Reputation: 59425
Just trying to understand how geom_abline works with facets in ggplot.
I have a dataset of student test scores. These are in a data table dt with 4 columns:
student: unique student ID
cohort: grouping factor for students (A, B, … H)
subject: subject of the test (English, Math, Science)
score: the test score for that student in that subject
The goal is to compare cohorts. The following snippet creates a sample dataset.
library(data.table)
## cohorts: list of cohorts with number of students in each
cohorts <- data.table(name=toupper(letters[1:8]),size=as.numeric(c(8,25,16,30,10,27,13,32)))
## base: assign students to cohorts
base <- data.table(student=c(1:sum(cohorts$size)),cohort=rep(cohorts$name,cohorts$size))
## scores for each subject
english <- data.table(base,subject="English", score=rnorm(nrow(base), mean=45, sd=50))
math <- data.table(base,subject="Math", score=rnorm(nrow(base), mean=55, sd=25))
science <- data.table(base,subject="Science", score=rnorm(nrow(base), mean=70, sd=25))
## combine
dt <- rbind(english,math,science)
## clip scores to (0,100)
dt$score<- (dt$score>=0) * dt$score
dt$score<- (dt$score<=100)*dt$score + (dt$score>100)*100
The following displays mean score by cohort with 95% CL, facetted by subject, and includes a (blue, dashed) reference line (using geom_abline).
library(ggplot2)
library(Hmisc)
ggp <- ggplot(dt,aes(x=cohort, y=score)) + ylim(0,100)
ggp <- ggp + stat_summary(fun.data="mean_cl_normal")
ggp <- ggp + geom_abline(aes(slope=0,intercept=mean(score)),color="blue",linetype="dashed")
ggp <- ggp + facet_grid(subject~.)
ggp
The problem is that the reference line (from geom_abline) is the same in all facets (= the grand average score for all students and all subjects). So stat_summary seems to respect the grouping implied in facet_grid (e.g., by subject), but abline does not. Can anyone explain why?
NB: I realize this problem can be solved by creating a separate table of group means and using that as the data source in geom_abline (below), but why is this necessary?
means <- dt[,list(mean.score=mean(score)),by="subject"]
ggp <- ggplot(dt,aes(x=cohort, y=score)) + ylim(0,100)
ggp <- ggp + stat_summary(fun.data="mean_cl_normal")
ggp <- ggp + geom_abline(data=means, aes(slope=0,intercept=mean.score),color="blue",linetype="dashed")
ggp <- ggp + facet_grid(subject~.)
ggp
Upvotes: 2
Views: 1499
Reputation: 6711
This should do what you want. The stat_*
functions use different collections of data for each facet. I think any expressions in the aes
of the geom_*
functions are intended to be used for the transformation of each y-value.
ggplot(dt,aes(x=cohort, y=score)) +
stat_summary(fun.data="mean_cl_normal") +
stat_smooth(formula=y~1,aes(group=1),method="lm",se=FALSE) +
facet_grid(subject~.) + ylim(0,100)
Upvotes: 3
Reputation: 173737
As golbasche mentioned, I would have probably done something more like this:
dt <- dt[,avg_score := mean(score),by = subject]
ggplot(dt,aes(x=cohort, y=score)) +
facet_grid(subject~.) +
stat_summary(fun.data="mean_cl_normal") +
geom_hline(aes(yintercept = avg_score),color = "blue",linetype = "dashed") +
ylim(0,100)
Upvotes: 0