Let’s use the wbstats
package for R
:
Search for indicators
https://data.worldbank.org/indicator
Figure out what country code is in World Bank (e.g. “US” for United States)
usa<-wb(country = c("US"),
indicator = c("NY.GDP.PCAP.CD", "NY.GDP.PCAP.KD.ZG", "SL.UEM.TOTL.ZS", "FP.CPI.TOTL.ZG", "GC.DOD.TOTL.GD.ZS", "SP.POP.TOTL", "SP.POP.GROW", "SI.POV.GINI", "SP.DYN.LE00.IN", "SP.DYN.IMRT.IN", "SI.POV.DDAY", "NE.TRD.GNFS.ZS"),
startdate = 1970, enddate = 2019,
return_wide = TRUE) %>%
mutate(date = as.numeric(date)) %>%
rename("Year" = date,
"GDP_per_Capita"= NY.GDP.PCAP.CD,
"GDP_growth" = NY.GDP.PCAP.KD.ZG,
"Unemployment" = SL.UEM.TOTL.ZS,
"Inflation" = FP.CPI.TOTL.ZG,
"Debt_pct_GDP" = GC.DOD.TOTL.GD.ZS,
"Population" = SP.POP.TOTL,
"Pop_growth" = SP.POP.GROW,
"Gini" = SI.POV.GINI,
"Life_Exp" = SP.DYN.LE00.IN,
"Infant_mortality" = SP.DYN.IMRT.IN,
"Poverty_pct" = SI.POV.DDAY,
"Trade_pct_GDP" = NE.TRD.GNFS.ZS)
ggplot(data = usa)+
aes(x = Year,
y = GDP_per_Capita)+
#geom_point()+
geom_path(size=2, color = "blue")+
scale_x_continuous(breaks=seq(1970,2020,10))+
scale_y_continuous(breaks=seq(10000,60000,10000),
labels=scales::dollar)+
theme_classic(base_family = "Fira Sans Condensed", base_size=18)+
labs(x = "Year",
y = "GDP per Capita (Current USD)")
## Warning: Removed 1 rows containing missing values (geom_path).