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#' Bin data for dot plot.
#'
#' Missing values are currently silently dropped.
#' If weights are used, they must be integer values.
#'
#' @section Aesthetics:
#' \Sexpr[results=rd,stage=build]{ggplot2:::rd_aesthetics("stat", "bindot")}
#'
#' @inheritParams stat_identity
#' @param binaxis The axis to bin along, "x" (default) or "y"
#' @param method "dotdensity" (default) for dot-density binning, or
#' "histodot" for fixed bin widths (like stat_bin)
#' @param binwidth When \code{method} is "dotdensity, this specifies maximum bin width.
#' When \code{method} is "histodot", this specifies bin width.
#' Defaults to 1/30 of the range of the data
#' @param binpositions When \code{method} is "dotdensity", "bygroup" (default)
#' determines positions of the bins for each group separately. "all" determines
#' positions of the bins with all the data taken together; this is used for
#' aligning dot stacks across multiple groups.
#' @param origin When \code{method} is "histodot", origin of first bin
#' @param right When \code{method} is "histodot", should intervals be closed
#' on the right (a, b], or not [a, b)
#' @param width When \code{binaxis} is "y", the spacing of the dot stacks
#' for dodging.
#' @param na.rm If \code{FALSE} (the default), removes missing values with
#' a warning. If \code{TRUE} silently removes missing values.
#' @param drop If TRUE, remove all bins with zero counts
#'
#' @return New data frame with additional columns:
#' \item{x}{center of each bin, if binaxis is "x"}
#' \item{y}{center of each bin, if binaxis is "x"}
#' \item{binwidth}{max width of each bin if method is "dotdensity";
#' width of each bin if method is "histodot"}
#' \item{count}{number of points in bin}
#' \item{ncount}{count, scaled to maximum of 1}
#' \item{density}{density of points in bin, scaled to integrate to 1,
#' if method is "histodot"}
#' \item{ndensity}{density, scaled to maximum of 1, if method is "histodot"}
#' @seealso See \code{\link{geom_dotplot}} for examples.
#' @export
#' @examples
#' # See geom_dotplot for examples
#'
stat_bindot <- function (mapping = NULL, data = NULL, geom = "dotplot", position = "identity",
binwidth = NULL, origin = NULL, width = 0.9, binaxis = "x", method = "dotdensity",
binpositions = "bygroup", drop = FALSE, right = TRUE, na.rm = FALSE, ...) {
StatBindot$new(mapping = mapping, data = data, geom = geom, position = position,
binwidth = binwidth, origin = origin, width = width, binaxis = binaxis,
method = method, binpositions = binpositions, drop = drop, right = right,
na.rm = na.rm, ...)
}
StatBindot <- proto(Stat, {
objname <- "bindot"
informed <- FALSE
calculate_groups <- function(., data, na.rm = FALSE, binwidth = NULL, binaxis = "x",
method = "dotdensity", binpositions = "bygroup", ...) {
data <- remove_missing(data, na.rm, c(binaxis, "weight"), name="stat_bindot",
finite = TRUE)
.$informed <- FALSE
# If using dotdensity and binning over all, we need to find the bin centers
# for all data before it's split into groups.
if (method == "dotdensity" && binpositions == "all") {
if (binaxis == "x") {
newdata <- densitybin(x = data$x, weight = data$weight, binwidth = binwidth,
method = method)
data <- arrange(data, x)
newdata <- arrange(newdata, x)
} else if (binaxis == "y") {
newdata <- densitybin(x = data$y, weight = data$weight, binwidth = binwidth,
method = method)
data <- arrange(data, y)
newdata <- arrange(newdata, x)
}
data$bin <- newdata$bin
data$binwidth <- newdata$binwidth
data$weight <- newdata$weight
data$bincenter <- newdata$bincenter
}
.super$calculate_groups(., data, binwidth = binwidth, binaxis = binaxis,
method = method, binpositions = binpositions, ...)
}
calculate <- function(., data, scales, binwidth = NULL, binaxis = "x",
method = "dotdensity", binpositions = "bygroup",
origin = NULL, breaks = NULL, width = 0.9, drop = FALSE,
right = TRUE, ...) {
# This function taken from integer help page
is.wholenumber <- function(x, tol = .Machine$double.eps^0.5) {
abs(x - round(x)) < tol
}
# Check that weights are whole numbers (for dots, weights must be whole)
if (!is.null(data$weight) && any(!is.wholenumber(data$weight)) &&
any(data$weight < 0)) {
stop("Weights for stat_bindot must be nonnegative integers.")
}
if (binaxis == "x") {
range <- scale_dimension(scales$x, c(0, 0))
values <- data$x
} else if (binaxis == "y") {
range <- scale_dimension(scales$y, c(0, 0))
values <- data$y
# The middle of each group, on the stack axis
midline <- mean(range(data$x))
}
if (is.null(breaks) && is.null(binwidth) && !is.integer(values) && !.$informed) {
message("stat_bindot: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.")
.$informed <- TRUE
}
if(method == "histodot") {
# Use the function from stat_bin
data <- bin(x = values, weight = data$weight, binwidth = binwidth, origin = origin,
breaks=breaks, range = range, width = width, drop = drop, right = right)
# Change "width" column to "binwidth" for consistency
names(data)[names(data) == "width"] <- "binwidth"
names(data)[names(data) == "x"] <- "bincenter"
} else if (method == "dotdensity") {
# If bin centers are found by group instead of by all, find the bin centers
# (If binpositions=="all", then we'll already have bin centers.)
if (binpositions == "bygroup")
data <- densitybin(x = values, weight = data$weight, binwidth = binwidth,
method = method, range = range)
# Collapse each bin and get a count
data <- ddply(data, .(bincenter), summarise, binwidth = binwidth[1], count = sum(weight))
if (sum(data$count, na.rm = TRUE) != 0) {
data$count[is.na(data$count)] <- 0
data$ncount <- data$count / max(abs(data$count), na.rm = TRUE)
if (drop) data <- subset(data, count > 0)
}
}
if (binaxis == "x") {
names(data)[names(data) == "bincenter"] <- "x"
# For x binning, the width of the geoms is same as the width of the bin
data$width <- data$binwidth
} else if (binaxis == "y") {
names(data)[names(data) == "bincenter"] <- "y"
# For y binning, set the x midline. This is needed for continuous x axis
data$x <- midline
}
return(data)
}
default_aes <- function(.) aes(y = ..count..)
required_aes <- c("x")
default_geom <- function(.) GeomDotplot
})
# This does density binning, but does not collapse each bin with a count.
# It returns a data frame with the original data (x), weights, bin #, and the bin centers.
densitybin <- function(x, weight = NULL, binwidth = NULL, method = method, range = NULL) {
if (length(na.omit(x)) == 0) return(data.frame())
if (is.null(weight)) weight <- rep(1, length(x))
weight[is.na(weight)] <- 0
if (is.null(range)) range <- range(x, na.rm = TRUE, finite = TRUE)
if (is.null(binwidth)) binwidth <- diff(range) / 30
# Sort weight and x, by x
weight <- weight[order(x)]
x <- x[order(x)]
cbin <- 0 # Current bin ID
bin <- rep.int(NA, length(x)) # The bin ID for each observation
binend <- -Inf # End position of current bin (scan left to right)
# Scan list and put dots in bins
for (i in 1:length(x)) {
# If past end of bin, start a new bin at this point
if (x[i] >= binend) {
binend <- x[i] + binwidth
cbin <- cbin + 1
}
bin[i] <- cbin
}
results <- data.frame(x, bin, binwidth, weight)
results <- ddply(results, .(bin), function(df) {
df$bincenter = (min(df$x) + max(df$x)) / 2
return(df)
})
return(results)
}
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