Where are we?

# library(mdsr) 
# library(tidyverse)
library(ggplot2)

ggplot2::mpg data example continued

We will continue to follow the examples in R for data science.

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = drv)) + 
  facet_wrap(~ class, nrow = 2)

Geometric objects

# left
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))

# right
ggplot(data = mpg) + 
  geom_smooth(mapping = aes(x = displ, y = hwy))
## `geom_smooth()` using method = 'loess'

A geom is the geometrical object that a plot uses to represent data. People often describe plots by the type of geom that the plot uses. For example, bar charts use bar geoms, line charts use line geoms, boxplots use boxplot geoms, and so on. Scatterplots break the trend; they use the point geom. As we see above, you can use different geoms to plot the same data. The plot on the left uses the point geom, and the plot on the right uses the smooth geom, a smooth line fitted to the data.

Every geom function in ggplot2 takes a mapping argument. However, not every aesthetic works with every geom. You could set the shape of a point, but you couldn’t set the “shape” of a line. On the other hand, you could set the linetype of a line. geom_smooth() will draw a different line, with a different linetype, for each unique value of the variable that you map to linetype.

ggplot(data = mpg) + 
  geom_smooth(mapping = aes(x = displ, y = hwy, linetype = drv))

To display multiple geoms in the same plot, add multiple geom functions to ggplot():

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) +
  geom_smooth(mapping = aes(x = displ, y = hwy))

This, however, introduces some duplication in our code. Imagine if you wanted to change the y-axis to display cty instead of hwy. You’d need to change the variable in two places, and you might forget to update one. You can avoid this type of repetition by passing a set of mappings to ggplot(). ggplot2 will treat these mappings as global mappings that apply to each geom in the graph. In other words, this code will produce the same plot as the previous code:

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point() + 
  geom_smooth()

If you place mappings in a geom function, ggplot2 will treat them as local mappings for the layer. It will use these mappings to extend or overwrite the global mappings for that layer only. This makes it possible to display different aesthetics in different layers.

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point(mapping = aes(color = class)) + 
  geom_smooth(method = "lm", se = FALSE) # We've been using method = "loess"

Think about this:

  1. What geom would you use to draw a line chart? A boxplot? A histogram? An area chart?

  2. What aesthetics can you use to each geom?

To get answers, Help > Cheatsheets > Data Visualization with ggplot2

Adding context by labels

The easiest place to start when turning an exploratory graphic into an expository graphic is with good labels. You add labels with the labs() function. This example adds a plot title:

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth(se = FALSE) +
  labs(
    title = "Fuel efficiency generally decreases with engine size",
    subtitle = "Two seaters (sports cars) are an exception because of their light weight",
    caption = "Data from fueleconomy.gov"
  )

You can also use labs() to replace the axis and legend titles. It’s usually a good idea to replace short variable names with more detailed descriptions, and to include the units.

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_smooth(se = FALSE) +
  labs(
    x = "Engine displacement (L)",
    y = "Highway fuel economy (mpg)",
    colour = "Car type"
  )

Context is also provided by guides (more commonly called legends). By mapping a discrete variable to one of the visual cues of shape, color or linetype, ggplot2 by default creates a legend. The geom_text() and geom_annotate() functions can also be used to provide specific textual annotations on the plot. We will see this in the lab activity.

Scales

It’s very useful to plot transformations of your variable. To elucidate this idea, let’s use diamond dataset which comes in ggplot2 and contains information about ~54,000 diamonds, including the price, carat, color, clarity, and cut of each diamond. It’s easier to see the precise relationship between carat and price if we log transform them:

ggplot(diamonds, aes(carat, price)) +
  geom_bin2d()
ggplot(diamonds, aes(log10(carat), log10(price))) +
  geom_bin2d()

However, the disadvantage of this transformation is that the axes are now labelled with the transformed values, making it hard to interpret the plot. Instead of doing the transformation in the aesthetic mapping, we can instead do it with the scale. This is visually identical, except the axes are labelled on the original data scale.

ggplot(diamonds, aes(carat, price)) +
  geom_bin2d() + 
  scale_x_log10() + 
  scale_y_log10()

Here’s an identical graph using scale_y_continuous() function:

ggplot(diamonds, aes(carat, price)) +
  geom_bin2d() + 
  scale_x_continuous(trans = "log10") + 
  scale_y_continuous(trans = "log10")

Another scale that is frequently customised is colour. Below, "Set1" is defined in RColorBrewer package; see Figure 2.11 in MDSR (textbook).

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(color = drv))

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(color = drv)) +
  scale_colour_brewer(palette = "Set1")

Statistical transformations

Next, let’s take a look at a bar chart. Bar charts seem simple, but they are interesting because they reveal something subtle about plots. Consider a basic bar chart, as drawn with geom_bar()

ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut))

On the x-axis, the chart displays cut, a variable from diamonds. On the y-axis, it displays count, but count is not a variable in diamonds! Where does count come from? Many graphs, like scatterplots, plot the raw values of your dataset. Other graphs, like bar charts, calculate new values to plot:

The algorithm used to calculate new values for a graph is called a stat, short for statistical transformation. The figure below describes how this process works with geom_bar().

You can learn which stat a geom uses by inspecting the default value for the stat argument. For example, ?geom_bar shows that the default value for stat is count, which means that geom_bar() uses stat_count(). stat_count() is documented on the same page as geom_bar(), and if you scroll down you can find a section called Computed variables. That describes how it computes two new variables: count and prop.

You can generally use geoms and stats interchangeably. For example, you can recreate the previous plot using stat_count() instead of geom_bar():

ggplot(data = diamonds) + 
  stat_count(mapping = aes(x = cut))

This works because every geom has a default stat; and every stat has a default geom. You might want to override the default mapping from transformed variables to aesthetics.

ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, y = ..prop.., group = 1))

group="whatever" is a “dummy” grouping to override the default behavior, which is to group by the x variable cut (in this example). The default for geom_bar is to group by the x variable in order to separately count the number of rows in each level of the x variable. To compute the proportion of each level of cut among all, we do not want to group by cut. Sepecifying a dummy group group = 1, i.e. all are in group 1, achieves this.

When there is no need for any statistical transformation, I can change the stat of geom_bar() from count (the default) to identity, as shown in the example below.

library(tibble)
demo <- tribble(
  ~cut,         ~freq,
  "Fair",       1610,
  "Good",       4906,
  "Very Good",  12082,
  "Premium",    13791,
  "Ideal",      21551
)

ggplot(data = demo) +
  geom_bar(mapping = aes(x = reorder(cut,freq), y = freq), stat = "identity") 

# see what happens if `reorder(cut,freq)` is replaced by `cut`. Type `head(diamonds$cut)`

Let us browse some other aesthetic options in geom_bar(). You can colour a bar chart using either the color aesthetic, or, more usefully, fill:

ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, fill = cut))

It is of course more useful when fill aesthetic is mapped to another categorical variable, like clarity.

library(dplyr)
diamondss <- diamonds %>% filter(color %in% c('D','E','F')) # don't worry about this for now
g <- ggplot(data = diamondss, mapping = aes(x = cut, fill = color)) 
g + geom_bar()

The stacking is performed automatically by the position adjustment specified by the position argument. If you don’t want a stacked bar chart, you can use one of three other options: "identity", "dodge" or "fill".

g + geom_bar(alpha = 1/5, position = "identity")
g + geom_bar(position = "fill")
g + geom_bar(position = "dodge")

Saving your plots

ggsave("my-plot.pdf")

ggplot2 continued in Lec 7.