Load and Summarize Gapminder Data Set

Load the gapminder data from gapminderDataFiveYear.txt into memory

gDat <- read.delim("gapminderDataFiveYear.txt")

Number of observations:

nrow(gDat)

## [1] 1704

Variables:

names(gDat)

## [1] "country"   "year"      "pop"       "continent" "lifeExp"   "gdpPercap"

Some statistical summary of the data set:

summary(gDat)

##         country          year           pop               continent  
##  Afghanistan:  12   Min.   :1952   Min.   :6.001e+04   Africa  :624  
##  Albania    :  12   1st Qu.:1966   1st Qu.:2.794e+06   Americas:300  
##  Algeria    :  12   Median :1980   Median :7.024e+06   Asia    :396  
##  Angola     :  12   Mean   :1980   Mean   :2.960e+07   Europe  :360  
##  Argentina  :  12   3rd Qu.:1993   3rd Qu.:1.959e+07   Oceania : 24  
##  Australia  :  12   Max.   :2007   Max.   :1.319e+09                 
##  (Other)    :1632                                                    
##     lifeExp        gdpPercap       
##  Min.   :23.60   Min.   :   241.2  
##  1st Qu.:48.20   1st Qu.:  1202.1  
##  Median :60.71   Median :  3531.8  
##  Mean   :59.47   Mean   :  7215.3  
##  3rd Qu.:70.85   3rd Qu.:  9325.5  
##  Max.   :82.60   Max.   :113523.1  
## 

Plot the corelation between lifeExp and gdpPercap in China after 1980

library(lattice)

## Warning: package 'lattice' was built under R version 3.0.2

xyplot(lifeExp ~ gdpPercap, data = gDat,subset = (country == "China" & year >=1980))