undpcomposite

The goal of undpcomposite is to provide tidy data from the UNDP composite data timeseries

Installation

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

# install.packages("devtools")
# devtools::install_github("openwashdata/undpcomposite")
install.packages("undpcomposite", 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)

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

1. Click Download CSV. A window opens that displays the CSV in your browser.
2. Right-click anywhere inside the window and select “Save Page As…”.
3. Save the file in a folder of your choice.

dataset	CSV	XLSX
undpcomposite	Download CSV	Download XLSX

Data

The package provides access to composite data from UNDP on all indices in the form of a timeseries.

library(undpcomposite)

undpcomposite

The dataset undpcomposite contains data about UNDP indicators over time. It has 6798 observations and 45 variables

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

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

Example

Less developed countries exhibit higher adolescent birth rates than more developed countries

library(undpcomposite)
library(ggplot2)
library(dplyr)

# Ensure UTF-8 encoding for character columns
undpcomposite <- undpcomposite |>
  mutate(across(where(is.character), ~ iconv(.,"UTF-8","UTF-8",sub="")))

# Keep four countries and the years with a value for `abr`
undpcomposite_clean <- undpcomposite |> 
  filter(country %in% c("United States", "Germany", "Niger", "Mali")) |> 
  mutate(
    year = as.numeric(year)  # Convert year to numeric
  ) |> 
  filter(!is.na(abr))  # Drop years without a value for abr

# Find min and max year
year_range <- range(undpcomposite_clean$year, na.rm = TRUE)

# Create the plot with decade-based x-axis
ggplot(undpcomposite_clean, aes(x = year, y = abr, color = country, group = country)) +
  geom_line() +
  scale_x_continuous(breaks = seq(floor(year_range[1] / 10) * 10, ceiling(year_range[2] / 10) * 10, by = 10)) +  
  labs(
    title = "Adolescent Birth Rate in Two High Income and Two Low Income Countries",
    x = "Year",
    y = "Adolescent Birth Rate (births per 1,000 women aged 15-19)"
  ) +
  theme_minimal()

License

Data are available as CC-BY.

Citation

Please cite this package using:

citation("undpcomposite")
#> To cite package 'undpcomposite' in publications use:
#> 
#>   Dubey Y, Schöbitz L (2025). "undpcomposite: UNDP Composite Indicators
#>   Timeseries." doi:10.5281/zenodo.14845847
#>   <https://doi.org/10.5281/zenodo.14845847>.
#>   <https://github.com/openwashdata/undpcomposite>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Misc{dubey_etall:2025,
#>     title = {undpcomposite: UNDP Composite Indicators Timeseries},
#>     author = {Yash Dubey and Lars Schöbitz},
#>     year = {2025},
#>     doi = {10.5281/zenodo.14845847},
#>     url = {https://github.com/openwashdata/undpcomposite},
#>     abstract = {Provides tidy data on the composite indices of the UNDP Human Development Report 2023/2024 (HDI, GDI, IHDI, GII and PHDI) and their components as a time series from 1990 to 2022 for 195 countries and territories and 11 UNDP aggregates.},
#>     keywords = {open data,washdata,human development index,gender development index,gender inequality index,inequality,planetary pressures,time series,sdgs},
#>     version = {1.0.2},
#>   }