Chapter 2 Getting started and loading data
What you want to do:
Install R Studio and R
Load data saved in a Microsoft Excel spreadsheet (
.xlsx), comma separated values file (.csv), SPSS file (.sav), or Google Sheet.
2.1 Install R Studio and R
First, you’ll need to download the latest versions of R Studio and R. Although we’ll exclusively be using R Studio, R Studio needs to have R installed, as well. You can find links here:
Download R Studio: https://www.rstudio.com/products/rstudio/download/#download
Download R: https://cran.r-project.org/
Don’t worry; you won’t mess anything up if you download (or even install!) the wrong file. Once you’ve installed both, we can get to work doing some data analysis.
2.2 Loading data
You might be thinking that an Excel file is the first that we would load, but there happens to be a format which you can open and edit in Excel that is even easier to use between Excel and R and among Excel, R, as well as SPSS and other statistical software, like MPlus, and even other programming languages, like Python. That format is CSV, or a comma-separated-values file.
The CSV file is useful because you can open it with Excel and save Excel files as CSV files. Additionally, and as its name indicates, a CSV file is rows of a spreadsheet with the columns separated by commas, so you can view it in a text editor, like TextEdit for Macintosh, as well. Not surprisingly, Google Sheets easily converts CSV files into a Sheet, and also easily saves Sheets as CSV files.
For these reasons, we start with reading CSV files.
2.2.1 Loading CSV files
The easiest way to read a CSV file is with the function read_csv(). It is from a package, or an add-on to R, called readr, but we are going to install readr as well as other packages that work well together as part of a group of packages named the tidyverse. To install all of the packages in the tidyverse, use the following command:
install.packages("tidyverse")You can also navigate to the Packages pane, and then click “Install”, which will work the same as the line of code above. Note, here there is a way to install a package using code or part of the R Studio interface. Usually, using code is a bit quicker, but sometimes (as we will see in a moment) using the interface can be very useful and sometimes complimentary to use of code.
We have now installed the tidyverse. We only have to install a package once, but to use it, we have to load it each time we start a new R session. We will discuss what an R session is later on; for now, know that we have to load a package to use it. We do that with library():
library(tidyverse)And now we can load a file. We are going to call the data student_responses:
Since we loaded the data, we now want to look at it. We can just type its name, and a summary of the data will print:
This was a minor task, but if you loaded a file and printed it, give yourself a pat on the back. It is no joke to say that many times simply being able to load a file into new software. We are now well on our way to carrying out analysis of our data.
2.2.2 Loading Excel files
We will now do the same with an Excel file. You might be thinking that you can simply open the file in Excel and then save it as a CSV. This is generally a good idea. At the same time, sometimes you may need to directly read a file from Excel, and it is easy enough to do this.
The package that we use, readxl, is not a part of the tidyverse, so we will have to install it first (remember, we only need to do this once), and then load it using library(readxl). Note that the command to install readxl is grayed-out below: The # symbol before install.packages("readxl") indicates that this line should be treated as a comment and not actually run, like the lines of code that are not grayed-out. It is here just as a reminder that the package needs to be installed if it is not already.
Once we have installed readxl, we have to load it (just like tidyverse):
# install.packages("readxl")
library(readxl)We can then use read_xlsx() in the same way as read_csv():
x <- read_xls()
x <- read_xlsx()And we can print the data we loaded in the same way:
2.2.3 Loading SAV files
The same factors that apply to reading Excel files apply to reading SAV files (from SPSS). First, install the package haven, load it, and the use the function read_sav():
# install.packages("haven")
library(haven)
x <- read_sav("")2.2.4 Google Sheets
Finally, it can sometimes be useful to load a file directly from Google Sheets, and this can be done using the Google Sheets package.
# install.packages("googlesheets")
library(googlesheets)When you run the command below, a link to authenticate with your Google account will open in your browser.
my_sheets <- gs_ls()You can then simply use the gs_title() function in conjunction with the gs_read() function:
df <- gs_title('title')
df <- gs_read(df)2.3 Conclusion
Great job! You’ve made it through the first chapter. For more on reading files, we will discuss how to use functions to read every file in a folder (or, to write many different files to a folder).