Package 'plotthis'

Description

Draws a stacked area plot showing how one or more groups' numeric values (or counts) accumulate across the progression of a discrete x-axis variable. Each group is rendered as a filled area stacked from baseline, making it easy to compare both individual magnitudes and the total across categories.

The function supports count aggregation (omit y to plot observation counts per x-category), proportion scaling (scale_y = TRUE normalises each x position to 100\ colour control, faceting, and splitting into separate sub-plots via split_by.

Usage

AreaPlot(
  data,
  x,
  y = NULL,
  x_sep = "_",
  split_by = NULL,
  split_by_sep = "_",
  group_by = NULL,
  group_by_sep = "_",
  group_name = NULL,
  scale_y = FALSE,
  theme = "theme_this",
  theme_args = list(),
  palette = "Paired",
  palcolor = NULL,
  palreverse = FALSE,
  alpha = 1,
  facet_by = NULL,
  facet_scales = "fixed",
  facet_ncol = NULL,
  facet_nrow = NULL,
  facet_byrow = TRUE,
  x_text_angle = 0,
  aspect.ratio = 1,
  legend.position = waiver(),
  legend.direction = "vertical",
  title = NULL,
  subtitle = NULL,
  xlab = NULL,
  ylab = NULL,
  keep_na = FALSE,
  keep_empty = FALSE,
  seed = 8525,
  combine = TRUE,
  nrow = NULL,
  ncol = NULL,
  byrow = TRUE,
  axes = NULL,
  axis_titles = axes,
  guides = NULL,
  design = NULL,
  ...
)

Arguments

Value

A ggplot object, a patchwork object, or a named list of ggplot objects (when combine = FALSE), each with height and width attributes in inches.

split_by workflow

When split_by is provided:

1. check_keep_na() and check_keep_empty() normalise the keep_na / keep_empty arguments for all columns (x, split_by, group_by, facet_by).

2. The split_by column is validated and its NA / empty levels are processed via process_keep_na_empty(). It is then removed from the per-column keep_na / keep_empty lists.

3. The data frame is split by split_by (preserving level order). If split_by is NULL, the data is wrapped in a single-element list with name "...".

4. Per-split palette, palcolor, legend.position, and legend.direction are resolved via check_palette(), check_palcolor(), and check_legend().

5. AreaPlotAtomic() is called for each split. If title is a function, it receives the split level name and can generate dynamic titles.

6. Results are combined via combine_plots() (when combine = TRUE) or returned as a named list.

Examples

set.seed(8525)
data <- data.frame(
    x = rep(c("A", "B", "C", "D"), 2),
    y = c(1, 3, 6, 4, 2, 5, 7, 8),
    group = rep(c("F1", "F2"), each = 4),
    split = rep(c("X", "Y"), 4)
)
# Basic stacked area
AreaPlot(data, x = "x", y = "y", group_by = "group")

# Scaled to proportions
AreaPlot(data, x = "x", y = "y", group_by = "group",
         scale_y = TRUE)

# Split into sub-plots (no group_by — single-colour fill)
AreaPlot(data, x = "x", y = "y", split_by = "group")

# Per-split palettes
AreaPlot(data, x = "x", y = "y", split_by = "group",
         palette = c(F1 = "Blues", F2 = "Reds"))

# Per-split legend positioning
AreaPlot(data, x = "x", y = "y", group_by = "group",
         split_by = "split",
         legend.direction = c(X = "horizontal", Y = "vertical"),
         legend.position = c(X = "top", Y = "right"))

# How keep_na and keep_empty work
data <- data.frame(
    x = factor(rep(c("A", NA, "C", "D"), 3),
               levels = c("A", "B", "C", "D")),
    y = c(1, 3, 6, 4, 2, 5, 7, 8, 4, 2, 3, 5),
    group = factor(sample(rep(c("F1", NA, "F3"), each = 4)),
                   levels = c("F1", "F2", "F3")),
    split = factor(sample(rep(c("X", "Y", NA), 4)),
                   levels = c("X", "Y", "Z")),
    facet = factor(sample(rep(c("M", "N", NA), 4)),
                   levels = c("M", "N", "O"))
)

# Default: NA and empty levels dropped
AreaPlot(data, x = "x", y = "y", group_by = "group")

# Keep NA and empty levels
AreaPlot(data, x = "x", y = "y", group_by = "group",
         keep_na = TRUE, keep_empty = TRUE)

# Keep NA, assign empty levels colours but don't show them
AreaPlot(data, x = "x", y = "y", group_by = "group",
         keep_na = TRUE, keep_empty = "level")

# Drop NA, keep empty levels
AreaPlot(data, x = "x", y = "y", group_by = "group",
         keep_na = FALSE, keep_empty = TRUE)

# Per-column keep_na / keep_empty via named lists
AreaPlot(data, x = "x", y = "y", group_by = "group",
         keep_na = list(x = TRUE, group = FALSE),
         keep_empty = list(x = FALSE, group = TRUE))
AreaPlot(data, x = "x", y = "y", group_by = "group",
         keep_na = list(x = FALSE, group = TRUE),
         keep_empty = list(x = TRUE, group = FALSE))

Description

Draws bar plots with flexible fill, grouping, labelling, and annotation options. Supports both simple single-colour bars and grouped bars (dodged or stacked). Bars can be filled by a categorical variable (discrete colour scale), a continuous variable (colour gradient), or a fixed colour.

The function supports count aggregation (omit y to plot observation counts), proportion scaling (scale_y = TRUE for grouped bars), background stripes (add_bg), bar labels, trend lines, horizontal reference lines, and splitting into separate sub-plots via split_by.

SplitBarPlot (also known as WaterfallPlot) draws a divergent bar plot where bars extend left (negative values) and right (positive values) from a central zero line. The bar fill colour and opacity can encode additional variables, and the vertical ordering of categories is fully customisable.

The function supports split_by to produce separate panels, facet_by for grouped views within panels, and alpha_by for encoding a secondary numeric variable via opacity.

Usage

BarPlot(
  data,
  x,
  x_sep = "_",
  y = NULL,
  flip = FALSE,
  fill_by = TRUE,
  fill_name = NULL,
  line_name = NULL,
  label_nudge = 0.02,
  label = NULL,
  label_fg = "black",
  label_size = 4,
  label_bg = "white",
  label_bg_r = 0.1,
  group_by = NULL,
  group_by_sep = "_",
  group_name = NULL,
  split_by = NULL,
  split_by_sep = "_",
  facet_by = NULL,
  facet_scales = "fixed",
  facet_ncol = NULL,
  facet_nrow = NULL,
  facet_byrow = TRUE,
  facet_args = list(),
  add_bg = FALSE,
  bg_palette = "stripe",
  bg_palcolor = NULL,
  bg_alpha = 0.2,
  add_line = NULL,
  line_color = "red2",
  line_width = 0.6,
  line_type = 2,
  add_trend = FALSE,
  trend_color = "black",
  trend_linewidth = 1,
  trend_ptsize = 2,
  theme = "theme_this",
  theme_args = list(),
  palette = NULL,
  palcolor = NULL,
  palreverse = FALSE,
  alpha = 1,
  lower_quantile = 0,
  upper_quantile = 0.99,
  lower_cutoff = NULL,
  upper_cutoff = NULL,
  x_text_angle = 0,
  aspect.ratio = 1,
  y_min = NULL,
  y_max = NULL,
  position = "auto",
  position_dodge_preserve = "total",
  legend.position = "right",
  legend.direction = "vertical",
  title = NULL,
  subtitle = NULL,
  xlab = NULL,
  ylab = NULL,
  keep_empty = FALSE,
  keep_na = FALSE,
  expand = waiver(),
  width = waiver(),
  combine = TRUE,
  nrow = NULL,
  ncol = NULL,
  byrow = TRUE,
  seed = 8525,
  axes = NULL,
  axis_titles = axes,
  guides = NULL,
  design = NULL,
  ...
)

SplitBarPlot(
  data,
  x,
  y,
  y_sep = "_",
  flip = FALSE,
  split_by = NULL,
  split_by_sep = "_",
  alpha_by = NULL,
  alpha_reverse = FALSE,
  alpha_name = NULL,
  order_y = list(`+` = c("x_desc", "alpha_desc"), `-` = c("x_desc", "alpha_asc")),
  bar_height = 0.9,
  lineheight = 0.5,
  max_charwidth = 80,
  fill_by = NULL,
  fill_by_sep = "_",
  fill_name = NULL,
  direction_name = "direction",
  direction_pos_name = "positive",
  direction_neg_name = "negative",
  theme = "theme_this",
  theme_args = list(),
  palette = "Spectral",
  palcolor = NULL,
  palreverse = FALSE,
  lower_quantile = 0,
  upper_quantile = 0.99,
  lower_cutoff = NULL,
  upper_cutoff = NULL,
  facet_by = NULL,
  facet_scales = "free_y",
  facet_nrow = NULL,
  facet_ncol = NULL,
  facet_byrow = TRUE,
  aspect.ratio = 1,
  x_min = NULL,
  x_max = NULL,
  legend.position = "right",
  legend.direction = "vertical",
  title = NULL,
  subtitle = NULL,
  xlab = NULL,
  ylab = NULL,
  keep_empty = FALSE,
  keep_na = FALSE,
  combine = TRUE,
  nrow = NULL,
  ncol = NULL,
  byrow = TRUE,
  seed = 8525,
  axes = NULL,
  axis_titles = axes,
  guides = NULL,
  design = NULL,
  ...
)

WaterfallPlot(
  data,
  x,
  y,
  y_sep = "_",
  flip = FALSE,
  split_by = NULL,
  split_by_sep = "_",
  alpha_by = NULL,
  alpha_reverse = FALSE,
  alpha_name = NULL,
  order_y = list(`+` = c("x_desc", "alpha_desc"), `-` = c("x_desc", "alpha_asc")),
  bar_height = 0.9,
  lineheight = 0.5,
  max_charwidth = 80,
  fill_by = NULL,
  fill_by_sep = "_",
  fill_name = NULL,
  direction_name = "direction",
  direction_pos_name = "positive",
  direction_neg_name = "negative",
  theme = "theme_this",
  theme_args = list(),
  palette = "Spectral",
  palcolor = NULL,
  palreverse = FALSE,
  lower_quantile = 0,
  upper_quantile = 0.99,
  lower_cutoff = NULL,
  upper_cutoff = NULL,
  facet_by = NULL,
  facet_scales = "free_y",
  facet_nrow = NULL,
  facet_ncol = NULL,
  facet_byrow = TRUE,
  aspect.ratio = 1,
  x_min = NULL,
  x_max = NULL,
  legend.position = "right",
  legend.direction = "vertical",
  title = NULL,
  subtitle = NULL,
  xlab = NULL,
  ylab = NULL,
  keep_empty = FALSE,
  keep_na = FALSE,
  combine = TRUE,
  nrow = NULL,
  ncol = NULL,
  byrow = TRUE,
  seed = 8525,
  axes = NULL,
  axis_titles = axes,
  guides = NULL,
  design = NULL,
  ...
)

Arguments

Value

A ggplot object, a patchwork object, or a named list of ggplot objects (when combine = FALSE), each with height and width attributes in inches.

A ggplot object, a patchwork object, or a named list of ggplot objects (when combine = FALSE), each with height and width attributes in inches.

split_by workflow

When split_by is provided:

1. check_keep_na() and check_keep_empty() normalise the keep_na / keep_empty arguments for all columns (x, split_by, facet_by, group_by).

2. The split_by column is validated and its NA / empty levels are processed. It is then removed from the per-column keep_na / keep_empty lists.

3. The data is split by split_by (preserving level order). If split_by is NULL, the data is wrapped in a single-element list with name "...".

4. Per-split palette, palcolor, legend.position, and legend.direction are resolved via check_palette(), check_palcolor(), and check_legend().

5. BarPlotAtomic() is called for each split.

6. Results are combined via combine_plots() (when combine = TRUE) or returned as a named list.

When split_by is provided:

1. check_keep_na() and check_keep_empty() normalise the keep_na / keep_empty arguments.

2. The split_by column is validated and its NA / empty levels are processed. It is then removed from the per-column lists.

3. The data is split by split_by (preserving level order).

4. Per-split palette, palcolor, legend.position, and legend.direction are resolved.

5. SplitBarPlotAtomic() is called for each split. When title is a function, it receives the split level name for dynamic title generation.

6. Results are combined via combine_plots().

Examples

data <- data.frame(
    x = c("A", "B", "C", "D", "E", "F", "G", "H"),
    y = c(10, 8, 16, 4, 6, 12, 14, 2),
    group = c("G1", "G1", "G2", "G2", "G3", "G3", "G4", "G4"),
    facet = c("F1", "F2", "F3", "F4", "F1", "F2", "F3", "F4")
)

# Single-colour bars
BarPlot(data, x = "x", y = "y")

# Solid fill (no colour mapping)
BarPlot(data, x = "x", y = "y", fill_by = FALSE)

# Label bar tops
BarPlot(data, x = "x", y = "y", label = TRUE)
BarPlot(data, x = "x", y = "y", label = "facet", label_nudge = 0)

# Grouped bars
BarPlot(data, x = "group", y = "y", group_by = "x")

# Dodged bars with background stripes
BarPlot(data,
    x = "group", y = "y", group_by = "x",
    position = "dodge", add_bg = TRUE)

# split_by with faceting
BarPlot(data,
    x = "x", y = "y", split_by = "group",
    facet_by = "facet", position = "dodge", facet_ncol = 1)

# split_by with collected guides
BarPlot(data,
    x = "x", y = "y", split_by = "group", facet_by = "facet",
    position = "dodge", facet_ncol = 1, guides = 'collect')

# Per-split palettes
BarPlot(data,
    x = "x", y = "y", split_by = "group",
    palette = list(G1 = "Reds", G2 = "Blues", G3 = "Greens", G4 = "Purp"),
    facet_by = "facet", position = "dodge", facet_ncol = 1)

# Background stripe palette
BarPlot(data,
    x = "group", y = "y", group_by = "x",
    position = "dodge", add_bg = TRUE, bg_palette = "Spectral")

# Count bars (y = NULL)
BarPlot(data, x = "group", ylab = "count")

# Flipped axes
BarPlot(data, x = "group", flip = TRUE, ylab = "count")

# Numeric fill_by with colour gradient
BarPlot(data, x = "x", y = "y", fill_by = "y", flip = TRUE)

# Control fill colour scale limits (quantile)
BarPlot(data, x = "x", y = "y", fill_by = "y", flip = TRUE,
        lower_quantile = 0.1, upper_quantile = 0.9)

# Control fill colour scale limits (explicit cutoff)
BarPlot(data, x = "x", y = "y", fill_by = "y", flip = TRUE,
        lower_cutoff = 5, upper_cutoff = 12)

# keep_na and keep_empty examples
data <- data.frame(
    x = factor(c("A", "B", "C", "D", "E", "F", NA, "H"),
               levels = LETTERS[1:10]),
    y = c(10, 8, 16, 4, 6, NA, 14, 2),
    group = factor(c("G1", "G1", "G2", NA, "G3", "G3", "G5", "G5"),
                   levels = c("G1", "G2", "G3", "G4", "G5")),
    facet = factor(c("F1", NA, "F3", "F4", "F1", "F2", "F3", "F4"),
                   levels = c("F1", "F2", "F3", "F4", "F5"))
)

# Default: NA and empty levels dropped
BarPlot(data, x = "x", y = "y")

# Keep both NA and empty levels
BarPlot(data, x = "x", y = "y",
        keep_na = TRUE, keep_empty = TRUE)

# With faceting
BarPlot(data, x = "x", y = "y",
        keep_na = TRUE, keep_empty = TRUE, facet_by = "facet")

# Keep NA, hide empty levels but reserve their colours
BarPlot(data, x = "x", y = "y",
        keep_na = TRUE, keep_empty = 'level')

# Per-column keep_na / keep_empty
BarPlot(data, x = "x", y = "y",
        keep_na = list(x = TRUE), keep_empty = list(x = FALSE))

# Grouped bars with keep_na / keep_empty
BarPlot(data, x = "group", y = "y", group_by = "x")
BarPlot(data, x = "group", y = "y", group_by = "x",
        keep_na = TRUE, keep_empty = TRUE)
BarPlot(data, x = "group", y = "y", group_by = "x",
        keep_na = TRUE, keep_empty = TRUE, facet_by = "facet")

# Per-column on grouped bars
BarPlot(data, x = "group", y = "y", group_by = "x",
        keep_na = list(x = TRUE, group = FALSE),
        keep_empty = list(x = FALSE, group = TRUE))


set.seed(8525)
data <- data.frame(
    word = c("apple", "banana", "cherry", "date", "elderberry",
             "It is a very long term with a lot of words"),
    count = c(-10, 20, -30, 40, 50, 34),
    score = c(1, 2, 3, 4, 5, 3.2),
    group = c("A", "A", "B", "B", "C", "C")
)

# Basic split bar plot with alpha encoding
SplitBarPlot(data, x = "count", y = "word", alpha_by = "score")

# Control label wrapping
SplitBarPlot(data, x = "count", y = "word", alpha_by = "score",
             max_charwidth = 30, lineheight = 1.1)

# Fill by categorical variable
SplitBarPlot(data, x = "count", y = "word", fill_by = "group")

# Faceting
SplitBarPlot(data, x = "count", y = "word", facet_by = "group",
             fill_name = "Direction")

# Per-split palettes
SplitBarPlot(data, x = "count", y = "word", alpha_by = "score",
             split_by = "group",
             palette = c(A = "Reds", B = "Blues", C = "Greens"))

# keep_na and keep_empty examples
data <- data.frame(
    word = factor(c("apple", "banana", "cherry", NA, "elderberry",
         "It is a very long term with a lot of words"),
         levels = c("apple", "banana", "cherry", "date", "elderberry",
             "unused", "It is a very long term with a lot of words")),
    count = c(-10, 20, NA, 40, 10, 34),
    score = c(1, 2, 3, 4, 5, 3.2),
    group = factor(sample(c("A", "A", "B", "B", "C", "C")),
         levels = c("A", "B", "C", "D"))
)

# Default: NA and empty levels dropped
SplitBarPlot(data, x = "count", y = "word", alpha_by = "score")

# Keep NA and empty levels
SplitBarPlot(data, x = "count", y = "word", alpha_by = "score",
             keep_na = TRUE, keep_empty = TRUE)

# Keep with faceting
SplitBarPlot(data, x = "count", y = "word", alpha_by = "score",
             keep_na = TRUE, keep_empty = TRUE, facet_by = "group")

# Keep NA, hide empty levels (reserve colours)
SplitBarPlot(data, x = "count", y = "word", alpha_by = "score",
             keep_na = TRUE, keep_empty = "level")

# Per-column control
SplitBarPlot(data, x = "count", y = "word", alpha_by = "score",
             keep_na = list(word = FALSE), keep_empty = list(word = TRUE))

# Control fill colour scale limits
SplitBarPlot(data, x = "count", y = "word", fill_by = "score",
             lower_cutoff = 1, upper_cutoff = 4)

Description

Draws box plots or bar plots (mean ± error bars) with extensive customisation options. Supports jittered or beeswarm points, paired observations with connecting lines, trend lines, statistical test annotations (pairwise or omnibus), background stripes, reference lines, point highlighting, and custom summary statistic overlays.

This is the public API — it delegates to BoxViolinPlot with base = "box" or base = "bar", which in turn dispatches to BoxViolinPlotAtomic for each split_by level.

Draws violin plots with extensive customisation options. Supports jittered or beeswarm points, box plot overlays, trend lines, statistical test annotations, background stripes, reference lines, point highlighting, and custom summary statistic overlays.

This is the public API — it delegates to BoxViolinPlot with base = "violin", which dispatches to BoxViolinPlotAtomic for each split_by level.

Draws beeswarm plots — points arranged by the beeswarm algorithm to avoid overlap while displaying the distribution density. This is a convenience wrapper that delegates to BoxViolinPlot with base = "none" and add_beeswarm = TRUE.

Requires the ggbeeswarm package. To get a beeswarm plot WITH a box plot, use BeeswarmPlot(..., add_box = TRUE). To get a violin plot with beeswarm points, use ViolinPlot(..., add_beeswarm = TRUE).

Usage

BoxPlot(
  data,
  x,
  x_sep = "_",
  y = NULL,
  base = c("box", "bar"),
  in_form = c("long", "wide"),
  split_by = NULL,
  split_by_sep = "_",
  symnum_args = NULL,
  sort_x = NULL,
  flip = FALSE,
  keep_empty = FALSE,
  keep_na = FALSE,
  group_by = NULL,
  group_by_sep = "_",
  group_name = NULL,
  paired_by = NULL,
  x_text_angle = ifelse(isTRUE(flip), 0, 45),
  step_increase = 0.1,
  fill_mode = ifelse(!is.null(group_by), "dodge", "x"),
  palreverse = FALSE,
  position_dodge_preserve = "total",
  theme = "theme_this",
  theme_args = list(),
  palette = "Paired",
  palcolor = NULL,
  alpha = 1,
  aspect.ratio = NULL,
  legend.position = "right",
  legend.direction = "vertical",
  add_point = FALSE,
  pt_color = if (isTRUE(add_beeswarm)) NULL else "grey30",
  pt_size = NULL,
  pt_alpha = 1,
  jitter_width = NULL,
  jitter_height = 0,
  stack = FALSE,
  y_max = NULL,
  y_min = NULL,
  y_brackets = NULL,
  add_beeswarm = FALSE,
  beeswarm_method = "swarm",
  beeswarm_cex = 1,
  beeswarm_priority = "ascending",
  beeswarm_dodge = 0.9,
  add_trend = FALSE,
  trend_color = NULL,
  trend_linewidth = 1,
  trend_ptsize = 2,
  add_stat = NULL,
  stat_name = NULL,
  stat_color = "black",
  stat_size = 1,
  stat_stroke = 1,
  stat_shape = 25,
  add_errorbar = "SEM",
  errorbar_color = "grey20",
  errorbar_width = 0.4,
  errorbar_linewidth = 0.6,
  add_bg = FALSE,
  bg_palette = "stripe",
  bg_palcolor = NULL,
  bg_alpha = 0.2,
  add_line = NULL,
  line_color = "red2",
  line_width = 0.6,
  line_type = 2,
  highlight = NULL,
  highlight_color = "red2",
  highlight_size = 1,
  highlight_alpha = 1,
  comparisons = NULL,
  ref_group = NULL,
  pairwise_method = "wilcox.test",
  multiplegroup_comparisons = FALSE,
  multiple_method = "kruskal.test",
  sig_label = "p.format",
  sig_labelsize = 3.5,
  hide_ns = FALSE,
  facet_by = NULL,
  facet_scales = "fixed",
  facet_ncol = NULL,
  facet_nrow = NULL,
  facet_byrow = TRUE,
  title = NULL,
  subtitle = NULL,
  xlab = NULL,
  ylab = NULL,
  seed = 8525,
  combine = TRUE,
  nrow = NULL,
  ncol = NULL,
  byrow = TRUE,
  axes = NULL,
  axis_titles = axes,
  guides = NULL,
  ...
)

ViolinPlot(
  data,
  x,
  x_sep = "_",
  y = NULL,
  in_form = c("long", "wide"),
  split_by = NULL,
  split_by_sep = "_",
  symnum_args = NULL,
  sort_x = NULL,
  flip = FALSE,
  keep_empty = FALSE,
  keep_na = FALSE,
  group_by = NULL,
  group_by_sep = "_",
  group_name = NULL,
  paired_by = NULL,
  x_text_angle = ifelse(isTRUE(flip), 0, 45),
  step_increase = 0.1,
  fill_mode = ifelse(!is.null(group_by), "dodge", "x"),
  palreverse = FALSE,
  position_dodge_preserve = "total",
  theme = "theme_this",
  theme_args = list(),
  palette = "Paired",
  palcolor = NULL,
  alpha = 1,
  aspect.ratio = NULL,
  legend.position = "right",
  legend.direction = "vertical",
  add_point = FALSE,
  pt_color = if (isTRUE(add_beeswarm)) NULL else "grey30",
  pt_size = NULL,
  pt_alpha = 1,
  jitter_width = NULL,
  jitter_height = 0,
  stack = FALSE,
  y_max = NULL,
  y_min = NULL,
  y_brackets = NULL,
  add_beeswarm = FALSE,
  beeswarm_method = "swarm",
  beeswarm_cex = 1,
  beeswarm_priority = "ascending",
  beeswarm_dodge = 0.9,
  add_box = FALSE,
  box_color = "black",
  box_width = 0.1,
  box_ptsize = 2.5,
  add_trend = FALSE,
  trend_color = NULL,
  trend_linewidth = 1,
  trend_ptsize = 2,
  add_stat = NULL,
  stat_name = NULL,
  stat_color = "black",
  stat_size = 1,
  stat_stroke = 1,
  stat_shape = 25,
  add_bg = FALSE,
  bg_palette = "stripe",
  bg_palcolor = NULL,
  bg_alpha = 0.2,
  add_line = NULL,
  line_color = "red2",
  line_width = 0.6,
  line_type = 2,
  highlight = NULL,
  highlight_color = "red2",
  highlight_size = 1,
  highlight_alpha = 1,
  comparisons = NULL,
  ref_group = NULL,
  pairwise_method = "wilcox.test",
  multiplegroup_comparisons = FALSE,
  multiple_method = "kruskal.test",
  sig_label = "p.format",
  sig_labelsize = 3.5,
  hide_ns = FALSE,
  facet_by = NULL,
  facet_scales = "fixed",
  facet_ncol = NULL,
  facet_nrow = NULL,
  facet_byrow = TRUE,
  title = NULL,
  subtitle = NULL,
  xlab = NULL,
  ylab = NULL,
  seed = 8525,
  combine = TRUE,
  nrow = NULL,
  ncol = NULL,
  byrow = TRUE,
  axes = NULL,
  axis_titles = axes,
  guides = NULL,
  ...
)

BeeswarmPlot(
  data,
  x,
  x_sep = "_",
  y = NULL,
  in_form = c("long", "wide"),
  split_by = NULL,
  split_by_sep = "_",
  symnum_args = NULL,
  sort_x = NULL,
  flip = FALSE,
  keep_empty = FALSE,
  keep_na = FALSE,
  group_by = NULL,
  group_by_sep = "_",
  group_name = NULL,
  paired_by = NULL,
  x_text_angle = ifelse(isTRUE(flip), 0, 45),
  step_increase = 0.1,
  fill_mode = ifelse(!is.null(group_by), "dodge", "x"),
  palreverse = FALSE,
  theme = "theme_this",
  theme_args = list(),
  palette = "Paired",
  palcolor = NULL,
  alpha = 1,
  aspect.ratio = NULL,
  legend.position = "right",
  legend.direction = "vertical",
  pt_color = NULL,
  pt_size = NULL,
  pt_alpha = 1,
  position_dodge_preserve = "total",
  jitter_width = NULL,
  jitter_height = 0,
  stack = FALSE,
  y_max = NULL,
  y_min = NULL,
  y_brackets = NULL,
  add_violin = FALSE,
  beeswarm_method = "swarm",
  beeswarm_cex = 1,
  beeswarm_priority = "ascending",
  beeswarm_dodge = 0.9,
  add_box = FALSE,
  box_color = "black",
  box_width = 0.1,
  box_ptsize = 2.5,
  add_trend = FALSE,
  trend_color = NULL,
  trend_linewidth = 1,
  trend_ptsize = 2,
  add_stat = NULL,
  stat_name = NULL,
  stat_color = "black",
  stat_size = 1,
  stat_stroke = 1,
  stat_shape = 25,
  add_bg = FALSE,
  bg_palette = "stripe",
  bg_palcolor = NULL,
  bg_alpha = 0.2,
  add_line = NULL,
  line_color = "red2",
  line_width = 0.6,
  line_type = 2,
  highlight = NULL,
  highlight_color = "red2",
  highlight_size = 1,
  highlight_alpha = 1,
  comparisons = NULL,
  ref_group = NULL,
  pairwise_method = "wilcox.test",
  multiplegroup_comparisons = FALSE,
  multiple_method = "kruskal.test",
  sig_label = "p.format",
  sig_labelsize = 3.5,
  hide_ns = FALSE,
  facet_by = NULL,
  facet_scales = "fixed",
  facet_ncol = NULL,
  facet_nrow = NULL,
  facet_byrow = TRUE,
  title = NULL,
  subtitle = NULL,
  xlab = NULL,
  ylab = NULL,
  seed = 8525,
  combine = TRUE,
  nrow = NULL,
  ncol = NULL,
  byrow = TRUE,
  axes = NULL,
  axis_titles = axes,
  guides = NULL,
  ...
)

Arguments

Value

A ggplot object, a patchwork object, or a named list of ggplot objects (when combine = FALSE), each with height and width attributes in inches.

Bar plots (base = "bar")

When base = "bar", bars display group means with optional error bars. add_errorbar controls the error bar type:

• "SEM" (default) — standard error of the mean.

• "SD" — standard deviation.

• "CI" or "CI95" — 95\

• "none" — no error bars.

Error bars are computed via a custom stat_summary(fun.data = ...) that handles per-group mean, SD, and sample size.

Examples

set.seed(8525)
data <- data.frame(
    x = rep(LETTERS[1:8], each = 40),
    y = c(rnorm(160), rnorm(160, mean = 1)),
    group1 = sample(c("g1", "g2"), 320, replace = TRUE),
    group2 = sample(c("h1", "h2", "h3", "h4"), 320, replace = TRUE)
)

# Basic box plot
BoxPlot(data, x = "x", y = "y")

# With beeswarm points
BoxPlot(data, x = "x", y = "y", add_beeswarm = TRUE, pt_color = "grey30")

# Stacked + flipped + faceted
BoxPlot(data,
    x = "x", y = "y",
    stack = TRUE, flip = TRUE, facet_by = "group1",
    add_bg = TRUE, bg_palette = "Paired")

# Stacked + flipped + split_by with per-split colours
BoxPlot(data,
    x = "x", y = "y",
    stack = TRUE, flip = TRUE, split_by = "group1",
    add_bg = TRUE, bg_palette = "Paired",
    palcolor = list(g1 = c("red", "blue"), g2 = c("blue", "red")))

# sort_x — order by mean(y)
data <- data.frame(
  x = factor(rep(LETTERS[1:5], each = 40),
     levels = c(LETTERS[1:2], "unused", LETTERS[3:5])),
  y = c(rnorm(40, mean = 5), rnorm(40, mean = 4), rnorm(40, mean = 3),
     rnorm(40, mean = 2), rnorm(40, mean = 1))
)
BoxPlot(data, x = "x", y = "y", sort_x = "mean(y)", keep_empty = TRUE)
BoxPlot(data, x = "x", y = "y", sort_x = "mean(-y)", keep_empty = TRUE)

# Wide-form data
data_wide <- data.frame(A = rnorm(100), B = rnorm(100), C = rnorm(100))
BoxPlot(data_wide, x = c("A", "B", "C"), in_form = "wide")

# Paired observations with connecting lines and paired test
paired_data <- data.frame(
    subject = rep(paste0("s", 1:10), each = 2),
    visit = rep(c("pre", "post"), times = 10),
    value = rnorm(20))
BoxPlot(paired_data,
    x = "visit", y = "value", comparisons = TRUE,
    paired_by = "subject", add_point = TRUE)

# Paired + grouped
paired_group_data <- data.frame(
    subject = rep(paste0("s", 1:6), each = 2),
    x = rep(c("A", "B"), each = 6),
    group = rep(c("before", "after"), times = 6),
    value = rnorm(12))
BoxPlot(paired_group_data,
    x = "x", y = "value",
    paired_by = "subject", group_by = "group",
    comparisons = TRUE, pt_size = 3, pt_color = "red")

# keep_na and keep_empty examples
data <- data.frame(
    x = factor(rep(c(LETTERS[1:3], NA, LETTERS[5:8]), each = 40),
       levels = c(LETTERS[1:8])),
    y = c(rnorm(160), rnorm(160, mean = 1)),
    group1 = sample(c("g1", "g2"), 320, replace = TRUE),
    group2 = factor(sample(c("h1", NA, "h3", "h4"), 320, replace = TRUE),
       levels = c("h1", "h2", "h3", "h4")))

BoxPlot(data, x = "x", y = "y")
BoxPlot(data, x = "x", y = "y", keep_na = TRUE, keep_empty = TRUE)
BoxPlot(data, x = "x", y = "y", keep_na = TRUE, keep_empty = TRUE,
        facet_by = "group2")
BoxPlot(data, x = "x", y = "y", keep_na = TRUE, keep_empty = 'level')
BoxPlot(data, x = "x", y = "y", group_by = "group2")
BoxPlot(data, x = "x", y = "y", group_by = "group2",
        keep_na = TRUE, keep_empty = TRUE)
BoxPlot(data, x = "x", y = "y", group_by = "group2",
        keep_na = TRUE, keep_empty = 'level')

# Per-column keep_na / keep_empty
BoxPlot(data, x = "x", y = "y", group_by = "group2",
        keep_na = list(x = TRUE, group2 = FALSE),
        keep_empty = list(x = FALSE, group2 = TRUE))

# Bar plot (base = "bar")
data$y <- abs(data$y)
BoxPlot(data, x = "x", y = "y", base = "bar")
BoxPlot(data, x = "x", y = "y", base = "bar", add_errorbar = "SD")
BoxPlot(data, x = "x", y = "y", base = "bar", add_errorbar = "CI95")
BoxPlot(data, x = "x", y = "y", base = "bar", add_errorbar = "none")
BoxPlot(data, x = "x", y = "y", base = "bar", group_by = "group1")
BoxPlot(data, x = "x", y = "y", base = "bar", add_point = TRUE)
BoxPlot(data, x = "x", y = "y", base = "bar",
        fill_mode = "mean", palette = "Blues")


ViolinPlot(data, x = "x", y = "y")
ViolinPlot(data, x = "x", y = "y", add_beeswarm = TRUE, pt_color = "grey30")
ViolinPlot(data, x = "x", y = "y", add_box = TRUE)
ViolinPlot(data, x = "x", y = "y", add_point = TRUE)
ViolinPlot(data, x = "x", y = "y", add_trend = TRUE)
ViolinPlot(data, x = "x", y = "y", add_stat = mean)
ViolinPlot(data, x = "x", y = "y", add_bg = TRUE)
ViolinPlot(data, x = "x", y = "y", add_line = 0)

# Grouped
ViolinPlot(data, x = "x", y = "y", group_by = "group1")

# Grouped + faceted + box overlay
ViolinPlot(data,
    x = "x", y = "y", group_by = "group1",
    facet_by = "group2", add_box = TRUE)

# Highlight
ViolinPlot(data,
    x = "x", y = "y", add_point = TRUE,
    highlight = 'group1 == "g1"', alpha = 0.8,
    highlight_size = 1.5, pt_size = 1, add_box = TRUE)

# Pairwise comparisons with formatted labels
ViolinPlot(data,
    x = "x", y = "y", group_by = "group1",
    comparisons = TRUE, sig_label = "p = {p}")

# Explicit comparison list + hide non-significant
ViolinPlot(data,
    x = "x", y = "y", sig_label = "p.format", hide_ns = TRUE,
    facet_by = "group2", comparisons = list(c("D", "E")))

# Continuous fill (mean) + omnibus test
ViolinPlot(data,
    x = "x", y = "y", fill_mode = "mean",
    facet_by = "group2", palette = "Blues",
    multiplegroup_comparisons = TRUE)

# Per-split palettes
ViolinPlot(data,
    x = "x", y = "y", fill_mode = "mean",
    split_by = "group1", palette = c(g1 = "Blues", g2 = "Reds"))

# Stacked faceting
ViolinPlot(data,
    x = "x", y = "y", stack = TRUE,
    facet_by = "group2", add_box = TRUE, add_bg = TRUE,
    bg_palette = "Paired")


# Basic beeswarm
BeeswarmPlot(data, x = "x", y = "y")

# Control point size
BeeswarmPlot(data, x = "x", y = "y", pt_size = 1)

# Beeswarm with box overlay
BeeswarmPlot(data, x = "x", y = "y", add_box = TRUE, pt_color = "grey30")

# Grouped
BeeswarmPlot(data, x = "x", y = "y", group_by = "group1")

# Grouped without dodging
BeeswarmPlot(data, x = "x", y = "y", group_by = "group1",
             beeswarm_dodge = NULL)

# Hex layout with wider spacing
BeeswarmPlot(data,
    x = "x", y = "y", beeswarm_method = "hex",
    beeswarm_cex = 2)

Description

Draws a chord diagram (also known as a circos plot) to visualise relationships between two categorical variables. Categories are arranged around a circle, and connecting ribbons (links) represent the flow or association between source and target nodes. The width of each link is proportional to the associated numeric value or observation count.

The function supports count aggregation (omit y to plot observation counts per pair), link colouring by source or target node, label rotation options, and splitting into separate sub-diagrams via split_by.

CircosPlot is an alias of ChordPlot.

CircosPlot is an alias for ChordPlot.

Usage

ChordPlot(
  data,
  y = NULL,
  from = NULL,
  from_sep = "_",
  to = NULL,
  to_sep = "_",
  split_by = NULL,
  split_by_sep = "_",
  flip = FALSE,
  links_color = c("from", "to"),
  theme = "theme_this",
  theme_args = list(),
  palette = "Paired",
  palcolor = NULL,
  palreverse = FALSE,
  alpha = 0.5,
  labels_rot = FALSE,
  title = NULL,
  subtitle = NULL,
  seed = 8525,
  keep_na = FALSE,
  keep_empty = FALSE,
  combine = TRUE,
  nrow = NULL,
  ncol = NULL,
  byrow = TRUE,
  axes = NULL,
  axis_titles = axes,
  guides = NULL,
  design = NULL,
  ...
)

CircosPlot(
  data,
  y = NULL,
  from = NULL,
  from_sep = "_",
  to = NULL,
  to_sep = "_",
  split_by = NULL,
  split_by_sep = "_",
  flip = FALSE,
  links_color = c("from", "to"),
  theme = "theme_this",
  theme_args = list(),
  palette = "Paired",
  palcolor = NULL,
  palreverse = FALSE,
  alpha = 0.5,
  labels_rot = FALSE,
  title = NULL,
  subtitle = NULL,
  seed = 8525,
  keep_na = FALSE,
  keep_empty = FALSE,
  combine = TRUE,
  nrow = NULL,
  ncol = NULL,
  byrow = TRUE,
  axes = NULL,
  axis_titles = axes,
  guides = NULL,
  design = NULL,
  ...
)

Arguments

Value

A patchwork object or a named list of wrapped elements (when combine = FALSE), each with height and width attributes in inches.

split_by workflow

When split_by is provided:

1. check_keep_na() and check_keep_empty() normalise the keep_na / keep_empty arguments for all columns (split_by, from, to).

2. The split_by column is validated and its NA / empty levels are processed. It is then removed from the per-column lists.

3. The data is split by split_by (preserving level order). If split_by is NULL, the data is wrapped in a single-element list with name "...".

4. Per-split palette and palcolor are resolved via check_palette() and check_palcolor().

5. ChordPlotAtomic() is called for each split. When title is a function, it receives the split level name for dynamic titles.

6. Results are combined via combine_plots().

Examples

set.seed(8525)
data <- data.frame(
    nodes1 = sample(c("Soure1", "Source2", "Source3"), 10, replace = TRUE),
    nodes2 = sample(letters[1:3], 10, replace = TRUE),
    y = sample(1:5, 10, replace = TRUE)
)

# Basic chord diagram (counts)
ChordPlot(data, from = "nodes1", to = "nodes2")

# Links coloured by target + rotated labels
ChordPlot(data, from = "nodes1", to = "nodes2",
          links_color = "to", labels_rot = TRUE)

# With explicit y values (link thickness)
ChordPlot(data, from = "nodes1", to = "nodes2", y = "y")

# Split by a column — one diagram per split level
ChordPlot(data, from = "nodes1", to = "nodes2", split_by = "y")

# Per-split palettes
ChordPlot(data, from = "nodes1", to = "nodes2", split_by = "y",
          palette = c("1" = "Reds", "2" = "Blues",
                      "3" = "Greens", "4" = "Purp"))

# Flip source/target direction
ChordPlot(data, from = "nodes1", to = "nodes2", flip = TRUE)

Description

Creates a clustree (clustering tree) plot visualising how cluster assignments change across increasing clustering resolutions. The plot helps identify stable clustering solutions and understand the hierarchical relationships among clusters at different resolution thresholds.

The function expects a data frame with columns named by a common prefix followed by numeric resolution values (e.g. "res_0.1", "res_0.3", "res_0.5"). Each column contains cluster labels (factor or character) for every observation at that resolution.

Internally, the function uses clustree::clustree() to compute a ggraph-based tree layout where nodes are clusters and edges represent cells transitioning between clusters at adjacent resolutions. Edge colour and width encode the number of transitioning cells.

Key features:

• Resolution-level node colouring — each resolution receives a distinct colour from the selected palette.

• Edge gradient — edges are coloured by transition count using a separate edge_palette colour gradient.

• Flip support — flip = TRUE places resolutions on the x-axis for left-to-right reading.

• Split by groups — split_by generates per-group clustree plots that are combined via patchwork.

• Automatic dimensions — plot height and width are automatically computed based on the number of resolutions, clusters, and the legend configuration.

Usage

ClustreePlot(
  data,
  prefix,
  flip = FALSE,
  split_by = NULL,
  split_by_sep = "_",
  palette = "Paired",
  palcolor = NULL,
  palreverse = FALSE,
  edge_palette = "Spectral",
  edge_palcolor = NULL,
  aspect.ratio = 1,
  legend.position = "right",
  legend.direction = "vertical",
  title = NULL,
  subtitle = NULL,
  xlab = NULL,
  ylab = NULL,
  expand = c(0.1, 0.1),
  theme = "theme_this",
  theme_args = list(),
  combine = TRUE,
  nrow = NULL,
  ncol = NULL,
  byrow = TRUE,
  seed = 8525,
  axes = NULL,
  axis_titles = axes,
  guides = NULL,
  design = NULL,
  ...
)

Arguments

Value

A ggplot object (single plot), a patchwork object (when combine = TRUE with split_by), or a list of ggplot objects (when combine = FALSE).

split_by Workflow (ClustreePlot)

When split_by is provided, the following pipeline executes:

1. Argument validation — validate_common_args() checks the seed value and sets the random seed.

2. Theme resolution — process_theme() resolves the theme string or function to a theme function.

3. Split column validation — check_columns() resolves split_by with force_factor = TRUE, allow_multi = TRUE, concat_multi = TRUE.

4. Data splitting — splits data by split_by levels (unused levels dropped), preserving factor level order.

5. Per-split palette / colour / legend — check_palette(), check_palcolor(), and check_legend() resolve per-split overrides for palette, palcolor, legend.position, and legend.direction.

6. Per-split title — when title is a function, it receives the default title (the split level name) and can return a custom string; otherwise title %||% split_level is used.

7. Dispatch — each split subset is passed to ClustreePlotAtomic with the per-split parameters.

8. Combination — combine_plots() assembles the list of plots via patchwork::wrap_plots, honouring nrow/ncol/byrow/design.

Examples

set.seed(8525)
N <- 100
data <- data.frame(
    p.0.4 = sample(LETTERS[1:5], N, replace = TRUE),
    p.0.5 = sample(LETTERS[1:6], N, replace = TRUE),
    p.0.6 = sample(LETTERS[1:7], N, replace = TRUE),
    p.0.7 = sample(LETTERS[1:8], N, replace = TRUE),
    p.0.8 = sample(LETTERS[1:9], N, replace = TRUE),
    p.0.9 = sample(LETTERS[1:10], N, replace = TRUE),
    p.1 = sample(LETTERS[1:30], N, replace = TRUE),
    split = sample(1:2, N, replace = TRUE)
)

# --- Basic clustree plot ---
ClustreePlot(data, prefix = "p")

# --- Flipped layout (resolutions on x-axis) ---
ClustreePlot(data, prefix = "p", flip = TRUE)

# --- Split by group ---
ClustreePlot(data, prefix = "p", split_by = "split")

# --- Split by group with per-split palettes ---
ClustreePlot(data, prefix = "p", split_by = "split",
             palette = c("1" = "Set1", "2" = "Paired"))

Description

Draws a grid of pairwise scatter plots for selected numeric columns, arranged in a scatterplot matrix layout. The upper or lower triangle displays correlation tiles while the opposite triangle shows scatter plots with regression lines. Diagonal cells can show density plots, violin plots, histograms, box plots, or a simple diagonal line.

NOTE: The facet_by parameter is not supported in CorPairsPlot (an error is raised if provided). Use split_by instead to create separate correlation pair matrices per group.

The function supports four layout orientations (layout), three correlation methods, configurable diagonal plots via other plotthis functions, custom correlation tile formatting, and splitting into separate sub-plots via split_by.

Usage

CorPairsPlot(
  data,
  columns = NULL,
  group_by = NULL,
  group_by_sep = "_",
  group_name = NULL,
  split_by = NULL,
  split_by_sep = "_",
  diag_type = NULL,
  diag_args = list(),
  layout = c(".\\", "\\.", "/.", "./"),
  cor_method = c("pearson", "spearman", "kendall"),
  cor_palette = "RdBu",
  cor_palcolor = NULL,
  cor_size = 3,
  cor_format = "corr: {round(corr, 2)}",
  cor_fg = "black",
  cor_bg = "white",
  cor_bg_r = 0.1,
  theme = "theme_this",
  theme_args = list(),
  palette = ifelse(is.null(group_by), "Spectral", "Paired"),
  palcolor = NULL,
  palreverse = FALSE,
  title = NULL,
  subtitle = NULL,
  facet_by = NULL,
  legend.position = "right",
  legend.direction = "vertical",
  seed = 8525,
  combine = TRUE,
  nrow = NULL,
  ncol = NULL,
  byrow = TRUE,
  axes = NULL,
  axis_titles = axes,
  guides = NULL,
  design = NULL,
  ...
)

Arguments

Value

A patchwork object (when combine = TRUE) or a named list of patchwork objects (when combine = FALSE), each with height and width attributes in inches.

split_by workflow

When split_by is provided:

1. The split_by column is validated via check_columns() with force_factor = TRUE. Empty levels are dropped.

2. The data frame is split by split_by (preserving level order). If split_by is NULL, the data is wrapped in a single-element list with name "...". The split_by column is removed from each split's data before plotting.

3. Per-split palette, palcolor, legend.position, and legend.direction are resolved via check_palette(), check_palcolor(), and check_legend().

4. CorPairsPlotAtomic() is called for each split. When title is a function, it receives the split level name and can generate dynamic titles.

5. Results are combined via combine_plots() (when combine = TRUE) or returned as a named list.

Examples

set.seed(8525)
data <- data.frame(x = rnorm(100))
data$y <- rnorm(100, 10, sd = 0.5)
data$z <- -data$x + data$y + rnorm(100, 20, 1)
data$g <- sample(1:4, 100, replace = TRUE)

# Histogram diagonal, slash layout
CorPairsPlot(data, diag_type = "histogram",
    diag_args = list(bins = 30, palette = "Paired"),
    layout = "/.")

# No diagonal with axis title styling
CorPairsPlot(data, group_by = "g", diag_type = "none", layout = "./",
    theme_args = list(axis.title = element_textbox(
        color = "black", box.color = "grey20", size = 16, halign = 0.5,
        fill = "grey90", linetype = 1,
        width = grid::unit(1, "npc"),
        padding = ggplot2::margin(5, 5, 5, 5))))

# Violin diagonal with custom format
CorPairsPlot(data, group_by = "g", diag_type = "violin", layout = "\\.",
    cor_format = "{x}\n{y}\ncorr: {round(corr, 2)}")

# Per-split with bottom legend
CorPairsPlot(data, split_by = "g", diag_type = "none", layout = ".\\",
    legend.position = "bottom", legend.direction = "horizontal",
    group_name = "group")

# Per-split with custom palette colours
CorPairsPlot(data, split_by = "g",
    palcolor = list("1" = "red", "2" = "blue", "3" = "green",
                    "4" = "yellow"))

Description

Draws a scatter plot of two numeric variables with a linear regression line, optional correlation statistics, and point highlighting. This is the public entry point that wraps CorPlotAtomic with split_by support.

Key features include group-based colouring (group_by), point highlighting by expression, rowname, or index, annotation items (regression equation, R-squared, p-value, Spearman/Pearson/Kendall rho, N), raster rendering for large datasets, faceting (facet_by), and splitting into separate sub-plots via split_by.

Usage

CorPlot(
  data,
  x,
  y,
  group_by = NULL,
  group_by_sep = "_",
  group_name = NULL,
  split_by = NULL,
  split_by_sep = "_",
  pt_size = 2,
  pt_shape = 16,
  raster = FALSE,
  alpha = 1,
  raster_dpi = c(512, 512),
  highlight = NULL,
  highlight_color = "black",
  highlight_size = 1,
  highlight_alpha = 1,
  highlight_stroke = 0.8,
  anno_items = c("eq", "r2", "p"),
  anno_size = 3,
  anno_fg = "black",
  anno_bg = "white",
  anno_bg_r = 0.1,
  anno_position = c("topleft", "topright", "bottomleft", "bottomright", "tl", "tr", "bl",
    "br"),
  add_smooth = TRUE,
  smooth_color = "red2",
  smooth_width = 1.5,
  smooth_se = FALSE,
  theme = "theme_this",
  theme_args = list(),
  palette = ifelse(is.null(group_by), "Spectral", "Paired"),
  palcolor = NULL,
  palreverse = FALSE,
  title = NULL,
  subtitle = NULL,
  xlab = NULL,
  ylab = NULL,
  facet_by = NULL,
  facet_scales = "fixed",
  facet_ncol = NULL,
  facet_nrow = NULL,
  facet_byrow = TRUE,
  aspect.ratio = 1,
  legend.position = waiver(),
  legend.direction = "vertical",
  seed = 8525,
  combine = TRUE,
  nrow = NULL,
  ncol = NULL,
  byrow = TRUE,
  axes = NULL,
  axis_titles = axes,
  guides = NULL,
  design = NULL,
  ...
)

Arguments