Video tutorial
Please watch this video (2:46), then read and follow along with the written tutorial below. Compare your own output to what you see printed below to make sure all of your code runs as expected.
Introduction
In some cases you might need to do additional calculations with your
data. In this tutorial, we show you how to define new variables and
overwrite existing ones using the mutate() function from tidyverse
functions and the diamonds dataset (which comes pre-loaded with
tidyverse so you don’t need to import it).
Let’s load the tidyverse package and have a look at the diamonds
dataset:
# load tidyverse
library(tidyverse)
# add diamonds to the environment
data(diamonds)
Creating new variables
The mutate() function is used to create new variables in a dataset.
The syntax is mutate(data, variable = expression). The data argument
is the dataset you want to modify, variable is the name of the new
variable, and expression is the calculation you want to perform. If
variable already exists in the dataset, mutate() will overwrite it.
Let’s create a new variable called price_per_carat that calculates the
price per carat of each diamond:
# create a new variable price_per_carat
mutate(diamonds, price_per_carat = price / carat)
## # A tibble: 53,940 × 11
## carat cut color clarity depth table price x y z price_per_carat
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43 1417.
## 2 0.21 Prem… E SI1 59.8 61 326 3.89 3.84 2.31 1552.
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31 1422.
## 4 0.29 Prem… I VS2 62.4 58 334 4.2 4.23 2.63 1152.
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75 1081.
## 6 0.24 Very… J VVS2 62.8 57 336 3.94 3.96 2.48 1400
## 7 0.24 Very… I VVS1 62.3 57 336 3.95 3.98 2.47 1400
## 8 0.26 Very… H SI1 61.9 55 337 4.07 4.11 2.53 1296.
## 9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49 1532.
## 10 0.23 Very… H VS1 59.4 61 338 4 4.05 2.39 1470.
## # ℹ 53,930 more rows
You can also create multiple variables at once by separating them with a comma. It is good practice to start each new variable on a new line to keep your code readable.
Let’s create a second variable: a logical (TRUE/FALSE) variable that checks if the diamond costs more than $10,000. In this case, the expression is a logical condition.
# create two new variables
mutate(diamonds,
price_per_carat = price / carat,
expensive = price > 10000)
## # A tibble: 53,940 × 12
## carat cut color clarity depth table price x y z price_per_carat
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43 1417.
## 2 0.21 Prem… E SI1 59.8 61 326 3.89 3.84 2.31 1552.
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31 1422.
## 4 0.29 Prem… I VS2 62.4 58 334 4.2 4.23 2.63 1152.
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75 1081.
## 6 0.24 Very… J VVS2 62.8 57 336 3.94 3.96 2.48 1400
## 7 0.24 Very… I VVS1 62.3 57 336 3.95 3.98 2.47 1400
## 8 0.26 Very… H SI1 61.9 55 337 4.07 4.11 2.53 1296.
## 9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49 1532.
## 10 0.23 Very… H VS1 59.4 61 338 4 4.05 2.39 1470.
## # ℹ 53,930 more rows
## # ℹ 1 more variable: expensive <lgl>
To be able to work with this new dataset, you need to save it to a new
object. Let’s assign the result of the mutate() function to a new
object called diamonds_new:
# save the result to a new object
diamonds_new <- mutate(diamonds,
price_per_carat = price / carat,
expensive = price > 10000)