wbstats 1.2.0 is now on CRAN

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Programmatic Access to Data and Statistics from the World Bank API
Author

Mauricio “Pachá” Vargas S.

Published

August 28, 2026

Before the main content: I am creating an R Community on Google Groups. You can join the group using this form.

Changes

  • Removed the dplyr, tidyr, readr, tidyselect, tibble, rlang, and magrittr dependencies in favor of data.table (for row-binding and reshaping data) and base R.
  • Removed the stringr and lubridate dependencies in favor of base R string and date handling.
  • Functions now return data.table objects instead of tibbles.
  • I added wb_country_coverage() to summarize the available data before downloading the series.
  • I removed all the retired indicators and those that return empty tables from wb_cachelist.

Installation

You can install the latest release version from CRAN with

install.packages("wbstats")

which is the same as the latest development version from GitHub this as of 2026-08-28

remotes::install_github("pachadotdev/wbstats")

Downloading data from the World Bank

library(wbstats)

# Population for every country from 1960 until present
d <- wb_data("SP.POP.TOTL")
    
head(d)

#> # A tibble: 6 × 9
#>   iso2c iso3c country    date SP.POP.TOTL unit  obs_status footnote last_updated
#>   <chr> <chr> <chr>     <dbl>       <dbl> <chr> <chr>      <chr>    <date>      
#> 1 AF    AFG   Afghanis…  2024    42647492 <NA>  <NA>       <NA>     2025-07-01  
#> 2 AF    AFG   Afghanis…  2023    41454761 <NA>  <NA>       <NA>     2025-07-01  
#> 3 AF    AFG   Afghanis…  2022    40578842 <NA>  <NA>       <NA>     2025-07-01  
#> 4 AF    AFG   Afghanis…  2021    40000412 <NA>  <NA>       <NA>     2025-07-01  
#> 5 AF    AFG   Afghanis…  2020    39068979 <NA>  <NA>       <NA>     2025-07-01  
#> 6 AF    AFG   Afghanis…  2019    37856121 <NA>  <NA>       <NA>     2025-07-01

The current World Bank API does not provide summaries of data availability. I added the wb_country_coverage() function to supply that, which reads pre-computed summaries from GitHub.

d <- wb_country_coverage("gross domestic product", c("Mexico", "Chile"), 2010, 2020)
d

#      iso2c  iso3c country pct_complete  from    to  nobs         indicator
#     <char> <char>  <char>        <num> <int> <int> <int>            <char>
#  1:     CL    CHL   Chile         56.5  1960  2100    52     CC.EG.INTS.KW
#  2:     MX    MEX  Mexico         56.5  1960  2100    52     CC.EG.INTS.KW
#  3:     CL    CHL   Chile         34.8  1960  2025    23 EG.EGY.PRIM.PP.KD
#  4:     MX    MEX  Mexico         34.8  1960  2025    23 EG.EGY.PRIM.PP.KD
#  5:     CL    CHL   Chile         53.0  1960  2025    35 EG.GDP.PUSE.KO.PP
# ---                                                                       
# 82:     MX    MEX  Mexico         54.5  1960  2025    36    PA.NUS.PRVT.PP
# 83:     CL    CHL   Chile         53.0  1960  2025    35 SL.GDP.PCAP.EM.KD
# 84:     MX    MEX  Mexico         53.0  1960  2025    35 SL.GDP.PCAP.EM.KD
# 85:     CL    CHL   Chile        100.0  2004  2023    20   SPI.D5.2.5.HOUS
# 86:     MX    MEX  Mexico         40.0  2004  2023     8   SPI.D5.2.5.HOUS

# countries with less than 15% coverage for any variable
d[pct_complete < 15, ]

#     iso2c  iso3c country pct_complete  from    to  nobs                 indicator
#    <char> <char>  <char>        <num> <int> <int> <int>                    <char>
# 1:     MX    MEX  Mexico         12.9  1960  2100    13 UIS.XUNIT.GDPCAP.02.FSGOV

Hans Rosling’s Gapminder using wbstats

library(wbstats)
library(data.table)
library(tinyplot)

my_indicators <- c(
  life_exp = "SP.DYN.LE00.IN",
  gdp_capita ="NY.GDP.PCAP.CD",
  pop = "SP.POP.TOTL"
)

d <- wb_data(my_indicators, start_date = 2016)

d <- merge(d, wb_countries(), "iso3c")
d <- na.omit(d)

png(file="man/figures/readme-gdppc-vs-lifexp.png", width = 900, height = 600)
tinyplot(
  life_exp ~ gdp_capita | region,
  data = d,
  cex = d$pop,
  pch = 19,
  alpha = 0.7,
  palette = "tableau",
  log = "x",
  xaxl = "$",
  main = "An Example of Hans Rosling's Gapminder using wbstats",
  xlab = "GDP per Capita (log scale)",
  ylab = "Life Expectancy at Birth",
  cap = "Source: World Bank"
)
dev.off()

How I create the package data

Just in case this is useful.

What worked for me to query the data was to use the format and per_page arguments. You do not need this to work with the package.

Endpoints used for the package data:

  • https://api.worldbank.org/v2/indicators?format=json&per_page=30000
  • https://api.worldbank.org/v2/source?format=json&per_page=100
  • https://api.worldbank.org/v2/topics?format=json&per_page=100
  • https://api.worldbank.org/v2/regions?format=json&per_page=100
  • https://api.worldbank.org/v2/incomelevel?format=json&per_page=10
  • https://api.worldbank.org/v2/lendingtypes?format=json&per_page=10
  • https://api.worldbank.org/v2/languages?format=json&per_page=100

Parts of this API endpoints description comes from https://dlthub.com/context/source/world-bank-indicators-api and other were just testing things like “language” and “languages” tp update wbstats. I did not create this package, I just assumed its maintenance as it is a very valuable resoure.

Resource Endpoint Method Description
indicators /v2/indicator GET Access to nearly 28,000 series indicators
countries /v2/country/all GET All countries
country_indicator /v2/country/{country_id}/indicator/{indicator_id} GET Specific indicator for a country
sources /v2/source GET All data sources
source_indicators /v2/source/{source_id}/indicators GET Indicators for a specific source
topics /v2/topics GET Metadata about indicator topics
regions /v2/country/all GET All regions
income_levels /v2/country/income_levels GET All income levels
lending_types /v2/country/lending_types GET All lending types
languages /v2/country/languages GET All languages

I added this because I did not find much information in the official documentation.