Part 1: Data visualisation with ggplot2

Overview

Teaching: 50 min
Exercises: 25 min

Questions

What are the components of a ggplot?

How do I create scatterplots, boxplots, and barplots?

How can I change the aesthetics (ex. colour, transparency) of my plot?

How can I create multiple plots at once?

Objectives

Produce scatter plots, boxplots, and time series plots using ggplot.

Set universal plot settings.

Describe what faceting is and apply faceting in ggplot.

Modify the aesthetics of an existing ggplot plot (including axis labels and color).

Build complex and customized plots from data in a data frame.

We start by loading the required package. ggplot2 is also included in the tidyverse package.

library(tidyverse)

If not still in the workspace, load the data we saved in the previous lesson.

test_1_raw <- read_csv('data_output/test_1_raw_totals.csv') %>%
  mutate(country = as.factor(country)) # changes the country variable to a factor

Parsed with column specification:
cols(
  ID = col_double(),
  country = col_character(),
  raw_total = col_double(),
  list_raw_total = col_double(),
  list_an_raw = col_double(),
  read_raw_total = col_double(),
  read_an_raw = col_double()
)

# test_results_2 <- read_csv('data/placement_2.csv') %>%
#   mutate(country = as.factor(country)) # changes the country variable to a factor

Plotting with `ggplot2`

ggplot2 is a plotting package that makes it simple to create complex plots from data stored in a data frame. It provides a programmatic interface for specifying what variables to plot, how they are displayed, and general visual properties. Therefore, we only need minimal changes if the underlying data change or if we decide to change from a bar plot to a scatterplot. This helps in creating publication quality plots with minimal amounts of adjustments and tweaking.

ggplot2 functions like data in the ‘long’ format, i.e., a column for every dimension, and a row for every observation. Well-structured data will save you lots of time when making figures with ggplot2

ggplot graphics are built step by step by adding new elements. Adding layers in this fashion allows for extensive flexibility and customization of plots.

To build a ggplot, we will use the following basic template that can be used for different types of plots:

ggplot(data = <DATA>, mapping = aes(<MAPPINGS>)) +  <GEOM_FUNCTION>()

use the ggplot() function and bind the plot to a specific data frame using the data argument

ggplot(data = test_1_raw)

define a mapping (using the aesthetic (aes) function), by selecting the variables to be plotted and specifying how to present them in the graph, e.g. as x/y positions or characteristics such as size, shape, color, etc.

ggplot(data = test_1_raw, aes(x = read_raw_total, y = list_raw_total))

add ‘geoms’ – graphical representations of the data in the plot (points, lines, bars). ggplot2 offers many different geoms; we will use some common ones today, including:
geom_point() for scatter plots, dot plots, etc.
geom_boxplot() for, well, boxplots!
geom_line() for trend lines, time series, etc.

To add a geom to the plot use the + operator. Because we have two continuous variables, let’s use geom_point() first:

ggplot(data = test_1_raw, aes(x = read_raw_total, y = list_raw_total)) +
    geom_point()

plot of chunk first-ggplot

The + in the ggplot2 package is particularly useful because it allows you to modify existing ggplot objects. This means you can easily set up plot templates and conveniently explore different types of plots, so the above plot can also be generated with code like this:

# Assign plot to a variable
sub_skill_scatter <- ggplot(data = test_1_raw, aes(x = read_raw_total, y = list_raw_total))

# Draw the plot
sub_skill_scatter +
    geom_point()

Notes

Anything you put in the ggplot() function can be seen by any geom layers that you add (i.e., these are universal plot settings). This includes the x- and y-axis mapping you set up in aes().

You can also specify mappings for a given geom independently of the mapping defined globally in the ggplot() function.

The + sign used to add new layers must be placed at the end of the line containing the previous layer. If, instead, the + sign is added at the beginning of the line containing the new layer, ggplot2 will not add the new layer and will return an error message.

## This is the correct syntax for adding layers
sub_skill_scatter +
    geom_point()

## This will not add the new layer and will return an error message
sub_skill_scatter
+ geom_point()

Building your plots iteratively

Building plots with ggplot2 is typically an iterative process. We start by defining the dataset we’ll use, lay out the axes, and choose a geom:

ggplot(data = test_1_raw, aes(x = read_raw_total, y = list_raw_total)) +
  geom_point()

plot of chunk create-ggplot-object

Then, we start modifying this plot to extract more information from it. For instance, we can add transparency (alpha) to avoid overplotting:

ggplot(data = test_1_raw, aes(x = read_raw_total, y = list_raw_total)) +
    geom_point(alpha = 0.5)

plot of chunk adding-transparency

That only helped a little bit with the overplotting problem. We can also introduce a little bit of randomness into the position of our points using the geom_jitter() function.

ggplot(data = test_1_raw, aes(x = read_raw_total, y = list_raw_total)) +
    geom_jitter(alpha = 0.5)

plot of chunk adding-jitter

We can also add colors for all the points:

ggplot(data = test_1_raw, aes(x = read_raw_total, y = list_raw_total)) +
  geom_jitter(alpha = 0.5, color = "blue")

plot of chunk adding-colors

Or to color dots by country of origin in the plot differently, you could use a vector as an input to the argument color. Because we are now mapping features of the data to a color, instead of setting one color for all points, the color now needs to be set inside a call to the aes function. ggplot2 will provide a different color corresponding to different values in the vector. We set the value of alpha outside of the aes function call because we are using the same value for all points. Here is an example where we color by country:

ggplot(data = test_1_raw, aes(x = read_raw_total, y = list_raw_total)) +
    geom_jitter(aes(color = country), alpha = 0.5)

plot of chunk color-by-species

There is a positive relationship between the raw scores on the reading and listening tests. There does not seem to be a difference in this relationship by country of origin.

Exercise

Use what you just learned to create a scatter plot of read_an_raw by list_an_raw with the country showing in different colors. Is this a good way to show this type of data?
Solution
ggplot(data = test_1_raw, aes(x = read_an_raw, y = list_an_raw)) +
   geom_jitter(aes(color = country), alpha = 0.5)

Boxplot

We can use boxplots to visualize the distribution of total raw test scores across countries of origin:

ggplot(data = test_1_raw, aes(x = country, y = raw_total)) +
    geom_boxplot()

plot of chunk boxplot

By adding points to a boxplot, we can have a better idea of the number of measurements and of their distribution:

ggplot(data = test_1_raw, aes(x = country, y = raw_total)) +
    geom_boxplot(alpha = 0) +
    geom_jitter(alpha = 0.5, color = "tomato", width = .20)

plot of chunk boxplot-with-points

Notice how the boxplot layer is behind the jitter layer? What do you need to change in the code to put the boxplot in front of the points such that it’s not hidden?

Exercise

Boxplots are useful summaries, but hide the shape of the distribution. For example, if the distribution is bimodal, we would not see it in a boxplot. An alternative to the boxplot is the violin plot, where the shape (of the density of points) is drawn.

Replace the box plot with a violin plot; see geom_violin().
Solution
ggplot(data = test_1_raw, aes(x = country, y = raw_total)) +
  geom_violin(alpha = 0) +
  geom_jitter(alpha = 0.5, color = "tomato")

Barplots

Barplots are also useful for visualizing categorical data. By default, geom_bar accepts a variable for x, and plots the number of instances each value of x (in this case, wall type) appears in the dataset.

ggplot(data = test_1_raw, aes(x = country)) +
    geom_bar()

plot of chunk barplot-1

Let’s say that, hypothetically, people who recieved a raw score of greater than a 50 are admitted into the university, or some kind of program. We can include mutate, case_when, %>%, and ggplot to create a new variable called admitted. Then we can plot it.

# we can add a new column to the dataset called `admitted`
test_1_raw <- test_1_raw %>%
  mutate(admitted = case_when(raw_total > 50 ~ 'yes',
                              raw_total <= 50 ~ 'no')) 
admit_bar <- test_1_raw%>%
  ggplot(., aes(x = country, fill = admitted)) +
  geom_bar(position = 'dodge') # places the bars next to each other

Exercise

Pretend that in order to be admitted, test takers need a reading subscore 25 or greater. Create a bar plot showing the counts of respondents from each country of origin who recieved a raw reading score greater or equal to than 25.
Solution
admit_bar_read <- test_1_raw %>%
 mutate(admitted_read = case_when(read_raw_total >= 25 ~ 'yes',
                             read_raw_total < 25 ~ 'no')) %>%
 ggplot(., aes(x = country, fill = admitted_read)) +
 geom_bar(position = 'dodge') # places the bars next to each other

Adding Labels and Titles

By default, the axes labels on a plot are determined by the name of the variable being plotted. However, ggplot2 offers lots of customization options, like specifying the axes labels, and adding a title to the plot with relatively few lines of code. We will add more informative x and y axis labels to our plot of proportion of house type by village and also add a title.

ggplot(data = test_1_raw, aes(x = read_raw_total, y = list_raw_total)) +
    geom_jitter(aes(color = country), alpha = 0.5) +
    xlab("Total Raw Reading Scores") +
    ylab("Total Raw Listening Scores") +
    ggtitle("Listening <> Reading Subscores") +
    scale_color_viridis_d(labels = c('China', 'Morocco', 'Russia'), # add caps to key
                          name = 'Country') # add caps to legend title

plot of chunk scatter-labeled

Faceting

Rather than creating a single plot with side-by-side bars or points for each country of origin, we may want to create multiple plot, where each plot shows the data for a single country of origin. This would be especially useful if we had a large number of villages that we had sampled, as a large number of side-by-side bars will become more difficult to read.

ggplot2 has a special technique called faceting that allows the user to split one plot into multiple plots based on a factor included in the dataset. We will use it to split our scatterplot of of listening and reading scores by country of origin so that each country of origin has it’s own panel in a multi-panel plot:

title_case <- as_labeller(c('china' = 'China',
                            'morocco' = 'Morocco',
                            'russia' = 'Russia'))

ggplot(data = test_1_raw, aes(x = read_raw_total, y = list_raw_total)) +
    geom_jitter(alpha = 0.5) +
    xlab("Total Raw Reading Scores") +
    ylab("Total Raw Listening Scores") +
    ggtitle("Listening <> Reading Subscores") +
    facet_wrap(~ country, nrow = 1, labeller = title_case)

plot of chunk barplot-faceting

Click the “Zoom” button in your RStudio plots pane to view a larger version of this plot.

Usually plots with white background look more readable when printed. We can set the background to white using the function theme_bw(). Additionally, you can remove the grid:

title_case <- as_labeller(c('china' = 'China',
                            'morocco' = 'Morocco',
                            'russia' = 'Russia'))

ggplot(data = test_1_raw, aes(x = read_raw_total, y = list_raw_total)) +
    geom_jitter(alpha = 0.5) +
    xlab("Total Raw Reading Scores") +
    ylab("Total Raw Listening Scores") +
    ggtitle("Listening <> Reading Subscores") +
    facet_wrap(~ country, nrow = 1, labeller = title_case) +
    theme_bw(base_size = 18) + # increase base font size to 18
    theme(panel.grid = element_blank())

plot of chunk barplot-theme-bw

What if we wanted to see histograms of the total scores on the test by country of origin:

total_score_hist <- test_1_raw %>%
  ggplot(., aes(x = raw_total)) +
  geom_histogram(binwidth = 5) + # each bin is contains a range of scores within 5 points
  facet_wrap(~ country, labeller = title_case, scales = 'fixed') +
  ylab('Counts') +
  xlab('Total Raw Test Score') +
  ggtitle('Total Raw Test Score Distribution by Country')

`ggplot2` themes

In addition to theme_bw(), which changes the plot background to white, ggplot2 comes with several other themes which can be useful to quickly change the look of your visualization. The complete list of themes is available at http://docs.ggplot2.org/current/ggtheme.html. theme_minimal() and theme_light() are popular, and theme_void() can be useful as a starting point to create a new hand-crafted theme.

The ggthemes package provides a wide variety of options (including an Excel 2003 theme). The ggplot2 extensions website provides a list of packages that extend the capabilities of ggplot2, including additional themes.

Exercise

Experiment with at least two different themes. Build the previous plot using each of those themes. Which do you like best?

Customization

Take a look at the ggplot2 cheat sheet, and think of ways you could improve the plot.

Exercise

With all of this information in hand, please take another five minutes to either improve one of the plots generated in this exercise or create a beautiful graph of your own. Use the RStudio ggplot2 cheat sheet for inspiration. Here are some ideas:

See if you can make the bars white with black outline.

Try using a different color palette (see http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/).

After creating your plot, you can save it to a file in your favorite format. The Export tab in the Plot pane in RStudio will save your plots at low resolution, which will not be accepted by many journals and will not scale well for posters.

Instead, use the ggsave() function, which allows you easily change the dimension and resolution of your plot by adjusting the appropriate arguments (width, height and dpi).

Make sure you have the fig_output/ folder in your working directory.

ggsave("fig_output/name_of_file.png", total_score_hist, width = 6, height = 4, units = 'in', dpi = 600)

Note: The parameters width and height also determine the font size in the saved plot.

Key Points

ggplot2 is a flexible and useful tool for creating plots in R.

The data set and coordinate system can be defined using the ggplot function.

Additional layers, including geoms, are added using the + operator.

Boxplots are useful for visualizing the distribution of a continuous variable.

Barplot are useful for visualizing categorical data.

Faceting allows you to generate multiple plots based on a categorical variable.

previous episode

R for Assessment Specialists

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Part 1: Data visualisation with ggplot2

Overview

Plotting with `ggplot2`

Notes

Building your plots iteratively

Exercise

Solution

Boxplot

Exercise

Solution

Barplots

Exercise

Solution

Adding Labels and Titles

Faceting

`ggplot2` themes

Exercise

Customization

Exercise

Key Points

previous episode

next episode

previous episode

R for Assessment Specialists

next episode

Part 1: Data visualisation with ggplot2

Overview

Plotting with ggplot2

Notes

Building your plots iteratively

Exercise

Solution

Boxplot

Exercise

Solution

Barplots

Exercise

Solution

Adding Labels and Titles

Faceting

ggplot2 themes

Exercise

Customization

Exercise

Key Points

previous episode

next episode

Plotting with `ggplot2`

`ggplot2` themes