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#' A quantile-quantile plot
#'
#' `geom_qq` and `stat_qq` produce quantile-quantile plots. `geom_qq_line` and
#' `stat_qq_line` compute the slope and intercept of the line connecting the
#' points at specified quartiles of the theoretical and sample distributions.
#'
#' @eval rd_aesthetics("stat", "qq")
#' @eval rd_aesthetics("stat", "qq_line")
#' @param distribution Distribution function to use, if x not specified
#' @param dparams Additional parameters passed on to `distribution`
#' function.
#' @inheritParams layer
#' @inheritParams geom_point
#' @section Computed variables:
#' Variables computed by `stat_qq`:
#' \describe{
#' \item{sample}{sample quantiles}
#' \item{theoretical}{theoretical quantiles}
#' }
#' Variables computed by `stat_qq_line`:
#' \describe{
#' \item{x}{x-coordinates of the endpoints of the line segment connecting the
#' points at the chosen quantiles of the theoretical and the sample
#' distributions}
#' \item{y}{y-coordinates of the endpoints}
#' }
#' @export
#' @examples
#' \donttest{
#' df <- data.frame(y = rt(200, df = 5))
#' p <- ggplot(df, aes(sample = y))
#' p + stat_qq() + stat_qq_line()
#'
#' # Use fitdistr from MASS to estimate distribution params
#' params <- as.list(MASS::fitdistr(df$y, "t")$estimate)
#' ggplot(df, aes(sample = y)) +
#' stat_qq(distribution = qt, dparams = params["df"]) +
#' stat_qq_line(distribution = qt, dparams = params["df"])
#'
#' # Using to explore the distribution of a variable
#' ggplot(mtcars, aes(sample = mpg)) +
#' stat_qq() +
#' stat_qq_line()
#' ggplot(mtcars, aes(sample = mpg, colour = factor(cyl))) +
#' stat_qq() +
#' stat_qq_line()
#' }
geom_qq <- function(mapping = NULL, data = NULL,
geom = "point", position = "identity",
...,
distribution = stats::qnorm,
dparams = list(),
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE) {
layer(
data = data,
mapping = mapping,
stat = StatQq,
geom = geom,
position = position,
show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(
distribution = distribution,
dparams = dparams,
na.rm = na.rm,
...
)
)
}
#' @export
#' @rdname geom_qq
stat_qq <- geom_qq
#' @rdname ggplot2-ggproto
#' @format NULL
#' @usage NULL
#' @export
StatQq <- ggproto("StatQq", Stat,
default_aes = aes(y = stat(sample), x = stat(theoretical)),
required_aes = c("sample"),
compute_group = function(data, scales, quantiles = NULL,
distribution = stats::qnorm, dparams = list(),
na.rm = FALSE) {
sample <- sort(data$sample)
n <- length(sample)
# Compute theoretical quantiles
if (is.null(quantiles)) {
quantiles <- stats::ppoints(n)
} else {
stopifnot(length(quantiles) == n)
}
theoretical <- do.call(distribution, c(list(p = quote(quantiles)), dparams))
new_data_frame(list(sample = sample, theoretical = theoretical))
}
)