Basics of ggplot2

A beginners guide to making figures in R

Jan 21, 2025

Nothing is worse than reading a scientific publication only to encounter muddled, overly busy figures. Clear storytelling is a cornerstone of scientific writing, and the saying “a picture is worth a thousand words” has never been more relevant. Let’s dive into creating clean, effective figures in R!

Import Data

First, we need to make sure that tidyverse is installed. ggplot is a package within tidyverse, which is one of the most useful tools in R for data wrangling and visualization.

install.packages("tidyverse")

# load the package
library(tidyverse)

Next, lets read in our data. We’ll be working with the palmer penguins dataset, which is free and publicly available.

install.packages("palmerpenguins")
library(palmerpenguins)

data <- pengiuns

First, let’s look at the data in a tabular form.

print(data)

So there are eight columns and 344 rows. Let’s say we are interested in penguin weight. We’ll need to look at the “body_mass_g” variable.

Basic Syntax

Let’s create a basic histogram to look at the distribution of this continuous variable.

To start, you call ggplot(). The first argument is our data.

ggplot(data, ...

# or

ggplot(data = data, ...

I could also code it with data = data, although the first argument with ggplot is always the “data = ” argument.

Next we have to specify the aesthetics. Since a histogram only has an x argument (our continuous variable: body_mass_g, in our case), that’s all we need.

ggplot(data, aes(x = body_mass_g))

# or

ggplot(data, aes(body_mass_g))

If we ran this, R would display a blank graph. We need to specify which type of graph. In this example, we will create a histogram. Each additional “geom” is added by a “+” sign. I generally like to start a new line after each “+”.

ggplot(data, aes(x = body_mass_g)) +
  geom_histogram()

Not bad! Now let’s customize it. I personally do not like the default ggplot look, so let’s try some built in themes.

ggplot(data, aes(x = body_mass_g)) +
  geom_histogram() +
  theme_bw()

ggplot(data, aes(x = body_mass_g)) +
  geom_histogram() +
  theme_minimal()

ggplot(data, aes(x = body_mass_g)) +
  geom_histogram() +
  theme_classic()

ggplot(data, aes(x = body_mass_g)) +
  geom_histogram() +
  theme_linedraw()

I prefer theme_bw() or theme_minimal(). Let’s stick with those going forward.

Get Colorful

Maybe you want to change the fill color of the histogram bars. Colors in R are always specified within quotes.

ggplot(data, aes(x = body_mass_g)) +
  geom_histogram(fill = "red") +
  theme_minimal()

Maybe you want you want each species to have a different color. We’ll need to specify which variable is our fill within the aes().

ggplot(data, aes(x = body_mass_g, fill = species)) +
  geom_histogram() +
  theme_minimal()

Great! But these are stacked bars. We want them to overlap.

ggplot(data, aes(x = body_mass_g, fill = species)) +
  geom_histogram(position = "identity") +
  theme_minimal()

But we can’t quite see some of the bars that overlap. Let’s change the transparency (alpha).

ggplot(data, aes(x = body_mass_g, fill = species)) +
  geom_histogram(position = "identity", alpha = 0.7) +
  theme_minimal()

Much better!

Labeling Your Graph

The x and y-axes aren’t very pretty right now. Let’s change that. We’ll use labs().

ggplot(data, aes(x = body_mass_g, fill = species)) +
  geom_histogram(position = "identity",
                              alpha = 0.7) +
  theme_minimal() +
  labs(x = "Body Mass (g)",
          y = "Frequency",
          fill = "Species")

Other geoms

Since this is a basic introduction to ggplot, we will not go into all the other geoms. However, there are several other types of graphs you can make:

geom_bar - bar graphs
geom_point - scatterplots
geom_line - linegraphs
geom_text - adding text to your figure
And so many more!

Exporting Figures

Now let’s save our figure. Most journals require between 300-600 dots per inch (dpi).

ggsave("penguins_histogram.jpg", width = 5, height = 3, dpi = 300)

And that’s it! Thanks for reading! Look out for more R tutorials.