Data Structures in R

Logical Vectors

Another common vector type you will encounter is the logical vector. Each element in a logical vector is either TRUE or FALSE (all uppercase letters). You could use the c() function to create a logical vector. For example, to create the original_member vector we could use the following syntax:

# Create vector
original_member = c(TRUE, TRUE, FALSE, TRUE, FALSE)

# View vector
original_member

[1]  TRUE  TRUE FALSE  TRUE FALSE

It is more common to create logical vectors through computation using logical operators. For example we might ask which elements of the age object are greater than 20.

# Which elements of age > 20
age > 20

[1] FALSE FALSE FALSE  TRUE FALSE

The result of using the logical operator > is a logical vector. There are several logical operators in addition to >:

# greater than or equal to 20
age >= 20

[1] FALSE  TRUE FALSE  TRUE  TRUE

# less than 20
age < 20

[1]  TRUE FALSE  TRUE FALSE FALSE

# less than or equal to 20
age <= 20

[1]  TRUE  TRUE  TRUE FALSE  TRUE

# equal to 20
age == 20

[1] FALSE  TRUE FALSE FALSE  TRUE

# not equal to 20
age != 20

[1]  TRUE FALSE  TRUE  TRUE FALSE

Note that the logical operator for “equal to” is two equals signs. This is because = (one equal sign) is what we use for assignment . If you wrote age=20 you would be assigning the value 20 to age, not asking whether the elements in age are equal to 20!

Logical elements have numeric values associated with them, namely,

• FALSE = 0; and
• TRUE = 1.

This means we can apply computations to a logical vector. For example, we could count the number of Spice Girls that were original members by summing the logical values in the original_members object. (Since all FALSE values are 0, this amounts to counting the number of TRUE values.)

sum(original_member)

[1] 3

We could also count the number of Spice Girls who are under the age of 20.

sum(age < 20)

[1] 2

Character Vectors

A third type of vector you will work with is a character vector. Character vectors (a.k.a., strings, literals) are vectors in which each element is a string of characters delimited by quotation marks. For example, the Spice names column is a character vector. We can again create this vector using the c() function.

# Create vector
spice = c("Scary", "Sporty", "Baby", "Ginger", "Posh")

# View vector
spice

[1] "Scary"  "Sporty" "Baby"   "Ginger" "Posh"  

Many computations that worked on numeric ectors do not work on character vectors. These will often return an error or unexpected result. In the syntax below, for example, we are told that the mean() function expects a numeric or logical vector, and since what we used was not either of those, the result returned was NA.

# Find mean name
mean(spice)

[1] NA

Some computations work the same way.

# Count the number of elements
length(spice)

[1] 5

Importing Data From a CSV File

In professional practice, you will often enter data into a spreadsheet and rather than typing it into R. When you save this work, many spreadsheet programs use a proprietary format for saving the information (e.g., Excel saves as a XLSX file; Google Sheets saves as a GSHEET file). These often include extraneous information (e.g., formatting) that is irrelevant to the raw data. While R includes libraries and functions that can import data in the XLSX and GSHEETS formats, it is generally easier to save or export your data to a CSV (comma separated value) file from within your spreadsheet program prior to importing it into R.

Figure 14: Export or save your data using the CSV file format. In Google Sheets, you can download the data as a CSV file.

Once your data are saved as a CSV file, it can be easily imported into R. To do so,

• Click the Import Dataset button under the Environment tab in RStudio and choose “From Text (readr).”
• Click the Browse button and navigate to where you saved your CSV file, select the file, and click “Open.”

This will open a preview of your data. Check to be sure the variable names are correct and that the data looks like what you entered into your spreadsheet.

• Change the text in the name box to correspond to the object name you want to use in R.
• Finally, click the Import button to import your data.

After importing the data you should see the object in your global environment.

Figure 15: Import data saved in a CSV file into R.

Even though you used the Import button—a point-and-click feature in RStudio—to import the data, behind the scenes, syntax was generated that was actually used to import the data into R. When we selected “From Text (readr),” the read_csv() function from the readr package was used to import the data. You can see the syntax generated in the Code Preview window after you selected your CSV file.

Figure 16: Code Preview window showing the syntax to import the spice_girls.csv data.

In the first line of syntax, the readr package is loaded using the library() function. The data is imported in the second line of syntax and assigned to an object, in this case spice_girls. The read_csv() function includes the path name for the CSV file on your computer.⁹ The View() function in the third line of syntax simply opens the spice_girls object in a view tab in RStudio.

It is a good idea to copy the first two lines of syntax from the Code Preview window into your script file. It will be faster to import the data in the future by running it from a script file rather than trying to reproduce all the steps to import your data. The third line of syntax, using View(), is not essential to importing your data, and there are better ways to actually “see” the contents of the data object.