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# Scales object encapsulates multiple scales.
# All input and output done with data.frames to facilitate
# multiple input and output variables
scales_list <- function() {
ggproto(NULL, ScalesList)
}
ScalesList <- ggproto("ScalesList", NULL,
scales = NULL,
find = function(self, aesthetic) {
vapply(self$scales, function(x) any(aesthetic %in% x$aesthetics), logical(1))
},
has_scale = function(self, aesthetic) {
any(self$find(aesthetic))
},
add = function(self, scale) {
if (is.null(scale)) {
return()
}
prev_aes <- self$find(scale$aesthetics)
if (any(prev_aes)) {
# Get only the first aesthetic name in the returned vector -- it can
# sometimes be c("x", "xmin", "xmax", ....)
scalename <- self$scales[prev_aes][[1]]$aesthetics[1]
cli::cli_inform(c(
"Scale for {.field {scalename}} is already present.",
"Adding another scale for {.field {scalename}}, which will replace the existing scale."
))
}
# Remove old scale for this aesthetic (if it exists)
self$scales <- c(self$scales[!prev_aes], list(scale))
},
n = function(self) {
length(self$scales)
},
input = function(self) {
unlist(lapply(self$scales, "[[", "aesthetics"))
},
# This actually makes a descendant of self, which is functionally the same
# as a actually clone for most purposes.
clone = function(self) {
ggproto(NULL, self, scales = lapply(self$scales, function(s) s$clone()))
},
non_position_scales = function(self) {
ggproto(NULL, self, scales = self$scales[!self$find("x") & !self$find("y")])
},
get_scales = function(self, output) {
scale <- self$scales[self$find(output)]
if (length(scale) == 0) return()
scale[[1]]
}
)
# Train scale from a data frame
scales_train_df <- function(scales, df, drop = FALSE) {
if (empty(df) || length(scales$scales) == 0) return()
lapply(scales$scales, function(scale) scale$train_df(df = df))
}
# Map values from a data.frame. Returns data.frame
scales_map_df <- function(scales, df) {
if (empty(df) || length(scales$scales) == 0) return(df)
mapped <- unlist(lapply(scales$scales, function(scale) scale$map_df(df = df)), recursive = FALSE)
data_frame0(!!!mapped, df[setdiff(names(df), names(mapped))])
}
# Transform values to cardinal representation
scales_transform_df <- function(scales, df) {
if (empty(df)) return(df)
# if the scale contains no trans or the trans is of identity, it doesn't need
# to be transformed.
idx_skip <- vapply(scales$scales, function(x) {
has_default_transform(x) &&
(is.null(x$trans) || identical(x$trans$transform, identity))
}, logical(1L))
scale_list <- scales$scales[!idx_skip]
if (length(scale_list) == 0L) return(df)
transformed <- unlist(lapply(scale_list, function(s) s$transform_df(df = df)),
recursive = FALSE)
untransformed <- df[setdiff(names(df), names(transformed))]
data_frame0(!!!transformed, untransformed)
}
scales_backtransform_df <- function(scales, df) {
# NOTE: no need to check empty(data) because it should be already checked
# before this function is called.
# if the scale contains no trans or the trans is of identity, it doesn't need
# to be backtransformed.
idx_skip <- vapply(scales$scales, function(x) {
is.null(x$trans) ||
identical(x$trans$inverse, identity)
}, logical(1L))
scale_list <- scales$scales[!idx_skip]
if (length(scale_list) == 0L) return(df)
backtransformed <- unlist(lapply(scale_list, function(scale) {
aesthetics <- intersect(scale$aesthetics, names(df))
if (length(aesthetics) == 0) return()
lapply(df[aesthetics], scale$trans$inverse)
}), recursive = FALSE)
new_data_frame(c(backtransformed, df[setdiff(names(df), names(backtransformed))]))
}
# @param aesthetics A list of aesthetic-variable mappings. The name of each
# item is the aesthetic, and the value of each item is the variable in data.
scales_add_defaults <- function(scales, data, aesthetics, env) {
if (is.null(aesthetics)) return()
names(aesthetics) <- unlist(lapply(names(aesthetics), aes_to_scale))
new_aesthetics <- setdiff(names(aesthetics), scales$input())
# No new aesthetics, so no new scales to add
if (is.null(new_aesthetics)) return()
datacols <- lapply(aesthetics[new_aesthetics], eval_tidy, data = data)
datacols <- compact(datacols)
for (aes in names(datacols)) {
scales$add(find_scale(aes, datacols[[aes]], env))
}
}
# Add missing but required scales.
# @param aesthetics A character vector of aesthetics. Typically c("x", "y").
scales_add_missing <- function(plot, aesthetics, env) {
# Keep only aesthetics that aren't already in plot$scales
aesthetics <- setdiff(aesthetics, plot$scales$input())
for (aes in aesthetics) {
scale_name <- paste("scale", aes, "continuous", sep = "_")
scale_f <- find_global(scale_name, env, mode = "function")
plot$scales$add(scale_f())
}
}
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