R ggplot2 aes_eval 控製審美評價

# These functions can be used inside the `aes()` function
# used as the `mapping` argument in layers, for example:
# geom_density(mapping = aes(y = after_stat(scaled)))

after_stat(x)

after_scale(x)

stage(start = NULL, after_stat = NULL, after_scale = NULL)

第一階段：直接輸入

默認是在開始時使用用戶提供的圖層數據進行映射。如果你想直接從圖層數據映射，你不應該做任何特殊的事情。這是可以訪問原始圖層數據的唯一階段。

# 'x' and 'y' are mapped directly
ggplot(mtcars) + geom_point(aes(x = mpg, y = disp))

第2階段：統計轉換後

第二階段是數據經過圖層統計轉換後。從 stat 轉換數據映射的最常見示例是 geom_histogram() 中的條形高度：高度並非來自基礎數據中的變量，而是映射到由 stat_bin() 計算的 count 。為了從 stat 轉換數據進行映射，您應該使用 after_stat() 函數來標記美學映射的評估應推遲到 stat 轉換之後。統計轉換後的評估將訪問統計計算的變量，而不是原始映射值。每個統計數據中的 'computed variables' 部分列出了可訪問的變量。

# The 'y' values for the histogram are computed by the stat
ggplot(faithful, aes(x = waiting)) +
  geom_histogram()

# Choosing a different computed variable to display, matching up the
# histogram with the density plot
ggplot(faithful, aes(x = waiting)) +
  geom_histogram(aes(y = after_stat(density))) +
  geom_density()

第三階段：規模轉型後

第三個也是最後一個階段是在數據經過繪圖比例轉換和映射之後。從縮放數據進行映射的一個示例可以是使用描邊顏色的去飽和版本進行填充。您應該使用 after_scale() 來標記數據縮放後的映射評估。縮放後的評估隻能訪問該圖層的最終美觀效果(包括非映射的默認美觀效果)。

# The exact colour is known after scale transformation
ggplot(mpg, aes(cty, colour = factor(cyl))) +
  geom_density()

# We re-use colour properties for the fill without a separate fill scale
ggplot(mpg, aes(cty, colour = factor(cyl))) +
  geom_density(aes(fill = after_scale(alpha(colour, 0.3))))

複雜的分期

如果您想多次映射相同的美學，例如將 x 映射到 stat 的數據列，但將其重新映射到 geom，您可以使用 stage() 函數來收集多個映射。

# Use stage to modify the scaled fill
ggplot(mpg, aes(class, hwy)) +
  geom_boxplot(aes(fill = stage(class, after_scale = alpha(fill, 0.4))))

# Using data for computing summary, but placing label elsewhere.
# Also, we're making our own computed variable to use for the label.
ggplot(mpg, aes(class, displ)) +
  geom_violin() +
  stat_summary(
    aes(
      y = stage(displ, after_stat = 8),
      label = after_stat(paste(mean, "±", sd))
    ),
    geom = "text",
    fun.data = ~ round(data.frame(mean = mean(.x), sd = sd(.x)), 2)
  )

# Default histogram display
ggplot(mpg, aes(displ)) +
  geom_histogram(aes(y = after_stat(count)))
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.


# Scale tallest bin to 1
ggplot(mpg, aes(displ)) +
  geom_histogram(aes(y = after_stat(count / max(count))))
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.


# Use a transparent version of colour for fill
ggplot(mpg, aes(class, hwy)) +
  geom_boxplot(aes(colour = class, fill = after_scale(alpha(colour, 0.4))))


# Use stage to modify the scaled fill
ggplot(mpg, aes(class, hwy)) +
  geom_boxplot(aes(fill = stage(class, after_scale = alpha(fill, 0.4))))


# Making a proportional stacked density plot
ggplot(mpg, aes(cty)) +
  geom_density(
    aes(
      colour = factor(cyl),
      fill = after_scale(alpha(colour, 0.3)),
      y = after_stat(count / sum(n[!duplicated(group)]))
    ),
    position = "stack", bw = 1
  ) +
  geom_density(bw = 1)


# Imitating a ridgeline plot
ggplot(mpg, aes(cty, colour = factor(cyl))) +
  geom_ribbon(
    stat = "density", outline.type = "upper",
    aes(
      fill = after_scale(alpha(colour, 0.3)),
      ymin = after_stat(group),
      ymax = after_stat(group + ndensity)
    )
  )


# Labelling a bar plot
ggplot(mpg, aes(class)) +
  geom_bar() +
  geom_text(
    aes(
      y = after_stat(count + 2),
      label = after_stat(count)
    ),
    stat = "count"
  )


# Labelling the upper hinge of a boxplot,
# inspired by June Choe
ggplot(mpg, aes(displ, class)) +
  geom_boxplot(outlier.shape = NA) +
  geom_text(
    aes(
      label = after_stat(xmax),
      x = stage(displ, after_stat = xmax)
    ),
    stat = "boxplot", hjust = -0.5
  )