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Mauricio “Pachá” Vargas Sepúlveda
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wbstats is back on CRAN
Mauricio “Pachá” Vargas S.
September 14, 2025
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Last week I needed to use wbstats to obtain some time series, and I realised it was removed from CRAN. However, it’s back now!
I contacted the maintainer, who explained that CRAN was asking for some time consuming changes to the package’s structure and documentation, and it was removed over CRAN’s policy on internet access.
Something similar happened to the tradestatistics package I created and that I have maintained in the last nine years. I adapted the tradestatistics approach to comply with CRAN’s policies for wbstats, which consisted in using mocked tests with pre-recorded API queries thanks to the vcr package, and now it’s back to CRAN. Other required changes where tiny changes about the documentation and the package structure.
I’ll be maintaining this package now, so please send me any issues or feature requests you may have on the package’s GitHub repository.
You can install:
The latest release version from CRAN with
if (!require(wbstats)) install.packages("wbstats")
Loading required package: 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> 2025-12-04 2 AF AFG Afghanis… 2023 41454761 <NA> <NA> <NA> 2025-12-04 3 AF AFG Afghanis… 2022 40578842 <NA> <NA> <NA> 2025-12-04 4 AF AFG Afghanis… 2021 40000412 <NA> <NA> <NA> 2025-12-04 5 AF AFG Afghanis… 2020 39068979 <NA> <NA> <NA> 2025-12-04 6 AF AFG Afghanis… 2019 37856121 <NA> <NA> <NA> 2025-12-04
Hans Rosling’s Gapminder using wbstats
if (!require(tidyverse)) install.packages("tidyverse")
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
if (!require(rnaturalearth)) install.packages("rnaturalearth")
if (!require(tidyverse)) install.packages("tidyverse")
if (!require(wbstats)) install.packages("wbstats")
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)
)
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