Use data from Gapminder in R

gapmind-r.r

# ------------------------------------------------------------------------------
# PREPARATION
# First download two Excel files from Gapminder, 
# I am here using the WHO alcohol consumption data from:
# http://spreadsheets.google.com/pub?key=0AgogXXPMARyldGJqTDRfNHBWODJMRWlZaVhNclhNZXc&output=xls
# And the World Bank GDP data from:
# http://spreadsheets.google.com/pub?key=0ArfEDsV3bBwCdHh3d1FPOVg1WXM3V2huRWc2cjM3TkE&output=xls
#
# Note that Gapminder uses '..' in cells to mark missing values
# Do a search and replace in Excel, replacing '..' with 'NA' (no quotes)

# ------------------------------------------------------------------------------
# IMPORT THE DATA
library("gdata")
alcohol <- read.xls(xls='path/to/alcoholfile.xls') # insert correct path
gdp <- read.xls(xls='path/to/gdpfile.xls') # insert correct path

# delete extra columns from the gdp dataframe (we only want to use 2005)
gdp <- gdp[,c('X','X2005')]

# merge the dataframes, joining by the country name
m <- merge(gdp, alcohol, by.x='X', by.y='X')

# relabel the columns
names(m)[names(m)=='X'] <- 'Country'
names(m)[names(m)=='X2005.x'] <- 'GDP'
names(m)[names(m)=='X2005.y'] <- 'alcohol'

# ------------------------------------------------------------------------------
# MANIPULATE AND VISUALIZE THE DATA
# peep at the top of the new dataframe
head(m)
summary(m) # summarize the three variables
plot(m) # and visualize their distribution

# plot the scatter of alcohol against the log(GDP) plus add smoothing
library("ggplot2")
p <- ggplot(m, aes(x=log(GDP), y=alcohol))
p <- p + geom_point()
p <- p + geom_smooth()
p