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<h1 class="title toc-ignore">Plotting</h1>
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<p>This page was last updated on January 20, 2026.</p>
<hr />
<div id="background" class="section level2">
<h2>Background</h2>
<p>Data visualization is an integral component of scientific progress.
The capacity to efficiently explore data and communicate complex and
often nuanced findings are essential skills for any scientist.</p>
<p>“<em>Most of us need to listen to the music to understand how
beautiful it is. But often that’s how we present statistics: we just
show the notes, we don’t play the music.</em>” - <a
href="https://www.ted.com/talks/hans_rosling_the_best_stats_you_ve_ever_seen">Hans
Rosling</a></p>
<p>This page provides basic guidance on how to plot data in
<code>R</code>. It is aimed at people who are new to <code>R</code>. It
is not aimed at providing guidance on data visualisation <em>per
se</em>, nor is it intended to be an exhaustive guidebook on plotting in
<code>R</code>.</p>
<hr />
</div>
<div id="the-plot-function" class="section level2">
<h2>The <code>plot()</code> function</h2>
<p>This section describes some of the basics of plotting objects in
<code>R</code>.</p>
<div id="inherent-flexibility" class="section level3">
<h3>Inherent flexibility</h3>
<p>R comes with a built in <code>plot</code> function that has the
capacity to generate a seemingly endless variety of graphical outputs.
The beauty of plotting in <code>R</code> is the flexibility of the
graphing system. This flexibility, however, can also make it challenging
to develop a good working knowledge of the <code>plot</code>
function.</p>
<p>On of the most important things to know about the <code>plot</code>
function is that it can be applied to just about any <code>R</code>
object. The reason for this is that because many analytical workflows
typically have standard data visualisation steps (e.g., model fitting is
usually followed by visualising the residuals), the people who develop
<code>R</code> and <code>R</code> packages tend to develop bespoke
versions of the <code>plot</code> function. As a result, there are
special plotting methods for functions, data.frames, density objects,
fitted model objects, and so on. While this flexibility can make it
straightforward to generate the graphs that are usually expected from
certain inputs, it also means you need to always ensure that
<code>plot</code> is doing what you want it to do.</p>
<p>For example, if we know that X and Y are inversely related to one
another, we might want to visualise this relationship.</p>
<pre class="r"><code>x <- 20:1
y <- 1:20
plot(y)</code></pre>
<p><img src="Plotting_files/figure-html/unnamed-chunk-1-1.png" width="576" /></p>
<p>Note here how <code>plot()</code> has returned a graph that, at a
glance, might look correct. The X and Y axes run from 1:20, there are 20
ordered data points, and our y data are on the y. The problem is that we
knew that X and Y were inversely related, but this figure shows a
positive relationship. The <code>plot</code> function’s flexibility is
reason for the discrepancy between the expectation and the graph that
has been returned. When provided with a vector of values,
<code>plot</code> displays these values based on a simple index of the
order the individual values were provided (i.e., the first Y value was
1, so is displayed at 1,1, the second Y value was 2, so it is displayed
at 2,2, and so on).</p>
<p>In this case, it is important to tell the <code>plot</code> function
which values belong on the X axis, and which belong on the Y.</p>
<pre class="r"><code>plot(x = x, y = y)</code></pre>
<p><img src="Plotting_files/figure-html/unnamed-chunk-2-1.png" width="576" /></p>
<p>Now the plot properly reflects our original expectation.</p>
</div>
<div id="standard-graphs" class="section level3 tabset">
<h3 class="tabset">Standard graphs</h3>
<p>Base <code>R</code> can generate a wide range of graphical outputs.
We will use the <code>iris</code> dataset to explore these.</p>
<pre class="r"><code>data("iris")</code></pre>
<div id="scatterplots" class="section level4">
<h4>Scatterplots</h4>
<p>By default, <code>plot</code> returns a scatter plot when it is
provided with numeric X and Y values. There are two ways of defining the
X and Y components of the plot. The first is by manually defining the X
and Y components.</p>
<pre class="r"><code>plot(x = iris$Sepal.Length,
y = iris$Sepal.Width)</code></pre>
<p><img src="Plotting_files/figure-html/unnamed-chunk-4-1.png" width="576" /></p>
<p>The second option is to define a formula, much as you would when
building a regression model.</p>
<pre class="r"><code>plot(formula = Sepal.Width ~ Sepal.Length,
data = iris)</code></pre>
<p><img src="Plotting_files/figure-html/unnamed-chunk-5-1.png" width="576" /></p>
<p>Be careful with these two formats because
<code>plot(Sepal.Width ~ Sepal.Length)</code> is <strong>not the
same</strong> as <code>plot(Sepal.Width, Sepal.Length)</code>.</p>
<pre class="r"><code>plot(iris$Sepal.Width ~ iris$Sepal.Length)
plot(iris$Sepal.Width, iris$Sepal.Length)</code></pre>
<p><img src="Plotting_files/figure-html/unnamed-chunk-6-1.png" width="50%" /><img src="Plotting_files/figure-html/unnamed-chunk-6-2.png" width="50%" /></p>
</div>
<div id="boxplots" class="section level4">
<h4>Boxplots</h4>
<p>In R, boxplots can be generated by using the <code>boxplot()</code>
function. The <code>boxplot()</code> function takes in any number of
numeric vectors, drawing a boxplot for each vector.</p>
<pre class="r"><code>boxplot(iris$Petal.Length)</code></pre>
<p><img src="Plotting_files/figure-html/unnamed-chunk-7-1.png" width="576" /></p>
<p>It is also possible to draw multiple boxplots on a single plot, by
passing in a list, data frame or multiple vectors</p>
<pre class="r"><code>boxplot(iris$Petal.Length, iris$Sepal.Width, iris$Petal.Length)</code></pre>
<p><img src="Plotting_files/figure-html/unnamed-chunk-8-1.png" width="576" /></p>
<p>The <code>boxplot()</code> function can also take formulas of the
form <code>y~x</code> where, <code>y</code> is a numeric vector which is
grouped according to the value of <code>x</code>.</p>
<pre class="r"><code>boxplot(formula = Petal.Length ~ Species,
data = iris)</code></pre>
<p><img src="Plotting_files/figure-html/unnamed-chunk-9-1.png" width="576" /></p>
</div>
<div id="barplots" class="section level4">
<h4>Barplots</h4>
<p>Barplots are useful for comparing values side by side. The tend not
to be used on raw data, but rather on statistics that are output from
some analysis. For example, we can use barplots to compare the mean
sepal length between irish species.</p>
<pre class="r"><code>MEANS <- aggregate(Sepal.Length ~ Species,
data = iris,
FUN = "mean")
barplot(MEANS[,2])</code></pre>
<p><img src="Plotting_files/figure-html/unnamed-chunk-10-1.png" width="576" /></p>
<p>Barplots can also be used to explore sampling structures. For
example, when paired with the table function, we can visualise the
sampling of trait values across the tree species of irish in the
dataset.</p>
<pre class="r"><code>COUNTS <- table(iris$Species)
barplot(COUNTS)</code></pre>
<p><img src="Plotting_files/figure-html/unnamed-chunk-11-1.png" width="576" /></p>
<p>We can see that the sampling is even across all three species of
iris.</p>
</div>
<div id="histograms-and-density-plots" class="section level4">
<h4>Histograms and Density Plots</h4>
<p>Histograms are useful for understanding the distribution of a
dataset. In <code>R</code>, histograms can be generated by using the
<code>hist()</code> function, which computes a histogram of the given
data values.</p>
<pre class="r"><code>hist(iris$Petal.Length)</code></pre>
<p><img src="Plotting_files/figure-html/unnamed-chunk-12-1.png" width="576" /></p>
<p>Histograms have discrete bins. Sometimes we might be interested in a
continuous representation of the distribution of a particular dataset.
Unlike the <code>hist()</code> function, this is a two step process that
involves first estimating the density, and then plotting the
results.</p>
<pre class="r"><code>DENSITY <- density(iris$Petal.Length)
plot(DENSITY)</code></pre>
<p><img src="Plotting_files/figure-html/unnamed-chunk-13-1.png" width="576" /></p>
</div>
</div>
<div id="improving-basic-plots" class="section level3">
<h3>Improving basic plots</h3>
<p>The basic plots that are returned by <code>R</code> can be perfectly
fine for first passes at data exploration. They are rarely publication
quality however. To counter this, the plot function has a large number
of arguments that can be used to improve the quality of a plot. Some of
the more useful ones include:</p>
<pre class="r"><code>main = "Title" # add a title above the graph
pch = 16 # set shape plot symbol (16 is a filled circle)
col = "red" # set the item color
xlim = c(-10,10) # set limits of the x-axis (horizontal axis)
ylim = c(0,100) # set limits of the y-axis (vertical axis)
lty = 2 # set line type to dashed
las = 2 # rotate axis labels to be perpendicular to axis
cex = 1.5 # magnify the plotting symbols 1.5-fold
cex.lab = 1.5 # magnify the axis labels 1.5-fold
cex.axis = 1.3 # magnify the axis annotation 1.3-fold
xlab = " X (units)" # label for the x-axis
ylab = "Y (units)" # label for the y-axis
ylab = "Y (units)" # label for the y-axis
family = "serif" # font ype to apply</code></pre>
<p>When used in conjunction, it can possible to produce high quality
plots.</p>
<pre class="r"><code>plot(formula = Sepal.Width ~ Sepal.Length,
data = iris,
main = "Iris traits",
pch = 16,
col = Species,
xlim = c(0,10),
ylim = c(0,5),
cex = 0.8,
cex.lab = 1.5,
cex.axis = 1.3,
xlab = " Sepal width (cm)",
ylab = "Sepal length (cm)",
family = "serif")</code></pre>
<p><img src="Plotting_files/figure-html/unnamed-chunk-15-1.png" width="576" /></p>
<hr />
</div>
</div>
<div id="ggplot2" class="section level2">
<h2>ggplot2</h2>
<p>Credit: This tutorial was developed in part by Jacob Hubner</p>
<p>An alternative to base <code>R</code> are the methods from the
<code>ggplot2</code> package. Building a graph using ggplot involves the
combination of components or “layers” including data, “aesthetics” that
map variables to visuals, and “geoms” that create different kinds of
plots.</p>
<pre class="r"><code>library(ggplot2)</code></pre>
<p>Let’s start with some data. Mtcars is a readily accessible dataset
within R itself, so you can call it anytime.</p>
<pre class="r"><code>data(mtcars)</code></pre>
<p>We can explore any plot we want using a mix of catagorical variables
(cyl,am,vs,gear,carb) and quantitative variable
(mpg,disp.hp,drat,wt,qsec)</p>
<p>To use ggplot you must always start with the ggplot function. This
doesn’t create anything, but sets the scene for whatever plots you are
wanting to make. It is here that you can choose to dictate the data you
will use for the rest of the plot, using the ‘data’ and ‘aes’ arguments.
‘aes’ defines your x and y coordinates, and also any groupings you might
want to make. It’s after this that you begin to define your graph
type.</p>
<pre class="r"><code># Common graphing options:
# Scatterplot: + geom_point() - good for two quantitative variables
# Line: + geom_line - good for trends over time
# Boxplot: + geom_boxplot() - good for one quantitatve and one catagorical variable
# Bar graph: + geom_bar() - similar situation as a boxplot.
# Histogram: + geom_histogram() - only x-axis used, good for counts.</code></pre>
<p>Let’s see some simple examples.</p>
<pre class="r"><code>ggplot(mtcars,aes(x=wt,y=qsec)) +
geom_point()</code></pre>
<p><img src="Plotting_files/figure-html/unnamed-chunk-19-1.png" width="576" /></p>
<pre class="r"><code># Now with colours!
ggplot(mtcars,aes(x=wt,y=qsec,col=cyl)) +
geom_point()</code></pre>
<p><img src="Plotting_files/figure-html/unnamed-chunk-19-2.png" width="576" /></p>
<pre class="r"><code>ggplot(mtcars,aes(x=wt,y=disp)) +
geom_line()</code></pre>
<p><img src="Plotting_files/figure-html/unnamed-chunk-19-3.png" width="576" /></p>
<pre class="r"><code>ggplot(mtcars,aes(x=cyl,y=qsec)) +
geom_boxplot()</code></pre>
<p><img src="Plotting_files/figure-html/unnamed-chunk-19-4.png" width="576" /></p>
<pre class="r"><code># You'll notice that ggplot has difficulty understanding that categorical variables with numbers are indeed categorical. To fix this, we can turn them into factors. We should do this in our own dataframe.
cars <- data.frame(mtcars,
am.f = as.factor(mtcars$am),
cyl.f=as.factor(mtcars$cyl))
# Now try...
ggplot(cars,aes(x=cyl.f,y=qsec)) +
geom_boxplot()</code></pre>
<p><img src="Plotting_files/figure-html/unnamed-chunk-19-5.png" width="576" /></p>
<pre class="r"><code># Much better!
ggplot(cars,aes(x=am.f,y=qsec)) +
geom_bar(stat="identity")</code></pre>
<p><img src="Plotting_files/figure-html/unnamed-chunk-19-6.png" width="576" /></p>
<pre class="r"><code>ggplot(cars,aes(x=qsec)) +
geom_histogram()</code></pre>
<p><img src="Plotting_files/figure-html/unnamed-chunk-19-7.png" width="576" /></p>
<pre class="r"><code># To do a histogram with categorical variables, make a barplot with only an x aesthetic instead.
ggplot(cars,aes(x=cyl.f)) +
geom_bar()</code></pre>
<p><img src="Plotting_files/figure-html/unnamed-chunk-19-8.png" width="576" /></p>
<p>Now lets go back to the scatterplot and add a trendline, the easiest
line to make in a linear one, but you can plot any model you can think
of with a couple steps. Let’s use geom_smooth…</p>
<pre class="r"><code>ggplot(mtcars,aes(x=wt,y=disp)) +
geom_point()</code></pre>
<p><img src="Plotting_files/figure-html/unnamed-chunk-20-1.png" width="576" /></p>
<pre class="r"><code>ggplot(mtcars,aes(x=wt,y=disp)) +
geom_point() +
geom_smooth(formula=y~x, se=F)</code></pre>
<p><img src="Plotting_files/figure-html/unnamed-chunk-20-2.png" width="576" /></p>
<pre class="r"><code>ggplot(mtcars,aes(x=wt,y=disp)) +
geom_point() +
geom_smooth(method="lm",formula=y~x, se=F)</code></pre>
<p><img src="Plotting_files/figure-html/unnamed-chunk-20-3.png" width="576" /></p>
<pre class="r"><code>ggplot(mtcars,aes(x=wt,y=disp)) +
geom_point() +
geom_smooth(method="lm",formula=y~x, se=T)</code></pre>
<p><img src="Plotting_files/figure-html/unnamed-chunk-20-4.png" width="576" /></p>
<p>When things get complicated, you can define aesthetics on a
case-by-case basis instead of cramming everything into one
dataframe.</p>
<pre class="r"><code>lm1 <- lm(disp~wt,data=cars) # This can be any model you can think of. GLM, non-linear, whatever!
df2 <- data.frame(cars,fit=fitted(lm1))
ggplot() +
geom_point(data=cars,aes(x=wt,y=disp,col=mpg)) +
geom_line(data=df2,aes(x=wt,y=fit))</code></pre>
<p><img src="Plotting_files/figure-html/unnamed-chunk-21-1.png" width="576" /></p>
<p>Once the general behaviour of the plot is working as expected, there
are a lot of options for personalising the look of the figure. There are
many useful tutorials for this online, however, here is a series of
arguments to get you started with an uncluttered figure.</p>
<pre class="r"><code>ggplot(mtcars,aes(x=wt,y=disp)) +
geom_point() +
geom_smooth(method="lm",
formula=y~x,
col="red") +
theme_bw() +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")) +
ggtitle("Effect of Car Weight on Displacement") +
xlab("Weight (1000lbs)") +
ylab("Displacement (cubic inches)") +
theme(plot.title = element_text(hjust = 0.5))</code></pre>
<p><img src="Plotting_files/figure-html/unnamed-chunk-22-1.png" width="576" /></p>
<p>There’s a lot more to discover but hopefully this is a useful
launchpad into the world of ggplot.</p>
</div>
</div>
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