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R for reproducible scientific analysis
Reading data
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Learning Objectives {.objectives}

  • To be able to read in regular data into R

Reading in data

Now that we've obtained the gapminder dataset, we want to load it into R. Before reading in data, it's a good idea to have a look at its structure. Let's take a look using our newly-learned shell skills:

cd gapminder/data/ # navigate to the data directory of the project folder
head gapminder-FiveYearData.csv 

country,year,pop,continent,lifeExp,gdpPercap
Afghanistan,1952,8425333,Asia,28.801,779.4453145
Afghanistan,1957,9240934,Asia,30.332,820.8530296
Afghanistan,1962,10267083,Asia,31.997,853.10071
Afghanistan,1967,11537966,Asia,34.02,836.1971382
Afghanistan,1972,13079460,Asia,36.088,739.9811058
Afghanistan,1977,14880372,Asia,38.438,786.11336
Afghanistan,1982,12881816,Asia,39.854,978.0114388
Afghanistan,1987,13867957,Asia,40.822,852.3959448
Afghanistan,1992,16317921,Asia,41.674,649.3413952

As its file extension would suggest, the file contains comma-separated values, and seems to contain a header row.

We can use read.table to read this into R

gapminder <- read.table(
  file="data/gapminder-FiveYearData.csv",
  header=TRUE, sep=","
)
head(gapminder)

      country year      pop continent lifeExp gdpPercap
1 Afghanistan 1952  8425333      Asia  28.801  779.4453
2 Afghanistan 1957  9240934      Asia  30.332  820.8530
3 Afghanistan 1962 10267083      Asia  31.997  853.1007
4 Afghanistan 1967 11537966      Asia  34.020  836.1971
5 Afghanistan 1972 13079460      Asia  36.088  739.9811
6 Afghanistan 1977 14880372      Asia  38.438  786.1134

Because we know the structure of the data, we're able to specify the appropriate arguments to read.table. Without these arguments, read.table will do its best to do something sensible, but it is always more reliable to explicitly tell read.table the structure of the data. read.csv function provides a convenient shortcut for loading in CSV files.

Miscellaneous Tips {.callout}

  1. Another type of file you might encounter are tab-separated format. To specify a tab as a separator, use "\t".

  2. You can also read in files from the internet by replacing the file paths with a web address.

  3. You can read directly from excel spreadsheets without converting them to plain text by using the xlsx package.

To make sure our analysis is reproducible, we should put the code into a script file so we can come back to it later.

Challenge {.challenge}

Go to file -> new file -> R script, and write an R script to load in the gapminder dataset. Put it in the scripts/ directory and add it to version control.

Run the script using the source function, using the file path as its argument (or by pressing the "source" button in RStudio).