worldhdi

The goal of worldhdi is to present Human Development Index (HDI) data from 1990 to 2022 in a tidy format. The data are sourced from the United Nations Development Programme (UNDP) Human Development Report 2023/2024 Statistical Annex.

Installation

You can install the development version of worldhdi from GitHub with:

# install.packages("devtools")
# devtools::install_github("openwashdata/worldhdi")
install.packages("worldhdi", repos = c('https://openwashdata.r-universe.dev', 'https://cloud.r-project.org'))

## 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.

dataset	CSV	XLSX
worldhdi	Download CSV	Download XLSX

Data

The package provides access to tidy Human Development Index (HDI) data for 195 countries and territories and 15 UNDP aggregates from 1990 to 2022. The data are sourced from the United Nations Development Programme (UNDP) Human Development Report 2023/2024 Statistical Annex, published under the Creative Commons Attribution 3.0 IGO licence.

library(worldhdi)

worldhdi

The dataset worldhdi contains Human Development Index (HDI) data for 195 countries and territories (193 of them ranked) and 15 UNDP aggregates from 1990 to 2022. It has 210 observations and 17 variables.

worldhdi |> 
  head(3) |> 
  gt::gt() |>
  gt::as_raw_html()

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

Example

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?

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

# 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.

Citation

Please cite this package using:

citation("worldhdi")
#> To cite package 'worldhdi' in publications use:
#> 
#>   Dubey Y, Schöbitz L (2024). "worldhdi: Human Development Index
#>   Worldwide 1990-2022 (2024)." doi:10.5281/zenodo.14006109
#>   <https://doi.org/10.5281/zenodo.14006109>.
#>   <https://openwashdata.github.io/worldhdi/>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Misc{dubey_etall:2024,
#>     title = {worldhdi: Human Development Index Worldwide 1990-2022 (2024)},
#>     author = {Yash Dubey and Lars Schöbitz},
#>     year = {2024},
#>     doi = {10.5281/zenodo.14006109},
#>     url = {https://openwashdata.github.io/worldhdi/},
#>     abstract = {Provides Human Development Index (HDI) trends from 1990 to 2022 for 195 countries and territories (193 of them ranked) and 15 UNDP aggregates (development groups, regions and the world), from the UNDP Human Development Report 2023/2024 Statistical Annex. It includes HDI ranks, changes in rank and average annual HDI growth.},
#>     keywords = {open data,washdata,human development index,hdi,undp,development indicators,sdgs},
#>     version = {1.0.2},
#>   }