(tidyverse) +library(gutenbergr) +library(tidyr) +library(dplyr) library(tidytext) +library(ggplot2) ``` ```{r} From b0d5185f4cfefe36ba7000be251806024c02d12c Mon Sep 17 00:00:00 2001 From: Halina Do-Linh <88209419+hdolinh@users.noreply.github.com> Date: Tue, 8 Aug 2023 09:38:39 -0700 Subject: [PATCH 024/104] 2023 08 delta (#336) resolved local conflicts --- .github/workflows/main.yml | 3 ++- materials/sections/intro-tidy-text-data.qmd | 6 ++++-- 2 files changed, 6 insertions(+), 3 deletions(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 6e14057c..fe35fa37 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -7,6 +7,7 @@ on: jobs: dispatch: runs-on: ubuntu-latest + steps: - name: Repository Dispatch uses: peter-evans/repository-dispatch@v2.1.1 @@ -14,4 +15,4 @@ jobs: token: ${{ secrets.RRCOURSE }} repository: nceas/repro-research-course event-type: quarto - client-payload: '{"branch": "${{ github.ref_name }}"}' \ No newline at end of file + client-payload: '{"branch": "${{ github.ref_name }}"}' diff --git a/materials/sections/intro-tidy-text-data.qmd b/materials/sections/intro-tidy-text-data.qmd index 8d207cbd..8754690b 100644 --- a/materials/sections/intro-tidy-text-data.qmd +++ b/materials/sections/intro-tidy-text-data.qmd @@ -60,9 +60,11 @@ By using tidy data principles, we can apply many "tidy" R packages including dpy Text analysis workflow using tidy data principles ```{r} -library(gutenbergr) # add to description and ask Nick to add to included crab -library(tidyverse) +library(gutenbergr) +library(tidyr) +library(dplyr) library(tidytext) +library(ggplot2) ``` ```{r} From 4c63eaac5eea84637b27e97ae3bfb08c041049ae Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Tue, 8 Aug 2023 10:55:25 -0700 Subject: [PATCH 025/104] adding information to census outline --- materials/sections/census-data.qmd | 1 + 1 file changed, 1 insertion(+) diff --git a/materials/sections/census-data.qmd b/materials/sections/census-data.qmd index d9f04e26..ee8f4aa8 100644 --- a/materials/sections/census-data.qmd +++ b/materials/sections/census-data.qmd @@ -5,6 +5,7 @@ ## Overview (Chapter 1 - 5 min) - General view of US Census Data +- What is an API and how to access ## Introduction to `tidycensus` (Chapter 2 - 25-30 min) From 2559eaf3077ded29fccc54f3f877dff9d7ed7eda Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Fri, 11 Aug 2023 09:09:53 -0700 Subject: [PATCH 026/104] adding to census lesson aoutline --- materials/sections/census-data.qmd | 18 +++++++++++++----- 1 file changed, 13 insertions(+), 5 deletions(-) diff --git a/materials/sections/census-data.qmd b/materials/sections/census-data.qmd index ee8f4aa8..ec29f0e9 100644 --- a/materials/sections/census-data.qmd +++ b/materials/sections/census-data.qmd @@ -5,22 +5,30 @@ ## Overview (Chapter 1 - 5 min) - General view of US Census Data -- What is an API and how to access +- Decennial US Census +- American Community Survey (ACS) + +"While the decennial US Census and American Community Survey are the most popular and widely used datasets produced by the US Census Bureau, the Bureau conducts hundreds of other surveys and disseminates data on a wide range of subjects to the public. These datasets include economic and business surveys, housing surveys, international data, population estimates and projections, and much more; a full listing is available on the Census website." + +- How is Census data available +- The Census API ## Introduction to `tidycensus` -(Chapter 2 - 25-30 min) +(Chapter 2 - 5 min) +"The `tidycensus` is an R package that provides an interface to access and work with the United States Census Bureau data. It simplifies the process of retrieving and analyzing census data by allowing users to query data directly from the Census Bureau's API and then organize the data into a tidy format for easy manipulation and analysis." +## Querying data - Basic data requests - Fundamentals of data package and the options it offers ## Getting Census Data ready for analysis -(Chapter 3 - 20-25 min) +(Chapter 3 - 10 min) -- Data wranglw again? Yes! +- Applying data wrangling skills - Handling errors in data ## Visualizing Censis Data with `ggplot2` -(Chapter 4 - 20 min) +(Chapter 4 - 10 min) ## Maps and Census Data ( Chapter 5 and 6 - 10 min?) From 85e723b87bb6cf3b1ccaa6d3b83ce993c40f9d07 Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Mon, 14 Aug 2023 15:56:39 -0700 Subject: [PATCH 027/104] reorganizing sessions qmds to reflect new schedule + updating yaml to reflec these changes too --- materials/_quarto.yml | 26 +++++++++++++------------- materials/session_02.qmd | 4 ++-- materials/session_03.qmd | 4 ++-- materials/session_04.qmd | 15 +++++++++++---- materials/session_05.qmd | 5 +++-- materials/session_06.qmd | 4 ++-- materials/session_07.qmd | 6 +++--- materials/session_08.qmd | 4 +++- materials/session_09.qmd | 4 ++-- materials/session_10.qmd | 4 ++-- materials/session_11.qmd | 5 ++--- materials/session_12.qmd | 7 +++++-- materials/session_14.qmd | 4 ++-- 13 files changed, 52 insertions(+), 40 deletions(-) diff --git a/materials/_quarto.yml b/materials/_quarto.yml index 0dd138f9..72c6cbd9 100755 --- a/materials/_quarto.yml +++ b/materials/_quarto.yml @@ -28,20 +28,20 @@ book: chapters: - index.qmd # preface - session_01.qmd # collaborating using git & github and merge conflicts - - session_02.qmd # social aspects of collaboration - - session_03.qmd # thinking preferences - - session_04.qmd # synthesis - - session_05.qmd # publishing to web - - session_06.qmd # data vis - - session_07.qmd # systematic reviews - - session_08.qmd # synthesis - - session_09.qmd # spatial data - - session_10.qmd # intro to meta-analysis - - session_11.qmd # text data - - session_12.qmd # census data + - session_02.qmd # publishing to web + - session_03.qmd # data viz + - session_04.qmd # Practice session + - session_05.qmd # synthesis: setting goals + - session_06.qmd # social aspects of collaboration + - session_07.qmd # thinking preferences + - session_08.qmd # synthesis: data policies + - session_09.qmd # synthesis + - session_10.qmd # spatial data + - session_11.qmd # census data + - session_12.qmd # text data - session_13.qmd # reproducible surveys - - session_14.qmd # spatial demographis specialist - - session_15.qmd # synthesis + - session_14.qmd # synthesis + # - session_15.qmd # synthesis - session_16.qmd # shiny - session_17.qmd # 2FA setup diff --git a/materials/session_02.qmd b/materials/session_02.qmd index 937a6178..541dd48e 100644 --- a/materials/session_02.qmd +++ b/materials/session_02.qmd @@ -1,6 +1,6 @@ --- -title: "Social Aspects of Collaboration" +title: "Publishing your analysis to the web with GitHub Pages" title-block-banner: true --- -{{< include /sections/collaboration-social-data-policies.qmd >}} \ No newline at end of file +{{< include /sections/git-github-publishing-analysis.qmd >}} \ No newline at end of file diff --git a/materials/session_03.qmd b/materials/session_03.qmd index 415dbe21..7e9f723d 100644 --- a/materials/session_03.qmd +++ b/materials/session_03.qmd @@ -1,8 +1,8 @@ --- -title: "Thinking Preferences" +title: "Data Visualization" title-block-banner: true --- -{{< include /sections/collaboration-thinking-preferences.qmd >}} +{{< include /sections/visualization-delta.qmd >}} diff --git a/materials/session_04.qmd b/materials/session_04.qmd index 03837356..f056216a 100644 --- a/materials/session_04.qmd +++ b/materials/session_04.qmd @@ -1,7 +1,14 @@ --- -title: "Synthesis Time" -from: markdown+emoji +title: "R Practice: Collaborating on, Wrangling & Visualizing Data" title-block-banner: true +execute: + eval: false +format: + html: + code-link: true + code-fold: true + code-summary: "Answer" + code-overflow: wrap --- - - + +{{< include /sections/r-practice-clean-wrangle-visualize.qmd >}} \ No newline at end of file diff --git a/materials/session_05.qmd b/materials/session_05.qmd index eacd74ba..c5673844 100644 --- a/materials/session_05.qmd +++ b/materials/session_05.qmd @@ -1,8 +1,9 @@ --- -title: "Publishing your Analysis to the Web" +title: "Collaborative Synthesis" title-block-banner: true --- -{{< include /sections/git-github-publishing-analysis.qmd >}} + + diff --git a/materials/session_06.qmd b/materials/session_06.qmd index e458fa0a..33e493f6 100644 --- a/materials/session_06.qmd +++ b/materials/session_06.qmd @@ -1,6 +1,6 @@ --- -title: "Data Visualization in R" +title: "Social Aspecst of Collaborations" title-block-banner: true --- -{{< include /sections/visualization-delta.qmd >}} \ No newline at end of file +{{< include /sections/collaboration-social-data-policies.qmd >}} \ No newline at end of file diff --git a/materials/session_07.qmd b/materials/session_07.qmd index 61475111..5832deda 100644 --- a/materials/session_07.qmd +++ b/materials/session_07.qmd @@ -1,5 +1,5 @@ --- -title: "Systematic Reviews" +title: "Thinking Preferences" title-block-banner: true format: html: @@ -7,6 +7,6 @@ format: code-link: true --- - +{{< include /sections/collaboration-thinking-preferences.qmd >}} + -ADD LINK TO MATERIAL diff --git a/materials/session_08.qmd b/materials/session_08.qmd index 324c3e6c..2b92fef0 100644 --- a/materials/session_08.qmd +++ b/materials/session_08.qmd @@ -1,6 +1,8 @@ --- -title: "Synthesis Time" +title: "Collaborative Synthesis: Authorship Guidlines & Data Ploicy" title-block-banner: true --- + +ADD INSTRUCTIONS \ No newline at end of file diff --git a/materials/session_09.qmd b/materials/session_09.qmd index 57a856e9..4e777a75 100644 --- a/materials/session_09.qmd +++ b/materials/session_09.qmd @@ -1,8 +1,8 @@ --- -title: "Using `sf` for Spatia Data & Intro to Making Maps" +title: "Collaborative Synthesis" title-block-banner: true --- -{{< include /sections/geospatial-vector-analysis.qmd >}} \ No newline at end of file + \ No newline at end of file diff --git a/materials/session_10.qmd b/materials/session_10.qmd index b01dac44..b517b50b 100644 --- a/materials/session_10.qmd +++ b/materials/session_10.qmd @@ -1,7 +1,7 @@ --- -title: "Introduction to Meta-Analysis" +title: "Using `sf` for Spatia Data & Intro to Making Maps" title-block-banner: true --- -ADD LINK TO MATERIALS \ No newline at end of file +{{< include /sections/geospatial-vector-analysis.qmd >}} \ No newline at end of file diff --git a/materials/session_11.qmd b/materials/session_11.qmd index 222a5429..32475c58 100644 --- a/materials/session_11.qmd +++ b/materials/session_11.qmd @@ -1,8 +1,7 @@ --- -title: "Working with Text Data in R" +title: "Working with US Census Data" title-block-banner: true --- -{{< include /sections/intro-tidy-text-data.qmd >}} +{{< include /sections/census-data.qmd >}} - diff --git a/materials/session_12.qmd b/materials/session_12.qmd index df85db61..0d834a7e 100644 --- a/materials/session_12.qmd +++ b/materials/session_12.qmd @@ -1,10 +1,13 @@ --- -title: "Working with US Census Data" +title: "Working with Text Data in R" title-block-banner: true --- -{{< include /sections/census-data.qmd >}} +{{< include /sections/intro-tidy-text-data.qmd >}} + + + diff --git a/materials/session_14.qmd b/materials/session_14.qmd index ac3c35d8..ca012e37 100644 --- a/materials/session_14.qmd +++ b/materials/session_14.qmd @@ -1,8 +1,8 @@ --- -title: "Guest Speaker" +title: "Collaboraive Synthesis" title-block-banner: true --- -Add material + From 748e332f139c20aa3d3967eb711e6e690eb46b75 Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Mon, 14 Aug 2023 16:10:54 -0700 Subject: [PATCH 028/104] deleting calling to reference.bib from rpractice session. References are pulled from book.bib --- materials/sections/r-practice-clean-wrangle-visualize.qmd | 3 --- materials/session_04.qmd | 1 + 2 files changed, 1 insertion(+), 3 deletions(-) diff --git a/materials/sections/r-practice-clean-wrangle-visualize.qmd b/materials/sections/r-practice-clean-wrangle-visualize.qmd index a7d21eb1..19c4275c 100644 --- a/materials/sections/r-practice-clean-wrangle-visualize.qmd +++ b/materials/sections/r-practice-clean-wrangle-visualize.qmd @@ -1,6 +1,3 @@ ---- -bibliography: references.bib ---- ## Learning Objectives {.unnumbered} diff --git a/materials/session_04.qmd b/materials/session_04.qmd index f056216a..1509783f 100644 --- a/materials/session_04.qmd +++ b/materials/session_04.qmd @@ -11,4 +11,5 @@ format: code-overflow: wrap --- + {{< include /sections/r-practice-clean-wrangle-visualize.qmd >}} \ No newline at end of file From e9b34ffff76f6bda8cb98b53d5236b57f4d41f6a Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Mon, 14 Aug 2023 18:48:42 -0700 Subject: [PATCH 029/104] Restructuring ggplot part for further customization. Adding notes on part that still need revision --- materials/sections/visualization-delta.qmd | 418 ++++++++++----------- 1 file changed, 205 insertions(+), 213 deletions(-) diff --git a/materials/sections/visualization-delta.qmd b/materials/sections/visualization-delta.qmd index 8b3da0d7..f6363adf 100644 --- a/materials/sections/visualization-delta.qmd +++ b/materials/sections/visualization-delta.qmd @@ -1,10 +1,9 @@ ## Learning Objectives {.unnumbered} -**REVISE** -- The basics of the `ggplot2` package to create static plots -- How to use `ggplot2`'s `theme` abilities to create publication-grade graphics -- The basics of the `leaflet` package to create interactive maps +- Explain the basics of how the `ggplot2` package works +- Use `ggplot2`'s `theme` abilities to customize plots and create publication-grade graphics +- Introduce the `leaflet` and `DT` package to create interactive maps and tables respectively ## Overview @@ -77,8 +76,8 @@ glimpse(delta_visits) range(delta_visits$date) ## First and last rows -head(delta_visits$date) -tail(delta_visits$date) +head(delta_visits) +tail(delta_visits) ## Which time of day? unique(delta_visits$time_of_day) @@ -90,7 +89,7 @@ unique(delta_visits$time_of_day) ## Getting the data ready -It is more frequent than not, that we need to do some wrangling before we can plot our data the way we want to. Now that we have read out data and have done some exploration, we'll put our data wrangling skills to practice to get our data in the desired format. +It is more frequent than not, that we need to do some wrangling before we can plot our data the way we want to. After reading and exploring our data, we'll put our data wrangling skills to practice to get our data in the desired format. With the [tidy data principles](https://learning.nceas.ucsb.edu/2023-06-delta/session_09.html#tidy-data) in mind. Is this data tidy? @@ -100,15 +99,20 @@ With the [tidy data principles](https://learning.nceas.ucsb.edu/2023-06-delta/se `ggplot2` for the most part likes data input to be in a long format (aka "tidy"). So let go ahead and make this data frame long instead of wide. Do you remember the name of the function we can use? +Let's refresh our memory on how this function works by accessing the `help` page. Type `?pivot_long()` in the console to see the documentation for this function. + ```{r} visits_long <- delta_visits %>% pivot_longer(cols = c("sm_boat", "med_boat", "lrg_boat", "bank_angler", "scientist", "cars"), names_to = "visitor_type", - values_to = "quantity") %>% + values_to = "quantity") %>% rename(restore_loc = eco_restore_approximate_location) %>% select(-notes) +## Checking the outcome +head(visits_long) + ``` @@ -130,7 +134,7 @@ head(daily_visits_loc) ``` -The chunk above usees some of the dplyr functions that we've used in the past. We use `group_by()` to indicate that we want to calculate our results for the unique combinations of type of visit, restoration location proximity, and day. We next use `summarize()` to calculate an daily visit value for each of these groups. Note we use the `%in%` operator to pipe in the result of one command as an argument to the next one. +The chunk above uses some of the dplyr functions that we've used in the past. We use `group_by()` to indicate that we want to calculate our results for the unique combinations of type of visit, restoration location proximity, and day. We next use `summarize()` to calculate an daily visit value for each of these groups. Note we use the `%in%` operator to pipe in the result of one command as an argument to the next one. ## Plotting with `ggplot2` @@ -152,7 +156,7 @@ To add layers to our plot, for example, additional geometries/aesthetics and the ::: -For example, let's plot total daily visits by restoration location. We will show this by creating the same plot in 3 slightly different ways. Each of the options below have the essential pieces of a `ggplot`. +Now, let's plot total daily visits by restoration location. We will show this by creating the same plot in 3 slightly different ways. Each of the options below have the 4 essential pieces of a `ggplot`. ```{r basic_esc_plot} @@ -178,6 +182,8 @@ ggplot() + They all will create the same plot: +(Apologies for the crumble text on the x-axis, we will learn how to make this look better soon) + ```{r esc_plot} #| echo: false @@ -227,29 +233,28 @@ daily_visits_loc %>% ``` - + ### Customizing our plot Let's go back to our base bar graph. What if we want our bars to be blue instead of gray? You might think we could run this: ```{r fill_blue} -#| eval: false -ggplot(annual_esc, - aes(x = Species, y = delta_visitsment, - fill = "blue")) + + +ggplot(data = daily_visits_loc, + aes(x = restore_loc, y = daily_visits, + fill = "blue"))+ geom_col() ``` @@ -260,20 +265,22 @@ Notice that we tried to set the fill color of the plot inside the mapping aesthe What we really wanted to do was just change the color of the bars. If we want do do that, we can call the color option in the `geom_col()` function, **outside** of the mapping aesthetics function call. ```{r fill_blue_geom} -#| eval: false -ggplot(annual_esc, - aes(x = Species, y = delta_visitsment)) + + +ggplot(data = daily_visits_loc, + aes(x = restore_loc, y = daily_visits))+ geom_col(fill = "blue") + ``` -What if we did want to map the color of the bars to a variable, such as region. `ggplot()` is really powerful because we can easily get this plot to visualize more aspects of our data. +What if we did want to map the color of the bars to a variable, such as `visitor_type`. `ggplot()` is really powerful because we can easily get this plot to visualize more aspects of our data. ```{r} -#| eval: false -ggplot(annual_esc, - aes(x = Species, y = delta_visitsment, - fill = SASAP.Region)) + + +ggplot(data = daily_visits_loc, + aes(x = restore_loc, y = daily_visits, + fill = visitor_type))+ geom_col() + ``` ::: {.callout-caution icon=false} @@ -287,65 +294,32 @@ ggplot(annual_esc, ::: -#### Creating multiple plots - -We know that in the graph we just plotted, each bar includes delta_visitsments for multiple years. Let's leverage the power of `ggplot` to plot more aspects of our data in one plot. - -We are going to plot delta_visitsment by species over time, from 2000 to 2016, for each region. - -An easy way to plot another aspect of your data is using the function `facet_wrap()`. This function takes a mapping to a variable using the syntax `~{variable_name}`. The `~` (tilde) is a model operator which tells `facet_wrap()` to model each unique value within `variable_name` to a facet in the plot. - -The default behavior of facet wrap is to put all facets on the same x and y scale. You can use the `scales` argument to specify whether to allow different scales between facet plots (e.g `scales = "free_y"` to free the y axis scale). You can also specify the number of columns using the `ncol = ` argument or number of rows using `nrow =`. - - -```{r facet_base_plot} -#| eval: false -## Subset with data from years 2000 to 2016 - -annual_esc_2000s <- annual_esc %>% - filter(Year %in% c(2000:2016)) - -## Quick check -unique(annual_esc_2000s$Year) - - -## Plot with facets -ggplot(annual_esc_2000s, - aes(x = Year, - y = delta_visitsment, - color = Species)) + - geom_line() + - geom_point() + - facet_wrap( ~ SASAP.Region, - scales = "free_y") - -``` - - #### Setting ggplot themes -Now let's work on making this plot look a bit nicer. We are going to" +We have successfully plotted our data. But, this is clearly not a nice plot. Let's work on making this plot look a bit nicer. We are going to" -- Add a title using `ggtitle()` -- Adjust labels using `ylab()` - Include a built in theme using `theme_bw()` +- Flip the x and y axis to better read the graph +- Add a title, subtitle and adjust labels using `labs()` -There are a wide variety of built in themes in `ggplot` that help quickly set the look of the plot. Use the RStudio autocomplete `theme_` `` to view a list of theme functions. + +There are a wide variety of built in themes in `ggplot` that help quickly set the look of the plot. Use the RStudio auto-complete `theme_` `` to view a list of theme functions. ```{r theme_bw_plot} -#| eval: false -ggplot(annual_esc_2000s, - aes(x = Year, - y = delta_visitsment, - color = Species)) + - geom_line() + - geom_point() + - facet_wrap( ~ SASAP.Region, - scales = "free_y") + - ylab("delta_visitsment") + - ggtitle("Annual Salmon delta_visitsment by Region") + - theme_bw() + +ggplot(data = daily_visits_loc, + aes(x = restore_loc, y = daily_visits, + fill = visitor_type))+ + geom_col()+ + theme_bw()+ + coord_flip()+ + labs(x = "Restoration Location", + y = "Number of Visits", + fill = "Type of Visitor", + title = "Total Number of Visits to Delta Restoration Areas by visitor type", + subtitle = "Sum of all visits during study period") + ``` You can see that the `theme_bw()` function changed a lot of the aspects of our plot! The background is white, the grid is a different color, etc. There are lots of other built in themes like this that come with the `ggplot2` package. @@ -368,62 +342,57 @@ The built in theme functions (`theme_*()`) change the default settings for many Let's look at an example of a `theme()` call, where we change the position of the legend from the right side to the bottom, and remove its title. ```{r} -#| eval: false -ggplot(annual_esc_2000s, - aes(x = Year, - y = delta_visitsment, - color = Species)) + - geom_line() + - geom_point() + - facet_wrap( ~ SASAP.Region, - scales = "free_y") + - ylab("delta_visitsment") + - ggtitle("Annual Salmon delta_visitsment by Region") + - theme_light() + - theme(legend.position = "bottom", - legend.title = element_blank()) +ggplot(data = daily_visits_loc, + aes(x = restore_loc, y = daily_visits, + fill = visitor_type))+ + geom_col()+ + theme_bw()+ + coord_flip()+ + labs(x = "Restoration Location", + y = "Number of Visits", + fill = "Type of Visitor", + title = "Total Number of Visits to Delta Restoration Areas by visitor type", + subtitle = "Sum of all visits during study period")+ + theme(legend.position = "bottom") ``` Note that the `theme()` call needs to come after any built-in themes like `theme_bw()` are used. Otherwise, `theme_bw()` will likely override any theme elements that you changed using `theme()`. + You can also save the result of a series of `theme()` function calls to an object to use on multiple plots. This prevents needing to copy paste the same lines over and over again! ```{r save_theme} -my_theme <- theme_light() + +my_theme <- theme_bw(base_size = 14) + theme(legend.position = "bottom", - legend.title = element_blank()) + axis.ticks.x = element_blank()) ``` So now our code will look like this: ```{r} -#| eval: false -ggplot(annual_esc_2000s, - aes(x = Year, - y = delta_visitsment, - color = Species)) + - geom_line() + - geom_point() + - facet_wrap( ~ SASAP.Region, - scales = "free_y") + - ylab("delta_visitsment") + - ggtitle("Annual Salmon delta_visitsment by Region") + +ggplot(data = daily_visits_loc, + aes(x = restore_loc, y = daily_visits, + fill = visitor_type))+ + geom_col()+ + coord_flip()+ + labs(x = "Restoration Location", + y = "Number of Visits", + fill = "Type of Visitor", + title = "Total Number of Visits to Delta Restoration Areas by visitor type", + subtitle = "Sum of all visits during study period")+ my_theme + ``` ::: callout-note #### Exercise - 1. Using whatever method you like, figure out how to rotate the x-axis tick labels to a 45 degree angle. - -**Hint:** You can start by looking at the documentation of the function by typing `?theme()` in the console. And googling is a great way to figure out how to do the modifications you want to your plot. +What changes do you expect to see in your plot by adding the following line of code? Discuss with your neighbor and then try it out! -2. What changes do you expect to see in your plot by adding the following line of code? Discuss with your neighbor and then try it out! - -`scale_x_continuous(breaks = seq(2000,2016, 2))` +`scale_y_continuous(breaks = seq(0,120, 20))` ::: @@ -431,138 +400,115 @@ ggplot(annual_esc_2000s, ```{r} #| code-fold: true #| code-summary: "Answer" -#| eval: false -## Useful baseline themes are -ggplot(annual_esc_2000s, - aes(x = Year, - y = delta_visitsment, - color = Species)) + - geom_line() + - geom_point() + - scale_x_continuous(breaks = seq(2000, 2016, 2)) + - facet_wrap( ~ SASAP.Region, - scales = "free_y") + - ylab("delta_visitsment") + - ggtitle("Annual Salmon delta_visitsment by Region") + - my_theme + - theme(axis.text.x = element_text(angle = 45, - vjust = 0.5)) +ggplot(data = daily_visits_loc, + aes(x = restore_loc, y = daily_visits, + fill = visitor_type))+ + geom_col()+ + coord_flip()+ + scale_y_continuous(breaks = seq(0,120, 20))+ + labs(x = "Restoration Location", + y = "Number of Visits", + fill = "Type of Visitor", + title = "Total Number of Visits to Delta Restoration Areas by visitor type", + subtitle = "Sum of all visits during study period")+ + my_theme ``` - -#### Smarter tick labels using `scales` - -Fixing tick labels in `ggplot` can be super annoying. The y-axis labels in the plot above don't look great. We could manually fix them, but it would likely be tedious and error prone. - -The `scales` package provides some nice helper functions to easily rescale and relabel your plots. Here, we use `scale_y_continuous()` from `ggplot2`, with the argument `labels`, which is assigned to the function name `comma`, from the `scales` package. This will format all of the labels on the y-axis of our plot with comma-formatted numbers. +Finally we are going to expand the bars all the way to the axis line. In other words, remove the gap between the bars and the vertical "x-axis" line. ```{r} -#| eval: false -ggplot(annual_esc_2000s, - aes(x = Year, - y = delta_visitsment, - color = Species)) + - geom_line() + - geom_point() + - scale_x_continuous(breaks = seq(2000, 2016, 2)) + - scale_y_continuous(labels = comma) + - facet_wrap( ~ SASAP.Region, - scales = "free_y") + - ylab("delta_visitsment") + - ggtitle("Annual Salmon delta_visitsment by Region") + - my_theme + - theme(axis.text.x = element_text(angle = 45, - vjust = 0.5)) +ggplot(data = daily_visits_loc, + aes(x = restore_loc, y = daily_visits, + fill = visitor_type))+ + geom_col()+ + theme_bw()+ + coord_flip()+ + scale_y_continuous(breaks = seq(0,120, 20), expand = c(0,0))+ + labs(x = "Restoration Location", + y = "Number of Visits", + fill = "Type of Visitor", + title = "Total Number of Visits to Delta Restoration Areas by visitor type", + subtitle = "Sum of all visits during study period")+ + my_theme ``` -You can also save all your code into an object in your working environment by assigning a name to the `ggplot()` code. +#### Reordering things -```{r} -#| eval: false -annual_region_plot <- ggplot(annual_esc_2000s, - aes(x = Year, - y = delta_visitsment, - color = Species)) + - geom_line() + - geom_point() + - scale_x_continuous(breaks = seq(2000, 2016, 2)) + - scale_y_continuous(labels = comma) + - facet_wrap( ~ SASAP.Region, - scales = "free_y") + - ylab("delta_visitsment") + - xlab("\nYear") + - ggtitle("Annual Salmon delta_visitsment by Region") + - my_theme + - theme(axis.text.x = element_text(angle = 45, - vjust = 0.5)) -``` +`ggplot()` loves putting things in alphabetical order. But more frequent than not, that's not the order you actually want things to be plotted. One way to do this is to use the `fct_reorder()` function from the `forcats` package. `forcats` provide tools for working with categorical variables. In this case, we want to reorder or categorical variable of "Restoration Location" base on the total number of visits. -And then call your object to see your plot. +The fist thing we need to do is to add a column to our data with the _total number of visits_ by location. ```{r} -#| eval: false -annual_region_plot -``` - +daily_visits_totals <- daily_visits_loc %>% + group_by(restore_loc) %>% + mutate(n = sum(daily_visits)) %>% + ungroup() -#### Reordering things +head(daily_visits_totals) +``` -`ggplot()` loves putting things in alphabetical order. But more frequent than not, that's not the order you actually want things to be plotted if you have categorical groups. Let’s find some total years of data by species for Kuskokwim. +Next, we will run the code for our plot adding the `fct_reorder()` function. -```{r n_year_data} -#| eval: false -#| warning: false -#| message: false +```{r} +ggplot(data = daily_visits_totals, + aes(x = fct_reorder(restore_loc, n), y = daily_visits, + fill = visitor_type))+ + geom_col()+ + theme_bw()+ + coord_flip()+ + scale_y_continuous(breaks = seq(0,120, 20), expand = c(0,0))+ + labs(x = "Restoration Location", + y = "Number of Visits", + fill = "Type of Visitor", + title = "Total Number of Visits to Delta Restoration Areas by visitor type", + subtitle = "Sum of all visits during study period")+ + my_theme -## Number Years of data for each salmon species at Kuskokwim -n_years <- annual_esc %>% - group_by(SASAP.Region, Species) %>% - summarize(n = n()) %>% - filter(SASAP.Region == "Kuskokwim") ``` -Now let's plot this using `geom_bar()`. +What if you want to plot the other way around? In this case from least to greater? We add the `desc()` to the variable we are sorting by. ```{r} -#| eval: false -## base plot -ggplot(n_years, - aes(x = Species, - y = n)) + - geom_bar(aes(fill = Species), - stat = "identity") +ggplot(data = daily_visits_totals, + aes(x = fct_reorder(restore_loc, desc(n)), y = daily_visits, + fill = visitor_type))+ + geom_col()+ + theme_bw()+ + coord_flip()+ + scale_y_continuous(breaks = seq(0,120, 20), expand = c(0,0))+ + labs(x = "Restoration Location", + y = "Number of Visits", + fill = "Type of Visitor", + title = "Total Number of Visits to Delta Restoration Areas by visitor type", + subtitle = "Sum of all visits during study period")+ + my_theme + ``` -Now, let's apply some of the customizations we have seen so far and learn some new ones. + -```{r} -#| eval: false -## Reordering, flipping coords and other customization -ggplot(n_years, - aes( - x = fct_reorder(Species, n), - y = n, - fill = Species - )) + - geom_bar(stat = "identity") + - coord_flip() + - theme_minimal() + - ## another way to customize labels - labs(x = "Species", - y = "Number of years of data", - title = "Number of years of delta_visitsment data for salmon species in Kuskokwim") + - theme(legend.position = "none") +?? Add somthing about reordering the legend?? -``` +#### Colors +Color can be fun and overwhelming +Introduce color pallets +things to keep in mind (how many colors you need) +Provide resources #### Saving plots +UDPDATE CODE + Saving plots using `ggplot` is easy! The `ggsave()` function will save either the last plot you created, or any plot that you have saved to a variable. You can specify what output format you want, size, resolution, etc. See `?ggsave()` for documentation. ```{r} @@ -579,6 +525,52 @@ ggsave(annual_region_plot, "figures/annual_esc_region.png", width = 12, height = ``` + + +#### Creating multiple plots + +UPDATE TEXT + +We know that in the graph we just plotted, each bar includes visit for multiple days. Let's leverage the power of `ggplot` to plot more aspects of our data in one plot. + +We are going to plot visits by **species over time, from 2000 to 2016, for each region. ** + +An easy way to plot another aspect of your data is using the function `facet_wrap()`. This function takes a mapping to a variable using the syntax `~{variable_name}`. The `~` (tilde) is a model operator which tells `facet_wrap()` to model each unique value within `variable_name` to a facet in the plot. + +The default behavior of facet wrap is to put all facets on the same x and y scale. You can use the `scales` argument to specify whether to allow different scales between facet plots (e.g `scales = "free_y"` to free the y axis scale). You can also specify the number of columns using the `ncol = ` argument or number of rows using `nrow =`. + + +ADD TEXT ABOUT SCALES + +```{r} +ggplot(data = daily_visits_totals, + aes(x = visitor_type, y = daily_visits, + fill = visitor_type))+ + geom_col()+ + theme_bw()+ + facet_wrap(~restore_loc, + scales = "free_y", + ncol = 5, + nrow = 2)+ + labs(x = "Type of visitor", + y = "Number of Visits", + title = "Total Number of Visits to Delta Restoration Areas", + subtitle = "Sum of all visits during study period")+ + theme_bw()+ + theme(legend.position = "bottom", + axis.ticks.x = element_blank(), + axis.text.x = element_blank()) +``` + + + +#### Smarter tick labels using `scales` + +Fixing tick labels in `ggplot` can be super annoying. The y-axis labels in the plot above don't look great. We could manually fix them, but it would likely be tedious and error prone. + +The `scales` package provides some nice helper functions to easily rescale and relabel your plots. Here, we use `scale_y_continuous()` from `ggplot2`, with the argument `labels`, which is assigned to the function name `comma`, from the `scales` package. This will format all of the labels on the y-axis of our plot with comma-formatted numbers. + + ## Interactive visualization ### Tables with `DT` From b7e7d81b2db52e21653173d9304d9087ed1d71c4 Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Tue, 15 Aug 2023 09:09:41 -0700 Subject: [PATCH 030/104] updating data viz learning goals --- materials/sections/visualization-delta.qmd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/materials/sections/visualization-delta.qmd b/materials/sections/visualization-delta.qmd index f6363adf..b556c36f 100644 --- a/materials/sections/visualization-delta.qmd +++ b/materials/sections/visualization-delta.qmd @@ -1,8 +1,8 @@ ## Learning Objectives {.unnumbered} -- Explain the basics of how the `ggplot2` package works -- Use `ggplot2`'s `theme` abilities to customize plots and create publication-grade graphics +- Understand the fundamentals of how the `ggplot2` package works +- Use `ggplot2`'s `theme` and other customization functions create publication-grade graphics - Introduce the `leaflet` and `DT` package to create interactive maps and tables respectively ## Overview From a1c066f5bd3fb456d8d5d11802e4a467b31b0bf5 Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Thu, 17 Aug 2023 21:13:11 -0700 Subject: [PATCH 031/104] revising static plot part of lesson. Added clyrifying text and reorder layers of plot --- materials/sections/visualization-delta.qmd | 88 ++++++++++------------ 1 file changed, 39 insertions(+), 49 deletions(-) diff --git a/materials/sections/visualization-delta.qmd b/materials/sections/visualization-delta.qmd index b556c36f..18118b5e 100644 --- a/materials/sections/visualization-delta.qmd +++ b/materials/sections/visualization-delta.qmd @@ -56,7 +56,7 @@ library(janitor) # expedite cleaning and exploring data #| warning: false delta_visits <- read_csv("https://portal.edirepository.org/nis/dataviewer?packageid=edi.587.1&entityid=cda8c1384af0089b506d51ad8507641f") %>% - clean_names() + janitor::clean_names() ``` @@ -209,7 +209,7 @@ We will use the pipe operator to pass into `ggplot()` a filtered version of `dai **Boxplot** -_Note: These examples are to demonstrate case uses of wrangling function proir to plotting. They are not necessarily plotting best practices._ +_Note: These examples are to demonstrate case uses of wrangling function prior to plotting. They are not necessarily plotting best practices._ ```{r boxplot_ex} daily_visits_loc %>% @@ -298,9 +298,10 @@ ggplot(data = daily_visits_loc, We have successfully plotted our data. But, this is clearly not a nice plot. Let's work on making this plot look a bit nicer. We are going to" -- Include a built in theme using `theme_bw()` -- Flip the x and y axis to better read the graph - Add a title, subtitle and adjust labels using `labs()` +- Flip the x and y axis to better read the graph +- Include a built in theme using `theme_bw()` + There are a wide variety of built in themes in `ggplot` that help quickly set the look of the plot. Use the RStudio auto-complete `theme_` `` to view a list of theme functions. @@ -312,13 +313,14 @@ ggplot(data = daily_visits_loc, aes(x = restore_loc, y = daily_visits, fill = visitor_type))+ geom_col()+ - theme_bw()+ - coord_flip()+ labs(x = "Restoration Location", y = "Number of Visits", fill = "Type of Visitor", title = "Total Number of Visits to Delta Restoration Areas by visitor type", - subtitle = "Sum of all visits during study period") + subtitle = "Sum of all visits during July 2017 and March 2018")+ + coord_flip()+ + theme_bw() + ``` @@ -346,14 +348,16 @@ ggplot(data = daily_visits_loc, aes(x = restore_loc, y = daily_visits, fill = visitor_type))+ geom_col()+ - theme_bw()+ - coord_flip()+ labs(x = "Restoration Location", y = "Number of Visits", fill = "Type of Visitor", title = "Total Number of Visits to Delta Restoration Areas by visitor type", subtitle = "Sum of all visits during study period")+ - theme(legend.position = "bottom") + coord_flip()+ + theme_bw()+ + theme(legend.position = "bottom", + axis.ticks.x = element_blank()) + ``` @@ -363,7 +367,7 @@ Note that the `theme()` call needs to come after any built-in themes like `theme You can also save the result of a series of `theme()` function calls to an object to use on multiple plots. This prevents needing to copy paste the same lines over and over again! ```{r save_theme} -my_theme <- theme_bw(base_size = 14) + +my_theme <- theme_bw(base_size = 16) + theme(legend.position = "bottom", axis.ticks.x = element_blank()) ``` @@ -376,12 +380,12 @@ ggplot(data = daily_visits_loc, aes(x = restore_loc, y = daily_visits, fill = visitor_type))+ geom_col()+ - coord_flip()+ labs(x = "Restoration Location", y = "Number of Visits", fill = "Type of Visitor", title = "Total Number of Visits to Delta Restoration Areas by visitor type", subtitle = "Sum of all visits during study period")+ + coord_flip()+ my_theme ``` @@ -423,14 +427,13 @@ ggplot(data = daily_visits_loc, aes(x = restore_loc, y = daily_visits, fill = visitor_type))+ geom_col()+ - theme_bw()+ - coord_flip()+ - scale_y_continuous(breaks = seq(0,120, 20), expand = c(0,0))+ labs(x = "Restoration Location", y = "Number of Visits", fill = "Type of Visitor", title = "Total Number of Visits to Delta Restoration Areas by visitor type", subtitle = "Sum of all visits during study period")+ + coord_flip()+ + scale_y_continuous(breaks = seq(0,120, 20), expand = c(0,0))+ my_theme ``` @@ -441,7 +444,7 @@ ggplot(data = daily_visits_loc, `ggplot()` loves putting things in alphabetical order. But more frequent than not, that's not the order you actually want things to be plotted. One way to do this is to use the `fct_reorder()` function from the `forcats` package. `forcats` provide tools for working with categorical variables. In this case, we want to reorder or categorical variable of "Restoration Location" base on the total number of visits. -The fist thing we need to do is to add a column to our data with the _total number of visits_ by location. +The fist thing we need to do is to add a column to our data with the _total number of visits_ by location. This will be our "sorting" variable. ```{r} daily_visits_totals <- daily_visits_loc %>% @@ -459,14 +462,13 @@ ggplot(data = daily_visits_totals, aes(x = fct_reorder(restore_loc, n), y = daily_visits, fill = visitor_type))+ geom_col()+ - theme_bw()+ - coord_flip()+ - scale_y_continuous(breaks = seq(0,120, 20), expand = c(0,0))+ labs(x = "Restoration Location", y = "Number of Visits", fill = "Type of Visitor", title = "Total Number of Visits to Delta Restoration Areas by visitor type", subtitle = "Sum of all visits during study period")+ + coord_flip()+ + scale_y_continuous(breaks = seq(0,120, 20), expand = c(0,0))+ my_theme ``` @@ -494,9 +496,10 @@ ggplot(data = daily_visits_totals, :::callout-important Note how `coord_flip()` changes the parameters ::: +?? Add somthing about reordering the legend?? ---> -?? Add somthing about reordering the legend?? + #### Colors Color can be fun and overwhelming @@ -507,41 +510,22 @@ Provide resources #### Saving plots -UDPDATE CODE - Saving plots using `ggplot` is easy! The `ggsave()` function will save either the last plot you created, or any plot that you have saved to a variable. You can specify what output format you want, size, resolution, etc. See `?ggsave()` for documentation. ```{r} #| eval: false -ggsave("figures/nyears_data_kus.jpg", width = 8, height = 6, units = "in") -``` - - -We can also save our facet plot showing annual delta_visitsments by region calling the plot's object. - -```{r} -#| eval: false -ggsave(annual_region_plot, "figures/annual_esc_region.png", width = 12, height = 8, units = "in") +ggsave("figures/visit_restore_site_delta.jpg", width = 8, height = 6, units = "in") ``` - #### Creating multiple plots -UPDATE TEXT - -We know that in the graph we just plotted, each bar includes visit for multiple days. Let's leverage the power of `ggplot` to plot more aspects of our data in one plot. - -We are going to plot visits by **species over time, from 2000 to 2016, for each region. ** - An easy way to plot another aspect of your data is using the function `facet_wrap()`. This function takes a mapping to a variable using the syntax `~{variable_name}`. The `~` (tilde) is a model operator which tells `facet_wrap()` to model each unique value within `variable_name` to a facet in the plot. The default behavior of facet wrap is to put all facets on the same x and y scale. You can use the `scales` argument to specify whether to allow different scales between facet plots (e.g `scales = "free_y"` to free the y axis scale). You can also specify the number of columns using the `ncol = ` argument or number of rows using `nrow =`. -ADD TEXT ABOUT SCALES - ```{r} ggplot(data = daily_visits_totals, aes(x = visitor_type, y = daily_visits, @@ -564,11 +548,11 @@ ggplot(data = daily_visits_totals, -#### Smarter tick labels using `scales` + ## Interactive visualization @@ -585,9 +569,13 @@ locations <- visits_long %>% distinct(restore_loc, .keep_all = T) %>% select(restore_loc, latitude, longitude) +head(locations) ``` -And display it as an interactive table using `datatable()` from the `DT` package. +The `dplyr::distinct()` function comes pretty handy when you want to filter unique values in a column. In this case we use the `.keep_all = T` argument to keep all the columns of our data frame so we can have the `lat` and `long` of each of the locations. If we don't add this argument, we would end up with a data frame with only one column: `restore_loc` and 10 rows, one for each of the unique locations. + + +Now we can display this table as an interactive table using `datatable()` from the `DT` package. ```{r display_locations} datatable(locations) @@ -595,7 +583,7 @@ datatable(locations) ### Maps with `leaflet` -Similar to `ggplot2`, you can make a basic `leaflet` map using just a couple lines of code. Note that unlike `ggplot2`, the `leaflet` package uses pipe operators (`%>%`) and not the additive operator (`+`). +The `leaflet()` package allows you to make basic interactive maps using just a couple lines of code. Note that unlike `ggplot2`, the `leaflet` package uses pipe operators (`%>%`) and not the additive operator (`+`). The `addTiles()` function without arguments will add base tiles to your map from [OpenStreetMap](https://www.openstreetmap.org/). `addMarkers()` will add a marker at each location specified by the latitude and longitude arguments. Note that the `~` symbol is used here to model the coordinates to the map (similar to `facet_wrap()` in `ggplot`). @@ -618,11 +606,13 @@ You can also use `leaflet` to import Web Map Service (WMS) tiles. Here is an exa ```{r} #| eval: false leaflet(locations) %>% - addWMSTiles( - "https://www.gebco.net/data_and_products/gebco_web_services/web_map_service/mapserv?request=getmap&service=wms&BBOX=-90,-180,90,360&crs=EPSG:4326&format=image/jpeg&layers=gebco_latest&width=1200&height=600&version=1.3.0", - layers = 'GEBCO_LATEST', - attribution = "Imagery reproduced from the GEBCO_2022 Grid, WMS 1.3.0 GetMap, www.gebco.net" - ) %>% + # addWMSTiles( + # "https://www.gebco.net/data_and_products/gebco_web_services/web_map_service/mapserv?request=getmap&service=wms&BBOX=-90,-180,90,360&crs=EPSG:4326&format=image/jpeg&layers=gebco_latest&width=1200&height=600&version=1.3.0", + # layers = 'GEBCO_LATEST', + # attribution = "Imagery reproduced from the GEBCO_2022 Grid, WMS 1.3.0 GetMap, www.gebco.net" + # ) %>% + addWMSTiles("https://basemap.nationalmap.gov/arcgis/services/USGSTopo/MapServer/WmsServer", + layers = "0") %>% addCircleMarkers( lng = ~ Longitude, lat = ~ Latitude, From 09a434c3eb019402a095111f153286a4ab165c57 Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Thu, 24 Aug 2023 20:53:44 -0700 Subject: [PATCH 032/104] revising data viz and addind color blurb --- materials/sections/visualization-delta.qmd | 52 ++++++++++++++-------- 1 file changed, 34 insertions(+), 18 deletions(-) diff --git a/materials/sections/visualization-delta.qmd b/materials/sections/visualization-delta.qmd index 18118b5e..ec97c5c1 100644 --- a/materials/sections/visualization-delta.qmd +++ b/materials/sections/visualization-delta.qmd @@ -46,17 +46,18 @@ library(leaflet) # interactive maps library(DT) # interactive tables library(scales) # scale functions for visualization library(janitor) # expedite cleaning and exploring data +library(veridis) # colorblind friendly color pallet ``` -2. Load the data directly from the [EDI Data Repository: Sacramento-San Joaquin Delta Socioecological Monitoring ](https://portal.edirepository.org/nis/mapbrowse?packageid=edi.587.1). Navegate to the link above, scroll down and under *Resources*, hover over the "Download" button for the "Socioecological monitoring data", right click, and select "Copy Link Address". +2. Load the data directly from the [EDI Data Repository: Sacramento-San Joaquin Delta Socioecological Monitoring ](https://portal.edirepository.org/nis/mapbrowse?packageid=edi.587.1). Navegate to the link above, scroll down and under *Resources*, hover over the "Download" button for the "Socioecological monitoring data", right click, and select "Copy Link Address". _Note:_ `janitor::clean_names()` is an awesome function to transform all column names into the same format. ```{r} #| message: false #| warning: false delta_visits <- read_csv("https://portal.edirepository.org/nis/dataviewer?packageid=edi.587.1&entityid=cda8c1384af0089b506d51ad8507641f") %>% - janitor::clean_names() + janitor::clean_names() ## Introducing this new package! ``` @@ -134,7 +135,7 @@ head(daily_visits_loc) ``` -The chunk above uses some of the dplyr functions that we've used in the past. We use `group_by()` to indicate that we want to calculate our results for the unique combinations of type of visit, restoration location proximity, and day. We next use `summarize()` to calculate an daily visit value for each of these groups. Note we use the `%in%` operator to pipe in the result of one command as an argument to the next one. +The chunk above uses some of the dplyr functions that we've used in the past. We use `group_by()` to indicate that we want to calculate our results for the unique combinations of type of visit, restoration location proximity, and day. We next use `summarise()` to calculate an daily visit value for each of these groups. Note we use the `%in%` operator to pipe in the result of one command as an argument to the next one. ## Plotting with `ggplot2` @@ -296,17 +297,13 @@ ggplot(data = daily_visits_loc, #### Setting ggplot themes -We have successfully plotted our data. But, this is clearly not a nice plot. Let's work on making this plot look a bit nicer. We are going to" +We have successfully plotted our data. But, this is clearly not a nice plot. Let's work on making this plot look a bit nicer. We are going to: - Add a title, subtitle and adjust labels using `labs()` - Flip the x and y axis to better read the graph - Include a built in theme using `theme_bw()` - -There are a wide variety of built in themes in `ggplot` that help quickly set the look of the plot. Use the RStudio auto-complete `theme_` `` to view a list of theme functions. - - ```{r theme_bw_plot} ggplot(data = daily_visits_loc, @@ -324,7 +321,7 @@ ggplot(data = daily_visits_loc, ``` -You can see that the `theme_bw()` function changed a lot of the aspects of our plot! The background is white, the grid is a different color, etc. There are lots of other built in themes like this that come with the `ggplot2` package. +You can see that the `theme_bw()` function changed a lot of the aspects of our plot! The background is white, the grid is a different color, etc. There are lots of other built in themes like this that come with the `ggplot2` package that help quickly set the look of the plot. Use the RStudio auto-complete `theme_` `` to view a list of theme functions. ```{r} @@ -341,7 +338,7 @@ theme_classic() The built in theme functions (`theme_*()`) change the default settings for many elements that can also be changed individually using the`theme()` function. The `theme()` function is a way to further fine-tune the look of your plot. This function takes MANY arguments (just have a look at `?theme`). Luckily there are many great `ggplot` resources online so we don't have to remember all of these, just Google ["ggplot cheat sheet"](https://www.google.com/search?q=ggplot+cheat+sheet&rlz=1C5CHFA_enUS989US989&oq=ggplot+chea&aqs=chrome.0.0i433i512j69i57j0i512l2j0i22i30i395l2.1681j1j7&sourceid=chrome&ie=UTF-8) and find one you like. -Let's look at an example of a `theme()` call, where we change the position of the legend from the right side to the bottom, and remove its title. +Let's look at an example of a `theme()` call, where we change the position of the legend from the right side to the bottom, and remove the ticks of our Locations axis. ```{r} ggplot(data = daily_visits_loc, @@ -356,7 +353,7 @@ ggplot(data = daily_visits_loc, coord_flip()+ theme_bw()+ theme(legend.position = "bottom", - axis.ticks.x = element_blank()) + axis.ticks.y = element_blank()) ## note we mention y-axis here ``` @@ -369,7 +366,7 @@ You can also save the result of a series of `theme()` function calls to an objec ```{r save_theme} my_theme <- theme_bw(base_size = 16) + theme(legend.position = "bottom", - axis.ticks.x = element_blank()) + axis.ticks.y = element_blank()) ``` So now our code will look like this: @@ -500,12 +497,31 @@ Note how `coord_flip()` changes the parameters ---> - #### Colors -Color can be fun and overwhelming -Introduce color pallets -things to keep in mind (how many colors you need) -Provide resources + +The last thing we will do to our plot is change the color. To do this we are going to use a functions from the `viridis` package. This package provides different color pallets that are designed to improve graph readability for readers with common forms of color blindness and/or color vision deficiency. As `viridis` there are multiple other color pallet packages or color pallets out there that you can use to customize your graphs. We could spend a whole session talking about colors in R! For the purpose of this lesson we are just going to keep it brief and show one function of the `viridis` package that will make our plot colors look better. + +```{r} + +ggplot(data = daily_visits_totals, + aes(x = fct_reorder(restore_loc, desc(n)), y = daily_visits, + fill = visitor_type))+ + geom_col()+ + theme_bw()+ + coord_flip()+ + scale_y_continuous(breaks = seq(0,120, 20), expand = c(0,0))+ + scale_fill_viridis_d()+ + labs(x = "Restoration Location", + y = "Number of Visits", + fill = "Type of Visitor", + title = "Total Number of Visits to Delta Restoration Areas by visitor type", + subtitle = "Sum of all visits during study period")+ + my_theme + + +``` + +Thing to keep in mind when choosing a color pallet is the number of variables you have and how many colors your pallet have. And if your need a discrete or a continuous color pallet. Find more information about colors in this [R color cheatsheet](https://www.nceas.ucsb.edu/sites/default/files/2020-04/colorPaletteCheatsheet.pdf). #### Saving plots @@ -514,7 +530,7 @@ Saving plots using `ggplot` is easy! The `ggsave()` function will save either th ```{r} #| eval: false -ggsave("figures/visit_restore_site_delta.jpg", width = 8, height = 6, units = "in") +ggsave("figures/visit_restore_site_delta.jpg", width = 12, height = 6, units = "in") ``` From a035e9bc97213b5761d7bc7d9efe06bff196f6d7 Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Thu, 24 Aug 2023 20:54:24 -0700 Subject: [PATCH 033/104] updating index and adding week 2 schedule --- materials/images/schedule.png | Bin 300561 -> 308062 bytes materials/index.qmd | 12 ++++-------- 2 files changed, 4 insertions(+), 8 deletions(-) diff --git a/materials/images/schedule.png b/materials/images/schedule.png index a138e8579157368d832e076472b0933fefb9aadb..f839f016c8773e0c1ed4406a77ac0f86353f2c56 100644 GIT binary patch literal 308062 zcmeFZWmH^S)-?m&WyB6@B zy4_EA=idIFxBKTi#>*H*9jeYg`<%VkUTdzo<|bH4LGm#M5e5Ov_kyZx<{Roh$1Lq9dL;=k!W z^8ShCe4cu~=oZ6i)+)C?GSsrM=s=64WKJAKPZ;-rTs(pG$(gg!8wqjF11evnSY@>b 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z{qL-peI~5Bwm>smMbPXH3puRsIE}#kn#KVs-!@u#eMWE^Z1VP^AY%MWBsPiQ^g@CS z4;e;)?Q&dKTzR9E*${16reFM%Ngl{d>Irsm5x(gwBfw=TS=H8?L~sUeloDq*k95GI z2OCBsuK@pYDxG6CM6f1G1Dj}?p#khcd}C)x%mOoo-e|yH +![](images/schedule.png) ::: - ## Next training @@ -49,7 +45,7 @@ UPDATE IMAGE WITH WEEK 2 SCHEDULE - Handling missing data - Big data workflows and parallel computing -- Building scientific websites with R and Shiny +- GitHub Workflows - Synthesis presentations and next steps From bcd16ca8993afda9a8f65fb36295c35de7097ec1 Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Thu, 24 Aug 2023 20:54:49 -0700 Subject: [PATCH 034/104] adding census lesson learning objectives --- materials/sections/census-data.qmd | 22 +++++++++++++++++++++- 1 file changed, 21 insertions(+), 1 deletion(-) diff --git a/materials/sections/census-data.qmd b/materials/sections/census-data.qmd index ec29f0e9..52e42d70 100644 --- a/materials/sections/census-data.qmd +++ b/materials/sections/census-data.qmd @@ -1,5 +1,7 @@ ## Learning Objectives {.unnumbered} - +- Provide an overview of US Census data +- Introduce the main functions of the `tidycensus` package to be able to work with census data +- Review data wrangling function to get census data ready for analysis ## Overview @@ -17,6 +19,24 @@ (Chapter 2 - 5 min) "The `tidycensus` is an R package that provides an interface to access and work with the United States Census Bureau data. It simplifies the process of retrieving and analyzing census data by allowing users to query data directly from the Census Bureau's API and then organize the data into a tidy format for easy manipulation and analysis." + + +## Getting Started +In order to be able to use the `tidycensus` package, we need to: + +- Install the package in our machine (run: `install.package("tidycensus")` in the console) +- Get an API key to connect our session with the census data and be able to retrieve data + +As we are working in the server, the package is already installed in our system (so we can skip this step). But when you go work in your own computers, you will have to install the package if you haven't done that already. + + + + +```{r} + +``` + + ## Querying data - Basic data requests - Fundamentals of data package and the options it offers From 2030d90511493ce8d7128630c4e8a91481d1c935 Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Thu, 24 Aug 2023 21:59:29 -0700 Subject: [PATCH 035/104] adding USGS base map to leaflet plots --- materials/sections/visualization-delta.qmd | 84 ++++++++++++++++------ 1 file changed, 62 insertions(+), 22 deletions(-) diff --git a/materials/sections/visualization-delta.qmd b/materials/sections/visualization-delta.qmd index ec97c5c1..cc1e777f 100644 --- a/materials/sections/visualization-delta.qmd +++ b/materials/sections/visualization-delta.qmd @@ -526,7 +526,7 @@ Thing to keep in mind when choosing a color pallet is the number of variables yo #### Saving plots -Saving plots using `ggplot` is easy! The `ggsave()` function will save either the last plot you created, or any plot that you have saved to a variable. You can specify what output format you want, size, resolution, etc. See `?ggsave()` for documentation. +Saving plots using `ggplot` is easy! The `ggsave()` function will save either the last plot you created, or any plot that you have saved to an object. You can specify what output format you want, size, resolution, etc. See `?ggsave()` for documentation. ```{r} #| eval: false @@ -543,7 +543,7 @@ The default behavior of facet wrap is to put all facets on the same x and y scal ```{r} -ggplot(data = daily_visits_totals, +facet_plot <- ggplot(data = daily_visits_totals, aes(x = visitor_type, y = daily_visits, fill = visitor_type))+ geom_col()+ @@ -552,6 +552,7 @@ ggplot(data = daily_visits_totals, scales = "free_y", ncol = 5, nrow = 2)+ + scale_fill_viridis_d()+ labs(x = "Type of visitor", y = "Number of Visits", title = "Total Number of Visits to Delta Restoration Areas", @@ -562,6 +563,14 @@ ggplot(data = daily_visits_totals, axis.text.x = element_blank()) ``` +We can save this plot to our figures folder too. Note that this time we are specifically mentioning the object we want to save. + +```{r} +#| eval: false + +ggsave("figures/visit_restore_site_facet.jpg", facet_plot, width = 12, height = 8, units = "in") + +``` -#### Survey Monkey {-} +### Survey Monkey Similar to Qualtrics and qualtRics, there is an open source R package for working with data in Survey Monkey: [Rmonkey](https://github.com/cloudyr/Rmonkey). However, the last updates were made 5 years ago, an eternity in the software world, so it may or may not still function as intended. diff --git a/materials/session_13.qmd b/materials/session_13.qmd index 785fa69b..dcdbad7c 100644 --- a/materials/session_13.qmd +++ b/materials/session_13.qmd @@ -2,5 +2,6 @@ title: "Reproducible Surveys" title-block-banner: true --- - + + {{< include /sections/survey-workflow.qmd >}} From b69c541ee0ccbcc9e090afa0135b9995ee090457 Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Thu, 24 Aug 2023 23:23:01 -0700 Subject: [PATCH 038/104] fixing typo in viridis package --- materials/sections/visualization-delta.qmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/materials/sections/visualization-delta.qmd b/materials/sections/visualization-delta.qmd index cc1e777f..6b51488d 100644 --- a/materials/sections/visualization-delta.qmd +++ b/materials/sections/visualization-delta.qmd @@ -46,7 +46,7 @@ library(leaflet) # interactive maps library(DT) # interactive tables library(scales) # scale functions for visualization library(janitor) # expedite cleaning and exploring data -library(veridis) # colorblind friendly color pallet +library(viridis) # colorblind friendly color pallet ``` From ee0b4fe4d802530c7f70d9d97b4ba550fb7956e7 Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Fri, 25 Aug 2023 09:47:35 -0700 Subject: [PATCH 039/104] commenting out census text while finishing lesson --- materials/sections/census-data.qmd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/materials/sections/census-data.qmd b/materials/sections/census-data.qmd index 52e42d70..3fc54de6 100644 --- a/materials/sections/census-data.qmd +++ b/materials/sections/census-data.qmd @@ -4,7 +4,7 @@ - Review data wrangling function to get census data ready for analysis -## Overview + From bb20f6f8311df6dfd89cc170ff212e980fc9a258 Mon Sep 17 00:00:00 2001 From: hdolinh Date: Fri, 25 Aug 2023 15:58:50 -0700 Subject: [PATCH 040/104] add text to section to 12.1.1 --- materials/sections/intro-tidy-text-data.qmd | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/materials/sections/intro-tidy-text-data.qmd b/materials/sections/intro-tidy-text-data.qmd index 8754690b..096c3dc1 100644 --- a/materials/sections/intro-tidy-text-data.qmd +++ b/materials/sections/intro-tidy-text-data.qmd @@ -19,8 +19,6 @@ Text data is information stored as character or string data types. It comes in v ### How do we talk about text data? -There's a lot of jargon that comes with working with text data. What do we know about these terms? When you think of text data or text analysis what do you think of? - Here is a list of text data or text analysis terms we'll be referring to throughout this lesson. Note this is not a comprehensive list of text analysis terms that are used beyond this lesson. | Term | Definition | @@ -35,6 +33,10 @@ Here is a list of text data or text analysis terms we'll be referring to through ### How is text data used in the environmental field? +As our knowledge about the environmental world grows, researchers will need new computational approaches for working with text data because reading and identifying all the relevant literature for literature syntheses is becoming an increasingly difficult task. + +Beyond literature syntheses, quantitative text analysis tools are extremely valuable for efficiently extracting information from texts and other text mining or text analysis tasks. + ## What is tidy text data? Let's recall what are the three tidy data principles: @@ -141,6 +143,8 @@ ggplot(data = count_bradbury, aes(n, reorder(word, n))) + ## Unstructured text data +typically raw text = unstructured + ## Structured text data ## Where can we find it and access it? From 7fdc1434c734bf441084946b2b9fbd759b281514 Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Fri, 25 Aug 2023 11:30:30 -0700 Subject: [PATCH 041/104] minor edits to survey lesson --- materials/sections/survey-workflow.qmd | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/materials/sections/survey-workflow.qmd b/materials/sections/survey-workflow.qmd index 91196939..43ad82f7 100644 --- a/materials/sections/survey-workflow.qmd +++ b/materials/sections/survey-workflow.qmd @@ -64,7 +64,7 @@ Next, we need to set the API credentials. This function modifies the `.Renviron` The API key is as good as a password, so care should be taken to not share it publicly. For example, you would never want to save it in a script. The function below is the rare exception of code that should be run in the console and not saved. It works in a way that you only need to run it once, unless you are working on a new computer or your credentials changed. Note that in this book, we have not shared the actual API key, for the reasons outlined above. You should have an e-mail with the API key in it. Copy and paste it as a string to the `api_key` argument in the function below: ```{r, eval = FALSE} -qualtrics_api_credentials(api_key = "", base_url = "ucsb.co1.qualtrics.com", install = TRUE) +qualtrics_api_credentials(api_key = "", base_url = "ucsb.co1.qualtrics.com", install = TRUE, overwrite = T) ``` :::callout-tip @@ -148,7 +148,8 @@ q3 <- survey_results %>% ``` ```{r, eval = FALSE} -ggplot(data = q3, mapping = aes(x = Q3, y = n)) + +ggplot(data = q3, + mapping = aes(x = Q3, y = n)) + geom_col() + labs(x = "What language do you currently use most frequently?", y = "Number of reponses") + theme_minimal() @@ -180,6 +181,7 @@ kable(q4, col.names = "What data science tool or language are you most excited t ``` + ## Other survey tools ### Google forms @@ -234,6 +236,9 @@ We can make the responses data frame more compact by renaming the columns of the ```{r} names(questions) <- paste0("Q", seq(1:4)) + +questions + colnames(responses) <- c("Timestamp", names(questions)) dplyr::glimpse(responses) ``` From c1c6bdb5f19594093b7da4f3f0d845049b3dce5b Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Fri, 25 Aug 2023 17:29:16 -0700 Subject: [PATCH 042/104] Replacing rmd with qmd and shortening collaboration-cleanining and wrangling-visualization practice session files --- .../r-practice-clean-wrangle-visualize.qmd | 23 +++++++++---------- .../r-practice-collaborator-clean-wrangle.qmd | 8 +++---- .../r-practice-collaborator-visualize.qmd | 16 ++++++------- .../r-practice-owner-clean-wrangle.qmd | 8 +++---- .../sections/r-practice-owner-visualize.qmd | 15 ++++++------ 5 files changed, 35 insertions(+), 35 deletions(-) diff --git a/materials/sections/r-practice-clean-wrangle-visualize.qmd b/materials/sections/r-practice-clean-wrangle-visualize.qmd index 19c4275c..891924df 100644 --- a/materials/sections/r-practice-clean-wrangle-visualize.qmd +++ b/materials/sections/r-practice-clean-wrangle-visualize.qmd @@ -33,12 +33,12 @@ These exercises will be using data on abundance, size, and trap counts (fishing 2. **Collaborator** creates new files for exercise a. The Collaborator creates the following directory: i. `analysis` - b. After creating the directories, create the following R Markdown files and store them in the listed folders: - i. Title it: "Owner Analysis", save it as: `owner-analysis.Rmd`, and store in `analysis` folder - ii. Title it: "Collaborator Analysis", save it as: `collaborator-analysis.Rmd`, and store in `analysis` folder - iii. Title it: "Lobster Report" and save it as: `lobster-report.Rmd` (do not need to put in a folder) + b. After creating the directories, create the following Quarto files and store them in the listed folders: + i. Title it: "Owner Analysis", save it as: `owner-analysis.qmd`, and store in `analysis` folder + ii. Title it: "Collaborator Analysis", save it as: `collaborator-analysis.qmd`, and store in `analysis` folder + iii. Title it: "Lobster Report" and save it as: `lobster-report.qmd` (do not need to put in a folder) b. After creating the files, the Collaborator will `stage`, `commit`, write a commit message, `pull`, and `push` the files to the remote repository (on GitHub) - c. The Owner `pull`s the changes and R Markdown files into their local repository (their workspace) + c. The Owner `pull`s the changes and Quarto files into their local repository (their workspace) 3. **Owner** downloads data from the EDI Data Portal [SBC LTER: Reef: Abundance, size and fishing effort for California Spiny Lobster (Panulirus interruptus), ongoing since 2012](https://portal.edirepository.org/nis/mapbrowse?packageid=knb-lter-sbc.77.8). a. Create two new directories one called `data` and one called `figs` @@ -84,18 +84,18 @@ Questions 1-3 you will be working independently since you're working with differ ### Collaborate on a report and publish using GitHub pages -The final step! Time to work together again. Collaborate with your partner in `lobster-report.Rmd` to create a report to publish to GitHub pages. +The final step! Time to work together again. Collaborate with your partner in `lobster-report.qmd` to create a report to publish to GitHub pages. ::: {.callout-caution icon=false} ## Code Review -As you're working on the `lobster-report.Rmd` you will be conducting [two types of code reviews](https://en.wikibooks.org/wiki/Introduction_to_Software_Engineering/Quality/Code_Review#:~:text=Code%20review%20practices%20fall%20into,review%20and%20lightweight%20code%20review): (1) pair programming and (2) lightweight code review. +As you're working on the `lobster-report.qmd` you will be conducting [two types of code reviews](https://en.wikibooks.org/wiki/Introduction_to_Software_Engineering/Quality/Code_Review#:~:text=Code%20review%20practices%20fall%20into,review%20and%20lightweight%20code%20review): (1) pair programming and (2) lightweight code review. - **Pair programming** is where two people develop code together at the same workstation. One person is the "driver" and one person is the "navigator". The driver writes the code while the navigator observes the code being typed, points out any immediate quick fixes, and will also Google / troubleshoot if errors occur. Both the Owner and the Collaborator should experience both roles, so switch halfway through or at a meaningful stopping point. -- A **lightweight code review** is brief and you will be giving feedback on code readability and code logic as you're adding Owner and Collaborator code from their respective `analysis.Rmd`s to the `lobster-report.Rmd`. Think of it as a walk through of your the code for the data visualizations you plan to include in the report (this includes the code you wrote to create the subset for the plot and the code to create the plot) and give quick feedback. +- A **lightweight code review** is brief and you will be giving feedback on code readability and code logic as you're adding Owner and Collaborator code from their respective `analysis.qmd`s to the `lobster-report.qmd`. Think of it as a walk through of your the code for the data visualizations you plan to include in the report (this includes the code you wrote to create the subset for the plot and the code to create the plot) and give quick feedback. ::: -Make sure your R Markdown is well organized and includes the following elements: +Make sure your Quarto document is well organized and includes the following elements: - citation of the data - brief summary of the abstract (i.e. 1-2 sentences) from the [EDI Portal](https://portal.edirepository.org/nis/mapbrowse?packageid=knb-lter-sbc.77.8) @@ -109,7 +109,7 @@ Make sure your R Markdown is well organized and includes the following elements: Finally, publish on GitHub pages (from Owner's repository). Refer back to [Chapter 9](http://localhost:4769/session_12.html) for steps on how to publish using GitHub pages. -## Bonus: Add marine protected area (MPA) designation to the data + diff --git a/materials/sections/r-practice-collaborator-clean-wrangle.qmd b/materials/sections/r-practice-collaborator-clean-wrangle.qmd index 1bdc64cf..0bd5cd64 100644 --- a/materials/sections/r-practice-collaborator-clean-wrangle.qmd +++ b/materials/sections/r-practice-collaborator-clean-wrangle.qmd @@ -5,10 +5,10 @@ format: html ::: callout-tip ## Setup -1. Open the R Markdown file `collaborator-analysis.Rmd` +1. Open the Quarto file `collaborator-analysis.qmd` a. Check the `YAML` and add your name to the `author` field b. Create a new section with a level 2 header and title it "Exercise: Explore, Clean, and Wrangle Data" -2. Load the following libraries at the top of your R Markdown file. +2. Load the following libraries at the top of your Quarto file. ```{r} #| code-fold: false @@ -46,7 +46,7 @@ lobster_traps <- lobster_traps %>% mutate(TRAPS = na_if(TRAPS, -99999)) ``` -## `filter()` practice + diff --git a/materials/sections/r-practice-collaborator-visualize.qmd b/materials/sections/r-practice-collaborator-visualize.qmd index df905f43..673947ab 100644 --- a/materials/sections/r-practice-collaborator-visualize.qmd +++ b/materials/sections/r-practice-collaborator-visualize.qmd @@ -5,13 +5,13 @@ format: html ::: callout-tip ## Setup -1. Stay in the R Markdown file `collaborator-analysis.Rmd` and create a new section with a level 2 header and title it "Exercise: Data Visualization" +1. Stay in the Quarto file `collaborator-analysis.qmd` and create a new section with a level 2 header and title it "Exercise: Data Visualization" **Structure of the data visualization exercises:** -- Questions 7-9 will have you create the necessary subsets to create the data visualizations, as well as the basic code to create a visualization. -- Question 10, return to the data visualization code you've written and add styling code to it. For this exercise, only add styling code to the visualization you want to include in the `lobster-report.Rmd` (start with just one plot and if there's time add styling code to another plot). -- Lastly, save the final visualizations to the `figs` folder before collaborating on the `lobster-report.Rmd`. +- Questions 7-8 will have you create the necessary subsets to create the data visualizations, as well as the basic code to create a visualization. +- Question 9, return to the data visualization code you've written and add styling code to it. For this exercise, only add styling code to the visualization you want to include in the `lobster-report.qmd` (start with just one plot and if there's time add styling code to another plot). +- Lastly, save the final visualizations to the `figs` folder before collaborating on the `lobster-report.qmd`. ::: ```{r} @@ -88,7 +88,7 @@ ggplot(data = lobsters_traps_summarize, aes(x = YEAR, y = TOTAL_TRAPS)) + ::: - + + ::: callout-note -### Question 10 +### Question 9 1. Go back to your visualization code and add some styling code (aka make your plots pretty!). Again, start with one plot and if there's time add styling code to additional plots. Here's a list of functions to help you get started (this is not an exhaustive list!) or revisit the [data visualization lesson](https://learning.nceas.ucsb.edu/2023-04-coreR/session_12.html): diff --git a/materials/sections/r-practice-owner-clean-wrangle.qmd b/materials/sections/r-practice-owner-clean-wrangle.qmd index 5bd8ae68..b8e74c58 100644 --- a/materials/sections/r-practice-owner-clean-wrangle.qmd +++ b/materials/sections/r-practice-owner-clean-wrangle.qmd @@ -5,10 +5,10 @@ format: html ::: callout-tip ## Setup -1. Open the R Markdown file `owner-analysis.Rmd` +1. Open the Quarto file `owner-analysis.qmd` a. Check the `YAML` and add your name to the `author` field b. Create a new section with a level 2 header and title it "Exercise: Explore, Clean, and Wrangle Data" -2. Load the following libraries at the top of your R Markdown file +2. Load the following libraries at the top of your Quarto file ```{r} #| code-fold: false @@ -47,7 +47,7 @@ lobster_abundance <- lobster_abundance %>% mutate(SIZE_MM = na_if(SIZE_MM, -99999)) ``` -## `filter()` practice + diff --git a/materials/sections/r-practice-owner-visualize.qmd b/materials/sections/r-practice-owner-visualize.qmd index d3021ac3..e6af7963 100644 --- a/materials/sections/r-practice-owner-visualize.qmd +++ b/materials/sections/r-practice-owner-visualize.qmd @@ -5,13 +5,13 @@ format: html ::: callout-tip ## Setup -1. Stay in the R Markdown file `owner-analysis.Rmd` and create a new section with a level 2 header and title it "Exercise: Data Visualization" +1. Stay in the Quarto file `owner-analysis.qmd` and create a new section with a level 2 header and title it "Exercise: Data Visualization" **Structure of the data visualization exercises:** -- Questions 7-9 will have you create the necessary subsets to create the data visualizations, as well as the basic code to create a visualization. -- Question 10, return to the data visualization code you've written and add styling code to it. For this exercise, only add styling code to the visualization you want to include in the `lobster-report.Rmd` (start with just one plot and if there's time add styling code to another plot). -- Lastly, save the final visualizations to the `figs` folder before collaborating on the `lobster-report.Rmd`. +- Questions 7-8 will have you create the necessary subsets to create the data visualizations, as well as the basic code to create a visualization. +- Question 9, return to the data visualization code you've written and add styling code to it. For this exercise, only add styling code to the visualization you want to include in the `lobster-report.qmd` (start with just one plot and if there's time add styling code to another plot). +- Lastly, save the final visualizations to the `figs` folder before collaborating on the `lobster-report.qmd`. ::: ```{r} @@ -85,7 +85,7 @@ ggplot(data = lobsters_summarize, aes(x = YEAR, y = COUNT)) + ::: -::: callout-note + + ::: callout-note -### Question 10 +### Question 9 1. Go back to your visualization code and add some styling code (aka make your plots pretty!). Again, start with one plot and if there's time add styling code to additional plots. Here's a list of functions to help you get started (this is not an exhaustive list!) or revisit the [data visualization lesson](https://learning.nceas.ucsb.edu/2023-04-coreR/session_12.html): From 3506236075f772d360fee5ebf1171495e8dcfe57 Mon Sep 17 00:00:00 2001 From: Matt Jones Date: Fri, 25 Aug 2023 17:54:11 -0800 Subject: [PATCH 043/104] Fix spelling error in title. --- materials/session_06.qmd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/materials/session_06.qmd b/materials/session_06.qmd index 33e493f6..8dc51f42 100644 --- a/materials/session_06.qmd +++ b/materials/session_06.qmd @@ -1,6 +1,6 @@ --- -title: "Social Aspecst of Collaborations" +title: "Social Aspects of Collaborations" title-block-banner: true --- -{{< include /sections/collaboration-social-data-policies.qmd >}} \ No newline at end of file +{{< include /sections/collaboration-social-data-policies.qmd >}} From ef13a33b802788d83c7501e6f49595ac2434d7b4 Mon Sep 17 00:00:00 2001 From: hdolinh Date: Fri, 25 Aug 2023 15:58:50 -0700 Subject: [PATCH 044/104] add text to section to 12.1.1 --- materials/sections/intro-tidy-text-data.qmd | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/materials/sections/intro-tidy-text-data.qmd b/materials/sections/intro-tidy-text-data.qmd index 8754690b..096c3dc1 100644 --- a/materials/sections/intro-tidy-text-data.qmd +++ b/materials/sections/intro-tidy-text-data.qmd @@ -19,8 +19,6 @@ Text data is information stored as character or string data types. It comes in v ### How do we talk about text data? -There's a lot of jargon that comes with working with text data. What do we know about these terms? When you think of text data or text analysis what do you think of? - Here is a list of text data or text analysis terms we'll be referring to throughout this lesson. Note this is not a comprehensive list of text analysis terms that are used beyond this lesson. | Term | Definition | @@ -35,6 +33,10 @@ Here is a list of text data or text analysis terms we'll be referring to through ### How is text data used in the environmental field? +As our knowledge about the environmental world grows, researchers will need new computational approaches for working with text data because reading and identifying all the relevant literature for literature syntheses is becoming an increasingly difficult task. + +Beyond literature syntheses, quantitative text analysis tools are extremely valuable for efficiently extracting information from texts and other text mining or text analysis tasks. + ## What is tidy text data? Let's recall what are the three tidy data principles: @@ -141,6 +143,8 @@ ggplot(data = count_bradbury, aes(n, reorder(word, n))) + ## Unstructured text data +typically raw text = unstructured + ## Structured text data ## Where can we find it and access it? From fd499956941b3dbb48709f51e58f9a208eb6cc50 Mon Sep 17 00:00:00 2001 From: hdolinh Date: Sat, 26 Aug 2023 19:17:00 -0700 Subject: [PATCH 045/104] add setup + instructions for practice tidy text workflow (section 12.3) --- materials/sections/intro-tidy-text-data.qmd | 139 +++++++++++++------- 1 file changed, 94 insertions(+), 45 deletions(-) diff --git a/materials/sections/intro-tidy-text-data.qmd b/materials/sections/intro-tidy-text-data.qmd index 096c3dc1..e533394f 100644 --- a/materials/sections/intro-tidy-text-data.qmd +++ b/materials/sections/intro-tidy-text-data.qmd @@ -22,7 +22,7 @@ Text data is information stored as character or string data types. It comes in v Here is a list of text data or text analysis terms we'll be referring to throughout this lesson. Note this is not a comprehensive list of text analysis terms that are used beyond this lesson. | Term | Definition | -|-----------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +|---------------|---------------------------------------------------------| | Corpus (corpora, plural) | Collection or database of text or multiple texts. These types of objects typically contain raw strings annotated with additional metadata and details. | | Document-term matrix | Represents the relationship between terms and documents, where each row stands for a term and each column for a document, and an entry is the number of occurrences of the term in the document. | | Natural Language Processing (NLP) | NLP is an interdisciplinary field used in computer science, data science, linguistics, and others to analyze, categorize, and work with computerized text. | @@ -35,7 +35,7 @@ Here is a list of text data or text analysis terms we'll be referring to through As our knowledge about the environmental world grows, researchers will need new computational approaches for working with text data because reading and identifying all the relevant literature for literature syntheses is becoming an increasingly difficult task. -Beyond literature syntheses, quantitative text analysis tools are extremely valuable for efficiently extracting information from texts and other text mining or text analysis tasks. +Beyond literature syntheses, quantitative text analysis tools are extremely valuable for efficiently extracting information from texts and other text mining or text analysis tasks. ## What is tidy text data? @@ -51,86 +51,135 @@ This one-token-per-row structure is in contrast to the ways text is often stored For tidy text mining, the token that is stored in each row is most often a single word, but can also be an n-gram, sentence, or paragraph. -By using tidy data principles, we can apply many "tidy" R packages including dpylr, tidyr, ggplot2, and more. +By using tidy data principles, we can apply many "tidy" R packages including `dpylr`, `tidyr`, `ggplot2`, and more. ### What is the `tidytext` R package? -### PROJECT GUTENBERG - practice putting text data in tidy text format using classic horror books +::: columns +::: {.column width="30%"} +![](images/tidytext-logo.png){fig-align="center"} +::: + +::: {.column width="70%"} +`tidytext` is a package that applies the tidy principles to analyzing text.
+ +The package contains many useful functions to wrangle text data into tidy formats. It has been built considering other text mining R packages so that it's easy to switch between text mining tools (e.g. `quanteda`, `stringr`, `wordcloud2`). +::: +::: + +## Practice: Tidy Text Workflow ![](images/intro-tidy-text-workflow-1.png) -Text analysis workflow using tidy data principles +We are going to use the `gutenbergr` package to access public domain texts from [Project Gutenberg](https://www.gutenberg.org/) (a library of free eBooks). We'll then use the `tidytext`, `dyplr` and `ggplot2` packages to practice the tidy text workflow. + +Break out into groups and then follow the exercise setup and instructions. + +::: {.callout-tip} + +### Setup and Instructions + +1. Create a new `qmd` file and title it "Intro to Text Data", name yourself as the author, and then save the file as `intro-text-data.qmd`. + +2. Create a new code chunk and attach the following libraries: ```{r} -library(gutenbergr) -library(tidyr) -library(dplyr) -library(tidytext) -library(ggplot2) +#| message: false +library(gutenbergr) # access public domain texts from Project Gutenberg +library(tidytext) # text mining using tidy tools +library(dplyr) # wrangle data +library(ggplot2) # plot data ``` -```{r} -# get id number -gutenberg_works(title == "Dracula") +3. Depending on which group you're in, use one of the following public domain texts: -dracula_corp <- gutenberg_download(345) +```{r} +#| eval: false +# Group A +gutenberg_works(title == "Dracula") # dracula text -tidy_dracula <- dracula_corp %>% - unnest_tokens(word, text) %>% - anti_join(stop_words, by = "word") +# Group B +gutenberg_works(title == "Frankenstein; Or, The Modern Prometheus") # frankenstein text -tidy_dracula %>% - count(word, sort = TRUE) +# Group C +gutenberg_works(title == "Carmilla") # carmilla text ``` -```{r} -# get id number -gutenberg_works(title == "Frankenstein; Or, The Modern Prometheus") +4. Get the id number from the `gutenberg_works()` function so that you can download the text as a corpus using the function `gutenberg_download()`. Save the corpus to an object called `{book-title}_corp`. View the object - is the data in a tidy format? -frank_corp <- gutenberg_download(84) +5. Tokenize the corpus data using `unnest_tokens()`. Take a look at the data - do we need every single token for our analysis? -tidy_frank <- frank_corp %>% - unnest_tokens(word, text) %>% - anti_join(stop_words, by = "word") +6. Remove "stop words" or words that can be safely removed or ignored without sacrificing the meaning of the sentence (e.g. "to", "in", "and") using `anti_join()`. Take a look at the data - are you satisfied with your data? We won't conduct any additional cleaning steps here, but consider how you would further clean the data. -tidy_frank %>% - count(word, sort = TRUE) -``` +7. Calculate the top 10 most frequent words using the functions `count()` and `slice_max()`. -```{r} -# get id number -gutenberg_works(title == "Carmilla") +8. Plot the top 10 most frequent words using `ggplot()`. We reccommend creating either a bar plot using `geom_col()` or a lollipop plot using both `geom_point()` and `geom_segment()`. -carmilla_corp <- gutenberg_download(10007) +9. **Bonus**: Consider elements in `theme()` and improve your plot. +::: -tidy_carmilla <- carmilla_corp %>% - unnest_tokens(word, text) %>% - anti_join(stop_words, by = "word") +### Example using Ray Bradbury's *Asleep in Armageddon* -tidy_carmilla %>% - count(word, sort = TRUE) -``` +The code chunks below follows the instructions from above using Ray Bradbury's *Asleep in Armageddon*. ```{r} # get id number gutenberg_works(title == "Asleep in Armageddon") +``` -# text data +```{r} +#| code-fold: true +#| code-summary: Steps 4-7 Code +#| message: false +# access text data using id number from `gutenberg_works()` bradbury_corp <- gutenberg_download(63827) -# tidy text +# tidy text data - unnest and remove stop words tidy_bradbury <- bradbury_corp %>% unnest_tokens(word, text) %>% anti_join(stop_words, by = "word") -# summarized text +# calculate top 10 most frequent words count_bradbury <- tidy_bradbury %>% count(word) %>% slice_max(n = 10, order_by = n) +``` -# visualize text +```{r} +#| code-fold: true +#| code-summary: Step 8 Code +# visualize text data # +# bar plot ggplot(data = count_bradbury, aes(n, reorder(word, n))) + - geom_col() + geom_col() + + labs(x = "Count", + y = "Token") +``` + +```{r} +#| code-fold: true +#| code-summary: Steps 9 Code +# visualize text data # +# initial lollipop plot +# ggplot(data = count_bradbury, aes(x=word, y=n)) + +# geom_point() + +# geom_segment(aes(x=word, xend=word, y=0, yend=n)) + +# coord_flip() + +# labs(x = "Token", +# y = "Count") + +# ascending order pretty lollipop plot +ggplot(data = count_bradbury, aes(x=reorder(word, n), y=n)) + + geom_point(color="cyan4") + + geom_segment(aes(x=word, xend=word, y=0, yend=n), color="cyan4") + + coord_flip() + + labs(title = "Top Ten Words in Ray Bradbury's Asleep in Armageddon", + x = NULL, + y = "Count") + + theme_minimal() + + theme( + panel.grid.major.y = element_blank() + ) ``` ## Text analysis workflow with goal of ending with a DTM @@ -143,7 +192,7 @@ ggplot(data = count_bradbury, aes(n, reorder(word, n))) + ## Unstructured text data -typically raw text = unstructured +typically raw text = unstructured ## Structured text data From 41f556e1fca192418c28e23fdd5688f08c1f6897 Mon Sep 17 00:00:00 2001 From: hdolinh Date: Sat, 26 Aug 2023 19:17:20 -0700 Subject: [PATCH 046/104] add tidytext logo for section 12.2.1 --- materials/images/tidytext-logo.png | Bin 0 -> 14680 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 materials/images/tidytext-logo.png diff --git a/materials/images/tidytext-logo.png b/materials/images/tidytext-logo.png new file mode 100644 index 0000000000000000000000000000000000000000..3cc709591405c1720869cf3c848c54a824308015 GIT binary patch literal 14680 zcmZ8|WmH^E6DIC1!QCOa1`X~$1Hs)jxVr=o5ZpZs0YZSmJwVWb;O_1a9Fo27x4(7| 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zDAZt{m1jL&iRYl=Ag|3GUeBEaFt+sG3wSrwr!t}uMtZaNm^1jeMqTb#dOWliojM=0 zuRkanBK!*m3}Vwg%2SDisZ0gX4{n0Ym%(lqJOs`xhF{jt=dfC@N-T{iSOI<{Vo|Yi z`0;yLdAJ~)hgUt)3z_Lbg%J zi#?JqhUp*Eb@q3w4?kGY@UURL#8L}e#7j;b_JOfB%E1;QiTAFX|4vYMOjz3wv65M% zm^P3*qpRrOz7tKn=iQ7PFz>{a*CazxBn8@pc!7kD6dEImu}(4fMQ2kd)YPZ z%Ew%B5dqC(|1B^C?O_26L6eWn0Ut5KgUoSGdYGE#f#?$T0CT8A>ra!NyIfg+TJQJZ zyAGwBZGXi8E+h#nf)elEw63J5ZYhjJ-x>bswo5F-zFMGpG8%W+4>5b`MuGNq?)%d7Apt zFf`%+Q=osivd(ksw#mBx^(ZoV$$#*&w(znMweqk59&mg-yds=Df}DKsK)iyY{34=! ze4IQ&qC7l{j!h2#&j1%UYX@8Z|9=3#D6hc(9nkq{@EI6D_TLP;Znj>&79KWm($;R4 gHnhqv7Iro|HWt?YA4hEdYj=cGme-K0lQEC@KOe1D{Qv*} literal 0 HcmV?d00001 From 662e89483cde8204167f162d4331da99a376b4f9 Mon Sep 17 00:00:00 2001 From: hdolinh Date: Sun, 27 Aug 2023 18:51:59 -0700 Subject: [PATCH 047/104] comment out unfinished sections --- materials/sections/intro-tidy-text-data.qmd | 33 +++++++++++---------- 1 file changed, 17 insertions(+), 16 deletions(-) diff --git a/materials/sections/intro-tidy-text-data.qmd b/materials/sections/intro-tidy-text-data.qmd index e533394f..72bb2561 100644 --- a/materials/sections/intro-tidy-text-data.qmd +++ b/materials/sections/intro-tidy-text-data.qmd @@ -10,7 +10,7 @@ This lesson has been adapted from the following resources: - [Welcome to Text Mining with R](https://www.tidytextmining.com/) by [Julia Silge](https://juliasilge.com/) and [David Robinson](http://varianceexplained.org/). Julia and David are also the developers of the [`tidytext`](https://juliasilge.github.io/tidytext/) package. -- [Computational Social Science: Theory & Application, Version: 17 June, 2021](https://bookdown.org/paul/2021_computational_social_science/) by Paul C. Bauer +- [Section 7.3: (R-) Workflow for Text Analysis from Computational Social Science: Theory & Application, Version: 17 June, 2021](https://bookdown.org/paul/2021_computational_social_science/r-workflow-for-text-analysis.html) by Paul C. Bauer ::: ## What is text data? @@ -67,7 +67,7 @@ The package contains many useful functions to wrangle text data into tidy format ::: ::: -## Practice: Tidy Text Workflow +## Exercise: Tidy Text Workflow ![](images/intro-tidy-text-workflow-1.png) @@ -147,7 +147,7 @@ count_bradbury <- tidy_bradbury %>% ```{r} #| code-fold: true -#| code-summary: Step 8 Code +#| code-summary: Step 8 Plot Code # visualize text data # # bar plot ggplot(data = count_bradbury, aes(n, reorder(word, n))) + @@ -158,7 +158,7 @@ ggplot(data = count_bradbury, aes(n, reorder(word, n))) + ```{r} #| code-fold: true -#| code-summary: Steps 9 Code +#| code-summary: Step 9 Plot Code # visualize text data # # initial lollipop plot # ggplot(data = count_bradbury, aes(x=word, y=n)) + @@ -182,24 +182,25 @@ ggplot(data = count_bradbury, aes(x=reorder(word, n), y=n)) + ) ``` -## Text analysis workflow with goal of ending with a DTM + From 733c0065ab1d812c35e7bb090caf0dacb4feef9f Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Sun, 27 Aug 2023 21:29:26 -0700 Subject: [PATCH 048/104] adding instruction on how to reset PAT (Appendix) --- materials/_quarto.yml | 2 +- materials/images/discovered_token.png | Bin 0 -> 208917 bytes materials/images/git_sitrep.png | Bin 0 -> 109697 bytes materials/images/unset_token.png | Bin 0 -> 346621 bytes materials/sections/github-check-pat.qmd | 38 ++++++++++++++++++++++++ materials/session_17.qmd | 9 +++++- 6 files changed, 47 insertions(+), 2 deletions(-) create mode 100644 materials/images/discovered_token.png create mode 100644 materials/images/git_sitrep.png create mode 100644 materials/images/unset_token.png create mode 100644 materials/sections/github-check-pat.qmd diff --git a/materials/_quarto.yml b/materials/_quarto.yml index 72c6cbd9..14f58966 100755 --- a/materials/_quarto.yml +++ b/materials/_quarto.yml @@ -43,7 +43,7 @@ book: - session_14.qmd # synthesis # - session_15.qmd # synthesis - session_16.qmd # shiny - - session_17.qmd # 2FA setup + - session_17.qmd # 2FA setup + reset token bibliography: book.bib diff --git a/materials/images/discovered_token.png b/materials/images/discovered_token.png new file mode 100644 index 0000000000000000000000000000000000000000..296af97b2df930506ae7417e74f5b116430dea77 GIT binary patch literal 208917 zcmeGEcUY6nwg(K;!60IxBSjJEy>}21mEL<%n)Dh%4`4w=L_~TOq!R)}2!s+5=~bx_ z5 z5)$eb5)$%08Y*H9^*paT2??OiT~pKWfu<(6VSulzyO#?I$^H1$bZS$R)vM9x;l7F& zL~bw$e2~*nB>nh|Q+?IU^v+f8_m{~vG)zsIEc~>tTxqW(t7M?GDDJVYb+n=IDqD#a zzPX05tog7j>PORjIuwp=>CKdKSQ=83I#)T*mm-b6v+(k6d5toO`xjmRmq|)If{HI< zxJW4qF7m5iV)1Kay=iV}NTTjCL9oNjQjWZDb&*Y7pUpqFFq+$uY9vwD;P2j(Y;@<( zS3h91xHUvUQm%nV2uW#~L{Pe9v3OtpMG(->nXeUilka-bRq?j)S8^9d5*p`A+Bq~N zR`dr_b&c^)IP3WOnC|4Mzmz=TX#7}Qyk~fCos38N-m_iPDAzNG-CG0^w-#x>xD@_h znXS6C?Iv$;JpAQ+o)29B>sVJfH@qONSiDv1OXIP+qO`8_0GD1T9_2qLOdB$~Gn#X2 zhc4g6=Y{rw@Sc1g;d^~~0=cV##@t}qQm=E|prl%dP*7Uu>W%N#7ar432nT)*1o8^Z z8-uUriwx$cFfb|fTgATYgyke(T~lLTxN(rcd*eId_zoAY34!_ETIzDU+yZb7)l}_5~HcH#SUloP$KBRlswPzCfk?^|8aP^1ozWNY^?p<4l z^Bac|VRC~xO0p4patcx*M|$BH5gID)6r{)6YqF#uqXx^j8Si@?KfqrPJJL2S?Xj9O znNN`DqEVSVI}N@kqIZJ6=iQSdbSms%TeyRmjKOAXzLVdD6S^@T-+n zTlqRgP4qLu)sbky5emd-nv4&46e-`_i{K4vG7XlORQ)8(w_>EZsf08{E$IR3SEi_f zV*xc>rcbT?D6W%3<{0GZMW3!0u+Navx0A3?iG5}Xp});d^F@>4C7}F{^;=bEfR%=B z59MwwS;1v~wpUND-nelqCg4M&;T2OdGj-|$#TB=eTS1%%z=1mMgT@nvF!IP3S28Y+ zI?_#%*po;-V%ngOYuA~-8$y{wzT9CmAJ7O;y$JujJiorp5keA0n#4nrtg-S^_VEkx zEA+Q6$7@`@EK$cKaJTQR;KwWO+KS0GJtVtVkDs+aDr2GQy14Lcr}LRpV+LbJeTGN| zKv$%MGVn?ut<+1_j))H9nl($4DxS2ooWTRf(%ahZBL<+{PU!LcEJsB$T;XyKy& zp}d2wv?wc_4NeJXo_wYhx86=S-?bCcB8XwYzNY^gxSe@KcII>T=*%iYEQ;kknOk$@ zrsb8E3lbDJ9+7`|ZD=5`xx)MU^6i(qcOwgVB;NM$xL!+$m-!m}Q}3s^f9ywf$y*93 zU+(vQJop%3%(Hs6Jb&N(-o1O;T^hG7?{TN_nTQ6OF1;Ek7~reBiF&r7@rGBY$EG)~ zXSoO0>np(UinKHFs%Gy;ui`NyQA17>)jngFK^Ih=G74CiS|?K1S_fTx8E_IH+~g9V z5a1Hf{mZAX^*vu7drx>z;NoPTMPL4VB%fNfx>=1Wjp==psX{}jpV@roP5W=^-*l2o zGoY4`5DovAuMC0ZoTj2cp86MI@rt?~-!8g)SxZ7*20xq7VmC21tuuOMXkBbt9%Stv z{bq!2#Vbs(H6!YER64=RN;9)~zyeb>Q#Mn1rs0zJDjVQXY*_r@*PA{!vlkWV9L})jQUwdYq1lmbd0;XD>@9DM9@(wW0K5kAT+>E+34qjjzd#b<0T0L`dId zYnBmx(lYkZ6XAZ_GtDFK+x786kESOVK0T}-@=$T(m#}GVWfJq zPWYW}Lq1!#r}`;FJa+FyP2@D-rf^M=u7QqkvVFDvQw(`zp{j}618{m&DS?_?^XdJk zdQ=dq;frP$Q7;r;(7nLlW8kqUV!UT|51#1XrMtke(AtH*T@w#^@a{ozub#=&*pR6s zV)B!lYH(Xe#Qa&sF>av8RTw4=qk?G!B0{5&srJnGENJ(cz6wvZ6$G}+(b^=KCa@*M zrM*gvN@EwE7MZoowDgAvLVRpYs#+nFR&Xn1gGBxO^agAdR{TS>4(&Ys=yZGtqwh8O zM9u^1#^r|WCUB;7U3~2G$lm|6Vxv-3p!~1x-fu$^qdOT3`TDN<0{RE~!%S?Vi(*w) z@{3gq1OBRW%5z`*;scMrxqLR8PF=A6b@EkjGI2S0#QT}cz(ZFK*VX{IA9J%@GkDu? 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Markdown syntax to create a link: From b47042573e47b14e401dad7bfd01480855390302 Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Sun, 27 Aug 2023 22:49:20 -0700 Subject: [PATCH 050/104] removing eval false and calling plot object in data vis lesson --- materials/sections/visualization-delta.qmd | 10 ++++++---- materials/session_03.qmd | 1 - 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/materials/sections/visualization-delta.qmd b/materials/sections/visualization-delta.qmd index 6b51488d..0cd175de 100644 --- a/materials/sections/visualization-delta.qmd +++ b/materials/sections/visualization-delta.qmd @@ -234,10 +234,10 @@ daily_visits_loc %>% ``` - **Line and point** +**Line and point** ```{r line_poin_plot} -#| eval: false + daily_visits_loc %>% filter(restore_loc == "Decker Island", visitor_type == "med_boat") %>% @@ -261,7 +261,7 @@ ggplot(data = daily_visits_loc, Why did that happen? -Notice that we tried to set the fill color of the plot inside the mapping aesthetic call. What we have done, behind the scenes, is create a column filled with the word "blue" in our data frame, and then mapped it to the `fill` aesthetic, which then chose the default fill color of red. +Notice that we tried to set the fill color of the plot inside the mapping aesthetic call. What we have done, behind the scenes, is create a column filled with the word "blue" in our data frame, and then mapped it to the `fill` aesthetic, which then chose the default fill color, salmon. What we really wanted to do was just change the color of the bars. If we want do do that, we can call the color option in the `geom_col()` function, **outside** of the mapping aesthetics function call. @@ -300,7 +300,7 @@ ggplot(data = daily_visits_loc, We have successfully plotted our data. But, this is clearly not a nice plot. Let's work on making this plot look a bit nicer. We are going to: - Add a title, subtitle and adjust labels using `labs()` -- Flip the x and y axis to better read the graph +- Flip the x and y axis to better read the graph using `coord_flip()` - Include a built in theme using `theme_bw()` @@ -561,6 +561,8 @@ facet_plot <- ggplot(data = daily_visits_totals, theme(legend.position = "bottom", axis.ticks.x = element_blank(), axis.text.x = element_blank()) + +facet_plot ``` We can save this plot to our figures folder too. Note that this time we are specifically mentioning the object we want to save. diff --git a/materials/session_03.qmd b/materials/session_03.qmd index 7e9f723d..efca8a17 100644 --- a/materials/session_03.qmd +++ b/materials/session_03.qmd @@ -3,6 +3,5 @@ title: "Data Visualization" title-block-banner: true --- - {{< include /sections/visualization-delta.qmd >}} From 225200ab27148cab176e398070ba44f0c7ac31f4 Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Mon, 28 Aug 2023 10:21:51 -0700 Subject: [PATCH 051/104] adding thinking preferences material to qmd file --- .../collaboration-thinking-preferences.qmd | 54 ++++++++++++++++++- materials/session_07.qmd | 6 +-- 2 files changed, 55 insertions(+), 5 deletions(-) diff --git a/materials/sections/collaboration-thinking-preferences.qmd b/materials/sections/collaboration-thinking-preferences.qmd index 18816ce7..44476246 100644 --- a/materials/sections/collaboration-thinking-preferences.qmd +++ b/materials/sections/collaboration-thinking-preferences.qmd @@ -1 +1,53 @@ -INSERT ONE OF THE THIKING PREFERENCES VERSIONS \ No newline at end of file + + +## Learning Objectives {.unnumbered} + +An activity and discussion that will provide: + +- Opportunity to get to know fellow participants and trainers +- An introduction to variation in thinking preferences + +## Thinking Preferences Activity + +Step 1: + +- Read through the statements contained within this [document](files/ThinkingPreferencesMapping.pdf) and determine which descriptors are most like you. Make a note of them. +- Review the descriptors again and determine which are quite like you. +- You are working towards identifying your top 20. If you have more than 20, discard the descriptors that resonate the least. +- Using the letter codes in the right hand column, count the number of descriptors that fall into the categories A B C and D. + +Step 2: Scroll to the second page and copy the graphic onto a piece of paper, completing the quadrant with your scores for A, B, C and D. + +Step 3: Reflect and share out: Do you have a dominant letter? Were some of the statements you included in your top 20 easier to resonate with than others? Were you answering based on how you *are* or how you wish to be? + + +## About the Whole Brain Thinking System + +Everyone thinks differently. The way individuals think guides the way they work, and the way groups of individuals think guides how teams work. Understanding thinking preferences facilitates effective collaboration and team work. + +The Whole Brain Model, developed by Ned Herrmann, builds upon early conceptualizations of brain functioning. For example, the left and right hemispheres were thought to be associated with different types of information processing while our neocortex and limbic system would regulate different functions and behaviours. + +![](images/Brain.jpg) + +The Herrmann Brain Dominance Instrument (HBDI) provides insight into dominant characteristics based on thinking preferences. There are four major thinking styles that reflect the left cerebral, left limbic, right cerebral and right limbic. + +- Analytical (Blue) +- Practical (Green) +- Relational (Red) +- Experimental (Yellow) + +![](images/WholeBrain.jpg) + +These four thinking styles are characterized by different traits. Those in the BLUE quadrant have a strong logical and rational side. They analyze information and may be technical in their approach to problems. They are interested in the 'what' of a situation. Those in the GREEN quadrant have a strong organizational and sequential side. They like to plan details and are methodical in their approach. They are interested in the 'when' of a situation. The RED quadrant includes those that are feelings-based in their apporach. They have strong interpersonal skills and are good communicators. They are interested in the 'who' of a situation. Those in the YELLOW quadrant are ideas people. They are imaginative, conceptual thinkers that explore outside the box. Yellows are interested in the 'why' of a situation. + +![](images/WholeBrainTraits.jpg) + +Most of us identify with thinking styles in more than one quadrant and these different thinking preferences reflect a complex self made up of our rational, theoretical self; our ordered, safekeeping self; our emotional, interpersonal self; and our imaginitive, experimental self. + +![](images/ComplexSelf.jpg) + +Undertsanding the complexity of how people think and process information helps us understand not only our own approach to problem solving, but also how individuals within a team can contribute. There is great value in diversity of thinking styles within collaborative teams, each type bringing stengths to different aspects of project development. + +![](images/WorkingStyles.jpg) + + diff --git a/materials/session_07.qmd b/materials/session_07.qmd index 5832deda..db0ab812 100644 --- a/materials/session_07.qmd +++ b/materials/session_07.qmd @@ -1,12 +1,10 @@ --- title: "Thinking Preferences" title-block-banner: true -format: - html: - code-overflow: wrap - code-link: true --- + + {{< include /sections/collaboration-thinking-preferences.qmd >}} From 83a4b986ced10127cb61dd5e962275a1345010a9 Mon Sep 17 00:00:00 2001 From: hdolinh Date: Mon, 28 Aug 2023 14:41:04 -0700 Subject: [PATCH 052/104] resolve merge conflict --- materials/sections/intro-tidy-text-data.qmd | 21 ++++++++++++++++----- 1 file changed, 16 insertions(+), 5 deletions(-) diff --git a/materials/sections/intro-tidy-text-data.qmd b/materials/sections/intro-tidy-text-data.qmd index c91ec07c..ddd9c9eb 100644 --- a/materials/sections/intro-tidy-text-data.qmd +++ b/materials/sections/intro-tidy-text-data.qmd @@ -35,11 +35,7 @@ Here is a list of text data or text analysis terms we'll be referring to through As our knowledge about the environmental world grows, researchers will need new computational approaches for working with text data because reading and identifying all the relevant literature for literature syntheses is becoming an increasingly difficult task. -<<<<<<< HEAD -Beyond literature syntheses, quantitative text analysis tools are extremely valuable for efficiently extracting information from texts and other text mining or text analysis tasks. -======= Beyond literature syntheses, quantitative text analysis tools are extremely valuable for efficiently extracting information from texts and other text mining or text analysis tasks. ->>>>>>> bb20f6f8311df6dfd89cc170ff212e980fc9a258 ## What is tidy text data? @@ -186,7 +182,22 @@ ggplot(data = count_bradbury, aes(x=reorder(word, n), y=n)) + ) ``` - -ADD INSTRUCTIONS \ No newline at end of file From cfae139651cc376722ba3c2e685214a7e021a15a Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Tue, 29 Aug 2023 06:19:39 -0700 Subject: [PATCH 056/104] Adding census data material --- materials/sections/census-data.qmd | 264 +++++++++++++++++++++++++++-- 1 file changed, 246 insertions(+), 18 deletions(-) diff --git a/materials/sections/census-data.qmd b/materials/sections/census-data.qmd index 3fc54de6..5dd3e504 100644 --- a/materials/sections/census-data.qmd +++ b/materials/sections/census-data.qmd @@ -2,10 +2,16 @@ - Provide an overview of US Census data - Introduce the main functions of the `tidycensus` package to be able to work with census data - Review data wrangling function to get census data ready for analysis +- Plot census data using `ggplot2` +:::callout-note +## Acknowledgement + +::: + ## Introduction to `tidycensus` -(Chapter 2 - 5 min) -"The `tidycensus` is an R package that provides an interface to access and work with the United States Census Bureau data. It simplifies the process of retrieving and analyzing census data by allowing users to query data directly from the Census Bureau's API and then organize the data into a tidy format for easy manipulation and analysis." + +When working with census data, the general workflow would be to go to [data.census.gov](data.census.gov), filter data and queries based on variables and geographies, and then download the data into a spreadsheet. People that work with census data do this over and over again. + +Kyle Walker, one of the authors of the `tidycensus` package, created the package as a way to systematize the process and do this systematization using R. He learned about the US Census Application Programming Interface (API) which allows developers to access census resources programmatically, therefore incorporated in the package to facilitate access to interact with census data. + +Note there are other R packages that interact with the census API, such as `censusapi` and `acs`. However, in this lesson, we are going to focus on the `tidycensus` package, given that is constantly being maintained and has good documentation on how it works. + +From Walkers Book: + +> "The `tidycensus` is an R package that provides an interface to access and work with the United States Census Bureau data. It simplifies the process of retrieving and analyzing census data by allowing users to query data directly from the Census Bureau's API and then organize the data into a tidy format for easy manipulation and analysis." + + +### General Structure + +Tidycensus takes an opinionated approach to accessing a selected number of census APIs. The main goal is to facilitate access in R to a few APIs. + +Through this package, you can access: +- Decennial census (200, 2010, available data from 2020) +- The American Community Survey (ACS) data sets +- Population estimate program (information on yearly estimates and components of change) +- The ASC Public Use Microdata. Anonymized individual-level records from the ACS organized by households. +- Migration Flows APIs which include information about in and outflows from several geographies from the 5-year ACS samples. +The idea behind this package is to make the tedious process of working with Census data more concise. It pulls data from the census API and returns it to the user in a “tidy” format. + +Can easily merge census geometries to data for mapping. Which apparently can be a very time-consuming task. Unfortunately, for this session, we will not get into mapping. See the resource below for Walker’s book and class on how to map census data. + + +Other features of this package: +- Includes tools for handling margins of errors in the ACS and working with survey weights in the ACS PUMS +- You can request data from states and counties by name instead of FIPS codes. + ## Getting Started -In order to be able to use the `tidycensus` package, we need to: -- Install the package in our machine (run: `install.package("tidycensus")` in the console) -- Get an API key to connect our session with the census data and be able to retrieve data +::: callout-tip +## Setup + +0. Make sure you’re in the right project (`training_{USERNAME}`) and use the `Git` workflow by `Pull`ing to check for any changes. Then, create a new Quarto document, delete the default text, and save this document. + +1. Load the packages we'll need: + +```{r load_packages} +#| message: false +#| warning: false +#| eval: false + +library(tidycensus) +library(dplyr) +library(tidyr) +library(ggplot2) +``` + +2. Get an API key to connect our session with the census data and be able to retrieve data +- Go to +- Fill out the form +- Check your email for your key. + +3. Use the `census_api_key()` function to set your key. Note: `install = TRUE` forces r to write this key to a file in our R environment that will be read every time you use R. This means, by setting this argument to `TRUE`, you only have to do it once in any computer you are working. If you see this argument as `FALSE`, R will not remember this key next time you come back. + +```{r} +#| eval: false +census_api_key("YOUR KEY GOES HERE", install = TRUE) +``` + +4. Restart R + +5. Run the library chunck again. + +::: + + +Note 1: As we are working on the server, the package is already installed in our system. But when you go work on your own computers, you will have to install the package using `install.package("tidycensus")`, if you haven't done that already. + +Note 2: The API key you received in your email is good to use in other instances too. Keep this email! We are using the key to connect your included-crab session with the US Census data. You can use the same key to connect your personal/work computer too. + + + +## Quering data (focus on 2020 decennial data) + +### The 2020 Decennial Data +Before we dive into retrieving data from the 2020 decennial census, we need to mention a couple of things. Based on all the challenges of running a decennial census during a pandemic, the Census Bureau had to make decisions and provide new and different functionalists. The pandemic also delayed the release of the 2020 census data. You can check [here](https://www2.census.gov/programs-surveys/decennial/2020/program-management/2010_20_data_product_release_dates.pdf) what data products have been released until now, and the Bureau’s timeline to release more data. + +One of the main files from this census is the PL94-171 Redistricting Summary File which is used for congressional appointments and redistricting. Variable available in this file are: +- Total counts (population & households) +- Occupied/vacant housing unit +- Total and voting age population breakdown by race & ethnicity +- Group quarter status +- Demographic and Housing Characteristics Summary Files (Different to summary file 1 form 2010). Contains age and sex breakdowns and detailed race and ethnicity data. + + +### Getting census data +The main functions from `tidycensus` represent the select number of datasets that this package provides access to. The following table provides the description for each of the core functions from [Walker's book](https://walker-data.com/census-r/an-introduction-to-tidycensus.html#decennial-census). + +| Function | Description | +|---------------|--------------------------------| +`get_decennial()`| Retrieves data from the US Decennial Census APIs for 2000, 2010, and 2020.| +|`get_acs()` | Requests data from the 1-year and 5-year American Community Survey samples. Data are available from the 1-year ACS back to 2005 and the 5-year ACS back to 2005-2009.| +|`get_estimates()`| Allows you to get the Population Estimates. These datasets include yearly estimates of population characteristics by state, county, and metropolitan area, along with components of change demographic estimates like births, deaths, and migration rates.| +|`get_pums()` | Accesses data from the ACS Public Use Microdata Sample APIs. These samples include anonymized individual-level records from the ACS organized by household and are highly useful for many different social science analyses | +| `get_flows()` | an interface to the ACS Migration Flows APIs. Includes information on in- and out-flows from various geographies for the 5-year ACS samples, enabling origin-destination analyses.| + + +Here we are going to focus on getting started with using `get_decennial()` as a way of understanding how this package works. With `get_decennial()`, you can query data from 2000, 2010 and 2020 decennial census. The 3 necessary arguments you need to provide are: +- Geography +- Variable +- Year + +So, to get the total population for 2020 by state the code woul look like this. + +```{r} +#| eval: false + +pop_2020 <- get_decennial( + geography = "state", + variable = "P1_001N", + year = 2020) + +``` + +The resulting data frame you get from this query is a tibble in "tidy" format with 4 columns: +- GEOID = Code for the geographic unit (in this case FIP codes) +- variable = census code for the variable (eg. P1_001N is the code for the total population in the 2020 population redistricting file) +- value = population count + +This table is the daa for one single variable. In this case the variable is "P1_001N" which refers to total population. + +:::callout-important +## Message error + + +You get this message the first time you run g`et_decennial()` in your session. It first makes sure your are retrieving the correct data. Then it mentions the fact that the 2020 census “Introduces errors differential privacy”. + +In the past other privacy methods have been used to preserve confidentiality. Differential privacy is a method that purposely introduces noise or error into the data in order to make it impossible or at least very difficult to reverse engineer the census and track where the responses are coming from. This has an impact on small area counts (e.g.: block with children but not adults). This is something to be aware of if you are working with small population geographies. + +Only the population data is differentially infused. The household data is not. +::: + +When using `get_decennial()`, you can also specify a specific table instead of only one variable. A table of data contains multiple variables. The code for getting the "P2" table from 2020 would look like this. +```{r} +#| eval: false + +table_p2_2020 <- get_decennial( + geography = "state", + table = "P2", + year = 2020) + +``` + + +Table P2 is one of the tables of the 2020 census, which provides counts by Race with Hispanic origins. + + +Let's check which variables we have in the P2 table. + +```{r} +#| eval: false + +unique(table_p2_2020$variable) + +``` + +One of the hardest things to wrap your head around when using `tidycensus` is understanding the difference between files, tables, and variables and how they work. + +My take on this is the following: data is released in files. Each file is a data product from the Census Bureau (find details of the Census data products [here](https://www.census.gov/programs-surveys/decennial-census/decade/2020/planning-management/release/about-2020-data-products.html#redistricting)), for example, PL 94-171. Each of these files contains many tables (P1, P2 .. P5). Each table covers a specific topic. For example, P2 provides counts by race by Hispanic origins. Then each table has multiple variables. The variable code, to my understanding, starts with the table code. For example, the unique codes values we printed from table P2. + +How do we navigate all these codes? There is no straightforward way. In part, this dilemma is part of working with census data, which in itself is complicated. However, `tidycensus` provides some help with a function called `load_variables()` + +This function scrapes the variables' names from the census website and gives you a data frame that you can interact with. This variable requires you to input a year and a file name. -As we are working in the server, the package is already installed in our system (so we can skip this step). But when you go work in your own computers, you will have to install the package if you haven't done that already. +So let’s run this function and create an object named `vars_pl_2020` This will give us all the variable codes and definitions for the PL94171 redistricting file. +```{r} +#| eval: false + +vars_pl_2020 <- load_variables(2020, "pl") +``` + + +Now, let’s take a look at this data frame. And interactively search for the variables in this file. This data frame has 3 columns, variable’s name, label, and concept, which represent the table’s name. + +For example, here we can see the variable “P1_001N” that we used earlier to retrieve the total population by state. We can also scroll down and see that all variables that start with P2, belong to the P2 table “HISPANIC OR LATINO, AND NOT HISPANIC OR LATINO BY RACE”. We can also see the variable codes for the H1 table for Occupancy or Housing. + +Tables available in the 2020 Census PL file: + +|Table Name | Description| +|-----------|------------| +|H1 |Occupancy status by household| +|P1 | Race by Hispanic origin| +|P3 | Race for the population 18+| +|P4| Race by Hispanic origin for the population 18+| +|P5| Group quarters status | + +Note: “Group quarters are places where people live or stay, in a group living arrangement, that is owned or managed by an entity or organization providing housing and/or services for the residents.” ([US Census Bureau Glossary](https://www.census.gov/glossary/?term=Group+quarters+population)) + + +We won’t get into much more detail, but, you can query any of the available files for the 2020 census data. Like the Demographic and Housing Characteristics File (DHC) by running `vars_dhc_2020 -> load_variables(2020, "dhc")`. This is a much larger file with much more tables than the PL file. + +The idea behind `load_variables()` is for you to be able to search for the variable code for the variable you need. +Now that we’ve talked about variables let’s talk a little bit about geography and how `tidycensus` makes it easy to query data within census geographies. As we mention in the introduction, census data has a hierarchy of geographies, plus other groups of geographies. This mean we can name the specific geography we want data from. For example, let's get data for Hispanic population the 6 counties around the Delta. + +```{r} +#| eval: false + +delta_hispanic <- get_decennial( + geography = "county", + state = "CA", + county = c("Alameda", "Contra Costa", "Sacramento", "San Joaquin", "Solano", "Yolo"), + variables = "P2_002N", + year = 2020) +``` + +To learn more about the arguments for geography for each core function of `tidycensus`, check out the documentation [here]() + + +#### Quering for multiple variabels + +The `varaible` argument can have as an input a vector of variables. We can create a vector and then call that vector as the input of the `variable` argument, or we can use the concatenate function `c()` and request data from multiple variables. ```{r} +#| eval: false + +## Vector with race variables codes +race_vars <- c( + Hispanic = "P2_002N", + White = "P2_005N", + Black = "P2_006N", + Native = "P2_007N", + Asian = "P2_008N", + HIPI = "P2_009N") ## Native Hawaiian and other Pacific Islander + +delta_race <- get_decennial( + geography = "county", + state = "CA", + county = c("Alameda", "Contra Costa", "Sacramento", "San Joaquin", "Solano", "Yolo"), + variables = race_vars, + summary_var = "P2_001N", + year = 2020) ``` +Note how this table returns the variable name we assigned in the vector above instead of the variable code! This is a handy option, given that the codes can be hard to remember what is what. + +Also, note that we added one more argument to our request `summary_var = "P2_001N`. This adds one more column to our output data frame, one with the summary variable value. + +In every table you can generally find a variable that is an appropriate denominators for a group of variables. Following the example above, the P2 table, which provides population by race with Hispanic origin, the variable "P2001N" represents the total population. Because this variable is an an appropriate denominator for the other variables in the table, it helps to have it in a different column to make it easier to calculate proportions or percentage. -## Querying data -- Basic data requests -- Fundamentals of data package and the options it offers -## Getting Census Data ready for analysis -(Chapter 3 - 10 min) + -## Visualizing Censis Data with `ggplot2` -(Chapter 4 - 10 min) -## Maps and Census Data + From ab83d53125fd6c07f7bbd7a46e91263a99807e58 Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Tue, 29 Aug 2023 07:03:32 -0700 Subject: [PATCH 057/104] adding collaboration guidelines instructions --- materials/session_08.qmd | 29 +++++++++++++++++++++++++++++ materials/session_12.qmd | 2 +- 2 files changed, 30 insertions(+), 1 deletion(-) diff --git a/materials/session_08.qmd b/materials/session_08.qmd index ebaa4f3d..94c5ae1b 100644 --- a/materials/session_08.qmd +++ b/materials/session_08.qmd @@ -5,3 +5,32 @@ title-block-banner: true +::: callout-tip +## Team Collaboration Guidelines + +1. Developing a code of conduct + +- Set of shared agreements about how people in the group will treat each other will help create the conditions for successful collaboration. +- What behaviors you want to encourage? +- What behaviors that will not be tolerated? + +See examples in [the book](https://learning.nceas.ucsb.edu/2023-08-delta/session_06.html#developing-a-code-of-conduct) + +2. Establishing Authorship and Credit Policies + +- Check the [International Committee of Medical Journal Editors guidelines for authorship and contribution](https://www.icmje.org/recommendations/browse/roles-and-responsibilities/defining-the-role-of-authors-and-contributors.html) +- Revise the [NISO Contributor Roles Taxonomy (CRediT)](https://credit.niso.org/) +- As a group answer the following questions relevant questions to your project: What are our criteria for authorship? (See the ICMJE guidelines for potential criteria) + - Will we extend the opportunity for authorship to all group members on every paper or product? + - Do we want to have an opt in or opt out policy? (In an opt out policy, all group members are considered authors from the outset and must request removal from the paper if they don't want think they meet the criteria for authorship) + - Who has the authority to make decisions about authorship? Lead author? PI? Group? + - How will we decide authorship order? + - In what other ways will we acknowledge contributions and extend credit to collaborators? + - How will we resolve conflicts if they arise? + +3. Data Sharing and Reuse Policies + +- Go over the Arctic Data Center Data Sharing and Reuse Policies Example [template](https://docs.google.com/document/d/16wNpk9vJiWqQQStmaEG66YgawU39hGC8x9KnbUqbyO4/edit#heading=h.gnbwiz3v0o8z) +- Use this template to start drafting a Data Sharing and Reuse Polices for your group. + +::: diff --git a/materials/session_12.qmd b/materials/session_12.qmd index 0d834a7e..a138d3bb 100644 --- a/materials/session_12.qmd +++ b/materials/session_12.qmd @@ -4,7 +4,7 @@ title-block-banner: true --- -{{< include /sections/intro-tidy-text-data.qmd >}} + From 164e936590f9bd0ceca8bee06fea91c5418ae59b Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Tue, 29 Aug 2023 07:06:24 -0700 Subject: [PATCH 058/104] fixing typos. mutting text data to get th ebook to build --- materials/session_08.qmd | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/materials/session_08.qmd b/materials/session_08.qmd index 94c5ae1b..856427d1 100644 --- a/materials/session_08.qmd +++ b/materials/session_08.qmd @@ -11,7 +11,7 @@ title-block-banner: true 1. Developing a code of conduct - Set of shared agreements about how people in the group will treat each other will help create the conditions for successful collaboration. -- What behaviors you want to encourage? +- What behaviors do you want to encourage? - What behaviors that will not be tolerated? See examples in [the book](https://learning.nceas.ucsb.edu/2023-08-delta/session_06.html#developing-a-code-of-conduct) @@ -22,7 +22,7 @@ See examples in [the book](https://learning.nceas.ucsb.edu/2023-08-delta/session - Revise the [NISO Contributor Roles Taxonomy (CRediT)](https://credit.niso.org/) - As a group answer the following questions relevant questions to your project: What are our criteria for authorship? (See the ICMJE guidelines for potential criteria) - Will we extend the opportunity for authorship to all group members on every paper or product? - - Do we want to have an opt in or opt out policy? (In an opt out policy, all group members are considered authors from the outset and must request removal from the paper if they don't want think they meet the criteria for authorship) + - Do we want to have an opt in or opt out policy? (In an opt out policy, all group members are considered authors from the outset and must request removal from the paper if they don't think they meet the criteria for authorship) - Who has the authority to make decisions about authorship? Lead author? PI? Group? - How will we decide authorship order? - In what other ways will we acknowledge contributions and extend credit to collaborators? @@ -31,6 +31,6 @@ See examples in [the book](https://learning.nceas.ucsb.edu/2023-08-delta/session 3. Data Sharing and Reuse Policies - Go over the Arctic Data Center Data Sharing and Reuse Policies Example [template](https://docs.google.com/document/d/16wNpk9vJiWqQQStmaEG66YgawU39hGC8x9KnbUqbyO4/edit#heading=h.gnbwiz3v0o8z) -- Use this template to start drafting a Data Sharing and Reuse Polices for your group. +- Use this template to start drafting a Data Sharing and Reuse Policies for your group. ::: From df8e48ec961b372ef4d7c4cb98fe11ef17e791ad Mon Sep 17 00:00:00 2001 From: hdolinh Date: Tue, 29 Aug 2023 08:32:36 -0700 Subject: [PATCH 059/104] replace bradbury with phantom of the opera --- materials/sections/intro-tidy-text-data.qmd | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/materials/sections/intro-tidy-text-data.qmd b/materials/sections/intro-tidy-text-data.qmd index 67991a66..3f5fa4ea 100644 --- a/materials/sections/intro-tidy-text-data.qmd +++ b/materials/sections/intro-tidy-text-data.qmd @@ -132,15 +132,15 @@ gutenberg_works(title == "The Phantom of the Opera") #| code-summary: Steps 4-7 Code #| message: false # access text data using id number from `gutenberg_works()` -bradbury_corp <- gutenberg_download(63827) +phantom_corp <- gutenberg_download(175) # tidy text data - unnest and remove stop words -tidy_bradbury <- bradbury_corp %>% +tidy_phantom <- phantom_corp %>% unnest_tokens(word, text) %>% anti_join(stop_words, by = "word") # calculate top 10 most frequent words -count_bradbury <- tidy_bradbury %>% +count_phantom <- tidy_phantom %>% count(word) %>% slice_max(n = 10, order_by = n) ``` @@ -150,7 +150,7 @@ count_bradbury <- tidy_bradbury %>% #| code-summary: Step 8 Plot Code # visualize text data # # bar plot -ggplot(data = count_bradbury, aes(n, reorder(word, n))) + +ggplot(data = count_phantom, aes(n, reorder(word, n))) + geom_col() + labs(x = "Count", y = "Token") @@ -161,7 +161,7 @@ ggplot(data = count_bradbury, aes(n, reorder(word, n))) + #| code-summary: Step 9 Plot Code # visualize text data # # initial lollipop plot -# ggplot(data = count_bradbury, aes(x=word, y=n)) + +# ggplot(data = count_phantom, aes(x=word, y=n)) + # geom_point() + # geom_segment(aes(x=word, xend=word, y=0, yend=n)) + # coord_flip() + @@ -169,11 +169,11 @@ ggplot(data = count_bradbury, aes(n, reorder(word, n))) + # y = "Count") # ascending order pretty lollipop plot -ggplot(data = count_bradbury, aes(x=reorder(word, n), y=n)) + +ggplot(data = count_phantom, aes(x=reorder(word, n), y=n)) + geom_point(color="cyan4") + geom_segment(aes(x=word, xend=word, y=0, yend=n), color="cyan4") + coord_flip() + - labs(title = "Top Ten Words in Ray Bradbury's Asleep in Armageddon", + labs(title = "Top Ten Words in The Phantom of the Opera", x = NULL, y = "Count") + theme_minimal() + From 1ae9c39cb44823d29ade221fb936f6444ce28c8a Mon Sep 17 00:00:00 2001 From: hdolinh Date: Tue, 29 Aug 2023 08:37:12 -0700 Subject: [PATCH 060/104] replace bradbury text with phantom --- materials/sections/intro-tidy-text-data.qmd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/materials/sections/intro-tidy-text-data.qmd b/materials/sections/intro-tidy-text-data.qmd index 3f5fa4ea..dbc2b931 100644 --- a/materials/sections/intro-tidy-text-data.qmd +++ b/materials/sections/intro-tidy-text-data.qmd @@ -118,9 +118,9 @@ gutenberg_works(title == "The Strange Case of Dr. Jekyll and Mr. Hyde") # jekyll 9. **Bonus**: Consider elements in `theme()` and improve your plot. ::: -### Example using Ray Bradbury's *Asleep in Armageddon* +### Example using *The Phantom of the Opera* -The code chunks below follows the instructions from above using Ray Bradbury's *Asleep in Armageddon*. +The code chunks below follows the instructions from above using the text of *The Phantom of the Opera*. ```{r} # get id number From 0bc8c300b09d3acd71b75c250675b20ef86ffaee Mon Sep 17 00:00:00 2001 From: hdolinh Date: Tue, 29 Aug 2023 11:34:07 -0700 Subject: [PATCH 061/104] add new refs for section 12.1.2 --- materials/book.bib | 45 +++++++++++++++++++++++++++++++ materials/sections/references.bib | 45 +++++++++++++++++++++++++++++++ 2 files changed, 90 insertions(+) diff --git a/materials/book.bib b/materials/book.bib index 1b04fe81..e7fa57b1 100644 --- a/materials/book.bib +++ b/materials/book.bib @@ -273,3 +273,48 @@ @article{michener2015 number = {10}, } + +@article{farrell2022, + title = {Past and future uses of text mining in ecology and evolution}, + author = {Farrell, Maxwell J. and Brierley, Liam and Willoughby, Anna and Yates, Andrew and Mideo, Nicole}, + year = {2022}, + month = {05}, + date = {2022-05-18}, + journal = {Proceedings of the Royal Society B: Biological Sciences}, + volume = {289}, + number = {1975}, + doi = {10.1098/rspb.2021.2721}, + url = {http://dx.doi.org/10.1098/rspb.2021.2721}, + langid = {en} +} + +@article{froehlich2017, + title = {Public Perceptions of Aquaculture: Evaluating Spatiotemporal Patterns of Sentiment around the World}, + author = {Froehlich, Halley E. and Gentry, Rebecca R. and Rust, Michael B. and Grimm, Dietmar and Halpern, Benjamin S.}, + editor = {Somers, Christopher M.}, + year = {2017}, + month = {01}, + date = {2017-01-03}, + journal = {PLOS ONE}, + pages = {e0169281}, + volume = {12}, + number = {1}, + doi = {10.1371/journal.pone.0169281}, + url = {http://dx.doi.org/10.1371/journal.pone.0169281}, + langid = {en} +} + +@article{vanhoutan2020, + title = {Sentiment Analysis of Conservation Studies Captures Successes of Species Reintroductions}, + author = {Van Houtan, Kyle S. and Gagne, Tyler and Jenkins, Clinton N. and Joppa, Lucas}, + year = {2020}, + month = {04}, + date = {2020-04}, + journal = {Patterns}, + pages = {100005}, + volume = {1}, + number = {1}, + doi = {10.1016/j.patter.2020.100005}, + url = {http://dx.doi.org/10.1016/j.patter.2020.100005}, + langid = {en} +} \ No newline at end of file diff --git a/materials/sections/references.bib b/materials/sections/references.bib index 128c7fd4..94ba85ff 100644 --- a/materials/sections/references.bib +++ b/materials/sections/references.bib @@ -20,3 +20,48 @@ @misc{lter2022 url = {https://portal.edirepository.org/nis/mapbrowse?packageid=knb-lter-sbc.77.8}, langid = {en} } + +@article{farrell2022, + title = {Past and future uses of text mining in ecology and evolution}, + author = {Farrell, Maxwell J. and Brierley, Liam and Willoughby, Anna and Yates, Andrew and Mideo, Nicole}, + year = {2022}, + month = {05}, + date = {2022-05-18}, + journal = {Proceedings of the Royal Society B: Biological Sciences}, + volume = {289}, + number = {1975}, + doi = {10.1098/rspb.2021.2721}, + url = {http://dx.doi.org/10.1098/rspb.2021.2721}, + langid = {en} +} + +@article{froehlich2017, + title = {Public Perceptions of Aquaculture: Evaluating Spatiotemporal Patterns of Sentiment around the World}, + author = {Froehlich, Halley E. and Gentry, Rebecca R. and Rust, Michael B. and Grimm, Dietmar and Halpern, Benjamin S.}, + editor = {Somers, Christopher M.}, + year = {2017}, + month = {01}, + date = {2017-01-03}, + journal = {PLOS ONE}, + pages = {e0169281}, + volume = {12}, + number = {1}, + doi = {10.1371/journal.pone.0169281}, + url = {http://dx.doi.org/10.1371/journal.pone.0169281}, + langid = {en} +} + +@article{vanhoutan2020, + title = {Sentiment Analysis of Conservation Studies Captures Successes of Species Reintroductions}, + author = {Van Houtan, Kyle S. and Gagne, Tyler and Jenkins, Clinton N. and Joppa, Lucas}, + year = {2020}, + month = {04}, + date = {2020-04}, + journal = {Patterns}, + pages = {100005}, + volume = {1}, + number = {1}, + doi = {10.1016/j.patter.2020.100005}, + url = {http://dx.doi.org/10.1016/j.patter.2020.100005}, + langid = {en} +} From c344137a371b9c04266ff4ffe52c67b3b8a5cfe0 Mon Sep 17 00:00:00 2001 From: hdolinh Date: Tue, 29 Aug 2023 11:34:35 -0700 Subject: [PATCH 062/104] images for tidy text workflow exercise --- materials/images/tidytext-barplot.png | Bin 0 -> 77592 bytes 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-## Tidy Text to Non-Tidy Text Workflow +## Tidy Text to Non-Tidy Text Workflows -![](images/tidytext-nontidy-workflow.png) +![A flowchart of a typical text analysis that combines `tidytext` with other tools and data formats, particularly the `tm` or `quanteda` packages. Source: Silge & Robinson](images/tidytext-nontidy-workflow.png) Many text analysis methods, in particular NLP techniques (e.g. topic models) require text data to be stored in a mathematical format. A common approach is to create a document term matrix (DTM), also called a document-feature matrix (DFM). In a matrix format, algorithms are able to more easily compare one document to many other documents to identify patterns. +In the [Tidy Text Workflow Exercise](#exercise-tidy-text-workflow), we converted our corpus into a data table that has "one-token-per-row". However, the tidy text format of "one-token-per-row" is not a common format for other R packages that work with text data or perform text analysis. Packages like `tm`, `quanteda`, `topicmodels` + + +:::{.callout-caution icon=false} +### R Text Mining Tools and Analysis Packages Resources + +- [CRAN Task View: Natural Language Processing](https://cran.r-project.org/web/views/NaturalLanguageProcessing.html) +- [Penn Libraries Guides: Text Analysis](https://guides.library.upenn.edu/penntdm/r) + +::: + + | +|Population estimate program | Information on yearly estimates and components of change| +| ACS Public Use Microdata | Anonymized individual-level records from the ACS organized by households| +|Migration Flows | Information about in and outflows from several geographies from the 5-year ACS samples.| The idea behind this package is to make the tedious process of working with Census data more concise. It pulls data from the census API and returns it to the user in a “tidy” format. From ff4699e3c066756bc969600fc59f0e4ae15391cf Mon Sep 17 00:00:00 2001 From: Matt Jones Date: Tue, 29 Aug 2023 21:07:10 -0700 Subject: [PATCH 071/104] Add alternate data download instructions. --- materials/sections/geospatial-vector-analysis.qmd | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/materials/sections/geospatial-vector-analysis.qmd b/materials/sections/geospatial-vector-analysis.qmd index dd54a95f..8a6eed90 100644 --- a/materials/sections/geospatial-vector-analysis.qmd +++ b/materials/sections/geospatial-vector-analysis.qmd @@ -40,6 +40,16 @@ The spatial data we will be using to create the map are: 1. Navigate to [this dataset](https://dev.nceas.ucsb.edu/view/urn:uuid:6f07cb25-a4a1-48e8-95cb-74f532f3ce2d) on KNB's test site and download the zip folder. 2. Upload the zip folder to the `data` folder in the `training_{USERNAME}` project. You don't need to unzip the folder ahead of time, uploading will automatically unzip the folder. + a. Alternatively, programatically download and extract the demo data with: + +```{r} +#| eval:false +knb_url <- 'https://dev.nceas.ucsb.edu/knb/d1/mn/v2/object/urn%3Auuid%3Aaceaecb2-1ce0-4d41-a839-d3607d32bb58' +download.file(url = knb_url, destfile = 'demo_data.zip') +unzip('demo_data.zip', exdir = 'data') +file.remove('demo_data.zip') +``` + 3. Create a new R Markdown file. a. Title it "Intro to sf package for Spatial Data and Making Maps" b. Save the file and name it "intro-sf-spatial-data-maps". From 3b6e1a1602c04f6de21895c6269e88aabfdd4d96 Mon Sep 17 00:00:00 2001 From: Matt Jones Date: Tue, 29 Aug 2023 22:37:03 -0700 Subject: [PATCH 072/104] Fix syntax error in chunk options --- materials/sections/geospatial-vector-analysis.qmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/materials/sections/geospatial-vector-analysis.qmd b/materials/sections/geospatial-vector-analysis.qmd index 8a6eed90..f2ce4a26 100644 --- a/materials/sections/geospatial-vector-analysis.qmd +++ b/materials/sections/geospatial-vector-analysis.qmd @@ -43,7 +43,7 @@ The spatial data we will be using to create the map are: a. Alternatively, programatically download and extract the demo data with: ```{r} -#| eval:false +#| eval: false knb_url <- 'https://dev.nceas.ucsb.edu/knb/d1/mn/v2/object/urn%3Auuid%3Aaceaecb2-1ce0-4d41-a839-d3607d32bb58' download.file(url = knb_url, destfile = 'demo_data.zip') unzip('demo_data.zip', exdir = 'data') From c3cfa470314afba8cdd975d597c2d220a538aa41 Mon Sep 17 00:00:00 2001 From: hdolinh Date: Tue, 29 Aug 2023 20:36:12 -0700 Subject: [PATCH 073/104] create regex lesson as qmd file --- materials/sections/intro-tidy-text-data.qmd | 122 ++++++-- materials/sections/regular-expressions.Rmd | 319 -------------------- 2 files changed, 102 insertions(+), 339 deletions(-) delete mode 100644 materials/sections/regular-expressions.Rmd diff --git a/materials/sections/intro-tidy-text-data.qmd b/materials/sections/intro-tidy-text-data.qmd index b15c5696..edccda51 100644 --- a/materials/sections/intro-tidy-text-data.qmd +++ b/materials/sections/intro-tidy-text-data.qmd @@ -90,7 +90,7 @@ We are going to use the `gutenbergr` package to access public domain texts from Break out into groups and then follow the exercise setup and instructions. -::: callout-tip +::: {.callout-tip} ### Setup and Instructions 1. Create a new `qmd` file and title it "Intro to Text Data", name yourself as the author, and then save the file as `intro-text-data.qmd`. @@ -264,7 +264,6 @@ all_books_sparse <- tidy_all_books %>% value = n) ``` - ```{r} #| eval: false @@ -276,7 +275,6 @@ all_books_dtm <- tidy_all_books %>% value = n) ``` - ```{r} #| eval: false @@ -289,7 +287,6 @@ all_books_dfm <- tidy_all_books %>% ``` - :::{.callout-caution icon=false} ### R Text Mining Tools and Analysis Packages Resources @@ -297,26 +294,99 @@ all_books_dfm <- tidy_all_books %>% - [Penn Libraries Guides: Text Analysis](https://guides.library.upenn.edu/penntdm/r) ::: -!Q zj-Jvcr9G~vJ<0+W)V#|&G4?BSw?FOI5BpGjev?_b(T9fR7W#C_8Cl}UdyS`V4nF9{ z+z(7wZTo3yvv@ng`X0HaYVpdy5c~@brnO}hh+Zq5KImI)bjk9}QMdmN!vN>+wcP?QYUduT4%&#~oJ}h zN$zbEPSN$NO+q;!cOdx}i6X@xJ#ZvmM^M?W+`+d#vP2-dBXT+Wjl6e~9>9mbbNESR39n|)sD1pX);bKWW)YoPEQC%O7U$tJ3 z9nwp071VkavAIMfBu%H>kvPF{W6(R^? zl6jQQ&CkonUtfCF_`BmxOHJ^8k>d$(8|^VirD;LSo%1v*k?z3YpC;8u(-rR5!gFG( 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hundreds of other surveys and disseminates data on a wide range of subjects to the public. These datasets include economic and business surveys, housing surveys, international data, population estimates and projections, and much more; a full listing is available on the Census website." +## Introduction to `tidycensus` + +::: columns +::: {.column width="30%"} +![](images/tidycensus_logo.png){fig-align="center"} +::: -- How is Census data available -- The Census API --> -## Introduction to `tidycensus` When working with census data, the general workflow would be to go to [data.census.gov](data.census.gov), filter data and queries based on variables and geographies, and then download the data into a spreadsheet. People that work with census data do this over and over again. - The `tidycensus` package (@tidycensus2021) came to be to systematize the process and do this systematization using R. In 2012 the US Census Bureau released the Census Application Programming Interface (API) as a way to disseminate government data resources to the public. This interface now provides programmatic access to hundreds of data resources from the Census Bureau. The vision behind this package was to incorporate the API access into an R package to facilitate access to census data using R. + The `tidycensus` package (@tidycensus2021) was developed to systematize this process and do this systematization using R. In 2012 the US Census Bureau released the Census Application Programming Interface (API) as a way to disseminate government data resources to the public. This interface now provides programmatic access to hundreds of data resources from the Census Bureau. The vision behind this package was to incorporate the API access into an R package to facilitate access to census data using R. Note there are other R packages that interact with the census APIs, such as [`censusapi`](https://cran.r-project.org/web/packages/censusapi/index.html) and [`acs`](https://cran.r-project.org/web/packages/acs/index.html). In this lesson, we are going to focus on the `tidycensus` package, given that is continuously maintained by the authors, has good documentation and it makes it easy to work with other `tidyverse` functions. @@ -43,20 +43,21 @@ Data files you can access through this package are named and described in the ta | Survey Name | Description | |-------------|-------------| -|Decennial census| 2000, 2010, available data from 2020| -|American Community Survey (ACS) | | -|Population estimate program | Information on yearly estimates and components of change| +|Decennial census| Complete enumeration of the US population to assist with apportionment. It asks a limited set of questions on race, ethnicity, age, sex, and housing tenure. Data from 2000, 2010, available data from 2020| +|American Community Survey (ACS) | Detailed demographic information about US population. Annual data updates. 1-year ACS greater, and the 5-year ACS, which is a moving average of data over a 5-year period that covers geographies down to the Census block group. ACS data represent estimates rather than precise counts. Data includes margin of error.| +|Population estimate program | These datasets include yearly estimates of population characteristics by state, county, and metropolitan area, along with components of change demographic estimates like births, deaths, and migration rates.| | ACS Public Use Microdata | Anonymized individual-level records from the ACS organized by households| |Migration Flows | Information about in and outflows from several geographies from the 5-year ACS samples.| The idea behind this package is to make the tedious process of working with Census data more concise. It pulls data from the census API and returns it to the user in a “tidy” format. -Can easily merge census geometries to data for mapping. Which apparently can be a very time-consuming task. Unfortunately, for this session, we will not get into mapping. See the resource below for Walker’s book and class on how to map census data. +Can easily merge census geometries to data for mapping. Which apparently can be a very time-consuming task. Unfortunately, for this session, we will not get into mapping. Check out Analyzing Census Data [Chapter 6: Mapping Census Data with R](https://walker-data.com/census-r/mapping-census-data-with-r.html) for more details on this subject. Other features of this package: -- Includes tools for handling margins of errors in the ACS and working with survey weights in the ACS PUMS + +- Includes tools for handling margins of errors in the ACS and working with survey weights in the ACS Public Use Microdata. - You can request data from states and counties by name instead of FIPS codes. @@ -94,7 +95,7 @@ census_api_key("YOUR KEY GOES HERE", install = TRUE) 4. Restart R -5. Run the library chunck again. +5. Run the library chunk again. ::: @@ -110,7 +111,7 @@ Note 2: The API key you received in your email is good to use in other instances ### The 2020 Decennial Data Before we dive into retrieving data from the 2020 decennial census, we need to mention a couple of things. Based on all the challenges of running a decennial census during a pandemic, the Census Bureau had to make decisions and provide new and different functionalists. The pandemic also delayed the release of the 2020 census data. You can check [here](https://www2.census.gov/programs-surveys/decennial/2020/program-management/2010_20_data_product_release_dates.pdf) what data products have been released until now, and the Bureau’s timeline to release more data. -One of the main files from this census is the PL94-171 Redistricting Summary File which is used for congressional appointments and redistricting. Variable available in this file are: +One of the main files from the 2020 census is the PL94-171 Redistricting Summary File which is used for congressional appointments and redistricting. Variable available in this file are: - Total counts (population & households) - Occupied/vacant housing unit - Total and voting age population breakdown by race & ethnicity @@ -119,6 +120,7 @@ One of the main files from this census is the PL94-171 Redistricting Summary Fil ### Getting census data + The main functions from `tidycensus` represent the select number of datasets that this package provides access to. The following table provides the description for each of the core functions from [Walker's book](https://walker-data.com/census-r/an-introduction-to-tidycensus.html#decennial-census). | Function | Description | @@ -130,12 +132,22 @@ The main functions from `tidycensus` represent the select number of datasets tha | `get_flows()` | an interface to the ACS Migration Flows APIs. Includes information on in- and out-flows from various geographies for the 5-year ACS samples, enabling origin-destination analyses.| -Here we are going to focus on getting started with using `get_decennial()` as a way of understanding how this package works. With `get_decennial()`, you can query data from 2000, 2010 and 2020 decennial census. The 3 necessary arguments you need to provide are: +Here we are going to focus on getting started with using `get_decennial()` as a way of understanding how this package works. + +With `get_decennial()`, you can query data from 2000, 2010 and 2020 decennial census. The 3 necessary arguments you need to provide are: - Geography - Variable - Year -So, to get the total population for 2020 by state the code woul look like this. +:::callout-tip +## Specifying a summary file +Another argument form `get_decennial()` is `sumfile =`. + +"The Census summary file; if NULL, defaults to "pl" when the year is 2020 and "sf1" for 2000 and 2010. Not all summary files are available for each decennial Census year. Make sure you are using the correct summary file for your requested variables, as variable IDs may be repeated across summary files and represent different topics" + +::: + +So, to get the total population for 2020 by state the code would look like this. ```{r} #| eval: false @@ -152,20 +164,36 @@ The resulting data frame you get from this query is a tibble in "tidy" format wi - variable = census code for the variable (eg. P1_001N is the code for the total population in the 2020 population redistricting file) - value = population count -This table is the daa for one single variable. In this case the variable is "P1_001N" which refers to total population. +This table provides data for one single variable. In this case the variable is "P1_001N" which refers to total population. :::callout-important -## Message error - +## Message -You get this message the first time you run g`et_decennial()` in your session. It first makes sure your are retrieving the correct data. Then it mentions the fact that the 2020 census “Introduces errors differential privacy”. +![](images/get_decennial_message.png) -In the past other privacy methods have been used to preserve confidentiality. Differential privacy is a method that purposely introduces noise or error into the data in order to make it impossible or at least very difficult to reverse engineer the census and track where the responses are coming from. This has an impact on small area counts (e.g.: block with children but not adults). This is something to be aware of if you are working with small population geographies. +You get this message the first time you run `get_decennial()` in your session. It first makes sure your are retrieving the correct data. Then it mentions the fact that the 2020 census “Introduces errors differential privacy”. -Only the population data is differentially infused. The household data is not. +In the past other privacy methods have been used to preserve confidentiality. Differential privacy is a method that purposely introduces noise or error into the data in order to make it impossible or at least very difficult to reverse engineer the census and track where the responses are coming from. This has an impact on small area counts (e.g.: block with children but not adults). This is something to be aware if you are working with small population geographies. + +Only the population data is differentially infused. The household data are not. ::: + +Note that to get total population data for the 2010 decennial census the variable code is different than the 2020 census (even though the arguments of the functions are the same). In this case the code would look like this. + +```{r} + +pop_2010 <- get_decennial( + geography = "state", + variables = "P001001", + year = 2010) + +``` + + When using `get_decennial()`, you can also specify a specific table instead of only one variable. A table of data contains multiple variables. The code for getting the "P2" table from 2020 would look like this. + + ```{r} #| eval: false @@ -191,18 +219,22 @@ unique(table_p2_2020$variable) One of the hardest things to wrap your head around when using `tidycensus` is understanding the difference between files, tables, and variables and how they work. -My take on this is the following: data is released in files. Each file is a data product from the Census Bureau (find details of the Census data products [here](https://www.census.gov/programs-surveys/decennial-census/decade/2020/planning-management/release/about-2020-data-products.html#redistricting)), for example, PL 94-171. Each of these files contains many tables (P1, P2 .. P5). Each table covers a specific topic. For example, P2 provides counts by race by Hispanic origins. Then each table has multiple variables. The variable code, to my understanding, starts with the table code. For example, the unique codes values we printed from table P2. +Let's take a look on how this works. Data is released in files. Each file is a data product from the Census Bureau (find details of the Census data products [here](https://www.census.gov/programs-surveys/decennial-census/decade/2020/planning-management/release/about-2020-data-products.html#redistricting)), for example, PL 94-171 is one of the files for the 2020 Decennial Census. Each of these files contains many tables (P1, P2 .. P5). Each table covers a specific topic. For example, P2 provides counts by race by Hispanic origins. Then each table has multiple variables. The variable code seems to, for the most part, start with the table code. For example, the unique codes values we printed from table P2 all start with P2_. How do we navigate all these codes? There is no straightforward way. In part, this dilemma is part of working with census data, which in itself is complicated. However, `tidycensus` provides some help with a function called `load_variables()` This function scrapes the variables' names from the census website and gives you a data frame that you can interact with. This variable requires you to input a year and a file name. -So let’s run this function and create an object named `vars_pl_2020` This will give us all the variable codes and definitions for the PL94171 redistricting file. +So let’s run this function and create an object named `vars_pl_2020` This will give us all the variable codes and definitions for the PL 94-171 redistricting file. ```{r} #| eval: false vars_pl_2020 <- load_variables(2020, "pl") + +## for 20210 +vars_pl_2010 <- load_variables(2010, "pl") + ``` @@ -223,11 +255,29 @@ Tables available in the 2020 Census PL file: Note: “Group quarters are places where people live or stay, in a group living arrangement, that is owned or managed by an entity or organization providing housing and/or services for the residents.” ([US Census Bureau Glossary](https://www.census.gov/glossary/?term=Group+quarters+population)) -We won’t get into much more detail, but, you can query any of the available files for the 2020 census data. Like the Demographic and Housing Characteristics File (DHC) by running `vars_dhc_2020 -> load_variables(2020, "dhc")`. This is a much larger file with much more tables than the PL file. +We won’t get into much more detail, but, you can query any of the available files for the 2020 census data. Like the Demographic and Housing Characteristics File (DHC) by running the following code. This is a much larger file with much more tables than the PL file. + +```{r} +#| eval: false + +vars_dhc_2020 <- load_variables(2020, "dhc") + +## Note you have to specify the file with sumfile = +household_2020 <- get_decennial( + geography = "state", + variable = "H10_001N", + year = 2020, + sumfile = "dhc") + +``` + The idea behind `load_variables()` is for you to be able to search for the variable code for the variable you need. -Now that we’ve talked about variables let’s talk a little bit about geography and how `tidycensus` makes it easy to query data within census geographies. As we mention in the introduction, census data has a hierarchy of geographies, plus other groups of geographies. This mean we can name the specific geography we want data from. For example, let's get data for Hispanic population the 6 counties around the Delta. +Now that we’ve talked about variables let’s talk a little bit about geography and how `tidycensus` makes it easy to query data within census geographies. Census data is tabulated in enumeration units. These units are specific geographies including legal entities such as states and counties, and statistical entities that are not official jurisdictions but used to standardize data. The graphic below, provided by [census.gov](https://www.census.gov/programs-surveys/geography/guidance/hierarchy.html) shows the standard hierarchy of census geographic entities. + +![](images/census_geos.png) +The parameter geography = in `get_acs()` and `get_decennial()` allows us to request data from common enumeration units. This mean we can name the specific geography we want data from. For example, let's get data for Hispanic population the 6 counties around the Delta. ```{r} #| eval: false @@ -238,21 +288,21 @@ delta_hispanic <- get_decennial( county = c("Alameda", "Contra Costa", "Sacramento", "San Joaquin", "Solano", "Yolo"), variables = "P2_002N", year = 2020) + ``` -To learn more about the arguments for geography for each core function of `tidycensus`, check out the documentation [here]() +To learn more about the arguments for geography for each core function of `tidycensus`, check out the documentation [here](https://walker-data.com/tidycensus/articles/basic-usage.html#geography-in-tidycensus). #### Quering for multiple variabels -The `varaible` argument can have as an input a vector of variables. We can create a vector and then call that vector as the input of the `variable` argument, or we can use the concatenate function `c()` and request data from multiple variables. +The `varaible` argument can take a vector of variables as an input, allowing to query for more than one variable at the time. We can create a vector and then call that vector as the input of the `variable` argument, or we can use the concatenate function `c()` and request data from multiple variables. ```{r} #| eval: false ## Vector with race variables codes - race_vars <- c( Hispanic = "P2_002N", White = "P2_005N", @@ -277,11 +327,103 @@ Also, note that we added one more argument to our request `summary_var = "P2_001 In every table you can generally find a variable that is an appropriate denominators for a group of variables. Following the example above, the P2 table, which provides population by race with Hispanic origin, the variable "P2001N" represents the total population. Because this variable is an an appropriate denominator for the other variables in the table, it helps to have it in a different column to make it easier to calculate proportions or percentage. - - diff --git a/materials/session_11.qmd b/materials/session_11.qmd index 32475c58..746a3752 100644 --- a/materials/session_11.qmd +++ b/materials/session_11.qmd @@ -3,5 +3,6 @@ title: "Working with US Census Data" title-block-banner: true --- + {{< include /sections/census-data.qmd >}} From 61551b4e203daba126ae082e7f6312c9bbc2927b Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Wed, 30 Aug 2023 00:33:09 -0700 Subject: [PATCH 080/104] setting chunks to eval false, fixinf fig name --- ...la_message.png => get_decennial_message.png} | Bin materials/sections/census-data.qmd | 16 +++++++++++++--- 2 files changed, 13 insertions(+), 3 deletions(-) rename materials/images/{get_decennila_message.png => get_decennial_message.png} (100%) diff --git a/materials/images/get_decennila_message.png b/materials/images/get_decennial_message.png similarity index 100% rename from materials/images/get_decennila_message.png rename to materials/images/get_decennial_message.png diff --git a/materials/sections/census-data.qmd b/materials/sections/census-data.qmd index b55defc7..6aff33f2 100644 --- a/materials/sections/census-data.qmd +++ b/materials/sections/census-data.qmd @@ -16,9 +16,10 @@ This lesson is based on [Analyzing US Census Data: Methods, Maps, and Models in ## Introduction to `tidycensus` -::: columns -::: {.column width="30%"} -![](images/tidycensus_logo.png){fig-align="center"} +::: column-margin + +![](images/tidycensus_logo.png){fig-align="right"} + ::: @@ -182,6 +183,7 @@ Only the population data is differentially infused. The household data are not. Note that to get total population data for the 2010 decennial census the variable code is different than the 2020 census (even though the arguments of the functions are the same). In this case the code would look like this. ```{r} +#| eval: false pop_2010 <- get_decennial( geography = "state", @@ -367,6 +369,8 @@ We can check our data by calling the `View(delta_track_hw)` function in the cons Now that we have our data, next thing we will do is calculate the percentage of White and Hispanic population in each track. Given that we have the summary variable within our data set we can easily add a new column with the percentage. And then, we will also clean the `NAMES` column and separate track, county and state into it's own column. ```{r} +#| eval: false + delta_track_clean <- delta_track_hw %>% mutate(percent = 100 * (value / summary_value)) %>% separate(NAME, into = c("tract", "county", "state"), @@ -382,6 +386,7 @@ Now that we have or "clean" data, with all the variables we need. Let's plot thi Applying all what we learned earlier this week, we are going to use `ggplot2` to create some wonderful plots! ```{r} +#| eval: false ggplot(delta_track_hw_cl, aes(x = percent, fill = county)) + @@ -401,6 +406,9 @@ ggplot(delta_track_hw_cl, - Example code to get median income for California by county ```{r} +#| eval: false + + ## 1-year survey median_income_1yr <- get_acs( geography = "county", @@ -420,6 +428,8 @@ median_income_5yr <- get_acs( - You can access to different variables in a survey with the `load_variable()` function ```{r} +#| eval: false + ## variables for 5-year 2017-2021 ACS vars <- load_variables(2021, "acs5") ``` From 538effb86cc8e5d941b9fd678106fc27b632e7a9 Mon Sep 17 00:00:00 2001 From: hdolinh Date: Wed, 30 Aug 2023 09:09:29 -0700 Subject: [PATCH 081/104] set lib chunk with gutenbergr package to eval:false --- materials/sections/intro-tidy-text-data.qmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/materials/sections/intro-tidy-text-data.qmd b/materials/sections/intro-tidy-text-data.qmd index 89f0f86d..087662dc 100644 --- a/materials/sections/intro-tidy-text-data.qmd +++ b/materials/sections/intro-tidy-text-data.qmd @@ -98,7 +98,7 @@ We are going to use the `gutenbergr` package to access public domain texts from 2. Create a new code chunk and attach the following libraries: ```{r} -#| message: false +#| eval: false library(gutenbergr) # access public domain texts from Project Gutenberg library(tidytext) # text mining using tidy tools library(dplyr) # wrangle data From 774cdd6c906bd5d7e033d683d7b343f17fbd47c6 Mon Sep 17 00:00:00 2001 From: hdolinh Date: Wed, 30 Aug 2023 09:16:58 -0700 Subject: [PATCH 082/104] uncomment text lesson --- materials/session_12.qmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/materials/session_12.qmd b/materials/session_12.qmd index d790347a..21ba1ad5 100644 --- a/materials/session_12.qmd +++ b/materials/session_12.qmd @@ -3,7 +3,7 @@ title: "Working with Text Data in R" title-block-banner: true --- - +{{< include /sections/intro-tidy-text-data.qmd >}} From 2bb938804018e049f38b547325fbbe77b225fa79 Mon Sep 17 00:00:00 2001 From: hdolinh Date: Wed, 30 Aug 2023 09:17:42 -0700 Subject: [PATCH 083/104] explicitly call dplyr for anti_join(); add theme to plots in 12.5 exercise --- materials/sections/intro-tidy-text-data.qmd | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/materials/sections/intro-tidy-text-data.qmd b/materials/sections/intro-tidy-text-data.qmd index 087662dc..2d0d2c06 100644 --- a/materials/sections/intro-tidy-text-data.qmd +++ b/materials/sections/intro-tidy-text-data.qmd @@ -173,7 +173,7 @@ Take a look at the data - are you satisfied with your data? We won't conduct any #| eval: false # remove stop words -tidy_phantom <- tidy_phantom %>% anti_join(stop_words, by = "word") +tidy_phantom <- tidy_phantom %>% dplyr::anti_join(stop_words, by = "word") ``` ::: {.callout-note} @@ -354,8 +354,9 @@ all_books_dfm <- tidy_all_books %>% library(tidytext) # tidy text tools library(quanteda) # create a corpus library(pdftools) # read in data +library(dplyr) # wrangle data library(stringr) # string manipulation -library(ggplot2) # for quick and dirty plots +library(ggplot2) # plots library(wordcloud) ``` @@ -466,7 +467,7 @@ Remove stop words using `anti_join()` and the `stop_words` data frame from `tidy #| code-summary: Answer #| message: false words_dp_ch8 <- unnest_dp_ch8 %>% - anti_join(stop_words) + dplyr::anti_join(stop_words) ``` @@ -499,7 +500,8 @@ ggplot(count_dp_ch8, aes(x = reorder(word, n), y = n)) + coord_flip() + labs(title = "Top 10 Most Frequently Occurring Words in Chapter 8 of the Delta Plan", x = NULL, - y = "count") + y = "count") + + theme_minimal() ``` ```{r} @@ -512,7 +514,8 @@ ggplot(data = count_dp_ch8, aes(x=reorder(word, n), y=n)) + coord_flip() + labs(title = "Top 10 Most Frequently Occurring Words in Chapter 8 of the Delta Plan", x = NULL, - y = "Count") + y = "Count") + + theme_minimal() ``` ```{r} From 85877be1ac3271512573776ffcb84b848cfcac39 Mon Sep 17 00:00:00 2001 From: hdolinh Date: Wed, 30 Aug 2023 13:35:52 -0700 Subject: [PATCH 084/104] include DFM, DTM, and sparse matrix in vocab table --- materials/sections/intro-tidy-text-data.qmd | 151 ++++++++++---------- 1 file changed, 72 insertions(+), 79 deletions(-) diff --git a/materials/sections/intro-tidy-text-data.qmd b/materials/sections/intro-tidy-text-data.qmd index 2d0d2c06..5a324946 100644 --- a/materials/sections/intro-tidy-text-data.qmd +++ b/materials/sections/intro-tidy-text-data.qmd @@ -6,7 +6,7 @@ bibliography: book.bib - Describe principles of tidy text data - Employ strategies to wrangle unstructured text data into a tidy text format using the `tidytext` package -- Become familiar non-tidy text formats and how to convert between tidy text and non-tidy text formats +- Become familiar non-tidy text formats and how to convert between tidy text and non-tidy text formats - Become familiar with text analysis (or text mining) methods and when to use them ::: callout-note @@ -30,15 +30,17 @@ Raw text data is often unstructured and quite "messy". This could include wrong Here is a list of text data or text analysis terms we'll be referring to throughout this lesson. Note this is not a comprehensive list of text analysis terms that are used beyond this lesson. -| Term | Definition | -|-----------------|-------------------------------------------------------| -| Corpus (corpora, plural) | Collection or database of text or multiple texts. These types of objects typically contain raw strings annotated with additional metadata and details. | -| Document-term matrix | Represents the relationship between terms and documents, where each row stands for a term and each column for a document, and an entry is the number of occurrences of the term in the document. | -| Natural Language Processing (NLP) | NLP is an interdisciplinary field used in computer science, data science, linguistics, and others to analyze, categorize, and work with computerized text. | -| String | Specific type of data whose values are enclosed within a set of quotes. Typically values or elements are characters (e.g. "Hello World!"). | -| Text analysis | The process of deriving high-quality information or patterns from text through evaluation and interpretation of the output. Also referred to as "text mining" or "text analytics". | -| Token | A meaningful unit of text, such as a word, to use for analysis. | -| Tokenization | The process of splitting text into tokens. | +| Term | Definition | +|------------------|------------------------------------------------------| +| Corpus (corpora, plural) | Collection or database of text or multiple texts. These types of objects typically contain raw strings annotated with additional metadata and details. | +| Document-feature matrix (DFM) | Represents the relationship between features and documents, where each row is a document and each column corresponds to a set of features. Features are not limited to terms and can include a variety of attributes that describe the documents. Each cell in the matrix contains a value that represents the presence, absence, or some quantitative measure of a specific feature in a particular document. | +| Document-term matrix (DTM) | Represents the relationship between terms and documents, where each row stands for a document and each column represents a term, and an entry is the number of occurrences of the term in the document. | +| Natural Language Processing (NLP) | NLP is an interdisciplinary field used in computer science, data science, linguistics, and others to analyze, categorize, and work with computerized text. | +| Sparse matrix | A matrix where the majority of the entries are zero. | +| String | Specific type of data whose values are enclosed within a set of quotes. Typically values or elements are characters (e.g. "Hello World!"). | +| Text analysis | The process of deriving high-quality information or patterns from text through evaluation and interpretation of the output. Also referred to as "text mining" or "text analytics". | +| Token | A meaningful unit of text, such as a word, to use for analysis. | +| Tokenization | The process of splitting text into tokens. | ### How is text data used in the environmental field? @@ -46,13 +48,13 @@ As our knowledge about the environmental world grows, researchers will need new Beyond literature syntheses, quantitative text analysis tools are extremely valuable for efficiently extracting information from texts and other text mining or text analysis tasks. -::: {.callout-caution icon=false} +::: {.callout-caution icon="false"} #### Environmental researchers have used text data to: - Understand how text mining is used in the field [@farrell2022] - Gain insights into public perception of a particular subject [@froehlich2017] - Highlight the power of NLP models to advance research [@vanhoutan2020] -::: +::: ## What is tidy text data? @@ -84,13 +86,13 @@ The package contains many useful functions to wrangle text data into tidy format ::: ::: -## Exercise: Tidy Text Workflow +## Exercise: Tidy Text Workflow {#exercise-tidy-text-workflow} ![Source: Silge & Robinson](images/tidytext-tidy-workflow.png) We are going to use the `gutenbergr` package to access public domain texts from [Project Gutenberg](https://www.gutenberg.org/) (a library of free eBooks). We'll then use the `tidytext`, `dyplr` and `ggplot2` packages to practice the tidy text workflow. -::: {.callout-tip} +::: callout-tip ### Setup 1. Create a new `qmd` file and title it "Intro to Text Data", name yourself as the author, and then save the file as `intro-text-data.qmd`. @@ -124,7 +126,7 @@ gutenberg_works(title == "The Strange Case of Dr. Jekyll and Mr. Hyde") # jekyll The answers in the code chunks are using the text of *The Phantom of the Opera*. -::: {.callout-note} +::: callout-note #### Question 1 Get the id number from the `gutenberg_works()` function so that you can download the text as a corpus using the function `gutenberg_download()`. Save the corpus to an object called `{book-title}_corp`. View the object - is the data in a tidy format? @@ -142,7 +144,7 @@ gutenberg_works(title == "The Phantom of the Opera") phantom_corp <- gutenberg_download(175) ``` -::: {.callout-note} +::: callout-note #### Question 2 Tokenize the corpus data using `unnest_tokens()`. Take a look at the data - do we need every single token for our analysis? @@ -158,11 +160,10 @@ tidy_phantom <- phantom_corp %>% unnest_tokens(word, text) ``` - -::: {.callout-note} +::: callout-note #### Question 3 -Remove "stop words" or words that can be safely removed or ignored without sacrificing the meaning of the sentence (e.g. "to", "in", "and") using `anti_join()`. +Remove "stop words" or words that can be safely removed or ignored without sacrificing the meaning of the sentence (e.g. "to", "in", "and") using `anti_join()`. Take a look at the data - are you satisfied with your data? We won't conduct any additional cleaning steps here, but consider how you would further clean the data. ::: @@ -176,7 +177,7 @@ Take a look at the data - are you satisfied with your data? We won't conduct any tidy_phantom <- tidy_phantom %>% dplyr::anti_join(stop_words, by = "word") ``` -::: {.callout-note} +::: callout-note #### Question 4 Calculate the top 10 most frequent words using the functions `count()` and `slice_max()`. @@ -193,10 +194,10 @@ count_phantom <- tidy_phantom %>% slice_max(n = 10, order_by = n) ``` -::: {.callout-note} +::: callout-note #### Question 5 -Plot the top 10 most frequent words using `ggplot()`. +Plot the top 10 most frequent words using `ggplot()`. We recommend creating either a bar plot using `geom_col()` or a lollipop plot using both `geom_point()` and `geom_segment()`. ::: @@ -213,8 +214,8 @@ ggplot(data = count_phantom, aes(n, reorder(word, n))) + y = "Token") ``` -

    + Step 8 Bar Plot ![](images/tidytext-barplot.png) @@ -223,8 +224,9 @@ ggplot(data = count_phantom, aes(n, reorder(word, n))) + ### Bonus Question -::: {.callout-note} +::: callout-note #### Question 6 + Consider elements in `theme()` and improve your plot. ::: @@ -256,20 +258,22 @@ ggplot(data = count_phantom, aes(x=reorder(word, n), y=n)) + ```
    - Step 9 Lollipop Plot + +Step 9 Lollipop Plot ![](images/tidytext-lollipopplot.png) +
    ## Tidy Text to Non-tidy Text Workflows ![A flowchart of a typical text analysis that combines `tidytext` with other tools and data formats, particularly the `tm` or `quanteda` packages. Source: Silge & Robinson](images/tidytext-nontidy-workflow.png) -In the [Tidy Text Workflow Exercise](#exercise-tidy-text-workflow), we converted our corpus into a data table that has "one-token-per-row". However, the tidy text format of one-token-per-row is not a common format for other R packages that work with text data or perform text analysis. Packages like `tm`, `quanteda`, `topicmodels`. +In the [Tidy Text Workflow Exercise](#exercise-tidy-text-workflow), we converted our corpus into a data table that has "one-token-per-row". However, the tidy text format of one-token-per-row is not a common format for other R packages that work with text data or perform text analysis. Packages like `tm`, `quanteda`, `topicmodels`. Many text analysis methods, in particular NLP techniques (e.g. topic models) require text data to be stored in a mathematical format. **A common approach is to create a matrix, such as a: sparse matrix, a document term matrix (DTM), or a document-feature matrix (DFM)**. In a matrix format, algorithms are able to more easily compare one document to many other documents to identify patterns. -Silge and Robinson kept this in mind as they built the `tidytext` package, and included helpful `cast()` functions to turn a tidy text object (again a table with one-token-per-row) into a matrix. +Silge and Robinson kept this in mind as they built the `tidytext` package, and included helpful `cast()` functions to turn a tidy text object (again a table with one-token-per-row) into a matrix. ### Use `cast()` to Convert to a Matrix (Non-tidy) Format @@ -330,24 +334,21 @@ all_books_dfm <- tidy_all_books %>% value = n) ``` - -:::{.callout-caution icon=false} +::: {.callout-caution icon="false"} ### R Text Mining Tools and Analysis Packages Resources -- [CRAN Task View: Natural Language Processing](https://cran.r-project.org/web/views/NaturalLanguageProcessing.html) -- [Penn Libraries Guides: Text Analysis](https://guides.library.upenn.edu/penntdm/r) +- [CRAN Task View: Natural Language Processing](https://cran.r-project.org/web/views/NaturalLanguageProcessing.html) +- [Penn Libraries Guides: Text Analysis](https://guides.library.upenn.edu/penntdm/r) ::: - ## Exercise: Explore Unstructured Text Data from a PDF -::: {.callout-tip} - +::: callout-tip ### Setup 1. In the `intro-text-data.qmd` file, create a new header for this exercise (e.g. "Explore Unstructured Text Data from a PDF"). -2. Create a new code chunk and attach the following libraries: +2. Create a new code chunk and attach the following libraries: ```{r} #| message: false @@ -362,10 +363,10 @@ library(wordcloud) 3. Depending on which group you're in, read in one of the following chapters of the Delta Plan. Access and download the chapters of the Delta Plan from [Delta Stewardship Council website](https://deltacouncil.ca.gov/delta-plan/). -**Notes for quick exploration of data:** +**Notes for quick exploration of data:** -- Check the `class()` of the pdf you just read in - is it what you expected? How does the object appear in the Global Environment? -- Call the object in the Console. What does your data look like? What can you infer from how it's structured? +- Check the `class()` of the pdf you just read in - is it what you expected? How does the object appear in the Global Environment? +- Call the object in the Console. What does your data look like? What can you infer from how it's structured? ```{r} #| eval: false @@ -395,13 +396,13 @@ path_df <- "data/text/dsc-plan-ch8.pdf" dp_ch8 <- pdftools::pdf_text(path_df) ``` -4. Using the `quanteda` package, turn the unstructured pdf text data into a corpus. +4. Using the `quanteda` package, turn the unstructured pdf text data into a corpus. -**Notes for quick exploration of data:** +**Notes for quick exploration of data:** -- Check the `class()` of the corpus you created - is it what you expected? How does the object appear in the Global Environment? -- Call the object in the Console. What does your data look like? How does this structure compare to the pdf object? -- Run `summary()` of the corpus in the Console. What insights can you glean? +- Check the `class()` of the corpus you created - is it what you expected? How does the object appear in the Global Environment? +- Call the object in the Console. What does your data look like? How does this structure compare to the pdf object? +- Run `summary()` of the corpus in the Console. What insights can you glean? ```{r} #| eval: false @@ -416,12 +417,12 @@ corpus_dp_ch <- quanteda::corpus(dp_ch) corpus_dp_ch8 <- quanteda::corpus(dp_ch8) ``` -5. Using `tidy()` from `tidytext`, make the corpus a tidy object. +5. Using `tidy()` from `tidytext`, make the corpus a tidy object. -**Notes for quick exploration of data:** +**Notes for quick exploration of data:** -- Check the `class()` of the corpus you created - is it what you expected? How does the object appear in the Global Environment? -- Call the object in the Console or use `View()`. What does your data look like? Is it what you expected? +- Check the `class()` of the corpus you created - is it what you expected? How does the object appear in the Global Environment? +- Call the object in the Console or use `View()`. What does your data look like? Is it what you expected? ```{r} #| eval: false @@ -441,7 +442,7 @@ tidy_dp_ch8 <- tidy(corpus_dp_ch8) Work independently or in groups for Question 1-5. The code solutions are based on the text data from Chapter 8 of the Delta Plan. -::: {.callout-note} +::: callout-note #### Question 1 Tokenize the tidy text data using `unnest_tokens()` @@ -455,8 +456,7 @@ unnest_dp_ch8 <- tidy_dp_ch8 %>% input = text) ``` - -::: {.callout-note} +::: callout-note #### Question 2 Remove stop words using `anti_join()` and the `stop_words` data frame from `tidytext`. @@ -470,8 +470,7 @@ words_dp_ch8 <- unnest_dp_ch8 %>% dplyr::anti_join(stop_words) ``` - -::: {.callout-note} +::: callout-note #### Question 3 Calculate the top 10 most frequently occurring words. Consider using `count()` and `slice_max()`. @@ -485,7 +484,7 @@ count_dp_ch8 <- words_dp_ch8 %>% slice_max(n = 10, order_by = n) ``` -::: {.callout-note} +::: callout-note #### Question 4 Visualize the results using a plot of your choice (e.g. bar plot, lollipop plot, or wordcloud). @@ -528,36 +527,35 @@ wordcloud(words = count_dp_ch8$word, ### Bonus Question -::: {.callout-note} +::: callout-note #### Question 5 What do you think of your plots? Are they helpful? Consider other techniques like adding custom stop words or stemming to improve your results. ::: - ## Common Text Analysis and Text Mining Methods -The text analysis tools, methods, and packages depend greatly on your specific text analysis goals and the nature of your text data. You may end up only using one method or one package, or many in combination. - -| Text Analysis Method | R Package | When to Use | -|------------------------------|----------------|---------------------------------------------------------| -| Document-Term Matrix (DTM) | `tm`, `quanteda`, `tm.plugin.webmining` | Represent text data as a matrix of word frequencies. | -| Named Entity Recognition (NER) | `openNLP`, `spacyr`, `udpipe` | Identify entities like names, locations, etc. in text. | -| Sentiment Analysis | `tidytext`, `sentimentr`, `syuzhet` | Determine sentiment (positive/negative) in text. | -| Stopword Removal | `tm`, `quanteda`, `tidytext` | Remove common and irrelevant words. | -| Text Classification | `caret`, `tm`, `quanteda` | Categorize text into predefined classes/categories. | -| Text Cleaning | `tm`, `tm.plugin.webmining`, `tidytext` | Preprocess text: convert to lowercase, remove special characters, etc. | -| Text Clustering | `tm`, `text2vec`, `tm.plugin.clustering` | Group similar documents together. | -| Text Summarization | `tm`, `textclean`, `textrank` | Condense the main points of a text into a summary. | -| TF-IDF | `tm`, `tm.plugin.webmining`, `tidytext` | Measure word importance in a document corpus. | -| Tokenization | `tm`, `quanteda`, `text2vec` | Split text into individual words/tokens. | -| Topic Modeling | `tm`, `topicmodels`, `lda2vec` | Discover hidden topics in a collection of documents. | -| Word Embeddings | `word2vec`, `text2vec`, `fastText` | Represent words as dense vectors for semantic analysis. | -| Dependency Parsing | `udpipe`, `spaCy`, `openNLP` | Analyze grammatical structure and word dependencies. | +The text analysis tools, methods, and packages depend greatly on your specific text analysis goals and the nature of your text data. You may end up only using one method or one package, or many in combination. + +| Text Analysis Method | R Package | When to Use | +|-------------------|----------------|-------------------------------------| +| Document-Term Matrix (DTM) | `tm`, `quanteda`, `tm.plugin.webmining` | Represent text data as a matrix of word frequencies. | +| Named Entity Recognition (NER) | `openNLP`, `spacyr`, `udpipe` | Identify entities like names, locations, etc. in text. | +| Sentiment Analysis | `tidytext`, `sentimentr`, `syuzhet` | Determine sentiment (positive/negative) in text. | +| Stopword Removal | `tm`, `quanteda`, `tidytext` | Remove common and irrelevant words. | +| Text Classification | `caret`, `tm`, `quanteda` | Categorize text into predefined classes/categories. | +| Text Cleaning | `tm`, `tm.plugin.webmining`, `tidytext` | Preprocess text: convert to lowercase, remove special characters, etc. | +| Text Clustering | `tm`, `text2vec`, `tm.plugin.clustering` | Group similar documents together. | +| Text Summarization | `tm`, `textclean`, `textrank` | Condense the main points of a text into a summary. | +| TF-IDF | `tm`, `tm.plugin.webmining`, `tidytext` | Measure word importance in a document corpus. | +| Tokenization | `tm`, `quanteda`, `text2vec` | Split text into individual words/tokens. | +| Topic Modeling | `tm`, `topicmodels`, `lda2vec` | Discover hidden topics in a collection of documents. | +| Word Embeddings | `word2vec`, `text2vec`, `fastText` | Represent words as dense vectors for semantic analysis. | +| Dependency Parsing | `udpipe`, `spaCy`, `openNLP` | Analyze grammatical structure and word dependencies. | ## Note About Python and NLP - +```{=html} +``` From dc811f638b040929727d94710b82e729ce1eb9a4 Mon Sep 17 00:00:00 2001 From: hdolinh Date: Wed, 30 Aug 2023 13:35:52 -0700 Subject: [PATCH 085/104] include DFM, DTM, and sparse matrix in vocab table --- materials/sections/intro-tidy-text-data.qmd | 151 ++++++++++---------- 1 file changed, 72 insertions(+), 79 deletions(-) diff --git a/materials/sections/intro-tidy-text-data.qmd b/materials/sections/intro-tidy-text-data.qmd index 2d0d2c06..5a324946 100644 --- a/materials/sections/intro-tidy-text-data.qmd +++ b/materials/sections/intro-tidy-text-data.qmd @@ -6,7 +6,7 @@ bibliography: book.bib - Describe principles of tidy text data - Employ strategies to wrangle unstructured text data into a tidy text format using the `tidytext` package -- Become familiar non-tidy text formats and how to convert between tidy text and non-tidy text formats +- Become familiar non-tidy text formats and how to convert between tidy text and non-tidy text formats - Become familiar with text analysis (or text mining) methods and when to use them ::: callout-note @@ -30,15 +30,17 @@ Raw text data is often unstructured and quite "messy". This could include wrong Here is a list of text data or text analysis terms we'll be referring to throughout this lesson. Note this is not a comprehensive list of text analysis terms that are used beyond this lesson. -| Term | Definition | -|-----------------|-------------------------------------------------------| -| Corpus (corpora, plural) | Collection or database of text or multiple texts. These types of objects typically contain raw strings annotated with additional metadata and details. | -| Document-term matrix | Represents the relationship between terms and documents, where each row stands for a term and each column for a document, and an entry is the number of occurrences of the term in the document. | -| Natural Language Processing (NLP) | NLP is an interdisciplinary field used in computer science, data science, linguistics, and others to analyze, categorize, and work with computerized text. | -| String | Specific type of data whose values are enclosed within a set of quotes. Typically values or elements are characters (e.g. "Hello World!"). | -| Text analysis | The process of deriving high-quality information or patterns from text through evaluation and interpretation of the output. Also referred to as "text mining" or "text analytics". | -| Token | A meaningful unit of text, such as a word, to use for analysis. | -| Tokenization | The process of splitting text into tokens. | +| Term | Definition | +|------------------|------------------------------------------------------| +| Corpus (corpora, plural) | Collection or database of text or multiple texts. These types of objects typically contain raw strings annotated with additional metadata and details. | +| Document-feature matrix (DFM) | Represents the relationship between features and documents, where each row is a document and each column corresponds to a set of features. Features are not limited to terms and can include a variety of attributes that describe the documents. Each cell in the matrix contains a value that represents the presence, absence, or some quantitative measure of a specific feature in a particular document. | +| Document-term matrix (DTM) | Represents the relationship between terms and documents, where each row stands for a document and each column represents a term, and an entry is the number of occurrences of the term in the document. | +| Natural Language Processing (NLP) | NLP is an interdisciplinary field used in computer science, data science, linguistics, and others to analyze, categorize, and work with computerized text. | +| Sparse matrix | A matrix where the majority of the entries are zero. | +| String | Specific type of data whose values are enclosed within a set of quotes. Typically values or elements are characters (e.g. "Hello World!"). | +| Text analysis | The process of deriving high-quality information or patterns from text through evaluation and interpretation of the output. Also referred to as "text mining" or "text analytics". | +| Token | A meaningful unit of text, such as a word, to use for analysis. | +| Tokenization | The process of splitting text into tokens. | ### How is text data used in the environmental field? @@ -46,13 +48,13 @@ As our knowledge about the environmental world grows, researchers will need new Beyond literature syntheses, quantitative text analysis tools are extremely valuable for efficiently extracting information from texts and other text mining or text analysis tasks. -::: {.callout-caution icon=false} +::: {.callout-caution icon="false"} #### Environmental researchers have used text data to: - Understand how text mining is used in the field [@farrell2022] - Gain insights into public perception of a particular subject [@froehlich2017] - Highlight the power of NLP models to advance research [@vanhoutan2020] -::: +::: ## What is tidy text data? @@ -84,13 +86,13 @@ The package contains many useful functions to wrangle text data into tidy format ::: ::: -## Exercise: Tidy Text Workflow +## Exercise: Tidy Text Workflow {#exercise-tidy-text-workflow} ![Source: Silge & Robinson](images/tidytext-tidy-workflow.png) We are going to use the `gutenbergr` package to access public domain texts from [Project Gutenberg](https://www.gutenberg.org/) (a library of free eBooks). We'll then use the `tidytext`, `dyplr` and `ggplot2` packages to practice the tidy text workflow. -::: {.callout-tip} +::: callout-tip ### Setup 1. Create a new `qmd` file and title it "Intro to Text Data", name yourself as the author, and then save the file as `intro-text-data.qmd`. @@ -124,7 +126,7 @@ gutenberg_works(title == "The Strange Case of Dr. Jekyll and Mr. Hyde") # jekyll The answers in the code chunks are using the text of *The Phantom of the Opera*. -::: {.callout-note} +::: callout-note #### Question 1 Get the id number from the `gutenberg_works()` function so that you can download the text as a corpus using the function `gutenberg_download()`. Save the corpus to an object called `{book-title}_corp`. View the object - is the data in a tidy format? @@ -142,7 +144,7 @@ gutenberg_works(title == "The Phantom of the Opera") phantom_corp <- gutenberg_download(175) ``` -::: {.callout-note} +::: callout-note #### Question 2 Tokenize the corpus data using `unnest_tokens()`. Take a look at the data - do we need every single token for our analysis? @@ -158,11 +160,10 @@ tidy_phantom <- phantom_corp %>% unnest_tokens(word, text) ``` - -::: {.callout-note} +::: callout-note #### Question 3 -Remove "stop words" or words that can be safely removed or ignored without sacrificing the meaning of the sentence (e.g. "to", "in", "and") using `anti_join()`. +Remove "stop words" or words that can be safely removed or ignored without sacrificing the meaning of the sentence (e.g. "to", "in", "and") using `anti_join()`. Take a look at the data - are you satisfied with your data? We won't conduct any additional cleaning steps here, but consider how you would further clean the data. ::: @@ -176,7 +177,7 @@ Take a look at the data - are you satisfied with your data? We won't conduct any tidy_phantom <- tidy_phantom %>% dplyr::anti_join(stop_words, by = "word") ``` -::: {.callout-note} +::: callout-note #### Question 4 Calculate the top 10 most frequent words using the functions `count()` and `slice_max()`. @@ -193,10 +194,10 @@ count_phantom <- tidy_phantom %>% slice_max(n = 10, order_by = n) ``` -::: {.callout-note} +::: callout-note #### Question 5 -Plot the top 10 most frequent words using `ggplot()`. +Plot the top 10 most frequent words using `ggplot()`. We recommend creating either a bar plot using `geom_col()` or a lollipop plot using both `geom_point()` and `geom_segment()`. ::: @@ -213,8 +214,8 @@ ggplot(data = count_phantom, aes(n, reorder(word, n))) + y = "Token") ``` -
    + Step 8 Bar Plot ![](images/tidytext-barplot.png) @@ -223,8 +224,9 @@ ggplot(data = count_phantom, aes(n, reorder(word, n))) + ### Bonus Question -::: {.callout-note} +::: callout-note #### Question 6 + Consider elements in `theme()` and improve your plot. ::: @@ -256,20 +258,22 @@ ggplot(data = count_phantom, aes(x=reorder(word, n), y=n)) + ```
    - Step 9 Lollipop Plot + +Step 9 Lollipop Plot ![](images/tidytext-lollipopplot.png) +
    ## Tidy Text to Non-tidy Text Workflows ![A flowchart of a typical text analysis that combines `tidytext` with other tools and data formats, particularly the `tm` or `quanteda` packages. Source: Silge & Robinson](images/tidytext-nontidy-workflow.png) -In the [Tidy Text Workflow Exercise](#exercise-tidy-text-workflow), we converted our corpus into a data table that has "one-token-per-row". However, the tidy text format of one-token-per-row is not a common format for other R packages that work with text data or perform text analysis. Packages like `tm`, `quanteda`, `topicmodels`. +In the [Tidy Text Workflow Exercise](#exercise-tidy-text-workflow), we converted our corpus into a data table that has "one-token-per-row". However, the tidy text format of one-token-per-row is not a common format for other R packages that work with text data or perform text analysis. Packages like `tm`, `quanteda`, `topicmodels`. Many text analysis methods, in particular NLP techniques (e.g. topic models) require text data to be stored in a mathematical format. **A common approach is to create a matrix, such as a: sparse matrix, a document term matrix (DTM), or a document-feature matrix (DFM)**. In a matrix format, algorithms are able to more easily compare one document to many other documents to identify patterns. -Silge and Robinson kept this in mind as they built the `tidytext` package, and included helpful `cast()` functions to turn a tidy text object (again a table with one-token-per-row) into a matrix. +Silge and Robinson kept this in mind as they built the `tidytext` package, and included helpful `cast()` functions to turn a tidy text object (again a table with one-token-per-row) into a matrix. ### Use `cast()` to Convert to a Matrix (Non-tidy) Format @@ -330,24 +334,21 @@ all_books_dfm <- tidy_all_books %>% value = n) ``` - -:::{.callout-caution icon=false} +::: {.callout-caution icon="false"} ### R Text Mining Tools and Analysis Packages Resources -- [CRAN Task View: Natural Language Processing](https://cran.r-project.org/web/views/NaturalLanguageProcessing.html) -- [Penn Libraries Guides: Text Analysis](https://guides.library.upenn.edu/penntdm/r) +- [CRAN Task View: Natural Language Processing](https://cran.r-project.org/web/views/NaturalLanguageProcessing.html) +- [Penn Libraries Guides: Text Analysis](https://guides.library.upenn.edu/penntdm/r) ::: - ## Exercise: Explore Unstructured Text Data from a PDF -::: {.callout-tip} - +::: callout-tip ### Setup 1. In the `intro-text-data.qmd` file, create a new header for this exercise (e.g. "Explore Unstructured Text Data from a PDF"). -2. Create a new code chunk and attach the following libraries: +2. Create a new code chunk and attach the following libraries: ```{r} #| message: false @@ -362,10 +363,10 @@ library(wordcloud) 3. Depending on which group you're in, read in one of the following chapters of the Delta Plan. Access and download the chapters of the Delta Plan from [Delta Stewardship Council website](https://deltacouncil.ca.gov/delta-plan/). -**Notes for quick exploration of data:** +**Notes for quick exploration of data:** -- Check the `class()` of the pdf you just read in - is it what you expected? How does the object appear in the Global Environment? -- Call the object in the Console. What does your data look like? What can you infer from how it's structured? +- Check the `class()` of the pdf you just read in - is it what you expected? How does the object appear in the Global Environment? +- Call the object in the Console. What does your data look like? What can you infer from how it's structured? ```{r} #| eval: false @@ -395,13 +396,13 @@ path_df <- "data/text/dsc-plan-ch8.pdf" dp_ch8 <- pdftools::pdf_text(path_df) ``` -4. Using the `quanteda` package, turn the unstructured pdf text data into a corpus. +4. Using the `quanteda` package, turn the unstructured pdf text data into a corpus. -**Notes for quick exploration of data:** +**Notes for quick exploration of data:** -- Check the `class()` of the corpus you created - is it what you expected? How does the object appear in the Global Environment? -- Call the object in the Console. What does your data look like? How does this structure compare to the pdf object? -- Run `summary()` of the corpus in the Console. What insights can you glean? +- Check the `class()` of the corpus you created - is it what you expected? How does the object appear in the Global Environment? +- Call the object in the Console. What does your data look like? How does this structure compare to the pdf object? +- Run `summary()` of the corpus in the Console. What insights can you glean? ```{r} #| eval: false @@ -416,12 +417,12 @@ corpus_dp_ch <- quanteda::corpus(dp_ch) corpus_dp_ch8 <- quanteda::corpus(dp_ch8) ``` -5. Using `tidy()` from `tidytext`, make the corpus a tidy object. +5. Using `tidy()` from `tidytext`, make the corpus a tidy object. -**Notes for quick exploration of data:** +**Notes for quick exploration of data:** -- Check the `class()` of the corpus you created - is it what you expected? How does the object appear in the Global Environment? -- Call the object in the Console or use `View()`. What does your data look like? Is it what you expected? +- Check the `class()` of the corpus you created - is it what you expected? How does the object appear in the Global Environment? +- Call the object in the Console or use `View()`. What does your data look like? Is it what you expected? ```{r} #| eval: false @@ -441,7 +442,7 @@ tidy_dp_ch8 <- tidy(corpus_dp_ch8) Work independently or in groups for Question 1-5. The code solutions are based on the text data from Chapter 8 of the Delta Plan. -::: {.callout-note} +::: callout-note #### Question 1 Tokenize the tidy text data using `unnest_tokens()` @@ -455,8 +456,7 @@ unnest_dp_ch8 <- tidy_dp_ch8 %>% input = text) ``` - -::: {.callout-note} +::: callout-note #### Question 2 Remove stop words using `anti_join()` and the `stop_words` data frame from `tidytext`. @@ -470,8 +470,7 @@ words_dp_ch8 <- unnest_dp_ch8 %>% dplyr::anti_join(stop_words) ``` - -::: {.callout-note} +::: callout-note #### Question 3 Calculate the top 10 most frequently occurring words. Consider using `count()` and `slice_max()`. @@ -485,7 +484,7 @@ count_dp_ch8 <- words_dp_ch8 %>% slice_max(n = 10, order_by = n) ``` -::: {.callout-note} +::: callout-note #### Question 4 Visualize the results using a plot of your choice (e.g. bar plot, lollipop plot, or wordcloud). @@ -528,36 +527,35 @@ wordcloud(words = count_dp_ch8$word, ### Bonus Question -::: {.callout-note} +::: callout-note #### Question 5 What do you think of your plots? Are they helpful? Consider other techniques like adding custom stop words or stemming to improve your results. ::: - ## Common Text Analysis and Text Mining Methods -The text analysis tools, methods, and packages depend greatly on your specific text analysis goals and the nature of your text data. You may end up only using one method or one package, or many in combination. - -| Text Analysis Method | R Package | When to Use | -|------------------------------|----------------|---------------------------------------------------------| -| Document-Term Matrix (DTM) | `tm`, `quanteda`, `tm.plugin.webmining` | Represent text data as a matrix of word frequencies. | -| Named Entity Recognition (NER) | `openNLP`, `spacyr`, `udpipe` | Identify entities like names, locations, etc. in text. | -| Sentiment Analysis | `tidytext`, `sentimentr`, `syuzhet` | Determine sentiment (positive/negative) in text. | -| Stopword Removal | `tm`, `quanteda`, `tidytext` | Remove common and irrelevant words. | -| Text Classification | `caret`, `tm`, `quanteda` | Categorize text into predefined classes/categories. | -| Text Cleaning | `tm`, `tm.plugin.webmining`, `tidytext` | Preprocess text: convert to lowercase, remove special characters, etc. | -| Text Clustering | `tm`, `text2vec`, `tm.plugin.clustering` | Group similar documents together. | -| Text Summarization | `tm`, `textclean`, `textrank` | Condense the main points of a text into a summary. | -| TF-IDF | `tm`, `tm.plugin.webmining`, `tidytext` | Measure word importance in a document corpus. | -| Tokenization | `tm`, `quanteda`, `text2vec` | Split text into individual words/tokens. | -| Topic Modeling | `tm`, `topicmodels`, `lda2vec` | Discover hidden topics in a collection of documents. | -| Word Embeddings | `word2vec`, `text2vec`, `fastText` | Represent words as dense vectors for semantic analysis. | -| Dependency Parsing | `udpipe`, `spaCy`, `openNLP` | Analyze grammatical structure and word dependencies. | +The text analysis tools, methods, and packages depend greatly on your specific text analysis goals and the nature of your text data. You may end up only using one method or one package, or many in combination. + +| Text Analysis Method | R Package | When to Use | +|-------------------|----------------|-------------------------------------| +| Document-Term Matrix (DTM) | `tm`, `quanteda`, `tm.plugin.webmining` | Represent text data as a matrix of word frequencies. | +| Named Entity Recognition (NER) | `openNLP`, `spacyr`, `udpipe` | Identify entities like names, locations, etc. in text. | +| Sentiment Analysis | `tidytext`, `sentimentr`, `syuzhet` | Determine sentiment (positive/negative) in text. | +| Stopword Removal | `tm`, `quanteda`, `tidytext` | Remove common and irrelevant words. | +| Text Classification | `caret`, `tm`, `quanteda` | Categorize text into predefined classes/categories. | +| Text Cleaning | `tm`, `tm.plugin.webmining`, `tidytext` | Preprocess text: convert to lowercase, remove special characters, etc. | +| Text Clustering | `tm`, `text2vec`, `tm.plugin.clustering` | Group similar documents together. | +| Text Summarization | `tm`, `textclean`, `textrank` | Condense the main points of a text into a summary. | +| TF-IDF | `tm`, `tm.plugin.webmining`, `tidytext` | Measure word importance in a document corpus. | +| Tokenization | `tm`, `quanteda`, `text2vec` | Split text into individual words/tokens. | +| Topic Modeling | `tm`, `topicmodels`, `lda2vec` | Discover hidden topics in a collection of documents. | +| Word Embeddings | `word2vec`, `text2vec`, `fastText` | Represent words as dense vectors for semantic analysis. | +| Dependency Parsing | `udpipe`, `spaCy`, `openNLP` | Analyze grammatical structure and word dependencies. | ## Note About Python and NLP - +```{=html} +``` From d96dc6eaee1b2d929e9271905f7af4c1e8035bd4 Mon Sep 17 00:00:00 2001 From: hdolinh Date: Wed, 30 Aug 2023 13:36:54 -0700 Subject: [PATCH 086/104] table formatting --- materials/sections/intro-tidy-text-data.qmd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/materials/sections/intro-tidy-text-data.qmd b/materials/sections/intro-tidy-text-data.qmd index 5a324946..554d964d 100644 --- a/materials/sections/intro-tidy-text-data.qmd +++ b/materials/sections/intro-tidy-text-data.qmd @@ -31,7 +31,7 @@ Raw text data is often unstructured and quite "messy". This could include wrong Here is a list of text data or text analysis terms we'll be referring to throughout this lesson. Note this is not a comprehensive list of text analysis terms that are used beyond this lesson. | Term | Definition | -|------------------|------------------------------------------------------| +|-----------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Corpus (corpora, plural) | Collection or database of text or multiple texts. These types of objects typically contain raw strings annotated with additional metadata and details. | | Document-feature matrix (DFM) | Represents the relationship between features and documents, where each row is a document and each column corresponds to a set of features. Features are not limited to terms and can include a variety of attributes that describe the documents. Each cell in the matrix contains a value that represents the presence, absence, or some quantitative measure of a specific feature in a particular document. | | Document-term matrix (DTM) | Represents the relationship between terms and documents, where each row stands for a document and each column represents a term, and an entry is the number of occurrences of the term in the document. | @@ -538,7 +538,7 @@ What do you think of your plots? Are they helpful? Consider other techniques lik The text analysis tools, methods, and packages depend greatly on your specific text analysis goals and the nature of your text data. You may end up only using one method or one package, or many in combination. | Text Analysis Method | R Package | When to Use | -|-------------------|----------------|-------------------------------------| +|--------------------------------|------------------------------------------|------------------------------------------------------------------------| | Document-Term Matrix (DTM) | `tm`, `quanteda`, `tm.plugin.webmining` | Represent text data as a matrix of word frequencies. | | Named Entity Recognition (NER) | `openNLP`, `spacyr`, `udpipe` | Identify entities like names, locations, etc. in text. | | Sentiment Analysis | `tidytext`, `sentimentr`, `syuzhet` | Determine sentiment (positive/negative) in text. | From a06767d792d0c226b07f9644ce832c40bb7ed43b Mon Sep 17 00:00:00 2001 From: hdolinh Date: Wed, 30 Aug 2023 13:59:05 -0700 Subject: [PATCH 087/104] remade tables --- materials/session_12.qmd | 5 ----- 1 file changed, 5 deletions(-) diff --git a/materials/session_12.qmd b/materials/session_12.qmd index 21ba1ad5..222a5429 100644 --- a/materials/session_12.qmd +++ b/materials/session_12.qmd @@ -6,8 +6,3 @@ title-block-banner: true {{< include /sections/intro-tidy-text-data.qmd >}} - - - - - From ecff1501f7b1231552c917a320dd722f6e941dad Mon Sep 17 00:00:00 2001 From: hdolinh Date: Wed, 30 Aug 2023 14:45:53 -0700 Subject: [PATCH 088/104] add text 12.4.1; move text mining tools and packages resources callout block to 12.6 --- materials/sections/intro-tidy-text-data.qmd | 27 +++++++++++++++------ 1 file changed, 19 insertions(+), 8 deletions(-) diff --git a/materials/sections/intro-tidy-text-data.qmd b/materials/sections/intro-tidy-text-data.qmd index cf6f8db5..3fc925af 100644 --- a/materials/sections/intro-tidy-text-data.qmd +++ b/materials/sections/intro-tidy-text-data.qmd @@ -277,7 +277,9 @@ Silge and Robinson kept this in mind as they built the `tidytext` package, and i ### Use `cast()` to Convert to a Matrix (Non-tidy) Format -let's create a matrix of all books we looked at +In these examples, we'll be using multiple books as our text: *The Phantom of the Opera*, *The Strange Case of Dr. Jekyll and Mr. Hyde*, *Frankenstein; Or, The Modern Prometheus*, and *Dracula*. + +We'll access these texts using `gutenbergr`'s `gutenberg_download()` function, and we'll make sure to include the titles in the download. ```{r} #| eval: false @@ -290,6 +292,8 @@ all_books_corp <- gutenberg_download(c(175, # phantom of the opera meta_fields = c("title")) ``` +This code chunk is formatting our corpus into the tidy text format, and it is counting the number of times each word occurs in each book. + ```{r} #| eval: false @@ -301,6 +305,8 @@ tidy_all_books <- all_books_corp %>% count(title, word) ``` +This code chunk is converting our corpus of all books into a sparse matrix. + ```{r} #| eval: false @@ -312,6 +318,8 @@ all_books_sparse <- tidy_all_books %>% value = n) ``` +This code chunk is converting our corpus of all books into a document-term matrix object from the `tm` package. + ```{r} #| eval: false @@ -323,6 +331,8 @@ all_books_dtm <- tidy_all_books %>% value = n) ``` +This code chunk is converting our corpus of all books into a document-feature matrix object from the `quanteda` package. + ```{r} #| eval: false @@ -334,13 +344,6 @@ all_books_dfm <- tidy_all_books %>% value = n) ``` -::: {.callout-caution icon="false"} -### R Text Mining Tools and Analysis Packages Resources - -- [CRAN Task View: Natural Language Processing](https://cran.r-project.org/web/views/NaturalLanguageProcessing.html) -- [Penn Libraries Guides: Text Analysis](https://guides.library.upenn.edu/penntdm/r) -::: - ## Exercise: Explore Unstructured Text Data from a PDF ::: callout-tip @@ -552,6 +555,14 @@ The text analysis tools, methods, and packages depend greatly on your specific t | Word Embeddings | `word2vec`, `text2vec`, `fastText` | Represent words as dense vectors for semantic analysis. | | Dependency Parsing | `udpipe`, `spaCy`, `openNLP` | Analyze grammatical structure and word dependencies. | +::: {.callout-caution icon="false"} +### R Text Mining Tools and Analysis Packages Resources + +- [CRAN Task View: Natural Language Processing](https://cran.r-project.org/web/views/NaturalLanguageProcessing.html) +- [Penn Libraries Guides: Text Analysis](https://guides.library.upenn.edu/penntdm/r) +- [Quanteda Tutorials By Kohei Watanabe and Stefan Müller](https://tutorials.quanteda.io/) +::: + ## Note About Python and NLP ```{=html} From 39ddf258a5d557335096106ecd952731478e4c37 Mon Sep 17 00:00:00 2001 From: hdolinh Date: Wed, 30 Aug 2023 14:52:51 -0700 Subject: [PATCH 089/104] fix typo --- materials/sections/intro-tidy-text-data.qmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/materials/sections/intro-tidy-text-data.qmd b/materials/sections/intro-tidy-text-data.qmd index 3fc925af..accf5edb 100644 --- a/materials/sections/intro-tidy-text-data.qmd +++ b/materials/sections/intro-tidy-text-data.qmd @@ -310,7 +310,7 @@ This code chunk is converting our corpus of all books into a sparse matrix. ```{r} #| eval: false -# convert tidy text table to spare matrix from `Matrix` package +# convert tidy text table to sparse matrix from `Matrix` package # requires `Matrix` to be installed all_books_sparse <- tidy_all_books %>% cast_sparse(row = title, From 6f0dc5f5bedf84a25b009f0d0ebf68e8557139e3 Mon Sep 17 00:00:00 2001 From: hdolinh Date: Wed, 30 Aug 2023 14:59:49 -0700 Subject: [PATCH 090/104] add more to sparse matrix definition --- materials/sections/intro-tidy-text-data.qmd | 53 +++++++++++++++------ 1 file changed, 39 insertions(+), 14 deletions(-) diff --git a/materials/sections/intro-tidy-text-data.qmd b/materials/sections/intro-tidy-text-data.qmd index accf5edb..6be715fb 100644 --- a/materials/sections/intro-tidy-text-data.qmd +++ b/materials/sections/intro-tidy-text-data.qmd @@ -30,17 +30,29 @@ Raw text data is often unstructured and quite "messy". This could include wrong Here is a list of text data or text analysis terms we'll be referring to throughout this lesson. Note this is not a comprehensive list of text analysis terms that are used beyond this lesson. -| Term | Definition | -|------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| Corpus (corpora, plural) | Collection or database of text or multiple texts. These types of objects typically contain raw strings annotated with additional metadata and details. | -| Document-feature matrix (DFM) | Represents the relationship between features and documents, where each row is a document and each column corresponds to a set of features. Features are not limited to terms and can include a variety of attributes that describe the documents. Each cell in the matrix contains a value that represents the presence, absence, or some quantitative measure of a specific feature in a particular document. | -| Document-term matrix (DTM) | Represents the relationship between terms and documents, where each row stands for a document and each column represents a term, and an entry is the number of occurrences of the term in the document. | -| Natural Language Processing (NLP) | NLP is an interdisciplinary field used in computer science, data science, linguistics, and others to analyze, categorize, and work with computerized text. | -| Sparse matrix | A matrix where the majority of the entries are zero. | -| String | Specific type of data whose values are enclosed within a set of quotes. Typically values or elements are characters (e.g. "Hello World!"). | -| Text analysis | The process of deriving high-quality information or patterns from text through evaluation and interpretation of the output. Also referred to as "text mining" or "text analytics". | -| Token | A meaningful unit of text, such as a word, to use for analysis. | -| Tokenization | The process of splitting text into tokens. | ++-----------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ +| Term | Definition | ++===================================+================================================================================================================================================================================================================================================================================================================================================================================================================+ +| Corpus (corpora, plural) | Collection or database of text or multiple texts. These types of objects typically contain raw strings annotated with additional metadata and details. | ++-----------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ +| Document-feature matrix (DFM) | Represents the relationship between features and documents, where each row is a document and each column corresponds to a set of features. Features are not limited to terms and can include a variety of attributes that describe the documents. Each cell in the matrix contains a value that represents the presence, absence, or some quantitative measure of a specific feature in a particular document. | ++-----------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ +| Document-term matrix (DTM) | Represents the relationship between terms and documents, where each row stands for a document and each column represents a term, and an entry is the number of occurrences of the term in the document. | ++-----------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ +| Natural Language Processing (NLP) | NLP is an interdisciplinary field used in computer science, data science, linguistics, and others to analyze, categorize, and work with computerized text. | ++-----------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ +| Sparse matrix | A matrix where the majority of the entries are zero. | +| | | +| | Both DFM and DTM are sparse matrices and this is normal. Typically, the bigger the DTM or DFM, the more zeros you'll see. | ++-----------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ +| String | Specific type of data whose values are enclosed within a set of quotes. Typically values or elements are characters (e.g. "Hello World!"). | ++-----------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ +| Text analysis | The process of deriving high-quality information or patterns from text through evaluation and interpretation of the output. Also referred to as "text mining" or "text analytics". | ++-----------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ +| Token | A meaningful unit of text, such as a word, to use for analysis. | ++-----------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ +| Tokenization | The process of splitting text into tokens. | ++-----------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ ### How is text data used in the environmental field? @@ -277,7 +289,7 @@ Silge and Robinson kept this in mind as they built the `tidytext` package, and i ### Use `cast()` to Convert to a Matrix (Non-tidy) Format -In these examples, we'll be using multiple books as our text: *The Phantom of the Opera*, *The Strange Case of Dr. Jekyll and Mr. Hyde*, *Frankenstein; Or, The Modern Prometheus*, and *Dracula*. +In these examples, we'll be using multiple books as our text: *The Phantom of the Opera*, *The Strange Case of Dr. Jekyll and Mr. Hyde*, *Frankenstein; Or, The Modern Prometheus*, and *Dracula*. We'll access these texts using `gutenbergr`'s `gutenberg_download()` function, and we'll make sure to include the titles in the download. @@ -540,27 +552,40 @@ What do you think of your plots? Are they helpful? Consider other techniques lik The text analysis tools, methods, and packages depend greatly on your specific text analysis goals and the nature of your text data. You may end up only using one method or one package, or many in combination. ++--------------------------------+------------------------------------------+---------------------------------------------------------+ | Text Analysis Method | R Package | When to Use | -|--------------------------------|------------------------------------------|---------------------------------------------------------| ++================================+==========================================+=========================================================+ | Document-Term Matrix (DTM) | `tm`, `quanteda`, `tm.plugin.webmining` | Represent text data as a matrix of word frequencies. | ++--------------------------------+------------------------------------------+---------------------------------------------------------+ | Named Entity Recognition (NER) | `openNLP`, `spacyr`, `udpipe` | Identify entities like names, locations, etc. in text. | ++--------------------------------+------------------------------------------+---------------------------------------------------------+ | Sentiment Analysis | `tidytext`, `sentimentr`, `syuzhet` | Determine sentiment (positive/negative) in text. | ++--------------------------------+------------------------------------------+---------------------------------------------------------+ | Stopword Removal | `tm`, `quanteda`, `tidytext` | Remove common and irrelevant words. | ++--------------------------------+------------------------------------------+---------------------------------------------------------+ | Text Classification | `caret`, `tm`, `quanteda` | Categorize text into predefined classes/categories. | ++--------------------------------+------------------------------------------+---------------------------------------------------------+ | Text Clustering | `tm`, `text2vec`, `tm.plugin.clustering` | Group similar documents together. | ++--------------------------------+------------------------------------------+---------------------------------------------------------+ | Text Summarization | `tm`, `textclean`, `textrank` | Condense the main points of a text into a summary. | ++--------------------------------+------------------------------------------+---------------------------------------------------------+ | TF-IDF | `tm`, `tm.plugin.webmining`, `tidytext` | Measure word importance in a document corpus. | ++--------------------------------+------------------------------------------+---------------------------------------------------------+ | Tokenization | `tm`, `quanteda`, `text2vec` | Split text into individual words/tokens. | ++--------------------------------+------------------------------------------+---------------------------------------------------------+ | Topic Modeling | `tm`, `topicmodels`, `lda2vec` | Discover hidden topics in a collection of documents. | ++--------------------------------+------------------------------------------+---------------------------------------------------------+ | Word Embeddings | `word2vec`, `text2vec`, `fastText` | Represent words as dense vectors for semantic analysis. | ++--------------------------------+------------------------------------------+---------------------------------------------------------+ | Dependency Parsing | `udpipe`, `spaCy`, `openNLP` | Analyze grammatical structure and word dependencies. | ++--------------------------------+------------------------------------------+---------------------------------------------------------+ ::: {.callout-caution icon="false"} ### R Text Mining Tools and Analysis Packages Resources - [CRAN Task View: Natural Language Processing](https://cran.r-project.org/web/views/NaturalLanguageProcessing.html) - [Penn Libraries Guides: Text Analysis](https://guides.library.upenn.edu/penntdm/r) -- [Quanteda Tutorials By Kohei Watanabe and Stefan Müller](https://tutorials.quanteda.io/) +- [Quanteda Tutorials By Kohei Watanabe and Stefan Müller](https://tutorials.quanteda.io/) ::: ## Note About Python and NLP From 526be0f2ff5806d8bec320b4bec3848bcda959d2 Mon Sep 17 00:00:00 2001 From: hdolinh Date: Wed, 30 Aug 2023 20:50:20 -0700 Subject: [PATCH 091/104] add text to 12.7 --- materials/sections/intro-tidy-text-data.qmd | 32 ++------------------- 1 file changed, 3 insertions(+), 29 deletions(-) diff --git a/materials/sections/intro-tidy-text-data.qmd b/materials/sections/intro-tidy-text-data.qmd index 6be715fb..a03204d9 100644 --- a/materials/sections/intro-tidy-text-data.qmd +++ b/materials/sections/intro-tidy-text-data.qmd @@ -590,34 +590,8 @@ The text analysis tools, methods, and packages depend greatly on your specific t ## Note About Python and NLP -```{=html} - -``` +It's important to note that with tools like Jupyter Notebook and Quarto you're not longer limited to just one language. With tools that support both `R` and `python` code, you have the capability to leverage the strengths of both languages. For example, you can build a NLP model using `python` and then create a visualization using `R`. From 0758491a7f4de965a3663b8d480d8f8d3efd759e Mon Sep 17 00:00:00 2001 From: hdolinh Date: Wed, 30 Aug 2023 22:43:04 -0700 Subject: [PATCH 092/104] add warning:false to 12.5 exercise --- materials/sections/intro-tidy-text-data.qmd | 1 + 1 file changed, 1 insertion(+) diff --git a/materials/sections/intro-tidy-text-data.qmd b/materials/sections/intro-tidy-text-data.qmd index a03204d9..78f1e37c 100644 --- a/materials/sections/intro-tidy-text-data.qmd +++ b/materials/sections/intro-tidy-text-data.qmd @@ -367,6 +367,7 @@ all_books_dfm <- tidy_all_books %>% ```{r} #| message: false +#| warning: false library(tidytext) # tidy text tools library(quanteda) # create a corpus library(pdftools) # read in data From abfb29fcff693b0e184e618cdc8ba485d7e45900 Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Thu, 31 Aug 2023 06:23:36 -0700 Subject: [PATCH 093/104] fixing census data list format so it renders correctly --- materials/sections/census-data.qmd | 180 +++++++++++++---------------- 1 file changed, 79 insertions(+), 101 deletions(-) diff --git a/materials/sections/census-data.qmd b/materials/sections/census-data.qmd index 6aff33f2..87363886 100644 --- a/materials/sections/census-data.qmd +++ b/materials/sections/census-data.qmd @@ -1,32 +1,30 @@ ## Learning Objectives {.unnumbered} -- Provide an overview of US Census data -- Introduce the main functions of the `tidycensus` package to be able to work with census data -- Review data wrangling function to get census data ready for analysis -- Plot census data using `ggplot2` +- Provide an overview of US Census data +- Introduce the main functions of the `tidycensus` package to be able to work with census data +- Review data wrangling function to get census data ready for analysis +- Plot census data using `ggplot2` -:::callout-note +::: callout-note ## Acknowledgement + This lesson is based on [Analyzing US Census Data: Methods, Maps, and Models in R](https://walker-data.com/census-r/index.html), (Chapter 1 through 4) by Kyle Walker. And Walker's workshop "Analyzing 2020 Census Data with R and `tidycensus`" for University of Michigan (2022). GitHub repository [here](https://github.com/walkerke/umich-workshop-2022). ::: +```{=html} - +``` ## Introduction to `tidycensus` ::: column-margin - ![](images/tidycensus_logo.png){fig-align="right"} - ::: +When working with census data, the general workflow would be to go to [data.census.gov](data.census.gov), filter data and queries based on variables and geographies, and then download the data into a spreadsheet. People that work with census data do this over and over again. - -When working with census data, the general workflow would be to go to [data.census.gov](data.census.gov), filter data and queries based on variables and geographies, and then download the data into a spreadsheet. People that work with census data do this over and over again. - - The `tidycensus` package (@tidycensus2021) was developed to systematize this process and do this systematization using R. In 2012 the US Census Bureau released the Census Application Programming Interface (API) as a way to disseminate government data resources to the public. This interface now provides programmatic access to hundreds of data resources from the Census Bureau. The vision behind this package was to incorporate the API access into an R package to facilitate access to census data using R. +The `tidycensus` package (@tidycensus2021) was developed to systematize this process and do this systematization using R. In 2012 the US Census Bureau released the Census Application Programming Interface (API) as a way to disseminate government data resources to the public. This interface now provides programmatic access to hundreds of data resources from the Census Bureau. The vision behind this package was to incorporate the API access into an R package to facilitate access to census data using R. Note there are other R packages that interact with the census APIs, such as [`censusapi`](https://cran.r-project.org/web/packages/censusapi/index.html) and [`acs`](https://cran.r-project.org/web/packages/acs/index.html). In this lesson, we are going to focus on the `tidycensus` package, given that is continuously maintained by the authors, has good documentation and it makes it easy to work with other `tidyverse` functions. @@ -42,34 +40,31 @@ Note that this lesson is just an introduction to this package. Here we aim to pr Data files you can access through this package are named and described in the table below. -| Survey Name | Description | -|-------------|-------------| -|Decennial census| Complete enumeration of the US population to assist with apportionment. It asks a limited set of questions on race, ethnicity, age, sex, and housing tenure. Data from 2000, 2010, available data from 2020| -|American Community Survey (ACS) | Detailed demographic information about US population. Annual data updates. 1-year ACS greater, and the 5-year ACS, which is a moving average of data over a 5-year period that covers geographies down to the Census block group. ACS data represent estimates rather than precise counts. Data includes margin of error.| -|Population estimate program | These datasets include yearly estimates of population characteristics by state, county, and metropolitan area, along with components of change demographic estimates like births, deaths, and migration rates.| -| ACS Public Use Microdata | Anonymized individual-level records from the ACS organized by households| -|Migration Flows | Information about in and outflows from several geographies from the 5-year ACS samples.| - +| Survey Name | Description | +|------------------------------------|------------------------------------| +| Decennial census | Complete enumeration of the US population to assist with apportionment. It asks a limited set of questions on race, ethnicity, age, sex, and housing tenure. Data from 2000, 2010, available data from 2020 | +| American Community Survey (ACS) | Detailed demographic information about US population. Annual data updates. 1-year ACS greater, and the 5-year ACS, which is a moving average of data over a 5-year period that covers geographies down to the Census block group. ACS data represent estimates rather than precise counts. Data includes margin of error. | +| Population estimate program | These datasets include yearly estimates of population characteristics by state, county, and metropolitan area, along with components of change demographic estimates like births, deaths, and migration rates. | +| ACS Public Use Microdata | Anonymized individual-level records from the ACS organized by households | +| Migration Flows | Information about in and outflows from several geographies from the 5-year ACS samples. | -The idea behind this package is to make the tedious process of working with Census data more concise. It pulls data from the census API and returns it to the user in a “tidy” format. +The idea behind this package is to make the tedious process of working with Census data more concise. It pulls data from the census API and returns it to the user in a "tidy" format. Can easily merge census geometries to data for mapping. Which apparently can be a very time-consuming task. Unfortunately, for this session, we will not get into mapping. Check out Analyzing Census Data [Chapter 6: Mapping Census Data with R](https://walker-data.com/census-r/mapping-census-data-with-r.html) for more details on this subject. - Other features of this package: -- Includes tools for handling margins of errors in the ACS and working with survey weights in the ACS Public Use Microdata. -- You can request data from states and counties by name instead of FIPS codes. - +- Includes tools for handling margins of errors in the ACS and working with survey weights in the ACS Public Use Microdata. +- You can request data from states and counties by name instead of FIPS codes. ## Getting Started ::: callout-tip ## Setup -0. Make sure you’re in the right project (`training_{USERNAME}`) and use the `Git` workflow by `Pull`ing to check for any changes. Then, create a new Quarto document, delete the default text, and save this document. +0. Make sure you're in the right project (`training_{USERNAME}`) and use the `Git` workflow by `Pull`ing to check for any changes. Then, create a new Quarto document, delete the default text, and save this document. -1. Load the packages we'll need: +1. Load the packages we'll need: ```{r load_packages} #| message: false @@ -82,70 +77,69 @@ library(tidyr) library(ggplot2) ``` -2. Get an API key to connect our session with the census data and be able to retrieve data -- Go to -- Fill out the form -- Check your email for your key. +2. Get an API key to connect our session with the census data and be able to retrieve data -3. Use the `census_api_key()` function to set your key. Note: `install = TRUE` forces r to write this key to a file in our R environment that will be read every time you use R. This means, by setting this argument to `TRUE`, you only have to do it once in any computer you are working. If you see this argument as `FALSE`, R will not remember this key next time you come back. +- Go to +- Fill out the form +- Check your email for your key. + +3. Use the `census_api_key()` function to set your key. Note: `install = TRUE` forces r to write this key to a file in our R environment that will be read every time you use R. This means, by setting this argument to `TRUE`, you only have to do it once in any computer you are working. If you see this argument as `FALSE`, R will not remember this key next time you come back. ```{r} #| eval: false census_api_key("YOUR KEY GOES HERE", install = TRUE) ``` -4. Restart R - -5. Run the library chunk again. +4. Restart R +5. Run the library chunk again. ::: - Note 1: As we are working on the server, the package is already installed in our system. But when you go work on your own computers, you will have to install the package using `install.package("tidycensus")`, if you haven't done that already. Note 2: The API key you received in your email is good to use in other instances too. Keep this email! We are using the key to connect your included-crab session with the US Census data. You can use the same key to connect your personal/work computer too. - - ## Quering data (focus on 2020 decennial data) ### The 2020 Decennial Data -Before we dive into retrieving data from the 2020 decennial census, we need to mention a couple of things. Based on all the challenges of running a decennial census during a pandemic, the Census Bureau had to make decisions and provide new and different functionalists. The pandemic also delayed the release of the 2020 census data. You can check [here](https://www2.census.gov/programs-surveys/decennial/2020/program-management/2010_20_data_product_release_dates.pdf) what data products have been released until now, and the Bureau’s timeline to release more data. + +Before we dive into retrieving data from the 2020 decennial census, we need to mention a couple of things. Based on all the challenges of running a decennial census during a pandemic, the Census Bureau had to make decisions and provide new and different functionalists. The pandemic also delayed the release of the 2020 census data. You can check [here](https://www2.census.gov/programs-surveys/decennial/2020/program-management/2010_20_data_product_release_dates.pdf) what data products have been released until now, and the Bureau's timeline to release more data. One of the main files from the 2020 census is the PL94-171 Redistricting Summary File which is used for congressional appointments and redistricting. Variable available in this file are: -- Total counts (population & households) -- Occupied/vacant housing unit -- Total and voting age population breakdown by race & ethnicity -- Group quarter status -- Demographic and Housing Characteristics Summary Files (Different to summary file 1 form 2010). Contains age and sex breakdowns and detailed race and ethnicity data. +- Total counts (population & households) +- Occupied/vacant housing unit +- Total and voting age population breakdown by race & ethnicity +- Group quarter status + +Demographic and Housing Characteristics Summary Files (Different to summary file 1 form 2010). Contains age and sex breakdowns and detailed race and ethnicity data. ### Getting census data The main functions from `tidycensus` represent the select number of datasets that this package provides access to. The following table provides the description for each of the core functions from [Walker's book](https://walker-data.com/census-r/an-introduction-to-tidycensus.html#decennial-census). -| Function | Description | -|---------------|--------------------------------| -`get_decennial()`| Retrieves data from the US Decennial Census APIs for 2000, 2010, and 2020.| -|`get_acs()` | Requests data from the 1-year and 5-year American Community Survey samples. Data are available from the 1-year ACS back to 2005 and the 5-year ACS back to 2005-2009.| -|`get_estimates()`| Allows you to get the Population Estimates. These datasets include yearly estimates of population characteristics by state, county, and metropolitan area, along with components of change demographic estimates like births, deaths, and migration rates.| -|`get_pums()` | Accesses data from the ACS Public Use Microdata Sample APIs. These samples include anonymized individual-level records from the ACS organized by household and are highly useful for many different social science analyses | -| `get_flows()` | an interface to the ACS Migration Flows APIs. Includes information on in- and out-flows from various geographies for the 5-year ACS samples, enabling origin-destination analyses.| - +| Function | Description | +|-----------------------|------------------------------------------------| +| `get_decennial()` | Retrieves data from the US Decennial Census APIs for 2000, 2010, and 2020. | +| `get_acs()` | Requests data from the 1-year and 5-year American Community Survey samples. Data are available from the 1-year ACS back to 2005 and the 5-year ACS back to 2005-2009. | +| `get_estimates()` | Allows you to get the Population Estimates. These datasets include yearly estimates of population characteristics by state, county, and metropolitan area, along with components of change demographic estimates like births, deaths, and migration rates. | +| `get_pums()` | Accesses data from the ACS Public Use Microdata Sample APIs. These samples include anonymized individual-level records from the ACS organized by household and are highly useful for many different social science analyses | +| `get_flows()` | an interface to the ACS Migration Flows APIs. Includes information on in- and out-flows from various geographies for the 5-year ACS samples, enabling origin-destination analyses. | -Here we are going to focus on getting started with using `get_decennial()` as a way of understanding how this package works. +Here we are going to focus on getting started with using `get_decennial()` as a way of understanding how this package works. With `get_decennial()`, you can query data from 2000, 2010 and 2020 decennial census. The 3 necessary arguments you need to provide are: -- Geography -- Variable -- Year -:::callout-tip +- Geography +- Variable +- Year + +::: callout-tip ## Specifying a summary file -Another argument form `get_decennial()` is `sumfile =`. -"The Census summary file; if NULL, defaults to "pl" when the year is 2020 and "sf1" for 2000 and 2010. Not all summary files are available for each decennial Census year. Make sure you are using the correct summary file for your requested variables, as variable IDs may be repeated across summary files and represent different topics" +Another argument form `get_decennial()` is `sumfile =`. +"The Census summary file; if NULL, defaults to"pl" when the year is 2020 and "sf1" for 2000 and 2010. Not all summary files are available for each decennial Census year. Make sure you are using the correct summary file for your requested variables, as variable IDs may be repeated across summary files and represent different topics" ::: So, to get the total population for 2020 by state the code would look like this. @@ -161,25 +155,25 @@ pop_2020 <- get_decennial( ``` The resulting data frame you get from this query is a tibble in "tidy" format with 4 columns: -- GEOID = Code for the geographic unit (in this case FIP codes) -- variable = census code for the variable (eg. P1_001N is the code for the total population in the 2020 population redistricting file) -- value = population count -This table provides data for one single variable. In this case the variable is "P1_001N" which refers to total population. +- GEOID = Code for the geographic unit (in this case FIP codes) +- variable = census code for the variable (eg. P1_001N is the code for the total population in the 2020 population redistricting file) +- value = population count -:::callout-important +This table provides data for one single variable. In this case the variable is "P1_001N" which refers to total population. + +::: callout-important ## Message ![](images/get_decennial_message.png) -You get this message the first time you run `get_decennial()` in your session. It first makes sure your are retrieving the correct data. Then it mentions the fact that the 2020 census “Introduces errors differential privacy”. +You get this message the first time you run `get_decennial()` in your session. It first makes sure your are retrieving the correct data. Then it mentions the fact that the 2020 census "Introduces errors differential privacy". In the past other privacy methods have been used to preserve confidentiality. Differential privacy is a method that purposely introduces noise or error into the data in order to make it impossible or at least very difficult to reverse engineer the census and track where the responses are coming from. This has an impact on small area counts (e.g.: block with children but not adults). This is something to be aware if you are working with small population geographies. Only the population data is differentially infused. The household data are not. ::: - Note that to get total population data for the 2010 decennial census the variable code is different than the 2020 census (even though the arguments of the functions are the same). In this case the code would look like this. ```{r} @@ -192,10 +186,8 @@ pop_2010 <- get_decennial( ``` - When using `get_decennial()`, you can also specify a specific table instead of only one variable. A table of data contains multiple variables. The code for getting the "P2" table from 2020 would look like this. - ```{r} #| eval: false @@ -206,10 +198,8 @@ table_p2_2020 <- get_decennial( ``` - Table P2 is one of the tables of the 2020 census, which provides counts by Race with Hispanic origins. - Let's check which variables we have in the P2 table. ```{r} @@ -221,13 +211,13 @@ unique(table_p2_2020$variable) One of the hardest things to wrap your head around when using `tidycensus` is understanding the difference between files, tables, and variables and how they work. -Let's take a look on how this works. Data is released in files. Each file is a data product from the Census Bureau (find details of the Census data products [here](https://www.census.gov/programs-surveys/decennial-census/decade/2020/planning-management/release/about-2020-data-products.html#redistricting)), for example, PL 94-171 is one of the files for the 2020 Decennial Census. Each of these files contains many tables (P1, P2 .. P5). Each table covers a specific topic. For example, P2 provides counts by race by Hispanic origins. Then each table has multiple variables. The variable code seems to, for the most part, start with the table code. For example, the unique codes values we printed from table P2 all start with P2_. +Let's take a look on how this works. Data is released in files. Each file is a data product from the Census Bureau (find details of the Census data products [here](https://www.census.gov/programs-surveys/decennial-census/decade/2020/planning-management/release/about-2020-data-products.html#redistricting)), for example, PL 94-171 is one of the files for the 2020 Decennial Census. Each of these files contains many tables (P1, P2 .. P5). Each table covers a specific topic. For example, P2 provides counts by race by Hispanic origins. Then each table has multiple variables. The variable code seems to, for the most part, start with the table code. For example, the unique codes values we printed from table P2 all start with P2\_. How do we navigate all these codes? There is no straightforward way. In part, this dilemma is part of working with census data, which in itself is complicated. However, `tidycensus` provides some help with a function called `load_variables()` This function scrapes the variables' names from the census website and gives you a data frame that you can interact with. This variable requires you to input a year and a file name. -So let’s run this function and create an object named `vars_pl_2020` This will give us all the variable codes and definitions for the PL 94-171 redistricting file. +So let's run this function and create an object named `vars_pl_2020` This will give us all the variable codes and definitions for the PL 94-171 redistricting file. ```{r} #| eval: false @@ -239,25 +229,23 @@ vars_pl_2010 <- load_variables(2010, "pl") ``` +Now, let's take a look at this data frame. And interactively search for the variables in this file. This data frame has 3 columns, variable's name, label, and concept, which represent the table's name. -Now, let’s take a look at this data frame. And interactively search for the variables in this file. This data frame has 3 columns, variable’s name, label, and concept, which represent the table’s name. - -For example, here we can see the variable “P1_001N” that we used earlier to retrieve the total population by state. We can also scroll down and see that all variables that start with P2, belong to the P2 table “HISPANIC OR LATINO, AND NOT HISPANIC OR LATINO BY RACE”. We can also see the variable codes for the H1 table for Occupancy or Housing. +For example, here we can see the variable "P1_001N" that we used earlier to retrieve the total population by state. We can also scroll down and see that all variables that start with P2, belong to the P2 table "HISPANIC OR LATINO, AND NOT HISPANIC OR LATINO BY RACE". We can also see the variable codes for the H1 table for Occupancy or Housing. Tables available in the 2020 Census PL file: -|Table Name | Description| -|-----------|------------| -|H1 |Occupancy status by household| -|P1 | Race by Hispanic origin| -|P3 | Race for the population 18+| -|P4| Race by Hispanic origin for the population 18+| -|P5| Group quarters status | - -Note: “Group quarters are places where people live or stay, in a group living arrangement, that is owned or managed by an entity or organization providing housing and/or services for the residents.” ([US Census Bureau Glossary](https://www.census.gov/glossary/?term=Group+quarters+population)) +| Table Name | Description | +|------------|------------------------------------------------| +| H1 | Occupancy status by household | +| P1 | Race by Hispanic origin | +| P3 | Race for the population 18+ | +| P4 | Race by Hispanic origin for the population 18+ | +| P5 | Group quarters status | +Note: "Group quarters are places where people live or stay, in a group living arrangement, that is owned or managed by an entity or organization providing housing and/or services for the residents." ([US Census Bureau Glossary](https://www.census.gov/glossary/?term=Group+quarters+population)) -We won’t get into much more detail, but, you can query any of the available files for the 2020 census data. Like the Demographic and Housing Characteristics File (DHC) by running the following code. This is a much larger file with much more tables than the PL file. +We won't get into much more detail, but, you can query any of the available files for the 2020 census data. Like the Demographic and Housing Characteristics File (DHC) by running the following code. This is a much larger file with much more tables than the PL file. ```{r} #| eval: false @@ -273,13 +261,11 @@ household_2020 <- get_decennial( ``` - The idea behind `load_variables()` is for you to be able to search for the variable code for the variable you need. -Now that we’ve talked about variables let’s talk a little bit about geography and how `tidycensus` makes it easy to query data within census geographies. Census data is tabulated in enumeration units. These units are specific geographies including legal entities such as states and counties, and statistical entities that are not official jurisdictions but used to standardize data. The graphic below, provided by [census.gov](https://www.census.gov/programs-surveys/geography/guidance/hierarchy.html) shows the standard hierarchy of census geographic entities. +Now that we've talked about variables let's talk a little bit about geography and how `tidycensus` makes it easy to query data within census geographies. Census data is tabulated in enumeration units. These units are specific geographies including legal entities such as states and counties, and statistical entities that are not official jurisdictions but used to standardize data. The graphic below, provided by [census.gov](https://www.census.gov/programs-surveys/geography/guidance/hierarchy.html) shows the standard hierarchy of census geographic entities. -![](images/census_geos.png) -The parameter geography = in `get_acs()` and `get_decennial()` allows us to request data from common enumeration units. This mean we can name the specific geography we want data from. For example, let's get data for Hispanic population the 6 counties around the Delta. +![](images/census_geos.png) The parameter `geography =` in `get_acs()` and `get_decennial()` allows us to request data from common enumeration units. This mean we can name the specific geography we want data from. For example, let's get data for Hispanic population the 6 counties around the Delta. ```{r} #| eval: false @@ -295,12 +281,10 @@ delta_hispanic <- get_decennial( To learn more about the arguments for geography for each core function of `tidycensus`, check out the documentation [here](https://walker-data.com/tidycensus/articles/basic-usage.html#geography-in-tidycensus). - -#### Quering for multiple variabels +#### Quering for multiple variables The `varaible` argument can take a vector of variables as an input, allowing to query for more than one variable at the time. We can create a vector and then call that vector as the input of the `variable` argument, or we can use the concatenate function `c()` and request data from multiple variables. - ```{r} #| eval: false @@ -324,11 +308,10 @@ delta_race <- get_decennial( Note how this table returns the variable name we assigned in the vector above instead of the variable code! This is a handy option, given that the codes can be hard to remember what is what. -Also, note that we added one more argument to our request `summary_var = "P2_001N`. This adds one more column to our output data frame, one with the summary variable value. +Also, note that we added one more argument to our request `summary_var = "P2_001N`. This adds one more column to our output data frame, one with the summary variable value. In every table you can generally find a variable that is an appropriate denominators for a group of variables. Following the example above, the P2 table, which provides population by race with Hispanic origin, the variable "P2001N" represents the total population. Because this variable is an an appropriate denominator for the other variables in the table, it helps to have it in a different column to make it easier to calculate proportions or percentage. - ## Getting Census Data ready for analysis Once we access the data we want, we can apply our data wrangling skills to get the data in the format that we want. @@ -337,15 +320,15 @@ Let's demonstrate this with an example. Let's compare the distribution of percen The first step is to get the data. -:::callout-note +::: callout-note ## Exercise Make a query to get White and Hispanic population data for Delta counties **by tracks** from the 2020 Decennial Census. Include the total population summary variable (`summary_var = "P2_001N"`). Hint: variable codes are: + - Total Hispanic population = P2_002N - Total White population = P2_005N - ::: ```{r} @@ -395,7 +378,6 @@ ggplot(delta_track_hw_cl, theme_light() ``` - ## Notes on `get_acs()` - The functions operates very similar to `get_decennial()` @@ -433,7 +415,3 @@ median_income_5yr <- get_acs( ## variables for 5-year 2017-2021 ACS vars <- load_variables(2021, "acs5") ``` - - - - From cd0c976ac1777046365d68b200305bd3ea6c5c10 Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Thu, 31 Aug 2023 06:40:23 -0700 Subject: [PATCH 094/104] formatting set up in survey lesson --- materials/sections/survey-workflow.qmd | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/materials/sections/survey-workflow.qmd b/materials/sections/survey-workflow.qmd index 43ad82f7..12020c43 100644 --- a/materials/sections/survey-workflow.qmd +++ b/materials/sections/survey-workflow.qmd @@ -46,7 +46,8 @@ The three main questions of the survey have three types of responses: a multiple ![](images/survey_main.png) - +:::callout-tip +## Set up First, open a new Quarto document and add a chunk to load the libraries we'll need for this lesson: @@ -67,7 +68,10 @@ The API key is as good as a password, so care should be taken to not share it pu qualtrics_api_credentials(api_key = "", base_url = "ucsb.co1.qualtrics.com", install = TRUE, overwrite = T) ``` -:::callout-tip +::: + + +:::callout-important ## Aside note From d1ba7564aab2014afe4d6a18e2f5211bc64017ba Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Thu, 31 Aug 2023 09:25:59 -0700 Subject: [PATCH 095/104] fixing title formatting --- materials/sections/survey-workflow.qmd | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/materials/sections/survey-workflow.qmd b/materials/sections/survey-workflow.qmd index 12020c43..15fa8fcf 100644 --- a/materials/sections/survey-workflow.qmd +++ b/materials/sections/survey-workflow.qmd @@ -45,6 +45,7 @@ The survey is very short, only four questions. The first question is on it's own The three main questions of the survey have three types of responses: a multiple choice answer, a multiple choice answer which also includes an "other" write in option, and a free text answer. We'll use the results of this survey, which was sent out to NCEAS staff to fill out, to learn about how to create a reproducible survey report. ![](images/survey_main.png) +### Working with `qualtiRcs` :::callout-tip ## Set up @@ -114,7 +115,7 @@ survey_results <- fetch_survey(id) The survey results table has tons of information in it, not all of which will be relevant depending on your survey. The table has identifying information for the respondents (eg: `ResponseID`, `IPaddress`, `RecipientEmail`, `RecipientFirstName`, etc), much of which will be empty for this survey since it is anonymous. It also has information about the process of taking the survey, such as the `StartDate`, `EndDate`, `Progress`, and `Duration`. Finally, there are the answers to the questions asked, with columns labeled according to the `qname` column in the questions table (eg: Q1, Q2, Q3). Depending on the type of question, some questions might have multiple columns associated with them. We'll have a look at this more closely in a later example. -### Question 2 +#### Question 2 Let's look at the responses to the second question in the survey, "How long have you been programming?" Remember, the first question was the consent question. @@ -138,7 +139,7 @@ kable(q2, col.names = c("How long have you been programming?", kable_styling() ``` -### Question 3 +#### Question 3 For question 3, we'll use a similar workflow. For this question, however there are two columns containing survey answers. One contains the answers from the controlled vocabulary, the other contains any free text answers users entered. @@ -170,7 +171,7 @@ kable(q3_text, col.names = c("Other responses to 'What language do you currently kable_styling() ``` -### Question 4 +#### Question 4 The last question is just a free text question, so we can just display the results as is. From fec161c4e28016b8b0b23e1c4a54df1e3becd903 Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Thu, 31 Aug 2023 09:30:49 -0700 Subject: [PATCH 096/104] more title formatting --- materials/sections/survey-workflow.qmd | 2 ++ materials/session_13.qmd | 1 - 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/materials/sections/survey-workflow.qmd b/materials/sections/survey-workflow.qmd index 15fa8fcf..7c78485b 100644 --- a/materials/sections/survey-workflow.qmd +++ b/materials/sections/survey-workflow.qmd @@ -45,6 +45,8 @@ The survey is very short, only four questions. The first question is on it's own The three main questions of the survey have three types of responses: a multiple choice answer, a multiple choice answer which also includes an "other" write in option, and a free text answer. We'll use the results of this survey, which was sent out to NCEAS staff to fill out, to learn about how to create a reproducible survey report. ![](images/survey_main.png) + + ### Working with `qualtiRcs` :::callout-tip diff --git a/materials/session_13.qmd b/materials/session_13.qmd index dcdbad7c..ff791d6e 100644 --- a/materials/session_13.qmd +++ b/materials/session_13.qmd @@ -3,5 +3,4 @@ title: "Reproducible Surveys" title-block-banner: true --- - {{< include /sections/survey-workflow.qmd >}} From b0736fbe021aa32d29a55f59fe2c7bac107227cf Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Thu, 31 Aug 2023 12:22:20 -0700 Subject: [PATCH 097/104] adding resource to how to get an qualtrics token --- materials/sections/survey-workflow.qmd | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/materials/sections/survey-workflow.qmd b/materials/sections/survey-workflow.qmd index 7c78485b..3c0da48c 100644 --- a/materials/sections/survey-workflow.qmd +++ b/materials/sections/survey-workflow.qmd @@ -201,7 +201,7 @@ To show how we can link R into our survey workflows, I've set up a simple exampl I've set up the results so that they are in a new spreadsheet [here:](https://docs.google.com/spreadsheets/d/1CSG__ejXQNZdwXc1QK8dKouxphP520bjUOnZ5SzOVP8/edit?resourcekey#gid=1527662370). To access them, we will use the `googlesheets4` package. -First, open up a new R script and load the `googlesheets4` library: +First, open up a new Quarto doc and load the `googlesheets4` library: ```{r} library(googlesheets4) @@ -295,3 +295,7 @@ Similar to Qualtrics and qualtRics, there is an open source R package for workin There are also commercial options available. For example, [cdata](https://www.cdata.com/kb/tech/surveymonkey-jdbc-r.rst) have a driver and R package that enable access to an analysis of Survey Monkey data through R. +## Resourcese +- [How to get your Qualtrics API key](https://www.qualtrics.com/support/integrations/api-integration/overview/#GeneratingAnAPIToken) + + From 01d88b680bc6654478f6ebbce93305e04b36256d Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Thu, 31 Aug 2023 12:25:47 -0700 Subject: [PATCH 098/104] makinng sure updates on survey lesson work --- materials/sections/survey-workflow.qmd | 1 + materials/session_13.qmd | 2 ++ 2 files changed, 3 insertions(+) diff --git a/materials/sections/survey-workflow.qmd b/materials/sections/survey-workflow.qmd index 3c0da48c..9cb57868 100644 --- a/materials/sections/survey-workflow.qmd +++ b/materials/sections/survey-workflow.qmd @@ -295,6 +295,7 @@ Similar to Qualtrics and qualtRics, there is an open source R package for workin There are also commercial options available. For example, [cdata](https://www.cdata.com/kb/tech/surveymonkey-jdbc-r.rst) have a driver and R package that enable access to an analysis of Survey Monkey data through R. + ## Resourcese - [How to get your Qualtrics API key](https://www.qualtrics.com/support/integrations/api-integration/overview/#GeneratingAnAPIToken) diff --git a/materials/session_13.qmd b/materials/session_13.qmd index ff791d6e..fdcb9acb 100644 --- a/materials/session_13.qmd +++ b/materials/session_13.qmd @@ -3,4 +3,6 @@ title: "Reproducible Surveys" title-block-banner: true --- + + {{< include /sections/survey-workflow.qmd >}} From eebccbca681c77f10ff4c86a2c6d8c034acd0c1c Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Fri, 1 Sep 2023 08:42:31 -0700 Subject: [PATCH 099/104] adding census exercises --- materials/sections/census-data.qmd | 102 ++++++++++++++++++++++++++--- materials/session_11.qmd | 1 - materials/session_12.qmd | 2 +- 3 files changed, 93 insertions(+), 12 deletions(-) diff --git a/materials/sections/census-data.qmd b/materials/sections/census-data.qmd index 87363886..25411a51 100644 --- a/materials/sections/census-data.qmd +++ b/materials/sections/census-data.qmd @@ -321,15 +321,16 @@ Let's demonstrate this with an example. Let's compare the distribution of percen The first step is to get the data. ::: callout-note -## Exercise +## Exercise 1: `get_decennial()` -Make a query to get White and Hispanic population data for Delta counties **by tracks** from the 2020 Decennial Census. Include the total population summary variable (`summary_var = "P2_001N"`). +1. Make a query to get White and Hispanic population data for Delta counties **by tracks** from the 2020 Decennial Census. Include the total population summary variable (`summary_var = "P2_001N"`). Hint: variable codes are: - Total Hispanic population = P2_002N - Total White population = P2_005N -::: + + ```{r} #| eval: false @@ -347,29 +348,33 @@ delta_track_hw <- get_decennial( ``` + We can check our data by calling the `View(delta_track_hw)` function in the console. -Now that we have our data, next thing we will do is calculate the percentage of White and Hispanic population in each track. Given that we have the summary variable within our data set we can easily add a new column with the percentage. And then, we will also clean the `NAMES` column and separate track, county and state into it's own column. +2. Now that we have our data, next thing we will do is calculate the percentage of White and Hispanic population in each track. Given that we have the summary variable within our data set we can easily add a new column with the percentage. And then, we will also clean the `NAMES` column and separate track, county and state into it's own column (hint: `tidyr::separate()`). ```{r} #| eval: false +#| code-fold: true +#| code-summary: "Answer" delta_track_clean <- delta_track_hw %>% mutate(percent = 100 * (value / summary_value)) %>% separate(NAME, into = c("tract", "county", "state"), sep = ", ") + ``` -Note that we can apply all other `dplyr` functions we have learned to this dataset depending on what we want to achieve. One of the main goals of `tidycensus` is to make the output data frames compatible with `tidyverse` functions. -Now that we have or "clean" data, with all the variables we need. Let's plot this data to compare the distribution of percentage White population and percentage Hispanic population by census track vary among the Delta Counties. +Note that we can apply all other `dplyr` functions we have learned to this dataset depending on what we want to achieve. One of the main goals of `tidycensus` is to make the output data frames compatible with `tidyverse` functions. -## Visualizing Census Data with `ggplot2` -Applying all what we learned earlier this week, we are going to use `ggplot2` to create some wonderful plots! +3. Now that we have or "clean" data, with all the variables we need. Let's plot this data to **compare the distribution of percentage** White population and percentage Hispanic population by census track vary among the Delta Counties (hint: `geom_density()`). ```{r} #| eval: false +#| code-fold: true +#| code-summary: "Answer" ggplot(delta_track_hw_cl, aes(x = percent, fill = county)) + @@ -378,6 +383,16 @@ ggplot(delta_track_hw_cl, theme_light() ``` + +::: + + + + + + ## Notes on `get_acs()` - The functions operates very similar to `get_decennial()` @@ -390,7 +405,6 @@ ggplot(delta_track_hw_cl, ```{r} #| eval: false - ## 1-year survey median_income_1yr <- get_acs( geography = "county", @@ -407,7 +421,7 @@ median_income_5yr <- get_acs( ``` -- You can access to different variables in a survey with the `load_variable()` function +- You can access to different variables in a survey with the `load_variables()` function ```{r} #| eval: false @@ -415,3 +429,71 @@ median_income_5yr <- get_acs( ## variables for 5-year 2017-2021 ACS vars <- load_variables(2021, "acs5") ``` + + +::: callout-note +## Exercise 2: `get_acs()` +1. Load variables for "acs5", year 2021. + +```{r} +#| eval: false +#| code-fold: true +#| code-summary: "Answer" + +vars_acs5_21 <- load_variables(2021, "acs5") +``` + + +2. Find code for total median gross rent. + + +3. Get acs data for median gross rent by county in California + +```{r} +#| eval: false +#| code-fold: true +#| code-summary: "Answer" + + +ca_rent <- get_acs( + geography = "county", + variables = "B25031_001", + state = "CA", + year = 2021) + +``` + +4. Visualize estimate by county using `geom_point`. (Hint: use y = reorder(NAME, estimate) to order counties by estimates) + +```{r} +#| eval: false +#| code-fold: true +#| code-summary: "Answer" + +ggplot(ca_rent, aes(x = estimate, y = reorder(NAME, estimate))) + + geom_point() +``` + + +5. Add the following layer, one by one, to your plot. Discuss with your neighbor what each line of code is doing. + +```{r} +#| eval: false +#| code-fold: true +#| code-summary: "Answer" + +geom_errorbar(aes(xmin = estimate - moe, xmax = estimate + moe), + width = 0.5, linewidth = 0.5) + +scale_x_continuous(labels = label_dollar()) + + +scale_y_discrete(labels = function(x) str_remove(x, " County, California|, California")) +``` + +6. Enhance you plot adding a theme_*, changing the color of the points, renaming the labels, adding a title, or any other modification you want to make. + + +::: + + diff --git a/materials/session_11.qmd b/materials/session_11.qmd index 746a3752..32475c58 100644 --- a/materials/session_11.qmd +++ b/materials/session_11.qmd @@ -3,6 +3,5 @@ title: "Working with US Census Data" title-block-banner: true --- - {{< include /sections/census-data.qmd >}} diff --git a/materials/session_12.qmd b/materials/session_12.qmd index 222a5429..41e13257 100644 --- a/materials/session_12.qmd +++ b/materials/session_12.qmd @@ -3,6 +3,6 @@ title: "Working with Text Data in R" title-block-banner: true --- -{{< include /sections/intro-tidy-text-data.qmd >}} + From 042edc9937333723d0028b4a1ed2886d1bd2592e Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Fri, 1 Sep 2023 08:44:45 -0700 Subject: [PATCH 100/104] uncommenting session 12 --- materials/session_12.qmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/materials/session_12.qmd b/materials/session_12.qmd index 41e13257..222a5429 100644 --- a/materials/session_12.qmd +++ b/materials/session_12.qmd @@ -3,6 +3,6 @@ title: "Working with Text Data in R" title-block-banner: true --- - +{{< include /sections/intro-tidy-text-data.qmd >}} From ac13d454e0b47798aaacf6abcbd4a2d2cc35378b Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Fri, 1 Sep 2023 09:57:11 -0700 Subject: [PATCH 101/104] minor change to trigger GHA --- materials/session_11.qmd | 1 + 1 file changed, 1 insertion(+) diff --git a/materials/session_11.qmd b/materials/session_11.qmd index 32475c58..746a3752 100644 --- a/materials/session_11.qmd +++ b/materials/session_11.qmd @@ -3,5 +3,6 @@ title: "Working with US Census Data" title-block-banner: true --- + {{< include /sections/census-data.qmd >}} From 98fc978ddefc94528ee9a0ebe3bdf7425b35c6e2 Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Fri, 6 Oct 2023 07:03:59 -0700 Subject: [PATCH 102/104] adding description to synthesis sections in the book --- materials/session_05.qmd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/materials/session_05.qmd b/materials/session_05.qmd index c5673844..509f7fa6 100644 --- a/materials/session_05.qmd +++ b/materials/session_05.qmd @@ -1,9 +1,9 @@ --- -title: "Collaborative Synthesis" +title: "Hands On Synthesis" title-block-banner: true --- - +During this session we will present the synthesis goals for the week and then the groups will meet to start planning for the week. From 5ff351aef27a7d17c5e5fe0c514a1c73ed980d3b Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Fri, 6 Oct 2023 13:22:55 -0700 Subject: [PATCH 103/104] descibing activityes i synthesis sections and renaming to match book week 1 --- materials/session_08.qmd | 2 +- materials/session_09.qmd | 4 ++-- materials/session_14.qmd | 4 +++- 3 files changed, 6 insertions(+), 4 deletions(-) diff --git a/materials/session_08.qmd b/materials/session_08.qmd index 856427d1..c8486948 100644 --- a/materials/session_08.qmd +++ b/materials/session_08.qmd @@ -1,5 +1,5 @@ --- -title: "Collaborative Synthesis: Authorship Guidlines & Data Ploicy" +title: "Group Work: Developing Authorship Guidlines & Data Ploicy" title-block-banner: true --- diff --git a/materials/session_09.qmd b/materials/session_09.qmd index 4e777a75..99f548d6 100644 --- a/materials/session_09.qmd +++ b/materials/session_09.qmd @@ -1,8 +1,8 @@ --- -title: "Collaborative Synthesis" +title: "Hand On Synthesis" title-block-banner: true --- - +Time for groups to work and advance in their synthesis projects. \ No newline at end of file diff --git a/materials/session_14.qmd b/materials/session_14.qmd index ca012e37..ae5d3f04 100644 --- a/materials/session_14.qmd +++ b/materials/session_14.qmd @@ -1,8 +1,10 @@ --- -title: "Collaboraive Synthesis" +title: "Hands On Synthesis" title-block-banner: true --- +Time for groups to work and advance in their synthesis projects. + From 6b7f40422f7049dc4c90439a9b6632d54864a076 Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Thu, 9 Nov 2023 12:51:59 -0800 Subject: [PATCH 104/104] deleting schedule image to bea able to merge branch to main --- materials/images/schedule.png | Bin 308062 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 materials/images/schedule.png diff --git a/materials/images/schedule.png b/materials/images/schedule.png deleted file mode 100644 index f839f016c8773e0c1ed4406a77ac0f86353f2c56..0000000000000000000000000000000000000000 GIT binary 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zT`s1@`g$()oU7W`vp7$G+xe|SyaFzl4HJjEj=tXzkp5zL*J#NBr;dVLPpWDy4~&EC zo&#BA!wrdCkUEC(2N^$}1iTB>j^eu;$~r4pLrqmp9USSciO>KCzk@TunFqjwXgmn` zV%30Sy}yU$Z6$z=87ez6&l(G&?^t0U3!>x~o+c#(ZUQzDh-4?+GoixC84L9HNgwrsb%dD;5={Ms(`?8hfz4oAXPo0L9Z zkK*HfXf7PDHp;gWv(MgshgV`X(b`RRcm@Z#)~g%#IqV6Z2j;7XlhCV|G_$Tw-6YWk zw@APEG{Hg6c^2KPjr$Dg1nN(5uawG*H;!B(VSf7a0Ho=fbzacsVE3%vjayxDzJ7|- zJ61+%aelc9#yx_Y+pa09V_KuvUG%a@FV*zY8K}5^t{}jC%wTNoKF{l7^Q_VHAZ5zSji)J0t4iNu(ClQ%ro@i5azx-_y(uHGQSZZPXt7xfm9Oa ziL7u4^So8?4Ys=B;FSM=CZC|xnaMBvK&S*O>iM5}D9HaR55>(*XmfS_&09BbwshRQ zHCq^_&G|5_%t7H|3^E3Pk1N)Z5Yf9jLGfi+*=3_J42hQurBuUlw|&XU Date: Tue, 29 Aug 2023 22:44:09 -0700 Subject: [PATCH 076/104] add exercise two; reorganized exercise one; add common text analysis methods section --- materials/sections/intro-tidy-text-data.qmd | 281 ++++++++++++++++---- 1 file changed, 226 insertions(+), 55 deletions(-) diff --git a/materials/sections/intro-tidy-text-data.qmd b/materials/sections/intro-tidy-text-data.qmd index edccda51..89f0f86d 100644 --- a/materials/sections/intro-tidy-text-data.qmd +++ b/materials/sections/intro-tidy-text-data.qmd @@ -4,8 +4,9 @@ bibliography: book.bib ## Learning Objectives {.unnumbered} -- Describe principles of tidy text -- Employ strategies to wrangle unstructured text data into a tidy format using the `tidytext` package +- Describe principles of tidy text data +- Employ strategies to wrangle unstructured text data into a tidy text format using the `tidytext` package +- Become familiar non-tidy text formats and how to convert between tidy text and non-tidy text formats - Become familiar with text analysis (or text mining) methods and when to use them ::: callout-note @@ -45,16 +46,17 @@ As our knowledge about the environmental world grows, researchers will need new Beyond literature syntheses, quantitative text analysis tools are extremely valuable for efficiently extracting information from texts and other text mining or text analysis tasks. -Environmental researchers have used text data to: +::: {.callout-caution icon=false} +#### Environmental researchers have used text data to: - Understand how text mining is used in the field [@farrell2022] - Gain insights into public perception of a particular subject [@froehlich2017] - Highlight the power of NLP models to advance research [@vanhoutan2020] - +::: ## What is tidy text data? -Let's recall what are the three tidy data principles: +Let's recall the **tidy data principles**: 1. Every column is a variable. 2. Every row is an observation. @@ -88,10 +90,8 @@ The package contains many useful functions to wrangle text data into tidy format We are going to use the `gutenbergr` package to access public domain texts from [Project Gutenberg](https://www.gutenberg.org/) (a library of free eBooks). We'll then use the `tidytext`, `dyplr` and `ggplot2` packages to practice the tidy text workflow. -Break out into groups and then follow the exercise setup and instructions. - ::: {.callout-tip} -### Setup and Instructions +### Setup 1. Create a new `qmd` file and title it "Intro to Text Data", name yourself as the author, and then save the file as `intro-text-data.qmd`. @@ -118,41 +118,74 @@ gutenberg_works(title == "Frankenstein; Or, The Modern Prometheus") # frankenste # Group C gutenberg_works(title == "The Strange Case of Dr. Jekyll and Mr. Hyde") # jekyll hyde text ``` +::: -4. Get the id number from the `gutenberg_works()` function so that you can download the text as a corpus using the function `gutenberg_download()`. Save the corpus to an object called `{book-title}_corp`. View the object - is the data in a tidy format? - -5. Tokenize the corpus data using `unnest_tokens()`. Take a look at the data - do we need every single token for our analysis? - -6. Remove "stop words" or words that can be safely removed or ignored without sacrificing the meaning of the sentence (e.g. "to", "in", "and") using `anti_join()`. Take a look at the data - are you satisfied with your data? We won't conduct any additional cleaning steps here, but consider how you would further clean the data. +### Questions -7. Calculate the top 10 most frequent words using the functions `count()` and `slice_max()`. +The answers in the code chunks are using the text of *The Phantom of the Opera*. -8. Plot the top 10 most frequent words using `ggplot()`. We reccommend creating either a bar plot using `geom_col()` or a lollipop plot using both `geom_point()` and `geom_segment()`. +::: {.callout-note} +#### Question 1 -9. **Bonus**: Consider elements in `theme()` and improve your plot. +Get the id number from the `gutenberg_works()` function so that you can download the text as a corpus using the function `gutenberg_download()`. Save the corpus to an object called `{book-title}_corp`. View the object - is the data in a tidy format? ::: -### Example using *The Phantom of the Opera* - -The code chunks below follows the instructions from above using the text of *The Phantom of the Opera*. - ```{r} +#| code-fold: true +#| code-summary: Answer +#| eval: false + # get id number gutenberg_works(title == "The Phantom of the Opera") + +# access text data using id number from `gutenberg_works()` +phantom_corp <- gutenberg_download(175) ``` +::: {.callout-note} +#### Question 2 + +Tokenize the corpus data using `unnest_tokens()`. Take a look at the data - do we need every single token for our analysis? +::: + ```{r} #| code-fold: true -#| code-summary: Steps 4-7 Code -#| message: false +#| code-summary: Answer #| eval: false -# access text data using id number from `gutenberg_works()` -phantom_corp <- gutenberg_download(175) # tidy text data - unnest and remove stop words tidy_phantom <- phantom_corp %>% - unnest_tokens(word, text) %>% - anti_join(stop_words, by = "word") + unnest_tokens(word, text) +``` + + +::: {.callout-note} +#### Question 3 + +Remove "stop words" or words that can be safely removed or ignored without sacrificing the meaning of the sentence (e.g. "to", "in", "and") using `anti_join()`. + +Take a look at the data - are you satisfied with your data? We won't conduct any additional cleaning steps here, but consider how you would further clean the data. +::: + +```{r} +#| code-fold: true +#| code-summary: Answer +#| eval: false + +# remove stop words +tidy_phantom <- tidy_phantom %>% anti_join(stop_words, by = "word") +``` + +::: {.callout-note} +#### Question 4 + +Calculate the top 10 most frequent words using the functions `count()` and `slice_max()`. +::: + +```{r} +#| code-fold: true +#| code-summary: Answer +#| eval: false # calculate top 10 most frequent words count_phantom <- tidy_phantom %>% @@ -160,11 +193,19 @@ count_phantom <- tidy_phantom %>% slice_max(n = 10, order_by = n) ``` +::: {.callout-note} +#### Question 5 + +Plot the top 10 most frequent words using `ggplot()`. + +We recommend creating either a bar plot using `geom_col()` or a lollipop plot using both `geom_point()` and `geom_segment()`. +::: + ```{r} #| code-fold: true -#| code-summary: Step 8 Plot Code +#| code-summary: Bar Plot Code #| eval: false -# visualize text data # + # bar plot ggplot(data = count_phantom, aes(n, reorder(word, n))) + geom_col() + @@ -180,11 +221,18 @@ ggplot(data = count_phantom, aes(n, reorder(word, n))) + +### Bonus Question + +::: {.callout-note} +#### Question 6 +Consider elements in `theme()` and improve your plot. +::: + ```{r} #| code-fold: true -#| code-summary: Step 9 Plot Code +#| code-summary: Lollipop Plot Code #| eval: false -# visualize text data # + # initial lollipop plot ggplot(data = count_phantom, aes(x=word, y=n)) + geom_point() + @@ -219,11 +267,7 @@ ggplot(data = count_phantom, aes(x=reorder(word, n), y=n)) + In the [Tidy Text Workflow Exercise](#exercise-tidy-text-workflow), we converted our corpus into a data table that has "one-token-per-row". However, the tidy text format of one-token-per-row is not a common format for other R packages that work with text data or perform text analysis. Packages like `tm`, `quanteda`, `topicmodels`. -Many text analysis methods, in particular NLP techniques (e.g. topic models) require text data to be stored in a mathematical format. A common approach is to create a matrix, such as a: sparse matrix, a document term matrix (DTM), or a document-feature matrix (DFM). In a matrix format, algorithms are able to more easily compare one document to many other documents to identify patterns. - -### What is a Document Term Matrix (DTM)? - -A DTM or document-feature matrix (DFM) +Many text analysis methods, in particular NLP techniques (e.g. topic models) require text data to be stored in a mathematical format. **A common approach is to create a matrix, such as a: sparse matrix, a document term matrix (DTM), or a document-feature matrix (DFM)**. In a matrix format, algorithms are able to more easily compare one document to many other documents to identify patterns. Silge and Robinson kept this in mind as they built the `tidytext` package, and included helpful `cast()` functions to turn a tidy text object (again a table with one-token-per-row) into a matrix. @@ -295,26 +339,32 @@ all_books_dfm <- tidy_all_books %>% ::: -## Exercise: Exploring Unstructured Text Data from a PDF - +## Exercise: Explore Unstructured Text Data from a PDF ::: {.callout-tip} ### Setup -1. In the `intro-text-data.qmd` files create a new header for this exercise (e.g. "Exploring Unstructured Text Data from a PDF"). +1. In the `intro-text-data.qmd` file, create a new header for this exercise (e.g. "Explore Unstructured Text Data from a PDF"). 2. Create a new code chunk and attach the following libraries: ```{r} +#| message: false library(tidytext) # tidy text tools library(quanteda) # create a corpus library(pdftools) # read in data library(stringr) # string manipulation library(ggplot2) # for quick and dirty plots +library(wordcloud) ``` -3. Depending on which group you're in, read in one of the following chapters of the Delta Plan: +3. Depending on which group you're in, read in one of the following chapters of the Delta Plan. Access and download the chapters of the Delta Plan from [Delta Stewardship Council website](https://deltacouncil.ca.gov/delta-plan/). + +**Notes for quick exploration of data:** + +- Check the `class()` of the pdf you just read in - is it what you expected? How does the object appear in the Global Environment? +- Call the object in the Console. What does your data look like? What can you infer from how it's structured? ```{r} #| eval: false @@ -337,53 +387,174 @@ dp_ch6 <- pdftools::pdf_text(path_df) ``` ```{r} +#| include: false + +# ch 8 is used for demonstration and testing path_df <- "data/text/dsc-plan-ch8.pdf" dp_ch8 <- pdftools::pdf_text(path_df) ``` 4. Using the `quanteda` package, turn the unstructured pdf text data into a corpus. +**Notes for quick exploration of data:** + +- Check the `class()` of the corpus you created - is it what you expected? How does the object appear in the Global Environment? +- Call the object in the Console. What does your data look like? How does this structure compare to the pdf object? +- Run `summary()` of the corpus in the Console. What insights can you glean? + ```{r} -dp_ch8_corpus <- quanteda::corpus(dp_ch8) +#| eval: false + +corpus_dp_ch <- quanteda::corpus(dp_ch) ``` -::: ```{r} -dp_ch8_tidy <- tidy(dp_ch8_corpus) +#| include: false + +# ch 8 is used for demonstration and testing +corpus_dp_ch8 <- quanteda::corpus(dp_ch8) ``` +5. Using `tidy()` from `tidytext`, make the corpus a tidy object. + +**Notes for quick exploration of data:** + +- Check the `class()` of the corpus you created - is it what you expected? How does the object appear in the Global Environment? +- Call the object in the Console or use `View()`. What does your data look like? Is it what you expected? + ```{r} -dp_ch8_unnest <- dp_ch8_tidy %>% - unnest_tokens(output = word, - input = text) %>% - anti_join(stop_words) +#| eval: false + +tidy_dp_ch <- tidytext::tidy(corpus_dp_ch) ``` ```{r} -# custom stop words -# stemming +#| include: false + +# ch 8 is used for demonstration and testing +tidy_dp_ch8 <- tidy(corpus_dp_ch8) ``` +::: + +### Questions + +Work independently or in groups for Question 1-5. The code solutions are based on the text data from Chapter 8 of the Delta Plan. +::: {.callout-note} +#### Question 1 + +Tokenize the tidy text data using `unnest_tokens()` +::: ```{r} -# create visual +#| code-fold: true +#| code-summary: Answer +unnest_dp_ch8 <- tidy_dp_ch8 %>% + unnest_tokens(output = word, + input = text) ``` +::: {.callout-note} +#### Question 2 + +Remove stop words using `anti_join()` and the `stop_words` data frame from `tidytext`. +::: +```{r} +#| code-fold: true +#| code-summary: Answer +#| message: false +words_dp_ch8 <- unnest_dp_ch8 %>% + anti_join(stop_words) +``` -::: {..callout-note} -### Question 1 + +::: {.callout-note} +#### Question 3 + +Calculate the top 10 most frequently occurring words. Consider using `count()` and `slice_max()`. ::: -::: {..callout-note} -### Question 2 +```{r} +#| code-fold: true +#| code-summary: Answer +count_dp_ch8 <- words_dp_ch8 %>% + count(word) %>% + slice_max(n = 10, order_by = n) +``` + +::: {.callout-note} +#### Question 4 + +Visualize the results using a plot of your choice (e.g. bar plot, lollipop plot, or wordcloud). ::: -::: {..callout-note} -### Question 3 +```{r} +#| code-fold: true +#| code-summary: Plot Code +# bar plot +ggplot(count_dp_ch8, aes(x = reorder(word, n), y = n)) + + geom_col() + + coord_flip() + + labs(title = "Top 10 Most Frequently Occurring Words in Chapter 8 of the Delta Plan", + x = NULL, + y = "count") +``` + +```{r} +#| code-fold: true +#| code-summary: Plot Code +# lollipop plot +ggplot(data = count_dp_ch8, aes(x=reorder(word, n), y=n)) + + geom_point() + + geom_segment(aes(x=word, xend=word, y=0, yend=n)) + + coord_flip() + + labs(title = "Top 10 Most Frequently Occurring Words in Chapter 8 of the Delta Plan", + x = NULL, + y = "Count") +``` + +```{r} +#| code-fold: true +#| code-summary: Plot Code +# wordcloud +wordcloud(words = count_dp_ch8$word, + freq = count_dp_ch8$n) +``` + +### Bonus Question + +::: {.callout-note} +#### Question 5 + +What do you think of your plots? Are they helpful? Consider other techniques like adding custom stop words or stemming to improve your results. ::: + +## Common Text Analysis and Text Mining Methods + +The text analysis tools, methods, and packages depend greatly on your specific text analysis goals and the nature of your text data. You may end up only using one method or one package, or many in combination. + +| Text Analysis Method | R Package | When to Use | +|------------------------------|----------------|---------------------------------------------------------| +| Document-Term Matrix (DTM) | `tm`, `quanteda`, `tm.plugin.webmining` | Represent text data as a matrix of word frequencies. | +| Named Entity Recognition (NER) | `openNLP`, `spacyr`, `udpipe` | Identify entities like names, locations, etc. in text. | +| Sentiment Analysis | `tidytext`, `sentimentr`, `syuzhet` | Determine sentiment (positive/negative) in text. | +| Stopword Removal | `tm`, `quanteda`, `tidytext` | Remove common and irrelevant words. | +| Text Classification | `caret`, `tm`, `quanteda` | Categorize text into predefined classes/categories. | +| Text Cleaning | `tm`, `tm.plugin.webmining`, `tidytext` | Preprocess text: convert to lowercase, remove special characters, etc. | +| Text Clustering | `tm`, `text2vec`, `tm.plugin.clustering` | Group similar documents together. | +| Text Summarization | `tm`, `textclean`, `textrank` | Condense the main points of a text into a summary. | +| TF-IDF | `tm`, `tm.plugin.webmining`, `tidytext` | Measure word importance in a document corpus. | +| Tokenization | `tm`, `quanteda`, `text2vec` | Split text into individual words/tokens. | +| Topic Modeling | `tm`, `topicmodels`, `lda2vec` | Discover hidden topics in a collection of documents. | +| Word Embeddings | `word2vec`, `text2vec`, `fastText` | Represent words as dense vectors for semantic analysis. | +| Dependency Parsing | `udpipe`, `spaCy`, `openNLP` | Analyze grammatical structure and word dependencies. | + +## Note About Python and NLP + + From f4780be0eada82fc046e8f4544c2b4f4295d2a8e Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Wed, 30 Aug 2023 00:16:23 -0700 Subject: [PATCH 079/104] Finalizing census data --- materials/images/census_geos.png | Bin 0 -> 357424 bytes materials/images/get_decennila_message.png | Bin 0 -> 113019 bytes materials/images/tidycensus_logo.png | Bin 0 -> 56756 bytes materials/sections/census-data.qmd | 210 +++++++++++++++++---- materials/session_11.qmd | 1 + 5 files changed, 177 insertions(+), 34 deletions(-) create mode 100644 materials/images/census_geos.png create mode 100644 materials/images/get_decennila_message.png create mode 100644 materials/images/tidycensus_logo.png diff --git a/materials/images/census_geos.png b/materials/images/census_geos.png new file mode 100644 index 0000000000000000000000000000000000000000..821b318ecf79f65daaca90b66e791e4c25039efa GIT binary patch literal 357424 zcmeEu1zQ~3(k>93024Go0wieA;K2uXcXxMpO#&o9Ai>?;g1ZC=?l!mvcNtu6v(L^x z``qu__XphP%=652)4ilu)mpXceXE*J^0H#+sD!96Ffiy6;=+nBFoLYxKd0-fQ+Q8Exf zkr?cPSmM;L-~&JFXlVby6%mHV6;XSGT8aST&Qw>2r_nuh-*;GO;HHtdq*g#4b~tci{)IEs3$pjjGqQ7ZnG>@evcm?_&P8VLn+pC3&*Dw%dZ6@W*gLK?@6~9 z630M`|76g6i)E2t?AOu*amp=?us$px;NMg0i7gq#`Eb*FK9sPX#%MV7Yak>R+O_88 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00000000..48b151e7 --- /dev/null +++ b/materials/sections/github-check-pat.qmd @@ -0,0 +1,38 @@ + + +## Steps to check if your Personal Access Token is valid + +1. Login to [included-crab](https://included-crab.nceas.ucsb.edu/) +2. Open training_LASTNAME Rproj +3. In the console run: `usethis::git_sitrep()` + +![](images/git_sitrep.png){width="80%" fig-align="center"} + +4. If your Personal Access Token is , you have to go ahead and reset it following the instructions on how to Set (or reset) your PAT. + +![](images/unset_token.png){width="80%" fig-align="center"} + + +5. Set (or reset) your PAT + + +::: callout-tip +## Setting up your PAT + +1. Run `usethis::create_github_token()` in the Console. +2. A new browser window should open up to GitHub, showing all the scopes options. You can review the scopes, but you don't need to worry about which ones to select this time. Using `create_github_token()` automatically pre-selects some recommended scopes. **Go ahead and scroll to the bottom and click "Generate Token"**. +3. Copy the generated token. +4. Back in RStudio, run `gitcreds::gitcreds_set()` in the Console. +5. Paste your PAT when the prompt asks for it. +6. Last thing, run `usethis::git_sitrep()` in the Console to check your `Git` configuration and that you've successful stored your PAT. +::: + + +6. Check your PAT is . + +- In the console run `usethis::git_sitrep()` +- Expect to see this result: + +![](images/discovered_token.png){width="80%" fig-align="center"} + + diff --git a/materials/session_17.qmd b/materials/session_17.qmd index f11893ec..4e2ec68c 100644 --- a/materials/session_17.qmd +++ b/materials/session_17.qmd @@ -3,6 +3,13 @@ title: "Appendix" title-block-banner: true --- -# Configuring Two-factor Authentication on GitHub {.unnumbered} + +# Configuring Two-factor Authentication on GitHub {{< include /sections/2fa-setup-github.qmd >}} + + + +# Check and Set your GitHub Personal Access Token (PAT) + +{{< include /sections/github-check-pat.qmd >}} From 21df699d8464dd724fb760532b85799589cb1b20 Mon Sep 17 00:00:00 2001 From: camilavargasp Date: Sun, 27 Aug 2023 21:50:49 -0700 Subject: [PATCH 049/104] swtiching remaining rmd toqmd in git publishing lesson --- materials/sections/git-github-publishing-analysis.qmd | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/materials/sections/git-github-publishing-analysis.qmd b/materials/sections/git-github-publishing-analysis.qmd index fcd72da9..42a03d87 100644 --- a/materials/sections/git-github-publishing-analysis.qmd +++ b/materials/sections/git-github-publishing-analysis.qmd @@ -84,11 +84,11 @@ Next, we will show how you can link different `qmd`s rendered into `html` so you In this exercise, you'll create a table of contents with the lessons of this course on the main page, and link some of the files we have work on so far. -- Go back to the RStudio server and to your `index.Rmd` file -- Create a table of contents with the names of the main technical lessons of this course, like so: +- Go back to the RStudio server and to your `index.qmd` file +- Create a table of contents with the names of the some of the technical lessons we have or will cover: ``` -## coreR workshop +## R workshop - Introduction to Quarto - Cleaning and Wrangling data @@ -96,9 +96,9 @@ In this exercise, you'll create a table of contents with the lessons of this cou - Spatial Analysis ``` -- Make sure you have the `html` versions of your `intro-to-rmd.Rmd` and `data-cleaning.Rmd` files. If you only see the `Rmd` version, you need to "Knit" your files first +- Make sure you have the `html` versions of your `intro-to-quarto.qmd` and `data-cleaning.qmd` files. If you only see the `qmd` version, you need to "Render" your files first -- In your `index.Rmd` let's add the links to the html files we want to show on our webpage. Do you remember the Markdown syntax to create a link? +- In your `index.qmd` let's add the links to the html files we want to show on our webpage. Do you remember the Markdown syntax to create a link?