Geoms and stats for creating dotplots that automatically determines a bin width that ensures the plot fits within the available space. Also ensures dots do not overlap, and allows generation of quantile dotplots using the quantiles argument to stat_dotsinterval()/stat_dots(). Generally follows the naming scheme and arguments of the geom_slabinterval() and stat_slabinterval() family of geoms and stats.

geom_dotsinterval(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  binwidth = NA,
  dotsize = 1.07,
  stackratio = 1,
  layout = "bin",
  orientation = NA,
  interval_size_domain = c(1, 6),
  interval_size_range = c(0.6, 1.4),
  fatten_point = 1.8,
  show_slab = TRUE,
  show_point = TRUE,
  show_interval = TRUE,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

geom_dots(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  binwidth = NA,
  dotsize = 1.07,
  stackratio = 1,
  layout = "bin",
  orientation = NA,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_dotsinterval(
  mapping = NULL,
  data = NULL,
  geom = "dotsinterval",
  position = "identity",
  ...,
  quantiles = NA,
  point_interval = "median_qi",
  .width = c(0.66, 0.95),
  orientation = NA,
  na.rm = FALSE,
  show.legend = c(size = FALSE),
  inherit.aes = TRUE
)

stat_dots(
  mapping = NULL,
  data = NULL,
  geom = "dots",
  position = "identity",
  ...,
  quantiles = NA,
  orientation = NA,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

Set of aesthetic mappings created by aes() or aes_(). If specified and inherit.aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.

data

The data to be displayed in this layer. There are three options:

If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot().

A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify() for which variables will be created.

A function will be called with a single argument, the plot data. The return value must be a data.frame, and will be used as the layer data. A function can be created from a formula (e.g. ~ head(.x, 10)).

stat

The statistical transformation to use on the data for this layer, as a string.

position

Position adjustment, either as a string, or the result of a call to a position adjustment function. Setting this equal to "dodge" (position_dodge()) or "dodgejust" (position_dodgejust()) can be useful if you have overlapping geometries.

...

Other arguments passed to layer(). These are often aesthetics, used to set an aesthetic to a fixed value, like colour = "red" or size = 3 (see Aesthetics, below). They may also be parameters to the paired geom/stat.

binwidth

The bin width to use for laying out the dots. One of:

  • NA (the default): Dynamically select the bin width based on the size of the plot when drawn. This will pick a binwidth such that the tallest stack of dots is at most scale in height (ideally exactly scale in height, though this is not guaranteed).

  • A length-1 (scalar) numeric or unit object giving the exact bin width.

  • A length-2 (vector) numeric or unit object giving the minimum and maximum desired bin width. The bin width will be dynamically selected within these bounds.

If the value is numeric, it is assumed to be in units of data. The bin width (or its bounds) can also be specified using unit(), which may be useful if it is desired that the dots be a certain point size or a certain percentage of the width/height of the viewport. For example, unit(0.1, "npc") would make dots that are exactly 10% of the viewport size along whichever dimension the dotplot is drawn; unit(c(0, 0.1), "npc") would make dots that are at most 10% of the viewport size (while still ensuring the tallest stack is less than or equal to scale).

dotsize

The width of the dots relative to the binwidth. The default, 1.07, makes dots be just a bit wider than the bin width, which is a manually-tuned parameter that tends to work well with the default circular shape, preventing gaps between bins from appearing to be too large visually (as might arise from dots being precisely the binwidth). If it is desired to have dots be precisely the binwidth, set dotsize = 1.

stackratio

The distance between the center of the dots in the same stack relative to the dot height. The default, 1, makes dots in the same stack just touch each other.

layout

The layout method used for the dots:

  • "bin" (default): places dots on the off-axis at the midpoint of their bins as in the classic Wilkinson dotplot. This maintains the alignment of rows and columns in the dotplot. This layout is slightly different from the classic Wilkinson algorithm in that: (1) it nudges bins slightly to avoid overlapping bins and (2) if the input data are symmetrical it will return a symmetrical layout.

  • "weave": uses the same basic binning approach of "bin", but places dots in the off-axis at their actual positions (modulo overlaps, which are nudged out of the way). This maintains the alignment of rows but does not align dots within columns. Does not work well when side = "both".

  • "swarm": uses the "compactswarm" layout from beeswarm::beeswarm(). Does not maintain alignment of rows or columns, but can be more compact and neat looking, especially for sample data (as opposed to quantile dotplots of theoretical distributions, which may look better with "bin" or "weave").

orientation

Whether this geom is drawn horizontally or vertically. One of:

  • NA (default): automatically detect the orientation based on how the aesthetics are assigned. Automatic detection works most of the time.

  • "horizontal" (or "y"): draw horizontally, using the y aesthetic to identify different groups. For each group, uses the x, xmin, xmax, and thickness aesthetics to draw points, intervals, and slabs.

  • "vertical" (or "x"): draw vertically, using the x aesthetic to identify different groups. For each group, uses the y, ymin, ymax, and thickness aesthetics to draw points, intervals, and slabs.

For compatibility with the base ggplot naming scheme for orientation, "x" can be used as an alias for "vertical" and "y" as an alias for "horizontal" (tidybayes had an orientation parameter before base ggplot did, hence the discrepancy).

interval_size_domain

A length-2 numeric vector giving the minimum and maximum of the values of the size aesthetic that will be translated into actual sizes for intervals drawn according to interval_size_range (see the documentation for that argument.)

interval_size_range

A length-2 numeric vector. This geom scales the raw size aesthetic values when drawing interval and point sizes, as they tend to be too thick when using the default settings of scale_size_continuous(), which give sizes with a range of c(1, 6). The interval_size_domain value indicates the input domain of raw size values (typically this should be equal to the value of the range argument of the scale_size_continuous() function), and interval_size_range indicates the desired output range of the size values (the min and max of the actual sizes used to draw intervals). Most of the time it is not recommended to change the value of this argument, as it may result in strange scaling of legends; this argument is a holdover from earlier versions that did not have size aesthetics targeting the point and interval separately. If you want to adjust the size of the interval or points separately, you can instead use the interval_size or point_size aesthetics; see scales.

fatten_point

A multiplicative factor used to adjust the size of the point relative to the size of the thickest interval line. If you wish to specify point sizes directly, you can also use the point_size aesthetic and scale_point_size_continuous() or scale_point_size_discrete(); sizes specified with that aesthetic will not be adjusted using fatten_point.

show_slab

Should the slab portion of the geom be drawn?

show_point

Should the point portion of the geom be drawn?

show_interval

Should the interval portion of the geom be drawn?

na.rm

If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

If FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. borders().

geom

Use to override the default connection between stat_slabinterval() and geom_slabinterval()

quantiles

Setting this to a value other than NA will produce a quantile dotplot: that is, a dotplot of quantiles from the sample or distribution (for analytical distributions, the default of NA is taken to mean 100 quantiles). The value of quantiles determines the number of quantiles to plot. See Kay et al. (2016) and Fernandes et al. (2018) for more information on quantile dotplots.

point_interval

A function from the point_interval() family (e.g., median_qi, mean_qi, mode_hdi, etc), or a string giving the name of a function from that family (e.g., "median_qi", "mean_qi", "mode_hdi", etc; if a string, the caller's environment is searched for the function, followed by the ggdist environment). This function determines the point summary (typically mean, median, or mode) and interval type (quantile interval, qi; highest-density interval, hdi; or highest-density continuous interval, hdci). Output will be converted to the appropriate x- or y-based aesthetics depending on the value of orientation. See the point_interval() family of functions for more information.

.width

The .width argument passed to point_interval: a vector of probabilities to use that determine the widths of the resulting intervals. If multiple probabilities are provided, multiple intervals per group are generated, each with a different probability interval (and value of the corresponding .width and level generated variables).

Value

A ggplot2::Geom or ggplot2::Stat representing a dotplot or combined dotplot+interval geometry which can be added to a ggplot() object.

Details

The dots geoms are similar to geom_dotplot() but with a number of differences:

  • Dots geoms act like slabs in geom_slabinterval() and can be given x positions (or y positions when in a horizontal orientation).

  • Given the available space to lay out dots, the dots geoms will automatically determine how many bins to use to fit the available space.

  • Dots geoms use a dynamic layout algorithm that lays out dots from the center out if the input data are symmetrical, guaranteeing that symmetrical data results in a symmetrical plot. The layout algorithm also prevents dots from overlapping each other.

  • The shape of the dots in a in these geoms can be changed using the slab_shape aesthetic (when using the dotsinterval family) or the shape or slab_shape aesthetic (when using the dots family)

stat_dots() and stat_dotsinterval(), when used with the quantiles argument, are particularly useful for constructing quantile dotplots, which can be an effective way to communicate uncertainty using a frequency framing that may be easier for laypeople to understand (Kay et al. 2016, Fernandes et al. 2018).

To visualize sample data, such as a data distribution, samples from a bootstrap distribution, or a Bayesian posterior, you can supply samples to the x or y aesthetic.

To visualize analytical distributions, you can use the xdist or ydist aesthetic. For historical reasons, you can also use dist to specify the distribution, though this is not recommended as it does not work as well with orientation detection. These aesthetics can be used as follows:

  • xdist, ydist, and dist can be any distribution object from the distributional package (dist_normal(), dist_beta(), etc) or can be a posterior::rvar() object. Since these functions are vectorized, other columns can be passed directly to them in an aes() specification; e.g. aes(dist = dist_normal(mu, sigma)) will work if mu and sigma are columns in the input data frame.

  • dist can be a character vector giving the distribution name. Then the arg1, ... arg9 aesthetics (or args as a list column) specify distribution arguments. Distribution names should correspond to R functions that have "p", "q", and "d" functions; e.g. "norm" is a valid distribution name because R defines the pnorm(), qnorm(), and dnorm() functions for Normal distributions.

    See the parse_dist() function for a useful way to generate dist and args values from human-readable distribution specs (like "normal(0,1)"). Such specs are also produced by other packages (like the brms::get_prior function in brms); thus, parse_dist() combined with the stats described here can help you visualize the output of those functions.

Aesthetics

The slab+interval stats and geoms have a wide variety of aesthetics that control the appearance of their three sub-geometries: the slab, the point, and the interval.

These stats support the following aesthetics:

  • x: x position of the geometry (when orientation = "vertical"); or sample data to be summarized (when orientation = "horizontal" with sample data).

  • y: y position of the geometry (when orientation = "horizontal"); or sample data to be summarized (when orientation = "vertical" with sample data).

  • xdist: When using analytical distributions, distribution to map on the x axis: a distributional object (e.g. dist_normal()) or a posterior::rvar() object.

  • ydist: When using analytical distributions, distribution to map on the y axis: a distributional object (e.g. dist_normal()) or a posterior::rvar() object.

  • dist: When using analytical distributions, a name of a distribution (e.g. "norm"), a distributional object (e.g. dist_normal()), or a posterior::rvar() object. See Details.

  • args: Distribution arguments (args or arg1, ... arg9). See Details.

In addition, in their default configuration (paired with geom_dotsinterval()) the following aesthetics are supported by the underlying geom:

Slab-specific aesthetics

  • thickness: The thickness of the slab at each x value (if orientation = "horizontal") or y value (if orientation = "vertical") of the slab.

  • side: Which side to place the slab on. "topright", "top", and "right" are synonyms which cause the slab to be drawn on the top or the right depending on if orientation is "horizontal" or "vertical". "bottomleft", "bottom", and "left" are synonyms which cause the slab to be drawn on the bottom or the left depending on if orientation is "horizontal" or "vertical". "topleft" causes the slab to be drawn on the top or the left, and "bottomright" causes the slab to be drawn on the bottom or the right. "both" draws the slab mirrored on both sides (as in a violin plot).

  • scale: What proportion of the region allocated to this geom to use to draw the slab. If scale = 1, slabs that use the maximum range will just touch each other. Default is 0.9 to leave some space.

  • justification: Justification of the interval relative to the slab, where 0 indicates bottom/left justification and 1 indicates top/right justification (depending on orientation). If justification is NULL (the default), then it is set automatically based on the value of side: when side is "top"/"right" justification is set to 0, when side is "bottom"/"left" justification is set to 1, and when side is "both" justification is set to 0.5.

  • datatype: When using composite geoms directly without a stat (e.g. geom_slabinterval()), datatype is used to indicate which part of the geom a row in the data targets: rows with datatype = "slab" target the slab portion of the geometry and rows with datatype = "interval" target the interval portion of the geometry. This is set automatically when using ggdist stats.

Interval-specific aesthetics

  • xmin: Left end of the interval sub-geometry (if orientation = "horizontal").

  • xmax: Right end of the interval sub-geometry (if orientation = "horizontal").

  • ymin: Lower end of the interval sub-geometry (if orientation = "vertical").

  • ymax: Upper end of the interval sub-geometry (if orientation = "vertical").

Point-specific aesthetics

  • shape: Shape type used to draw the point sub-geometry.

Color aesthetics

  • colour: (or color) The color of the interval and point sub-geometries. Use the slab_color, interval_color, or point_color aesthetics (below) to set sub-geometry colors separately.

  • fill: The fill color of the slab and point sub-geometries. Use the slab_fill or point_fill aesthetics (below) to set sub-geometry colors separately.

  • alpha: The opacity of the slab, interval, and point sub-geometries. Use the slab_alpha, interval_alpha, or point_alpha aesthetics (below) to set sub-geometry colors separately.

  • colour_ramp: (or color_ramp) A secondary scale that modifies the color scale to "ramp" to another color. See scale_colour_ramp() for examples.

  • fill_ramp: A secondary scale that modifies the fill scale to "ramp" to another color. See scale_fill_ramp() for examples.

Line aesthetics

  • size: Width of the outline around the slab (if visible). Also determines the width of the line used to draw the interval and the size of the point, but raw size values are transformed according to the interval_size_domain, interval_size_range, and fatten_point parameters of the geom (see above). Use the slab_size, interval_size, or point_size aesthetics (below) to set sub-geometry line widths separately (note that when size is set directly using the override aesthetics, interval and point sizes are not affected by interval_size_domain, interval_size_range, and fatten_point).

  • stroke: Width of the outline around the point sub-geometry.

  • linetype: Type of line (e.g., "solid", "dashed", etc) used to draw the interval and the outline of the slab (if it is visible). Use the slab_linetype or interval_linetype aesthetics (below) to set sub-geometry line types separately.

Slab-specific color/line override aesthetics

  • slab_fill: Override for fill: the fill color of the slab.

  • slab_colour: (or slab_color) Override for colour/color: the outline color of the slab.

  • slab_alpha: Override for alpha: the opacity of the slab.

  • slab_size: Override for size: the width of the outline of the slab.

  • slab_linetype: Override for linetype: the line type of the outline of the slab.

  • slab_shape: Override for shape: the shape of the dots used to draw the dotplot slab.

Interval-specific color/line override aesthetics

  • interval_colour: (or interval_color) Override for colour/color: the color of the interval.

  • interval_alpha: Override for alpha: the opacity of the interval.

  • interval_size: Override for size: the line width of the interval.

  • interval_linetype: Override for linetype: the line type of the interval.

Point-specific color/line override aesthetics

  • point_fill: Override for fill: the fill color of the point.

  • point_colour: (or point_color) Override for colour/color: the outline color of the point.

  • point_alpha: Override for alpha: the opacity of the point.

  • point_size: Override for size: the size of the point.

Other aesthetics (these work as in standard geoms)

  • width

  • height

  • group

See examples of some of these aesthetics in action in vignette("dotsinterval"). Learn more about the sub-geom override aesthetics (like interval_color) in the scales documentation. Learn more about basic ggplot aesthetics in vignette("ggplot2-specs").

References

Kay, M., Kola, T., Hullman, J. R., & Munson, S. A. (2016). When (ish) is My Bus? User-centered Visualizations of Uncertainty in Everyday, Mobile Predictive Systems. Conference on Human Factors in Computing Systems - CHI '16, 5092--5103. doi: 10.1145/2858036.2858558 .

Fernandes, M., Walls, L., Munson, S., Hullman, J., & Kay, M. (2018). Uncertainty Displays Using Quantile Dotplots or CDFs Improve Transit Decision-Making. Conference on Human Factors in Computing Systems - CHI '18. doi: 10.1145/3173574.3173718 .

See also

See the stat_slabinterval() family for other stats built on top of geom_slabinterval(). See vignette("dotsinterval") for a variety of examples of use.

Author

Matthew Kay

Examples


library(dplyr)
library(ggplot2)

data(RankCorr_u_tau, package = "ggdist")

# orientation is detected automatically based on
# which axis is discrete

RankCorr_u_tau %>%
  ggplot(aes(x = u_tau)) +
  geom_dots()


RankCorr_u_tau %>%
  ggplot(aes(y = u_tau)) +
  geom_dots()


# stat_dots can summarize quantiles, creating quantile dotplots

RankCorr_u_tau %>%
  ggplot(aes(x = u_tau, y = factor(i))) +
  stat_dots(quantiles = 100)


# color and fill aesthetics can be mapped within the geom
# dotsinterval adds an interval

RankCorr_u_tau %>%
  ggplot(aes(x = u_tau, y = factor(i), fill = stat(x > 6))) +
  stat_dotsinterval(quantiles = 100)