worlbank 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.
The worldbank package provides a set of functions to
interact with various endpoints of the World Bank Indicators API. Each
function is designed to retrieve specific types of data, making it
easier to access and analyze World Bank datasets. Below is an overview
of the available endpoints and their corresponding functions in the
package:
wb_language): Retrieves a
list of all languages supported by the World Bank API. Useful for
obtaining language-specific data.wb_lending_type):
Fetches information about different lending types as recognized by the
World Bank.wb_income_level):
Allows users to access data about various income levels defined by the
World Bank.wb_source): Provides details
about the different data sources available within the World Bank’s
datasets.wb_topic): Lists all topics
covered by the World Bank API, helping users to narrow down their data
search to specific areas of interest.wb_region): Offers
information on different geographical regions as categorized by the
World Bank.wb_country): Enables access
to detailed data about individual countries, including socio-economic
and developmental indicators.wb_country_indicator): Specific to retrieving indicators
for a particular country or countries, allowing for more targeted data
analysis.wb_indicator): This
endpoint gives users access to a wide array of indicators used by the
World Bank in its data analysis and reports.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")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': 296 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" "SAS" "NA" ...
#> $ region_code : chr "ZJ" "NA" "8S" "NA" ...
#> $ region_value : chr "Latin America & Caribbean" "Aggregates" "South A"..
#> $ admin_region_id : chr NA NA "SAS" NA ...
#> $ admin_region_code : chr NA NA "8S" NA ...
#> $ admin_region_value: chr NA NA "South Asia" NA ...
#> $ 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 specific indicator
ind <- wb_indicator()
ind <- subset(
ind,
grepl("GDP", id, fixed = TRUE) & source_value == "World Development Indicators"
)
str(ind)
#> 'data.frame': 37 obs. of 9 variables:
#> $ id : chr "EG.GDP.PUSE.KO.PP" "EG.GDP.PUSE.KO.PP.KD" "EN.G"..
#> $ name : chr "GDP per unit of energy use (PPP $ per kg of oil"..
#> $ unit : chr NA NA NA NA ...
#> $ source_id : chr "2" "2" "2" "2" ...
#> $ source_value : chr "World Development Indicators" "World Developmen"..
#> $ source_note : chr "GDP per unit of energy use is the PPP GDP per k"..
#> $ source_organization: chr "IEA Statistics © OECD/IEA 2014 (https://www.iea"..
#> $ topic_id : chr "5" "5" "6" "6" ...
#> $ topic_value : chr "Energy & Mining" "Energy & Mining" "Environment"..
# fetch indicator data for specific or all countries (default)
gdp <- wb_country_indicator("NY.GDP.MKTP.CD", c("US", "DE", "FR", "CH", "JP"))
str(gdp)
#> 'data.frame': 320 obs. of 10 variables:
#> $ date : int 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 ...
#> $ 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 8.85e+11 8.18e+11 8.13e+11 7.42e+11 7.21e+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 ...