Visualisation with ggplot2
Last updated on 2022-11-29 | Edit this page
Overview
Questions
- How do I access my data in R?
- How do I visualise my data with ggplot2?
Objectives
- Read data into R
- To be able to use
ggplot2
to generate publication quality graphics. - To understand the basic grammar of graphics, including the aesthetics and geometry layers, adding statistics, transforming scales, and colouring or panelling by groups.
Motivation
Plotting the data is one of the best ways to quickly explore it and generate hypotheses about various relationships between variables.
There are several plotting systems in R, but today we will focus on
ggplot2
which implements grammar of
graphics - a coherent system for describing components that
constitute visual representation of data. For more information regarding
principles and thinking behind ggplot2
graphic system,
please refer to Layered grammar
of graphics by Hadley Wickham (@hadleywickham).
The advantage of ggplot2
is that it allows R users to
create publication quality graphics with a few lines of code.
ggplot2
has a large user base and is constantly developed
and extended by the community.
Getting data into R
We will start by reading the data into R, from the data
folder you placed them in the last part of the introduction.
R
penguins <- read.csv("data/penguins.csv")
This is our first bit of R code to “assign” data to an object in our “R environment”. The R environment can be seen in the upper right hand corner, and it lists everything R has access to at the moment. You should see an object called “penguins”, which is a Dataset with 344 observations and 8 variables. We created this object with the line of code we just ran. You can “read” the line, right to left as: “read the penguins.csv into R, and assign (<-) it to an object called penguins”. The arrow, or assignment, is R’s way of creating new objects to work on.
Note a key difference from R and programs like SPSS or excel, is that when data is used in R, we do not automatically alter the data in the file we read it from. Everything we do with the penguins data in R from now on, only happens in R, and does not change the originating file. This way we cannot easily accidentally alter our raw data, which is a very good thing.
Tip: We can inspect the data in several ways
- Click the data name in the Environment, and the data opens as a tab
in the scripts pane.
- Click the little arrow next to the data name in the Evironment, and
you’ll see a short preview of the data.
- Type
penguins
in the R console, and a preview will be shown of the data.
The dataset contains the following fields:
- species: penguin species
- island: island of observation
- bill_length_mm: bill length in millimetres
- bill_depth_mm: bill depth in millimetres
- flipper_length_mm: flipper length in millimetres
- body_mass_g: body mass in grams
- sex: penguin sex
- year: year of observation
Introduction to ggplot2
ggplot2
is a core member of tidyverse
family of packages. Installing and loading the package under the same
name will load all of the packages we will need for this workshop. Lets
get started!
R
# install.packages("tidyverse")
library(tidyverse)
1.3.2 ──
── Attaching packages ─────────────────────────────────────── tidyverse 3.3.6 ✔ purrr 0.3.4
✔ ggplot2 3.1.8 ✔ dplyr 1.0.10
✔ tibble 1.2.0 ✔ stringr 1.4.1
✔ tidyr 2.1.2 ✔ forcats 0.5.2
✔ readr tidyverse_conflicts() ──
── Conflicts ────────────────────────────────────────── ::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag() ✖ dplyr
Here’s a question that we would like to answer using
penguins
data: Do penguins with deep beaks also have
long beaks? This might seem like a silly question, but it gets us
exploring our data.
To plot penguins
, run the following code in the R-chunk
or in console. The following code will put bill_depth_mm
on
the x-axis and bill_length_mm
on the y-axis:
R
ggplot(data = penguins) +
geom_point(
mapping = aes(x = bill_depth_mm,
y = bill_length_mm)
): Removed 2 rows containing missing values (geom_point). Warning
Note that we split the function into several lines. In R, any
function has a name and is followed by parentheses. Inside the
parentheses we place any information the function needs to run. Here, we
are using two main functions, ggplot()
and
geom_point()
. To save screen space, we have placed each
function on its own line, and also split up arguments into several
lines. How this is done depends on you, there are no real rules for
this. We will use the tidyverse coding style throughout this course, to
be consistent and also save space on the screen. The plus sign indicates
that the ggplot is not over yet and that the next line should be
interpreted as additional layer to the preceding ggplot()
function. In other words, when writing a ggplot()
function
spanning several lines, the +
sign goes at the end of the
line, not in the beginning.
Note that in order to create a plot using
ggplot2
system, you should start your command with
ggplot()
function. It creates an empty coordinate system
and initializes the dataset to be used in the graph (which is supplied
as a first argument into the ggplot()
function). In order
to create graphical representation of the data, we can add one or more
layers to our otherwise empty graph. Functions starting with the prefix
geom_
create a visual representation of data. In this case
we added scattered points, using geom_point()
function.
There are many geoms
in ggplot2
, some of which
we will learn in this lesson.
geom_
functions create mapping of variables
from the earlier defined dataset to certain aesthetic elements of the
graph, such as axis, shapes or colours. The first argument of any
geom_
function expects the user to specify these mappings,
wrapped in the aes()
(short for aesthetics)
function. In this case, we mapped bill_depth_mm
and
bill_length_mm
variables from penguins
dataset
to x and y-axis, respectively (using x
and y
arguments of aes()
function).
The* penguins
*dataset has a column called
year
, which should appear on the x-axis.
R
ggplot(data = penguins) +
geom_point(
mapping = aes(x = year,
y = bill_length_mm)
): Removed 2 rows containing missing values (geom_point). Warning
R
ggplot(data = penguins) +
geom_jitter(
mapping = aes(x = year,
y = bill_length_mm)
): Removed 2 rows containing missing values (geom_point). Warning
Mapping data
What if we want to combine graphs from the previous two challenges and show the relationship between three variables in the same graph? Turns out, we don’t necessarily need to use third geometrical dimension, we can employ colour.
The following graph maps island
variable from
penguins
dataset to the colour
aesthetic of
the plot. Let’s take a look:
R
ggplot(data = penguins) +
geom_jitter(
mapping = aes(x = bill_depth_mm,
y = bill_length_mm,
colour = island)
): Removed 2 rows containing missing values (geom_point). Warning
R
ggplot(data = penguins) +
geom_jitter(
mapping = aes(x = bill_depth_mm,
y = bill_length_mm,
colour = year)
): Removed 2 rows containing missing values (geom_point). Warning
Island is a categorical variable, in R we call it a factor. The
colours get tagged with their factor in the legend, so we can interpret
which colour belongs to which factor. year
is a numerical
variable, so the colour becomes a gradient colour bar, rather than
showing fewer distinctly different colours. ggplot treats numerical and
factors different in this way precisely.
There are other aesthetics that can come handy. One of them is
size
. The idea is that we can vary the size of data points
to illustrate another continuous variable, such as species bill depth.
Lets look at four dimensions at once!
R
ggplot(data = penguins) +
geom_jitter(
mapping = aes(x = bill_depth_mm,
y = bill_length_mm,
colour = species,
size = year)
): Removed 2 rows containing missing values (geom_point). Warning
It might be even better to try another type of aesthetic, like shape, for categorical data like species.
R
ggplot(data = penguins) +
geom_jitter(
mapping = aes(x = bill_depth_mm,
y = bill_length_mm,
colour = species,
shape = species)
): Removed 2 rows containing missing values (geom_point). Warning
Playing around with different aesthetic mappings until you find something that really makes the data “pop” is a good idea. A plot is rarely made nice on the first try, we all try different configurations until we find the one we like.
Setting values
Until now, we explored different aesthetic properties of a graph
mapped to certain variables. What if you want to recolour or use a
certain shape to plot all data points? Well, that means that such colour
or shape will no longer be mapped to any data, so you need to
supply it to geom_
function as a separate argument (outside
of the mapping
). This is called “setting” in the
ggplot2-world. We “map” aesthetics to data columns, or we “set” single
values outside aesthetics to apply to the entire geom or plot. Here’s
our initial graph with all colours coloured in blue.
R
ggplot(data = penguins) +
geom_point(
mapping = aes(x = bill_depth_mm,
y = bill_length_mm),
colour = "blue"
): Removed 2 rows containing missing values (geom_point). Warning
Once more, observe that the colour is now not mapped to any
particular variable from the penguins
dataset and applies
equally to all data points, therefore it is outside the
mapping
argument and is not wrapped into aes()
function. Note that set colours are supplied as characters (in
quotes).
alpha
takes a value from 0 (transparent) to 1
(solid).
R
ggplot(data = penguins) +
geom_point(
mapping = aes(x = bill_depth_mm,
y = bill_length_mm,
alpha = year)
): Removed 2 rows containing missing values (geom_point). Warning
R
ggplot(data = penguins) +
geom_point(
mapping = aes(x = bill_depth_mm,
y = bill_length_mm),
alpha = 0.5)
: Removed 2 rows containing missing values (geom_point). Warning
Geometrical objects
Next, we will consider different options for geoms
.
Using different geom_
functions user can highlight
different aspects of data.
A useful geom function is geom_boxplot()
. It adds a
layer with the “box and whiskers” plot illustrating the distribution of
values within categories. The following chart breaks down bill length by
island, where the box represents first and third quartile (the 25th and
75th percentiles), the middle bar signifies the median value and the
whiskers extent to cover 95% confidence interval. Outliers (outside of
the 95% confidence interval range) are shown separately.
R
ggplot(data = penguins) +
geom_boxplot(
mapping = aes(x = species,
y = bill_length_mm)
): Removed 2 rows containing non-finite values (stat_boxplot). Warning
Layers can be added on top of each other. In the following graph we will place the boxplots over jittered points to see the distribution of outliers more clearly. We can map two aesthetic properties to the same variable. Here we will also use different colour for each island.
R
ggplot(data = penguins) +
geom_jitter(
mapping = aes(x = species,
y = bill_length_mm,
colour = species)
+
) geom_boxplot(
mapping = aes(x = species,
y = bill_length_mm)
): Removed 2 rows containing non-finite values (stat_boxplot).
Warning: Removed 2 rows containing missing values (geom_point). Warning
Now, this was slightly inefficient due to duplication of code - we
had to specify the same mappings for two layers. To avoid it, you can
move common arguments of geom_
functions to the main
ggplot()
function. In this case every layer will “inherit”
the same arguments, specified in the “parent” function.
R
ggplot(data = penguins,
mapping = aes(x = island,
y = bill_length_mm)
+
) geom_jitter(aes(colour = island)) +
geom_boxplot(alpha = .6)
: Removed 2 rows containing non-finite values (stat_boxplot).
Warning: Removed 2 rows containing missing values (geom_point). Warning
You can still add layer-specific mappings or other arguments by specifying them within individual geoms. Here, we’ve set the transparency of the boxplot to .6, so we can see the points behind it, and also mapped colour to island in the points. We would recommend building each layer separately and then moving common arguments up to the “parent” function.
We can use linear models to highlight differences in dependency
between bill length and body mass by island. Notice that we added a
separate argument to the geom_smooth()
function to specify
the type of model we want ggplot2
to built using the data
(linear model). The geom_smooth()
function has also
helpfully provided confidence intervals, indicating “goodness of fit”
for each model (shaded gray area). For more information on statistical
models, please refer to help (by typing ?geom_smooth
)
R
ggplot(data = penguins,
mapping = aes(x = bill_depth_mm,
y = bill_length_mm)
+
) geom_point(alpha = 0.5) +
geom_smooth(method = "lm")
`geom_smooth()` using formula 'y ~ x'
: Removed 2 rows containing non-finite values (stat_smooth).
Warning: Removed 2 rows containing missing values (geom_point). Warning
R
ggplot(data = penguins,
mapping = aes(x = bill_depth_mm,
y = bill_length_mm)) +
geom_point(mapping = aes(colour = species),
alpha = 0.5) +
geom_smooth(method = "lm")
`geom_smooth()` using formula 'y ~ x'
: Removed 2 rows containing non-finite values (stat_smooth).
Warning: Removed 2 rows containing missing values (geom_point). Warning
Add another geom!
R
ggplot(penguins,
aes(x = bill_depth_mm,
y = bill_length_mm)) +
geom_point(aes(colour = species),
alpha = 0.5) +
geom_smooth(method = "lm",
aes(colour = species)) +
geom_smooth(method = "lm",
colour = "black")
`geom_smooth()` using formula 'y ~ x'
: Removed 2 rows containing non-finite values (stat_smooth).
Warning`geom_smooth()` using formula 'y ~ x'
: Removed 2 rows containing non-finite values (stat_smooth).
Warning: Removed 2 rows containing missing values (geom_point). Warning
Sub-plots (plot panels)
The last thing we will cover for plots is creating sub-plots. Often, we’d like to create the same set of plots, but as distinctly different subplots. This way, we dont need to map soo many aesthetics (it can end up being really messy).
Lets say, the last plot we made, we want to understand if there are
also differences between male and female penguins. In ggplot2, this is
called a “facet”, and the function we use is called either
facet_wrap
or facet_grid
.
R
ggplot(penguins,
aes(x = bill_depth_mm,
y = bill_length_mm,
colour = species)) +
geom_point(alpha = 0.5) +
geom_smooth(method = "lm") +
facet_wrap(~ sex)
`geom_smooth()` using formula 'y ~ x'
: Removed 2 rows containing non-finite values (stat_smooth).
Warning: Removed 2 rows containing missing values (geom_point). Warning
The facet’s take formula arguments, meaning they contain the
tilde
(~). The way often we think about it, trying to
“read” the code, is that we facet “over” sex (in this case).
This plot looks a little crazy though, as we have penguins with missing sex information getting their own panel, and really, it makes more sense to compare the sexes within each species rather than the other way around. Let us swap the places of species and sex.
R
ggplot(penguins,
aes(x = bill_depth_mm,
y = bill_length_mm,
colour = sex)) +
geom_point(alpha = 0.5) +
geom_smooth(method = "lm") +
facet_wrap(~ species)
`geom_smooth()` using formula 'y ~ x'
: Removed 2 rows containing non-finite values (stat_smooth).
Warning: Removed 2 rows containing missing values (geom_point). Warning
The NA’s still look weird, but its definitely better, I think.
Add another facet variable with the +
R
ggplot(penguins,
aes(x = bill_depth_mm,
y = bill_length_mm,
colour = sex)) +
geom_point(alpha = 0.5) +
geom_smooth(method = "lm") +
facet_wrap(~ species + island)
`geom_smooth()` using formula 'y ~ x'
: Removed 2 rows containing non-finite values (stat_smooth).
Warning: Removed 2 rows containing missing values (geom_point). Warning