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")

## 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()

iso3	country	hdicode	region	hdi_rank_2022	year	hdi	le	eys	mys	gnipc	gdi_group	gdi	hdi_f	le_f	eys_f	mys_f	gni_pc_f	hdi_m	le_m	eys_m	mys_m	gni_pc_m	ihdi	coef_ineq	loss	ineq_le	ineq_edu	ineq_inc	gii_rank	gii	mmr	abr	se_f	se_m	pr_f	pr_m	lfpr_f	lfpr_m	rankdiff_hdi_phdi	phdi	diff_hdi_phdi	co2_prod	mf	pop_total
AFG	Afghanistan	Low	SA	182	1990	0.284	45.967	2.936460	0.8719620	3115.670	NA	NA	NA	48.397	2.117230	0.2016592	NA	NA	43.709	4.532768	1.493952	NA	NA	NA	NA	NA	NA	NA	NA	NA	1377.859	142.960	1.107733	7.899011	NA	NA	NA	NA	NA	0.281	1.056338	0.1892790	2.1809	10.69480
AFG	Afghanistan	Low	SA	182	1991	0.292	46.663	3.228456	0.9152675	2817.305	NA	NA	NA	49.144	2.246242	0.2189443	NA	NA	44.353	4.768261	1.578809	NA	NA	NA	NA	NA	NA	NA	NA	NA	1392.786	147.525	1.221396	8.137953	NA	NA	NA	NA	NA	0.289	1.027397	0.1781545	2.5264	10.74517
AFG	Afghanistan	Low	SA	182	1992	0.299	47.596	3.520452	0.9585729	2474.682	NA	NA	NA	50.320	2.383115	0.2362294	NA	NA	45.070	5.015989	1.663665	NA	NA	NA	NA	NA	NA	NA	NA	NA	1451.594	147.521	1.335059	8.376896	NA	NA	NA	NA	NA	0.296	1.003344	0.1229200	2.6421	12.05743

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

variable_name	variable_type	description
iso3	character	ISO3 country code
country	character	Country name
hdicode	character	Human Development Group
region	character	UNDP Developin Regions
hdi_rank_2022	numeric	HDI Rank in 2022
year	character	Year of value
hdi	numeric	Human Development Index
le	numeric	Life Expectancy at Birth (years)
eys	numeric	Expected Years of Schooling (years)
mys	numeric	Mean Years of Schooling (years)
gnipc	numeric	Gross National Income Per Capita (2017 PPP USD)
gdi_group	numeric	GDI Group
gdi	numeric	Gender Development Index (value)
hdi_f	numeric	HDI female
le_f	numeric	Life Expectancy at Birth, female (years)
eys_f	numeric	Expected Years of Schooling, female (years)
mys_f	numeric	Mean Years of Schooling, female (years)
gni_pc_f	numeric	Gross National Income Per Capita, female (2017 PPP USD)
hdi_m	numeric	HDI male
le_m	numeric	Life Expectancy at Birth, male (years)
eys_m	numeric	Expected Years of Schooling, male (years)
mys_m	numeric	Mean Years of Schooling, male (years)
gni_pc_m	numeric	Gross National Income Per Capita, male (2017 PPP USD)
ihdi	numeric	Inequality-adjusted Human Development Index (value)
coef_ineq	numeric	Coefficient of human inequality
loss	numeric	Overall loss (%)
ineq_le	numeric	Inequality in life expectancy
ineq_edu	numeric	Inequality in eduation
ineq_inc	numeric	Inequality in income
gii_rank	numeric	GII Rank
gii	numeric	Gender Inequality Index (value)
mmr	numeric	Maternal Mortality Ratio (deaths per 100,000 live births)
abr	numeric	Adolescent Birth Rate (births per 1,000 women ages 15-19)
se_f	numeric	Population with at least some secondary education, female (% ages 25 and older)
se_m	numeric	Population with at least some secondary education, male (% ages 25 and older)
pr_f	numeric	Share of seats in parliament, female (% held by women)
pr_m	numeric	Share of seats in parliament, male (% held by men)
lfpr_f	numeric	Labour force participation rate, female (% ages 15 and older)
lfpr_m	numeric	Labour force participation rate, male (% ages 15 and older)
rankdiff_hdi_phdi	numeric	Difference from HDI rank
phdi	numeric	Planetary pressuresadjusted Human Development Index (value)
diff_hdi_phdi	numeric	Difference from HDI value (%)
co2_prod	numeric	Carbon dioxide emissions per capita (production) (tonnes)
mf	numeric	Material footprint per capita (tonnes)
pop_total	numeric	Population, total (millions)

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="")))

# Handle missing values in `abr`
undpcomposite_clean <- undpcomposite |> 
  filter(country %in% c("United States", "Germany", "Niger", "Mali")) |> 
  mutate(
    year = as.numeric(year),  # Convert year to numeric
    abr = ifelse(is.na(abr), 0, abr)  # Replace NA values in 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 15-29)"
  ) +
  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 (2025). "undpcomposite: UNDP Composite Indicators
#>   Timeseries." doi:10.5281/zenodo.14845848
#>   <https://doi.org/10.5281/zenodo.14845848>,
#>   <https://github.com/openwashdata/undpcomposite>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Misc{dubey:2025,
#>     title = {undpcomposite: UNDP Composite Indicators Timeseries},
#>     author = {Yash Dubey},
#>     year = {2025},
#>     doi = {10.5281/zenodo.14845848},
#>     url = {https://github.com/openwashdata/undpcomposite},
#>     abstract = {Provides tidy data about all UNDP indicators in a composite timeseries.},
#>     version = {0.1.0},
#>   }