Reputation: 907
Let's say I have two different sources of data. One is of repeated observations, and one is just a mean +/- standard error predicted by a model.
n <- 30
obs <- data.frame(
group = rep(c("A", "B"), each = n*3),
level = rep(rep(c("low", "med", "high"), each = n), 2),
yval = c(
rnorm(n, 30), rnorm(n, 50), rnorm(n, 90),
rnorm(n, 40), rnorm(n, 55), rnorm(n, 70)
)
) %>%
mutate(level = factor(level, levels = c("low", "med", "high")))
model_preds <- data.frame(
group = c("A", "A", "A", "B", "B", "B"),
level = rep(c("low", "med", "high"), 2),
mean = c(32,56,87,42,51,74),
sem = runif(6, min = 2, max = 5)
)
now I can plot these on the same graph easily enough
p <- ggplot(obs, aes(x = level, y = yval, fill = group)) +
geom_boxplot() +
geom_point(data = model_preds, aes(x = level, y = mean), size = 2, colour = "forestgreen") +
geom_errorbar(data = model_preds, aes(x = level, y = mean, ymax = mean + sem, ymin = mean - sem), colour = "forestgreen", size = 1) +
facet_wrap(~group)
and use that the visually look at the difference between the model predictions and the observed results.
But I think this looks a bit ugly, so ideally would want to 'dodge' the point-and-errorbars geom(s) from the boxplot geom.
If you'll forgive my quick paint drawing, something like this:
It seems like position_dodge() might be the way to go but I haven't figured out how to combine two different geoms this way and the docs don't have any examples.
Might be that it's impossible, but thought I'd ask to check
Upvotes: 5
Views: 1302
Reputation: 38003
As a consequence of the grammer of graphics, which clearly separates various aspects of plotting, there is no way to communicate information between different layers (geoms and stats) of a plot. This also means that a position adjustment cannot be shared across layers, such that they can be dodged in a multi-layer fashion.
The next best thing you could do, is to use position = position_nudge()
in every layer, so that across the layers they seem dodged. You might also want to adjust the width
parameter of the boxplot and errorbar for this. Example below:
library(tidyverse)
n <- 30
obs <- data.frame(
group = rep(c("A", "B"), each = n*3),
level = rep(rep(c("low", "med", "high"), each = n), 2),
yval = c(
rnorm(n, 30), rnorm(n, 50), rnorm(n, 90),
rnorm(n, 40), rnorm(n, 55), rnorm(n, 70)
)
) %>%
mutate(level = factor(level, levels = c("low", "med", "high")))
model_preds <- data.frame(
group = c("A", "A", "A", "B", "B", "B"),
level = rep(c("low", "med", "high"), 2),
mean = c(32,56,87,42,51,74),
sem = runif(6, min = 2, max = 5)
)
ggplot(obs, aes(x = level, y = yval, fill = group)) +
geom_boxplot(position = position_nudge(x = -0.3),
width = 0.5) +
geom_point(data = model_preds, aes(x = level, y = mean),
size = 2, colour = "forestgreen",
position = position_nudge(x = 0.3)) +
geom_errorbar(data = model_preds,
aes(x = level, y = mean, ymax = mean + sem, ymin = mean - sem),
colour = "forestgreen", size = 1, width = 0.5,
position = position_nudge(x = 0.3)) +
facet_wrap(~group)
Created on 2021-01-17 by the reprex package (v0.3.0)
Upvotes: 5