A collection of development indicators

Anne Sophie Gill https://www.skemagloballab.io/gillAnneSophie.html (SKEMA Global Lab in AI)https://skemagloballab.io , Thierry Warin https://www.nuance-r.com/principalInvestigator.html (SKEMA Business School (Raleigh, NC))https://www.skemagloballab.io

Using the SKEMA Quantum Studio(Warin 2019) framework, we will teach you how to use the WDI package

The World Development Indicators is a compilation of relevant, high-quality, and internationally comparable statistics about global development and the fight against poverty. The database contains 1,600 time series indicators for 217 economies and more than 40 country groups, with data for many indicators going back more than 50 years.

How to use the WDI package

Step One : Retrieve data

# Load the WDI library 

# Search for an indicator
WDIdata <- WDIsearch("emission")

Step Two : Extract the data

# Search for data on CO2 emissions in 2007 around the world
pollution <- WDI(indicator="EN.ATM.CO2E.KT", start = 2007, end = 2007)

# Data Wrangling :

  names(pollution)[names(pollution) == "EN.ATM.CO2E.KT"] <- "pollution_qty" #rename column
  names(pollution)[names(pollution) == "country"] <- "ADMIN" #rename column

  # gsub function to rename ADMIN regions in "pollution" data frame to match "world@data" ADMIN regions

    pollution$ADMIN <- gsub("United States", "United States of America", pollution$ADMIN)
    pollution$ADMIN <- gsub("Russian Federation", "Russia", pollution$ADMIN)
    pollution$ADMIN <- gsub("Egypt, Arab Rep.", "Egypt", pollution$ADMIN)
    pollution$ADMIN <- gsub("Iran, Islamic Rep.", "Iran", pollution$ADMIN)
    pollution$ADMIN <- gsub("Venezuela, RB", "Venezuela", pollution$ADMIN)
    pollution$ADMIN <- gsub("Congo, Rep.", "Republic of the Congo", pollution$ADMIN)
    pollution$ADMIN <- gsub("Congo, Dem. Rep.", "Democratic Republic of the Congo", pollution$ADMIN)
    pollution$ADMIN <- gsub("Yemen, Rep.", "Yemen", pollution$ADMIN)
    pollution$ADMIN <- gsub("Tanzania", "United Republic of Tanzania", pollution$ADMIN)
    pollution$ADMIN <- gsub("Syrian Arab Republic", "Syria", pollution$ADMIN)
    pollution$ADMIN <- gsub("Korea, Dem. People’s Rep.", "North Korea", pollution$ADMIN)
    pollution$ADMIN <- gsub("Korea, Rep.", "South Korea", pollution$ADMIN)
    pollution$ADMIN <- gsub("Kyrgyz Republic", "Kyrgyzstan", pollution$ADMIN)
    pollution$ADMIN <- gsub("Slovak Republic", "Slovakia", pollution$ADMIN)
    pollution$ADMIN <- gsub("Czech Republic", "Czechia", pollution$ADMIN)
    pollution$ADMIN <- gsub("Serbia", "Republic of Serbia", pollution$ADMIN)
    pollution$ADMIN <- gsub("Timor-Leste", "East Timor", pollution$ADMIN)
    pollution$ADMIN <- gsub("Bahamas, The", "The Bahamas", pollution$ADMIN)

Step 3 : Visualize your data

# Load the following libraries

# Open your shp file with the readOGR function

world <- readOGR('world.shp')

OGR data source with driver: ESRI Shapefile 
Source: "/home/gilla/mondo/skemalab/blog/_posts/2020-02-21-wdi/world.shp", layer: "world"
with 241 features
It has 94 fields
Integer64 fields read as strings:  POP_EST NE_ID 

Shapefile link Natural Earth

# Use "Left join" function to combine your two data frames

world@data <- left_join(world@data, pollution, by = "ADMIN")

# Create labels

world@data$ADMIN <- as.character(world@data$ADMIN)

labels <- sprintf("<strong>%s</strong><br/>%g", world@data$ADMIN, world@data$pollution_qty) %>% lapply(htmltools::HTML)

# Determinate the intervalls that will be shown in the map legend
bins <- c(0, 100000, 200000, 300000, 400000, 500000, Inf)

# Choose a color scheme for your map

colors <- colorBin("BuPu", domain = world@data$pollution_qty, bins = bins)

# Plot the data using leaflet

leaflet(world) %>%
  addProviderTiles(providers$CartoDB.Positron) %>%
  addLegend(pal = colors, values = world@data$pollution_qty, opacity = 0.7, title = NULL, position = "bottomleft") %>%
  addPolygons(fillColor = ~colors(world@data$pollution_qty),
              weight = 2,
              opacity = 1,
              color = "white",
              dashArray = 1,
              fillOpacity = 0.8,
              highlight = highlightOptions(weight = 2,
                                           color = "black",
                                           dashArray = 1,
                                           fillOpacity = 0.7,
                                           bringToFront = TRUE),
              label = labels