• line chart: geom_line()
• boxplot: geom_boxplot()
• histogram: geom_histogram()
• area chart: geom_area()

This code produces a scatter plot with displ on the x-axis, hwy on the y-axis, and the points colored by drv. There will be a smooth line, without standard errors, fit through each drv group.

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

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

The theme option show.legend = FALSE hides the legend box.

Consider this example earlier in the chapter.

ggplot(data = mpg) +
  geom_smooth(
    mapping = aes(x = displ, y = hwy, colour = drv),
    show.legend = FALSE
  )

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

In that plot, there is no legend. Removing the show.legend argument or setting show.legend = TRUE will result in the plot having a legend displaying the mapping between colors and drv.

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

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

In the chapter, the legend is suppressed because with three plots, adding a legend to only the last plot would make the sizes of plots different. Different sized plots would make it more difficult to see how arguments change the appearance of the plots. The purpose of those plots is to show the difference between no groups, using a group aesthetic, and using a color aesthetic, which creates implicit groups. In that example, the legend isn’t necessary since looking up the values associated with each color isn’t necessary to make that point.

It adds standard error bands to the lines.

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

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

By default se = TRUE:

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

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

No. Because both geom_point() and geom_smooth() will use the same data and mappings. They will inherit those options from the ggplot() object, so the mappings don’t need to specified again.

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

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

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

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

The following code will generate those plots.

ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point() +
  geom_smooth(se = FALSE)

ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_smooth(mapping = aes(group = drv), se = FALSE) +
  geom_point()

ggplot(mpg, aes(x = displ, y = hwy, colour = drv)) +
  geom_point() +
  geom_smooth(se = FALSE)

ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point(aes(colour = drv)) +
  geom_smooth(se = FALSE)

ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point(aes(colour = drv)) +
  geom_smooth(aes(linetype = drv), se = FALSE)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point(size = 4, color = "white") +
  geom_point(aes(colour = drv))

The “previous plot” referred to in the question is the following.

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

The arguments fun.ymin, fun.ymax, and fun.y have been deprecated and replaced with fun.min, fun.max, and fun in ggplot2 v 3.3.0.

The default geom for stat_summary() is geom_pointrange(). The default stat for geom_pointrange() is identity() but we can add the argument stat = "summary" to use stat_summary() instead of stat_identity().

ggplot(data = diamonds) +
  geom_pointrange(
    mapping = aes(x = cut, y = depth),
    stat = "summary"
  )

## No summary function supplied, defaulting to `mean_se()`

The resulting message says that stat_summary() uses the mean and sd to calculate the middle point and endpoints of the line. However, in the original plot the min and max values were used for the endpoints. To recreate the original plot we need to specify values for fun.min, fun.max, and fun.

ggplot(data = diamonds) +
  geom_pointrange(
    mapping = aes(x = cut, y = depth),
    stat = "summary",
    fun.min = min,
    fun.max = max,
    fun = median
  )

The geom_col() function has different default stat than geom_bar(). The default stat of geom_col() is stat_identity(), which leaves the data as is. The geom_col() function expects that the data contains x values and y values which represent the bar height.

The default stat of geom_bar() is stat_count(). The geom_bar() function only expects an x variable. The stat, stat_count(), preprocesses input data by counting the number of observations for each value of x. The y aesthetic uses the values of these counts.

The following tables lists the pairs of geoms and stats that are almost always used in concert.

These pairs of geoms and stats tend to have their names in common, such stat_smooth() and geom_smooth() and be documented on the same help page. The pairs of geoms and stats that are used in concert often have each other as the default stat (for a geom) or geom (for a stat).

The following tables contain the geoms and stats in ggplot2 and their defaults as of version 3.3.0. Many geoms have stat_identity() as the default stat.

The function stat_smooth() calculates the following variables:

• y: predicted value
• ymin: lower value of the confidence interval
• ymax: upper value of the confidence interval
• se: standard error

The “Computed Variables” section of the stat_smooth() documentation contains these variables.

The parameters that control the behavior of stat_smooth() include:

• method: This is the method used to compute the smoothing line. If NULL, a default method is used based on the sample size: stats::loess() when there are less than 1,000 observations in a group, and mgcv::gam() with formula = y ~ s(x, bs = "CS) otherwise. Alternatively, the user can provide a character vector with a function name, e.g. "lm", "loess", or a function, e.g. MASS::rlm.

• formula: When providing a custom method argument, the formula to use. The default is y ~ x. For example, to use the line implied by lm(y ~ x + I(x ^ 2) + I(x ^ 3)), use method = "lm" or method = lm and formula = y ~ x + I(x ^ 2) + I(x ^ 3).

• method.arg(): Arguments other than than the formula, which is already specified in the formula argument, to pass to the function inmethod`.

• se: If TRUE, display standard error bands, if FALSE only display the line.

• na.rm: If FALSE, missing values are removed with a warning, if TRUE the are silently removed. The default is FALSE in order to make debugging easier. If missing values are known to be in the data, then can be ignored, but if missing values are not anticipated this warning can help catch errors.

TODO: Plots with examples illustrating the uses of these arguments.

If group = 1 is not included, then all the bars in the plot will have the same height, a height of 1. The function geom_bar() assumes that the groups are equal to the x values since the stat computes the counts within the group.

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

The problem with these two plots is that the proportions are calculated within the groups.

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

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

The following code will produce the intended stacked bar charts for the case with no fill aesthetic.

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

With the fill aesthetic, the heights of the bars need to be normalized.

ggplot(data = diamonds) + 
  geom_bar(aes(x = cut, y = ..count.. / sum(..count..), fill = color))

There is overplotting because there are multiple observations for each combination of cty and hwy values.

I would improve the plot by using a jitter position adjustment to decrease overplotting.

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

The relationship between cty and hwy is clear even without jittering the points but jittering shows the locations where there are more observations.

From the geom_jitter() documentation, there are two arguments to jitter:

• width controls the amount of horizontal displacement, and
• height controls the amount of vertical displacement.

The defaults values of width and height will introduce noise in both directions. Here is what the plot looks like with the default values of height and width.

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_point(position = position_jitter())

However, we can change these parameters. Here are few a examples to understand how these parameters affect the amount of jittering. Whenwidth = 0 there is no horizontal jitter.

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_jitter(width = 0)

When width = 20, there is too much horizontal jitter.

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_jitter(width = 20)

When height = 0, there is no vertical jitter.

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_jitter(height = 0)

When height = 15, there is too much vertical jitter.

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_jitter(height = 15)

When width = 0 and height = 0, there is neither horizontal or vertical jitter, and the plot produced is identical to the one produced with geom_point().

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_jitter(height = 0, width = 0)

Note that the height and width arguments are in the units of the data. Thus height = 1 (width = 1) corresponds to different relative amounts of jittering depending on the scale of the y (x) variable. The default values of height and width are defined to be 80% of the resolution() of the data, which is the smallest non-zero distance between adjacent values of a variable. When x and y are discrete variables, their resolutions are both equal to 1, and height = 0.4 and width = 0.4 since the jitter moves points in both positive and negative directions.

The default values of height and width in geom_jitter() are non-zero, so unless both height and width are explicitly set set 0, there will be some jitter.

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

The geom geom_jitter() adds random variation to the locations points of the graph. In other words, it “jitters” the locations of points slightly. This method reduces overplotting since two points with the same location are unlikely to have the same random variation.

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

However, the reduction in overlapping comes at the cost of slightly changing the x and y values of the points.

The geom geom_count() sizes the points relative to the number of observations. Combinations of (x, y) values with more observations will be larger than those with fewer observations.

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

The geom_count() geom does not change x and y coordinates of the points. However, if the points are close together and counts are large, the size of some points can itself create overplotting. For example, in the following example, a third variable mapped to color is added to the plot. In this case, geom_count() is less readable than geom_jitter() when adding a third variable as a color aesthetic.

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

ggplot(data = mpg, mapping = aes(x = cty, y = hwy, color = class)) +
  geom_count()

Combining geom_count() with jitter, which is specified with the position argument to geom_count() rather than its own geom, helps overplotting a little.

ggplot(data = mpg, mapping = aes(x = cty, y = hwy, color = class)) +
  geom_count(position = "jitter")

But as this example shows, unfortunately, there is no universal solution to overplotting. The costs and benefits of different approaches will depend on the structure of the data and the goal of the data scientist.

The default position for geom_boxplot() is "dodge2", which is a shortcut for position_dodge2. This position adjustment does not change the vertical position of a geom but moves the geom horizontally to avoid overlapping other geoms. See the documentation for position_dodge2() for additional discussion on how it works.

When we add colour = class to the box plot, the different levels of the drv variable are placed side by side, i.e., dodged.

ggplot(data = mpg, aes(x = drv, y = hwy, colour = class)) +
  geom_boxplot()

If position_identity() is used the boxplots overlap.

ggplot(data = mpg, aes(x = drv, y = hwy, colour = class)) +
  geom_boxplot(position = "identity")

A pie chart is a stacked bar chart with the addition of polar coordinates. Take this stacked bar chart with a single category.

ggplot(mpg, aes(x = factor(1), fill = drv)) +
  geom_bar()

Now add coord_polar(theta="y") to create pie chart.

ggplot(mpg, aes(x = factor(1), fill = drv)) +
  geom_bar(width = 1) +
  coord_polar(theta = "y")

The argument theta = "y" maps y to the angle of each section. If coord_polar() is specified without theta = "y", then the resulting plot is called a bulls-eye chart.

ggplot(mpg, aes(x = factor(1), fill = drv)) +
  geom_bar(width = 1) +
  coord_polar()

The labs function adds axis titles, plot titles, and a caption to the plot.

ggplot(data = mpg, mapping = aes(x = class, y = hwy)) +
  geom_boxplot() +
  coord_flip() +
  labs(y = "Highway MPG",
       x = "Class",
       title = "Highway MPG by car class",
       subtitle = "1999-2008",
       caption = "Source: http://fueleconomy.gov")

The arguments to labs() are optional, so you can add as many or as few of these as are needed.

ggplot(data = mpg, mapping = aes(x = class, y = hwy)) +
  geom_boxplot() +
  coord_flip() +
  labs(y = "Highway MPG",
       x = "Year",
       title = "Highway MPG by car class")

The labs() function is not the only function that adds titles to plots. The xlab(), ylab(), and x- and y-scale functions can add axis titles. The ggtitle() function adds plot titles.

The coord_map() function uses map projections to project the three-dimensional Earth onto a two-dimensional plane. By default, coord_map() uses the Mercator projection. This projection is applied to all the geoms in the plot. The coord_quickmap() function uses an approximate but faster map projection. This approximation ignores the curvature of Earth and adjusts the map for the latitude/longitude ratio. The coord_quickmap() project is faster than coord_map() both because the projection is computationally easier, and unlike coord_map(), the coordinates of the individual geoms do not need to be transformed.

See the coord_map() documentation for more information on these functions and some examples.

The function coord_fixed() ensures that the line produced by geom_abline() is at a 45-degree angle. A 45-degree line makes it easy to compare the highway and city mileage to the case in which city and highway MPG were equal.

p <- ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_point() +
  geom_abline()
p + coord_fixed()

If we didn’t include coord_fixed(), then the line would no longer have an angle of 45 degrees.

p

On average, humans are best able to perceive differences in angles relative to 45 degrees. See @Cleveland1993, @Cleveland1994,@Cleveland1993a, @ClevelandMcGillMcGill1988, @HeerAgrawala2006 for discussion on how the aspect ratio of a plot affects perception of the values it encodes, evidence that 45-degrees is generally the optimal aspect ratio, and methods to calculate the optimal aspect ratio of a plot. The function ggthemes::bank_slopes() will calculate the optimal aspect ratio to bank slopes to 45-degrees.