SLCU R Course

Reading data into R - solution to exercise

Exercise:

Create a script that does the following:

• Load the tidyverse package (using library())
• Set the working directory to the module01_data_and_files folder (using setwd())
• Read the CSV file that you previously exported from the data directory (using read_csv())

The full solution is given here, and an explanation follows below:

# First load all the libraries needed for this script
library(tidyverse)

# Check what the working directory is at the moment
#  this is what "folder" R is working from at the moment
getwd()

# Set working directory to the course materials folder
# Change this to be suitable to your situation
setwd("/home/slcu.user/Desktop/slcu_r_course/module01_data_and_files")

# Read the CSV file and store the table in an object called "my_surveys"
## In this case, the CSV file is located in the "data" folder
my_surveys <- read_csv("data/dataset_tidy.csv")

# View the loaded table
View(my_surveys)

Exercise explanation

When you start an analysis script, it’s always best to load all the necessary packages right at the top of your code - this way anyone using this code will know what packages they need installed in order for the code to work:

library(tidyverse)

Then, it’s good to see what is the working directory that R is using at the moment

getwd()

The output of that command will vary depending on your operating system and username.

For example, for a user called “slcu.user”, this might be the default working directory on a Mac:

/Users/slcu.user/

And this one on a Windows:

C:/Users/slcu.user/Documents

When doing an analysis, it’s best to set the working directory to be in the folder where your project is stored in.

Let’s say that the course materials are on your Desktop. So, schematically, this is where your data file is located:

Desktop
  |
  |_slcu_r_course
      |
      |_module01_data_and_files
          |
          |_data
             |
             |_dataset_tidy.csv

Therefore, it’s best if you first change your working directory to be in module01_data_and_files.

For our previous example this might be something like:

# On a Mac
setwd("/Users/slcu.user/Desktop/slcu_r_course/module01_data_and_files")

# On a Windows
setwd("C:/Users/slcu.user/Desktop/slcu_r_course/module01_data_and_files")

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Now that you’ve set the working directory, you can read the data in by using the function called read_csv():

surveys <- read_csv("data/dataset_tidy.csv")

Note: there is another function called read.csv(), which is the usual default function to read CSV files, and is very similar to read_csv(). During the workshops we will use read_csv() as it is more convenient.

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