How to collapse multiple rows based on a column

dplyr group_by()
paste0 in R
Learn how to collapse multiple rows based on a column with this comprehensive R tutorial. Includes practical examples and code snippets.
Published

June 29, 2022

Introduction

Collapsing multiple rows based on a column is a common data manipulation task that involves combining or aggregating data from several rows that share the same value in a grouping variable. This technique is essential when you need to consolidate information, calculate summary statistics, or create concatenated strings from multiple observations.

You’ll frequently use this approach when working with survey data where respondents have multiple entries, sales data with multiple transactions per customer, or any dataset where you need to group related observations together. The process typically involves identifying a grouping column, then applying functions like sum(), mean(), paste(), or list() to combine the values from other columns within each group.

Getting Started

First, let’s load the required packages and examine our dataset:

library(tidyverse)
library(palmerpenguins)

# Load the penguins dataset
data(penguins)
head(penguins)

Example 1: Basic Usage

Let’s start with a simple example using the mtcars dataset. We’ll collapse multiple car models by their number of cylinders, calculating average miles per gallon:

# Create a simple dataset for demonstration
data(mtcars)

# Add car names as a column
mtcars_with_names <- mtcars |>
  rownames_to_column("car_name")

# Basic collapse: group by cylinders and calculate mean mpg
collapsed_basic <- mtcars_with_names |>
  group_by(cyl) |>
  summarise(
    count = n(),
    avg_mpg = mean(mpg),
    car_names = paste(car_name, collapse = ", ")
  )

print(collapsed_basic)

This example groups cars by their cylinder count and creates summary statistics including the count of cars, average MPG, and a concatenated string of all car names in each group.

Example 2: Practical Application

Now let’s work with the penguins dataset for a more complex real-world scenario. We’ll collapse penguin observations by species and island, creating comprehensive summaries:

# Remove rows with missing values for cleaner results
penguins_clean <- penguins |>
  filter(!is.na(bill_length_mm), !is.na(body_mass_g))

# Complex collapse with multiple aggregations
penguin_summary <- penguins_clean |>
  group_by(species, island) |>
  summarise(
    observation_count = n(),
    avg_bill_length = round(mean(bill_length_mm), 2),
    avg_body_mass = round(mean(body_mass_g), 0),
    mass_range = paste(min(body_mass_g), max(body_mass_g), sep = "-"),
    years_observed = paste(sort(unique(year)), collapse = ", "),
    .groups = "drop"
  ) |>
  arrange(species, island)

print(penguin_summary)

For scenarios where you need to preserve all individual values as lists rather than calculating summaries:

# Collapse while preserving individual values as lists
penguin_lists <- penguins_clean |>
  group_by(species) |>
  summarise(
    total_observations = n(),
    islands = list(unique(island)),
    bill_lengths = list(bill_length_mm),
    body_masses = list(body_mass_g),
    .groups = "drop"
  )

# View the structure
str(penguin_lists)

# Access list elements (example for first species)
penguin_lists$islands[[1]]

You can also collapse rows while applying different functions to different columns:

# Advanced example with conditional aggregation
advanced_summary <- penguins_clean |>
  group_by(species, sex) |>
  summarise(
    n_observations = n(),
    bill_stats = paste0(
      "Mean: ", round(mean(bill_length_mm), 1), 
      " (", round(min(bill_length_mm), 1), 
      "-", round(max(bill_length_mm), 1), ")"
    ),
    common_island = names(sort(table(island), decreasing = TRUE))[1],
    study_years = paste(range(year), collapse = "-"),
    .groups = "drop"
  ) |>
  filter(!is.na(sex))

print(advanced_summary)

Summary

Collapsing multiple rows based on a column is accomplished using group_by() combined with summarise() in the tidyverse. Key takeaways include:

  • Use group_by() to specify the grouping column(s)
  • Apply aggregation functions like mean(), sum(), n() for numerical summaries
  • Use paste() with collapse parameter to concatenate strings
  • Employ list() to preserve individual values within groups
  • The .groups = "drop" argument prevents grouped output
  • Multiple grouping variables and complex aggregations are supported for sophisticated data consolidation

This approach provides flexible solutions for data aggregation tasks across various analytical scenarios.