README.Rmd

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# worldhdi

<!-- badges: start -->

[![License: CC BY
4.0](https://img.shields.io/badge/License-CC_BY_4.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/)

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.14006110.svg)](https://zenodo.org/doi/10.5281/zenodo.14006110)
<!-- badges: end -->

The goal of worldhdi is to present Human Development Index Data from 1990-2022
in a tidy format. The data is sourced from the United Nations Development

## Installation

You can install the development version of worldhdi from
[GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("openwashdata/worldhdi")
```

```{r}
## Run the following code in console if you don't have the packages
## install.packages(c("dplyr", "knitr", "readr", "stringr", "gt", "kableExtra"))
library(dplyr)
library(knitr)
library(readr)
library(stringr)
library(gt)
library(kableExtra)
library(tidyverse)
library(lubridate)
```

Alternatively, you can download the individual datasets as a CSV or XLSX
file from the table below.

```{r, echo=FALSE, message=FALSE, warning=FALSE}

extdata_path <- "https://github.com/openwashdata/worldhdi/raw/main/inst/extdata/"

read_csv("data-raw/dictionary.csv") |> 
  distinct(file_name) |> 
  dplyr::mutate(file_name = str_remove(file_name, ".rda")) |> 
  dplyr::rename(dataset = file_name) |> 
  mutate(
    CSV = paste0("[Download CSV](", extdata_path, dataset, ".csv)"),
    XLSX = paste0("[Download XLSX](", extdata_path, dataset, ".xlsx)")
  ) |> 
  knitr::kable()

```

## Data

The package provides access to tidy human development index (HDI) for 193 countries from 1990-2022. The data is sourced from the United Nations Development Programme (UNDP)

```{r}
library(worldhdi)
```

### worldhdi

The dataset `worldhdi` contains data about human development index (HDI) for 193 countries from 1990-2022.
It has `r nrow(worldhdi)` observations and `r ncol(worldhdi)` variables

```{r}
worldhdi |> 
  head(3) |> 
  gt::gt() |>
  gt::as_raw_html()
```

For an overview of the variable names, see the following table.

```{r echo=FALSE, message=FALSE, warning=FALSE}
readr::read_csv("data-raw/dictionary.csv") |>
  dplyr::filter(file_name == "worldhdi.rda") |>
  dplyr::select(variable_name:description) |> 
  knitr::kable() |> 
  kableExtra::kable_styling("striped") |> 
  kableExtra::scroll_box(height = "200px")
```


## Example

```{r}
library(worldhdi)
library(ggplot2)
library(rnaturalearthdata)
library(rnaturalearth)

# 2022 HDI worldwide 
world <- ne_countries(scale = "medium", returnclass = "sf")

world_map_data <- world |> left_join(worldhdi, by = c("iso_a3" = "iso3c"))

hdi_colors <- c("#d73027", "#fc8d59", "#fee08b", "#fdae61", "#fdd49e", "#feedde", 
                "#d9ef8b", "#a6d96a", "#66bd63", "#1a9850", "#00441b", "#003300", "#001a00", 
                "#e0e0e0") 

ggplot(data = world_map_data) +
  geom_sf(aes(fill = cut(hdi_2022, 
                         breaks = c(-Inf, 0.399, 0.449, 0.499, 0.549, 0.599, 0.649, 0.699, 
                                    0.749, 0.799, 0.849, 0.899, 0.950, Inf), 
                         labels = c("≤ 0.399", "0.400–0.449", "0.450–0.499", "0.500–0.549", 
                                    "0.550–0.599", "0.600–0.649", "0.650–0.699", 
                                    "0.700–0.749", "0.750–0.799", "0.800–0.849", 
                                    "0.850–0.899", "0.900–0.950", "≥ 0.950")))) +
  scale_fill_manual(values = hdi_colors, na.value = "gray90", name = "HDI 2022 Brackets") +
  theme_minimal() +
  labs(title = "World HDI (2022)") +
  theme(axis.text = element_blank(),
        axis.ticks = element_blank(),
        panel.grid = element_blank())
```

### Which countries saw the biggest increases in HDI over this period? 
```{r}
worldhdi |> 
  filter(!is.na(avg_growth_1990_2022)) |> 
  arrange(desc(avg_growth_1990_2022)) |> 
  select(country, avg_growth_1990_2022) |>
  head(10) |> 
  gt::gt() |>
  gt::as_raw_html()

```

### Trends in HDI by region
```{r}
# Use the rows where country is Organisation for Economic Co-operation and Development,
# Arab States, East Asia and the Pacific, Europe and Central Asia, Latin America and the Caribbean, World and plot the hdi trends using hdi_1990, hdi_2000, hdi_2010, hdi_2015, hdi_2022

worldhdi |>
  filter(country %in% c("Organisation for Economic Co-operation and Development", 
                        "Arab States", "East Asia and the Pacific", 
                        "Europe and Central Asia", "Latin America and the Caribbean", "World", "Sub-Saharan Africa", "South Asia")) |>
  pivot_longer(cols = starts_with("hdi"), 
               names_to = "year", 
               values_to = "hdi") |>
  mutate(year = gsub("hdi_", "", year),  # Remove "hdi_" prefix
         year = ymd(paste0(year, "-01-01")),  # Convert to date format
         country = ifelse(country == "Organisation for Economic Co-operation and Development", "OECD", country)) |>
  ggplot(aes(x = year, y = hdi, group = country, color = country)) +
  geom_line() +
  geom_point() +
  scale_x_date(date_labels = "%Y", date_breaks = "10 years") +  # Format x-axis as date and show every 10 years
  labs(title = "Trends in HDI by Region", y = "HDI", x = "Year", color = "Country") +  # Set legend title
  theme_minimal()
```

## License

Data are available as
[CC-BY](https://github.com/openwashdata/worldhdi/blob/main/LICENSE.md).

## Citation

Please cite this package using:

```{r}
citation("worldhdi")
```