geom_lv {lvplot}

Side-by-side LV boxplots with ggplot2.

Description

An extension of standard boxplots which draws k letter statistics. Conventional boxplots (Tukey 1977) are useful displays for conveying rough information about the central 50% of the data and the extent of the data. For moderate-sized data sets (n < 1000), detailed estimates of tail behavior beyond the quartiles may not be trustworthy, so the information provided by boxplots is appropriately somewhat vague beyond the quartiles, and the expected number of “outliers” and “far-out” values for a Gaussian sample of size n is often less than 10 (Hoaglin, Iglewicz, and Tukey 1986). Large data sets (n \approx 10,000-100,000) afford more precise estimates of quantiles in the tails beyond the quartiles and also can be expected to present a large number of “outliers” (about 0.4 + 0.007 n). The letter-value box plot addresses both these shortcomings: it conveys more detailed information in the tails using letter values, only out to the depths where the letter values are reliable estimates of their corresponding quantiles (corresponding to tail areas of roughly 2^{-i}); “outliers” are defined as a function of the most extreme letter value shown. All aspects shown on the letter-value boxplot are actual observations, thus remaining faithful to the principles that governed Tukey's original boxplot.

Usage

geom_lv(
  mapping = NULL,
  data = NULL,
  stat = "lv",
  position = "dodge",
  outlier.colour = "black",
  outlier.shape = 19,
  outlier.size = 1.5,
  outlier.stroke = 0.5,
  na.rm = TRUE,
  varwidth = FALSE,
  width.method = "linear",
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

GeomLv

scale_fill_lv(...)

stat_lv(
  mapping = NULL,
  data = NULL,
  geom = "lv",
  position = "dodge",
  na.rm = TRUE,
  conf = 0.95,
  percent = NULL,
  k = NULL,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

StatLv

Arguments

mapping

Set of aesthetic mappings created by 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)).

position

Position adjustment, either as a string naming the adjustment (e.g. "jitter" to use position_jitter), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.

outlier.colour

Override aesthetics used for the outliers. Defaults come from geom_point().

outlier.shape

Override aesthetics used for the outliers. Defaults come from geom_point().

outlier.size

Override aesthetics used for the outliers. Defaults come from geom_point().

outlier.stroke

Override aesthetics used for the outliers. Defaults come from geom_point().

na.rm

If FALSE (the default), removes missing values with a warning. If TRUE silently removes missing values.

varwidth

if FALSE (default) draw boxes that are the same size for each group. If TRUE, boxes are drawn with widths proportional to the square-roots of the number of observations in the groups (possibly weighted, using the weight aesthetic).

width.method

character, one of 'linear' (default), 'area', or 'height'. This parameter determines whether the width of the box for letter value LV(i) should be proportional to i (linear), proportional to $2^-i$ (height), or whether the area of the box should be proportional to $2^-i$ (area).

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().

...

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

geom, stat

Use to override the default connection between geom_lv and stat_lv.

conf

confidence level

percent

numeric value: percent of data in outliers

k

number of letter values shown

Format

An object of class GeomLv (inherits from Geom, ggproto, gg) of length 6.

An object of class StatLv (inherits from Stat, ggproto, gg) of length 5.

Computed/reported variables

k

Number of Letter Values used for the display

LV

Name of the Letter Value

width

width of the interquartile box

References

McGill, R., Tukey, J. W. and Larsen, W. A. (1978) Variations of box plots. The American Statistician 32, 12-16.

See Also

stat_quantile to view quantiles conditioned on a continuous variable.

Examples

library(ggplot2)
p <- ggplot(mpg, aes(class, hwy))
p + geom_lv(aes(fill = after_stat(LV))) + scale_fill_brewer()
p + geom_lv() + geom_jitter(width = 0.2)
p + geom_lv(aes(fill = after_stat(LV))) + scale_fill_lv()

# Outliers
p + geom_lv(varwidth = TRUE, aes(fill = after_stat(LV))) + scale_fill_lv()
p + geom_lv(fill = "grey80", colour = "black")
p + geom_lv(outlier.colour = "red", outlier.shape = 1)

# Plots are automatically dodged when any aesthetic is a factor
p + geom_lv(aes(fill = drv))

# varwidth adjusts the width of the boxes according to the number of observations
ggplot(ontime, aes(UniqueCarrier, TaxiIn + TaxiOut)) +
  geom_lv(aes(fill = after_stat(LV)), varwidth=TRUE) +
  scale_fill_lv() +
  scale_y_sqrt() +
  theme_bw()

ontime$DayOfWeek <- as.POSIXlt(ontime$FlightDate)$wday
ggplot(ontime, aes(factor(DayOfWeek), TaxiIn + TaxiOut)) +
  geom_lv(aes(fill = after_stat(LV))) +
  scale_fill_lv() +
  scale_y_sqrt() +
  theme_bw()