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#' @rdname geom_boxplot
#' @param coef Length of the whiskers as multiple of IQR. Defaults to 1.5.
#' @inheritParams stat_identity
#' @export
#' @eval rd_computed_vars(
#' .details = "`stat_boxplot()` provides the following variables, some of
#' which depend on the orientation:",
#' width = "width of boxplot.",
#' "ymin|xmin" = "lower whisker = smallest observation greater than or equal
#' to lower hinger - 1.5 * IQR.",
#' "lower|xlower" = "lower hinge, 25% quantile.",
#' notchlower = "lower edge of notch = median - 1.58 * IQR / sqrt(n).",
#' "middle|xmiddle" = "median, 50% quantile.",
#' notchupper = "upper edge of notch = median + 1.58 * IQR / sqrt(n).",
#' "upper|xupper" = "upper hinge, 75% quantile.",
#' "ymax|xmax" = "upper whisker = largest observation less than or equal to
#' upper hinger + 1.5 * IQR."
#' )
stat_boxplot <- function(mapping = NULL, data = NULL,
geom = "boxplot", position = "dodge2",
...,
coef = 1.5,
na.rm = FALSE,
orientation = NA,
show.legend = NA,
inherit.aes = TRUE) {
layer(
data = data,
mapping = mapping,
stat = StatBoxplot,
geom = geom,
position = position,
show.legend = show.legend,
inherit.aes = inherit.aes,
params = list2(
na.rm = na.rm,
orientation = orientation,
coef = coef,
...
)
)
}
#' @rdname ggplot2-ggproto
#' @format NULL
#' @usage NULL
#' @export
StatBoxplot <- ggproto("StatBoxplot", Stat,
required_aes = c("y|x"),
non_missing_aes = "weight",
# either the x or y aesthetic will get dropped during
# statistical transformation, depending on the orientation
dropped_aes = c("x", "y", "weight"),
setup_data = function(self, data, params) {
data <- flip_data(data, params$flipped_aes)
data$x <- data$x %||% 0
data <- remove_missing(
data,
na.rm = params$na.rm,
vars = "x",
name = "stat_boxplot"
)
flip_data(data, params$flipped_aes)
},
setup_params = function(self, data, params) {
params$flipped_aes <- has_flipped_aes(data, params, main_is_orthogonal = TRUE,
group_has_equal = TRUE,
main_is_optional = TRUE)
data <- flip_data(data, params$flipped_aes)
has_x <- !(is.null(data$x) && is.null(params$x))
has_y <- !(is.null(data$y) && is.null(params$y))
if (!has_x && !has_y) {
cli::cli_abort("{.fn {snake_class(self)}} requires an {.field x} or {.field y} aesthetic.")
}
params$width <- params$width %||% (resolution(data$x %||% 0, discrete = TRUE) * 0.75)
if (!is_mapped_discrete(data$x) && is.double(data$x) && !has_groups(data) && any(data$x != data$x[1L])) {
cli::cli_warn(c(
"Continuous {.field {flipped_names(params$flipped_aes)$x}} aesthetic",
"i" = "did you forget {.code aes(group = ...)}?"
))
}
params
},
extra_params = c("na.rm", "orientation"),
compute_group = function(data, scales, width = NULL, na.rm = FALSE, coef = 1.5, flipped_aes = FALSE) {
data <- flip_data(data, flipped_aes)
qs <- c(0, 0.25, 0.5, 0.75, 1)
if (!is.null(data$weight)) {
mod <- quantreg::rq(y ~ 1, weights = weight, data = data, tau = qs)
stats <- as.numeric(stats::coef(mod))
} else {
stats <- as.numeric(stats::quantile(data$y, qs))
}
names(stats) <- c("ymin", "lower", "middle", "upper", "ymax")
iqr <- diff(stats[c(2, 4)])
outliers <- data$y < (stats[2] - coef * iqr) | data$y > (stats[4] + coef * iqr)
if (any(outliers)) {
stats[c(1, 5)] <- range(c(stats[2:4], data$y[!outliers]), na.rm = TRUE)
}
if (vec_unique_count(data$x) > 1)
width <- diff(range(data$x)) * 0.9
df <- data_frame0(!!!as.list(stats))
df$outliers <- list(data$y[outliers])
if (is.null(data$weight)) {
n <- sum(!is.na(data$y))
} else {
# Sum up weights for non-NA positions of y and weight
n <- sum(data$weight[!is.na(data$y) & !is.na(data$weight)])
}
df$notchupper <- df$middle + 1.58 * iqr / sqrt(n)
df$notchlower <- df$middle - 1.58 * iqr / sqrt(n)
df$x <- if (is.factor(data$x)) data$x[1] else mean(range(data$x))
df$width <- width
df$relvarwidth <- sqrt(n)
df$flipped_aes <- flipped_aes
flip_data(df, flipped_aes)
}
)
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