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Density Plot / Histogram

Source: R/densityplot.R
densityhistoplot.Rd

Density plot for visualising the distribution of a numeric variable. Uses ggplot2::geom_density() to render smooth kernel density estimates, with optional grouping, faceting, split-by splitting, and data-distribution rug bars along the baseline.

This is the public entry point for density plots; the companion Histogram() function provides binned-histogram rendering. Both dispatch to the same internal engine (DensityHistoPlotAtomic) with type = "density" or type = "histogram" respectively.

Histogram for visualising the distribution of a numeric variable via binned counts. Uses ggplot2::geom_histogram(), with optional trend-line overlays, zero-skip interpolation, grouping, faceting, and split-by splitting.

This is the histogram companion to DensityPlot(). Both dispatch to the same internal engine (DensityHistoPlotAtomic) with type = "histogram" or type = "density" respectively.

When use_trend = TRUE, the histogram bars are replaced entirely by a point-and-line trend; when add_trend = TRUE, the trend is overlaid on top of the bars. The trend_skip_zero option uses zoo::na.approx() to interpolate across empty bins for a continuous trend curve — particularly useful with transformed y-axes.

Usage

DensityPlot(
  data,
  x,
  group_by = NULL,
  group_by_sep = "_",
  group_name = NULL,
  xtrans = "identity",
  ytrans = "identity",
  split_by = NULL,
  split_by_sep = "_",
  flip = FALSE,
  position = "identity",
  palette = "Paired",
  palcolor = NULL,
  palreverse = FALSE,
  alpha = 0.5,
  theme = "theme_this",
  theme_args = list(),
  add_bars = FALSE,
  bar_height = 0.025,
  bar_alpha = 1,
  bar_width = 0.1,
  keep_na = FALSE,
  keep_empty = FALSE,
  title = NULL,
  subtitle = NULL,
  xlab = NULL,
  ylab = NULL,
  expand = c(bottom = 0, left = 0, right = 0),
  facet_by = NULL,
  facet_scales = "free_y",
  facet_ncol = NULL,
  facet_nrow = NULL,
  facet_byrow = TRUE,
  aspect.ratio = 1,
  legend.position = ifelse(is.null(group_by), "none", "right"),
  legend.direction = "vertical",
  seed = 8525,
  combine = TRUE,
  nrow = NULL,
  ncol = NULL,
  byrow = TRUE,
  axes = NULL,
  axis_titles = axes,
  guides = NULL,
  design = NULL,
  ...
)

Histogram(
  data,
  x,
  group_by = NULL,
  group_by_sep = "_",
  group_name = NULL,
  xtrans = "identity",
  ytrans = "identity",
  split_by = NULL,
  split_by_sep = "_",
  flip = FALSE,
  bins = NULL,
  binwidth = NULL,
  trend_skip_zero = FALSE,
  add_bars = FALSE,
  bar_height = 0.025,
  bar_alpha = 1,
  bar_width = 0.1,
  position = "identity",
  keep_na = FALSE,
  keep_empty = FALSE,
  use_trend = FALSE,
  add_trend = FALSE,
  trend_alpha = 1,
  trend_linewidth = 0.8,
  trend_pt_size = 1.5,
  palette = "Paired",
  palcolor = NULL,
  palreverse = FALSE,
  alpha = 0.5,
  theme = "theme_this",
  theme_args = list(),
  title = NULL,
  subtitle = NULL,
  xlab = NULL,
  ylab = NULL,
  expand = c(bottom = 0, left = 0, right = 0),
  facet_by = NULL,
  facet_scales = "free_y",
  facet_ncol = NULL,
  facet_nrow = NULL,
  facet_byrow = TRUE,
  aspect.ratio = 1,
  legend.position = ifelse(is.null(group_by), "none", "right"),
  legend.direction = "vertical",
  seed = 8525,
  combine = TRUE,
  nrow = NULL,
  ncol = NULL,
  byrow = TRUE,
  axes = NULL,
  axis_titles = axes,
  guides = NULL,
  design = NULL,
  ...
)

Arguments

data

A data frame.

x

A character string specifying the column name of the data frame to plot for the x-axis.

group_by

Columns to group the data for plotting For those plotting functions that do not support multiple groups, They will be concatenated into one column, using group_by_sep as the separator

group_by_sep

The separator for multiple group_by columns. See group_by

group_name

A character string used as the legend title for the group_by aesthetic. When NULL (default), the (possibly concatenated) group_by column name is used.

xtrans

A character string specifying the transformation applied to the x-axis. Passed to ggplot2::scale_x_continuous(transform = ...). Supported values include "identity" (default), "log10", "log2", "sqrt", "reverse", etc.

ytrans

A character string specifying the transformation applied to the y-axis. Passed to ggplot2::scale_y_continuous(transform = ...). Used by trend_skip_zero to correctly interpolate across zero bins on a transformed scale. Default: "identity".

split_by

The column(s) to split data by and plot separately.

split_by_sep

The separator for multiple split_by columns. See split_by

flip

A logical value. If TRUE, the x and y axes are swapped via coord_flip(). Dimension calculation accounts for the flip.

position

A character string specifying the position adjustment for the bars or density curves. Default: "identity", which shows the actual count / density per group (unlike ggplot2's default "stack"). Other options: "stack", "dodge", "fill".

palette

A character string specifying the palette to use. A named list or vector can be used to specify the palettes for different split_by values.

palcolor

A character string specifying the color to use in the palette. A named list can be used to specify the colors for different split_by values. If some values are missing, the values from the palette will be used (palcolor will be NULL for those values).

palreverse

A logical value indicating whether to reverse the palette. Default is FALSE.

alpha

A numeric value specifying the transparency of the plot.

theme

A character string or a theme class (i.e. ggplot2::theme_classic) specifying the theme to use. Default is "theme_this".

theme_args

A list of arguments to pass to the theme function.

add_bars

A logical value. If TRUE, a data-distribution rug is drawn along the y = 0 axis using geom_linerange(). Each group's bars are vertically offset to avoid overlap.

bar_height

A numeric value specifying the height (in data units, relative to the maximum y) of the rug bars added by add_bars. The actual pixel height scales with max_y. Default: 0.025.

bar_alpha

A numeric value in [0, 1] for the transparency of the rug bars. Default: 1.

bar_width

A numeric value passed as the linewidth aesthetic of geom_linerange(). Controls the thickness of each rug tick. Default: 0.1.

keep_na

A logical value or a character to replace the NA values in the data. It can also take a named list to specify different behavior for different columns. If TRUE or NA, NA values will be replaced with NA. If FALSE, NA values will be removed from the data before plotting. If a character string is provided, NA values will be replaced with the provided string. If a named vector/list is provided, the names should be the column names to apply the behavior to, and the values should be one of TRUE, FALSE, or a character string. Without a named vector/list, the behavior applies to categorical/character columns used on the plot, for example, the x, group_by, fill_by, etc.

keep_empty

One of FALSE, TRUE and "level". It can also take a named list to specify different behavior for different columns. Without a named list, the behavior applies to the categorical/character columns used on the plot, for example, the x, group_by, fill_by, etc.

  • FALSE (default): Drop empty factor levels from the data before plotting.

  • TRUE: Keep empty factor levels and show them as a separate category in the plot.

  • "level": Keep empty factor levels, but do not show them in the plot. But they will be assigned colors from the palette to maintain consistency across multiple plots. Alias: levels

title

A character string specifying the title of the plot. A function can be used to generate the title based on the default title. This is useful when split_by is used and the title needs to be dynamic.

subtitle

A character string specifying the subtitle of the plot.

xlab

A character string specifying the x-axis label.

ylab

A character string specifying the y-axis label.

expand

The values to expand the x and y axes. It is like CSS padding. When a single value is provided, it is used for both axes on both sides. When two values are provided, the first value is used for the top/bottom side and the second value is used for the left/right side. When three values are provided, the first value is used for the top side, the second value is used for the left/right side, and the third value is used for the bottom side. When four values are provided, the values are used for the top, right, bottom, and left sides, respectively. You can also use a named vector to specify the values for each side. When the axis is discrete, the values will be applied as 'add' to the 'expansion' function. When the axis is continuous, the values will be applied as 'mult' to the 'expansion' function. See also https://ggplot2.tidyverse.org/reference/expansion.html

facet_by

A character string specifying the column name of the data frame to facet the plot. Otherwise, the data will be split by split_by and generate multiple plots and combine them into one using patchwork::wrap_plots

facet_scales

Whether to scale the axes of facets. Default is "fixed" Other options are "free", "free_x", "free_y". See ggplot2::facet_wrap

facet_ncol

A numeric value specifying the number of columns in the facet. When facet_by is a single column and facet_wrap is used.

facet_nrow

A numeric value specifying the number of rows in the facet. When facet_by is a single column and facet_wrap is used.

facet_byrow

A logical value indicating whether to fill the plots by row. Default is TRUE.

aspect.ratio

A numeric value specifying the aspect ratio of the plot.

legend.position

A character string specifying the position of the legend. if waiver(), for single groups, the legend will be "none", otherwise "right".

legend.direction

A character string specifying the direction of the legend.

seed

The random seed to use. Default is 8525.

combine

Whether to combine the plots into one when facet is FALSE. Default is TRUE.

nrow

A numeric value specifying the number of rows in the facet.

ncol

A numeric value specifying the number of columns in the facet.

byrow

A logical value indicating whether to fill the plots by row.

axes

A string specifying how axes should be treated. Passed to patchwork::wrap_plots(). Only relevant when split_by is used and combine is TRUE. Options are:

  • 'keep' will retain all axes in individual plots.

  • 'collect' will remove duplicated axes when placed in the same run of rows or columns of the layout.

  • 'collect_x' and 'collect_y' will remove duplicated x-axes in the columns or duplicated y-axes in the rows respectively.

axis_titles

A string specifying how axis titltes should be treated. Passed to patchwork::wrap_plots(). Only relevant when split_by is used and combine is TRUE. Options are:

  • 'keep' will retain all axis titles in individual plots.

  • 'collect' will remove duplicated titles in one direction and merge titles in the opposite direction.

  • 'collect_x' and 'collect_y' control this for x-axis titles and y-axis titles respectively.

guides

A string specifying how guides should be treated in the layout. Passed to patchwork::wrap_plots(). Only relevant when split_by is used and combine is TRUE. Options are:

  • 'collect' will collect guides below to the given nesting level, removing duplicates.

  • 'keep' will stop collection at this level and let guides be placed alongside their plot.

  • 'auto' will allow guides to be collected if a upper level tries, but place them alongside the plot if not.

design

Specification of the location of areas in the layout, passed to patchwork::wrap_plots(). Only relevant when split_by is used and combine is TRUE. When specified, nrow, ncol, and byrow are ignored. See patchwork::wrap_plots() for more details.

...

Additional arguments.

bins

A numeric value specifying the number of bins for the histogram. Ignored when type = "density". Defaults to 30 when neither bins nor binwidth is provided.

binwidth

A numeric value specifying the width of individual bins for the histogram. Ignored when type = "density". Takes precedence over bins when both are set.

trend_skip_zero

A logical value. If TRUE, bins with zero count are set to NA before the trend line is computed, and zoo::na.approx() is used to interpolate across the gaps — producing a continuous curve even when some bins are empty. Requires ytrans to be correctly specified. Only applies when type = "histogram" and use_trend or add_trend is active.

use_trend

A logical value. If TRUE, the histogram bars are replaced entirely by a trend line (points + connecting line). Only applies when type = "histogram".

add_trend

A logical value. If TRUE, a trend line is overlaid on top of the histogram bars. Only applies when type = "histogram".

trend_alpha

A numeric value in [0, 1] controlling the transparency of the trend points and line. Default: 1.

trend_linewidth

A numeric value for the thickness of the trend line. Default: 0.8.

trend_pt_size

A numeric value for the size of the trend points. Default: 1.5.

Value

A ggplot object (single plot), a patchwork / wrap_plots object (when split_by is provided and combine = TRUE), or a list of ggplot objects (when split_by is provided and combine = FALSE).

A ggplot object (single plot), a patchwork / wrap_plots object (when split_by is provided and combine = TRUE), or a list of ggplot objects (when split_by is provided and combine = FALSE).

split_by Workflow

When split_by is specified, DensityPlot() executes the following pipeline:

  1. Argument validation — validate_common_args() checks the seed and facet-by consistency.

  2. NA / empty normalisation — check_keep_na() / check_keep_empty() convert keep_na / keep_empty to per-column lists.

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

  4. Split column resolution — check_columns() validates split_by (force_factor, concat_multi).

  5. Pre-filtering — process_keep_na_empty() removes NA / empty levels from the split column, then data is split by split_by levels (order preserved).

  6. Per-split parameter resolution — check_palette(), check_palcolor(), check_legend() resolve palette, palcolor, legend.position, and legend.direction for each split.

  7. Per-split dispatch — each split is passed to DensityHistoPlotAtomic(type = "density", ...) with its resolved parameters. Title defaults to the split level name unless title is a function.

  8. Combination — combine_plots() assembles the list of plots via patchwork::wrap_plots(), applying nrow, ncol, byrow, axes, axis_titles, guides, and design.

When split_by is specified, Histogram() executes the following pipeline:

  1. Argument validation — validate_common_args() checks the seed and facet-by consistency.

  2. NA / empty normalisation — check_keep_na() / check_keep_empty() convert keep_na / keep_empty to per-column lists.

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

  4. Split column resolution — check_columns() validates split_by (force_factor, concat_multi).

  5. Pre-filtering — process_keep_na_empty() removes NA / empty levels from the split column, then data is split by split_by levels (order preserved).

  6. Per-split parameter resolution — check_palette(), check_palcolor(), check_legend() resolve palette, palcolor, legend.position, and legend.direction for each split.

  7. Per-split dispatch — each split is passed to DensityHistoPlotAtomic(type = "histogram", ...) with its resolved parameters (including bins, binwidth, use_trend, add_trend, trend_skip_zero, trend_alpha, trend_linewidth, trend_pt_size). Title defaults to the split level name unless title is a function.

  8. Combination — combine_plots() assembles the list of plots via patchwork::wrap_plots(), applying nrow, ncol, byrow, axes, axis_titles, guides, and design.

Examples

# \donttest{
set.seed(8525)
data <- data.frame(
    x = c(rnorm(500, -1), rnorm(500, 1)),
    group = factor(rep(c("A", NA, "C", "D"), each = 250), levels = LETTERS[1:4]),
    facet = sample(c("F1", "F2"), 1000, replace = TRUE)
)

# basic density
DensityPlot(data, x = "x")

DensityPlot(data, x = "x", group_by = "group")


# NA / empty level handling
DensityPlot(data, x = "x", group_by = "group",
    keep_na = TRUE, keep_empty = TRUE)

DensityPlot(data, x = "x", group_by = "group",
    keep_na = TRUE, keep_empty = 'level')


# faceting and splitting
DensityPlot(data, x = "x", group_by = "group", facet_by = "facet")

DensityPlot(data, x = "x", split_by = "facet", add_bars = TRUE)

DensityPlot(data, x = "x", split_by = "facet", add_bars = TRUE,
    palette = c(F1 = "Set1", F2 = "Set2"))

# }
set.seed(8525)
data <- data.frame(
    x = sample(setdiff(1:100, c(30:36, 50:55, 70:77)), 1000, replace = TRUE),
    group = factor(rep(c("A", "B", NA, "D"), each = 250), levels = LETTERS[1:4]),
    facet = sample(c("F1", "F2"), 1000, replace = TRUE)
)

# basic histogram
Histogram(data, x = "x")
#> Using `bins = 30`. Pick better value with `binwidth`.

Histogram(data, x = "x", group_by = "group")
#> Using `bins = 30`. Pick better value with `binwidth`.


# NA / empty level handling
Histogram(data, x = "x", group_by = "group", keep_na = TRUE, keep_empty = 'level')
#> Using `bins = 30`. Pick better value with `binwidth`.


# add_bars and trend overlays
Histogram(data, x = "x", split_by = "facet", add_bars = TRUE)
#> Using `bins = 30`. Pick better value with `binwidth`.
#> Using `bins = 30`. Pick better value with `binwidth`.

Histogram(data, x = "x", group_by = "group", add_trend = TRUE)
#> Using `bins = 30`. Pick better value with `binwidth`.

Histogram(data, x = "x", group_by = "group", add_trend = TRUE, trend_skip_zero = TRUE)
#> Using `bins = 30`. Pick better value with `binwidth`.


# use_trend replaces bars entirely
Histogram(data, x = "x", group_by = "group", split_by = "facet",
 use_trend = TRUE, trend_pt_size = 3)
#> Using `bins = 30`. Pick better value with `binwidth`.
#> Using `bins = 30`. Pick better value with `binwidth`.


# per-split palettes
Histogram(data, x = "x", group_by = "group", split_by = "facet",
 palette = c(F1 = "Paired", F2 = "Spectral"))
#> Using `bins = 30`. Pick better value with `binwidth`.
#> Using `bins = 30`. Pick better value with `binwidth`.