R Markdown Using the GapMinder Data

This page will demonstrate some of the basics of using R Markdown. R Markdown is a user-friendly/powerful tool that will allow you to professionally display your analysis on the world wide web with little or no HTML experience.

Before we start, you can find the Gapminder Data here.

Let's get started:

• Load in the Gapminder data found in the above link.

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

• Understand the structure of your data.

str(gapData)

## '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 ...

This is very informative. It tells us how many observation we are working with and what variables. From this simple command, we know there are 1704 observations and 6 variables: country, year, pop, continent, lifeExp, gdpPercap. We also now know to what class these objects belong.

• Peek at the data and make sure it is good to go.
  

head(gapData)

##       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
## 4 Afghanistan 1967 11537966      Asia   34.02     836.2
## 5 Afghanistan 1972 13079460      Asia   36.09     740.0
## 6 Afghanistan 1977 14880372      Asia   38.44     786.1

tail(gapData)

##       country year      pop continent lifeExp gdpPercap
## 1699 Zimbabwe 1982  7636524    Africa   60.36     788.9
## 1700 Zimbabwe 1987  9216418    Africa   62.35     706.2
## 1701 Zimbabwe 1992 10704340    Africa   60.38     693.4
## 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

• Get a an overview summary of the gapminder data.

summary(gapData)

##         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  
## 

• You can also display plots to describe your data. Below is a scatterplot of the population in Afghanistan versus year with a linear trend line included. It is interesting to note the drop in population around 1980 as this corresponds with the start of the Soviet War in Afghanistan.
  

library(lattice)
xyplot(pop ~ year, gapData, subset = country == "Afghanistan", main = "Scatterplot of Population vs Year for Afghanistan", 
    xlab = "Year", ylab = "Population", type = c("p", "r"))