The goal of worldbank is to provide a simple interface to the World Bank API v2. The main difference to other packages is that it’s a modern implementation using the httr2 package without any other dependencies.
The worldbank package provides a set of functions to interact with
various endpoints of the World Bank 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:
- Languages (
wb_language): Retrieves a list of all languages supported by the World Bank API. Useful for obtaining language-specific data. - Lending Types (
wb_lending_type): Fetches information about different lending types as recognized by the World Bank. - Income Levels (
wb_income_level): Allows users to access data about various income levels defined by the World Bank. - Sources (
wb_source): Provides details about the different data sources available within the World Bank’s datasets. - Topics (
wb_topic): Lists all topics covered by the World Bank API, helping users to narrow down their data search to specific areas of interest. - Regions (
wb_region): Offers information on different geographical regions as categorized by the World Bank. - Countries (
wb_country): Enables access to detailed data about individual countries, including socio-economic and developmental indicators. - Country Indicators (
wb_country_indicator): Specific to retrieving indicators for a particular country or countries, allowing for more targeted data analysis. - Indicators (
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 development version of worldbank 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"))
#> # A tibble: 2 × 18
#> country_id country_code country_name region_id region_code region_value
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 DEU DE Germany ECS Z7 Europe & Central …
#> 2 USA US United States NAC XU North America
#> # ℹ 12 more variables: admin_region_id <chr>, admin_region_code <chr>,
#> # admin_region_value <chr>, income_level_id <chr>, income_level_code <chr>,
#> # income_level_value <chr>, lending_type_id <chr>, lending_type_code <chr>,
#> # lending_type_value <chr>, capital_city <chr>, longitude <dbl>,
#> # latitude <dbl>
# or fetch all (default)
wb_country()
#> # A tibble: 296 × 18
#> country_id country_code country_name region_id region_code region_value
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 ABW AW Aruba LCN ZJ Latin Ameri…
#> 2 AFE ZH Africa Eastern and… NA NA Aggregates
#> 3 AFG AF Afghanistan SAS 8S South Asia
#> 4 AFR A9 Africa NA NA Aggregates
#> 5 AFW ZI Africa Western and… NA NA Aggregates
#> # ℹ 291 more rows
#> # ℹ 12 more variables: admin_region_id <chr>, admin_region_code <chr>,
#> # admin_region_value <chr>, income_level_id <chr>, income_level_code <chr>,
#> # income_level_value <chr>, lending_type_id <chr>, lending_type_code <chr>,
#> # lending_type_value <chr>, capital_city <chr>, longitude <dbl>,
#> # latitude <dbl>
# fetch indicator data for specific country
indicators <- wb_country_indicator("NY.GDP.MKTP.CD")
indicators
#> # A tibble: 13,198 × 10
#> date indicator_id indicator_name country_id country_name country_code value
#> <int> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 2022 NY.GDP.MKTP… GDP (current … ZH Africa East… AFE 1.19e12
#> 2 2021 NY.GDP.MKTP… GDP (current … ZH Africa East… AFE 1.09e12
#> 3 2020 NY.GDP.MKTP… GDP (current … ZH Africa East… AFE 9.29e11
#> 4 2019 NY.GDP.MKTP… GDP (current … ZH Africa East… AFE 1.01e12
#> 5 2018 NY.GDP.MKTP… GDP (current … ZH Africa East… AFE 1.01e12
#> # ℹ 13,193 more rows
#> # ℹ 3 more variables: unit <chr>, obs_status <chr>, decimal <int>
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