I've had to do this enough times (and have to look it up each time) that I decided to memorialize it here.
The issue: I have a two-way repeated measures design and I want to visualize all four cells. I'd like one plot to contain the individuals responses as well as the cell means. But I also want to link individuals together.
The solution: Plot the individual differences within each level of one of the factors using separate lines for each subject, plus an additional line for the cell means.
Here's a simple demo (with a bonus example of how to simulate such a dataset).
require(tidyverse)
# simulate 2x2 interaction with 40 subjects
n <- 40
# define effects for intercept, A, B, and A*B
a <- 5.0 # intercept
b1 <- 0.3 # main effect of A
b2 <- -0.5 # main effect of B
b3 <- 1.0 # interaction
# simulate outcomes
A <- rep(c(0,1), each=n*2)
B <- rep(c(0,1), each=n, 2)
e <- rnorm(n*4)
id <- rep(c(1:n), 4)
y <- a + b1*A + b2*B + b3*A*B + e
d <- data.frame(A=A, B=B, y=y, id=id)
d$id <- factor(id)
# use group_by to create a new data.frame object with cell means
g <- d %>%
group_by(A, B) %>%
summarize(y = mean(y))
ggplot(d, aes(x=A, y=y)) +
geom_line(aes(group=id), alpha=.3) + # plot by individual
geom_point(data=g, size=2) + # plot cell means as points
geom_path(data=g, aes(group=B), size=1) + # plot mean lines
facet_wrap(~B) # split plots by Factor B
