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wbstats: An R package for searching and downloading data from the World Bank API

CRAN status Monthly Lifecycle: maturing R-CMD-check

You can install:

The latest release version from CRAN with

install.packages("wbstats")

or

The latest development version from github with

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

Hans Rosling’s Gapminder using wbstats

library(tidyverse)
library(wbstats)

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 %>%
  left_join(wb_countries(), "iso3c") %>%
  ggplot() +
  geom_point(
    aes(
      x = gdp_capita, 
      y = life_exp, 
      size = pop, 
      color = region
      )
    ) +
  scale_x_continuous(
    labels = scales::dollar_format(),
    breaks = scales::log_breaks(n = 10)
    ) +
  coord_trans(x = 'log10') +
  scale_size_continuous(
    labels = scales::number_format(scale = 1/1e6, suffix = "m"),
    breaks = seq(1e8,1e9, 2e8),
    range = c(1,20)
    ) +
  theme_minimal() +
  labs(
    title = "An Example of Hans Rosling's Gapminder using wbstats",
    x = "GDP per Capita (log scale)",
    y = "Life Expectancy at Birth",
    size = "Population",
    color = NULL,
    caption = "Source: World Bank"
  )

Using ggplot2 to map wbstats data

library(rnaturalearth)
library(tidyverse)
library(wbstats)

ind <- "SL.EMP.SELF.ZS"
indicator_info <- filter(wb_cachelist$indicators, indicator_id == ind)

ne_countries(returnclass = "sf") %>%
  left_join(
    wb_data(
      c(self_employed = ind), 
         mrnev = 1
          ),
    c("iso_a3" = "iso3c")
  ) %>%
  filter(iso_a3 != "ATA") %>% # remove Antarctica
  ggplot(aes(fill = self_employed)) +
  geom_sf() +
  scale_fill_viridis_c(labels = scales::percent_format(scale = 1)) +
  theme(legend.position="bottom") +
  labs(
    title = indicator_info$indicator,
    fill = NULL,
    caption = paste("Source:", indicator_info$source_org) 
  )

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.