A simple solution to ditch the question "what's the path of my data" when importing data to R

Sebastian Sauer / 4 mins read
2022-05-11

Load packages

library(tidyverse)

Motivation: Get your data into R, different ways

Importing data into R can cause headaches for newbies. For some, the concept of relative and absolute paths is new. That’s why I compiled here some recommendations on how to important data into R and on how to ditch the “what’s my path” problem.

Pragmatic goal

If you are in a hurry, just pick one way, maybe the first approach. The pragmatic goal is to get your data into R. The rest is mere detail 😄.

Approach 1: Quick and easy

This approach is useful for beginners, as it is quick and simple. This approach assumes the data file (as csv format) is located somewhere on your disk (and you know where to find it).

  1. In RStudio, click on the pane Environment and then on Import Dataset and then on From Text (readr).
  2. In the menu, click on Browse to search the file on your computer.
  3. Finally, click on Import to import the CSV file into R.

Note that the menu offers some options such as skipping the first few lines, changing the name of the R tibble, or adjusting the CSV file details (such as different delimiters eg “;” used in German Excel files).

See this picture for an illustration of the respective RStudio menu.

What RStudio does is to get the absolute path to your data file and paste it into read_csv().

For example, an abolute path on my machine to a data file is this: /Users/sebastiansaueruser/datasets/tips.csv.

An absolute path is great, because it’s simple, but the downside is this path will not work on your machine. As long as you are not concerned with other machines, you can safely work with absolute path. If you would like to make your work portable, consider understanding relativ paths. Read on.

Approach 2: Start an RStudio project

That’s an approach I generally recommend.

  1. Start an RStudio project.
  2. Put your code files and your data files in this very folder.
  3. Import the data without specifying any paths, eg., d <- read.csv("mydata.csv)

RStudio will consider the RStudio project folder which you specified as the “root” of any (relative) paths you specify. As a consequence, if you put your data in this path, you will not need to specify any path - just give the file name to read_csv().

In case the data are stored in a sub folder in your project folder, you will need to specify this subfolder.

An helpful approach is provided by the package {{here}}. As an example, let’s assume the subfolder where your data file resides is called data, and the data file is named mydata.csv:

d <- read_csv(here("data", "mydata.csv"))

This command essentially tells R “Hey R, look here, that is, in my current project folder, then look in the subfolder”data” and then take the file “mydata.csv”.

In case you are unsure where you current project folder (also called working directory) is, try

getwd()
#> [1] "/Users/sebastiansaueruser/Google Drive/research/Publikationen/blog_ses/sesa-blog/content/post/2020-10-19-a-simple-solution-to-ditch-the-question-what-s-the-path-of-my-data-when-importing-data-to-r"

Approach 3: Import from an online source

That’s also simple and convenient – as long as someone has put the data online.

  1. Identify the URL of your data set, and copy it.
  2. Open up an R/Rmd script file and paste the URL to your import function, e.g,. d <- read.csv("https://vincentarelbundock.github.io/Rdatasets/csv/reshape2/tips.csv")

Note that the URL provides an absolute URL that will work irrespective from which machine you access it. Handy.

Note: In case you download a zip file, remember to unzip it before accessing the files.

Approach 4: Learn what a path means

This is a more thorough approach, but one which will pay out in the long run.

It is helpful to understand the difference between an abolute path and a relative path. There is a ton of tutorials on that in the web, e.g., checkout this one or this one.

Here’s an approach for absolute file paths:

  1. Identify the location (path) of your data file on your computer.
  2. Copy it to the clipboard (depends on your operating system)^[Mac: Click right, hold Option key, select “copy path name”. Windows: Click right, hold shift, select “copy path name”. Linux users will know in the first place how to proceed 🥳).
  3. Paste it into your import function, e.g, `d <- read.csv(“/Users/sebastiansaueruser/datasets/tips.csv”)

Example time – dataset tips

tips <- read.csv("https://vincentarelbundock.github.io/Rdatasets/csv/reshape2/tips.csv")

Exercises:

  1. Import from the URL.
  2. Download the file to your PC and import it via the absolute path.
  3. Download the file to your PC, move it to your current project folder, and import it via the relativ path.