12 More data visualization

Graphs and charts let you explore and learn about the structure of the information you collect. Good data visualizations also make it easier to communicate your ideas and findings to other people. - Kieran Healy, Data Visualization: A Practical Introduction (2019)

What’s covered in this short exercise barely scratches the surface of what’s possible in the {ggplot2} package. We will delve deeper into plotting in [communication-plot].

12.1 Reading and reference

{ggplot2} reference site

  • Gina Reynolds, the ggplot flipbook has some great examples of different types of plots made using {ggplot2}. Like an animation flipbook, you have the opportunity to “flip” through the code one line at a time, to see what changes with the addition of a line of code (or backwards, as things are removed).

12.2 Plot types

When we seek to visualize our data, we are confronted with the realization that there are many different ways to do that—there are many different plot types. How can we go about deciding what sort of plot to choose?

Your decision should be based on the answer to the question “What is the chart going to show?”

In this exercise, we will look at four common plot types.

  • scatterplots (as we used with the Anscombe’s Quartet data) show correlations between two variables

  • bar charts (and the type that is known as a dot plot or a Cleveland dot plot) are good for showing the difference between categories

  • histograms show the distribution of cases that are measured on a continuous scale and are categorized by bins of the same size

  • line plots are good for showing the change of a value over time

After we have explored how to build those, we move on to some basic plot formatting.

12.3 Scatterplot

(See Creating a Scatter Plot) in the R Graphics Cookbook

We’ve seen scatterplots in Anscombe’s Quartet, but here’s another example using the {palmerpenguin} data, which has a variety of measurements from some penguins near the Palmer Research Station in Antartica.

penguins
## # A tibble: 344 × 8
##    species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex     year
##    <fct>   <fct>              <dbl>         <dbl>             <int>       <int> <fct>  <int>
##  1 Adelie  Torgersen           39.1          18.7               181        3750 male    2007
##  2 Adelie  Torgersen           39.5          17.4               186        3800 female  2007
##  3 Adelie  Torgersen           40.3          18                 195        3250 female  2007
##  4 Adelie  Torgersen           NA            NA                  NA          NA <NA>    2007
##  5 Adelie  Torgersen           36.7          19.3               193        3450 female  2007
##  6 Adelie  Torgersen           39.3          20.6               190        3650 male    2007
##  7 Adelie  Torgersen           38.9          17.8               181        3625 female  2007
##  8 Adelie  Torgersen           39.2          19.6               195        4675 male    2007
##  9 Adelie  Torgersen           34.1          18.1               193        3475 <NA>    2007
## 10 Adelie  Torgersen           42            20.2               190        4250 <NA>    2007
## # … with 334 more rows

The question we are asking might be “Is there a relationship between the length of a bird’s flipper and their weight?” In our data, flipper length (in millimeters) is reported in the variable flipper_length_mm. For weight, we use body_mass_g, which is measured in grams.

A visualization of the relationship (the correlation) between these two variables would be shown using a scatterplot.

The {ggplot2} code for this is below:

ggplot(data = penguins, 
       aes(x = flipper_length_mm, y = body_mass_g)) +
  geom_point()

With this plot, we can overlay a straight line (the line of best fit calculated through a regression equation).

ggplot(data = penguins, 
       aes(x = flipper_length_mm, y = body_mass_g)) +
  geom_point() +
  geom_smooth(method = "lm")

This plot show that birds with longer flippers do indeed weigh more.

12.4 Bar chart

(See Creating a Bar Graph) in the R Graphics Cookbook

Bar charts are good for showing the difference between categories.

The first bar will show the number of birds (that is, rows) of each species. Note that there is only an x = variable: the value shown on the y axis, “count”, is calculated for us.

ggplot(penguins, aes(x = species)) +
  geom_bar()

12.4.1 Bar by another variable

In bar plots, we sometimes want to show a value other than the count of cases.

In this example, we first calculate the average length of the bird’s bill by species and then plot those values. The n value calculated in the summarise() function is the count of the number of cases in each class, which is to say, the same values shown in the bar chart above.

(Note that we have to use na.rm = TRUE in the calculation of mean bill length, in order to remove the NA values that appear in the variable bill_length_mm.)

# the summary data table
penguin_species_bill <- penguins %>% 
  group_by(species) %>% 
  summarise(n = n(),
            bill_length_mean = mean(bill_length_mm, na.rm = TRUE)) 

penguin_species_bill
## # A tibble: 3 × 3
##   species       n bill_length_mean
##   <fct>     <int>            <dbl>
## 1 Adelie      152             38.8
## 2 Chinstrap    68             48.8
## 3 Gentoo      124             47.5

To show the bill_length_mean value as the height of the bars, we use the geom_col() function:

ggplot(penguin_species_bill, 
       aes(x = species, 
           y = bill_length_mean)) +
    geom_col()

Note that the bars are arranged alphabetically, starting with Adelie.

Sorting categorical visualizations is often a useful way to demonstrate the differences between the categories. Here we add a reorder() argument to the x variable. You could read this as “reorder the variable”species” by the values in “bill length”.

ggplot(penguin_species_bill, 
       aes(x = reorder(species, bill_length_mean), 
           y = bill_length_mean)) +
    geom_col()

12.4.1.1 Your turn

What happens if you were to add a minus sign (hyphen) in front of the bill_length_mean of the reorder() function?

Solution

The full code would look like this:

ggplot(penguin_species_bill, 
       aes(x = reorder(species, -bill_length_mean), 
           y = bill_length_mean)) +
    geom_col()

Using the hyphen reverses the order, from longest bill to shortest.

12.4.2 Cleveland dot plot

(See Making a Cleveland Dot Plot) in the R Graphics Cookbook

Another way to show the same values as above is the “Cleveland dot plot”, which uses a point for the top of the column and a line to show its length. These plots are less cluttered, and particularly when there are many variables are easier to read.

In {ggplot2}, the code to visualize the same data would be:

ggplot(penguin_species_bill, 
       aes(x = reorder(species, -bill_length_mean), 
           y = bill_length_mean)) +
  geom_point() +
  # add the line
  geom_segment(aes(xend = species), yend = 0) +
  # extend the limits of the Y axis from zero to 50
  ylim(0, 50)

A variation on the Cleveland dotplot is the dumbell chart, which can be an effective way to show a change. The “Make a dumbbell chart” entry at the BBC Visual and Data Journalism cookbook for R graphics gives an example using {gapminder} data.

12.5 Histogram

(See Creating a Histogram) in the R Graphics Cookbook

Histograms show the distribution of cases that are measured on a continuous scale and are categorized by bins of the same size

In this example, we want to see the number of penguins by their weight.

ggplot(penguins, aes(x = body_mass_g)) +
  geom_histogram()

In that version, we end with 30 thin categories—what are called “bins”.

When you create a histogram without specifying the bin width, ggplot() prints out a message telling you that it’s defaulting to 30 bins, and to pick a better bin width. This is because it’s important to explore your data using different bin widths; the default of 30 may or may not show you something useful about your data. (_R)

We can specify the width of the bins in the geom_histogram() function. Let’s check the appearance of bins that are 500 units (in this case, 500 grams) wide.

ggplot(penguins, aes(x = body_mass_g)) +
  geom_histogram(binwidth = 500)

12.6 Line graph

Line plots are good for showing the change of a value of a variable (shown on the Y axis) over time (which is shown on the X axis).

In this example, we will use the gapminder data to show the increase in life expectancy in Canada.

# first, filter the data to have just the records from Canada
gapminder %>% 
  filter(country == "Canada") %>% 
  # now plot
  ggplot(aes(x = year, y = lifeExp)) +
    geom_line()

A more complex version of the plot could compare three separate countries.

# first, filter the data to have just the records from Canada
gapminder %>% 
  filter(country %in% c("Canada", "Japan", "China")) %>% 
  # now plot
  ggplot(aes(x = year, y = lifeExp, colour = country)) +
    geom_line()

In this plot, we see that in 1950, life expectancy was higher in Canada than the other two countries, but by the early 1970s Japanese people had longer life expectancy. While China still has a lower life expectancy than both, “China’s growth in life expectancy between 1950 and 1980 ranks as among the most rapid sustained increases in documented global history.”6

12.7 Formatting our plot

The plot of life expectancy shown above uses the default formatting of {ggplot2}. While the default is not terrible, there are some things we can do to get the plot ready for presentation.

In R, one of the things we can do is assign things to an object—and we can do the same with our plots. Here, the output of our plotting call will be assigned to an object called pl_lifeexp (my shorthand for “plot: life expectancy”). This code will create the object and store it in the environment, but not display it.

This code has one change from what’s above—the line size is defined as “1.5” to make it a bit heavier and therefore more visible.

pl_lifeexp <- 
# first, filter the data to have just the records from Canada
gapminder %>% 
  filter(country %in% c("Canada", "Japan", "China")) %>% 
  # now plot
  ggplot(aes(x = year, 
             y = lifeExp, 
             colour = country)) +
    geom_line(size = 1.5)

12.7.1 Labels: titles etc

We can now use that plot object and add some formatting. The first thing the plot needs is some text to tell us what it is. For that, we use the labs() function.

The labs() also allows us to change the labels on the x and y axis. For this chart, the year is self-evident, so there’s no need to label it. We us “NULL” to remove it.

pl_lifeexp +
  labs(title = "Life expectancy in Canada, China, and Japan",
       subtitle = "1952-2007",
       caption = "Source: Gapminder",
       y = "Life expectancy at birth",
       x = NULL)

Nice. We could have/should have assigned that to an object, since there’s still more we can do.

pl_lifeexp <- pl_lifeexp +
  labs(title = "Life expectancy in Canada, China, and Japan",
       subtitle = "1952-2007",
       caption = "Source: Gapminder",
       y = "Life expectancy at birth",
       x = NULL)

pl_lifeexp

12.7.2 Themes

The theme() function controls “the display of all non-data elements of the plot.” Everything from the axis ticks to the background of the legend can all be modified within this theme.

{ggplot2} has some built-in themes, controlling all of those things, which can be very useful in formatting.

The default theme is called theme_gray() (or theme_grey()). Here’s theme_bw() (for “black and white”) applied to our chart:

pl_lifeexp +
  theme_bw()

12.7.2.1 Your turn

What does your chart look like with theme_classic()?

Solution
pl_lifeexp +
  theme_classic()

You may want to investigate some of the other complete themes: https://ggplot2.tidyverse.org/reference/index.html#themes

12.7.3 Theme elements

Individual theme elements can be changed with the theme() function. Below is a single example. For a full list of all of the elements that can be changed and the terms used to change them, see the {ggplot2} reference page Modify components of a theme. The R Graphics Cookbook, 2nd ed. is another valuable resource for understanding how to alter individual elements of your plot.

To move the legend, we add a legend.position = argument to the theme() function.

pl_lifeexp +
  theme(legend.position = "bottom")

Based on the article “A background color that works with black and white elements”, I decided to try #E5E5E3 as background colour. The idea behind the very neutral grey is that it provides contrast for both black and white elements.

pl_lifeexp + 
  theme(panel.background = element_rect(fill = "#E5E5E3"))

I’m not sure it’s all that different from the default {ggplot2} colour!


12.8 Exercise - plotting

BIDA302: Plotting with {ggplot2}

  • an introduction to plotting

-30-