internals_ggbuild.R

# The ggbuild() function mimics the behaviour of the ggplot_build()
# function (from version 3.3.0.9000). It is a direct copy of the source
# code from the original, with calls to internal ggplot2 functions
# namespaced via :::
#
# The only substantive modification from the original is that it maintains
# a list "all_steps" that records the state of the data at various points in
# the build process, in addition to the final ggplot_built object.
#
# Lines that marked with # ****** are those that have been modified or inserted


ggbuild <- function(plot) {

  all_steps <- list() # ******

  plot <- ggplot2:::plot_clone(plot) # ******
  if (length(plot$layers) == 0) {
    plot <- plot + geom_blank()
  }

  layers <- plot$layers
  layer_data <- lapply(layers, function(y) y$layer_data(plot$data))

  scales <- plot$scales
  # 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
  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 <- ggplot2:::create_layout(plot$facet, plot$coordinates) # ******
  data <- layout$setup(data, plot$data, plot$plot_env)

  # Compute aesthetics to produce data with generalised variable names
  data <- by_layer(function(l, d) l$compute_aesthetics(d, plot))

  # Record the data at the end of the "preparation" stage
  all_steps$prepared <- data # ******


  # Transform all scales
  data <- lapply(data, ggplot2:::scales_transform_df, scales = scales) # ******

  # Record the layer data after scale transformation applied
  all_steps$transformed <- data # ******

  # 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)

  # Record the layer data after position adjustment
  all_steps$positioned <- 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))

  # Record the state of the layer data after position adjustment
  all_steps$poststat <- data # ******

  # Make sure missing (but required) aesthetics are added
  ggplot2:::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))

  # Apply position adjustments
  data <- by_layer(function(l, d) l$compute_position(d, layout))

  # Record the state of the data once geom and position adjustments are made
  all_steps$geompos <- 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, ggplot2:::scales_train_df, scales = npscales) # ******
    data <- lapply(data, ggplot2:::scales_map_df, scales = npscales) # ******
  }

  # Fill in defaults etc.
  data <- by_layer(function(l, d) l$compute_geom_2(d))

  # 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)


  # Record the data at the end of the "preparation" stage
  all_steps$built <-  structure(                        # ******
    list(data = data, layout = layout, plot = plot),    # ******
    class = "ggplot_built"                              # ******
  )                                                     # ******

  return(all_steps) # ******
}