World Bank Data Demo

1 Background

The World Bank collects statistical information from countries around the world. A particularly useful data set is the World Development Indicators (WDI) which are country level statistical information from around the world.

R is unique in that using library(WDI) you can download indicator data directly from the World Bank, read it into a data set, and put it to use. Using library(plotly) you can even make cool looking motion charts, somewhat reminiscent of those popularized by Hans Rosling.

While the code below is seemingly arcane, it is important to recognize that it is simple in structure. It is very possible to re-purpose the code below using some of the many 1,000’s of WDI indicators that are of interest to you.

2 Call Relevant Libraries

library(WDI) # for accessing World Bank data

library(dplyr) # data wrangling

library(ggplot2) # beautiful graphs

library(plotly) # for updated version of cool Hans Rosling style visualizations

library(DT) # data table

options(scipen = 999) # penalize scientific notation

3 Get Some Data From the World Development Indicators (WDI)

# get names of specific indicators from WDI Data Catalog

mydata <- WDI(country="all", 
              indicator=c("SI.POV.GINI", # Gini
                          "NY.GDP.PCAP.CD", # GDP
                          "SE.ADT.LITR.ZS", # adult literacy
                          "SP.DYN.LE00.IN", # life expectancy
                          "VC.BTL.DETH", # battle related deaths
                          "SP.POP.TOTL", # population
                          "SN.ITK.DEFC.ZS"), # undernourishment
              start = 1980, 
              end = 2023, 
              extra = TRUE) 


save(mydata, file="WorldBankData.RData")

4 Rename Some Variables

# think about renaming some variables with more intuitive names
# e.g....

# rename some variables with dplyr (just copy and paste your indicators)

mydata <- dplyr::rename(mydata, 
                        GDP = NY.GDP.PCAP.CD,
                        adult_literacy = SE.ADT.LITR.ZS,
                        life_expectancy = SP.DYN.LE00.IN, 
                        battle_death = VC.BTL.DETH,
                        population = SP.POP.TOTL,
                        Gini = SI.POV.GINI,
                        undernourishment = SN.ITK.DEFC.ZS)

mydata$country_name <- mydata$country

mydata$country <-as.factor(mydata$country)

save(mydata, file="WorldBankData.RData")

5 Take a Quick Look at the Data

# head(mydata) # look at the data

mydata %>% 
  select(country, 
         region,
         year,
         GDP, 
         adult_literacy,
         life_expectancy, 
         battle_death,
         population, 
         Gini,
         undernourishment) %>%
  datatable(rownames = FALSE,
            filter = 'top', 
            extensions = 'Buttons', 
            options = list(
              dom = 'Bfrtip',
              buttons = c('copy', 'csv', 'excel', 'pdf', 'print')),
              caption = 'World Bank Data')

6 ggplot Graphs

p1 <- ggplot(mydata,
       aes(x = GDP,
           y = life_expectancy,
           color = region)) +
  geom_point() +
  geom_smooth() +
  scale_color_viridis_d() +
  labs(title = "Life Expectancy by GDP",
       x = "GDP",
       y = "Life Expectancy")

p1

p1 + facet_wrap(~region)

7 Map

mymap <- mydata %>% 
  filter(year == 2020) %>% 
  plot_geo(locations = ~iso3c,
           color = ~life_expectancy,
           z = ~life_expectancy,
           text = ~country) %>% 
  layout(title = "Countries by Life Expectancy in 2020", 
         geo = list(showland = FALSE,
                    showcountries = TRUE)) %>%
  colorbar(title = 'life expectancy') 
         
mymap

8 Globe

myglobe <- mydata %>% 
  filter(year == 2020) %>% 
  plot_geo(locations = ~iso3c,
           color = ~life_expectancy,
           z = ~life_expectancy,
           text = ~country) %>% 
  layout(title = "Countries by Life Expectancy in 2020", 
         geo = list(showland = FALSE,
                    showcountries = TRUE,
                    projection = list(type = 'orthographic',
                                      rotation = list(lon = -30,
                                                      lat = 10,
                                                      roll = 0)))) %>%
  colorbar(title = 'life expectancy') 

Warning: Ignoring 8 observations

myglobe

9 Plot the Motion Charts

mydata <- mydata %>% 
  filter(region != "Aggregates") # remove aggregates

9.1 Life Expectancy by Year

9.1.1 ggplot with ggplotly

p0 <- ggplot(mydata,
       aes(x = year,
           y = life_expectancy,
           color = region,
           size = population,
           frame = year)) + 
  geom_point() +
  labs(title = "Life Expectancy by Year",
       x = "Year",
       y = "Life Expectancy") +
  scale_color_discrete(name = "Region")

ggplotly(p0)

9.1.2 plotly

p1 <- plot_ly(mydata,
              x = ~year, 
              y = ~life_expectancy, 
              size = ~population, 
              fill = ~'',
              color = ~region,
              frame = ~year, 
              text = ~country, 
              hoverinfo = "text",
              type = 'scatter',
              mode = 'markers',
              showlegend = FALSE) %>% 
  layout(title = "Life Expectancy by Year",
         yaxis = list(title = "life expectancy"))

p1

Warning: Ignoring 546 observations

9.2 Life Expectancy by GDP

p2 <- mydata %>%
  # filter(!is.na(GDP)) %>% 
  # filter(is.finite(GDP)) %>%
  plot_ly(x = ~GDP, 
          y = ~life_expectancy, 
          size = ~population, 
          fill = ~'',
          color = ~region,
          frame = ~year, 
          text = ~country, 
          hoverinfo = "text",
          type = 'scatter',
          mode = 'markers',
          showlegend = FALSE) %>%
  layout(title = "Life Expectancy by GDP", 
         yaxis = list(title = "life expectancy"))


p2

Warning: Ignoring 1278 observations

9.3 Life Expectancy by Logged GDP

Using logged GDP on the x axis means that we are looking at relative, instead of absolute changes in GDP.

p2 %>% 
  layout(xaxis = list(type = "log")) 

Warning: Ignoring 1278 observations