ggplot

From bad graphs to the grammar of graphics

A grammar of graphics

“A grammar of graphics is a tool that enables us to concisely describe the components of a graphic. Such a grammar allows us to move beyond named graphics (e.g., the”scatterplot“) and gain insight into the deep structure that underlies statistical graphics.” — Hadley Wickham

ggplot2 is an R implementation of the concept:
- A coherent system for describing and creating graphs.
- Based on The Grammar of Graphics.
- Learn one system and apply it in many places.
- The equivalent of `dplyr} for graphs.
To learn more, read A Layered Grammar of Graphics.
Implementations exist in other languages (e.g., Python)

The `mpg` data frame

Data from the US EPA on 38 models of car:

mpg %>% print(n = 5)

## # A tibble: 234 x 11
##   manufacturer model displ  year   cyl trans   drv     cty   hwy fl   
##   <chr>        <chr> <dbl> <int> <int> <chr>   <chr> <int> <int> <chr>
## 1 audi         a4      1.8  1999     4 auto(l~ f        18    29 p    
## 2 audi         a4      1.8  1999     4 manual~ f        21    29 p    
## 3 audi         a4      2    2008     4 manual~ f        20    31 p    
## 4 audi         a4      2    2008     4 auto(a~ f        21    30 p    
## 5 audi         a4      2.8  1999     6 auto(l~ f        16    26 p    
## # ... with 229 more rows, and 1 more variable: class <chr>

Among the variables in mpg are:
- displ, a car’s engine size, in litres.
- hwy, a car’s fuel efficiency on the highway (in miles per gallon).

Creating a plot

All 336,776 flights that departed from NYC in 2013

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

A graphing template

ggplot(data = <DATA>) +
  <GEOM_FUNCTION>(mapping = aes(<MAPPINGS>))

Aesthetics

Aesthetic

How to add a third variable to a two dimensional scatterplot?
By mapping it to an aesthetic:
- A visual property of the objects in your plot.
- Include the size, the shape, or the color of the points.

Adding classes to your plot

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

If you prefer British English, use colour instead of color.

The size aesthetic

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

## Warning: Using size for a discrete variable is not advised.

The alpha aesthetic

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

## Warning: Using alpha for a discrete variable is not advised.

The shape aesthetic

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

## Warning: The shape palette can deal with a maximum of 6 discrete values because
## more than 6 becomes difficult to discriminate; you have 7. Consider
## specifying shapes manually if you must have them.

## Warning: Removed 62 rows containing missing values (geom_point).

The hollow shapes (0–14) have a border determined by colour; the solid shapes (15–20) are filled with colour; the filled shapes (21–24) have a border of colour and are filled with fill.

Set the aesthetics manually

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

Set the aesthetics manually

Need values that make sense for that aesthetic:
- The name of a color as a character string.
- The size of a point in mm.
- The shape of a point as a number.

Facetting

Facets wrap

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy), size = 0.5) +
  facet_wrap(~ class, nrow = 2)

Facets grid

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy), size = 0.5) +
  facet_grid(drv ~ cyl)

Geometric objects and statistical transformations

How are these two plots similar?

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

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

Geometric objects

A geom:
- The object that a plot uses to represent data.
- Plots often describeds by the geom type:
  - Bar charts use bar geoms.
  - Line charts use line geoms.
  - Boxplots use boxplot geoms.
- An exception:
  - Scatterplots use the point geom.
Every geom function takes a mapping argument.
But not every aesthetic works with every geom:
- shape exists for geom_point but not for geom_line,
- and conversely for linetype.

The linetype aesthetic

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

Multiple geoms in the same plot

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

A better way

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

Local vs global mappings

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

Layer dependent data

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

Beyond scatterplots

Other graphs, like bar charts, calculate new values to plot.
- Bar charts, histograms, and frequency polygons:
  - Bin data.
  - Plot bin counts (number of points falling in each bin).
- Smoothers:
  - Fit a model to your data.
  - Plot predictions from the model.
- Boxplots:
  - Compute a robust summary of the distribution.
  - Display a specially formatted box.

Statistical transformations

Bar charts

The diamonds dataset:
- About 54,000 diamonds.
- Information about price, carat, color, clarity, and cut for each.

print(diamonds)

## # A tibble: 53,940 x 10
##    carat cut       color clarity depth table price     x     y     z
##    <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
##  1  0.23 Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43
##  2  0.21 Premium   E     SI1      59.8    61   326  3.89  3.84  2.31
##  3  0.23 Good      E     VS1      56.9    65   327  4.05  4.07  2.31
##  4  0.29 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63
##  5  0.31 Good      J     SI2      63.3    58   335  4.34  4.35  2.75
##  6  0.24 Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48
##  7  0.24 Very Good I     VVS1     62.3    57   336  3.95  3.98  2.47
##  8  0.26 Very Good H     SI1      61.9    55   337  4.07  4.11  2.53
##  9  0.22 Fair      E     VS2      65.1    61   337  3.87  3.78  2.49
## 10  0.23 Very Good H     VS1      59.4    61   338  4     4.05  2.39
## # ... with 53,930 more rows

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

A stat:
- The algorithm used to calculate new values for a graph.
- Short for statistical transformation.

ggplot2 provides over 20 stats.
Each stat is a function, get help as usual, e.g. ?stat_bin.
Use RStudio’s data visualization cheatsheet for a complete list.

Geom and stat

Every geom has a default stat and conversely.
- ?geom_bar shows that the default value for stat is “count”.
- Means that geom_bar() uses stat_count().
- ?stat_count has a section called “Computed variables” with two new variables: count and prop.
You can generally use geoms and stats interchangeably!

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

Typically, use geoms without worrying about the stat.
Three reasons to use a stat explicitly:
- To override the default stat.
- To override the default mapping from transformed variables to aesthetics.
- To draw greater attention to the stat in your code.

Use a stat explicitely

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

The fill aesthetic

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

Fill and position ajustements

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

Automatically stacked by the position adjustment.
?position_stack to learn more.

Fill with `position = "identity"`

ggplot(data = diamonds, mapping = aes(x = cut, fill = clarity)) +
  geom_bar(alpha = 1/5, position = "identity")

Not very useful for bars because of overlap.
?position_identity to learn more.

Fill with `position = "fill"`

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

Makes it easier to compare proportions across groups.
?position_fill to learn more.

Fill with `position = "dodge"`

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

Makes it easier to compare individual values.
?position_dodge to learn more.

`position = "jitter"`

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

Graph less/more accurate/revealing at small/large scales.
?position_jitter to learn more.

Coordinate systems

The most complicated part of ggplot2.
Default: the Cartesian coordinate system.
Other systems occasionally helpful:
- coord_flip() switches the x and y axes.
- coord_quickmap() sets the aspect ratio correctly for maps.
- coord_polar() uses polar coordinates.

`coord_flip()`

ggplot(data = mpg, mapping = aes(x = class, y = hwy)) +
  geom_boxplot() + 
  theme(axis.text=element_text(size=5))

ggplot(data = mpg, mapping = aes(x = class, y = hwy)) +
  geom_boxplot() +
  coord_flip() + 
  theme(axis.text=element_text(size=5))

Useful for:
- horizontal boxplots,
- and long labels.

`coord_polar()`

bar <- ggplot(data = diamonds) +
  geom_bar(mapping = aes(x = cut, fill = cut),
           show.legend = FALSE, width = 1) +
  theme(aspect.ratio = 1, axis.text=element_text(size=6)) + 
  labs(x = NULL, y = NULL)
bar + coord_flip()

bar <- ggplot(data = diamonds) +
  geom_bar(mapping = aes(x = cut, fill = cut),
           show.legend = FALSE, width = 1) +
  theme(aspect.ratio = 1, axis.text=element_text(size=6)) + 
  labs(x = NULL, y = NULL)
bar + coord_polar()

The layered grammar of graphics

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

A formal system for building plots,
Uniquely describes any plot as a combination of
- a dataset,
- a geom,
- a set of mappings,
- a stat,
- a position adjustment,
- a coordinate system,
- and a faceting scheme.

Label

ggplot(mpg, aes(displ, hwy)) + geom_point(aes(color = class)) +
  geom_smooth(se = FALSE) +
  labs(title = "Fuel efficiency decreases with engine size")

Avoid titles that just describe what the plot is!

More text

subtitle: additional details beneath the title.
caption: text at the bottom right of the plot.

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

Axes

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

Mathematical expressions

df <- tibble(x = runif(10),
y = runif(10))
ggplot(df, aes(x, y)) + geom_point() +
  labs(x = quote(sum(x[i] ^ 2, i == 1, n)),
       y = quote(alpha + beta + frac(delta, theta)))

Annotations

best_in_class <- mpg %>%
  group_by(class) %>%
  filter(row_number(desc(hwy)) == 1)
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(color = class)) +
  geom_text(aes(label = model), data = best_in_class, size = 3)

Or better

Use the ggrepel package!

best_in_class <- mpg %>%
  group_by(class) %>%
  filter(row_number(desc(hwy)) == 1)
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(color = class)) +
  geom_point(size = 3, shape = 1, data = best_in_class) +
  ggrepel::geom_label_repel(aes(label = model), data = best_in_class)

Replace legend by labels on the plot

class_avg <- mpg %>% group_by(class) %>%
  summarise(displ = median(displ), hwy = median(hwy))
ggplot(mpg, aes(displ, hwy, color = class)) +
  ggrepel::geom_label_repel(aes(label = class), data = class_avg,
                            size = 6, label.size = 0,
                            segment.color = NA) +
  geom_point() + theme(legend.position = "none")

To add a single label to the plot

label <- mpg %>%
  summarise(displ = max(displ), hwy = max(hwy),
            label = "Increasing engine size is
            related to decreasing fuel economy.")
ggplot(mpg, aes(displ, hwy)) + geom_point() +
  geom_text(aes(label = label), data = label,
            vjust = "top", hjust = "right")

To control the alignment of the label

An alternative

label <- tibble(displ = Inf, hwy = Inf,
                label = "Increasing engine size is
                related to decreasing fuel economy.")
ggplot(mpg, aes(displ, hwy)) + geom_point() +
  geom_text(aes(label = label), data = label,
            vjust = "top", hjust = "right")

Geoms to help annotate your plot

geom_hline() and geom_vline():
- Add reference lines.
- Using e.g. size = 2 is often a good idea.
geom_rect():
- Draw a rectangle around points of interest.
- Boundaries defined by xmin, xmax, ymin, ymax.
geom_segment() with the arrow argument:
- Draw attention to a point with an arrow.
- x/xend and y/yend define the start/end locations.
The only limit is your imagination (and patience)!

Scales

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

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(color = class)) +
  scale_x_continuous() + 
  scale_y_continuous()

How could we improve the scale?

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

Log-transform the variables

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

or simply replace the scale

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

Axis ticks and legend keys

To control the ticks on the axes and the keys on the legend:
- breaks: controls the position of the ticks, or the values associated with the keys.
- labels: controls the text label associated with each tick/key.

`breaks`

Allow e.g. to highlight exactly where the observations occur:

ggplot(mpg, aes(displ, hwy)) + geom_point() +
  scale_y_continuous(breaks = seq(15, 40, by = 5))

presidential %>%
  mutate(id = 33 + row_number()) %>%
  ggplot(aes(start, id)) + geom_point() +
    geom_segment(aes(xend = end, yend = id)) +
    scale_x_date(NULL, breaks = presidential$start, date_labels = "'%y")

`labels`

A useful trick for maps, or for publishing plots where you can’t share the absolute numbers:

ggplot(mpg, aes(displ, hwy)) + geom_point() +
  scale_x_continuous(labels = NULL) +
  scale_y_continuous(labels = NULL)

ggplot

Jitong

11/29/2021

From bad graphs to the grammar of graphics

A grammar of graphics

The mpg data frame

Creating a plot

A graphing template

Aesthetics

Aesthetic

Adding classes to your plot

The size aesthetic

The alpha aesthetic

The shape aesthetic

Set the aesthetics manually

Set the aesthetics manually

Facetting

Facets wrap

Facets grid

Geometric objects and statistical transformations

How are these two plots similar?

Geometric objects

The linetype aesthetic

More on geoms

Multiple geoms in the same plot

A better way

Local vs global mappings

Layer dependent data

Beyond scatterplots

Statistical transformations

Bar charts

Geom and stat

Use a stat explicitely

The fill aesthetic

Fill and position ajustements

Fill with position = "identity"

Fill with position = "fill"

Fill with position = "dodge"

position = "jitter"

Coordinate systems

Coordinate systems

coord_flip()

coord_polar()

The layered grammar of graphics

Label

More text

Axes

Mathematical expressions

Annotations

Or better

Replace legend by labels on the plot

To add a single label to the plot

To control the alignment of the label

An alternative

Geoms to help annotate your plot

Scales

How could we improve the scale?

Log-transform the variables

or simply replace the scale

Axis ticks and legend keys

breaks

labels

The `mpg` data frame

Fill with `position = "identity"`

Fill with `position = "fill"`

Fill with `position = "dodge"`

`position = "jitter"`

`coord_flip()`

`coord_polar()`

`breaks`

`labels`