例如使用数据mtcars 和函数coord_flip 考虑这个绘图
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()
误差线在图表上是水平的，但在图例中是垂直的，这让我很困扰 :) 如何旋转这些符号？
【问题讨论】：
标签： r plot graph graphics ggplot2
我没有想出一个适用于正常 ggplot2 工作流程的答案，所以现在，这里是一个 hacky 答案。关闭 stat_summary 图例。然后，使用超出您要绘制的实际数据范围的数据添加点和线几何。这将创建您想要的点和水平线图例。然后将绘图轴范围设置为仅包含真实数据的范围，这样虚假数据点就不会出现。
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$cyl))
另一种选择是使用网格函数将图例键 grobs 旋转 90 度，但我会将其留给比我更熟悉 grid 的人。
【讨论】：
调整图例键
GeomPointrange$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))
}
然后绘制
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()
【讨论】：
按照@eipi10 的建议使用grid 函数来编辑grobs - 相关grobs 是段。有两种可能性：1）旋转段grobs；或 2) 编辑段 grobs 端点的 x 和 y 坐标。
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)
【讨论】：
ggstance 包在这里提供了一个易于实施的解决方案：
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))
或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))
【讨论】：
编辑自：https://gist.github.com/grantmcdermott/d86af2b8f21f4082595c0e717eea5a90
要点是使用geom_pointrangeh from ggstance 并记住指定aes w.r.t。 x 轴。
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))
【讨论】：