Matt Gingerich
Loading a Gapminder data set and printing basic info:
gDat <- read.delim("gapminderDataFiveYear.txt")
print(paste("Number of observations: ", nrow(gDat)))
## [1] "Number of observations: 1704"
colnames(gDat)
## [1] "country" "year" "pop" "continent" "lifeExp" "gdpPercap"
summary(gDat)
## country year pop continent
## Afghanistan: 12 Min. :1952 Min. :6.00e+04 Africa :624
## Albania : 12 1st Qu.:1966 1st Qu.:2.79e+06 Americas:300
## Algeria : 12 Median :1980 Median :7.02e+06 Asia :396
## Angola : 12 Mean :1980 Mean :2.96e+07 Europe :360
## Argentina : 12 3rd Qu.:1993 3rd Qu.:1.96e+07 Oceania : 24
## Australia : 12 Max. :2007 Max. :1.32e+09
## (Other) :1632
## lifeExp gdpPercap
## Min. :23.6 Min. : 241
## 1st Qu.:48.2 1st Qu.: 1202
## Median :60.7 Median : 3532
## Mean :59.5 Mean : 7215
## 3rd Qu.:70.8 3rd Qu.: 9325
## Max. :82.6 Max. :113523
##
Plotting per capita GDP of South Korea (Korea, Rep.) and North Korea (Korea, Dem. Rep.) from 1952 to 2007:
library(lattice)
xyplot(gdpPercap ~ year | country, gDat, subset = (country == "Korea, Rep." |
country == "Korea, Dem. Rep."), type = c("p", "smooth"))