geom_lvplot: Side-by-side LV boxplots.
In heike/gglvplot: Letter Value Plots in ggplot2

Description Usage Arguments Computed/reported variables References See Also Examples

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.

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

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

`mapping`	Set of aesthetic mappings created by `aes` or `aes_`. If specified and `inherit.aes = TRUE` (the default), is combined with the default mapping at the top level of the plot. You only need to supply `mapping` if there isn't a mapping defined for the plot.
`data`	A data frame. If specified, overrides the default data frame defined at the top level of the plot.
`position`	Position adjustment, either as a string, or the result of a call to a position adjustment function.
`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).
`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 shouldn't inherit behaviour from the default plot specification, e.g. `borders`.
`...`	other arguments passed on to `layer`. There are three types of arguments you can use here: Aesthetics: to set an aesthetic to a fixed value, like `color = "red"` or `size = 3`. Other arguments to the layer, for example you override the default `stat` associated with the layer. Other arguments passed on to the stat.
`geom,stat`	Use to override the default connection between `geom_lvplot` and `stat_lvplot`.
`conf`	confidence level
`percent`	numeric value: percent of data in outliers
`k`	number of letter values shown

k: Number of Letter Values used for the display
LV: Name of the Letter Value
width: width of the interquartile box

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

stat_quantile to view quantiles conditioned on a continuous variable.

library(ggplot2)
p <- ggplot(mpg, aes(class, hwy))
p + geom_lvplot(aes(fill=..LV..)) + scale_fill_brewer()
p + geom_lvplot() + geom_jitter(width = 0.2)
p + geom_lvplot(alpha=1, aes(fill=..LV..)) + scale_fill_brewer()

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

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

## Not run: 
# just for now: read On_Time data into object ot from lvplot paper
library(RColorBrewer)
cols <- c("white",brewer.pal(9, "Greys"), "Red", "Pink")
reds <- brewer.pal(5, "Reds")[-1]
cols <- c("grey50", cols[2:3], reds[1], cols[4:6], reds[2], cols[7:9], reds[3], cols[10:11])

ggplot(data=ot) + geom_lvplot(aes(x=UniqueCarrier, y=sqrt(TaxiOut+TaxiIn),
  fill=..LV..), alpha=1) +
  scale_fill_manual(values=cols)
ggplot(data=ot) + geom_lvplot(aes(x=factor(DayOfWeek), y=sqrt(TaxiOut+TaxiIn),
  fill=..LV..), alpha=1) +
  scale_fill_manual(values=cols) + facet_wrap(~UniqueCarrier)

## End(Not run)