Package {ggnext}

ggnext: A Next-Generation Grammar of Graphics

Description

An implementation of the Grammar of Graphics described by Wilkinson (2005, ISBN:978-0-387-24544-7), built on 'S7' classes. The familiar grammar vocabulary of aesthetics, geometries, statistics, scales, coordinates, facets and themes composed with '+' is preserved; every constructor that takes an argument also accepts a plot as the first argument of a native pipe ('|>') stage, so the two styles are interchangeable. The package is extended with built-in interactivity, animation, an exact-data export, a plot linter that flags common statistical-graphics mistakes before a figure ships, and a catalogue covering layout diagrams (Sankey, treemap, network, radar), machine-learning diagnostics (SHAP, receiver operating characteristic, calibration, partial dependence) and clinical reporting (Kaplan-Meier, forest, swimmer, CONSORT). Static output is written to Scalable Vector Graphics; interactive output is a self-contained 'HTML' document using a canvas element and vanilla 'JavaScript'. Both render targets consume the same computed-geometry buffer, giving a single source of truth for layer geometry. Layout and estimation algorithms follow their published descriptions, including Bruls, Huizing and van Wijk (2000) doi:10.1007/978-3-7091-6783-0_4 for squarified treemaps, Fruchterman and Reingold (1991) doi:10.1002/spe.4380211102 for force-directed graphs, and Kaplan and Meier (1958) doi:10.1080/01621459.1958.10501452 for survival curves.

Author(s)

Maintainer: Mahesh Divakaran itsmdivakaran@gmail.com [copyright holder]

Authors:

• Mahesh Divakaran itsmdivakaran@gmail.com [copyright holder]

See Also

Useful links:

• https://itsmdivakaran.github.io/ggnext/

• https://github.com/itsmdivakaran/ggnext

• Report bugs at https://github.com/itsmdivakaran/ggnext/issues

Animation: a transition spec bound to a data variable

Description

Created by animate(). Frames are computed from the same geometry pipeline as static output — one buffer per frame value — and the interactive target plays them with a scrubber.

Usage

Animation(
  var = character(0),
  duration = integer(0),
  easing = character(0),
  loop = logical(0)
)

Arguments

Value

An S7 object of class Animation; usually built by animate().

ColorScale: palette override for the color aesthetic

Description

Created by scale_color_manual() / scale_color_gradient().

Usage

ColorScale(palette = character(0), name = NULL, type = character(0))

Arguments

Value

An S7 object of class ColorScale; usually built by scale_color_manual() or scale_color_gradient().

Coord: position transformation for normalized coordinates

Description

Base class for coordinate systems. A coord receives positions already normalized to [0, 1] by the scales and returns (possibly transformed) normalized positions. Cartesian is the identity; polar / flipped / 3D coords will override this seam without touching scales or geoms.

Usage

Coord(name = character(0))

Arguments

Value

An S7 object of class Coord, the base class for coordinate systems.

CoordCartesian: the identity coordinate system

Description

CoordCartesian: the identity coordinate system

Usage

CoordCartesian(flip = FALSE)

Arguments

Value

An S7 object of class CoordCartesian; usually built by coord_cartesian() or coord_flip().

CoordPolar: polar coordinate system

Description

Maps the normalized x axis onto angle and the normalized y axis onto radius, so lines, areas, and points become circular. This is the engine behind radar/spider charts, pie-like wedges, and circular bar charts.

Usage

CoordPolar(theta = "x", start = 0, direction = 1, inner = 0)

Arguments

Value

An S7 object of class CoordPolar; usually built by coord_polar().

Facet: split a plot into panels by data values

Description

Base class for faceting. Created by facet_wrap() / facet_grid().

Usage

Facet(
  vars = character(0),
  ncol = NULL,
  nrow = NULL,
  scales = character(0),
  type = character(0)
)

Arguments

Value

An S7 object of class Facet; usually built by facet_wrap() or facet_grid().

Geom: geometric representation of computed values

Description

Base class for geoms. A geom turns scaled (normalized-to-[0, 1]) aesthetic values into a list of render-target-agnostic drawing primitives ("marks"); see build_marks().

Usage

Geom(name = character(0), default_params = list(), required_aes = c("x", "y"))

Arguments

Value

An S7 object of class Geom, the base class for geometric layers.

GgnextPlot: the complete plot specification

Description

Built by ggnext() and grown with +. Holds everything needed to compute geometry; rendering never reaches back past the computed buffer.

Usage

GgnextPlot(
  data = NULL,
  mapping = NULL,
  layers = list(),
  scales = list(),
  coord = Coord(),
  interaction = NULL,
  theme = NULL,
  labels = NULL,
  color_scale = NULL,
  facet = NULL,
  animation = NULL,
  size = NULL
)

Arguments

Value

An S7 object of class GgnextPlot, as returned by ggnext().

Add interactivity to a plot

Description

Plots are static-first: render(p) and printing produce a static SVG image. Adding interact() with + opts the plot into the interactive HTML/canvas target — render(p) then produces the interactive page instead (an explicit render(p, target = ) always wins).

Usage

Interact(tooltip = NULL, zoom = logical(0), brush = logical(0))

interact(tooltip = TRUE, zoom = TRUE, brush = TRUE)

Arguments

Value

An Interact spec to add to a plot with +.

Examples

p <- ggnext(cars, aes(speed, dist)) + geom_point()
render(p)                 # static SVG
pi <- p + interact()      # same plot, interactive by default
html <- render(pi)        # HTML/canvas with tooltip + zoom + brush

Labels: plot title block and axis/legend titles

Description

Created by labs(), ggtitle(), xlab(), ylab(); added with +. Any field left NULL falls back to the default (for axes, the deparsed aesthetic mapping).

Usage

Labels(
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  tag = NULL,
  x = NULL,
  y = NULL,
  color = NULL,
  size = NULL,
  fill = NULL
)

Arguments

Value

An S7 object of class Labels; usually built by labs().

Layer: one geom + stat + data/mapping overrides

Description

Layer: one geom + stat + data/mapping overrides

Usage

Layer(
  geom = Geom(),
  stat = Stat(),
  mapping = NULL,
  data = NULL,
  params = list(),
  inherit = TRUE
)

Arguments

Value

An S7 object of class Layer, as returned by the ⁠geom_*()⁠ constructors.

Scale: map data values onto a normalized visual range

Description

Base class for scales. A scale owns one positional aesthetic, learns its domain from data ("training"), and maps data values onto [0, 1].

Usage

Scale(aesthetic = character(0), name = NULL, breaks = NULL, labels = NULL)

Arguments

Value

An S7 object of class Scale, the base class for scales.

ScaleContinuous: linear continuous positional scale

Description

ScaleContinuous: linear continuous positional scale

Usage

ScaleContinuous(
  aesthetic = "x",
  limits = NULL,
  name = NULL,
  breaks = NULL,
  labels = NULL,
  trans = "identity",
  expand = 0.05
)

Arguments

Value

An S7 object of class ScaleContinuous; usually built by scale_x_continuous() rather than called directly.

ScaleDiscrete: positional scale for categorical data

Description

Automatically selected when a positional aesthetic maps to character, factor, or logical data; construct explicitly via scale_x_discrete() / scale_y_discrete() to fix the level order.

Usage

ScaleDiscrete(aesthetic = "x", limits = NULL, name = NULL)

Arguments

Value

An S7 object of class ScaleDiscrete; usually built by scale_x_discrete() rather than called directly.

Stat: statistical transformation applied to layer data

Description

Base class for statistical transformations. A stat receives the evaluated aesthetic values and returns (possibly new) values for the geom to draw.

Usage

Stat(
  name = character(0),
  provides = character(0),
  discrete_provides = character(0)
)

Arguments

Value

An S7 object of class Stat, the base class for statistical transformations.

Customize theme settings

Description

Overrides individual theme settings. By default the overrides apply to theme_ggnext(); pass base to restyle a different preset.

Usage

Theme(
  name = character(0),
  background = character(0),
  panel_fill = character(0),
  grid_color = character(0),
  grid_color_minor = character(0),
  axis_color = character(0),
  label_color = character(0),
  title_color = character(0),
  subtitle_color = character(0),
  strip_fill = character(0),
  strip_color = character(0),
  legend_text_color = character(0),
  panel_border = character(0),
  font = character(0),
  title_font = character(0),
  tick_font_size = integer(0),
  title_font_size = integer(0),
  plot_title_size = integer(0),
  plot_subtitle_size = integer(0),
  caption_size = integer(0),
  strip_font_size = integer(0),
  legend_font_size = integer(0),
  title_face = character(0),
  tick_len = integer(0),
  grid_major_x = logical(0),
  grid_major_y = logical(0),
  axis_line_x = logical(0),
  axis_line_y = logical(0),
  ticks_x = logical(0),
  ticks_y = logical(0),
  axis_text_x = logical(0),
  axis_text_y = logical(0),
  axis_title_x = logical(0),
  axis_title_y = logical(0),
  legend_position = character(0),
  point_palette = character(0),
  gradient_low = character(0),
  gradient_high = character(0)
)

theme(..., base = NULL)

Arguments

Value

A Theme to add to a plot with +.

Examples

ggnext(cars, aes(speed, dist)) + geom_point() +
  theme(panel_fill = "#FFF8F0", grid_major_x = FALSE)

# Restyle a preset rather than the default.
ggnext(cars, aes(speed, dist)) + geom_point() +
  theme(base = theme_dark(), plot_title_size = 22)

Construct aesthetic mappings

Description

aes() captures unevaluated expressions that describe how columns of the data map onto visual properties. The first two unnamed arguments are taken as x and y. Expressions are evaluated later, against the layer data, in the environment where aes() was called.

Usage

aes(...)

Arguments

Value

An object of class ggnext_aes.

Examples

aes(speed, dist)
aes(x = speed, y = dist, color = gear)

Animate a plot over a transition variable

Description

Animation is a property of the plot, not a separate verb chain: the same geometry pipeline runs once per level of by, and the interactive target plays the resulting frames with a scrubber and play/pause control. Because frames are ordinary geometry buffers, positions are interpolated by the player rather than recomputed.

Usage

animate(by, duration = 800, easing = "cubic-in-out", loop = TRUE)

Arguments

Details

Animated plots render to the interactive target (like interact()). render(p, target = "static") still produces a static SVG of the whole data, so animation never blocks a static export.

Value

An Animation spec to add to a plot with +.

Examples

d <- data.frame(
  x = rep(1:5, 3), y = c(1:5, (1:5)^1.5, (1:5)^2),
  step = rep(c(1, 2, 3), each = 5)
)
p <- ggnext(d, aes(x, y)) + geom_point(size = 5) + animate(step)
html <- render(p)

Build drawing primitives ("marks") from scaled aesthetic values

Description

The single seam between the grammar and the renderers. Each geom method returns a list of marks; a mark is a plain named list with a type field (currently "circle"; the full catalog will add "rect", "line", "path", "polygon", "text") plus type-specific fields in normalized panel coordinates (x/y in [0, 1], y pointing up). Because both render targets consume marks, adding a geom never touches renderer code, and adding a renderer never touches geom code.

Usage

build_marks(geom, ...)

Arguments

Value

List of marks.

Build the ggnext documentation site

Description

Renders every gallery example with the package itself and writes a self-contained static site. No external site generator, no CDN assets.

Usage

build_site(dir, quiet = FALSE, gallery = TRUE, cookbook = TRUE)

Arguments

Value

The output directory, invisibly.

Examples

# The figures and the cookbook are the slow parts of a real build; this
# writes the pages only. For the full site, pass a directory of your
# choosing, e.g. build_site("docs").
out <- file.path(tempdir(), "ggnext-site")
build_site(out, quiet = TRUE, gallery = FALSE, cookbook = FALSE)
list.files(out)

Apply a stat's transformation to evaluated aesthetic values

Description

Methods receive values, a named list of evaluated aesthetic vectors, and return a named list of (possibly transformed) aesthetic vectors.

Usage

compute_stat(stat, ...)

Arguments

Value

Named list of (possibly transformed) aesthetic vectors.

Cartesian coordinate system

Description

The identity coordinate system.

Usage

coord_cartesian(flip = FALSE)

Arguments

Value

A CoordCartesian object to add with +.

Flipped Cartesian coordinates

Description

Draws the plot with x and y swapped — the usual way to get horizontal bars or a readable categorical axis with long labels.

Usage

coord_flip()

Value

A CoordCartesian object to add with +.

Examples

d <- data.frame(g = c("alpha", "beta", "gamma"), v = c(3, 7, 5))
ggnext(d, aes(g, v)) + geom_col() + coord_flip()

Polar coordinates

Description

Bends the panel into a circle: the theta axis becomes the angle and the other axis becomes the radius. This is the engine behind radar charts (geom_radar()), pie/donut wedges, and circular bar charts.

Usage

coord_polar(theta = "x", start = 0, direction = 1, inner = 0)

Arguments

Value

A CoordPolar object to add to a plot with +.

Examples

d <- data.frame(g = c("A", "B", "C", "D"), v = c(4, 7, 3, 6))
ggnext(d, aes(g, v)) + geom_col() + coord_polar()

Transform normalized positions through a coordinate system

Description

Transform normalized positions through a coordinate system

Usage

coord_transform(coord, ...)

Arguments

Value

List with transformed x and y.

Lay panels out in a rows-by-columns grid

Description

facet_grid(rows, cols) builds a two-way panel matrix: one row per level of rows, one column per level of cols.

Usage

facet_grid(rows, cols = NULL, scales = "fixed")

Arguments

Value

A Facet object to add to a plot with +.

Examples

d <- transform(mtcars, cyl = factor(cyl), am = factor(am))
ggnext(d, aes(disp, mpg)) + geom_point() + facet_grid(am, cyl)

Wrap panels into a grid

Description

Splits the data by one or more variables and lays the resulting panels out in a rectangular grid, wrapping to a new row as needed.

Usage

facet_wrap(vars, ncol = NULL, nrow = NULL, scales = "fixed")

Arguments

Value

A Facet object to add to a plot with +.

Examples

ggnext(iris, aes(Sepal.Length, Sepal.Width)) +
  geom_point() +
  facet_wrap(Species)

Sloped reference line

Description

Draws y = intercept + slope * x across the panel. Like geom_hline() and geom_vline() it ignores the plot's aes(), so it never disturbs the data mapping.

Usage

geom_abline(
  intercept = 0,
  slope = 1,
  color = NULL,
  linewidth = NULL,
  alpha = NULL,
  dash = NULL
)

Arguments

Value

A Layer to add with +.

Examples

ggnext(cars, aes(speed, dist)) +
  geom_point() +
  geom_abline(intercept = 0, slope = 3, dash = "5,4")

Adverse-event incidence heatmap

Description

Incidence by preferred term and treatment arm (or severity grade), shaded by rate and annotated with counts.

Usage

geom_ae_heatmap(mapping = NULL, data = NULL, label = TRUE, label_size = NULL)

Arguments

Value

A Layer to add with +.

Examples

d <- expand.grid(arm = c("Placebo", "Low", "High"),
                 ae = c("Nausea", "Fatigue", "Headache"))
d$pct <- c(5, 12, 22, 8, 15, 26, 3, 6, 11)
ggnext(d, aes(arm, ae, size = pct)) + geom_ae_heatmap()

Alluvial diagram (ordered, repeated-measures categorical flow)

Description

Tracks the same subjects through an ordered sequence of stages (e.g. visit 1 category -> visit 2 category -> visit 3 category), unlike geom_sankey(), which draws a general directed flow graph from an edge list. Internally it tallies every consecutive-stage transition and reuses geom_sankey()'s node-stacking and ribbon-path layout.

Usage

geom_alluvial(
  mapping = NULL,
  data = NULL,
  alpha = NULL,
  node_width = NULL,
  label = TRUE
)

Arguments

Value

A Layer to add with +.

Examples

d <- data.frame(
  subject = rep(1:8, each = 3),
  visit = rep(c("Baseline", "Week 4", "Week 8"), 8),
  response = c(
    "SD", "SD", "PR", "SD", "PR", "PR", "SD", "SD", "SD",
    "PR", "PR", "CR", "SD", "PR", "PR", "SD", "SD", "PD",
    "PR", "CR", "CR", "SD", "PD", "PD"
  )
)
d$visit <- factor(d$visit, levels = c("Baseline", "Week 4", "Week 8"))
ggnext(d, aes(x = visit, y = response, group = subject)) +
  geom_alluvial() +
  theme_void()

Area layer (filled to the zero baseline)

Description

Area layer (filled to the zero baseline)

Usage

geom_area(mapping = NULL, data = NULL, color = NULL, alpha = NULL)

Arguments

Value

A Layer.

Bar chart layer (counts per category)

Description

Counts rows at each x. For pre-computed heights use geom_col().

Usage

geom_bar(
  mapping = NULL,
  data = NULL,
  color = NULL,
  alpha = NULL,
  width = 0.8,
  position = "stack"
)

Arguments

Value

A Layer.

Bland-Altman agreement plot

Description

Difference against mean for two measurement methods, with the mean bias and 95% limits of agreement drawn as reference lines — the standard method-comparison plot.

Usage

geom_bland_altman(
  mapping = NULL,
  data = NULL,
  color = NULL,
  size = NULL,
  alpha = NULL
)

Arguments

Value

A Layer to add with +.

Examples

set.seed(1)
a <- rnorm(60, 100, 12)
d <- data.frame(method_a = a, method_b = a + rnorm(60, 2, 5))
ggnext(d, aes(method_a, method_b)) + geom_bland_altman()

Blank layer

Description

Draws nothing. Useful to establish scales without showing data, or as a placeholder in code that conditionally adds a layer.

Usage

geom_blank(mapping = NULL, data = NULL)

Arguments

Value

A Layer to add with +.

Examples

ggnext(cars, aes(speed, dist)) + geom_blank()

Boxplot layer

Description

Boxplot layer

Usage

geom_boxplot(
  mapping = NULL,
  data = NULL,
  color = NULL,
  alpha = NULL,
  width = 0.6,
  coef = 1.5
)

Arguments

Value

A Layer.

Bump chart (rank over time)

Description

Rank trajectories drawn with sigmoid interpolation between periods, so crossings read cleanly instead of as sharp zigzags.

Usage

geom_bump(
  mapping = NULL,
  data = NULL,
  color = NULL,
  alpha = NULL,
  linewidth = NULL,
  points = TRUE
)

Arguments

Value

A Layer to add with +.

Examples

d <- data.frame(
  year = rep(2021:2023, 3),
  rank = c(1, 2, 3, 2, 1, 1, 3, 3, 2),
  team = rep(c("A", "B", "C"), each = 3)
)
ggnext(d, aes(year, rank, color = team)) +
  geom_bump() + scale_y_reverse()

Calibration curve

Description

Bins predicted probabilities and plots the observed event rate in each bin against the mean prediction, with the diagonal marking perfect calibration. Points off the diagonal show over- or under-confidence.

Usage

geom_calibration(
  mapping = NULL,
  data = NULL,
  bins = 10,
  color = NULL,
  diagonal = TRUE
)

Arguments

Value

A Layer to add with +.

Examples

set.seed(1)
p <- runif(300)
d <- data.frame(pred = p, obs = rbinom(300, 1, p^1.3))
ggnext(d, aes(pred, obs)) + geom_calibration()

Chord diagram

Description

Entities sit on a circle; each relationship is a ribbon whose ends are arcs proportional to the flow. Ribbon interiors are quadratic curves pulled toward the circle center.

Usage

geom_chord(
  mapping = NULL,
  data = NULL,
  alpha = NULL,
  label = TRUE,
  label_size = NULL
)

Arguments

Value

A Layer to add with +.

Examples

d <- data.frame(
  from = c("A", "A", "B", "C"), to = c("B", "C", "C", "A"),
  n = c(5, 3, 7, 2)
)
ggnext(d, aes(x = from, xend = to, y = n)) + geom_chord() + theme_void()

Column chart layer (bar heights from the data)

Description

Column chart layer (bar heights from the data)

Usage

geom_col(
  mapping = NULL,
  data = NULL,
  color = NULL,
  alpha = NULL,
  width = 0.8,
  position = "stack"
)

Arguments

Value

A Layer.

Concordance / agreement plot

Description

Scatter of two methods' measurements against the identity line, with Lin's concordance correlation coefficient (CCC) annotated in the corner. Unlike geom_bland_altman() (difference vs. mean, for spotting systematic bias), this reads agreement directly off how close the cloud sits to y = x.

Usage

geom_concordance(
  mapping = NULL,
  data = NULL,
  color = NULL,
  size = NULL,
  alpha = NULL
)

Arguments

Value

A Layer to add with +.

Examples

set.seed(1)
a <- rnorm(50, 10, 2)
d <- data.frame(method1 = a, method2 = a * 0.95 + rnorm(50, 0, 0.5))
ggnext(d, aes(method1, method2)) + geom_concordance()

Confusion matrix

Description

A tile heatmap of predicted vs actual classes, shaded by row-normalized rate and annotated with counts, so class imbalance does not hide errors.

Usage

geom_confusion_matrix(
  mapping = NULL,
  data = NULL,
  normalize = "row",
  label_size = NULL
)

Arguments

Value

A Layer to add with +.

Examples

d <- data.frame(
  predicted = c("cat", "cat", "dog", "dog", "dog", "cat"),
  actual    = c("cat", "dog", "dog", "dog", "cat", "cat")
)
ggnext(d, aes(predicted, actual)) + geom_confusion_matrix()

CONSORT participant flow diagram

Description

Boxes and arrows tracing participants from screening through analysis, laid out automatically from a stage/count table.

Usage

geom_consort(mapping = NULL, data = NULL, box_fill = NULL, label_size = NULL)

Arguments

Value

A Layer to add with +.

Examples

d <- data.frame(
  stage = c("Assessed for eligibility", "Randomised",
            "Received allocation", "Analysed"),
  n = c(420, 300, 291, 285)
)
ggnext(d, aes(label = stage, size = n)) + geom_consort()

Correlation matrix heatmap

Description

All pairwise Pearson correlations among the numeric columns of data, shaded on a diverging scale (negative to positive) and annotated with the coefficient. Takes the raw wide data frame directly rather than through aes(), since the stat needs the whole numeric block at once to compute pairwise correlations, not row-wise aesthetic vectors — the same pattern geom_dendrogram() uses.

Usage

geom_cor(
  data,
  vars = NULL,
  label = TRUE,
  label_size = NULL,
  low = "#C1462F",
  mid = "#F2F0EA",
  high = "#12A594"
)

Arguments

Value

A Layer to add with +.

Examples

ggnext() +
  geom_cor(mtcars, vars = c("mpg", "cyl", "disp", "hp", "wt")) +
  labs(title = "Correlation matrix")

Counted scatter

Description

A scatter where the point area encodes how many observations share that position — the fix for a scatter of rounded or discrete values where overplotting hides the mass.

Usage

geom_count(mapping = NULL, data = NULL, color = NULL, alpha = NULL)

Arguments

Value

A Layer to add with +.

Examples

d <- data.frame(x = c(1, 1, 1, 2, 2, 3), y = c(1, 1, 2, 2, 2, 3))
ggnext(d, aes(x, y)) + geom_count()

Crossbar: an interval box with the estimate marked

Description

A hollow box spanning ymin to ymax with a heavier line at y — the compact summary used in dose-response and subgroup tables.

Usage

geom_crossbar(
  mapping = NULL,
  data = NULL,
  color = NULL,
  linewidth = NULL,
  alpha = NULL,
  width = NULL
)

Arguments

Value

A Layer to add with +.

Examples

d <- data.frame(g = c("a", "b"), m = c(2, 3), lo = c(1, 2), hi = c(4, 5))
ggnext(d, aes(g, m, ymin = lo, ymax = hi)) + geom_crossbar()

Cumulative incidence (competing risks)

Description

Aalen-Johansen cumulative incidence curves by event type, the correct estimator when competing events make 1 - Kaplan-Meier biased upward.

Usage

geom_cuminc(mapping = NULL, data = NULL, linewidth = NULL)

Arguments

Value

A Layer to add with +.

Examples

set.seed(1)
d <- data.frame(
  t = rexp(120, 0.1),
  ev = sample(0:2, 120, replace = TRUE, prob = c(.4, .35, .25))
)
ggnext(d, aes(time = t, status = ev)) + geom_cuminc()

Curved connector

Description

A quadratic curve between two points, for annotation leaders and relationship diagrams where a straight segment would overlap the data.

Usage

geom_curve(
  mapping = NULL,
  data = NULL,
  curvature = NULL,
  color = NULL,
  linewidth = NULL,
  alpha = NULL
)

Arguments

Value

A Layer to add with +.

Examples

d <- data.frame(x = 1, y = 1, xe = 3, ye = 3)
ggnext(d, aes(x, y, xend = xe, yend = ye)) + geom_curve()

Decision-boundary region plot

Description

Shades a grid of predictions to show a two-dimensional classifier's decision regions; overlay geom_point() for the training data.

Usage

geom_decision_boundary(mapping = NULL, data = NULL, alpha = NULL)

Arguments

Value

A Layer to add with +.

Examples

g <- expand.grid(x = seq(0, 1, 0.05), y = seq(0, 1, 0.05))
g$cls <- ifelse(g$x + g$y > 1, "a", "b")
ggnext(g, aes(x, y, color = cls)) + geom_decision_boundary()

Hierarchical clustering dendrogram

Description

Runs stats::hclust() (base R, not an added dependency) on the Euclidean distances between rows of data and draws the merge tree as the classic bracket shape: two vertical drops joined by one horizontal bar at the merge height. Takes the raw wide data frame directly (like geom_cor()), since the layout needs the whole numeric block at once, not row-wise aesthetics.

Usage

geom_dendrogram(
  data,
  vars = NULL,
  method = "complete",
  color = NULL,
  linewidth = NULL
)

Arguments

Value

A Layer to add with +.

Examples

ggnext() +
  geom_dendrogram(mtcars[1:10, c("mpg", "hp", "wt", "qsec")]) +
  theme_void()

Density curve layer

Description

Density curve layer

Usage

geom_density(
  mapping = NULL,
  data = NULL,
  color = NULL,
  alpha = NULL,
  linewidth = NULL,
  adjust = 1
)

Arguments

Value

A Layer.

Dose-response curve

Description

A four-parameter log-logistic curve fitted to dose-response data, with a confidence band and the fitted EC50 marked.

Usage

geom_dose_response(
  mapping = NULL,
  data = NULL,
  color = NULL,
  points = TRUE,
  ec50 = TRUE
)

Arguments

Value

A Layer to add with +.

Examples

d <- data.frame(
  dose = rep(c(0.1, 1, 10, 100, 1000), each = 3),
  resp = c(5, 7, 6, 18, 22, 20, 52, 48, 55, 82, 79, 85, 95, 97, 93)
)
ggnext(d, aes(dose, resp)) + geom_dose_response() + scale_x_log10()

Dot plot

Description

A histogram built from one dot per observation, stacked within its bin. Better than bars for small samples, where it shows every data point.

Usage

geom_dotplot(
  mapping = NULL,
  data = NULL,
  binwidth = NULL,
  color = NULL,
  size = NULL,
  alpha = NULL
)

Arguments

Value

A Layer to add with +.

Examples

ggnext(cars, aes(speed)) + geom_dotplot(binwidth = 2)

Dumbbell layer (before/after per category)

Description

Map x (start value), xend (end value), and y (category).

Usage

geom_dumbbell(
  mapping = NULL,
  data = NULL,
  color = NULL,
  size = NULL,
  alpha = NULL,
  linewidth = NULL,
  color_start = NULL,
  color_end = NULL
)

Arguments

Value

A Layer.

Embedding scatter (t-SNE / UMAP / PCA)

Description

A two-dimensional embedding scatter with optional cluster hulls, so cluster shape is visible rather than inferred from point color alone.

Usage

geom_embedding(
  mapping = NULL,
  data = NULL,
  hull = TRUE,
  size = NULL,
  alpha = NULL
)

Arguments

Value

A Layer to add with +.

Examples

set.seed(1)
d <- data.frame(
  d1 = c(rnorm(30), rnorm(30, 4)), d2 = c(rnorm(30), rnorm(30, 3)),
  cl = rep(c("a", "b"), each = 30)
)
ggnext(d, aes(d1, d2, color = cl)) + geom_embedding()

Error bar layer (ymin to ymax with caps)

Description

Error bar layer (ymin to ymax with caps)

Usage

geom_errorbar(
  mapping = NULL,
  data = NULL,
  color = NULL,
  alpha = NULL,
  linewidth = NULL,
  width = NULL
)

Arguments

Value

A Layer.

Horizontal error bars

Description

The horizontal counterpart of geom_errorbar(), for intervals that run along x — forest plots and tornado charts.

Usage

geom_errorbarh(
  mapping = NULL,
  data = NULL,
  color = NULL,
  linewidth = NULL,
  alpha = NULL,
  height = NULL
)

Arguments

Value

A Layer to add with +.

Examples

d <- data.frame(g = c("a", "b"), lo = c(1, 2), hi = c(4, 5))
ggnext(d, aes(y = g, xmin = lo, xmax = hi)) + geom_errorbarh()

Time-series forecast with confidence bands

Description

Historical actuals as a solid line, the forecast dashed, and one or two nested confidence ribbons.

Usage

geom_forecast_band(mapping = NULL, data = NULL, color = NULL, linewidth = NULL)

Arguments

Value

A Layer to add with +.

Examples

d <- data.frame(
  t = 1:10, v = c(1:6, 7, 8, 9, 10),
  lo = c(rep(NA, 6), 6, 6.5, 7, 7.5),
  hi = c(rep(NA, 6), 8, 9.5, 11, 12.5),
  part = rep(c("actual", "forecast"), c(6, 4))
)
ggnext(d, aes(t, v, ymin = lo, ymax = hi, group = part)) +
  geom_forecast_band()

Forest plot

Description

Point estimates with confidence intervals down a category axis, with a dashed no-effect reference line — the standard display for hazard/odds ratios, subgroup analyses, and meta-analyses. Marker area encodes study weight when size is mapped.

Usage

geom_forest(
  mapping = NULL,
  data = NULL,
  ref = 1,
  color = NULL,
  linewidth = NULL
)

Arguments

Value

A Layer to add with +.

Examples

d <- data.frame(
  study = c("Trial A", "Trial B", "Trial C", "Pooled"),
  hr = c(0.82, 0.71, 0.95, 0.83),
  lo = c(0.65, 0.52, 0.78, 0.74),
  hi = c(1.03, 0.97, 1.16, 0.93),
  weight = c(30, 22, 28, 100)
)
ggnext(d, aes(hr, study, ymin = lo, ymax = hi, size = weight)) +
  geom_forest() +
  labs(title = "Hazard ratio by trial", x = "Hazard ratio (95% CI)", y = NULL)

Frequency polygon

Description

The same binning as geom_histogram(), drawn as a line through the bin centres. Easier to overlay across groups than filled bars.

Usage

geom_freqpoly(
  mapping = NULL,
  data = NULL,
  bins = 30,
  binwidth = NULL,
  color = NULL,
  linewidth = NULL,
  alpha = NULL
)

Arguments

Value

A Layer to add with +.

Examples

ggnext(cars, aes(speed)) + geom_freqpoly(bins = 8)

Curve of a function

Description

Evaluates fn over the panel's x range and draws the result. Handy for overlaying a theoretical density or a reference curve on data.

Usage

geom_function(
  fn,
  xlim = c(0, 1),
  n = 101,
  color = NULL,
  linewidth = NULL,
  alpha = NULL
)

Arguments

Value

A Layer to add with +.

Examples

ggnext(data.frame(x = c(-3, 3)), aes(x)) +
  geom_function(dnorm, xlim = c(-3, 3))

Conversion funnel

Description

Horizontally centered bars, one per stage, whose widths are proportional to the value — the standard conversion/drop-off view.

Usage

geom_funnel(
  mapping = NULL,
  data = NULL,
  alpha = NULL,
  label = TRUE,
  label_size = NULL
)

Arguments

Value

A Layer to add with +.

Examples

d <- data.frame(
  stage = factor(c("Visits", "Signups", "Trials", "Paid"),
                 levels = c("Visits", "Signups", "Trials", "Paid")),
  n = c(10000, 3200, 1100, 420)
)
ggnext(d, aes(stage, n, color = stage)) + geom_funnel() + theme_void()

Histogram layer

Description

Histogram layer

Usage

geom_histogram(
  mapping = NULL,
  data = NULL,
  color = NULL,
  alpha = NULL,
  bins = 30,
  binwidth = NULL,
  position = "stack"
)

Arguments

Value

A Layer.

Horizontal reference line

Description

Horizontal reference line

Usage

geom_hline(
  yintercept,
  color = NULL,
  linewidth = NULL,
  alpha = NULL,
  dash = NULL
)

Arguments

Value

A Layer.

Hazard ratio forest plot (Cox proportional hazards)

Description

Fits a Cox proportional-hazards model from scratch (Newton-Raphson on the Breslow partial likelihood; see stat_coxph()) and plots the resulting hazard ratio and Wald 95% CI per group, down a category axis with a no-effect reference line at HR = 1 — geom_forest() paired with the Cox engine so the whole model-to-plot pipeline is one call.

Usage

geom_hr(
  mapping = NULL,
  data = NULL,
  ref_level = NULL,
  color = NULL,
  linewidth = NULL
)

Arguments

Value

A Layer to add with +.

Examples

set.seed(1)
d <- data.frame(
  time = c(rexp(60, 0.08), rexp(60, 0.05)),
  status = rbinom(120, 1, 0.8),
  arm = rep(c("placebo", "treatment"), each = 60)
)
ggnext(d, aes(time = time, status = status, group = arm)) +
  geom_hr(ref_level = "placebo") +
  labs(title = "Hazard ratio (Cox model)", x = "Hazard ratio (95% CI)", y = NULL)

Feature importance plot

Description

A ranked point-and-whisker plot of feature importance, with optional whiskers from ymin/ymax (e.g. permutation-importance standard errors) and a reference line at zero importance. Built on the same point+whisker rendering as geom_forest().

Usage

geom_importance(mapping = NULL, data = NULL, color = NULL, linewidth = NULL)

Arguments

Details

A discrete axis otherwise sorts alphabetically; pass y as a factor already ordered by importance (ascending, since the first level sits at the panel's bottom) to get the classic "most important at the top" layout — the same convention geom_waterfall() uses for category order.

Value

A Layer to add with +.

Examples

d <- data.frame(
  feature = c("age", "income", "tenure", "region"),
  imp = c(0.42, 0.31, 0.18, 0.05),
  se = c(0.05, 0.04, 0.03, 0.02)
)
d$feature <- factor(d$feature, levels = d$feature[order(d$imp)])
ggnext(d, aes(imp, feature, ymin = imp - se, ymax = imp + se)) +
  geom_importance() +
  labs(title = "Permutation importance", x = "Importance", y = NULL)

Jittered point layer (strip charts)

Description

geom_point() with uniform positional noise; deterministic per seed.

Usage

geom_jitter(
  mapping = NULL,
  data = NULL,
  color = NULL,
  size = NULL,
  alpha = NULL,
  width = NULL,
  height = NULL,
  seed = 42
)

Arguments

Value

A Layer.

Kaplan-Meier survival curve layer

Description

Map time and status (1/TRUE = event, 0/FALSE = censored); map color to compare groups. Pair with geom_km_risktable() for the classic number-at-risk strip.

Usage

geom_km(
  mapping = NULL,
  data = NULL,
  color = NULL,
  alpha = NULL,
  linewidth = NULL,
  conf_int = FALSE
)

Arguments

Value

A Layer.

Examples

set.seed(1)
d <- data.frame(
  t = c(rexp(40, 0.1), rexp(40, 0.07)),
  ev = rbinom(80, 1, 0.7),
  arm = rep(c("placebo", "drug"), each = 40)
)
ggnext(d, aes(time = t, status = ev, color = arm)) +
  geom_km(conf_int = TRUE)

Number-at-risk table for a Kaplan-Meier curve

Description

Draws the classic "number at risk" strip beneath a survival curve: for each group, the count of subjects still at risk (time >= tick) at a handful of tick times, laid out as an overlay in the bottom ~18% of the panel. It is a single layer, not a separate sub-panel — add it alongside geom_km() on the same plot.

Usage

geom_km_risktable(
  mapping = NULL,
  data = NULL,
  breaks = NULL,
  label_size = NULL
)

Arguments

Details

This layer must share the same time/status/grouping mapping and data as the geom_km() layer it accompanies, so the two are trained on the same time domain and its tick columns line up with the curve above them.

Value

A Layer to add with +.

Examples

set.seed(1)
d <- data.frame(
  t = c(rexp(40, 0.1), rexp(40, 0.07)),
  ev = rbinom(80, 1, 0.7),
  arm = rep(c("placebo", "drug"), each = 40)
)
ggnext(d, aes(time = t, status = ev, color = arm)) +
  geom_km() +
  geom_km_risktable()

Text on a background plate

Description

Like geom_text(), but each label sits on an opaque rounded box, so it stays readable over dense data.

Usage

geom_label(
  mapping = NULL,
  data = NULL,
  color = NULL,
  fill = NULL,
  size = NULL,
  alpha = NULL,
  padding = NULL
)

Arguments

Value

A Layer to add with +.

Examples

d <- data.frame(x = c(1, 2), y = c(2, 1), l = c("alpha", "beta"))
ggnext(d, aes(x, y, label = l)) + geom_label()

Learning curve

Description

Training and validation score against training-set size or epoch, the standard read on whether a model is data-limited or over-fitting.

Usage

geom_learning_curve(
  mapping = NULL,
  data = NULL,
  band = TRUE,
  linewidth = NULL,
  points = TRUE
)

Arguments

Value

A Layer to add with +.

Examples

d <- data.frame(
  n = rep(c(50, 100, 200, 400), 2),
  score = c(.75, .82, .86, .88, .70, .78, .83, .86),
  split = rep(c("train", "validation"), each = 4)
)
ggnext(d, aes(n, score, color = split)) + geom_learning_curve()

Leverage / Cook's distance plot

Description

Standardized residual against leverage (hat value), the companion to geom_residual() for spotting influential observations. Map size to Cook's distance for the classic three-way read (leverage, residual size, influence) in one scatter.

Usage

geom_leverage(
  mapping = NULL,
  data = NULL,
  color = NULL,
  size = NULL,
  alpha = NULL
)

Arguments

Value

A Layer to add with +.

Examples

m <- lm(dist ~ speed, cars)
d <- data.frame(
  hat = hatvalues(m), rstd = rstandard(m), cooksd = cooks.distance(m)
)
ggnext(d, aes(hat, rstd, size = cooksd)) +
  geom_leverage() +
  labs(title = "Leverage vs standardized residual",
       x = "Leverage (hat value)", y = "Standardized residual")

Lift and cumulative gain curves

Description

Sorts observations by predicted score and plots the cumulative share of positives captured against the share of the population targeted — the standard way to size a marketing or triage cutoff.

Usage

geom_lift_gain(
  mapping = NULL,
  data = NULL,
  type = "gain",
  color = NULL,
  baseline = TRUE
)

Arguments

Value

A Layer to add with +.

Examples

set.seed(1)
s <- runif(200)
d <- data.frame(score = s, y = rbinom(200, 1, s))
ggnext(d, aes(score = score, truth = y)) + geom_lift_gain()

Line layer (points connected in x order)

Description

Line layer (points connected in x order)

Usage

geom_line(
  mapping = NULL,
  data = NULL,
  color = NULL,
  linewidth = NULL,
  alpha = NULL,
  dash = NULL
)

Arguments

Value

A Layer.

Vertical interval without a marker

Description

The interval alone; add geom_point() for an estimate marker, or use geom_pointrange() which draws both.

Usage

geom_linerange(
  mapping = NULL,
  data = NULL,
  color = NULL,
  linewidth = NULL,
  alpha = NULL
)

Arguments

Value

A Layer to add with +.

Examples

d <- data.frame(g = c("a", "b"), lo = c(1, 2), hi = c(4, 5))
ggnext(d, aes(g, ymin = lo, ymax = hi)) + geom_linerange()

Missing-data pattern plot

Description

A tile grid of which variables are observed vs. missing, one row per distinct missingness pattern (most frequent at the top, annotated with how many observations share it) rather than one row per observation — the standard compact view for spotting structured missingness (e.g. a block of variables always missing together).

Usage

geom_missing_pattern(
  data,
  vars = NULL,
  label = TRUE,
  label_size = NULL,
  observed = "#D8DCE6",
  missing = "#C1462F"
)

Arguments

Value

A Layer to add with +.

Examples

d <- data.frame(
  age = c(25, NA, 30, 40, NA, 33),
  bmi = c(22, 24, NA, NA, 27, 23),
  sbp = c(120, 118, 130, NA, 125, 121)
)
ggnext() + geom_missing_pattern(d) + labs(title = "Missing-data patterns")

Nelson-Aalen cumulative hazard curve layer

Description

Map time and status (1/TRUE = event, 0/FALSE = censored) exactly as for geom_km(); map color to compare groups. The cumulative hazard H(t) is the additive counterpart of the Kaplan-Meier survival curve — useful when the rate of events, not the surviving fraction, is what matters.

Usage

geom_nelson_aalen(
  mapping = NULL,
  data = NULL,
  color = NULL,
  alpha = NULL,
  linewidth = NULL
)

Arguments

Value

A Layer to add with +.

Examples

set.seed(1)
d <- data.frame(t = rexp(80, 0.08), ev = rbinom(80, 1, 0.75))
ggnext(d, aes(time = t, status = ev)) +
  geom_nelson_aalen() +
  labs(title = "Cumulative hazard", x = "Time", y = "H(t)")

Network (node-link) diagram

Description

Lays out a graph with a from-scratch Fruchterman-Reingold force simulation (repulsion between all nodes, attraction along edges, with a cooling schedule) and draws nodes and edges in one unified grammar — node and edge aesthetics come from the same aes().

Usage

geom_network(
  mapping = NULL,
  data = NULL,
  node_size = NULL,
  edge_color = NULL,
  edge_width = NULL,
  label = TRUE,
  label_size = NULL,
  alpha = NULL,
  iterations = 200,
  seed = 1
)

Arguments

Value

A Layer to add with +.

Examples

d <- data.frame(
  from = c("A", "A", "B", "C", "D", "E"),
  to = c("B", "C", "C", "D", "E", "A")
)
ggnext(d, aes(x = from, xend = to)) + geom_network() + theme_void()

Parallel coordinates plot

Description

Each observation is a line crossing one vertical axis per variable — the standard way to eyeball high-dimensional structure and clusters. Each axis is independently rescaled to [0, 1] by the stat, so variables in different units are comparable.

Usage

geom_parallel(
  mapping = NULL,
  data = NULL,
  color = NULL,
  alpha = NULL,
  linewidth = NULL
)

Arguments

Value

A Layer to add with +.

Examples

d <- data.frame(
  id = rep(1:3, each = 3),
  var = rep(c("a", "b", "c"), 3),
  val = c(1, 9, 4, 3, 5, 8, 7, 2, 6)
)
ggnext(d, aes(var, val, group = id, color = factor(id))) + geom_parallel()

Partial dependence and ICE curves

Description

A thick average partial-dependence line over thin per-observation ICE curves. Map group to the observation id to draw the ICE spaghetti.

Usage

geom_partial_dependence(
  mapping = NULL,
  data = NULL,
  color = NULL,
  ice = TRUE,
  ice_alpha = 0.25,
  linewidth = NULL
)

Arguments

Value

A Layer to add with +.

Examples

d <- data.frame(
  x = rep(1:10, 5), id = rep(1:5, each = 10),
  pred = as.vector(sapply(1:5, function(i) (1:10) * 0.1 * i + rnorm(10, 0, .1)))
)
ggnext(d, aes(x, pred, group = id)) + geom_partial_dependence()

Path layer (points connected in data order)

Description

Path layer (points connected in data order)

Usage

geom_path(
  mapping = NULL,
  data = NULL,
  color = NULL,
  linewidth = NULL,
  alpha = NULL,
  dash = NULL
)

Arguments

Value

A Layer.

Scatter-plot layer: one point per observation

Description

Draws a circle mark for each row of the layer data - the default view of a relationship between two continuous variables.

Usage

geom_point(
  mapping = NULL,
  data = NULL,
  stat = stat_identity(),
  color = NULL,
  size = NULL,
  alpha = NULL
)

Arguments

Value

A Layer object to add to a plot with +.

Examples

ggnext(cars, aes(speed, dist)) + geom_point(color = "steelblue", size = 4)

Point-range layer (interval plus midpoint; forest-plot building block)

Description

A horizontal forest plot is this geom with the categorical variable on x — a dedicated geom_forest() with flipped coordinates is a milestone.

Usage

geom_pointrange(
  mapping = NULL,
  data = NULL,
  color = NULL,
  size = NULL,
  alpha = NULL,
  linewidth = NULL
)

Arguments

Value

A Layer.

Polygons

Description

Joins the points of each group into a closed shape, in data order. Map group when the data holds several polygons.

Usage

geom_polygon(
  mapping = NULL,
  data = NULL,
  color = NULL,
  alpha = NULL,
  linewidth = NULL,
  border = NULL
)

Arguments

Value

A Layer to add with +.

Examples

d <- data.frame(x = c(1, 3, 2), y = c(1, 1, 3))
ggnext(d, aes(x, y)) + geom_polygon()

Precision-recall curve

Description

Sweeps the classification threshold and plots recall (x) against precision (y), with a horizontal baseline at the class prevalence (instead of the diagonal geom_roc() uses) — the more informative curve when positives are rare.

Usage

geom_pr(
  mapping = NULL,
  data = NULL,
  color = NULL,
  alpha = NULL,
  linewidth = NULL
)

Arguments

Value

A Layer to add with +.

Examples

set.seed(1)
s <- runif(200)
d <- data.frame(truth = rbinom(200, 1, s * 0.3), score = s)
ggnext(d, aes(truth = truth, score = score)) + geom_pr()

Quantile-quantile plot

Description

Sample quantiles against the quantiles of a reference distribution; points on a straight line mean the sample follows it. Add geom_qq_line() for the reference.

Usage

geom_qq(
  mapping = NULL,
  data = NULL,
  distribution = stats::qnorm,
  color = NULL,
  size = NULL,
  alpha = NULL
)

geom_qq_line(
  mapping = NULL,
  data = NULL,
  distribution = stats::qnorm,
  color = NULL,
  linewidth = NULL,
  alpha = NULL
)

Arguments

Value

A Layer to add with +.

Examples

set.seed(1)
d <- data.frame(v = rnorm(100))
ggnext(d, aes(v)) + geom_qq() + geom_qq_line()

Quantile regression lines

Description

Fits a linear model to chosen conditional quantiles, showing how the spread of y changes with x — not just its mean, as geom_smooth() does.

Usage

geom_quantile(
  mapping = NULL,
  data = NULL,
  quantiles = c(0.25, 0.5, 0.75),
  linewidth = NULL,
  alpha = NULL
)

Arguments

Value

A Layer to add with +.

Examples

ggnext(cars, aes(speed, dist)) +
  geom_point(alpha = 0.5) +
  geom_quantile()

Radar (spider / star) chart

Description

Draws one closed polygon per group across a set of categorical axes. Pair with coord_polar() for the familiar circular form; without it the same layer reads as a parallel-coordinates profile.

Usage

geom_radar(
  mapping = NULL,
  data = NULL,
  color = NULL,
  alpha = NULL,
  linewidth = NULL,
  points = TRUE
)

Arguments

Value

A Layer to add with +.

Examples

d <- data.frame(
  axis = rep(c("Speed", "Power", "Range", "Cost", "Safety"), 2),
  value = c(8, 6, 7, 4, 9, 5, 9, 4, 8, 6),
  model = rep(c("A", "B"), each = 5)
)
ggnext(d, aes(axis, value, color = model)) +
  geom_radar() +
  coord_polar() +
  theme_minimal()

Raster

Description

A tile grid drawn without cell borders — the right choice for a dense heatmap or an image, where borders would swamp the data.

Usage

geom_raster(mapping = NULL, data = NULL, color = NULL, alpha = NULL)

Arguments

Value

A Layer to add with +.

Examples

g <- expand.grid(x = 1:20, y = 1:20)
g$z <- as.vector(outer(1:20, 1:20, function(a, b) sin(a / 3) * cos(b / 3)))
ggnext(g, aes(x, y, color = z)) + geom_raster()

Rectangles

Description

Draws an axis-aligned rectangle per row from explicit corners. Use it for highlight bands, annotation boxes, and any hand-placed block; for a regular grid shaded by value use geom_tile().

Usage

geom_rect(
  mapping = NULL,
  data = NULL,
  color = NULL,
  alpha = NULL,
  linewidth = NULL,
  border = NULL
)

Arguments

Value

A Layer to add with +.

Examples

d <- data.frame(x1 = c(1, 3), x2 = c(2, 5), y1 = c(1, 2), y2 = c(4, 3))
ggnext(d, aes(xmin = x1, xmax = x2, ymin = y1, ymax = y2)) + geom_rect()

Residual diagnostic plot

Description

Residuals against fitted values with a zero reference line and a loess trend, the first plot to look at when checking a linear model.

Usage

geom_residual(
  mapping = NULL,
  data = NULL,
  color = NULL,
  smooth = TRUE,
  size = NULL,
  alpha = NULL
)

Arguments

Value

A Layer to add with +.

Examples

m <- lm(dist ~ speed, cars)
d <- data.frame(fitted = fitted(m), resid = resid(m))
ggnext(d, aes(fitted, resid)) + geom_residual()

Ribbon layer (band between ymin and ymax)

Description

Ribbon layer (band between ymin and ymax)

Usage

geom_ribbon(mapping = NULL, data = NULL, color = NULL, alpha = NULL)

Arguments

Value

A Layer.

Ridgeline plot (joyplot)

Description

One density curve per group, offset vertically so distributions can be compared at a glance. Groups are ordered by their factor levels, first level at the bottom.

Usage

geom_ridgeline(
  mapping = NULL,
  data = NULL,
  scale = 1.6,
  color = NULL,
  alpha = NULL,
  linewidth = NULL,
  bw = NULL
)

Arguments

Value

A Layer to add with +.

Examples

ggnext(iris, aes(Sepal.Length, Species)) + geom_ridgeline()

ROC curve layer

Description

Map truth (actual class; for factors the second level is the positive class) and score (predicted score); map color to compare models.

Usage

geom_roc(
  mapping = NULL,
  data = NULL,
  color = NULL,
  alpha = NULL,
  linewidth = NULL
)

Arguments

Value

A Layer.

Marginal rug

Description

A short tick per observation along the panel edges, showing the raw values behind a density, smooth, or scatter.

Usage

geom_rug(
  mapping = NULL,
  data = NULL,
  sides = NULL,
  length = NULL,
  color = NULL,
  linewidth = NULL,
  alpha = NULL
)

Arguments

Value

A Layer to add with +.

Examples

ggnext(cars, aes(speed, dist)) + geom_point() + geom_rug()

Sankey / alluvial flow diagram

Description

Shows how quantities move between stages. Nodes are drawn as bars at each stage and flows as curved ribbons whose thickness is the value.

Usage

geom_sankey(
  mapping = NULL,
  data = NULL,
  alpha = NULL,
  node_width = NULL,
  label = TRUE
)

Arguments

Details

The layout (node positions, ribbon paths) is computed from scratch: nodes are stacked within their stage in decreasing size, and ribbons leave and enter nodes in the same order, which is what keeps the crossings readable.

Value

A Layer to add with +.

Examples

d <- data.frame(
  from = c("Visited", "Visited", "Signed up", "Signed up"),
  to = c("Signed up", "Left", "Purchased", "Churned"),
  n = c(400, 600, 150, 250)
)
ggnext(d, aes(x = from, xend = to, y = n)) +
  geom_sankey() +
  theme_void()

Segment layer (x,y) to (xend,yend)

Description

Segment layer (x,y) to (xend,yend)

Usage

geom_segment(
  mapping = NULL,
  data = NULL,
  color = NULL,
  linewidth = NULL,
  alpha = NULL
)

Arguments

Value

A Layer.

SHAP value beeswarm

Description

One horizontal swarm per feature showing every observation's SHAP value, with points nudged vertically so overlapping values stay countable. Color the points by the feature value to read the direction of effect.

Usage

geom_shap(mapping = NULL, data = NULL, size = NULL, alpha = NULL)

Arguments

Value

A Layer to add with +.

Examples

set.seed(1)
d <- data.frame(
  feature = rep(c("age", "income", "tenure"), each = 40),
  shap = c(rnorm(40, 0.3, 0.2), rnorm(40, -0.1, 0.3), rnorm(40, 0, 0.15)),
  value = runif(120)
)
ggnext(d, aes(shap, feature, color = value)) +
  geom_shap() +
  geom_vline(0, dash = "3,3") +
  labs(title = "SHAP value by feature", x = "SHAP value", y = NULL)

Categorical shift plot

Description

How subjects move between categories from baseline to follow-up (lab grade shifts, response categories), drawn as a counted tile grid.

Usage

geom_shift(mapping = NULL, data = NULL, label = TRUE)

Arguments

Value

A Layer to add with +.

Examples

d <- data.frame(
  baseline = c("G0", "G0", "G1", "G1", "G2", "G0"),
  followup = c("G0", "G1", "G1", "G2", "G2", "G0")
)
ggnext(d, aes(baseline, followup)) + geom_shift()

Clustering silhouette plot

Description

Sorted silhouette widths grouped by cluster, with the mean marked — the standard visual check on how well-separated a clustering is.

Usage

geom_silhouette(mapping = NULL, data = NULL, alpha = NULL)

Arguments

Value

A Layer to add with +.

Examples

set.seed(1)
d <- data.frame(
  cluster = rep(c("1", "2", "3"), each = 20),
  width = c(runif(20, .3, .9), runif(20, .1, .7), runif(20, -.1, .6))
)
ggnext(d, aes(width, cluster, color = cluster)) + geom_silhouette()

Baseline covariate balance (standardized mean difference) plot

Description

The standardized mean difference (Cohen's d) between two groups for each covariate, down a category axis with a reference line at SMD = 0 and a dashed line at the conventional 0.1 "acceptable imbalance" threshold — the standard baseline balance check for a randomized or propensity-matched comparison.

Usage

geom_smd(data, group, vars = NULL, threshold = 0.1, color = NULL)

Arguments

Value

A Layer to add with +.

Examples

set.seed(1)
d <- data.frame(
  arm = rep(c("treatment", "control"), each = 50),
  age = c(rnorm(50, 55, 8), rnorm(50, 58, 9)),
  bmi = c(rnorm(50, 27, 4), rnorm(50, 27.5, 4)),
  sbp = c(rnorm(50, 130, 12), rnorm(50, 129, 11))
)
ggnext() +
  geom_smd(d, group = "arm") +
  labs(title = "Baseline balance", x = "Standardized mean difference", y = NULL)

Smoothed trend layer

Description

Smoothed trend layer

Usage

geom_smooth(
  mapping = NULL,
  data = NULL,
  color = NULL,
  alpha = NULL,
  method = "loess",
  se = TRUE,
  linewidth = NULL,
  level = 0.95
)

Arguments

Value

A Layer.

Individual longitudinal trajectories (spaghetti plot)

Description

One thin line per subject with an optional bold group mean overlaid — the standard way to show within-subject change without hiding spread.

Usage

geom_spaghetti(
  mapping = NULL,
  data = NULL,
  mean_line = TRUE,
  alpha = NULL,
  linewidth = NULL
)

Arguments

Value

A Layer to add with +.

Examples

set.seed(1)
d <- data.frame(
  week = rep(0:4, 8), id = rep(1:8, each = 5),
  score = as.vector(sapply(1:8, function(i) 50 + i + (0:4) * 2 + rnorm(5, 0, 3)))
)
ggnext(d, aes(week, score, group = id)) + geom_spaghetti()

Oncology spider plot

Description

Percent change from baseline over time, one trajectory per subject, with the +20% (progression) and -30% (response) RECIST thresholds marked. Distinct from geom_radar(), which is a multivariate star chart.

Usage

geom_spider_response(
  mapping = NULL,
  data = NULL,
  thresholds = TRUE,
  linewidth = NULL,
  points = TRUE
)

Arguments

Value

A Layer to add with +.

Examples

d <- data.frame(
  month = rep(c(0, 2, 4, 6), 3),
  pct = c(0, -20, -35, -40, 0, 10, 25, 40, 0, -5, -10, -8),
  subject = rep(c("S1", "S2", "S3"), each = 4)
)
ggnext(d, aes(month, pct, color = subject)) + geom_spider_response()

Spokes (vector field)

Description

A segment from each point at a given angle and radius — the usual way to draw a vector or wind field.

Usage

geom_spoke(
  mapping = NULL,
  data = NULL,
  color = NULL,
  linewidth = NULL,
  alpha = NULL
)

Arguments

Value

A Layer to add with +.

Examples

g <- expand.grid(x = 1:5, y = 1:5)
g$angle <- atan2(g$y - 3, g$x - 3)
g$radius <- 0.4
ggnext(g, aes(x, y, xend = angle, yend = radius)) + geom_spoke()

Step layer (staircase line)

Description

Step layer (staircase line)

Usage

geom_step(
  mapping = NULL,
  data = NULL,
  color = NULL,
  linewidth = NULL,
  alpha = NULL,
  dash = NULL
)

Arguments

Value

A Layer.

Streamgraph

Description

Stacked areas centered on a wiggle baseline rather than zero, which keeps every band's thickness readable as it changes over time.

Usage

geom_stream(mapping = NULL, data = NULL, alpha = NULL)

Arguments

Value

A Layer to add with +.

Examples

d <- data.frame(
  t = rep(1:6, 3),
  v = c(2, 4, 6, 5, 3, 2, 1, 3, 5, 8, 6, 4, 5, 4, 3, 4, 6, 7),
  grp = rep(c("a", "b", "c"), each = 6)
)
ggnext(d, aes(t, v, color = grp)) + geom_stream() + theme_minimal()

Swimmer plot

Description

One horizontal bar per subject showing time on treatment, with optional arrowheads for subjects still ongoing at the data cutoff — the standard patient-level timeline in oncology reporting.

Usage

geom_swimmer(
  mapping = NULL,
  data = NULL,
  bar_height = 0.6,
  arrow = TRUE,
  alpha = NULL
)

Arguments

Value

A Layer to add with +.

Examples

d <- data.frame(
  subject = paste0("S", 1:6),
  months = c(4, 9, 14, 6, 20, 11),
  response = c("PR", "CR", "CR", "SD", "PR", "SD"),
  ongoing = c(FALSE, FALSE, TRUE, FALSE, TRUE, FALSE)
)
ggnext(d, aes(months, subject, color = response, label = ongoing)) +
  geom_swimmer() +
  labs(title = "Time on treatment", x = "Months", y = NULL)

Text label layer

Description

Text label layer

Usage

geom_text(
  mapping = NULL,
  data = NULL,
  color = NULL,
  fontsize = NULL,
  alpha = NULL,
  anchor = NULL
)

Arguments

Value

A Layer.

Tile layer (heatmap cells)

Description

Map a continuous color for the classic heatmap look.

Usage

geom_tile(
  mapping = NULL,
  data = NULL,
  color = NULL,
  alpha = NULL,
  width = NULL,
  height = NULL
)

Arguments

Value

A Layer.

Treemap

Description

Area-proportional nested rectangles, laid out with the squarified algorithm (Bruls, Huizing & van Wijk 2000) so tiles stay close to square and stay comparable by area.

Usage

geom_treemap(
  mapping = NULL,
  data = NULL,
  alpha = NULL,
  label = TRUE,
  label_size = NULL
)

Arguments

Value

A Layer to add with +.

Examples

d <- data.frame(
  region = c("North", "South", "East", "West", "Central"),
  revenue = c(52, 38, 27, 19, 11)
)
ggnext(d, aes(size = revenue, label = region, color = region)) +
  geom_treemap() +
  theme_void()

UpSet plot (set intersections)

Description

Replaces unreadable 4+ way Venn diagrams: a bar chart of intersection sizes above a dot matrix showing which sets each bar belongs to.

Usage

geom_upset(mapping = NULL, data = NULL, alpha = NULL, dot_size = NULL)

Arguments

Value

A Layer to add with +.

Examples

d <- data.frame(sets = c("A", "A&B", "B", "A&B&C", "C", "A&B", "A"))
ggnext(d, aes(label = sets)) + geom_upset() + theme_void()

Violin layer

Description

Violin layer

Usage

geom_violin(
  mapping = NULL,
  data = NULL,
  color = NULL,
  alpha = NULL,
  width = 0.9
)

Arguments

Value

A Layer.

Vertical reference line

Description

Vertical reference line

Usage

geom_vline(
  xintercept,
  color = NULL,
  linewidth = NULL,
  alpha = NULL,
  dash = NULL
)

Arguments

Value

A Layer.

Waterfall chart layer (running total of signed changes)

Description

Categories plot in level order; pass x = factor(x, levels = unique(x)) to keep them in data order.

Usage

geom_waterfall(
  mapping = NULL,
  data = NULL,
  alpha = NULL,
  width = 0.8,
  color_pos = NULL,
  color_neg = NULL
)

Arguments

Value

A Layer.

RECIST best-response waterfall

Description

Subjects ordered by best percent change from baseline, one bar each, with the +20% / -30% RECIST thresholds marked. Distinct from geom_waterfall(), which shows a running total.

Usage

geom_waterfall_response(
  mapping = NULL,
  data = NULL,
  thresholds = TRUE,
  alpha = NULL
)

Arguments

Value

A Layer to add with +.

Examples

set.seed(1)
d <- data.frame(
  subject = paste0("S", 1:20),
  pct = sort(runif(20, -75, 45), decreasing = TRUE)
)
ggnext(d, aes(subject, pct)) +
  geom_waterfall_response() +
  labs(title = "Best response", y = "% change from baseline", x = NULL)

Create a new ggnext plot

Description

The entry point of the grammar: bind a default data frame and a default aesthetic mapping, then add layers, scales, and coordinate systems with +.

Usage

ggnext(data = NULL, mapping = NULL, width = 640, height = 480)

Arguments

Value

A GgnextPlot object.

Examples

p <- ggnext(cars, aes(speed, dist)) + geom_point()
svg <- render(p)

Draw the ggnext hex sticker

Description

Generates the package logo as an SVG: a hexagon containing a small scatter with a fitted trend, drawn with the same coordinate and color machinery the package uses for real plots.

Usage

ggnext_logo(
  file = NULL,
  width = 520,
  dark = FALSE,
  style = c("wordmark", "monogram"),
  tagline = c("NEXT-GENERATION", "GRAMMAR OF GRAPHICS")
)

Arguments

Details

Two styles are available. "wordmark" (the default) spells out ggnext under the artwork with a tagline; "monogram" sets a large GG in the middle, which stays legible at favicon and app-icon sizes where a seven-character wordmark turns to mush.

Value

The SVG document as a length-1 character vector, invisibly when file is supplied.

Examples

svg <- ggnext_logo()
substr(svg, 1, 30)

# App-icon variant.
icon <- ggnext_logo(style = "monogram", width = 256)

Set the plot title (and optionally the subtitle)

Description

Set the plot title (and optionally the subtitle)

Usage

ggtitle(label, subtitle = NULL)

Arguments

Value

A Labels object to add to a plot with +.

Set plot and axis labels

Description

Every argument is optional; only the ones you supply are changed, so labs() can be added repeatedly to build up a title block.

Usage

labs(
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  tag = NULL,
  x = NULL,
  y = NULL,
  color = NULL,
  size = NULL,
  fill = NULL
)

Arguments

Value

A Labels object to add to a plot with +.

Examples

ggnext(cars, aes(speed, dist)) +
  geom_point() +
  labs(
    title = "Stopping distance rises with speed",
    subtitle = "1920s road tests, 50 observations",
    x = "Speed (mph)",
    y = "Stopping distance (ft)",
    caption = "Source: datasets::cars"
  )

Set both axis limits at once

Description

Set both axis limits at once

Usage

lims(x = NULL, y = NULL)

Arguments

Value

A list of scales, which + adds one at a time.

Print a plot's validation findings and return the plot, unchanged

Description

A pipe-friendly wrapper around validate_plot(): prints the report as a side effect and returns plot invisibly, so it can sit inside a pipeline without interrupting it.

Usage

plot_check(plot)

Arguments

Value

plot, invisibly.

Examples

ggnext(cars, aes(speed, dist)) |>
  geom_point() |>
  plot_check() |>
  invisible()

Extract the exact data a plot draws

Description

Returns the computed values behind each layer — post-stat, post-position, post-facet — in data units (never normalized coordinates). For a geom_point() layer that is the x/y you supplied; for geom_histogram() it is the bin centers, counts, and bin edges actually drawn; for geom_boxplot() the quartiles and whisker ends.

Usage

plot_data(plot, layer = NULL, panel = NULL)

Arguments

Details

Only columns the layer genuinely uses are returned. Constant grouping columns are dropped, so a plot with no group aesthetic has no group column.

Value

A data frame, or a list of data frames.

Examples

p <- ggnext(cars, aes(speed, dist)) + geom_point()
head(plot_data(p))

# Stats: what geom_histogram() actually drew.
h <- ggnext(cars, aes(speed)) + geom_histogram(bins = 5)
plot_data(h)

# Facets keep a panel column.
f <- ggnext(iris, aes(Sepal.Length, Sepal.Width)) +
  geom_point() + facet_wrap(Species)
head(plot_data(f))

Set the output size

Description

Set the output size

Usage

plot_size(width, height)

Arguments

Value

A ggnext_size object to add to a plot with +.

Examples

ggnext(cars, aes(speed, dist)) + geom_point() + plot_size(900, 600)

Render a ggnext plot

Description

Computes the plot geometry once and serializes it to the chosen render target: a standalone SVG document ("static") or a self-contained interactive HTML page with a ⁠<canvas>⁠ element, hover tooltips, and scroll-to-zoom ("interactive").

Usage

render(plot, ...)

Arguments

Value

The rendered document as a length-1 character vector of class ggnext_render (printed as a short summary, not the full source), invisibly when file is given.

Examples

p <- ggnext(cars, aes(speed, dist)) + geom_point()
svg <- render(p)                          # static SVG (the default)
html <- render(p + interact())            # interactive HTML/canvas

Compute tick breaks for a trained scale

Description

Compute tick breaks for a trained scale

Usage

scale_breaks(scale, ...)

Arguments

Value

Numeric vector of tick values in data units.

Set the continuous color gradient

Description

Set the continuous color gradient

Usage

scale_color_gradient(low = "#1A2E59", high = "#5FD0A5", name = NULL)

Arguments

Value

A ColorScale object to add with +.

Examples

ggnext(iris, aes(Sepal.Length, Sepal.Width, color = Petal.Length)) +
  geom_point() +
  scale_color_gradient(low = "#FFF3B0", high = "#9E2A2B")

Set the discrete color palette

Description

Overrides the default colorblind-safe palette for discrete color mappings. Levels take colors in order.

Usage

scale_color_manual(values, name = NULL)

Arguments

Value

A ColorScale object to add with +.

Examples

ggnext(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
  geom_point() +
  scale_color_manual(c("#D55E00", "#0072B2", "#009E73"))

Map data values through a trained scale onto [0, 1]

Description

Map data values through a trained scale onto [0, 1]

Usage

scale_map(scale, ...)

Arguments

Value

Numeric vector of normalized positions.

Train a scale on data values

Description

Learns the scale's domain (its trained data range). User-supplied limits win over the data range. Returns an updated scale — S7 objects are values, so training is a functional update, not a mutation.

Usage

scale_train(scale, ...)

Arguments

Value

The scale, with domain filled in.

Continuous scale for the x axis

Description

Controls the axis title, limits, tick positions, tick labels, the padding around the data, and the axis transform.

Usage

scale_x_continuous(
  name = NULL,
  limits = NULL,
  breaks = NULL,
  labels = NULL,
  expand = 0.05,
  trans = "identity"
)

scale_y_continuous(
  name = NULL,
  limits = NULL,
  breaks = NULL,
  labels = NULL,
  expand = 0.05,
  trans = "identity"
)

Arguments

Value

A ScaleContinuous object to add with +.

Examples

ggnext(cars, aes(speed, dist)) +
  geom_point() +
  scale_x_continuous(
    name = "Speed (mph)",
    breaks = c(5, 10, 15, 20, 25),
    expand = 0
  ) +
  scale_y_continuous(labels = function(v) paste0(v, " ft"))

Discrete positional scale for the x axis

Description

Discrete positional scale for the x axis

Usage

scale_x_discrete(limits = NULL, name = NULL)

Arguments

Value

A ScaleDiscrete object to add with +.

Log10 and square-root scales

Description

Shorthand for ⁠scale_*_continuous(trans = "log10")⁠ / "sqrt".

Usage

scale_x_log10(
  name = NULL,
  limits = NULL,
  breaks = NULL,
  labels = NULL,
  expand = 0.05
)

scale_y_log10(
  name = NULL,
  limits = NULL,
  breaks = NULL,
  labels = NULL,
  expand = 0.05
)

scale_x_sqrt(
  name = NULL,
  limits = NULL,
  breaks = NULL,
  labels = NULL,
  expand = 0.05
)

scale_y_sqrt(
  name = NULL,
  limits = NULL,
  breaks = NULL,
  labels = NULL,
  expand = 0.05
)

scale_x_reverse(
  name = NULL,
  limits = NULL,
  breaks = NULL,
  labels = NULL,
  expand = 0.05
)

scale_y_reverse(
  name = NULL,
  limits = NULL,
  breaks = NULL,
  labels = NULL,
  expand = 0.05
)

Arguments

Value

A ScaleContinuous object to add with +.

Examples

d <- data.frame(x = 10^(1:5), y = 1:5)
ggnext(d, aes(x, y)) + geom_point() + scale_x_log10()

Discrete positional scale for the y axis

Description

Discrete positional scale for the y axis

Usage

scale_y_discrete(limits = NULL, name = NULL)

Arguments

Value

A ScaleDiscrete object to add with +.

Bin observations (histogram counts)

Description

Bin observations (histogram counts)

Usage

stat_bin(bins = 30, binwidth = NULL)

Arguments

Value

A StatBin object, to pass as a layer's stat.

Five-number summary for boxplots

Description

Five-number summary for boxplots

Usage

stat_boxplot(width = 0.6, coef = 1.5)

Arguments

Value

A StatBoxplot object, to pass as a layer's stat.

Count observations at each x

Description

Count observations at each x

Usage

stat_count(width = 0.8)

Arguments

Value

A StatCount object, to pass as a layer's stat.

Hazard ratios from a Cox proportional-hazards model

Description

Hazard ratios from a Cox proportional-hazards model

Usage

stat_coxph(ref_level = NULL)

Arguments

Value

A StatCoxph object, to pass as a layer's stat.

Kernel density estimate

Description

Kernel density estimate

Usage

stat_density(n = 256, adjust = 1)

Arguments

Value

A StatDensity object, to pass as a layer's stat.

Identity statistical transformation

Description

Passes layer data through untransformed. This is the default stat for geoms that draw the data as given, such as geom_point() and geom_line().

Usage

stat_identity()

Value

A StatIdentity object, to pass as a layer's stat.

Jitter positions for strip charts

Description

Jitter positions for strip charts

Usage

stat_jitter(width = NULL, height = NULL, seed = 42)

Arguments

Value

A StatJitter object, to pass as a layer's stat.

Kaplan-Meier survival estimate

Description

Kaplan-Meier survival estimate

Usage

stat_km(conf_int = FALSE)

Arguments

Value

A StatKM object, to pass as a layer's stat.

Number-at-risk table for a Kaplan-Meier curve

Description

Number-at-risk table for a Kaplan-Meier curve

Usage

stat_km_risktable(breaks = NULL)

Arguments

Value

A StatKMRiskTable object, to pass as a layer's stat.

Nelson-Aalen cumulative hazard estimate

Description

Nelson-Aalen cumulative hazard estimate

Usage

stat_nelson_aalen()

Value

A StatNelsonAalen object, to pass as a layer's stat.

Empirical precision-recall curve

Description

Empirical precision-recall curve

Usage

stat_pr()

Value

A StatPR object, to pass as a layer's stat.

Empirical ROC curve

Description

Empirical ROC curve

Usage

stat_roc()

Value

A StatROC object, to pass as a layer's stat.

Fitted trend line with confidence band

Description

Fitted trend line with confidence band

Usage

stat_smooth(method = "loess", se = TRUE, n = 80, level = 0.95)

Arguments

Value

A StatSmooth object, to pass as a layer's stat.

Running totals for waterfall charts

Description

Running totals for waterfall charts

Usage

stat_waterfall(width = 0.8)

Arguments

Value

A StatWaterfall object, to pass as a layer's stat.

Mirrored y-density for violins

Description

Mirrored y-density for violins

Usage

stat_ydensity(width = 0.9, n = 101)

Arguments

Value

A StatYdensity object, to pass as a layer's stat.

Classic theme: white panel, black axes, no gridlines

Description

The traditional statistical-journal look — axis lines and ticks only.

Usage

theme_classic()

Value

A Theme to add to a plot with +.

Dark theme

Description

Dark theme

Usage

theme_dark()

Value

A Theme to add to a plot with +.

The default ggnext theme

Description

Light background with a softly tinted panel and white gridlines.

Usage

theme_ggnext()

Value

A Theme to add to a plot with +.

Examples

p <- ggnext(cars, aes(speed, dist)) + geom_point() + theme_dark()

Minimal theme: no panel fill, light grey gridlines

Description

Minimal theme: no panel fill, light grey gridlines

Usage

theme_minimal()

Value

A Theme to add to a plot with +.

Modern theme: airy, high-contrast, horizontal rules only

Description

Editorial styling — a large title, hairline horizontal gridlines, no vertical grid or axis lines.

Usage

theme_modern()

Value

A Theme to add to a plot with +.

Void theme: data only, no chrome at all

Description

Useful for treemaps, network graphs, and sparkline-style output.

Usage

theme_void()

Value

A Theme to add to a plot with +.

Check a plot for common statistical-graphics mistakes

Description

Runs a small set of rules over a plot's layers and scales - an empty plot, a continuous geom (geom_point(), geom_line(), ...) mapped to a categorical y, a mapped aesthetic that is mostly missing, a discrete color/fill scale with more levels than a legend can usefully show, and a sqrt-transformed scale fed negative values. Each is a heuristic, not a guarantee - validate_plot() reports what commonly goes wrong, not what is definitely wrong with this particular plot.

Usage

validate_plot(plot)

Arguments

Value

An object of class ggnext_check: a list of findings, each with rule, status (currently always "warn"), and message. Prints as a short report; empty when nothing was found.

Examples

p <- ggnext(mtcars, aes(mpg, as.character(cyl))) + geom_point()
validate_plot(p)

Write the exact plot data to CSV

Description

Convenience wrapper around plot_data() and utils::write.csv() for publishing the numbers behind a figure next to the figure itself.

Usage

write_plot_data(plot, file, layer = NULL)

Arguments

Value

The file path(s) written, invisibly.

Examples

p <- ggnext(cars, aes(speed, dist)) + geom_point()
out <- file.path(tempdir(), "cars.csv")
write_plot_data(p, out)

Set the x axis title

Description

Set the x axis title

Usage

xlab(label)

Arguments

Value

A Labels object to add to a plot with +.

Set continuous axis limits

Description

A shorthand for scale_x_continuous(limits = ). Data outside the limits is still computed by stats but clipped to the panel when drawn.

Usage

xlim(...)

ylim(...)

Arguments

Value

A scale object to add to a plot with +.

Examples

ggnext(cars, aes(speed, dist)) + geom_point() + xlim(0, 30) + ylim(0, 150)

Set the y axis title

Description

Set the y axis title

Usage

ylab(label)

Arguments

Value

A Labels object to add to a plot with +.