Just trying to understand how geom_abline works with facets in ggplot.
By way of example, 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.
## Code to generate dt
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. *Why*?
NB: I realize this problem can be solved by creating a 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
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