knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/" )
You can install:
The latest release version from CRAN with
install.packages("wbstats")
or
The latest development version from github with
remotes::install_github("nset-ornl/wbstats")
library(wbstats) # Population for every country from 1960 until present d <- wb_data("SP.POP.TOTL") head(d)
wbstats
library(tidyverse) library(wbstats) my_indicators <- c( life_exp = "SP.DYN.LE00.IN", gdp_capita ="NY.GDP.PCAP.CD", pop = "SP.POP.TOTL" ) d <- wb_data(my_indicators, start_date = 2016) d %>% left_join(wb_countries(), "iso3c") %>% ggplot() + geom_point( aes( x = gdp_capita, y = life_exp, size = pop, color = region ) ) + scale_x_continuous( labels = scales::dollar_format(), breaks = scales::log_breaks(n = 10) ) + coord_trans(x = 'log10') + scale_size_continuous( labels = scales::number_format(scale = 1/1e6, suffix = "m"), breaks = seq(1e8,1e9, 2e8), range = c(1,20) ) + theme_minimal() + labs( title = "An Example of Hans Rosling's Gapminder using wbstats", x = "GDP per Capita (log scale)", y = "Life Expectancy at Birth", size = "Population", color = NULL, caption = "Source: World Bank" )
ggplot2
to map wbstats
datalibrary(rnaturalearth) library(tidyverse) library(wbstats) ind <- "SL.EMP.SELF.ZS" indicator_info <- filter(wb_cachelist$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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