5.2: Creating or Modifying Variables Using Mutate()

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Often we will want to either create a new variable based on an existing variable, or modify the value of an existing variable. Within the tidyverse, we do this using a function called mutate(). Let’s start with a toy example by creating a data frame containing a single variable.

toy_df <- data.frame(x = c(1,2,3,4))
glimpse(toy_df)

## Observations: 4
## Variables: 1
## $ x <dbl> 1, 2, 3, 4

Let’s say that we wanted to create a new variable called y that would contain the value of x multiplied by 10. We could do this using mutate() and then assign the result back to the same data frame:

toy_df <- toy_df %>%
  # create a new variable called y that contains x*10
  mutate(y = x*10)
glimpse(toy_df)

## Observations: 4
## Variables: 2
## $ x <dbl> 1, 2, 3, 4
## $ y <dbl> 10, 20, 30, 40

We could also overwrite a variable with a new value:

toy_df2 <- toy_df %>%
  # create a new variable called y that contains x*10
  mutate(y = y + 1)
glimpse(toy_df2)

## Observations: 4
## Variables: 2
## $ x <dbl> 1, 2, 3, 4
## $ y <dbl> 11, 21, 31, 41

We will use mutate() often so it’s an important function to understand.

Here we can use it with our example data frame to create a new variable that is the sum of several other variables.

myDataFrame <- 
  myDataFrame %>%
  mutate(total = x + y + z)

kable(myDataFrame)

n	x	y	z	total
russ	1	4	7	12
lucy	2	5	8	15
jaclyn	3	6	9	18
tyler	4	7	10	21

mutate() is a function that creates a new variable in a data frame using the existing variables. In this case, it creates a variable called total that is the sum of the existing variables x, y, and z.

5.2.1 Remove a column using the select() function

Adding a minus sign to the name of a variable within the select() command will remove that variable, leaving all of the others.

myDataFrame <- 
  myDataFrame %>%
  dplyr::select(-total)

kable(myDataFrame)

n	x	y	z
russ	1	4	7
lucy	2	5	8
jaclyn	3	6	9
tyler	4	7	10