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.

# Uncomment and run this line once
#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.

# Uncomment and run this line once
#install.packages("palmerpenguins")
library(palmerpenguins)

data <- penguins

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

print(data)

# A tibble: 344 × 8
   species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
   <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
 1 Adelie  Torgersen           39.1          18.7               181        3750
 2 Adelie  Torgersen           39.5          17.4               186        3800
 3 Adelie  Torgersen           40.3          18                 195        3250
 4 Adelie  Torgersen           NA            NA                  NA          NA
 5 Adelie  Torgersen           36.7          19.3               193        3450
 6 Adelie  Torgersen           39.3          20.6               190        3650
 7 Adelie  Torgersen           38.9          17.8               181        3625
 8 Adelie  Torgersen           39.2          19.6               195        4675
 9 Adelie  Torgersen           34.1          18.1               193        3475
10 Adelie  Torgersen           42            20.2               190        4250
# ℹ 334 more rows
# ℹ 2 more variables: sex <fct>, year <int>

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.

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 = data, aes(x = body_mass_g))

# or
ggplot(data, aes(body_mass_g))

However, this is just 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)