Required packages

Load the tidyverse packages, which include dplyr:

library(tidyverse)

Demo dataset

We’ll use the R built-in iris data set, which we start by converting into a tibble data frame (tbl_df) for easier data analysis.

my_data <- as_tibble(iris)
my_data

## # A tibble: 150 x 5
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
##          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
## 1          5.1         3.5          1.4         0.2 setosa 
## 2          4.9         3            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           3.6          1.4         0.2 setosa 
## 6          5.4         3.9          1.7         0.4 setosa 
## # ... with 144 more rows

mutate: Add new variables by preserving existing ones

Add new columns (sepal_by_petal_*) by preserving existing ones:

my_data %>% 
  mutate(sepal_by_petal_l = Sepal.Length/Petal.Length)

## # A tibble: 150 x 6
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
##          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
## 1          5.1         3.5          1.4         0.2 setosa 
## 2          4.9         3            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           3.6          1.4         0.2 setosa 
## 6          5.4         3.9          1.7         0.4 setosa 
## # ... with 144 more rows, and 1 more variable: sepal_by_petal_l <dbl>

transmute: Make new variables by dropping existing ones

Add new columns (sepal_by_petal_*) by dropping existing ones:

my_data %>%
  transmute(
    sepal_by_petal_l = Sepal.Length/Petal.Length,
    sepal_by_petal_w = Sepal.Width/Petal.Width
    )

## # A tibble: 150 x 2
##   sepal_by_petal_l sepal_by_petal_w
##              <dbl>            <dbl>
## 1             3.64            17.5 
## 2             3.5             15   
## 3             3.62            16   
## 4             3.07            15.5 
## 5             3.57            18   
## 6             3.18             9.75
## # ... with 144 more rows

Modify multiple columns at once

We start by creating a demo data set, my_data2, which contains only numeric columns. To do so, we’ll remove the column Species as follow:

my_data2 <- my_data %>%
  select(-Species)

The functions mutate_all() / transmute_all(), mutate_at() / transmute_at() and mutate_if() / transmute_if() can be used to modify multiple columns at once.

The simplified formats are as follow:

# Mutate variants
mutate_all(.tbl, .funs, ...)
mutate_if(.tbl, .predicate, .funs, ...)
mutate_at(.tbl, .vars, .funs, ...)

# Transmute variants
transmute_all(.tbl, .funs, ...)
transmute_if(.tbl, .predicate, .funs, ...)
transmute_at(.tbl, .vars, .funs, ...)

• .tbl: a tbl data frame
• .funs: List of function calls generated by funs(), or a character vector of function names, or simply a function.
• …: Additional arguments for the function calls in .funs.
• .predicate: A predicate function to be applied to the columns or a logical vector. The variables for which .predicate is or returns TRUE are selected.

In the following sections, we’ll present only the variants of mutate(). The transmute() variants can be used similarly.

my_data2 %>%
  mutate_all(funs(./2.54))

## # A tibble: 150 x 4
##   Sepal.Length Sepal.Width Petal.Length Petal.Width
##          <dbl>       <dbl>        <dbl>       <dbl>
## 1         2.01        1.38        0.551      0.0787
## 2         1.93        1.18        0.551      0.0787
## 3         1.85        1.26        0.512      0.0787
## 4         1.81        1.22        0.591      0.0787
## 5         1.97        1.42        0.551      0.0787
## 6         2.13        1.54        0.669      0.157 
## # ... with 144 more rows

my_data2 %>%
  mutate_all(funs(cm = ./2.54))

## # A tibble: 150 x 8
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Sepal.Length_cm
##          <dbl>       <dbl>        <dbl>       <dbl>           <dbl>
## 1          5.1         3.5          1.4         0.2            2.01
## 2          4.9         3            1.4         0.2            1.93
## 3          4.7         3.2          1.3         0.2            1.85
## 4          4.6         3.1          1.5         0.2            1.81
## 5          5           3.6          1.4         0.2            1.97
## 6          5.4         3.9          1.7         0.4            2.13
## # ... with 144 more rows, and 3 more variables: Sepal.Width_cm <dbl>,
## #   Petal.Length_cm <dbl>, Petal.Width_cm <dbl>

my_data2 %>%
  mutate_at(
    c("Sepal.Length", "Petal.Width"),
    funs(cm = ./2.54)
    )

my_data %>% mutate_if(is.factor, as.character)

my_data %>% mutate_if(is.numeric, round, digits = 0)

Summary

This article describe how to add new variable columns into a data frame using the dplyr functions: mutate(), transmute() and variants.

• mutate(iris, sepal = 2*Sepal.Length): Computes and appends new variable(s).
• transmute(iris, sepal = 2*Sepal.Length): Makes new variable(s) and drops existing ones.