Displaying multiple factors with Sina plots - r

Question

NOTE: I have updated this post following discussion with Z. Lin. Originally, I had simplified my problem to a two factor design (see section "Original question"). However, my actual data consists of four factors, requiring facet_grid. I am therefore providing an example for a four factor design further below (see section "Edit").
Original question
Let's assume I have a two factor design with dv as my dependent variable and iv.x and iv.y as my factors/independent variables. Some quick sample data:
DF <- data.frame(dv = rnorm(900),
iv.x = sort(rep(letters[1:3], 300)),
iv.y = rep(sort(rep(rev(letters)[1:3], 100)), 3))
My goal is to display each condition separately as can nicely be done with violin plots:
ggplot(DF, aes(iv.x, dv, colour=iv.y)) + geom_violin()
I have recently come across Sina plots and would like to do the same here. Unfortunately Sina plots don't do this, collapsing the data instead.
ggplot(DF, aes(iv.x, dv, colour=iv.y)) + geom_sina()
An explicit call to position dodge doesn't help either, as this produces an error message:
ggplot(DF, aes(iv.x, dv, colour=iv.y)) + geom_sina(position = position_dodge(width = 0.5))
The authors of Sina plots have already been made aware of this issue in 2016:
https://github.com/thomasp85/ggforce/issues/47
My problem is more in terms of time. We soon want to submit a manuscript and Sina plots would be a great way to display our data. Can anyone think of a workaround for Sina plots such that I can still display two factors as in the example with violin plots above?
Edit
Sample data for a four factor design:
DF <- data.frame(dv=rnorm(400),
iv.w=sort(rep(letters[1:2],200)),
iv.x=rep(sort(rep(letters[3:4],100)), 2),
iv.y=rep(sort(rep(rev(letters)[1:2],50)),4),
iv.z=rep(sort(rep(letters[5:6],25)),8))
An example with violin plots of what I would like to create using Sina plots:
ggplot(DF, aes(iv.x, dv, colour=iv.y)) +
facet_grid(iv.w ~ iv.z) +
geom_violin(aes(y = dv, fill = iv.y),
position = position_dodge(width = 1))+
stat_summary(aes(y = dv, fill = iv.y), fun.y=mean, geom="point",
colour="black", show.legend = FALSE, size=.2,
position=position_dodge(width=1))+
stat_summary(aes(y = dv, fill = iv.y), fun.data=mean_cl_normal, geom="errorbar",
position=position_dodge(width=1), width=.2, show.legend = FALSE,
colour="black", size=.2)

Answer 1

Edited solution, since OP clarified that facets are required:
ggplot(DF, aes(x = interaction(iv.y, iv.x),
y = dv, fill = iv.y, colour = iv.y)) +
facet_grid(iv.w ~ iv.z) +
geom_sina() +
stat_summary(fun.y=mean, geom="point",
colour="black", show.legend = FALSE, size=.2,
position=position_dodge(width=1))+
stat_summary(fun.data=mean_cl_normal, geom="errorbar",
position=position_dodge(width=1), width=.2,
show.legend = FALSE,
colour="black", size=.2) +
scale_x_discrete(name = "iv.x",
labels = c("c", "", "d", "")) +
theme(panel.grid.major.x = element_blank(),
axis.text.x = element_text(hjust = -4),
axis.ticks.x = element_blank())
Instead of using facets to simulate dodging between colours, this approach creates a new variable interaction(colour.variable, x.variable) to be mapped to the x-axis.
The rest of the code in scale_x_discrete() & theme() are there to hide the default x-axis labels / ticks / grid lines.
axis.text.x = element_text(hjust = -4) is a hack that shifts x-axis labels to approximately the right position. It's ugly, but considering the use case is for a manuscript submission, I assume the size of plots will be fixed, and you just need to tweak it once.
Original solution:
Assuming your plots don't otherwise require facetting, you can simulate the appearance with facets:
ggplot(DF, aes(x = iv.y, y = dv, colour = iv.y)) +
geom_sina() +
facet_grid(~iv.x, switch = "x") +
labs(x = "iv.x") +
theme(axis.text.x = element_blank(), # hide iv.y labels
axis.ticks.x = element_blank(), # hide iv.y ticks
strip.background = element_blank(), # make facet strip background transparent
panel.spacing.x = unit(0, "mm")) # remove horizontal space between facets