Number of observations in quadrants

stat_quadrant_counts() counts the number of observations in each quadrant of a plot panel. By default it adds a text label to the far corner of each quadrant. It can also be used to obtain the total number of observations in each of two pairs of quadrants or in the whole panel. Grouping is ignored, so en every case a single count is computed for each quadrant in a plot panel.

stat_quadrant_counts(
  mapping = NULL,
  data = NULL,
  geom = "text_npc",
  position = "identity",
  quadrants = NULL,
  pool.along = c("none", "x", "y", "xy"),
  xintercept = 0,
  yintercept = 0,
  label.x = NULL,
  label.y = NULL,
  digits = 2,
  na.rm = FALSE,
  show.legend = FALSE,
  inherit.aes = TRUE,
  ...
)

Arguments

mapping: The aesthetic mapping, usually constructed with aes or aes_. Only needs to be set at the layer level if you are overriding the plot defaults.
data: A layer specific dataset - only needed if you want to override the plot defaults.
geom: The geometric object to use display the data
position: The position adjustment to use on this layer
quadrants: integer vector indicating which quadrants are of interest, with a OL indicating the whole plot.
pool.along: character, one of "none", "x" or "y", indicating which quadrants to pool to calculate counts by pair of quadrants.
xintercept, yintercept: numeric the coordinates of the origin of the quadrants.
label.x, label.y: numeric Coordinates (in npc units) to be used for absolute positioning of the labels.
digits: integer Number of digits for fraction and percent labels.
na.rm: a logical indicating whether NA values should be stripped before the computation proceeds.
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.
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 should not inherit behaviour from the default plot specification, e.g., borders.
...: other arguments passed on to layer. This can include aesthetics whose values you want to set, not map. See layer for more details.

Value

A plot layer instance. Using as output data the counts of observations per plot quadrant.

Details

This statistic can be used to automatically count observations in each of the four quadrants of a plot, and by default add these counts as text labels. Values exactly equal to xintercept or yintercept are counted together with those larger than the intercepts. An argument value of zero, passed to formal parameter quadrants is interpreted as a request for the count of all observations in each plot panel.

The default origin of quadrants is at xintercept = 0, yintercept = 0. Also by default, counts are computed for all quadrants within the x and y scale limits, but ignoring any marginal scale expansion. The default positions of the labels is in the farthest corner or edge of each quadrant using npc coordinates. Consequently, when using facets even with free limits for x and y axes, the location of the labels is consistent across panels. This is achieved by use of geom = "text_npc" or geom = "label_npc". To pass the positions in native data units, pass geom = "text" explicitly as argument.

Computed variables

Data frame with one to four rows, one for each quadrant for which counts are counted in data.

quadrant: integer, one of 0:4
x: x value of label position in data units
y: y value of label position in data units
npcx: x value of label position in npc units
npcy: y value of label position in npc units
count: number of observations in the quadrant(s)
total: number of onservations in data
count.label: number of observations as character
pc.label: percent of observations as character
fr.label: fraction of observations as character

As shown in one example below geom_debug can be used to print the computed values returned by any statistic. The output shown includes also values mapped to aesthetics, like label in the example.

Examples


# generate artificial data
set.seed(4321)
x <- -50:50
y <- rnorm(length(x), mean = 0)
my.data <- data.frame(x, y)

# using automatically generated text labels, default origin at (0, 0)

ggplot(my.data, aes(x, y)) +
  geom_point() +
  geom_quadrant_lines() +
  stat_quadrant_counts()


ggplot(my.data, aes(x, y)) +
  geom_point() +
  geom_quadrant_lines() +
  stat_quadrant_counts(aes(label = after_stat(pc.label)))


ggplot(my.data, aes(x, y)) +
  geom_point() +
  geom_quadrant_lines() +
  stat_quadrant_counts(aes(label = after_stat(fr.label)))


ggplot(my.data, aes(x, y)) +
  geom_point() +
  geom_quadrant_lines() +
  stat_quadrant_counts(aes(label = after_stat(dec.label)))


ggplot(my.data, aes(x, y)) +
 geom_point() +
  geom_quadrant_lines() +
  stat_quadrant_counts(aes(label = sprintf("%i observations", after_stat(count)))) +
  scale_y_continuous(expand = expansion(c(0.05, 0.15))) # reserve space


# user specified origin

ggplot(my.data, aes(x, y)) +
  geom_quadrant_lines(colour = "blue", xintercept = 10, yintercept = -1) +
  stat_quadrant_counts(colour = "blue", xintercept = 10, yintercept = -1) +
  geom_point() +
  scale_y_continuous(expand = expansion(mult = 0.15))


ggplot(my.data, aes(x, y)) +
  geom_quadrant_lines(colour = "blue", xintercept = 10, yintercept = -1) +
  stat_quadrant_counts(aes(label = after_stat(pc.label)),
                       colour = "blue", xintercept = 10, yintercept = -1) +
  geom_point() +
  scale_y_continuous(expand = expansion(mult = 0.15))


# more digits in labels

ggplot(my.data, aes(x, y)) +
  geom_quadrant_lines(colour = "blue", xintercept = 10, yintercept = -1) +
  stat_quadrant_counts(aes(label = after_stat(pc.label)), digits = 3,
                       colour = "blue", xintercept = 10, yintercept = -1) +
  geom_point() +
  scale_y_continuous(expand = expansion(mult = 0.15))


ggplot(my.data, aes(x, y)) +
  geom_quadrant_lines(colour = "blue", xintercept = 10, yintercept = -1) +
  stat_quadrant_counts(aes(label = after_stat(fr.label)),
                       colour = "blue", xintercept = 10, yintercept = -1) +
  geom_point() +
  scale_y_continuous(expand = expansion(mult = 0.15))


# grouped quadrants

ggplot(my.data, aes(x, y)) +
  geom_quadrant_lines(colour = "blue",
                      pool.along = "x") +
  stat_quadrant_counts(colour = "blue", label.x = "right",
                       pool.along = "x") +
  geom_point() +
  scale_y_continuous(expand = expansion(mult = 0.15))


# whole panel

ggplot(my.data, aes(x, y)) +
  geom_point() +
  stat_quadrant_counts(quadrants = 0, label.x = "left", label.y = "bottom") +
  scale_y_continuous(expand = expansion(mult = c(0.15, 0.05)))


# use a different geometry

ggplot(my.data, aes(x, y)) +
  geom_point() +
  stat_quadrant_counts(geom = "text") # use geom_text()


# Numeric values can be used to build labels with alternative formats
# Here with sprintf(), but paste() and format() also work.

ggplot(my.data, aes(x, y)) +
  geom_quadrant_lines(colour = "blue") +
  stat_quadrant_counts(aes(label = sprintf("%i of %i genes",
                       after_stat(count), after_stat(total))),
                       colour = "blue") +
  geom_point() +
  scale_y_continuous(expand = expansion(mult = 0.15))


# We use geom_debug() to see the computed values

gginnards.installed <- requireNamespace("gginnards", quietly = TRUE)
if (gginnards.installed) {
  library(gginnards)

  ggplot(my.data, aes(x, y)) +
    geom_point() +
    stat_quadrant_counts(geom = "debug")

  ggplot(my.data, aes(x, y)) +
    geom_point() +
    stat_quadrant_counts(geom = "debug", xintercept = 50)
}

#> [1] "PANEL 1; group(s) NULL; 'draw_function()' input 'data' (head):"
#>   npcx npcy label PANEL   x         y quadrant count total count.label pc.label
#> 1 0.95 0.95   n=1     1  50  2.080248        1     1   101         n=1     p=1%
#> 2 0.05 0.05  n=42     1 -50 -2.450016        3    42   101        n=42    p=42%
#> 3 0.05 0.95  n=58     1 -50  2.080248        4    58   101        n=58    p=57%
#> 4 0.95 0.05   n=0     1  50 -2.450016        2     0   101         n=0     p=0%
#>   dec.label fr.label xintercept yintercept
#> 1    f=0.01  1 / 101         50          0
#> 2    f=0.42 42 / 101         50          0
#> 3    f=0.57 58 / 101         50          0
#> 4    f=0.00  0 / 101         50          0

Arguments

Value

Details

Computed variables

See also

Examples