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

CRAN status Monthly Lifecycle: maturing

You can install:

The latest release version from CRAN with

install.packages("wbstats")

or

The latest development version from github with

remotes::install_github("nset-ornl/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 x 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~  2019    38041754 <NA>  <NA>       <NA>     2020-07-01  
#> 2 AF    AFG   Afghanis~  2018    37172386 <NA>  <NA>       <NA>     2020-07-01  
#> 3 AF    AFG   Afghanis~  2017    36296400 <NA>  <NA>       <NA>     2020-07-01  
#> 4 AF    AFG   Afghanis~  2016    35383128 <NA>  <NA>       <NA>     2020-07-01  
#> 5 AF    AFG   Afghanis~  2015    34413603 <NA>  <NA>       <NA>     2020-07-01  
#> 6 AF    AFG   Afghanis~  2014    33370794 <NA>  <NA>       <NA>     2020-07-01

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.