我想看看例如因子值变为数字值。我尝试过,例如在任何地方添加print
语句......
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
)
和
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
}
)
但是当我跑步时。
ggplot(data.frame(x = rep(c("y", "n"), 6), y = rep(c("y", "n"), each = 6)), aes(x = x, y = y)) + geom_tile2()
x
和y
是来自setup_data
及以后的stat
函数的数字...并且通过Github查看我似乎无法找到这种转换到坐标实际上发生了什么?
答案 0 :(得分:1)
通过ggplot2:::Layout$map_position()
函数完成x / y从因子到数值比例的转换,当前代码在这里:layout.r
我通常会想到在两个阶段中使用ggplot2
包创建绘图的步骤:
ggplot()
初始化)和所有geom_*
/ stat_*
/ facet_*
/ scale_*
/ coord_*
层添加到它被组合成一个ggplot对象。如果我们写类似p <- ggplot(mpg, aes(class)) + geom_bar()
的东西,我们就在这里停止。此处的GH代码:plot-construction.r ggplot_build()
)并进一步转换为grob的gtable(通过ggplot_gtable()
)的时候。这通常是通过ggplot对象的print / plot方法触发的(请参见here),但是我们也可以使用ggplotGrob()
,它直接返回转换后的gtable对象,减去打印步骤。 ggplot_build
/ ggplot_gtable
的GH代码在这里:plot-build.r 根据我的经验,我们可能想调整的大多数步骤都是在情节渲染阶段内进行的,在ggplot2:::ggplot_build.ggplot
/ ggplot2:::ggplot_gtable.ggplot_built
上运行调试是弄清楚事情在哪里的很好的第一步。发生。
在这种情况下,运行后
debugonce(ggplot2:::ggplot_build.ggplot)
ggplot(data.frame(x = rep(c("y", "n"), 6),
y = rep(c("y", "n"), each = 6)),
aes(x = x, y = y)) +
geom_tile() # no need to use the self-defined geom_tile2 here
我们开始逐步执行该功能:
> ggplot2:::ggplot_build.ggplot
function (plot)
{
plot <- plot_clone(plot)
if (length(plot$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$finish_data(data)
structure(list(data = data, layout = layout, plot = plot),
class = "ggplot_built")
}
在调试模式下,我们可以在每一步之后检查str(data[[i]])
,以检查与ggplot对象的图层i
相关的数据(在这种情况下,i
= 1,因为只有1个geom层。
Browse[2]>
debug: data <- lapply(data, scales_transform_df, scales = scales)
Browse[2]>
debug: scale_x <- function() scales$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*
的{{1}}由setup_data
触发(请参阅data <- by_layer(function(l, d) l$compute_statistic(d, layout))
here),这发生在 之后 。这就是为什么在ggplot2:::Layer$compute_statistic
中插入打印语句时,数据已经是数字形式的原因。
(并且StatIdentity2$setup_data
的{{1}}由Geom*
触发,甚至更晚发生。)
在将setup_data
确定为发生事情的步骤之后,我们可以再次运行调试模式并进入此函数以查看发生了什么。在这一点上,恐怕我真的不知道您的用例是什么,所以我将不得不留给您。