Import and Export Data with readr

Slides in full screen     Download PDF slides

1 Task

Note

Find the solution here.

1.1 Get started with readr and the tidyverse

Before you start, make sure to install the tidyverse packages by calling

install.packages("tidyverse")

This will install readr along with other tidyverse packages.

Remember to put library(tidyverse) (or library(readr)) on top of your script to access the readr functions.

1.1.1 Write a tibble to disk

Let’s use the animals tibble from the previous task and write it into the data folder in our project.

Before writing the tibble

• Create a data sub-folder in your RStudio project (if you don’t have one yet)
  • Hint: You can do that from within RStudio by using the New Folder button in the Files pane

Now write the animals tibble into that /data sub-directory as animals.csv using a comma separator.

Check if the file was written into the correct folder.

1.2 Read data into R

Now, try to read the data set back into R using the appropriate read_* function.

Make sure that you save the table you read in in a new variable to have it available for later use.

Tip

Don’t type the path of the table to read. Instead, type only the ““, then us the tab key on your keyboard to auto-complete the path. This way you avoid typing mistakes.

1.3 For the fast ones

• Download the csv or excel file below. The files are a bit messy. Find out what is messy about the data sets (try reading them in R and see what happens, open the files in Excel or a text editor and check out the structure). Then read them into R correctly. Try the janitor::clean_names function on the data after you read it in.

• Try reading in some of your own research data. First, add them to the /data folder of your project. Then read them into R using the appropriate read_* function. Can you manage to read the data in a clean format? What are the challenges you face?