How to use mutate() in R

Learn how to use mutate() in R with practical examples. Step-by-step guide with code you can copy and run immediately.

Introduction

The mutate() function from dplyr is used to create new columns or modify existing columns in a data frame. It’s essential for data transformation tasks like calculating new variables, converting data types, or applying functions to existing columns.

Getting Started

library(tidyverse)
library(palmerpenguins)

Example 1: Basic Usage

The Problem

We want to add a new column to the penguins dataset that converts body mass from grams to kilograms. We also need to create a column that combines species and island information.

Step 1: Create a Simple New Column

Let’s start by adding a body mass column in kilograms.

penguins_kg <- penguins |>
  mutate(body_mass_kg = body_mass_g / 1000)

head(penguins_kg)

This creates a new column body_mass_kg by dividing the existing body_mass_g column by 1000.

Step 2: Create Multiple Columns at Once

Now we’ll add several new columns in a single mutate() call.

penguins_enhanced <- penguins |>
  mutate(
    body_mass_kg = body_mass_g / 1000,
    bill_ratio = bill_length_mm / bill_depth_mm,
    species_island = paste(species, island, sep = "_")
  )

This adds three new columns simultaneously: weight in kg, bill length-to-depth ratio, and a combined species-island identifier.

Step 3: Modify Existing Columns

We can also use mutate() to transform existing columns in place.

penguins_modified <- penguins |>
  mutate(
    species = toupper(species),
    year = as.character(year),
    bill_length_mm = round(bill_length_mm, 1)
  )

str(penguins_modified)

This converts species names to uppercase, changes year to character type, and rounds bill length to one decimal place.

Example 2: Practical Application

The Problem

We’re analyzing penguin data for a research project and need to create standardized measurements, categorize penguins by size, and calculate condition indices. This requires multiple data transformations that build upon each other.

Step 1: Create Standardized Measurements

First, we’ll standardize the body measurements using z-scores.

penguins_std <- penguins |>
  drop_na() |>
  mutate(
    bill_length_z = scale(bill_length_mm)[,1],
    bill_depth_z = scale(bill_depth_mm)[,1],
    flipper_length_z = scale(flipper_length_mm)[,1]
  )

This creates z-score standardized versions of three measurement variables, removing any rows with missing values first.

Step 2: Categorize Penguins by Size

Next, we’ll create size categories based on body mass quartiles.

penguins_categorized <- penguins_std |>
  mutate(
    size_category = case_when(
      body_mass_g < quantile(body_mass_g, 0.25, na.rm = TRUE) ~ "Small",
      body_mass_g < quantile(body_mass_g, 0.75, na.rm = TRUE) ~ "Medium",
      TRUE ~ "Large"
    )
  )

This uses case_when() within mutate() to create three size categories based on the 25th and 75th percentiles of body mass.

Step 3: Calculate Condition Indices

Finally, we’ll create a body condition index and efficiency ratio.

penguins_final <- penguins_categorized |>
  mutate(
    body_condition = body_mass_g / flipper_length_mm,
    bill_efficiency = (bill_length_mm * bill_depth_mm) / body_mass_g,
    is_large_penguin = body_mass_g > mean(body_mass_g, na.rm = TRUE)
  )

summary(penguins_final)

This creates a condition index (mass per unit flipper length), bill efficiency metric, and a logical column identifying above-average sized penguins.

Step 4: Combine with Grouping

We can use mutate() with group_by() to create species-specific calculations.

penguins_grouped <- penguins_final |>
  group_by(species) |>
  mutate(
    mass_rank_in_species = rank(body_mass_g),
    mass_deviation = body_mass_g - mean(body_mass_g, na.rm = TRUE)
  ) |>
  ungroup()

This ranks each penguin’s mass within their species and calculates how much each penguin’s mass deviates from their species average.

Summary

• Use mutate() to create new columns or modify existing ones without changing the number of rows
• Multiple columns can be created simultaneously by separating them with commas
• New columns can reference previously created columns within the same mutate() call
• Combine mutate() with case_when() for conditional column creation

• When used with group_by(), calculations are performed within each group

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