A quick guide to the mutate functions in dplyr

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Data wrangling is an essential part of the data analysis process. In R, the dplyr package provides a set of tools to make this task more efficient. Among these tools is the mutate family of functions, offering versatile ways to add or modify columns in a data frame.

In this post , we will look into five key functions of the mutate family - mutate_all, mutate_at, mutate_each, mutate_each_, and mutate_if.

We will utilize the mtcars and iris datasets to see the practical applications of each function.

1. mutate_all

The mutate_all function empowers you to apply a specified operation to all columns in a data frame. This is particularly useful when you want to uniformly transform every column.

library(dplyr)

mtcars %>%
  mutate_all(~ . * 2)  # Multiply all numeric columns by 2

In this example, we use mutate_all to double the values of all numeric columns in the mtcars dataset.

2. mutate_at

mutate_at allows selective column manipulation by specifying the columns you want to transform. This is handy when you need to apply a function to specific columns.

mtcars %>%
  mutate_at(vars(mpg, hp), funs(. * 2))  # Multiply 'mpg' and 'hp' columns by 2

Here, we use mutate_at to double the values in the 'mpg' and 'hp' columns of the mtcars dataset.

3. mutate_each

Unlike mutate_all, mutate_each provides flexibility by allowing you to apply a function to all columns while excluding specific ones.RCopy code

iris %>%
  mutate_each(funs(log(.)), -Species)  # Apply log transformation to numeric columns except 'Species'

This example showcases the use of mutate_each to apply a logarithmic transformation to numeric columns in the iris dataset, excluding the 'Species' column.

4. mutate_each_

mutate_each_ is a non-standard evaluation version of mutate_each, enabling dynamic column selection through external variables.

columns_to_transform <- c("Sepal.Length", "Petal.Length")

iris %>%
  mutate_each_(funs(log(.)), columns_to_transform)  

In this illustration, we utilize mutate_each_ to apply a logarithmic transformation to specific columns determined by the columns_to_transform vector.

5. mutate_if

mutate_if shines when you want to apply a function based on a logical condition, selectively transforming columns that meet specific criteria.

mtcars %>%
  mutate_if(is.numeric, funs(. * 2))  # Multiply all numeric columns by 2

Here, we use mutate_if to double the values of numeric columns in the mtcars dataset, based on the condition that the column is numeric.