recreate scale_fill_brewer with scale_fill_manual

The easiest way is to trace it is to think in terms of (1) setting up the plot data structure, and (2) resolving the aesthetics. It uses S3 so the branching is implicit

The setup call sequence

1. [scale-brewer.R] scale_fill_brewer(type="seq", palette="Blues")

2. [scale-.R] discrete_scale(...) - return an object representing the scale

  structure(list(
    call = match.call(),

    aesthetics = aesthetics,
    scale_name = scale_name,
    palette = palette,

    range = DiscreteRange$new(),        ## this is scales::DiscreteRange 
    ...), , class = c(scale_name, "discrete", "scale"))

The resolve call sequence

1. [plot-build.R] ggplot_build(plot) - for non-position scales, apply scales_train_df

    # Train and map non-position scales
    npscales <- scales$non_position_scales()       ## scales is plot$scales, S4 type Scales
    if (npscales$n() > 0) {
      lapply(data, scales_train_df, scales = npscales)
      data <- lapply(data, scales_map_df, scales = npscales)
    }

2. [scales-.r] scales_train_df(...) - iterate again over scales$scales (list)

3. [scale-.r] scale_train_df(...) - iterate again

4. [scale-.r] scale_train(...) - S3 generic function

5. [scale-.r] scale_train.discrete(...) - almost there...

    scale$range$train(x, drop = scale$drop)

6. but scale$range is a DiscreteRange instance, so it calls (scales::DiscreteRange$new())$train, which overwrites scale$range!

    range <<- train_discrete(x, range, drop)

7. scales:::train_discrete(...) - again, almost there...

8. scales:::discrete_range(...) - still not there..

9. scales:::clevels(...) - there it is!

As of this point, scale$range has been overwritten by the levels of the factor. Unwinding the call stack to #1, we now call scales_map_df

1. [plot-build.R] ggplot_build(plot) - for non-position scales, apply scales_train_df

    # Train and map non-position scales
    npscales <- scales$non_position_scales()       ## scales is plot$scales, S4 type Scales
    if (npscales$n() > 0) {
      lapply(data, scales_train_df, scales = npscales)
      data <- lapply(data, scales_map_df, scales = npscales)
    }

2. [scales-.r] scale_maps_df(...) - iterate

3. [scale-.r] scale_map_df(...) - iterate

4. [scale-.r] scale_map.discrete - fill up the palette (non-position scale!)

   scale_map.discrete <- function(scale, x, limits = scale_limits(scale)) { n <- sum(!is.na(limits)) pal <- scale$palette(n) ... }