We're now going to work with some actual data. The data we'll be using is the gapminder dataset which contains information on life expectancy, population, and GDP over time for 142 countries. You should have downloaded this data and placed it on your desktop for now.
To load data, R needs to know where to find it. We can use the
getwd() function to figure out what our current working directory is.
To view the files in the current working directory, use
If we want to move to a different working directory, use
setwd("~/Desktop") ## ~/ is a shortcut for the "home" directory
However beware that if you write a script that uses
setwd(), others may not be able to run that script on their computers if they have organized their directories differently from you.
For now, let's set the working directory back to our project directory since that is where the data is.
To load the data into R, we use a function called
gapminder <- read.csv("gapminder.csv")
A very useful function for examining the first few rows of your data is
country continent year lifeExp pop gdpPercap 1 Afghanistan Asia 1952 28.801 8425333 779.4453 2 Afghanistan Asia 1957 30.332 9240934 820.8530 3 Afghanistan Asia 1962 31.997 10267083 853.1007 4 Afghanistan Asia 1967 34.020 11537966 836.1971 5 Afghanistan Asia 1972 36.088 13079460 739.9811 6 Afghanistan Asia 1977 38.438 14880372 786.1134
Many of the functions we used to examine vectors can also be applied to data frames.
Based on the output of
str(gapminder), can you answer the following questions?