Modern ggplot2

Teun van den Brand

Recent release of new major version

https://www.tidyverse.org/blog/2025/09/ggplot2-4-0-0/

Today’s topics

  • Headings
  • Patterns & gradients
  • Delayed evaluation
  • Polar coordinates
  • Facets

Headings

Better default titles for variables

  • Column metadata
  • Data dictionary

Column metadata

  • ‘Pretty labels’ implemented as "label" attribute in columns.
  • Implemented in Hmisc, tinylabels, haven, labelled & sjlabelled
df <- mtcars

df$mpg <- haven::labelled(df$mpg, label = "Miles per gallon")

head(df$mpg)
## <labelled<double>[6]>: Miles per gallon
## [1] 21.0 21.0 22.8 21.4 18.7 18.1

attr(df$mpg, "label")
## [1] "Miles per gallon"

Column metadata

  • ‘Pretty labels’ implemented as "label" attribute in columns.
  • Implemented in Hmisc, tinylabels, haven, labelled & sjlabelled
  • Careful with label attribute stability
df <- mtcars

attr(df$mpg, "label") <- "Miles per gallon"

head(df$mpg)
## [1] 21.0 21.0 22.8 21.4 18.7 18.1

vctrs::vec_slice(df$mpg, 1:6)
## [1] 21.0 21.0 22.8 21.4 18.7 18.1
## attr(,"label")
## [1] "Miles per gallon"

Column metadata

Label attribute automatically detected.

library(ggplot2)
library(patchwork)

df <- mtcars
attr(df$mpg, "label") <- "Miles per gallon"

ggplot(df, aes(mpg, disp)) +
  geom_point()

Data dictionary

Example dictionary for mtcars

dict <- tibble::tribble(
  ~column, ~label,                ~unit,    ~note,
  "mpg",   "Efficiency",          "mi/gal", "Gallons are US gallons",
  "cyl",   "Number of cylinders", "",       "",
  "disp",  "Engine Displacement", "in^3",   "",
  "am",    "Transmission",        "",       "0 = automatic, 1 = manual"
  # Additional rows for the other variables
)
dict
# A tibble: 4 × 4
  column label               unit     note                       
  <chr>  <chr>               <chr>    <chr>                      
1 mpg    Efficiency          "mi/gal" "Gallons are US gallons"   
2 cyl    Number of cylinders ""       ""                         
3 disp   Engine Displacement "in^3"   ""                         
4 am     Transmission        ""       "0 = automatic, 1 = manual"

Data dictionary

Preparing the dictionary for ggplot2 labels

# Format label as named vector
named_vec <- setNames(dict$label, dict$column)
# or:
named_vec <- dplyr::pull(dict, label, name = column)

named_vec
##                   mpg                   cyl                  disp 
##          "Efficiency" "Number of cylinders" "Engine Displacement" 
##                    am 
##        "Transmission"

Data dictionary

ggplot(mtcars, aes(mpg, disp, colour = cyl)) +
  geom_point() +
  labs(dictionary = named_vec)

Pros

  • Label variables directly, rather than aesthetics
  • Rewards habit of annotating data
  • Reusable within document
ggplot(mtcars, aes(cyl, mpg, group = cyl)) +
  geom_boxplot() +
  labs(dictionary = named_vec)

Pros

  • Label variables directly, rather than aesthetics
  • Rewards habit of annotating data
  • Reusable within document

Cons

  • Extra effort for ‘naked’ data
  • Expressions like factor(cyl) or cyl + 1 do not get automatic labels

Headings: summary

  • attr(data$var, "label")
  • labs(dictionary)

Exercise 2.1

05:00

Patterns and gradients

In R 4.1 the grid package introduced patterns and gradients.

  • grid::linearGradient()
  • grid::radialGradient()
  • grid::pattern()

We allow these as fill aesthetic in ggplot2.
Patterns can aid in cases of colour vision deficiency.

Gradients

Simple examples of linear and radial gradients.

# A vector of 15 colours
colours <- rev(hcl.colors(15, "Sunset"))

library(grid)

linear <- linearGradient(
  colours = colours,
  # Parametrised like a rectangle
  x1 = 0.5, x2 = 0.5,
  y1 = 0.0, y2 = 1.0
)

radial <- radialGradient(
  colours = colours, 
  # Parametrised like two circles
  cx1 = 0.8, cy1 = 0.8, r1 = 0.2,
  cx2 = 0.5, cy2 = 0.5, r2 = 0.5,
  # Draw separately for every area
  group = FALSE
)

Gradients

Use these gradients by providing them as a list.

p <- ggplot(mtcars) +
  aes(factor(vs))

p1 <- p + geom_bar(fill = list(linear))
p2 <- p + geom_bar(fill = list(radial))

p1 / p2

Ribbon gradient

Ribbon geometries now render a varying fill aesthetic as a gradient.

ggplot(economics) +
  aes(date, unemploy, fill = uempmed) +
  geom_area()

Patterns

Patterns are more complicated. You may need to know a little bit of grid to get these right. Here we’re using a diagonal line as a pattern.

width <- height <- unit(3, "mm")

inner_drawing <- segmentsGrob(
  # Diagonal line
  x0 = 0, x1 = 1,
  y0 = 0, y1 = 1,
  gp = gpar(col = "black"),
  vp = viewport(width = width, height = height)
)

hatching <- pattern(
  inner_drawing,
  width = width, height = height,
  extend = "repeat"
)

Patterns

Like gradients, patterns can be given as a list.

ggplot(mtcars) +
  aes(factor(cyl)) +
  geom_bar(
    fill = list(hatching),
    colour = "black"
  )

Patterns

To ‘fix’ patterning artefacts, you may need to adjust the strokes in the inner drawing.

width <- height <- unit(3, "mm")
inner_drawing <- segmentsGrob(
  x0 = c(-1, -1, 0), x1 = c(1, 2, 2),
  y0 = c(0, -1, -1), y1 = c(2, 2, 1),
  gp = gpar(col = "black"),
  vp = viewport(width = width, height = height)
)
hatching <- pattern(
  inner_drawing,
  width = width, height = height,
  extend = "repeat"
)

Patterns

Using patterns as a scale.

ggplot(mtcars) +
  aes(factor(cyl), fill = factor(cyl)) +
  geom_bar(colour = "black") +
  scale_fill_manual(
    values = list(linear, radial, hatching)
  )

Patterns galore

Using the gridpattern package to easily generate patterns.

herringbone <- gridpattern::patternFill(
  pattern = "polygon_tiling",
  type = "herringbone",
  spacing = 0.2,
  units = "cm",
  colour = "grey40",
  linewidth = 0.3
)
hexagons <- gridpattern::patternFill(
  pattern = "polygon_tiling",
  type = "hexagonal",
  spacing = 0.2,
  units = "cm",
  colour = "grey40",
  linewidth = 0.3
)
waves <- gridpattern::patternFill(
  pattern = "wave",
  spacing = 0.2,
  units = "cm",
  colour = "grey40",
  linewidth = 0.3
)

Patterns galore

Using the gridpattern package to easily generate patterns.

ggplot(mtcars) +
  aes(factor(cyl), fill = factor(cyl)) +
  geom_bar(colour = "grey40") +
  scale_fill_manual(
    values = list(herringbone, hexagons, waves)
  )

Patterns galore

Parametrised patterns with the ggpattern package.

library(ggpattern)

ggplot(mtcars) +
  aes(
    x = factor(cyl),
    pattern_spacing = cyl
  ) +
  geom_bar_pattern(
    pattern_fill = "black", 
    colour = "black", fill = "white"
  ) +
  scale_pattern_spacing_continuous(
    range = c(0.02, 0.05)
  )

Patterns galore

Emoji isotype plot using text patterns.

Code 
# Helper function
width <- unit(20, "pt")
patternise_text <- function(text) {
  lapply(text, function(string) {
    grob <- textGrob(string, x = 0, hjust = 0, gp = gpar(fontsize = 18))
    pattern(
      grob,
      x = 0, hjust = 0,
      width = width, 
      extend = "repeat", 
      # Center text per bar using height/group
      height = unit(1, "npc"),
      group = FALSE
    )
  })
}

# Stats for the Netherlands
df <- data.frame(
  animal = c("chickens", "pigs",   "cows", "sheep", "goats", "humans"),
  amount = c(99900000,   11400000, 3800000, 850000, 480000,  17990000)
)

ggplot(df, aes(amount, animal, fill = animal)) +
  geom_col() +
  scale_fill_manual(
    values = patternise_text(c(
      "chickens" = "🐓",
      "pigs"     = "🐖",
      "cows"     = "🐄",
      "sheep"    = "🐑",
      "goats"    = "🐐",
      "humans"   = "🧍"
    ))
  ) +
  scale_x_continuous(
    labels = scales::label_number(scale = 1e-6, suffix = "M")
  ) +
  theme(
    legend.key.width = width,
    legend.key.height = unit(18, "pt") # see fontsize in pattern
  )

Patterns and gradients: summary

  • grid for most control over patterns.
    • grid::pattern(), grid::linearGradient(), grid::radialGradient()
  • gridpattern for preformatted patterns.
    • gridpattern::patternFill()
  • ggpattern for mapping data to patterns.
    • Aesthetics (pattern_density)
    • Geom layers (ggpattern::geom_boxplot_pattern())
    • Scales (ggpattern::scale_pattern_density_continuous())

Exercise 2.2

05:00

Delayed evaluation

With regards to evaluation, there are three stages:

  1. Direct input at start
  2. After computing stat
  3. After scale mapping

Direct input

Data available from the start, when mapped from data columns.

✅ aes(x = displ, y = hwy)

❌ Unmapped aesthetics like geom_bar(fill = "red")

❌ Data columns that are not included in aes()

After computing stat

In addition to aesthetics, computed variables become available.

  • Section in e.g. ?stat_density
  • Accessible via after_stat()
  • Redirection in Stat*$default_aes
# Inspect default aesthetics
StatDensity$default_aes
Aesthetic mapping: 
* `x`      -> `after_stat(density)`
* `y`      -> `after_stat(density)`
* `fill`   -> NA
* `weight` -> NULL

After computing stat

Using after_stat() yourself to redirect/modify computed variables.

ggplot(mpg, aes(displ, drv)) +
  stat_density(
    geom = "tile", position = "identity",
    aes(fill = after_stat(scaled))
  )

After computing stat

You may have run into a histogram/density misalignment problem.

binwidth <- 2

ggplot(faithful, aes(waiting)) +
  geom_histogram(binwidth = binwidth) +
  geom_density()

After computing stat

This can be fixed by using the density computed variable in the histogram.

ggplot(faithful, aes(waiting)) +
  geom_histogram(
    aes(y = after_stat(density)), 
    binwidth = binwidth
  ) +
  geom_density()

After computing stat

Or scaling the count computed variable in the density.

ggplot(faithful, aes(waiting)) +
  geom_histogram(binwidth = binwidth) +
  geom_density(
    aes(y = after_stat(count * binwidth))
  )

After scales

At this stage in the plot, we have mapped variables.

  • Determined by scale’s palette
  • Values now have graphical interpretation
    • colour: "#4B0055"
    • size: 12
    • shape: "circle filled"/21
    • linetype: "solid"/1
  • Access via after_scale()

After scales

A typical use of after_scale() is to derive colours from colour to fill or vice versa.

ggplot(mpg, aes(displ, fill = drv)) +
  geom_density(
    aes(colour = after_scale(
      scales::col_mix(fill, "black")
    ))
  )

After scales

A nice benefit of using after_scale() is that you derive colours, so you can still swap out scales.

last_plot() + 
  scale_fill_viridis_d()

After scales

Another use case can be to create half-geometries.

ggplot(mpg, aes(class, displ)) +
  geom_boxplot(aes(xmin = after_scale(x)), staplewidth = 0.3) +
  geom_violin(aes(xmax = after_scale(x)))

Staging

When you need a combination of direct input, after stat or after scale modifications, you can use stage().

  • stage(x) is equivalent to x.
  • stage(after_stat = x) is equivalent to after_stat(x).
  • stage(after_scale = x) is equivalent to after_scale(x).

Staging

A typical use case is when you want to initialise the aesthetic with one column, and later modify the mapped values.

ggplot(mpg, aes(drv, displ)) +
  geom_violin(
    aes(fill = stage(
      start = drv, 
      after_scale = scales::col_mix(fill, "white")
    ))
  )

Staging

Another use case is to reposition labels after computing a statistic.

ggplot(mpg, aes(drv, displ, fill = drv)) +
  geom_violin(show.legend = FALSE) +
  stat_summary(
    fun.data = ~ data.frame(
      mean = mean(.x), 
      sd   = sd(.x), 
      max  = max(.x)
    ),
    aes(
      y = stage(displ, after_stat = max + 0.4),
      label = after_stat(sprintf("%.2f±%.2f", mean, sd))
    ),
    geom = "text"
  )

Caveat

Staging function on their own are inert.

after_stat(10)
## [1] 10

after_scale(10)
## [1] 10

stage(10, "A", mpg)
## [1] 10

They need to be put in aes().

aes(
  a = after_stat(10),
  b = after_scale(10),
  c = stage(10, "A", mpg)
)
## Aesthetic mapping: 
## * `a` -> `after_stat(10)`
## * `b` -> `after_scale(10)`
## * `c` -> `stage(10, "A", mpg)`

Delayed evaluation: summary

  • after_stat() to access computed variables.

  • after_scale() to redirect mapped values.

  • stage() to initiate and delay modification.

Exercise 2.3

03:00

Polar coordinates

The classic coord_polar() is superseded by coord_radial().

  • expand parameter
  • Arbitrary sectors
  • Donuts

Polar coordinates

Helpful to always examine plot in Cartesian coordinates.

Code 
p <- ggplot(mpg, aes(y = factor(1), fill = factor(drv))) +
  geom_bar() +
  # Add labels
  stat_count(
    aes(label = after_stat(paste0(fill, " =\n", count))),
    geom = "text",
    position = position_stack(vjust = 0.5)
  ) +
  # Turn off y-axis and legend
  scale_y_discrete(guide = "none", name = NULL) +
  scale_fill_discrete(guide = "none")
p

Polar versus radial

Differences between coord_polar() and coord_radial().

polar <- p + 
  coord_polar() + 
  labs(title = "coord_polar()")
radial <- p + 
  coord_radial() + 
  labs(title = "coord_radial()")

# Thomas will explain this here mystery later
polar + radial

Polar versus radial

Set expand = FALSE for use in pie charts.

polar <- p + 
  coord_polar() + 
  labs(title = "coord_polar()")
radial <- p + 
  coord_radial(expand = FALSE) + 
  labs(title = "coord_radial()")

polar + radial

Polar axes

coord_radial() interfaces with guide system mostly via guide_axis_theta(). Also note the text angles.

red_axis <- scale_x_continuous(
  guide = guide_axis_theta(
    angle = 0, 
    theme = theme_gray(ink = "red")
  )
)

# Ignores guide
(polar + red_axis) + 
  # Uses correct guide
  (radial + red_axis)

Partial circles

We’re no longer restricted to complete circles.

p + coord_radial(start = -0.4 * pi, end = 0.4 * pi)

Partial circles

Switching a pie chart to a donut chart is as easy as setting the inner.radius argument.

p + coord_radial(
  expand = FALSE, 
  inner.radius = 0.5
)

Partial circles

We can combine partial polar coordinates with donuts.

p + coord_radial(
  start = 0, end = 0.5 * pi, 
  inner.radius = 0.5
)

Polar coordinates: summary

  • coord_radial() replaces coord_polar()
  • Partial circles: start & end
  • Donut: inner.radius

Exercise 2.4

03:00

Facets

  • Display of inner axes
  • Layer layout
  • Panel ordering

Display of inner axes

p <- ggplot(penguins) +
  aes(bill_len, bill_dep, colour = sex) +
  geom_point(na.rm = TRUE)
p + facet_grid(island ~ species)

Display of inner axes

Inner axes can be exposed, for all directions or x or y individually.

p + facet_grid(island ~ species, axes = "all")

Display of inner axes

We can confine labels, so inner axes only display tick marks.

p + facet_grid(island ~ species, axes = "all", axis.labels = "margins")

Layout

Layers have a layout argument that can be interpreted by facets.

p +
  geom_point(
    colour ="grey", shape = 1, 
    na.rm = TRUE,
    layout = "fixed"
  ) +
  geom_point(na.rm = TRUE) +
  facet_grid(island ~ species)

Layout

facet_wrap() and facet_grid() allow placement at certain panels.

p +
  annotate(
    geom = "text", x = I(0.7), y = I(0.25), size = 2,
    label = "Adelie Penguins\non Dream island",
    layout = 4
  ) +
  facet_grid(island ~ species)

Wrap panel order

New panel ordering settings in dir argument.

  • as.table is now absorbed in dir
  • Use two-letter combination of t, r, b, l
    • t = top
    • r = right
    • b = bottom
    • l = left
  • Combinations determines starting point, e.g. "br" starts in the bottom-right.
  • First letter indicates growing direction, e.g. "br" grows bottom-to-top before right-to-left.

Wrap panel order

The default order is "lt".

panels <- ~ as.integer(interaction(species, island, drop = TRUE))
p + facet_wrap(panels, dir = "lt")

Wrap panel order

p + facet_wrap(panels, dir = "tr")

Facets: summary

  • Display of inner axes
    • axes = "margins"/"all"/"all_x"/"all_y"
    • axis.labels = "all"/"margins"/"all_x"/"all_y"
  • layer(layout) argument
    • Repeat data across panels
    • Confine data to individual panels
  • facet_wrap(dir) sets panel layout
    • Two letter code determine start position
    • First letter determines growing direction

Exercise 2.5

03:00

Next session: Text rendering and font use