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ggpointless

ggpointless is an extension of the ggplot2 package providing additional layers. These layers group into two categories:

Data transformations — geoms that fit or transform data:

Visual effects — purely aesthetic layers that change how data looks without transforming it. The following layers are mostly drop-in replacements for their ggplot2 counterparts. They add visual flair like alpha gradients but no statistical transformation:

See the Examples article for more examples.

Installation

ggpointless requires R ≥ 4.2 and ggplot2 ≥ 4.0.0. You can install it from CRAN with:

install.packages("ggpointless")

Usage

The chunk below sets a colour palette so the README plots share a consistent look. It’s not required; only the look of your plots will differ.

library(ggplot2)
library(ggpointless)

# Brand palette -- kept in sync with the vignette
cols <- c("#311dfc", "#a84dbd", "#d77e7b", "#f4ae1b")
theme_set(
  theme_minimal() +
    theme(
      legend.position           = "bottom",
      geom                      = element_geom(fill = cols[1]),
      palette.fill.discrete     = cols,
      palette.colour.discrete   = cols,
      palette.fill.continuous   = cols,
      palette.colour.continuous = cols
    )
)

Statistical transformations

Catenary curves and arches 

set.seed(5)
df_catenary <- data.frame(x = 1:4, y = sample(4))
ggplot(df_catenary, aes(x, y)) +
  geom_catenary() +
  geom_point(size = 3)

Four points connected by a catenary curve sagging between adjacent points.

ggplot(df_catenary, aes(x, y)) +
  geom_catenary(
    sag = c(2, .5, NA),
    chain_length = c(NA, 4, 6)) +
  geom_point(size = 3)
#> Both `sag` and `chain_length` supplied for 1 segment; using `sag`.
#> This message is displayed once every 8 hours.

Same four-point catenary with per-segment overrides — the first segment sags deeper, the second tighter, the third uses a longer chain length.

ggplot(df_catenary, aes(x, y)) +
  geom_arch(
    arch_height = c(1.5, NA, 0.5),
    arch_length = c(NA, 6, NA)
    ) +
  geom_point(size = 3)

Inverted catenary arches connecting four points; each arch peaks midway between two adjacent points.

Chaikin’s corner cutting algorithm 

lst <- list(
  data = list(
    whale = data.frame(x = c(.5, 4, 4, 3.5, 2), y = c(.5, 1, 1.5, .5, 3)),
    closed_square = data.frame(x = c(0, 0, 1, 1), y = c(2, 3, 3, 2)),
    open_triangle = data.frame(x = c(3, 3, 5), y = c(2, 3, 3)),
    closed_triangle = data.frame(x = c(3.5, 5, 5), y = c(0, 0, 1.5))
  ),
  color = cols,
  mode = c("closed", "closed", "open", "closed")
)

ggplot(mapping = aes(x, y)) +
  lapply(lst$data, \(i) {
    geom_polygon(data = i, fill = NA, linetype = "12", color = "#333333")
  }) +
  Map(f = \(data, color, mode) {
    geom_chaikin(data = data, color = color, mode = mode)
  }, data = lst$data, color = lst$color, mode = lst$mode) +
  geom_point(data = data.frame(x = 1.5, y = 1.5)) +
  coord_equal()

Four polygon shapes drawn with sharp dashed outlines and overlaid with smoothed Chaikin corner-cut curves.

Discrete Fourier transform 

x_d <- seq(0, 4 * pi, length.out = 15)
df_d <- data.frame(
  x = x_d,
  y = sin(x_d) + x_d * 0.4 + rnorm(15, sd = 0.2)
)

p <- ggplot(df_d, aes(x, y)) +
  geom_point(alpha = 0.35)
p + geom_fourier()

Noisy point cloud with a single Fourier-series fit overlaid; the curve follows the upward drift while smoothing the cyclic component.

p +
  geom_fourier(
    aes(colour = "detrend = NULL"),
    n_harmonics = 3
  ) +
  geom_fourier(
    aes(colour = "detrend = \"lm\""),
    n_harmonics = 3,
    detrend = "lm"
  )

Noisy point cloud with two overlaid Fourier-series fits: one without detrending shows the linear drift, one with linear detrending isolates the cyclic component.

Draw (grid)lines above bars and other geoms 

ggplot(mpg, aes(class)) +
  geom_bar() +
  geom_gridline()

Vertical bar chart of mpg car-class counts with horizontal grid lines drawn on top of the bars.

p <- ggplot(mpg, aes(y = class)) +
  geom_bar() +
  geom_gridline(grids = "x")

# geom_gridline inherits its line properties from theme
p + theme(panel.grid = element_line(colour = "white"))

Horizontal bar chart of mpg car-class counts with vertical white grid lines drawn on top of the bars.

# The x/y positions are deducted from the scale(s)
p + scale_x_sqrt()

Same horizontal bar chart on a log10 x scale; grid line positions follow the transformed breaks.

# You can overwrite these properties of course
p + 
  geom_gridline(grids = "x", colour = cols[4], linewidth = 1.5) +
  scale_x_sqrt(breaks = c(10, 20, 30, 40, 50))

Same horizontal bar chart with bolder white grid lines drawn via an explicit colour and linewidth override.

Lexis diagrams 

df_lexis <- data.frame(
  key = c("A", "B", "B", "C", "D"),
  x = c(0, 1, 6, 5, 6),
  xend = c(5, 4, 10, 8, 10)
)

ggplot(df_lexis, aes(x = x, xend = xend, color = key)) +
  geom_lexis(aes(linetype = after_stat(type)), size = 2) +
  coord_equal() +
  scale_x_continuous(breaks = c(df_lexis$x, df_lexis$xend)) +
  scale_linetype_identity() +
  theme(panel.grid.minor = element_blank())

Lexis diagram showing five 45-degree lifelines coloured by category, with dotted gap-fillers bridging gaps between events of the same cohort.

Emphasise some observations 

sunspot_df <- data.frame(
  year = time(datasets::sunspot.year),
  sunspots = unclass(datasets::sunspot.year)
)
ggplot(tail(sunspot_df, 12), aes(year, sunspots)) + 
  geom_step(colour = cols[4]) +
  geom_pointless(location = c("first", "last"), size = 3, colour = cols[4])

Yearly sunspot data from 1977 to 1986 as a step chart.

Visual effects

Fading area, density, and ridgeline charts 

set.seed(42)
df_fade <- data.frame(
  x = seq_len(60),
  y = cumsum(rnorm(60, sd = 0.35))
)

p <- ggplot(df_fade, aes(x, y))
p + geom_area_fade()

Area chart of a 60-step random walk, fill fading from opaque at the line to transparent at the y=0 baseline.

ggplot(iris, aes(Sepal.Length)) +
  geom_freqpoly_fade(alpha = 0, alpha_fade_to = 1)
#> `stat_bin()` using `bins = 30`. Pick better value `binwidth`.

Frequency polygon of iris Sepal.Length filled below the line with the gradient reversed — transparent at the line, opaque at the y=0 baseline.

dmn <- list(
  month.abb,
  time(datasets::nottem) |> floor() |> unique()
)
df_nottem <- datasets::nottem |> 
  matrix(data = _, 12, dimnames = dmn) |> 
  as.data.frame() |> 
  stack() |> 
  cbind(month = factor(month.abb, levels = month.abb))

p <- ggplot(df_nottem, aes(x = values, group = month, fill = after_stat(x))) +
  labs(
    x = NULL,
    y = NULL,
    caption = "Average air temperatures at Nottingham Castle in degrees Fahrenheit (1920–1939)"
  ) +
  guides(fill = "none") +
  scale_x_continuous(expand = 0)

p + geom_density_fade(
  outline.type = "none"
  )

Twelve overlapping monthly density curves of Nottingham temperatures, fill mapped to temperature so colour shifts from indigo (cold) through pink to amber (warm); each fades to transparent at the baseline.

p <- p + aes(y = month)
p + geom_ridgeline_density_fade(
  alpha_scope = "global",
  outline.type = "none"
  )
#> ℹ Using auto-computed `scale = 6.5`.
#> • Pass an explicit `scale` to override.

Twelve ridgeline density curves stacked from January at the bottom to December at the top, fill mapped to temperature, fading to transparent at each row's baseline.

p + geom_ridgeline_freqpoly_fade(
  alpha_scope = "global",
  linewidth = 0.25
  )
#> ℹ Using auto-computed `scale = 0.2`.
#> • Pass an explicit `scale` to override.

Same twelve monthly ridgelines drawn as binned frequency polygons (step-shaped) instead of smooth densities.

p + geom_ridgeline_histogram_fade(
  alpha_scope = "group",
  bins = 40,
  linewidth = 0.25
  )
#> ℹ Using auto-computed `scale = 0.29`.
#> • Pass an explicit `scale` to override.

Same twelve monthly ridgelines drawn as 40-bin histograms — each row is a stack of fading bars instead of a smooth curve.

Points that glow 

anscombe_long <- reshape(anscombe, varying = TRUE, sep = "", direction = "long", timevar = "tv")
ggplot(anscombe_long, aes(x, y)) +
  geom_point_glow(colour = cols[1]) +
  facet_wrap(~tv) +
  coord_equal(clip = "off")

Anscombe's quartet rendered as four scatter facets where each point carries a soft purple radial glow.

Fading curves, lines, and paths 

b <- ggplot(mtcars, aes(wt, mpg)) +
  geom_point()

df <- data.frame(x1 = 2.62, x2 = 3.57, y1 = 21.0, y2 = 15.0)
b +
  geom_curve_fade(
    aes(x = x1, y = y1, xend = x2, yend = y2, colour = "curve"),
    arrow = arrow(),
    data = df
  ) +
  geom_segment_fade(
    aes(x = x1, y = y1, xend = x2, yend = y2, colour = "segment"),
    data = df
  )

mtcars weight-vs-mpg scatter with two fading arrows between the same two points — one curved, one straight — colour-labelled by geom.

theta <- seq(1.3, -1.3, length.out = 101)
ichthys <- data.frame(
  x = theta^2,
  y = 0.5 * theta * (theta^2 - 1)
)

ggplot(ichthys, aes(x, y)) + 
  geom_path_fade(
    linewidth = 1.5,
    fade_direction = c("start", "end"),
    alpha_mode = "gradient"
  ) +
  coord_fixed()

A stylised fish (ichthys) outline drawn as a single path with the line fading to transparent at both the start and the end.

p <- ggplot() +
  geom_abline_fade(intercept = 0, colour = "#a84dbd", linewidth = 1) +
  geom_hline_fade(yintercept = 1, colour = "#d77e7b", linewidth = 1) +
  geom_vline_fade(xintercept = 1, colour = "#f4ae1b", linewidth = 1) +
  xlim(0, 2) +
  ylim(0, 2)
  
p

Three coloured reference lines on a unit panel — diagonal (purple), horizontal (pink), and vertical (amber) — each fading to transparent along its length.

The same three reference lines drawn under coord_polar, becoming concentric arcs and rays with the fade preserved along each line.

ggplot() +
  stat_function(
    alpha = 0.5,
    fun = dnorm,
    n = 100,
    xlim = c(-4, 4),
    geom = "area_fade",
    outline.type = "none" # remove solid outline
  ) +
  # Add fading outline instead
  stat_function(
    fun = dnorm, n = 100,
    xlim = c(-4, 4),
    geom = "line_fade",
    colour = cols[1],
    linewidth = 1,
    fade_direction = c("start", "end")
  )

Standard normal density curve filled as a fading area beneath the curve, with the outline drawn as a separate line that fades to transparent at both tails.

ggplot(head(economics, 25), aes(date, unemploy)) +
  geom_step_fade(alpha_fade_to = 0.1)

Step chart of US unemployment for the first 25 economics observations, the line fading from opaque on the right to nearly transparent on the left.

Fading rectangles and bar charts with rounded corners 

ggplot(head(economics, 25), aes(date, unemploy)) +
  geom_rect_fade(
    data = data.frame(
      xmin = as.Date("1968-07-01"),
      xmax = as.Date("1969-07-01"),
      ymin = -Inf, ymax = 2800
    ),
    inherit.aes = FALSE,
    alpha = 0,
    alpha_fade_to = 0.3,
    aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax)
  ) +
  geom_step_fade(alpha_fade_to = 0.1)

Same unemployment step chart with a vertical band from July 1968 to July 1969 shaded by a rectangle that fades to transparent at its top edge.

ggplot(mpg, aes(y = class)) +
  geom_bar_fade()

Vertical bar chart of mpg class counts with each bar filled by an alpha gradient that fades from opaque at the top to transparent at the baseline; bars have rounded corners.

ggplot(datasets::BOD, aes(Time, demand)) +
  geom_col_fade(
    radius = unit(10, "pt"),
    alpha = 0.75,
    alpha_fade_to = 0.25,
    alpha_scope = "global"
    )

Vertical bar chart of BOD demand over time with rounded corners and a global alpha gradient — fade depth scales with each bar's height relative to the tallest.

ggplot(faithful, aes(eruptions)) +
  geom_histogram_fade(
    alpha = 0.8,
    alpha_fade_to = 0.1,
    alpha_scope = "global"
    ) +
  geom_step(
    stat = "bin",
    direction = "mid",
    colour = cols[1]
    )
#> `stat_bin()` using `bins = 30`. Pick better value `binwidth`.
#> `stat_bin()` using `bins = 30`. Pick better value `binwidth`.

Histogram of Old Faithful eruption durations (30 bins) with each bar filled by an alpha gradient; gradient strength is shared across bars (global scope). ### Unit bar charts and histograms

bins <- 30
bw <- diff(range(faithful$eruptions)) / bins

ggplot(faithful, aes(eruptions)) +
  geom_unit_histogram(
    aes(fill = after_stat(x)),
    radius = unit(1, "pt"),
    bins = bins
  ) +
  labs(
    y = NULL
  ) +
  guides(fill = "none") +
  scale_x_continuous(
    breaks = 2:5,
    labels = paste(2:5, "min")
  ) +
  coord_equal(ratio = 1/3 * bw, clip = "off") +
  theme(
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_blank(),
    axis.ticks    = element_blank()
  )

Old Faithful eruption-duration histogram drawn as a stack of unit cells per bar, with cell fill running from indigo blue for short eruptions to amber for long ones.

cs <- 100
ggplot(diamonds, aes(y = cut, fill = color)) +
  geom_unit_bar(
    cell_size = cs,
    radius = unit(1, "pt"),
    position = "dodge"
    ) +
  labs(
    x = NULL,
    y = NULL,
    caption = sprintf("One cell equals %d observations.", cs)) +
  coord_equal(ratio = 7 * cs) +
  theme_minimal()
#> ! `radius` of 1 pt exceeds the largest displayable corner radius for the
#>   rendered shape.
#> ℹ Maximum displayable radius is 0.52 pt; falling back to that.

Horizontal isotype bar chart counting diamonds per cut; each cell represents one hundred observation.

The geom_unit_* family was inspired by the work of pudding.cool.

Accessibility

Visual effects can quietly exclude readers. The faded end of any gradient geom drops below WCAG contrast thresholds for low-vision readers. Pair colour with linetype, shape, or labels for categorical encodings; prefer colour-vision-deficiency-safe (CVD-safe) palettes like scale_colour_viridis_d() or Okabe–Ito; and raise alpha_fade_to when the faded region carries data your readers need to find.

The 4-stop palette cols used in this README and the Examples article is monotonic in luminance, so it survives greyscale conversion and the stop ordering is preserved under CVD. The middle hues collapse under deuteranopia, however, so for cases where hue identity carries information rather than ordering, prefer scale_fill_viridis_c() / scale_colour_viridis_c().

Device compatibility

The 2D gradients used by geom_area_fade() and friends depend on Porter-Duff compositing1, a feature of R’s graphics engine added in R 4.2. When the active device does not support compositing (e.g. grDevices::pdf()), the fade family falls back to a single-colour vertical fade — the horizontal colour gradient is lost, only the vertical alpha fade survives, and a one-time message is emitted:

! geom_area_fade(): the graphics device does not support gradient fills. The fill colour gradient is replaced by a single colour. Switch to a device that supports gradients (e.g. ragg::agg_png(), svg(), cairo_pdf()) for the full effect. This message is displayed once per session.

Most modern raster devices — including ragg::agg_png() and the Cairo-backed png() shipped on Linux and macOS — do support compositing, so the 2D gradient works out of the box. In RStudio, go to Tools > Global Options > Graphics > Backend and select AGG to ensure full support.

Code of Conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.