Client for the 'World Bank' APIs

Download and search data from the 'World Bank' APIs, including the 'Indicators' API, the 'Poverty and Inequality Platform (PIP)' API, the 'Finances One' API, the 'Projects' API, and the 'Documents & Reports' API. See < https://datahelpdesk.worldbank.org/knowledgebase/articles/889386-developer-information-overview> for further details.

worldbank

Overview

worldbank provides a simple interface to the following World Bank APIs:

The main difference to other packages is that it’s a modern implementation using the httr2 package and supports all available endpoints and parameters.

Installation

You can install the released version of worldbank from CRAN with:

install.packages("worldbank")

And the development version from GitHub with:

# install.packages("pak")
pak::pak("m-muecke/worldbank")

Usage

worldbank functions are prefixed with wb_ and follow the naming convention of the World Bank API v2.

library(worldbank)

# filter by specific country
wb_country(c("US", "DE"))
#>   country_id country_code  country_name region_id region_code
#> 1        DEU           DE       Germany       ECS          Z7
#> 2        USA           US United States       NAC          XU
#>            region_value admin_region_id admin_region_code admin_region_value
#> 1 Europe & Central Asia            <NA>              <NA>               <NA>
#> 2         North America            <NA>              <NA>               <NA>
#>   income_level_id income_level_code income_level_value lending_type_id
#> 1             HIC                XD        High income             LNX
#> 2             HIC                XD        High income             LNX
#>   lending_type_code lending_type_value    capital_city longitude latitude
#> 1                XX     Not classified          Berlin   13.4115  52.5235
#> 2                XX     Not classified Washington D.C.  -77.0320  38.8895

# or fetch all (default)
country <- wb_country()
str(country)
#> 'data.frame':    295 obs. of  18 variables:
#>  $ country_id        : chr  "ABW" "AFE" "AFG" "AFR" ...
#>  $ country_code      : chr  "AW" "ZH" "AF" "A9" ...
#>  $ country_name      : chr  "Aruba" "Africa Eastern and Southern" "Afghanista"..
#>  $ region_id         : chr  "LCN" "NA" "MEA" "NA" ...
#>  $ region_code       : chr  "ZJ" "NA" "ZQ" "NA" ...
#>  $ region_value      : chr  "Latin America & Caribbean" "Aggregates" "Middle "..
#>  $ admin_region_id   : chr  NA NA "MNA" NA ...
#>  $ admin_region_code : chr  NA NA "XQ" NA ...
#>  $ admin_region_value: chr  NA NA "Middle East, North Africa, Afghanistan & P"..
#>  $ income_level_id   : chr  "HIC" "NA" "LIC" "NA" ...
#>  $ income_level_code : chr  "XD" "NA" "XM" "NA" ...
#>  $ income_level_value: chr  "High income" "Aggregates" "Low income" "Aggregat"..
#>  $ lending_type_id   : chr  "LNX" NA "IDX" NA ...
#>  $ lending_type_code : chr  "XX" NA "XI" NA ...
#>  $ lending_type_value: chr  "Not classified" "Aggregates" "IDA" "Aggregates" ...
#>  $ capital_city      : chr  "Oranjestad" NA "Kabul" NA ...
#>  $ longitude         : num  -70 NA 69.2 NA NA ...
#>  $ latitude          : num  12.5 NA 34.5 NA NA ...

# search for indicators by keyword across id, name, and description
ind <- wb_search("GDP")
ind <- subset(ind, source_value == "World Development Indicators")
str(ind)
#> 'data.frame':    103 obs. of  9 variables:
#>  $ id                 : chr  "BG.GSR.NFSV.GD.ZS" "BM.KLT.DINV.WD.GD.ZS" "BN.C"..
#>  $ name               : chr  "Trade in services (% of GDP)" "Foreign direct i"..
#>  $ unit               : chr  NA NA NA NA ...
#>  $ source_id          : int  2 2 2 2 2 2 2 2 2 2 ...
#>  $ source_value       : chr  "World Development Indicators" "World Developmen"..
#>  $ source_note        : chr  "Total trade in services includes services provi"..
#>  $ source_organization: chr  "Balance of Payments Statistics Yearbook and dat"..
#>  $ topic_id           : chr  "3;12;21" "3;7" "3" "3;7;19" ...
#>  $ topic_value        : chr  "Economy & Growth;Private Sector;Trade" "Economy"..

# fetch indicator data for specific or all countries (default)
gdp <- wb_data("NY.GDP.MKTP.CD", c("US", "DE", "FR", "CH", "JP"))
str(gdp)
#> 'data.frame':    330 obs. of  10 variables:
#>  $ date          : int  2025 2024 2023 2022 2021 2020 2019 2018 2017 2016 ...
#>  $ indicator_id  : chr  "NY.GDP.MKTP.CD" "NY.GDP.MKTP.CD" "NY.GDP.MKTP.CD" "N"..
#>  $ indicator_name: chr  "GDP (current US$)" "GDP (current US$)" "GDP (current"..
#>  $ country_id    : chr  "CH" "CH" "CH" "CH" ...
#>  $ country_name  : chr  "Switzerland" "Switzerland" "Switzerland" "Switzerlan"..
#>  $ country_code  : chr  "CHE" "CHE" "CHE" "CHE" ...
#>  $ value         : num  1.04e+12 9.70e+11 9.28e+11 8.58e+11 8.41e+11 ...
#>  $ unit          : chr  NA NA NA NA ...
#>  $ obs_status    : chr  NA NA NA NA ...
#>  $ decimal       : int  0 0 0 0 0 0 0 0 0 0 ...

# plot the indicator data
library(ggplot2)

subset(gdp, date >= 1980) |>
  ggplot(aes(x = date, y = value, color = country_name)) +
  geom_line() +
  theme_minimal() +
  theme(
    legend.title = element_blank(),
    legend.position = "bottom",
    plot.title = element_text(face = "bold"),
    panel.grid.major.y = element_line(color = "black", linewidth = 0.2),
    panel.grid.major.x = element_blank(),
    panel.grid.minor = element_blank(),
    axis.text = element_text(color = "black"),
    axis.title = element_blank()
  ) +
  scale_y_continuous(
    labels = scales::label_currency(scale_cut = scales::cut_short_scale())
  ) +
  labs(title = "GDP in Current U.S. Dollars", color = "Country")