expand_grid(): Create all possible combinations of variables

tidyr
tidyr expand_grid()
Learn expand_grid(): create all possible combinations of variables with this comprehensive R tutorial. Includes practical examples and code snippets.
Published

August 26, 2024

Introduction

The expand_grid() function from tidyr creates a data frame containing all possible combinations of the values you provide. It’s particularly useful when you need to generate combinations for data analysis, create grids for plotting, or set up experimental designs with multiple variables.

Getting Started

library(tidyverse)
library(palmerpenguins)

Example 1: Basic Usage

The Problem

Let’s say you want to create all possible combinations of different species and islands from the penguins dataset. You need a systematic way to generate every possible pairing without manually typing each combination.

Step 1: Identify the unique values

First, let’s examine what unique values we have for species and islands in our dataset.

species_list <- penguins |> 
  distinct(species) |> 
  pull(species)

island_list <- penguins |> 
  distinct(island) |> 
  pull(island)

print(species_list)
print(island_list)

This gives us vectors containing the unique species (Adelie, Chinstrap, Gentoo) and islands (Biscoe, Dream, Torgersen).

Step 2: Create all combinations

Now we’ll use expand_grid() to create every possible combination of species and islands.

all_combinations <- expand_grid(
  species = species_list,
  island = island_list
)

print(all_combinations)

This creates a 9-row data frame (3 species × 3 islands) showing every possible species-island combination, even ones that don’t exist in nature.

Step 3: Compare with actual data

Let’s see which combinations actually exist in our real dataset.

actual_combinations <- penguins |> 
  distinct(species, island) |> 
  arrange(species, island)

print(actual_combinations)

You’ll notice that not all theoretical combinations exist - for example, there are no Chinstrap penguins on Biscoe Island in our dataset.

Example 2: Practical Application

The Problem

You’re planning a visualization that shows penguin body mass across different years and species, but you want to ensure your plot shows all possible combinations even if some don’t have data. This requires creating a complete grid first, then joining it with your actual data.

Step 1: Create a complete grid of years and species

We’ll generate all combinations of years and species from our dataset.

complete_grid <- expand_grid(
  year = unique(penguins$year),
  species = unique(penguins$species)
) |> 
  arrange(year, species)

print(complete_grid)

This creates a systematic grid ensuring we account for every year-species combination, providing a foundation for complete data visualization.

Step 2: Calculate summary statistics

Now let’s compute the average body mass for each actual combination in our data.

penguin_summary <- penguins |> 
  filter(!is.na(body_mass_g)) |> 
  group_by(year, species) |> 
  summarise(
    avg_mass = mean(body_mass_g),
    count = n(),
    .groups = "drop"
  )

This gives us the actual average body mass for each year-species combination that exists in our dataset.

Step 3: Join with complete grid

Finally, we’ll join our complete grid with the summary statistics to see where we have data gaps.

complete_analysis <- complete_grid |> 
  left_join(penguin_summary, by = c("year", "species")) |> 
  mutate(
    has_data = !is.na(avg_mass)
  )

print(complete_analysis)

This reveals which year-species combinations have data and which are missing, helping you make informed decisions about your analysis and visualization approach.

Summary

  • expand_grid() creates all possible combinations of the variables you provide, forming a complete factorial design
  • It’s essential for ensuring completeness in data analysis and avoiding gaps in visualizations
  • The function works with any number of variables and automatically handles different data types
  • Use it before joining with actual data to identify missing combinations and ensure comprehensive analysis

  • It’s particularly valuable for experimental design, grid-based plotting, and systematic data exploration tasks