Practice: Graphics with ggplot2 (part 1)

Stat 133

Author

Gaston Sanchez

Learning Objectives
  • Define a function that takes arguments
  • Return a value from a function
  • Test a function
  • Set default values for function arguments

The content of this module is an adapted excerpt from Winston Chang’s R Graphics Cookbook. Specifically, see Appendix A: Understanding ggplot2.

1 First contact with ggplot()

In this module you will learn how to create graphics with "ggplot2" which is part of the "tidyverse" ecosystem of packages.

library(tidyverse)

The package "ggplot2" is probably the most popular package in R to create beautiful static graphics. Compared to the functions in the base package "graphics", the package "ggplot2” follows a somewhat different philosophy, and it tries to be more consistent and modular as possible.

2 Nobel Prizes Toy Data

We’ll walk through a simple example using a toy data set based on Nobel Prizes. First, we’ll make a table with some Nobel prizes data in Economics and Chemistry, from USA, France, Germany and UK (years 1901-2019).

# nobel prizes data
econ_chem = data.frame(
  Country = c('USA', 'France', 'Germany', 'UK'),
  Economics = c(47, 3, 1, 6),
  Chemistry = c(56, 8, 24, 23)
)

econ_chem
  Country Economics Chemistry
1     USA        47        56
2  France         3         8
3 Germany         1        24
4      UK         6        23

With this data, we may be interested in obtaining a graphic like the following scatter plot:

3 Some Terminology

Before we go any further, it’ll be helpful to define some of the terminology used in ggplot2:

  • The data is what we want to visualize. It consists of variables, which are stored as columns in a tabular object (e.g. data.frame, tibble)

  • Geoms are the geometric objects that are drawn to represent the data, such as bars, lines, points, polygons, etc.

  • Aesthetic attributes, or aesthetics, are visual properties of geoms, such as \(x\) and \(y\) positions, line color, fill color, point shapes, etc.

  • There are mappings from data values to aesthetics.

  • Scales control the mapping from the values in the data space to values in the aesthetic space. A continuous \(y\) scale maps larger numerical values to vertically higher positions in space.

  • Guides show the viewer how to map the visual properties back to the data space. The most commonly used guides are the tick marks and labels on an axis.

In a data graphic, there is a mapping (or correspondence) from properties of the data to visual properties in the graphic. The data properties are typically numerical or categorical values, while the visual properties include the \(x\) and \(y\) positions of points, colors of lines, height of bars, and so on.

On the surface, representing a number with an \(x\) coordinate may seem very different from representing a number with a color of a point, but at an abstract level, they are the same.

In the grammar of graphics, this deep similarity is not just recognized, but made central.

4 Basic Scatterplot

A basic ggplot() specification looks like this:

ggplot(data = econ_chem, aes(x = Economics, y = Chemistry))

This creates a ggplot object using the data frame econ_chem. It also specifies default aesthetic mappings within aes():

  • x = Economics maps the column Economics to the \(x\) position

  • y = Chemistry maps the column Chemistry to the \(y\) position

"ggplot2" has a simple requirement for data structures: they must be stored in data frames or tibbles, and each type of variable that is mapped to an aesthetic must be stored in its own column.

After we’ve given ggplot() the data frame and the aesthetic mappings, there’s one more critical component. We need to tell it what geometric objects to put there. At this point, "ggplot2" doesn’t know if we want bars, lines, points, or something else to be drawn on the graph.

We’ll add geom_point() to draw points, resulting in a scatter plot:

ggplot(data =econ_chem, aes(x = Economics, y = Chemistry)) +
geom_point()

A basic scatter plot

4.1 Storing a ggplot object

If you are going to reuse some of these components, you can store them in variables. We can save the ggplot object in an object called gg, and then add geom_point() to it.

gg = ggplot(econ_chem, aes(x = Economics, y = Chemistry)) 

gg + geom_point()

4.2 More Mappings

We can also map the variable Country to the color of the points, by putting aes() inside the call to geom_point(), and specifying color = Country

gg + geom_point(aes(color = Country))

This doesn’t alter the default aesthetic mappings that we defined previously, inside of ggplot(...). What it does is add an aesthetic mapping for this particular geom, geom_point(). If we added other geoms, this mapping would not apply to them.

4.3 Setting Values

Contrast this aesthetic mapping with aesthetic setting. This time, we won’t use aes(); we’ll just set the value of color directly to "red". And we’ll also increase the size of the dots by setting size:

gg + geom_point(color = "red", size = 3)

4.4 Customizing Scales

We can also modify the scales; that is, the mappings from data to visual attributes. Here, we’ll change the \(x\) scale so that it has a larger range:

gg + geom_point() + scale_x_continuous(limits = c(0, 60))


If we go back to the example with the color = Country mapping, we can also modify the color scale and customize them with our own values:

gg + 
  geom_point(aes(color = Country)) +
  scale_color_manual(values = c("blue", "orange", "cyan", "magenta"))

5 Themes, Annotations, etc

Some aspects of a graph’s appearance fall outside the scope of the grammar of graphics. These include the color of the background and grid lines in the graphing area, the fonts used in the axis labels, annotations, text in the graph title & subtitle, legend details, and things like that. These are controlled with auxiliary functions such as labs(), theme(), or annotate().

gg + 
  geom_point(aes(color = Country), size = 3) +
  scale_x_continuous(limits = c(0, 60)) +
  scale_y_continuous(limits = c(0, 60)) +
  labs(title = "Economics and Chemistry Nobel Prizes (1901-2019)",
       subtitle = "(USA eclipses other countries)",
       x = "Prizes in Economics",
       y = "Prizes in Chemistry") +
  annotate(geom= "text", x = 47, y = 52, label = "USA") +
  theme(legend.position = "bottom") +
  theme_minimal()

6 Web interactive ggplot graphics with "plotly"

Graphics created with ggplot are static graphics (i.e. lack interactive features). However, you can use functions from the package "plotly" (this is NOT part of "tidyverse") to take a ggplot object and make it into a web-interactive plot.

As usual, you will need to install "plotly". Assuming this package is installed, simply call it with library()

library(plotly)

Let’s take the previous scatter plot, and assign the ggplot to its own object econ_chem_scatter. After that, pass this object to ggplotly(). A web interactive graphic will be produced, which you can zoom-in or zoom-out, hover over the points to see their specific values, reset axes, etc.

# create ggplot object
econ_chem_scatter = gg + 
  geom_point(aes(color = Country), size = 3) +
  scale_x_continuous(limits = c(0, 60)) +
  scale_y_continuous(limits = c(0, 60)) +
  labs(title = "Economics and Chemistry Nobel Prizes (1901-2019)",
       subtitle = "(USA eclipses other countries)",
       x = "Prizes in Economics",
       y = "Prizes in Chemistry") +
  annotate(geom= "text", x = 47, y = 52, label = "USA") +
  theme(legend.position = "bottom") +
  theme_minimal()

# convert ggplot to plotly
ggplotly(econ_chem_scatter)


Notice that not all elements specified in the ggplot object are preserved when passed to ggplotly(). For example, the generated plotly has no subtitle.