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#' @importFrom ggplot2 ggplot_build geom_blank waiver
#' @importFrom utils packageVersion
#' @export
ggplot_build.gganim <- function(plot) {
plot <- plot_clone(plot)
if (length(plot$layers) == 0) {
plot <- plot + geom_blank()
}
scene <- create_scene(plot$transition, plot$view, plot$shadow, plot$ease, plot$transmuters, plot$nframes)
layers <- plot$layers
layer_data <- lapply(layers, function(y) y$layer_data(plot$data))
scales <- plot$scales
# Extract scale names and merge it with label list
scale_labels <- lapply(scales$scales, `[[`, 'name')
names(scale_labels) <- vapply(scales$scales, function(sc) sc$aesthetics[1], character(1))
lapply(scales$scales, function(sc) sc$name <- waiver())
scale_labels <- scale_labels[!vapply(scale_labels, is.waive, logical(1))]
plot$labels[names(scale_labels)] <- scale_labels
# Apply function to layer and matching data
by_layer <- function(f) {
out <- vector("list", length(data))
for (i in seq_along(data)) {
out[[i]] <- f(l = layers[[i]], d = data[[i]])
}
out
}
# Allow all layers to make any final adjustments based
# on raw input data and plot info
data <- layer_data
if (packageVersion("ggplot2") > "3.1.1") {
# ggplot2 versions 3.1.0 or earlier do not support `setup_layer()`
data <- by_layer(function(l, d) l$setup_layer(d, plot))
}
# Initialise panels, add extra data for margins & missing faceting
# variables, and add on a PANEL variable to data
layout <- create_layout(plot$facet, plot$coordinates)
data <- layout$setup(data, plot$data, plot$plot_env)
scene$setup(data)
# Compute aesthetics to produce data with generalised variable names
data <- by_layer(function(l, d) l$compute_aesthetics(d, plot))
scene$identify_layers(data, layers)
# Transform all scales
data <- lapply(data, scales_transform_df, scales = scales)
# Map and train positions so that statistics have access to ranges
# and all positions are numeric
scale_x <- function() scales$get_scales("x")
scale_y <- function() scales$get_scales("y")
layout$train_position(data, scale_x(), scale_y())
data <- layout$map_position(data)
data <- scene$before_stat(data)
# Apply and map statistics
data <- by_layer(function(l, d) l$compute_statistic(d, layout))
data <- by_layer(function(l, d) l$map_statistic(d, plot))
data <- scene$after_stat(data)
# Make sure missing (but required) aesthetics are added
scales_add_missing(plot, c("x", "y"), plot$plot_env)
# Reparameterise geoms from (e.g.) y and width to ymin and ymax
data <- by_layer(function(l, d) l$compute_geom_1(d))
data <- scene$before_position(data)
# Apply position adjustments
data <- by_layer(function(l, d) l$compute_position(d, layout))
data <- scene$after_position(data)
# Reset position scales, then re-train and map. This ensures that facets
# have control over the range of a plot: is it generated from what is
# displayed, or does it include the range of underlying data
layout$reset_scales()
layout$train_position(data, scale_x(), scale_y())
layout$setup_panel_params()
data <- layout$map_position(data)
# Train and map non-position scales
npscales <- scales$non_position_scales()
if (npscales$n() > 0) {
lapply(data, scales_train_df, scales = npscales)
data <- lapply(data, scales_map_df, scales = npscales)
}
# Fill in defaults etc.
data <- by_layer(function(l, d) l$compute_geom_2(d))
data <- scene$after_defaults(data)
# Let layer stat have a final say before rendering
data <- by_layer(function(l, d) l$finish_statistics(d))
# Let Layout modify data before rendering
data <- layout$finish_data(data)
data <- scene$finish_data(data)
structure(
list(data = data, layout = layout, plot = plot, scene = scene),
class = "gganim_built"
)
}
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