使用 ecdf 图表中的值创建 table

Question

我正在尝试使用 ecdf 图中的值创建 table。我在下面重新创建了一个示例。

#Data
data(mtcars)

#Sort by mpg
mtcars <- mtcars[order(mtcars$mpg),]

#Make arbitrary ranking variable based on mpg
mtcars <- mtcars %>% mutate(Rank = dense_rank(mpg))

#Make variable for percent picked
mtcars <- mutate(mtcars, Percent_Picked = Rank/max(mtcars$Rank))

#Make cyl categorical
mtcars$cyl<-cut(mtcars$cyl, c(3,5,7,9), right=FALSE, labels=c(4,6,8))

#Make the graph
ggplot(mtcars, aes(Percent_Picked, color = cyl)) + 
  stat_ecdf(size=1) + 
  scale_x_continuous(labels = scales::percent) +
  scale_y_continuous(labels = scales::percent)

创建此图的原因

我想在整体 Percent_Picked 分别为 25%、50% 和 75% 时为每种圆柱体类型的值创建一个 table。所以显示 4-cylander 为 0%，6 为 28% 左右，8 为 85% 左右。

按组计算分位数并没有给我想要的结果（它显示了当特定圆柱类型的 25%、50% 和 75% 被选中时所有圆柱的百分比）。（例如，tbradley1013 on their blog 的建议仅有助于每个特定圆柱体的分位数，而不是每个圆柱体在 Percent_Picked 的给定分位数处的整体 cdf。）

如有任何线索，我们将不胜感激！

Answer 1

环顾四周，我发现 . Yours extends this a little by asking for group specific ecdf values, so we can use the do function in dplyr (here's an example] 可以这样做。比较这个 table 和你的 ggplot 中的值时，值有一些轻微差异，我不确定为什么会这样。可能只是 mtcars 数据集有点小，所以如果你运行在更大的数据集上这样做，我希望它更接近实际值。


#Sort by mpg
mtcars <- mtcars[order(mtcars$mpg),]

#Make arbitrary ranking variable based on mpg
mtcars <- mtcars %>% mutate(Rank = dense_rank(mpg))

#Make variable for percent picked
mtcars <- mutate(mtcars, Percent_Picked = Rank/max(mtcars$Rank))

#Make cyl categorical
mtcars$cyl<-cut(mtcars$cyl, c(3,5,7,9), right=FALSE, labels=c(4,6,8))

#Make the graph
ggplot(mtcars, aes(Percent_Picked, color = cyl)) + 
  stat_ecdf(size=1) + 
  scale_x_continuous(labels = scales::percent) +
  scale_y_continuous(labels = scales::percent)


create_ecdf_vals <- function(vec){
  df <- data.frame(
    x = unique(vec),
    y = ecdf(vec)(unique(vec))*length(vec)
  ) %>%
    mutate(y = scale(y, center = min(y), scale = diff(range(y)))) %>%
    union_all(data.frame(x=c(0,1),
                         y=c(0,1))) # adding in max/mins
  return(df)
}

mt.ecdf <- mtcars %>%
  group_by(cyl) %>%
  do(create_ecdf_vals(.$Percent_Picked))


mt.ecdf %>%
  summarise(q25 = y[which.max(x[x<=0.25])],
            q50 = y[which.max(x[x<=0.5])],
            q75 = y[which.max(x[x<=0.75])])

ggplot(mt.ecdf,aes(x,y,color = cyl)) +
  geom_step()

~编辑~
在 ggplot2 文档中进行一些挖掘之后，我们实际上可以使用 layer_data 函数显式地从图中提取数据。

my.plt <- ggplot(mtcars, aes(Percent_Picked, color = cyl)) + 
  stat_ecdf(size=1) + 
  scale_x_continuous(labels = scales::percent) +
  scale_y_continuous(labels = scales::percent)

plt.data <- layer_data(my.plt) # magic happens here

# and here's the table you want
plt.data %>%
  group_by(group) %>%
  summarise(q25 = y[which.max(x[x<=0.25])],
            q50 = y[which.max(x[x<=0.5])],
            q75 = y[which.max(x[x<=0.75])])

Answer 2

一个更短的答案，我不敢相信我之前没有看到。本质上，对于每个柱体，我只是将等于或小于 .25、.5 和 .75 的行数除以总行数。

cyl.table<-mtcars %>%
  group_by(cyl) %>%
    summarise("25% Picked" = sum(Percent_Picked<=0.25)/(sum(Percent_Picked<=1)),
              "50% Picked" = sum(Percent_Picked<=0.5)/(sum(Percent_Picked<=1)),
              "75% Picked" = sum(Percent_Picked<=0.75)/(sum(Percent_Picked<=1)))
cyl.table

使用 ecdf 图表中的值创建 table

Create a table with values from ecdf graph

r

ecdf