Note: I had written post about 3 years back on World Bank Data Analysis using gVisMotionCharts see Analyzing World Bank data with WDI, googleVis Motion Charts about 3 years back,similar to the famed Hans Rosling’s Gapminder charts. But the motion charts stopped working after some time ago. I have always been wanting to fix this and I now got to actually doing it. The issue was 2 of the WDI indicators were changed. After I fixed this, I was able to host using github.io pages. Also note if you are using Chrome make sure you enable flash player
Please check out the 2 motions charts with World Bank data 1. World Bank Chart 1 New 2. World Bank Chart 2 New
##1. Analyzing World Bank data with WDI and googleVis Recently I was surfing the web, when I came across a real cool post New R package to access World Bank data, by Markus Gesmann on using googleVis and motion charts with World Bank Data. The post also introduced me to Hans Rosling, Professor of Sweden’s Karolinska Institute. Hans Rosling, the creator of the famous Gapminder chart, the “Heath and Wealth of Nations” displays global trends through animated charts (do take a look). As they say in Hans Rosling’s hands, data sings. Take a look at some of his Ted talks for e.g. Hans Rosling:New insights on poverty. Rosling developed the breakthrough software behind his visualizations in the Gapminder. The free software - which can be loaded with any data - was purchased by Google in March 2007.
In this post I recreate some of the Gapminder charts with the help of R packages WDI and googleVis. The WDI package provides a set of really useful functions to get the data based on the World Bank Data indicator.googleVis provides motion charts in which the data is animated.
##2. World bank Chart 1
library(WDI)
library(ggplot2)
library(googleVis)## Creating a generic function for 'toJSON' from package 'jsonlite' in package 'googleVis'##
## Welcome to googleVis version 0.6.6
##
## Please read Google's Terms of Use
## before you start using the package:
## https://developers.google.com/terms/
##
## Note, the plot method of googleVis will by default use
## the standard browser to display its output.
##
## See the googleVis package vignettes for more details,
## or visit https://github.com/mages/googleVis.
##
## To suppress this message use:
## suppressPackageStartupMessages(library(googleVis))library(plyr)##3. Get the data from 1960 to 2019 for the following
# World population total
population = WDI(indicator='SP.POP.TOTL', country="all",start=1960, end=2019)
# GDP in US $
gdp= WDI(indicator='NY.GDP.MKTP.CD', country="all",start=1960, end=2019)
# Life expectancy at birth (Years)
lifeExpectancy= WDI(indicator='SP.DYN.LE00.IN', country="all",start=1960, end=2019)
# GDP Per capita
income = WDI(indicator='NY.GDP.PCAP.PP.CD', country="all",start=1960, end=2019)
# Fertility rate (births per woman)
fertility = WDI(indicator='SP.DYN.TFRT.IN', country="all",start=1960, end=2019)
# Poverty head count
#poverty= WDI(indicator='SI.POV.2DAY', country="all",start=1960, end=2016)
poverty= WDI(indicator='SI.POV.NAHC', country="all",start=1960, end=2019)names(population)[3]="Total population"
names(lifeExpectancy)[3]="Life Expectancy (Years)"
names(gdp)[3]="GDP (US$)"
names(income)[3]="GDP per capita income"
names(fertility)[3]="Fertility (Births per woman)"
names(poverty)[3]="Poverty headcount ratio"Join the individual data frames to one large wide data frame with all the indicators for the countries
j1 <- join(population, gdp)## Joining by: iso2c, country, yearj2 <- join(j1,lifeExpectancy)## Joining by: iso2c, country, yearj3 <- join(j2,income)## Joining by: iso2c, country, yearj4 <- join(j3,poverty)## Joining by: iso2c, country, yearwbData <- join(j4,fertility)## Joining by: iso2c, country, yearUse WDI_data to get the list of indicators and the countries. Join the countries and region
#This returns list of 2 matrixes
wdi_data =WDI_data
# The 1st matrix is the list is the set of all World Bank Indicators
indicators=wdi_data[[1]]
# The 2nd matrix gives the set of countries and regions
countries=wdi_data[[2]]
df = as.data.frame(countries)
aa <- df$region != "Aggregates"
# Remove the aggregates
countries_df <- df[aa,]
# Subset from the development data only those corresponding to the countries
bb = subset(wbData, country %in% countries_df$country)
cc = join(bb,countries_df)## Joining by: iso2c, countrydd = complete.cases(cc)
developmentDF = cc[dd,]gg<- gvisMotionChart(cc,
idvar = "country",
timevar = "year",
xvar = "GDP (US$)",
yvar = "Life Expectancy (Years)",
sizevar ="Total population",
colorvar = "region")
plot(gg)## starting httpd help server ... donecat(gg$html$chart, file="WorldBank_chart1.html")# World population
population = WDI(indicator='SP.POP.TOTL', country="all",start=1960, end=2019)
# GDP in US $
gdp= WDI(indicator='NY.GDP.MKTP.CD', country="all",start=1960, end=2019)
# Access to electricity (% population)
elecAccess= WDI(indicator='EG.ELC.ACCS.ZS', country="all",start=1960, end=2019)
# Electric power consumption Kwh per capita
elecConsumption= WDI(indicator='EG.USE.ELEC.KH.PC', country="all",start=1960, end=2019)
#CO2 emissions
co2Emissions= WDI(indicator='EN.ATM.CO2E.KT', country="all",start=1960, end=2019)
# Access to sanitation (% population)
#sanitationAccess= WDI(indicator='SH.STA.ACSN', country="all",start=1960, end=2019)
sanitationAccess= WDI(indicator='SH.STA.BASS.ZS', country="all",start=1960, end=2019)names(population)[3]="Total population"
names(gdp)[3]="GDP US($)"
names(elecAccess)[3]="Access to Electricity (% popn)"
names(elecConsumption)[3]="Electric power consumption (KWH per capita)"
names(co2Emissions)[3]="CO2 emisions"
names(sanitationAccess)[3]="Access to sanitation(% popn)"Join the individual data frames to one large wide data frame with all the indicators for the countries
j1 <- join(population, gdp)## Joining by: iso2c, country, yearj2 <- join(j1,elecAccess)## Joining by: iso2c, country, yearj3 <- join(j2,elecConsumption)## Joining by: iso2c, country, yearj4 <- join(j3,co2Emissions)## Joining by: iso2c, country, yearwbData1 <- join(j4,sanitationAccess)## Joining by: iso2c, country, yearUse WDI_data to get the list of indicators and the countries. Join the countries and region
#This returns list of 2 matrixes
wdi_data =WDI_data
# The 1st matrix is the list is the set of all World Bank Indicators
indicators=wdi_data[[1]]
# The 2nd matrix gives the set of countries and regions
countries=wdi_data[[2]]
df = as.data.frame(countries)
aa <- df$region != "Aggregates"
# Remove the aggregates
countries_df <- df[aa,]
# Subset from the development data only those corresponding to the countries
ee = subset(wbData1, country %in% countries_df$country)
ff = join(ee,countries_df)## Joining by: iso2c, countrygg1<- gvisMotionChart(ff,
idvar = "country",
timevar = "year",
xvar = "GDP US($)",
yvar = "Access to Electricity (% popn)",
sizevar ="Total population",
colorvar = "region")
plot(gg1)
cat(gg1$html$chart, file="WorldBank_chart2.html")print(getwd())## [1] "C:/Users/compu lab/AppData/Local/Temp/Rar$DI70.224"