使用带分位数的geom_boxplot

Question

Question

I'd like to use ggplot's geom_boxplot and use my own data columns for the quantile segments, instead of those returned by stat_boxplot .

The data, after doing some transformations, looks like this:

> allquartile                                                      
      T method       s.0%      s.25%      s.50%      s.75%     s.100%                                                                                                    
1     2    LDA -196.76273 -190.38842 -184.01411 -177.63979 -171.26548                                                                                                    
2     3    LDA -171.53987 -166.16923 -160.79859 -115.28652  -69.77446                                                                                                    
3     4    LDA -161.17590 -157.61372 -149.71026 -124.68926  -69.77446                                                                                                    
4     5    LDA -194.10553 -179.83165 -175.14337 -168.46104 -159.07206 

After doing a lot of searching and digging, I figured out that my plotting command should look like this:

p <- ggplot(allquartile,aes(x=T, ymin=`s.0%`, lower=`s.25%`,
                            middle=`s.50%`, upper=`s.75%`,
                            ymax=`s.100%`, color=method)) + 
     geom_boxplot(stat="identity")

This should use s.0% as the min, s.25% as the lower, etc etc. But when i try to display p , i get the following error:

Error in eval(expr, envir, enclos) : object 's.0%' not found                                                                                                             
Calls: print ... lapply -> is.vector -> lapply -> FUN -> eval -> eval

I've also tried using aes_string in place of aes , and I instead get this error:

Error in aes_string(x = T, ymin = `s.0%`, lower = `s.25%`, middle = `s.50%`,  :                                                                                            
object 's.0%' not found 

I'm fairly new to both R and ggplot2, so i'm not realy sure how to interpret this, but I'm assuming it's because of the . in s.0% .

I'd greatly appreciate any suggestions on how to get around this.

Edit: I've dug around more and I think this is due to my misunderstanding of the quantile method. I created allquartile by this command:

allquartile <-aggregate(list(s=topicquality$score), list(T=topicquality$T,method=topicquality$method),FUN=quantile,probs=seq(0, 1, .25)) 

And I realize that there are no columns named score.0% , score.25% , etc. There is just the score column with 5 values. So this boils down to: how do i access those 5 values within score ?

SOLUTION

I've found the issue with my dataset. As i mentioned in my edit, the columns score.0% , score.25% , etc didn't exist based on how i formed the data frame. For example, running colnames(allquartile) returned:

[1] "T"      "method" "score"

It turns out that the score column is a vector of values. Running allquartile$score gives me:

            0%       25%       50%       75%       100%
[1,] -196.7627 -190.3884 -184.0141 -177.6398 -171.26548
[2,] -171.5399 -166.1692 -160.7986 -115.2865  -69.77446
[3,] -161.1759 -157.6137 -149.7103 -124.6893  -69.77446
[4,] -194.1055 -179.8316 -175.1434 -168.4610 -159.07206
[5,] -200.1544 -174.2835 -167.7209 -145.3432 -129.54586

I can then access each individual quantile's values by doing

> allquartile$score[,1]
[1] -196.7627 -171.5399 -161.1759 -194.1055 -200.1544

I'm not familiar with R enough to know what kind of data structure this is, but I would call it a matrix. So like any good matrix object, m[,column] returns the values of the column while m[row,] returns the values of the row, and m[row, column] gets the cell value.

With that in mind, I've realized that the propper plotting command should be

p <- ggplot(allquartile,
            aes(x=T,
                ymin=score[,1],
                lower=score[,2],
                middle=score[,3],
                upper=score[,4], 
                ymax=score[,5], 
                color=method)) + 
     geom_boxplot(stat="identity") 

And this plots out everything perfectly.

Thanks to everyone for the good suggestions, even though they didn't fix the problem, they helped a lot in figuring things out.

Answer 1

Here is how to solve it. The issue is with your column names. If you type names(allquartile) , you will notice that your column names are s.0. , s.25. etc. My recommendation would to be avoid all punctuations in column names save for _ or . .

require(stringr)
names(allquartile) = str_replace_all(names(allquartile), "\\.", '')
p <- ggplot(allquartile2, aes_string(x = "T", ymin = "s0", lower = "s25", 
      middle = "s50", upper = "s75", ymax = "s100", color = "method")) + 
     geom_boxplot(stat = "identity")

Answer 2

Actually, based on your edits, I think your real problem is that you shouldn't have been using aggregate . If the function you are applying returns multiple values (like quantile ), aggregate returns the results in the somewhat inconvenient format you observed, by default.

What's happening is this. A data frame, somewhat confusingly, is actually a list, with each column being an element of the list. The only requirement being that each 'column' has the same number of rows. So you're getting a data frame back with three 'columns': the third column is a just a matrix!

Doing this with aggregate is possible, but there are more convenient tools out there. (For instance, you could call cbind(allquartile[,1:2],allquartile[,3]) to create a data frame of the 'correct' dimensions.)

For example, a very popular one is ddply from the plyr package. Here's an example using some made up data, but following the general structure of your data:

topicquality <- data.frame(score = runif(20),
                            T = rep(letters[1:2],each = 10),
                            method = rep(letters[3:4],length.out = 20))

ddply(topicquality,.(T,method),FUN = function(x,...){quantile(x$score,...)},probs = seq(0,1,0.25))

You'll note that this will return a data frame of the dimensions you expect, but you still have to deal with the inconvenient column names. That's best dealt with in the function you apply to each piece:

myQuantile <- function(x,...){
    tmp <- quantile(x,...)
    names(tmp) <- NULL #Or something else convenient
    tmp
}
ddply(topicquality,.(T,method),FUN = myQuantile,probs = seq(0,1,0.25))