STAT545A hw02: Exploring gapminder data

When you need to explore “big data”, it is extremely important to:

1. create graphical visualisations;

2. generate basic summary statistics.

I will use R to accomplish both of these tasks using the gapminder data (available here).

TO BEGIN:

• Save the gapminder data as a .txt file, then read the file into R using the read.delim function:

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

• Ensure the data was read in correctly using a few key functions:

head(gDat, n = 3)  #first three rows of gDat

##       country year      pop continent lifeExp gdpPercap
## 1 Afghanistan 1952  8425333      Asia   28.80     779.4
## 2 Afghanistan 1957  9240934      Asia   30.33     820.9
## 3 Afghanistan 1962 10267083      Asia   32.00     853.1

tail(gDat, n = 3)  #last three rows of gDat

##       country year      pop continent lifeExp gdpPercap
## 1702 Zimbabwe 1997 11404948    Africa   46.81     792.4
## 1703 Zimbabwe 2002 11926563    Africa   39.99     672.0
## 1704 Zimbabwe 2007 12311143    Africa   43.49     469.7

str(gDat)  #data structure, one-line summary for each component within gDat

## 'data.frame':    1704 obs. of  6 variables:
##  $ country  : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ year     : int  1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
##  $ pop      : num  8425333 9240934 10267083 11537966 13079460 ...
##  $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ lifeExp  : num  28.8 30.3 32 34 36.1 ...
##  $ gdpPercap: num  779 821 853 836 740 ...

• Generate summary statistics:

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  
## 

• Use the lattice package to visualise the data:

library(lattice)
xyplot(lifeExp ~ year | continent, gDat)

xyplot(lifeExp ~ gdpPercap, gDat, subset = year == 2002, group = continent, 
    auto.key = TRUE)

This is only a taste of the possible graphical visualisations you can perform, but already we can observe a large spread in the data, outliers, and trends that can be investigated further!