data
Importing data into R
Intro
Most of the data in this course will come from packages. But most of the data in the world is not in a package. How can I read this kind of data into R?
There’s ways to do this in RStudio through drop-down menus but I call that the “no learning” approach.
To read data into R you need to know two things:
What kind of file is it? What is its extension?
Where is it? What is its file path?
What kind of file is it?
There are many different types of files out there. You can tell what type of file a dataset is by looking at its extension - the bit in the file name that comes after the period. Some common file types/extensions are .csv, .dta, .xlsx, etc.
R has different functions you can use to import different types of files. For a .csv file you would use read_csv() or read.csv(). For a .dta you would use read_dta() from the {haven} package. For an .xlsx file you would use read_excel() from {readxl}. If you’re not sure, google what function to use for a particular data file.
Where is it?
This part is tricky for people who don’t have a lot of experience working with computers. But, in short: every file that is on your computer has a “path” or “address” that uniquely identifies it on your computer. This address changes with your file’s location; if you moved the file for whatever reason, the file path would change. You need the file path in order to tell R where to look at your data.
How to find it? Operating systems are always changing so in general the best thing is to google how to find a file’s filepath and then remember it. But the following seems to have worked on Mac for a long time now:
find the file in your Finder right-click and select “get info”
at the top left, under “General”, you’ll see the file path next to “Where:”
right-click that path and select “copy as Pathname”
And on Windows you can do this.
Code to import data into R might look like this:
df = read_csv("/Users/juan/Dropbox/teaching/co2-data/owid-co2-data.csv")How to do this better
For working on longer-term projects, it is more convenient to use relative file paths in conjunction with something like R projects (.Rproj) + the {here} library.