Aesthetic mapping Exercises

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy, color = "blue"))

In the above example, the dots are red despite the color = “blue” specification because it is specified inside the aes() field. Setting the colour and other such parameters uniformly should be outside this mapping field in the layer field.

Possible aesthetics:

• alpha (transparency)
• colour
• fill (the fill arsthetic can also be used in a variable capacity to show different colours for different observations)
• group (use categorical variable here. It can replace color, and groups objects on the plot. See further down.)
• shape
• size
• stroke (understand a little)

You can also map subject to certain constraints e.g.

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy, color = cyl < 6))

This will show which of the observations adhere to a particular condition or not.

Facets

One way to add additional variables is with aesthetics. Another way, particularly useful for categorical variables, is to split your plot into facets, subplots that each display one subset of the data.

ggplot(mpg) +
  geom_point(aes(x = cyl, y = hwy, col = trans)) +
  facet_wrap(~ class, nrow = 3)

Note: “The first argument of facet_wrap() should be a formula, which you create with ~ followed by a variable name (here “formula” is the name of a data structure in R, not a synonym for “equation”). The variable that you pass to facet_wrap() should be discrete.”

Facets can be done for two variables. To do this, add facet_grid() to the plot call. The first argument of this mapping is also a formula.

ggplot(mpg) +
  geom_point(aes(x = displ, y = hwy)) +
  facet_grid(drv ~ cyl)

To facet with just one variable, use a “.” At the other end of the formula.

Geoms

“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.”

We can turn this…

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

…into this…

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

## `geom_smooth()` using method = 'loess'

# Loess regression is the default

…by changing the geom. Every geom takes a mapping argument, but not every aes() works with every geom. For example, with line types:

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

## `geom_smooth()` using method = 'loess'

Many geoms use a single geometric object to display multiple rows of data. For these geoms, you can set the group aesthetic to a categorical variable to draw multiple objects (or use color, as previously mentioned).

We can plot multiple geoms for the same data and layer them over each other. This, however, causes repetition in the code. If, for example, the axes change, every geom’s x and y specs would have to change.

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

## `geom_smooth()` using method = 'loess'

In the case of layering different geoms, we can specify parameters like the axes as global options in the ggplot chart object:

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

## `geom_smooth()` using method = 'loess'

 # Same data passed to different geoms.

Epicly enough, we can still add features to each geom that will affect that layer only. For example, notice the colour in the following:

ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth()

## `geom_smooth()` using method = 'loess'

I can also subset the data in a particular geom with filter() from the dplyr package.

ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth(data = filter(mpg, class == "subcompact"), se = FALSE)

## `geom_smooth()` using method = 'loess'

# se = TRUE/FALSE displays confidence interval around the geom_smooth line or not.

Geom Exercises

Here I write code to recreate the graphs given in the book.

# Plot 1
ggplot(mpg, aes(displ, hwy)) +
  geom_point(size = 5) +
  geom_smooth(se = F)

## `geom_smooth()` using method = 'loess'

# Plot 2
ggplot(mpg, aes(displ, hwy)) +
  geom_point(size = 5) +
  geom_smooth(se = F, aes(group = drv))

## `geom_smooth()` using method = 'loess'

# Plot 3
ggplot(mpg, aes(displ, hwy, colour =  drv)) +
  geom_point() +
  geom_smooth(se = F)

## `geom_smooth()` using method = 'loess'

# Plot 4
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(col = drv)) +
  geom_smooth(se = F)

## `geom_smooth()` using method = 'loess'

# Plot 5
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour =  drv)) +
  geom_smooth(se = F, aes(linetype = drv))

## `geom_smooth()` using method = 'loess'

# Plot 6 -> think it's with shape? verify
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour =  drv)) +
  geom_smooth(se = F, aes(linetype = drv))

## `geom_smooth()` using method = 'loess'

Statistical Transformations

Consider a bar chart based on the diamonds data set:

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

As you can see, this graph does not use a y-variable. This is because a bar chart creates new values to plot by design. It works with the number of observations within a range or bin and displays the count per bin. This is the same for histograms and frequency polygons. The algorithm that calculates these new values for the graph is called a stat(), short short for statistical transformation.

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.

Generally, stats and geoms are interchangable. We recreate the same plot with the following.

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. This means that you can typically use geoms without worrying about the underlying statistical transformation. There are three reasons you would use stat() explicitly:

1. Overiding the default stat in a particular ggplot because you want a different stat. In the example below we change the default stat from “count” to “identity”. Identity uses the actual value assigned to a variable instead of its count.

# Create a small table with the tribble command.
demo <- tribble(
  ~cut,         ~freq,
  "Fair",       1610,
  "Good",       4906,
  "Very Good",  12082,
  "Premium",    13791,
  "Ideal",      21551
)

# Now plot the demo table, but override the default stat in the bar chart geom.
# The count geom is overriden to
ggplot(data = demo) +
  geom_bar(mapping = aes(x = cut, y = freq), stat = "identity")

2. Override the default mapping of transformed variables e.g. display proportion on y-axis rather than count.

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

3. You might want to draw greater attention to the statistical transformation in your code.

ggplot(data = diamonds) +
  stat_summary(
    mapping = aes(x = cut, y = depth),
    fun.ymin = min,
    fun.ymax = max,
    fun.y = median
  )

Transformation Exercises

ggplot(data = diamonds) +
  geom_pointrange(mapping = aes(x = cut, y = depth, ymin = min(diamonds$depth), ymax = max(diamonds$depth))) #Close enough

# Automatically groups according to cut, or x variable.
ggplot(data = diamonds) +
  geom_bar(mapping = aes(x = cut, y = ..prop..))

# Needs to group relative to whole x variable. Can say group=1 or group = "x"
ggplot(data = diamonds) +
  geom_bar(mapping = aes(x = cut, fill = color, y = ..prop..,  group = 1))

Position Adjustments

You can colour a bar chart using the colour or fill aesthetics. The fill fill argument is more useful, since you can map a variable to this aesthetic to display different colours. A stacked bar chart of sorts.

# The "colour aesthetic" adds the border.
ggplot(data = diamonds) +
  geom_bar(mapping = aes(x = cut, colour = cut))

# The "fill" aesthetic adds the interior colour.
ggplot(data = diamonds) +
  geom_bar(mapping = aes(x = cut, fill = cut))

The fill aesthetic can also be used as a variable to show different combinations.

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

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”.

# Position = identity uses the raw value of the observation. Here, it places the bar at it's exact location
ggplot(data = diamonds, mapping = aes(x = cut, fill = clarity)) +
  geom_bar(alpha = 0.2, position = "identity")

# Here we specify the fill as NA. and empty the bars.
ggplot(data = diamonds, mapping = aes(x = cut, colour = clarity)) +
  geom_bar(fill = NA, position = "identity")

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

ggplot(data = diamonds) +
  geom_bar(mapping = aes(x = cut, fill = clarity), position = "dodge")

ggplot(mpg) + geom_point(aes(displ, hwy)) + ggtitle("Scatter 1")

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy), position = "jitter")

Position Adjustment exercises

# How to fix this
ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_point()

# Do this:
ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_count()

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_jitter()

# Default postion is dodge, putting data displays next to each other.
ggplot(data = mpg, mapping = aes(x = trans, y = hwy)) +
  geom_boxplot()

Coordinate Systems

The default coordinate system is the Cartesian coordinate system where the x and y positions act independently to determine the location of each point. There are a number of other coordinate systems that are occasionally helpful.

# Normal Plot
ggplot(data = mpg, mapping = aes(x = class, y = hwy)) +
  geom_boxplot()

# Add new coordinate system:
ggplot(data = mpg, mapping = aes(x = class, y = hwy)) +
  geom_boxplot() +
  coord_flip()

bar <- ggplot(data = diamonds) +
  geom_bar(
    mapping = aes(x = cut, fill = cut),
    show.legend = FALSE,
    width = 1
  ) +
  theme(aspect.ratio = 1) +
  labs(x = NULL, y = NULL)

# flipped bar chart
bar + coord_flip()

# Coxcomb chart
bar + coord_polar()

Coordinate Systems Exercises

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_point() +
  geom_abline() + #adds line to graph by slope and intercept
  coord_fixed() # Default ratio is 1. 1 unit of x is the same as 1 unit of y

Layered Grammar of Graphics

Read more on that here

To conclude, the chunk below shows the basic syntax structure of ggplot grammar:

# ggplot(data = <DATA>) +
#   <GEOM_FUNCTION>(
#      mapping = aes(<MAPPINGS>),
#      stat = <STAT>,
#      position = <POSITION>
#   ) +
#   <COORDINATE_FUNCTION> +
#   <FACET_FUNCTION>