This tutorial explains how to use the mutate() function in R to add new variables to a data frame.
Adding New Variables in R
The following functions from the dplyr library can be used to add new variables to a data frame:
mutate() – adds new variables to a data frame while preserving existing variables
transmute() – adds new variables to a data frame and drops existing variables
mutate_all() – modifies all of the variables in a data frame at once
mutate_at() – modifies specific variables by name
mutate_if() – modifies all variables that meet a certain condition
mutate()
The mutate() function adds new variables to a data frame while preserving any existing variables. The basic synax for mutate() is as follows:
data mutate(new_variable = existing_variable/3)
- data: the new data frame to assign the new variables to
- new_variable: the name of the new variable
- existing_variable: the existing variable in the data frame that you wish to perform some operation on to create the new variable
For example, the following code illustrates how to add a new variable root_sepal_width to the built-in iris dataset:
#define data frame as the first six lines of the iris dataset data #view data data # Sepal.Length Sepal.Width Petal.Length Petal.Width Species #1 5.1 3.5 1.4 0.2 setosa #2 4.9 3.0 1.4 0.2 setosa #3 4.7 3.2 1.3 0.2 setosa #4 4.6 3.1 1.5 0.2 setosa #5 5.0 3.6 1.4 0.2 setosa #6 5.4 3.9 1.7 0.4 setosa #load dplyr library library(dplyr) #define new column root_sepal_width as the square root of the Sepal.Width variable data %>% mutate(root_sepal_width = sqrt(Sepal.Width)) # Sepal.Length Sepal.Width Petal.Length Petal.Width Species root_sepal_width #1 5.1 3.5 1.4 0.2 setosa 1.870829 #2 4.9 3.0 1.4 0.2 setosa 1.732051 #3 4.7 3.2 1.3 0.2 setosa 1.788854 #4 4.6 3.1 1.5 0.2 setosa 1.760682 #5 5.0 3.6 1.4 0.2 setosa 1.897367 #6 5.4 3.9 1.7 0.4 setosa 1.974842
transmute()
The transmute() function adds new variables to a data frame and drops existing variables. The following code illustrates how to add two new variables to a dataset and remove all existing variables:
#define data frame as the first six lines of the iris dataset data #view data data # Sepal.Length Sepal.Width Petal.Length Petal.Width Species #1 5.1 3.5 1.4 0.2 setosa #2 4.9 3.0 1.4 0.2 setosa #3 4.7 3.2 1.3 0.2 setosa #4 4.6 3.1 1.5 0.2 setosa #5 5.0 3.6 1.4 0.2 setosa #6 5.4 3.9 1.7 0.4 setosa #define two new variables and remove all existing variables data %>% transmute(root_sepal_width = sqrt(Sepal.Width), root_petal_width = sqrt(Petal.Width)) # root_sepal_width root_petal_width #1 1.870829 0.4472136 #2 1.732051 0.4472136 #3 1.788854 0.4472136 #4 1.760682 0.4472136 #5 1.897367 0.4472136 #6 1.974842 0.6324555
mutate_all()
The mutate_all() function modifies all of the variables in a data frame at once, allowing you to perform a specific function on all of the variables by using the funs()function. The following code illustrates how to divide all of the columns in a data frame by 10 using mutate_all():
#define new data frame as the first six rows of iris without the Species variable data2 % select(-Species) #view the new data frame data2 # Sepal.Length Sepal.Width Petal.Length Petal.Width #1 5.1 3.5 1.4 0.2 #2 4.9 3.0 1.4 0.2 #3 4.7 3.2 1.3 0.2 #4 4.6 3.1 1.5 0.2 #5 5.0 3.6 1.4 0.2 #6 5.4 3.9 1.7 0.4 #divide all variables in the data frame by 10 data2 %>% mutate_all(funs(./10)) # Sepal.Length Sepal.Width Petal.Length Petal.Width #1 0.51 0.35 0.14 0.02 #2 0.49 0.30 0.14 0.02 #3 0.47 0.32 0.13 0.02 #4 0.46 0.31 0.15 0.02 #5 0.50 0.36 0.14 0.02 #6 0.54 0.39 0.17 0.04
Note that additional variables can be added to the data frame by specifying a new name to be appended to the old variable name:
data2 %>% mutate_all(funs(mod = ./10))
# Sepal.Length Sepal.Width Petal.Length Petal.Width Sepal.Length_mod
#1 5.1 3.5 1.4 0.2 0.51
#2 4.9 3.0 1.4 0.2 0.49
#3 4.7 3.2 1.3 0.2 0.47
#4 4.6 3.1 1.5 0.2 0.46
#5 5.0 3.6 1.4 0.2 0.50
#6 5.4 3.9 1.7 0.4 0.54
# Sepal.Width_mod Petal.Length_mod Petal.Width_mod
#1 0.35 0.14 0.02
#2 0.30 0.14 0.02
#3 0.32 0.13 0.02
#4 0.31 0.15 0.02
#5 0.36 0.14 0.02
#6 0.39 0.17 0.04
mutate_at()
The mutate_at() function modifies specific variables by name. The following code illustrates how to divide two specific variables by 10 using mutate_at():
data2 %>% mutate_at(c("Sepal.Length", "Sepal.Width"), funs(mod = ./10))
# Sepal.Length Sepal.Width Petal.Length Petal.Width Sepal.Length_mod
#1 5.1 3.5 1.4 0.2 0.51
#2 4.9 3.0 1.4 0.2 0.49
#3 4.7 3.2 1.3 0.2 0.47
#4 4.6 3.1 1.5 0.2 0.46
#5 5.0 3.6 1.4 0.2 0.50
#6 5.4 3.9 1.7 0.4 0.54
# Sepal.Width_mod
#1 0.35
#2 0.30
#3 0.32
#4 0.31
#5 0.36
#6 0.39
mutate_if()
The mutate_if() function modifies all variables that meet a certain condition. The following code illustrates how to use the mutate_if() function to convert any variables of type factor to type character:
#find variable type of each variable in a data frame
data #convert any variable of type factor to type character
new_data % mutate_if(is.factor, as.character)
sapply(new_data, class)
#Sepal.Length Sepal.Width Petal.Length Petal.Width Species
# "numeric" "numeric" "numeric" "numeric" "character"
The following code illustrates how to use the mutate_if() function to round any variables of type numeric to one decimal place:
#define data as first six rows of iris dataset data #view data data # Sepal.Length Sepal.Width Petal.Length Petal.Width Species #1 5.1 3.5 1.4 0.2 setosa #2 4.9 3.0 1.4 0.2 setosa #3 4.7 3.2 1.3 0.2 setosa #4 4.6 3.1 1.5 0.2 setosa #5 5.0 3.6 1.4 0.2 setosa #6 5.4 3.9 1.7 0.4 setosa #round any variables of type numeric to one decimal place data %>% mutate_if(is.numeric, round, digits = 0) # Sepal.Length Sepal.Width Petal.Length Petal.Width Species #1 5 4 1 0 setosa #2 5 3 1 0 setosa #3 5 3 1 0 setosa #4 5 3 2 0 setosa #5 5 4 1 0 setosa #6 5 4 2 0 setosa
Further reading:
A Guide to apply(), lapply(), sapply(), and tapply() in R
How to Arrange Rows in R
How to Filter Rows in R