Plotting cumulative counts in ggplot2

问题:

There are some posts about plotting cumulative densities in ggplot. I'm currently using the accepted answer from Easier way to plot the cumulative frequency distribution in ggplot? for plotting my cumulative counts. But this solution involves pre-calculating the values beforehand.

Here I'm looking for a pure ggplot solution. Let's show what I have so far:

x <- data.frame(A=replicate(200,sample(c("a","b","c"),1)),X=rnorm(200))

ggplot's stat_ecdf

I can use ggplot's stat_ecdf, but it only plots cumulative densities:

ggplot(x,aes(x=X,color=A)) + geom_step(aes(y=..y..),stat="ecdf")

I'd like to do something like the following, but it doesn't work:

ggplot(x,aes(x=X,color=A)) + geom_step(aes(y=..y.. * ..count..),stat="ecdf")

cumsum and stat_bin

I found an idea about using cumsum and stat_bin:

ggplot(x,aes(x=X,color=A)) + stat_bin(aes(y=cumsum(..count..)),geom="step")

But as you can see, the next color doesn't start at y=0, but where the last color ended.

What I ask for

What I'd like to have from best to worst:

1. Ideally a simple fix to the not working

   ggplot(x,aes(x=X,color=A)) + geom_step(aes(y=..y.. * ..count..),stat="ecdf")
   

2. A more complicated way to use stat_ecdf with counts.

3. Last resort would be to use the cumsum approach, since it gives worse (binned) results.

回答1:

This will not solve directly problem with grouping of lines but it will be workaround.

You can add three calls to stat_bin() where you subset your data according to A levels.

ggplot(x,aes(x=X,color=A)) +
  stat_bin(data=subset(x,A=="a"),aes(y=cumsum(..count..)),geom="step")+
  stat_bin(data=subset(x,A=="b"),aes(y=cumsum(..count..)),geom="step")+
  stat_bin(data=subset(x,A=="c"),aes(y=cumsum(..count..)),geom="step")

UPDATE - solution using geom_step()

Another possibility is to multiply values of ..y.. with number of observations in each level. To get this number of observations at this moment only way I found is to precalculate them before plotting and add them to original data frame. I named this column len. Then in geom_step() inside aes() you should define that you will use variable len=len and then define y values as y=..y.. * len.

set.seed(123)
x <- data.frame(A=replicate(200,sample(c("a","b","c"),1)),X=rnorm(200))
library(plyr)
df <- ddply(x,.(A),transform,len=length(X))
ggplot(df,aes(x=X,color=A)) + geom_step(aes(len=len,y=..y.. * len),stat="ecdf") 

回答2:

You can apply row_number over the groups, and utilize that as the Y aesthetic in a geom_step or other geometry. You'll just have to sort by X, or the values will appear as they do in the data frame, unordered.

ggplot(x %>% 
         group_by(A) %>% 
         arrange(X) %>% 
         mutate(rn = row_number())) + 
  geom_step(aes(x=X, y=rn, color=A))

回答3:

The parameters len and y inside the layer geom_step(aes(len=len,y=..y.. * len),stat="ecdf") does not seem to be needed. And len doesn't appear to be a parameter inside geom_step. We just have to use

geom_step(aes(stat="ecdf"))