diff --git a/materials/sections/spatial-data-viz-us.qmd b/materials/sections/spatial-data-viz-us.qmd
index f2a060b1..369f775c 100644
--- a/materials/sections/spatial-data-viz-us.qmd
+++ b/materials/sections/spatial-data-viz-us.qmd
@@ -1,13 +1,15 @@
## Learning Objectives
-- Introduce the `sf` package for working with spatial data in R
-- Create a U.S map including territories using `tigris` and `ggplot2`
-- Enhance and customize maps to create publication grade figures
+- Introduce the `sf` package in R for working with spatial data
+- Walk through a conceptual framework on how you can create a map of the US and its territories
+- Explore how you can combine different layers of spatial data in one plot
+- Provide additional resources for your to explore and be able to achieve the plots you want
+
## Big Idea
-The primary purpose of this workshop is to demonstrate how to use R to visualize spatial data. Particularly how to create a map of the U.S and all its territory. We will briefly review the basic concepts of working with geospatial data. Therefore we may not cover key concepts wehn doing spatial data analysis. For more in-depth learning on spatial analysis in R, you can check out the online book [“Geocomputation with R”](https://r.geocompx.org/) by Lovelace et al. (2024).
+The primary purpose of this workshop is to demonstrate how to use R to visualize spatial data. Particularly how to create a map of the U.S and its main territories. We will briefly review the basic concepts of working with geospatial data. Therefore we may not cover key concepts when doing spatial data analysis. For more in-depth learning on spatial analysis in R, you can check out the online book [“Geocomputation with R”](https://r.geocompx.org/) by Lovelace et al. (2024).
## Introduction to spatial data with `sf`
@@ -23,89 +25,376 @@ The sf package is an R implementation of Simple Features. This package incorpora
- Tools for spatial operations on vectors
- Most of the functions in this package starts with prefix st_ which stands for spatial and temporal.
-For this lesson we are going to polygons for the entire U.S by states including territories from the `tirgris` package AND **ADDD***
+
+
+
+
+
+
+
+## About this lesson
+
+In this lesson we will walk through how to plot the a map of the continental US with additional insets of US territories. For this example we use the `shift_geometry()` function from the `tigris` package to plot the continental US, Alaska, Hawaii and Puerto Rico in one map.
+
::: column-margin
{fig-align="right"}
`tigris` is an R package that allows users to directly download and use [TIGER/Line shapefiles](https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html) from the US Census Bureau.
:::
+The idea of this script is to present an example of how to work with spatial data in R using the `sf` package and introduce tools and techniques that will help you create the maps you like for your project.
-## About the Data
-
-## Getting Started
-
-:::callout-tip
-## Setup
-
-- Create a new R project
+:::callout-warning
+## Spatial Data
+Large spatial data files can take some time to process and plot.If you are running this code on your local machine, be patient! Your session can freeze or take some time to see the desired outcome.
+:::
-- Open a new R Script (File > New File > R Script)
-- Load the packages we will use for this session
+## Set up
+We will start by loading the packages we will be using to create our map.
```{r}
library(tigris)
library(sf)
library(dplyr)
library(ggplot2)
-library(here)
+library(janitor)
+library(readr)
+library(stringr)
+library(ggrepel)
+library(patchwork)
```
-- Save your R Script
-:::
+## Read data
+Next step is to read the data we are going to be using today. In this case we will use 4 data sources.
+
+- The US state boundaries that we can load directly from the `tigris` package.
+- The continental US EPA Ecoregions used in the National Aquatic Resource Surveys.
+- The Level 3 EPA Ecoregions by state for the state od Alaska
+- List of states and their corresponding CASC region (this data set was manually created, see us-casc-regions.R script)
-## Load and Read data
-Read Ecoregion data
+
+### 1. Load US map from `tigris`
```{r}
-ecoregions_us <- read_sf(here("materials/data/shapefiles/Aggr_Ecoregions_2015.shp"))
+## read data
+# cb = T + 20m resol, removes American Samoa, Maraina Islands, Guam and Virgin Islands
+us_states <- states(cb = TRUE,
+ resolution = "20m")
-st_crs(ecoregions_us)
+## shift geometries for easy plotting
+us_states_shift <- us_states %>%
+ shift_geometry()
```
+### 2. Read in Ecoregions
+
+**a) Continental US**
+
+For the continental US we are using the aggregated ecoregions used in the National Aquatic Resource Surveys. You can download this data at the [EPA website](https://www.epa.gov/national-aquatic-resource-surveys/ecoregions-used-national-aquatic-resource-surveys).
-We will first load the U.S states + territories polygons and then read in the ecoregion data.
+These data divides the continental US in 9 ecoregions.
+**Note:** We downloade the data and saved in to the `data` folder in our Rproj.
```{r}
-us_states <- states(cb = TRUE,
- resolution = "20m") %>%
- shift_geometry()
+## read data
+ecoregions_cont_us <- read_sf("data/Aggr_Ecoregions_2015.shp")
-us_states2 <- states()
+unique(ecoregions_cont_us$WSA9_NAME)
+st_crs(ecoregions_cont_us) #Albers, NAD83
-continental_us <- us_states2 %>%
- filter(!NAME %in% c("Alaska", "American Samoa", "Commonwealth of the Northern Mariana Islands", "Guam", "Hawaii", "Puerto Rico", "United States Virgin Islands", "District of Columbia"))
+```
+
+
+**b) Alaska L3 Ecoregions**
+
+To add additional territories, we will have to download individual files for each area. Here an example for Alaska. In this case we are downloading the shapefiles for [Level III EPA ecoregions for Alaska](https://www.epa.gov/eco-research/ecoregion-download-files-state-region-10#pane-01).
-class(us_states)
-st_crs(us_states)
+**Note:** We downloade the data and saved in to the `data` folder in our Rproj.
+```{r}
+## Read data
+ecoregion_ak_l3 <- read_sf("data/ak_eco_l3.shp")
+st_crs(ecoregion_ak_l3) # EPSG",3338, Alaska Albers
+plot(ecoregion_ak_l3$geometry)
```
-## plotting multiple layers
+### 3. US major cities
+
+Shapefile indicating the locatio of the major cities across the US. Data downloaded form [here](https://hub.arcgis.com/datasets/esri::usa-major-cities/explore?location=11.509491%2C76.498957%2C3.25)
+
+**Note:** We downloade the data and saved in to the `data` folder in our Rproj.
+
+```{r}
+## read
+us_cities <- read_sf("data/USA_Major_Cities.shp")
+st_crs(us_cities) #WGS 84 / Pseudo-Mercator
+plot(us_cities$geometry)
+
+## transform to desired crs
+us_cities_nad83 <- st_transform(us_cities,
+ crs = st_crs(us_states))
+st_crs(us_cities_nad83)
+plot(us_cities_nad83$geometry)
+
+## shift geometries for easy plotting
+us_cities_shift <- us_cities_nad83 %>%
+ shift_geometry()
+
+plot(us_cities_shift$geometry)
+
+```
+
+
+#### 4. States by CASC region
+
+This data was created using the code on [this script](https://github.com/camilavargasp/usgs-cap-map-example/blob/main/us-casc-regions.R).
+
+
+```{r}
+## read data
+us_state_casc <- read_csv("data/state_by_casc_region.csv")
+
+## merge polygons to get CASC region areas
+casc_shp <- us_states %>%
+ left_join(us_state_casc, by= c("GEOID", "STUSPS", "NAME")) %>%
+ group_by(casc_region) %>%
+ summarise(geometry = st_union(geometry))
+
+plot(casc_shp$geometry)
+
+## shift geometries for easy plotting
+casc_shift <- casc_shp %>%
+ shift_geometry()
+
+plot(casc_shift$geometry)
+
+```
+
+
+
+## Cleaning Ecoregion data
+To be able to plot all the regions in one map, we need to get both ecoregions files in the same format. This means column names should match so we can bind both files.
+
+```{r}
+## Rename columns to a generic name
+ecoregions_cont_us_clean <- ecoregions_cont_us %>%
+ rename(code = WSA9,
+ name = WSA9_NAME)
+
+## Select two main colums from Alaska ecoregion file and rename to match continental file + transforming to the smae CRS than continental file.
+## note: choosing L1 because it is the mos broad ecoregion (4 ecoregions for alaska)
+ecoregion_ak_clean <- ecoregion_ak_l3 %>%
+ select(code = NA_L1CODE,
+ name = NA_L1NAME) %>%
+ mutate(name = str_to_sentence(name)) %>%
+ st_transform(st_crs(ecoregions_cont_us_clean))
+
+##checking outcomes
+unique(ecoregion_ak_clean$name)
+st_crs(ecoregion_ak_clean)
+plot(ecoregion_ak_clean$geometry)
+
+```
+
+## Bind ecoregions data into one file
+We use the `dplyr::bind_rows()` function to combine both data frames into one with all the ecoregions. One We have all the information in one place we apply the `shift_geometry()` to get the polygons in the projection we want for plotting.
+
+
+```{r}
+ecoregions_all <- bind_rows(ecoregions_cont_us_clean,
+ ecoregion_ak_clean)
+
+
+# shift geom
+ecoregions_shift <- ecoregions_all %>%
+ shift_geometry()
+
+plot(ecoregions_shift$geometry)
+```
+
+
+## Plot ecoregions + cities + casc
+Below is the initial plot that shows how we can layer different `sf` objects in s `ggplot` by using the `geom_sf()` function.
+
```{r}
-ggplot()+
- geom_sf(data = ecoregions_us,
- aes(fill = WSA9_NAME))+
- geom_sf(data = continental_us)+
-scale_fill_viridis_d()+
- theme_bw()
+us_plot <- ggplot()+
+ geom_sf(data = ecoregions_shift,
+ aes(fill = name))+
+ geom_sf(data = us_states_shift,
+ fill = NA,
+ color = "darkgray")+
+ geom_sf(data = us_cities_shift)+
+ geom_sf(data = casc_shift,
+ color = "red",
+ fill = NA)+
+ theme_void()+
+ scale_fill_viridis_d()
+
+us_plot
```
+## Customize map
+Finally, we are adding some customization to our plot to make it look "publication-grade". Note that these are just a few ways we could customize this plot. `ggplot2` and all the packages around `ggplot` provide endless possibilities on how to go about your plots. There is lot's of documentation about `ggplot2` online, feel free to search other possibilities to add or modify from this plot.
+
+```{r}
+
+finalized_plot <- ggplot()+
+ geom_sf(data = ecoregions_shift,
+ aes(fill = name),
+ alpha = 0.5)+
+ geom_sf(data = us_states_shift,
+ fill = NA,
+ color = "darkgray")+
+ geom_sf(data = us_cities_shift,
+ alpha = 0.3)+
+ geom_sf(data = casc_shift,
+ color = "black",
+ size = 6,
+ fill = NA)+
+ #an alternative to label your can use geom_sf_text, plots just the text not the rectagle aroud it.
+ geom_sf_label(data = casc_shift[c(1:7), ],
+ aes(label = casc_region),
+ size = 3)+
+ geom_label_repel(
+ data = casc_shift[c(8:10), ],
+ aes(label = casc_region,
+ geometry = geometry),
+ stat = "sf_coordinates",
+ size = 3,
+ min.segment.length = 2)+
+ # colour = "magenta",
+ # segment.colour = "magenta")+
+ theme_void()+
+ scale_fill_viridis_d(name = "Ecoregion")+
+ labs(title = "Cities across Ecoregions in the US",
+ subtitle = "Map is devided into CASC regions")+
+ theme(plot.title = element_text(hjust = 0.5),
+ plot.subtitle = element_text(hjust = 0.5))
+
+
+finalized_plot
+
+```
+
+
+## Additional Information
+
+**More on `shift_geometry()`**
+
+There are different option of output for this function. Remember you can always search for the the help by running `?shift_geometry()` in the console and get more details about how this function works. Let's see other possible outcomes when using this function.
+
+```{r}
+## Alaska, Hawaii and Puerto Rico "outside" of the continental US as opposed to below.
+us_states_outside <- us_states %>%
+ shift_geometry(position = "outside")
+
+plot(us_states_outside$geometry)
+
+```
+
+We can also set `preserve_area = TRUE` to keep the area of each territory the relative size compared to the continental US.
+
+```{r}
+us_states_area <- us_states %>%
+ shift_geometry(preserve_area = TRUE)
+
+plot(us_states_area$geometry)
+```
+
+Or, combine both options presented above.
+
+
+```{r}
+us_states_outside_area <- us_states %>%
+ shift_geometry(preserve_area = TRUE,
+ position = "outside")
+
+plot(us_states_outside_area$geometry)
+
+```
+
+
+**Exploring other ways to plot the US and its territorises**
+
+
+Break down one of the `sf` data frames with a `shift_geometry()` to inspect in more details the CRS for each territory.
+
+```{r}
+## Filtering each data set to plot independently
+alaska <- us_states_shift %>%
+ filter(NAME == "Alaska")
+
+hawaii <- us_states_shift %>%
+ filter(NAME == "Hawaii")
+
+puerto_rico <- us_states_shift %>%
+ filter(NAME == "Puerto Rico")
+
+continent <- us_states_shift %>%
+ filter(!NAME %in% c("Alaska", "Hawaii", "Puerto Rico"))
+
+
+```
+
+Exploring other ways of plotting the US and its territories using `patchwork`
+```{r}
+
+alaska_plot <- ggplot()+
+ geom_sf(data = alaska)+
+ theme_void()
+
+hawaii_plot <- ggplot()+
+ geom_sf(data = hawaii)+
+ theme_void()
+
+pr_plot <- ggplot()+
+ geom_sf(data = puerto_rico)+
+ theme_void()
+
+
+cont_plot <- ggplot()+
+ geom_sf(data = continent)+
+ theme_void()
+
+
+## combined plot
+cont_plot + {
+ alaska_plot | hawaii_plot | pr_plot +
+ plot_layout(ncol = 3)
+ } +
+ plot_layout(ncol = 1)
+
+```
+
+## Resurces
+
+**`patchwork`**
+
+- [ggplot multiple plots made ridiculuous simple using patchwork R package](https://www.datanovia.com/en/blog/ggplot-multiple-plots-made-ridiculuous-simple-using-patchwork-r-package/)
+
+- [Patchwork documentation on controlling layouts](https://patchwork.data-imaginist.com/articles/guides/layout.html)
+
+
+**Spatial Data Visualization**
+
+- [Visualizing Spatial Data, NCEAS Learning Hub Lesson by Rachel King](https://learning.nceas.ucsb.edu/2024-03-ucsb-faculty/session_05.html)
+
+- [Visualizing geospatial data by Claus Wilke](https://clauswilke.com/dataviz/geospatial-data.html)
+
+- [Mapping Census data in R by Kyle Walker](https://walker-data.com/census-r/mapping-census-data-with-r.html) (author of the `tigirs` package)
-## Bonus
+- [`ggcart`](https://uncoast-unconf.github.io/ggcart/), an interesting package to look into. "The goal of ggcart is to include Puerto Rico, the Virgin Islands and Guam in the traditional Albers maps"
-CREATE A GITHUB REPO VERSION OF THIS PROJECT!
-
-
+- [Blog on `geom_sf_text()` and `geom_sf_label()`](https://yutani.rbind.io/post/geom-sf-text-and-geom-sf-label-are-coming/)
\ No newline at end of file