Consider for example this plot using the data mtcars and the function coord_flip
library(ggplot2)
library(Hmisc)
ggplot(mtcars,aes(x=gear,y=cyl)) + stat_summary(aes(color=as.factor(rep(1:2,16))),
fun.data=mean_cl_boot, position=position_dodge(0.4)) + coord_flip()
The fact that error bars are horizontal on the graph but vertical in the legend bothers me :) How can I rotate these symbols?
Tweak the legend key
GeomPointrange$draw_key <- function (data, params, size) {
draw_key_vpath <- function (data, params, size) {
# only need to change the x&y coords so that the line is horizontal
# originally, the vertical line was `0.5, 0.1, 0.5, 0.9`
segmentsGrob(0.1, 0.5, 0.9, 0.5,
gp = gpar(col = alpha(data$colour, data$alpha),
lwd = data$size * .pt, lty = data$linetype,
lineend = "butt"), arrow = params$arrow)
}
grobTree(draw_key_vpath(data, params, size),
draw_key_point(transform(data, size = data$size * 4), params))
}
Then plot
ggplot(mtcars,aes(x=gear,y=cyl)) +
stat_summary(aes(color=as.factor(rep(1:2,16))),
fun.data=mean_cl_boot, position=position_dodge(0.4)) +
coord_flip()
I'm not coming up with an answer that works within the normal ggplot2 workflow, so for now, here's a hacky answer. Turn off the stat_summary legend. Then, add point and line geoms with data that is outside the range of the actual data you want to plot. This will create the point and horizontal line legend that you want. Then set the plot axis limits to include only the range of your real data, so that the fake data points are not visible.
ggplot(mtcars, aes(x=gear, y=cyl, color=as.factor(rep(1:2,16)))) +
stat_summary(fun.data=mean_cl_boot, position=position_dodge(0.4), show.legend=FALSE) +
geom_line(aes(y=cyl-100)) +
geom_point(aes(y=cyl-100), size=2.5) +
coord_flip(ylim=range(mtcars$cyl))
Another option would be to rotate the legend-key grobs by 90 degrees using grid functions, but I'll leave that for someone who's more skilled with grid than I am.
ggplot2 3.3.0 fixed this. Using the geom_pointrange() function will render horizontal error bars in the legend when using the xmin and xmax args:
library(ggplot2)
library(dplyr)
df <- mtcars |>
mutate(gear = as.factor(gear),
am = as.factor(am)) |>
group_by(gear, am) |>
summarise(cyl_mean = mean(cyl),
cyl_upr = mean(cyl) + sd(cyl)/sqrt(length(cyl)),
cyl_lwr = mean(cyl) - sd(cyl)/sqrt(length(cyl)))
ggplot(df,
aes(x=cyl_mean, y=gear,
color = am, group = am)) +
geom_pointrange(aes(xmin = cyl_lwr,
xmax = cyl_upr),
position = position_dodge(0.25))
Created on 2023-05-16 with reprex v2.0.2
The ggstance package provides an easy to implement solution here:
library(ggplot2)
library(ggstance)
ggplot(mtcars,aes(x=cyl,y=gear)) + stat_summaryh(aes(color=as.factor(rep(1:2,16))),
fun.data=mean_cl_boot_h, position = position_dodgev(height = 0.4))
or as a geom:
df <- data.frame(x = 1:3, y = 1:3)
ggplot(df, aes(x, y, colour = factor(x))) +
geom_pointrangeh(aes(xmin = x - 1, xmax = x + 1))
Following up @eipi10's suggestion to use grid functions to edit the grobs - the relevant grobs are segments. There are two possibilities: 1) rotate the segment grobs; or 2) edit the x and y coordinates of the endpoints of the segment grobs.
library(ggplot2)
library(Hmisc)
library(grid)
p = ggplot(mtcars,aes(x=gear,y=cyl)) +
stat_summary(aes(color=as.factor(rep(1:2,16))),
fun.data=mean_cl_boot, position=position_dodge(0.4)) +
coord_flip()
g = ggplotGrob(p)
# Get names of segment grobs
grid.ls(grid.force(g))$name # "GRID.segments"
# Check the structure of the segment grobs
str(getGrob(grid.force(g), gPath("GRID.segments"), grep = TRUE, global = TRUE))
# Edit the segment grobs using the editGrob() function
# 1) Rotate the segments
g <- editGrob(grid.force(g), gPath("GRID.segments"), grep = TRUE, global = TRUE,
vp = viewport(angle = 90))
# 2) set end points of segments
# g <- editGrob(grid.force(g), gPath("GRID.segments"), grep = TRUE, global = TRUE,
# x0 = unit(0.1, "npc"), y0 = unit(0.5, "npc"), x1 = unit(0.9, "npc"), y1 = unit(0.5, "npc"))
# Draw it
grid.newpage()
grid.draw(g)
Edited from: https://gist.github.com/grantmcdermott/d86af2b8f21f4082595c0e717eea5a90
The main point is to use geom_pointrangeh from ggstance and remember to specify aes w.r.t. x-axis.
library(tidyverse)
library(broom)
library(hrbrthemes)
library('ggstance')
library('jtools')
df =
mtcars %>%
mutate(vs = factor(vs), am = factor(am))
fit1 = lm(mpg ~ vs * am * wt, data = df)
fit1_coefs = tidy(fit1, conf.int = T)
fit2 = lm(mpg ~ vs / am / wt, data = df)
fit2_coefs = tidy(fit2, conf.int = T)
bind_rows(
fit1_coefs %>% mutate(model = "Model 1"),
fit2_coefs %>% mutate(model = "Model 2")
) %>%
filter(grepl("wt", term)) %>%
## Optional regexp work to make plot look nicier
mutate(
am = ifelse(grepl("am1", term), "Automatic", "Manual"),
vs = ifelse(grepl("vs1", term), "V-shaped", "Straight"),
x_lab = paste(am, vs, sep="\n")
) %>%
ggplot(aes(col = model,y=x_lab, x=estimate, xmin=conf.low, xmax=conf.high)) +
geom_pointrangeh(position = position_dodge(width = 0.5)) +
guides(color = guide_legend(reverse = TRUE)) +
geom_vline(xintercept = 0, col = "black",lty=4) +
labs(x = NULL, y = NULL,title = "Title") +
theme_nice() +
theme(plot.title = element_text(hjust = 0.5))
