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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 ggplot ecdf graph

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!

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  • And, I should also say, if parts of the code above look sketchy, let me know what I should do differently! Commented Feb 4, 2020 at 19:35

2 Answers 2

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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])])
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4 Comments

Thanks for your help on this! I think the issue with the slight differences in the ecdf graphs is that the one using the function you created doesn't start counting/accumulating until after the first instance for each cyl. So for example, when the first 4-cylander car is chosen, the y variable does not increase - it only starts to increase after the next 4-cylander car is chosen. I couldn't figure out where that was happening in the code - do you know?
Just found a better answer - let me revise what I've given you here
The update works like a charm. I had no idea layer_data existed, thank you!
As an update, I had to update a bit of the summarize function to q25 = y[x<=0.25][which.max(x[x<=0.25])] - see stackoverflow.com/questions/60728218/… for more details
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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
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