How to use arrange() in R

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

February 21, 2026

Introduction

The arrange() function from the dplyr package is used to sort rows in a data frame based on one or more columns. This function is essential when you need to reorder your data for analysis, visualization, or presentation purposes.

Getting Started

library(tidyverse)
library(palmerpenguins)

Example 1: Basic Sorting

The Problem

We have penguin data that appears in random order, but we want to sort it by body mass to identify the smallest and largest penguins. Let’s start with simple sorting operations.

Step 1: Sort by a single column (ascending)

We’ll arrange penguins from lightest to heaviest using body mass.

penguins |>
  arrange(body_mass_g) |>
  head()

This sorts all penguins in ascending order by body mass, with the lightest penguins appearing first.

Step 2: Sort in descending order

To see the heaviest penguins first, we use desc() to reverse the sort order.

penguins |>
  arrange(desc(body_mass_g)) |>
  head()

Now the heaviest penguins appear at the top of our dataset.

Step 3: Sort by multiple columns

We can sort by species first, then by body mass within each species.

penguins |>
  arrange(species, body_mass_g) |>
  head(10)

This groups penguins by species alphabetically, then sorts by body mass within each species group.

Example 2: Practical Application

The Problem

As a researcher studying penguin populations, you need to create a report showing penguins organized by island location, with the largest individuals listed first within each island group. You also want to handle missing data appropriately.

Step 1: Remove missing values

First, we’ll filter out penguins with missing body mass data to ensure clean sorting.

clean_penguins <- penguins |>
  filter(!is.na(body_mass_g), !is.na(island))

clean_penguins |> nrow()

This removes rows with missing values and shows us how many complete records remain.

Step 2: Sort by island and body mass

Now we’ll arrange by island first, then by body mass in descending order within each island.

penguin_report <- clean_penguins |>
  arrange(island, desc(body_mass_g)) |>
  select(island, species, body_mass_g, sex)

head(penguin_report, 12)

This creates our organized report with penguins grouped by island and sorted by size within each group.

Step 3: Add ranking within groups

To make the report more informative, we’ll add a rank number for each penguin within its island group.

penguin_report |>
  group_by(island) |>
  mutate(size_rank = row_number()) |>
  filter(size_rank <= 5) |>
  ungroup()

This shows the top 5 heaviest penguins from each island with their ranking.

Step 4: Compare sorting approaches

Let’s see how different sorting criteria affect our results by comparing two approaches.

# Approach 1: Sort by species, then body mass
approach1 <- clean_penguins |>
  arrange(species, desc(body_mass_g)) |>
  slice_head(n = 8)

# Approach 2: Sort by body mass only
approach2 <- clean_penguins |>
  arrange(desc(body_mass_g)) |>
  slice_head(n = 8)

These two approaches will likely show different penguins in the top results, demonstrating how sort order affects analysis outcomes.

Summary

  • arrange() sorts data frame rows based on column values in ascending order by default
  • Use desc() within arrange() to sort in descending order for any column
  • Multiple columns can be specified to create hierarchical sorting (first by column A, then by column B within A groups)
  • Always consider handling missing values with filter() before sorting to avoid unexpected results

  • Combine arrange() with group_by() and ranking functions for advanced sorting and analysis tasks