I wanted to see where factor values are turned into numeric ones. I tried to achieve this by simply adding print statements everywhere...
geom_tile2 <- function(mapping = NULL, data = NULL,
stat = "identity2", position = "identity",
...,
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE) {
layer(
data = data,
mapping = mapping,
stat = stat,
geom = GeomTile2,
position = position,
show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(
na.rm = na.rm,
...
)
)
}
GeomTile2 <- ggproto("GeomTile2", GeomRect,
extra_params = c("na.rm", "width", "height"),
setup_data = function(data, params) {
print(data)
data$width <- data$width %||% params$width %||% resolution(data$x, FALSE)
data$height <- data$height %||% params$height %||% resolution(data$y, FALSE)
transform(data,
xmin = x - width / 2, xmax = x + width / 2, width = NULL,
ymin = y - height / 2, ymax = y + height / 2, height = NULL
)
},
default_aes = aes(fill = "grey20", colour = NA, size = 0.1, linetype = 1,
alpha = NA),
required_aes = c("x", "y"),
draw_key = draw_key_polygon
)
and
stat_identity2 <- function(mapping = NULL, data = NULL,
geom = "point", position = "identity",
...,
show.legend = NA,
inherit.aes = TRUE) {
layer(
data = data,
mapping = mapping,
stat = StatIdentity2,
geom = geom,
position = position,
show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(
na.rm = FALSE,
...
)
)
}
StatIdentity2 <- ggproto("StatIdentity2", Stat,
setup_data = function(data, params) {
print(data)
data
},
compute_layer = function(data, scales, params) {
print(data)
print("stat end")
data
}
)
but when I run e.g.
ggplot(data.frame(x = rep(c("y", "n"), 6), y = rep(c("y", "n"), each = 6)),
aes(x = x, y = y)) +
geom_tile2()
The x and y are numeric from the setup_data function in the stat and onwards. Looking through the package's Github repo, I just can't seem to find where this conversion to coordinates actually happens?
The conversion from factors to numerical scale for x / y is done by the ggplot2:::Layout$map_position() function, current code here: layout.r
I usually think of the steps involved in creating a plot using ggplot2 package in two stages:
ggplot()) & all geom_* / stat_* / facet_* / scale_* / coord_* layers added to it are combined into a single ggplot object. If we write something like p <- ggplot(mpg, aes(class)) + geom_bar(), we stop here. GH code here: plot-construction.rggplot_build()) and further converted into a gtable of grobs (via ggplot_gtable()). This is usually triggered via the ggplot object's print / plot methods (see here), but we can also use ggplotGrob(), which returns the converted gtable object directly, minus the printing step. GH code for ggplot_build / ggplot_gtable here: plot-build.rIn my experience, most of the steps we might be interested to tweak are those within the plot rendering stage, and running debug on ggplot2:::ggplot_build.ggplot / ggplot2:::ggplot_gtable.ggplot_built is a good first step to figure out where things happen.
In this case, after running
debugonce(ggplot2:::ggplot_build.ggplot)
ggplot(data.frame(x = rep(c("y", "n"), 6),
y = rep(c("y", "n"), each = 6)),
aes(x = x, y = y)) +
geom_tile() # no need to use the self-defined geom_tile2 here
We begin to step through the function:
> ggplot2:::ggplot_build.ggplot
function (plot)
{
plot <- plot_clone(plot)
if (length(plot$layers) == 0) {
plot <- plot + geom_blank()
}
layers <- plot$layers
layer_data <- lapply(layers, function(y) y$layer_data(plot$data))
scales <- plot$scales
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
}
data <- layer_data
data <- by_layer(function(l, d) l$setup_layer(d, plot))
layout <- create_layout(plot$facet, plot$coordinates)
data <- layout$setup(data, plot$data, plot$plot_env)
data <- by_layer(function(l, d) l$compute_aesthetics(d, plot))
data <- lapply(data, scales_transform_df, scales = scales)
scale_x <- function() scales$get_scales("x")
scale_y <- function() scales$get_scales("y")
layout$train_position(data, scale_x(), scale_y())
data <- layout$map_position(data)
data <- by_layer(function(l, d) l$compute_statistic(d, layout))
data <- by_layer(function(l, d) l$map_statistic(d, plot))
scales_add_missing(plot, c("x", "y"), plot$plot_env)
data <- by_layer(function(l, d) l$compute_geom_1(d))
data <- by_layer(function(l, d) l$compute_position(d, layout))
layout$reset_scales()
layout$train_position(data, scale_x(), scale_y())
layout$setup_panel_params()
data <- layout$map_position(data)
npscales <- scales$non_position_scales()
if (npscales$n() > 0) {
lapply(data, scales_train_df, scales = npscales)
data <- lapply(data, scales_map_df, scales = npscales)
}
data <- by_layer(function(l, d) l$compute_geom_2(d))
data <- by_layer(function(l, d) l$finish_statistics(d))
data <- layout$finish_data(data)
structure(list(data = data, layout = layout, plot = plot),
class = "ggplot_built")
}
In debug mode, we can check str(data[[i]]) after every step, to examine the data associated with layer i of the ggplot object (i = 1 in this case, since there's only 1 geom layer).
Browse[2]>
debug: data <- lapply(data, scales_transform_df, scales = scales)
Browse[2]>
debug: scale_x <- function() scales$get_scales("x")
Browse[2]> str(data[[1]]) # still factor after scale_transform_df step
'data.frame': 12 obs. of 4 variables:
$ x : Factor w/ 2 levels "n","y": 2 1 2 1 2 1 2 1 2 1 ...
$ y : Factor w/ 2 levels "n","y": 2 2 2 2 2 2 1 1 1 1 ...
$ PANEL: Factor w/ 1 level "1": 1 1 1 1 1 1 1 1 1 1 ...
$ group: int 4 2 4 2 4 2 3 1 3 1 ...
..- attr(*, "n")= int 4
# ... omitted
debug: data <- layout$map_position(data)
Browse[2]>
debug: data <- by_layer(function(l, d) l$compute_statistic(d, layout))
Browse[2]> str(data[[1]]) # numerical after map_position step
'data.frame': 12 obs. of 4 variables:
$ x : int 2 1 2 1 2 1 2 1 2 1 ...
$ y : int 2 2 2 2 2 2 1 1 1 1 ...
$ PANEL: Factor w/ 1 level "1": 1 1 1 1 1 1 1 1 1 1 ...
$ group: int 4 2 4 2 4 2 3 1 3 1 ...
..- attr(*, "n")= int 4
Stat*'s setup_data is triggered by data <- by_layer(function(l, d) l$compute_statistic(d, layout)) (see ggplot2:::Layer$compute_statistic here), which happens after this step. This is why when you insert a print statement in StatIdentity2$setup_data, the data is already in numerical form.
(And Geom*'s setup_data is triggered by data <- by_layer(function(l, d) l$compute_geom_1(d)), which happens even later.)
After identifying map_position as the step where things happen, we can run debug mode again & step into this function to see exactly what's going on. At this point, I'm afraid I don't really know what your use case is, so I'll have to leave you to it.