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#' @rdname ggplot2-ggproto
#' @format NULL
#' @usage NULL
#' @export
StatBindot <- ggproto("StatBindot", Stat,
required_aes = "x",
non_missing_aes = "weight",
default_aes = aes(y = after_stat(count)),
dropped_aes = c("bin", "bincenter"), # these are temporary variables that are created and then removed by the stat
setup_params = function(data, params) {
if (is.null(params$binwidth)) {
cli::cli_inform("Bin width defaults to 1/30 of the range of the data. Pick better value with {.arg binwidth}.")
}
params
},
compute_layer = function(self, data, params, layout) {
data <- remove_missing(data, params$na.rm, name = snake_class(self))
ggproto_parent(Stat, self)$compute_layer(data, params, layout)
},
compute_panel = function(self, data, scales, na.rm = FALSE, binwidth = NULL,
binaxis = "x", method = "dotdensity",
binpositions = "bygroup", origin = NULL,
width = 0.9, drop = FALSE,
right = TRUE) {
# If using dotdensity and binning over all, we need to find the bin centers
# for all data before it's split into groups.
if (method == "dotdensity" && binpositions == "all") {
if (binaxis == "x") {
newdata <- densitybin(x = data$x, weight = data$weight, binwidth = binwidth,
method = method)
data <- data[order(data$x), ]
newdata <- newdata[order(newdata$x), ]
} else if (binaxis == "y") {
newdata <- densitybin(x = data$y, weight = data$weight, binwidth = binwidth,
method = method)
data <- data[order(data$y), ]
newdata <- newdata[order(newdata$x), ]
}
data$bin <- newdata$bin
data$binwidth <- newdata$binwidth
data$weight <- newdata$weight
data$bincenter <- newdata$bincenter
}
ggproto_parent(Stat, self)$compute_panel(data, scales, binwidth = binwidth,
binaxis = binaxis, method = method, binpositions = binpositions,
origin = origin, width = width, drop = drop,
right = right)
},
compute_group = function(self, data, scales, binwidth = NULL, binaxis = "x",
method = "dotdensity", binpositions = "bygroup",
origin = NULL, width = 0.9, drop = FALSE,
right = TRUE) {
# Check that weights are whole numbers (for dots, weights must be whole)
if (!is.null(data$weight) && !(is_integerish(data$weight) && all(data$weight >= 0))) {
cli::cli_abort("Weights must be nonnegative integers.")
}
if (binaxis == "x") {
range <- scales$x$dimension()
values <- data$x
} else if (binaxis == "y") {
range <- scales$y$dimension()
values <- data$y
# The middle of each group, on the stack axis
midline <- mean(range(data$x))
}
if (method == "histodot") {
closed <- if (right) "right" else "left"
if (!is.null(binwidth)) {
bins <- bin_breaks_width(range, binwidth, boundary = origin, closed = closed)
} else {
bins <- bin_breaks_bins(range, 30, boundary = origin, closed = closed)
}
data <- bin_vector(values, bins, weight = data$weight, pad = FALSE)
# Change "width" column to "binwidth" for consistency
names(data)[names(data) == "width"] <- "binwidth"
names(data)[names(data) == "x"] <- "bincenter"
} else if (method == "dotdensity") {
# If bin centers are found by group instead of by all, find the bin centers
# (If binpositions=="all", then we'll already have bin centers.)
if (binpositions == "bygroup")
data <- densitybin(x = values, weight = data$weight, binwidth = binwidth,
method = method, range = range)
# Collapse each bin and get a count
data <- dapply(data, "bincenter", function(x) {
data_frame0(
binwidth = .subset2(x, "binwidth")[1],
count = sum(.subset2(x, "weight")),
.size = 1
)
})
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$x <- midline
}
return(data)
}
)
# This does density binning, but does not collapse each bin with a count.
# It returns a data frame with the original data (x), weights, bin #, and the bin centers.
densitybin <- function(x, weight = NULL, binwidth = NULL, method = method, range = NULL) {
if (length(stats::na.omit(x)) == 0) return(data_frame0())
if (is.null(weight)) weight <- rep(1, length(x))
weight[is.na(weight)] <- 0
if (is.null(range)) range <- range(x, na.rm = TRUE, finite = TRUE)
if (is.null(binwidth)) binwidth <- diff(range) / 30
# Sort weight and x, by x
weight <- weight[order(x)]
x <- x[order(x)]
cbin <- 0 # Current bin ID
bin <- rep.int(NA, length(x)) # The bin ID for each observation
binend <- -Inf # End position of current bin (scan left to right)
# Scan list and put dots in bins
for (i in 1:length(x)) {
# If past end of bin, start a new bin at this point
if (x[i] >= binend) {
binend <- x[i] + binwidth
cbin <- cbin + 1
}
bin[i] <- cbin
}
results <- data_frame0(
x = x,
bin = bin,
binwidth = binwidth,
weight = weight,
.size = length(x)
)
results <- dapply(results, "bin", function(df) {
df$bincenter = (min(df$x) + max(df$x)) / 2
return(df)
})
return(results)
}
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