How to use pivot_wider() in R

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

February 21, 2026

Introduction

The pivot_wider() function from the tidyr package transforms long data into wide format by spreading values across multiple columns. This is essential when you need to reshape data for analysis, create summary tables, or prepare data for specific visualization requirements.

Getting Started

library(tidyverse)
library(palmerpenguins)

Example 1: Basic Usage

The Problem

We have penguin data in long format where species and measurements are in separate rows. We want to create a wide format where each species becomes its own column with corresponding values.

Step 1: Create sample long data

First, let’s create a simple dataset to demonstrate the basic concept.

penguin_summary <- penguins |>
  drop_na() |>
  group_by(species, island) |>
  summarise(avg_mass = mean(body_mass_g), .groups = "drop") |>
  slice_head(n = 6)

penguin_summary

This creates a summary table with species, island, and average body mass in long format.

Step 2: Apply basic pivot_wider

Now we’ll transform this data so each species becomes a separate column.

wide_penguins <- penguin_summary |>
  pivot_wider(
    names_from = species,
    values_from = avg_mass
  )

wide_penguins

The data is now restructured with species as column headers and their corresponding average masses as values.

Step 3: Handle missing values

We can specify how to handle missing combinations using the values_fill parameter.

wide_penguins_filled <- penguin_summary |>
  pivot_wider(
    names_from = species,
    values_from = avg_mass,
    values_fill = 0
  )

wide_penguins_filled

Any missing species-island combinations are now filled with zeros instead of NA values.

Example 2: Practical Application

The Problem

You’re analyzing car performance data and need to compare fuel efficiency (mpg) across different cylinder counts for each car manufacturer. The current format makes it difficult to see these comparisons side-by-side.

Step 1: Prepare the automotive data

Let’s create a meaningful summary from the mtcars dataset for our analysis.

# Add car names and create manufacturer column
cars_data <- mtcars |>
  rownames_to_column("car_name") |>
  mutate(manufacturer = word(car_name, 1)) |>
  select(manufacturer, cyl, mpg) |>
  group_by(manufacturer, cyl) |>
  summarise(avg_mpg = round(mean(mpg), 1), .groups = "drop")

cars_data

This creates a summary showing average fuel efficiency by manufacturer and cylinder count.

Step 2: Transform to comparison format

Now we’ll pivot the data to compare fuel efficiency across cylinder counts.

mpg_comparison <- cars_data |>
  pivot_wider(
    names_from = cyl,
    values_from = avg_mpg,
    names_prefix = "cyl_"
  )

mpg_comparison

Each cylinder count is now a separate column, making it easy to compare fuel efficiency across engine types.

Step 3: Create multiple value columns

We can also pivot multiple measurements simultaneously for comprehensive analysis.

detailed_cars <- mtcars |>
  rownames_to_column("car_name") |>
  mutate(manufacturer = word(car_name, 1)) |>
  group_by(manufacturer, cyl) |>
  summarise(
    avg_mpg = round(mean(mpg), 1),
    avg_hp = round(mean(hp), 0),
    .groups = "drop"
  ) |>
  pivot_wider(
    names_from = cyl,
    values_from = c(avg_mpg, avg_hp),
    names_sep = "_cyl_"
  )

detailed_cars

This creates columns for both MPG and horsepower across different cylinder counts, providing a comprehensive comparison matrix.

Summary

  • pivot_wider() transforms long data into wide format by spreading key-value pairs across columns
  • Use names_from to specify which column contains the new column names
  • Use values_from to specify which column contains the values to spread
  • The values_fill parameter handles missing combinations with specified default values

  • Multiple value columns can be pivoted simultaneously using vectors in values_from