library(wbstats)# Population for every country from 1960 until presentd<- 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> 2025-07-01library(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" )
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) )
