Create a table with values from ecdf graph

Question

I am trying to create a table using values from an ecdf plot. I\'ve recreated an example below.

#Data
data(mtcars)

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


        

Answer 1

So looking around I found this question. 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] to do so. There's some slight differences in the values when comparing between this table and the values in your ggplot and I'm not exactly sure why that is. It could be just that the mtcars data set is somewhat small, so if you run this on a larger data set, I'd expect it to be closer to the actual values.

#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()

~EDIT~
After some digging around in the ggplot2 docs, we can actually explicitly pull out the data from the plot using the layer_data function.

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])])