How to use geom_col() in R

ggplot2

ggplot2 geom_col()

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

Published

February 21, 2026

Introduction

The geom_col() function in ggplot2 creates column charts where the height of each bar represents values in your data. Unlike geom_bar() which counts observations, geom_col() uses the actual values from a specified column, making it perfect for displaying pre-calculated statistics, totals, or measurements.

Getting Started

library(tidyverse)
library(palmerpenguins)

Example 1: Basic Usage

The Problem

We want to create a simple column chart showing the average body mass of each penguin species. This requires calculating summary statistics and then visualizing them as columns.

Step 1: Calculate Summary Statistics

We need to group our data by species and calculate the mean body mass.

penguin_summary <- penguins |>
  filter(!is.na(body_mass_g)) |>
  group_by(species) |>
  summarise(avg_mass = mean(body_mass_g))

penguin_summary

This creates a summary table with species names and their corresponding average body masses.

Step 2: Create Basic Column Chart

Now we’ll use geom_col() to display these calculated values as columns.

ggplot(penguin_summary, aes(x = species, y = avg_mass)) +
  geom_col()

The function automatically creates bars with heights matching our avg_mass values for each species.

Step 3: Improve the Appearance

Let’s add better labels and formatting to make the chart more professional.

ggplot(penguin_summary, aes(x = species, y = avg_mass)) +
  geom_col(fill = "steelblue", alpha = 0.7) +
  labs(title = "Average Body Mass by Penguin Species",
       x = "Species",
       y = "Average Body Mass (g)")

Column chart in R using geom_col() showing average penguin body mass by species with custom fill color in ggplot2

The chart now has color, transparency, and clear labels that make it publication-ready.

Example 2: Practical Application

The Problem

A researcher wants to compare total flipper length measurements across different islands, broken down by penguin species. This represents a real-world scenario where we need to show multiple categories and their relationships in a grouped column chart.

Step 1: Prepare Multi-Group Data

We’ll calculate total flipper lengths by both island and species to create grouped data.

flipper_data <- penguins |>
  filter(!is.na(flipper_length_mm)) |>
  group_by(island, species) |>
  summarise(total_flipper = sum(flipper_length_mm), .groups = "drop")

head(flipper_data)

This creates a dataset with totals for each combination of island and species.

Step 2: Create Grouped Column Chart

We’ll use the fill aesthetic to create side-by-side columns for each species.

ggplot(flipper_data, aes(x = island, y = total_flipper, fill = species)) +
  geom_col(position = "dodge")

The position = "dodge" argument places columns side-by-side rather than stacking them.

Step 3: Add Professional Styling

Let’s enhance the chart with custom colors and improved formatting.

ggplot(flipper_data, aes(x = island, y = total_flipper, fill = species)) +
  geom_col(position = "dodge", alpha = 0.8) +
  scale_fill_manual(values = c("Adelie" = "#FF6B6B", 
                              "Chinstrap" = "#4ECDC4", 
                              "Gentoo" = "#45B7D1"))

Custom colors make each species easily distinguishable and visually appealing.

Step 4: Finalize with Labels and Theme

Complete the visualization with comprehensive labels and a clean theme.

ggplot(flipper_data, aes(x = island, y = total_flipper, fill = species)) +
  geom_col(position = "dodge", alpha = 0.8) +
  scale_fill_manual(values = c("Adelie" = "#FF6B6B",
                              "Chinstrap" = "#4ECDC4",
                              "Gentoo" = "#45B7D1")) +
  labs(title = "Total Flipper Length by Island and Species",
       x = "Island", y = "Total Flipper Length (mm)",
       fill = "Species") +
  theme_minimal()

Grouped column chart in R using geom_col() with position dodge showing total flipper length by island and species with custom colors in ggplot2

The final chart clearly shows the distribution and comparison of flipper measurements across all categories.

Summary

geom_col() uses actual data values to determine bar heights, unlike geom_bar() which counts observations
Always prepare your data first by calculating the summary statistics you want to display
Use position = "dodge" to create side-by-side grouped columns instead of stacked bars
The fill aesthetic creates multiple series within each x-axis category for comparative analysis
Combine with custom colors, labels, and themes to create professional, publication-ready visualizations

--- title: "How to use geom_col() in R" description: "Learn how to use geom_col() in R with practical examples. Step-by-step guide with code you can copy and run immediately." date: 2026-02-21 categories: ['ggplot2', 'ggplot2 geom_col()'] format: html: code-fold: false code-tools: true --- ## Introduction The `geom_col()` function in ggplot2 creates column charts where the height of each bar represents values in your data. Unlike [`geom_bar()`](/ggplot2/how-to-use-geombar-in-r.html) which counts observations, `geom_col()` uses the actual values from a specified column, making it perfect for displaying pre-calculated statistics, totals, or measurements. ## Getting Started ```r library(tidyverse) library(palmerpenguins) ``` ## Example 1: Basic Usage ### The Problem We want to create a simple column chart showing the average body mass of each penguin species. This requires calculating summary statistics and then visualizing them as columns. ### Step 1: Calculate Summary Statistics We need to group our data by species and calculate the mean body mass. ```r penguin_summary <- penguins |> filter(!is.na(body_mass_g)) |> group_by(species) |> summarise(avg_mass = mean(body_mass_g)) penguin_summary ``` This creates a summary table with species names and their corresponding average body masses. ### Step 2: Create Basic Column Chart Now we'll use `geom_col()` to display these calculated values as columns. ```r ggplot(penguin_summary, aes(x = species, y = avg_mass)) + geom_col() ``` The function automatically creates bars with heights matching our `avg_mass` values for each species. ### Step 3: Improve the Appearance Let's add better labels and formatting to make the chart more professional. ```r ggplot(penguin_summary, aes(x = species, y = avg_mass)) + geom_col(fill = "steelblue", alpha = 0.7) + labs(title = "Average Body Mass by Penguin Species", x = "Species", y = "Average Body Mass (g)") ``` ![Column chart in R using geom_col() showing average penguin body mass by species with custom fill color in ggplot2](/images/ggplot2/geom-col-in-r-average-body-mass-by-species-ggplot.png) The chart now has color, transparency, and clear labels that make it publication-ready. ## Example 2: Practical Application ### The Problem A researcher wants to compare total flipper length measurements across different islands, broken down by penguin species. This represents a real-world scenario where we need to show multiple categories and their relationships in a grouped column chart. ### Step 1: Prepare Multi-Group Data We'll calculate total flipper lengths by both island and species to create grouped data. ```r flipper_data <- penguins |> filter(!is.na(flipper_length_mm)) |> group_by(island, species) |> summarise(total_flipper = sum(flipper_length_mm), .groups = "drop") head(flipper_data) ``` This creates a dataset with totals for each combination of island and species. ### Step 2: Create Grouped Column Chart We'll use the `fill` aesthetic to create side-by-side columns for each species. ```r ggplot(flipper_data, aes(x = island, y = total_flipper, fill = species)) + geom_col(position = "dodge") ``` The `position = "dodge"` argument places columns side-by-side rather than stacking them. ### Step 3: Add Professional Styling Let's enhance the chart with custom colors and improved formatting. ```r ggplot(flipper_data, aes(x = island, y = total_flipper, fill = species)) + geom_col(position = "dodge", alpha = 0.8) + scale_fill_manual(values = c("Adelie" = "#FF6B6B", "Chinstrap" = "#4ECDC4", "Gentoo" = "#45B7D1")) ``` Custom colors make each species easily distinguishable and visually appealing. ### Step 4: Finalize with Labels and Theme Complete the visualization with comprehensive labels and a clean theme. ```r ggplot(flipper_data, aes(x = island, y = total_flipper, fill = species)) + geom_col(position = "dodge", alpha = 0.8) + scale_fill_manual(values = c("Adelie" = "#FF6B6B", "Chinstrap" = "#4ECDC4", "Gentoo" = "#45B7D1")) + labs(title = "Total Flipper Length by Island and Species", x = "Island", y = "Total Flipper Length (mm)", fill = "Species") + theme_minimal() ``` ![Grouped column chart in R using geom_col() with position dodge showing total flipper length by island and species with custom colors in ggplot2](/images/ggplot2/geom-col-in-r-grouped-column-chart-by-island-species-ggplot.png) The final chart clearly shows the distribution and comparison of flipper measurements across all categories. ## Summary - `geom_col()` uses actual data values to determine bar heights, unlike `geom_bar()` which counts observations - Always prepare your data first by calculating the summary statistics you want to display - Use `position = "dodge"` to create side-by-side grouped columns instead of stacked bars - The `fill` aesthetic creates multiple series within each x-axis category for comparative analysis - Combine with custom colors, labels, and themes to create professional, publication-ready visualizations --- ## Related Posts - [How to use geom_point() in R](/ggplot2/how-to-use-geompoint-in-r.html) - [How to use geom_area() in R](/ggplot2/how-to-use-geomarea-in-r.html) - [How to use geom_tile() in R](/ggplot2/how-to-use-geomtile-in-r.html) - [How to use select() in R](/dplyr/how-to-use-select-in-r.html) - [How to use mutate() in R](/dplyr/how-to-use-mutate-in-r.html)