扩展ggplot2:如何构建geom和stat？

Question

我正处于学习如何扩展ggplot2的早期阶段。我想创建一个自定义geom和相关的stat。我的出发点是格列奈特。此外，我还从这和这中受益。我正在努力拼凑一个模板来教我自己和其他人。

主要问题：

在我的函数calculate_shadows() params$anchor 中的所需的参数是 NULL**.我怎样才能访问它？**

下面描述的目标仅仅是为了学习如何创建自定义的stat和geom函数，这并不是一个真正的目标:正如您可以从屏幕截图中看到的那样，我确实知道如何利用ggplot2的力量来生成图形。

1. geom将读取数据，而对于所提供的变量，("x", "y")将绘制(因为缺少更好的单词) shadows：默认y=0处的水平线min(x)--max(x)和默认x=0处的垂直线min(y)--max(y)。如果提供了选项，这些“锚”可以更改，例如，如果用户提供x = 35, y = 1，则在拦截y = 1处绘制水平线，而在拦截x = 35处绘制垂直线。用法： 库(Ggplot2) ggplot(data =mtcar，aes(x = mpg，y= wt)) + geom_point() + geom_shadows(x = 35，y= 1)

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1. stat将读取数据，对于所提供的变量，("x", "y")将根据stat的值计算shadows。例如，通过传递stat = "identity"，阴影将计算数据的最小值和最大值(如geom_shadows所做的)。但通过stat = "quartile"，阴影将计算第一和第三四分位数。更广泛地说，我们可以传递一个函数，比如带有参数args = list(probs = c(0.10, 0.90), type = 6)的args = list(probs = c(0.10, 0.90), type = 6)函数，以使用第10和第90百分位数和类型6的分位数方法来计算阴影。 geom_point() + stat_shadows(stat =“四分位数”)

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不幸的是，我对扩展ggplot2的不熟悉使我无法达到我的目标。这些情节是用geom_segment“伪造”的。基于上面提到的教程和讨论，以及检查现有代码(如stat-qq或stat-smooth )，我为这个目标构建了一个基本的体系结构。它必须包含许多错误，我将感谢您的指导。另外，请注意，这两种方法都很好：geom_shadows(anchor = c(35, 1))或geom_shadows(x = 35, y = 1)。

下面是我的努力。首先，geom-shadows.r定义geom_shadows()。第二，stat-shadows.r定义stat_shadows()。代码不能按原样工作。但是如果我执行它的内容，它确实会产生所需的统计数据。为了清晰起见，我已经删除了stat_shadows()中的大多数计算，比如四分位数，来关注本质。布局上有明显的错误吗？

geom-shadows.r

#' documentation ought to be here
geom_shadows <- function(
  mapping = NULL, 
  data = NULL, 
  stat = "shadows", 
  position = "identity", 
  ...,
  anchor = list(x = 0, y = 0),
  shadows = list("x", "y"), 
  type = NULL,
  na.rm = FALSE,
  show.legend = NA, 
  inherit.aes = TRUE) {
    layer(
      data = data,
      mapping = mapping,
      stat = stat,
      geom = GeomShadows,
      position = position,
      show.legend = show.legend,
      inherit.aes = inherit.aes,
      params = list(
        anchor = anchor,
        shadows = shadows,
        type = type,  
        na.rm = na.rm,
        ...
    )
  )
}

GeomShadows <- ggproto("GeomShadows", Geom, 

  # set up the data, e.g. remove missing data
  setup_data = function(data, params) { 
    data 
  }, 

  # set up the parameters, e.g. supply warnings for incorrect input
  setup_params = function(data, params) {
    params
  },

  draw_group = function(data, panel_params, coord, anchor, shadows, type) { 
    # draw_group uses stats returned by compute_group

    # set common aesthetics
    geom_aes <- list(
      alpha = data$alpha,
      colour = data$color,
      size = data$size,
      linetype = data$linetype,
      fill = alpha(data$fill, data$alpha),
      group = data$group
    )

    # merge aesthetics with data calculated in setup_data
    geom_stats <- new_data_frame(c(list(
          x = c(data$x.xmin, data$y.xmin),
          xend = c(data$x.xmax, data$y.xmax),
          y = c(data$x.ymin, data$y.ymin),
          yend = c(data$x.ymax, data$y.ymax),
          alpha = c(data$alpha, data$alpha) 
        ), geom_aes
      ), n = 2) 

    # turn the stats data into a GeomPath
    geom_grob <- GeomSegment$draw_panel(unique(geom_stats), 
        panel_params, coord) 

    # pass the GeomPath to grobTree
    ggname("geom_shadows", grobTree(geom_grob)) 
  },

  # set legend box styles
  draw_key = draw_key_path,

  # set default aesthetics 
  default_aes = aes(
    colour = "blue",
    fill = "red",
    size = 1,
    linetype = 1,
    alpha = 1
  )

)

stat-shadows.r

#' documentation ought to be here
stat_shadows <-  
  function(mapping = NULL, 
           data = NULL,
           geom = "shadows", 
           position = "identity",
           ...,
           # do I need to add the geom_shadows arguments here?
           anchor = list(x = 0, y = 0),
           shadows = list("x", "y"), 
           type = NULL,
           na.rm = FALSE,
           show.legend = NA,
           inherit.aes = TRUE) {
  layer(
    stat = StatShadows,  
    data = data,
    mapping = mapping,
    geom = geom,
    position = position,
    show.legend = show.legend,
    inherit.aes = inherit.aes,
    params = list(
      # geom_shadows argument repeated here?
      anchor = anchor,  
      shadows = shadows,
      type = type,
      na.rm = na.rm,
      ...
    )
  )
}

StatShadows <- 
  ggproto("StatShadows", Stat,

    # do I need to repeat required_aes?
    required_aes = c("x", "y"), 

    # set up the data, e.g. remove missing data
    setup_data = function(data, params) {
      data
    },

    # set up parameters, e.g. unpack from list
    setup_params = function(data, params) {
      params
    },

    # calculate shadows: returns data_frame with colnames: xmin, xmax, ymin, ymax 
    compute_group = function(data, scales, anchor = list(x = 0, y = 0), shadows = list("x", "y"), type = NULL, na.rm = TRUE) {

      .compute_shadows(data = data, anchor = anchor, shadows = shadows, type = type)

  }
)

# Calculate the shadows for each type / shadows / anchor
.compute_shadows <- function(data, anchor, shadows, type) {

# Deleted all type-checking, etc. for MWE
# Only 'type = c(double, double)' accepted, e.g. type = c(0, 1)

qs <- type

# compute shadows along the x-axis
if (any(shadows == "x")) {
    shadows.x <- c(
    xmin = as.numeric(stats::quantile(data[, "x"], qs[[1]])),
    xmax = as.numeric(stats::quantile(data[, "x"], qs[[2]])),
    ymin = anchor[["y"]], 
    ymax = anchor[["y"]]) 
}

# compute shadows along the y-axis
if (any(shadows == "y")) {
    shadows.y <- c(
    xmin = anchor[["x"]], 
    xmax = anchor[["x"]], 
    ymin = as.numeric(stats::quantile(data[, "y"], qs[[1]])),
    ymax = as.numeric(stats::quantile(data[, "y"], qs[[2]])))
} 

# store shadows in one data_frame
stats <- new_data_frame(c(x = shadows.x, y = shadows.y))

# return the statistics
stats
}

.

Answer 1

直到有一个更彻底的答案:你失踪了

extra_params = c("na.rm", "shadows", "anchor", "type"),

内部GeomShadows <- ggproto("GeomShadows", Geom,

也可能在StatShadows <- ggproto("StatShadows", Stat,内部。

在geom-.r和stat-.r中，有许多非常有用的注释来说明geoms和stats是如何工作的。特别是( github杂志上的Claus Wilke )：

# Most parameters for the geom are taken automatically from draw_panel() or
# draw_groups(). However, some additional parameters may be needed
# for setup_data() or handle_na(). These can not be imputed automatically,
# so the slightly hacky "extra_params" field is used instead. By
# default it contains `na.rm`
extra_params = c("na.rm"),