Create a table with values from ecdf graph - r

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$mpg),]
#Make arbitrary ranking variable based on mpg
mtcars <- mtcars %>% mutate(Rank = dense_rank(mpg))
#Make variable for percent picked
mtcars <- mutate(mtcars, Percent_Picked = Rank/max(mtcars$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)
Which creates this plot
I want to create a table for the value of each of the cylinder types when the overall Percent_Picked is at 25%, 50%, and 75%. So something that shows that 4-cylander is at 0%, 6 is around 28%, and 8 is around 85%.
Calculating quantiles by group doesn't give me what I want (it shows the percent of all cylinders picked when 25%, 50%, and 75% of the particular cylinder type was picked). (For example, the suggestions by tbradley1013 on their blog only help with quantiles for each particular cylinder, not the overall cdf for each cylinder at given quantiles for Percent_Picked.)
Any leads would be appreciated!

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])])

Answer 2

A much shorter answer that I can't believe I didn't see earlier. Essentially I just divide the number of rows equal to or less than .25, .5, and .75 by the total number of rows, for each cyl.
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