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

Code

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

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

Code

# 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
                          "SP.POP.TOTL", # population
                          "SN.ITK.DEFC.ZS"), # undernourishment
              start = 1980, 
              end = 2021, 
              extra = TRUE) 


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

4 Rename Some Variables

Code

# 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, 
                        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

Code

# head(mydata) # look at the data

mydata %>% 
  select(country, 
         region,
         year,
         GDP, 
         adult_literacy,
         life_expectancy, 
         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

Code

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

Code

p1 + facet_wrap(~region)

7 Map

Code

mymap <- mydata %>% 
  filter(year == 2015) %>% 
  plot_geo(locations = ~iso3c,
           color = ~life_expectancy,
           z = ~life_expectancy,
           text = ~country) %>% 
  layout(title = "Countries by Life Expectancy in 2015", 
         geo = list(showland = FALSE,
                    showcountries = TRUE)) %>%
  colorbar(title = 'life expectancy') 
         
mymap

8 Globe

Code

myglobe <- mydata %>% 
  filter(year == 2015) %>% 
  plot_geo(locations = ~iso3c,
           color = ~life_expectancy,
           z = ~life_expectancy,
           text = ~country) %>% 
  layout(title = "Countries by Life Expectancy in 2015", 
         geo = list(showland = FALSE,
                    showcountries = TRUE,
                    projection = list(type = 'orthographic',
                                      rotation = list(lon = -30,
                                                      lat = 10,
                                                      roll = 0)))) %>%
  colorbar(title = 'life expectancy') 


myglobe

9 Plot the Motion Charts

Code

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

9.1 Life Expectancy by Year

9.1.1 ggplot with ggplotly

Code

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

Code

p1 <- plot_ly(mydata,
              x = ~year, 
              y = ~life_expectancy, 
              size = ~population, 
              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

9.2 Life Expectancy by GDP

Code

p2 <- mydata %>%
  # filter(!is.na(GDP)) %>% 
  # filter(is.finite(GDP)) %>%
  plot_ly(x = ~GDP, 
          y = ~life_expectancy, 
          size = ~population, 
          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

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.

Code

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

10 Three Dimensional Scatterplot

Code

p2 <- plot_ly(mydata,
              x = ~year, 
              y = ~GDP,
              z = ~life_expectancy, 
              size = ~population, 
              color = ~region,
              text = ~country, 
              # hoverinfo = "text",
              # type = 'scatter',
              # mode = 'markers',
              theta = 45,
              showlegend = FALSE) %>% 
  layout(title = "Life Expectancy by Year",
         yaxis = list(title = "life expectancy"))

p2