# DataJam 2021 Lesson 3 : Intro to ggplot2

Workshop info

• When: September 18th, 1:00pm (PST, Vancouver, BC)
• Where: Virtual
• Requirements: Participants must have a laptop or desktop with a Mac, Linux, or Windows operating system. (Tablets and Chromebooks are not advised.) Please have the latest version of R and RStudio downloaded and running (free!).
• Code of conduct: Everyone participating in the Vancouver DataJam activities are required to conform to the Code of Conduct

These materials have been adapted from the Software Carpentry: R Novice Lesson. You can find the original materials here.

Plotting our data is one of the best ways to quickly explore it and the various relationships between variables.

There are three main plotting systems in R, the base plotting system, the lattice package, and the ggplot2 package.

Today we’ll be learning about the ggplot2 package, because it is the most effective for creating publication quality graphics. ggplot2 is built on the grammar of graphics, the idea that any plot can be expressed from the same set of components: a data set, a coordinate system, and a set of geoms, the visual representation of data points.

The key to understanding ggplot2 is thinking about a figure in layers. This idea may be familiar to you if you have used image editing programs like Photoshop, Illustrator, or Inkscape.

Illustration by Allison Horst

First, let’s make sure everything’s loaded:

``````# Download the packages
# install.packages(c("tidyverse", "gapminder"))

# Load the packages for use
library(tidyverse)
library(gapminder)``````

## Simple ggplot

Let’s start off with an example:

``````ggplot(data = gapminder, aes(x = gdpPercap, y = lifeExp)) +
geom_point()``````

So the first thing we do is call the `ggplot()`. This function lets R know that we’re creating a new plot, and any of the arguments we give the `ggplot()` are the global options for the plot: they apply to all layers on the plot.

We’ve passed in two arguments to `ggplot()`. First, we provide what data we want to show on our figure, in this example the `gapminder` data we read in earlier. For the second argument we passed `aes()` that tells which variables/columns from the data we want to use. Here we told ggplot that we want to plot the “gdpPercap” column of the gapminder data frame on the x-axis, and the “lifeExp” column on the y-axis.

By itself, `ggplot()` only has the perameters of what to draw, but we haven’t indicated how we want the data to be drawn.

``ggplot(data = gapminder, aes(x = gdpPercap, y = lifeExp))``

Next we need to indicate how we want to visually represent the data, which we do by adding `geom_()` layers. In our example, we use `geom_point()` to visually represent the relationship between x and y as a scatterplot of points:

``````ggplot(data = gapminder, aes(x = gdpPercap, y = lifeExp)) +
geom_point()``````

### Challenge 1: (< 5 mins)

Modify the example so that the figure shows how life expectancy has changed over time:

``````# Previous example:
ggplot(data = gapminder, aes(x = gdpPercap, y = lifeExp)) +
geom_point()``````

Hint: use `glimpse()` to explore the different columns that are available in the `gapminer` data frame.

### Challenge 2: (< 5 mins)

Another aesthetic property we can modify is the point colour. Modify the code from the previous challenge to colour the points by the “continent” column. What trends do you see in the data? Are they what you expected?

## Layers

Using a scatterplot probably isn’t the best for visualizing change over time. Instead, let’s tell `ggplot` to visualize the data as a line plot. Here we’ll also shorten our code by omitting some of the declarations (e.g. x = year). By default, the first object passed to `ggplot()` is assumed to be the `data` argument. Similarly, the first two column names of `aes()` are assumed to be the x and y arguments.

``````ggplot(gapminder, aes(x = year, y = lifeExp, by = country, colour = continent)) +
geom_line()``````

Instead of adding a `geom_point` layer, we’ve added a `geom_line` layer. We’ve added the by aesthetic, which tells `ggplot` to draw a line for each country.

But what if we want to visualize both lines and points on the plot? We can simply add another layer to the plot:

``````ggplot(gapminder, aes(year, lifeExp, by = country, colour = continent)) +
geom_line() +
geom_point()``````

It’s important to note that each layer is drawn on top of the previous layer. In this example, the points have been drawn on top of the lines. Here’s a demonstration:

``````ggplot(gapminder, aes(year, lifeExp, by = country)) +
geom_line(aes(colour = continent)) +
geom_point()``````

In this example, the aesthetic mapping of colour has been moved from the global plot options in `ggplot` to the `geom_line` layer so it no longer applies to the points. Now we can clearly see that the points are drawn on top of the lines.

### Tip: Setting an aesthetic to a value instead of a mapping

So far, we’ve seen how to use an aesthetic (such as colour) as a mapping to a variable in the data. For example, when we use `geom_line(aes(colour = continent))`, ggplot will give a different colour to each continent. But what if we want to change the colour of all lines to blue? You may think that `geom_line(aes(colour = "blue"))` should work, but it doesn’t. Since we don’t want to create a mapping to a specific variable, we simply move the colour specification outside of the `aes()` function, like this: `geom_line(colour = "blue")`.

## Transformations and statistics

ggplot2 also makes it easy to overlay statistical models over the data. To demonstrate we’ll go back to our first example:

``````ggplot(gapminder, aes(gdpPercap, lifeExp, colour = continent)) +
geom_point()``````

Currently it’s hard to see the relationship between the points due to some strong outliers in GDP per capita. We can change the scale of units on the x axis using the scale functions. These control the mapping between the data values and visual values of an aesthetic. We can also modify the transparency of the points, using the alpha function, which is especially helpful when you have a large amount of data which is very clustered.

``````ggplot(gapminder, aes(gdpPercap, lifeExp)) +
geom_point(alpha = 0.5) +
scale_x_log10()``````

The `log10` function applied a transformation to the values of the gdpPercap column before rendering them on the plot, so that each multiple of 10 now only corresponds to an increase in 1 on the transformed scale, e.g. a GDP per capita of 1,000 is now 3 on the y axis, a value of 10,000 corresponds to 4 on the y axis and so on. This makes it easier to visualize the spread of data on the x-axis.

### Reminder: Setting an aesthetic to a value instead of a mapping

Notice that we used `geom_point(alpha = 0.5)`. As the previous tip mentioned, using a setting outside of the `aes()` function will cause this value to be used for all points, which is what we want in this case. But just like any other aesthetic setting, alpha can also be mapped to a variable in the data. For example, we can give a different transparency to each continent with `geom_point(aes(alpha = continent))`.

We can fit a simple relationship to the data by adding another layer, `geom_smooth`:

``````ggplot(gapminder, aes(gdpPercap, lifeExp)) +
geom_point() +
scale_x_log10() +
geom_smooth(method = "lm")``````

We can make the line thicker by setting the size aesthetic in the `geom_smooth` layer:

``````ggplot(gapminder, aes(gdpPercap, lifeExp)) +
geom_point() +
scale_x_log10() +
geom_smooth(method = "lm", size = 1.5)``````

There are two ways an aesthetic can be specified. Here we set the size aesthetic by passing it as an argument to `geom_smooth`. Previously in the lesson we’ve used the `aes` function to define a mapping between data variables and their visual representation.

### Challenge 4a: (< 5 mins)

Modify the colour and size of the points on the point layer in the previous example. Hint: do not use the `aes` function.

### Challenge 4b: (< 5 mins)

Modify your solution to Challenge 4a so that the points are now a different shape and are coloured by continent with new trend lines. Hint: The colour argument can be used inside the aesthetic.

## Multi-panel figures

Earlier we visualized the change in life expectancy over time across all countries in one plot. Alternatively, we can split this out over multiple panels by adding a layer of facet panels. Here we will first make a smaller version of the data to make it easier to work with. The `%in%` operator just means that country must be equal to one of the four specified countries.

``````gapminder_small <- gapminder %>%
filter(country %in% c("Canada", "United States", "France", "Australia"))

ggplot(gapminder_small, aes(year, lifeExp, colour = continent)) +
geom_line() +
facet_wrap(~country)``````

The `facet_wrap` layer took a “formula” as its argument, denoted by the tilde (`~`). This tells R to draw a panel for each unique value in the country column of the `gapminder_small` data.

## Modifying text

To clean this figure up for a publication we need to change some of the text elements. The x-axis is too cluttered, and the y axis should read “Life expectancy”, rather than the column name in the data frame.

We can do this by adding a couple of different layers. The theme layer controls the axis text, and overall text size. Labels for the axes, plot title and any legend can be set using the `labs` function. Legend titles are set using the same names we used in the `aes` specification. Thus below the colour legend title is set using `colour = "Continent"`, while the title of a fill legend would be set using `fill = "MyTitle"`.

``````gapminder_small <- gapminder %>%
filter(country %in% c("Canada", "United States", "France", "Australia"))

ggplot(gapminder_small, aes(year, lifeExp, colour = continent)) +
geom_line() +
facet_wrap(~country) +
labs(x = "Year", y = "Life expectancy", title = "Figure 1", colour = "Continent") +
theme_bw() +
theme(axis.text.x = element_blank(), axis.ticks.x = element_blank())``````

## Exporting the plot

The `ggsave()` function allows you to export a plot created with ggplot. You can specify the dimension and resolution of your plot by adjusting the appropriate arguments (`width`, `height` and `dpi`) to create high quality graphics for publication. In order to save the plot from above, we first assign it to a variable `lifeExp_plot`, then tell `ggsave` to save that plot in `png` format to a directory called `results`. (Make sure you have a `results/` folder in your working directory.)

``````lifeExp_plot <- ggplot(gapminder_small, aes(year, lifeExp, colour = continent)) +
geom_line() +
facet_wrap(~country) +
labs(x = "Year", y = "Life expectancy", title = "Figure 1", colour = "Continent") +
theme_bw() +
theme(axis.text.x = element_blank(), axis.ticks.x = element_blank())

ggsave(plot = lifeExp_plot, filename = "results/lifeExp.png", units = "cm", width = 12, height = 10, dpi = 300)``````

There are two nice things about `ggsave`. First, it defaults to the last plot, so if you omit the `plot` argument it will automatically save the last plot you created with `ggplot`. Secondly, it tries to determine the format you want to save your plot in from the file extension you provide for the filename (for example `.png` or `.pdf`). If you need to, you can specify the format explicitly in the `device` argument.

## Summary

This is a taste of what you can do with ggplot2. RStudio provides a really useful cheat sheet of the different layers available, and more extensive documentation is available on the ggplot2 website. Finally, if you have no idea how to change something, a quick Google search will usually send you to a relevant question and answer on Stack Overflow with reusable code to modify!

These lessons are a part of a two full-day R Carpentires workshop. If you are interested in a in depth dive into R for you and your organization please feel free to contact me and I would be happy to tailor a workshop for your organization, otherwise follow me on Twitter as I periodically offer this workshop.

## Final thoughts…

### Good luck on your R journey!

Illustration by Allison Horst

## Extra plotting exercises

### Challenge 5: (5 mins)

Create a density plot of GDP per capita, filled by continent.

Advanced:

• Transform the x axis to better visualise the data spread
• Add a facet layer to panel the density plots by year
• Make the y-axis range independent for each facet (year)

## Making Heatmaps with `corrplot`

While there is a lot that ggplot2 can do, one limitation is in the construction of heatmaps, another common type of visualiztion in many disciplines of science. Let’s start by installing, then loading it into RStudio.

``````# install.packages("corrplot")
library(corrplot)``````

Great! Now we’ll use the `mtcars` data set included in R to try out different heatmaps.

``````# Quick look at mtcars.
head(mtcars)``````
``````                   mpg cyl disp  hp drat    wt  qsec vs am gear carb
Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1``````
``````# Pearson correlation is default. See ?cor for other available methods
cor_mtcars <- cor(mtcars)
head(cor_mtcars)``````
``````            mpg        cyl       disp         hp       drat         wt
mpg   1.0000000 -0.8521620 -0.8475514 -0.7761684  0.6811719 -0.8676594
cyl  -0.8521620  1.0000000  0.9020329  0.8324475 -0.6999381  0.7824958
disp -0.8475514  0.9020329  1.0000000  0.7909486 -0.7102139  0.8879799
hp   -0.7761684  0.8324475  0.7909486  1.0000000 -0.4487591  0.6587479
drat  0.6811719 -0.6999381 -0.7102139 -0.4487591  1.0000000 -0.7124406
wt   -0.8676594  0.7824958  0.8879799  0.6587479 -0.7124406  1.0000000
qsec         vs         am       gear       carb
mpg   0.41868403  0.6640389  0.5998324  0.4802848 -0.5509251
cyl  -0.59124207 -0.8108118 -0.5226070 -0.4926866  0.5269883
disp -0.43369788 -0.7104159 -0.5912270 -0.5555692  0.3949769
hp   -0.70822339 -0.7230967 -0.2432043 -0.1257043  0.7498125
drat  0.09120476  0.4402785  0.7127111  0.6996101 -0.0907898
wt   -0.17471588 -0.5549157 -0.6924953 -0.5832870  0.4276059``````
``````# Plot heatmap
corrplot(cor_mtcars, method = "circle")``````

In this heat map both size and colour corresponds to the Pearson coefficient. There are also other correlative measures we can explore:

``corrplot(cor_mtcars, method = "square")``

``corrplot(cor_mtcars, method = "number")``

Heat maps are redundant, with the top-right and bottom-left portions of the plot being the same. We can eliminate this by taking a slice of the map:

``corrplot(cor_mtcars, type = "upper")``

``corrplot(cor_mtcars, type = "lower")``

We can also mix together shapes and numbers (or any two combinations of methods) to increase the amount of information we can represent in a heat map.

``corrplot.mixed(cor_mtcars, lower = "number", upper = "circle")``

The correlation matrix can be reordered according to the correlation coefficient. This is important to identify the hidden structure and pattern in the matrix. There are multiple methods available for clustering, but we will only use the k-means hierarchical clustering method here:

``corrplot(cor_mtcars, order = "hclust")``

We can also direct `corrplot` to identify the two distinct groups we can see by eye.

``corrplot(cor_mtcars, order = "hclust", addrect = 2)``

And also groups that might not be immediately clear to us.

``corrplot(cor_mtcars, order = "hclust", addrect = 3) ``

## Challenge solutions

Challenge 1 :Here is one possible solution:

``````ggplot(data = gapminder, aes(x = year, y = lifeExp)) +
geom_point()``````

Challenge 2 :

``````ggplot(data = gapminder, aes(x = year, y = lifeExp, colour = continent)) +
geom_point()``````

### Solution to challenge 4a

``````ggplot(gapminder, aes(gdpPercap, lifeExp)) +
geom_point(size = 3, colour = "orange") +
scale_x_log10() +
geom_smooth(method = "lm", size = 1.5)``````

### Solution to Challenge 4b

Modify Challenge 4 so that the points are now a different shape and are coloured by continent with new trend lines. Hint: The colour argument can be used inside the aesthetic.

``````ggplot(gapminder, aes(gdpPercap, lifeExp, colour = continent)) +
geom_point(size = 2, alpha = 0.5, shape = "triangle") +
scale_x_log10() +
geom_smooth(method = "lm", size = 1.5)``````

### Solution to Challenge 5

``````ggplot(gapminder, aes(gdpPercap, fill = continent)) +
geom_density(alpha = 0.6) +
facet_wrap(~year, scales = "free_y") +
scale_x_log10()``````