我有按三个变量交叉分类的数据，类似这样...
library(tidyverse)
d0 <- mtcars %>%
group_by(cyl, am, vs) %>%
summarise(ave_wt = mean(wt)) %>%
ungroup() %>%
complete(cyl, am, vs) %>%
replace_na(list(ave_wt = 0)) %>%
mutate_if(names(.) %in% names(.)[1:3], as.factor)
d0
# # A tibble: 12 x 4
# cyl am vs ave_wt
# <fctr> <fctr> <fctr> <dbl>
# 1 4 0 0 0.000000
# 2 4 0 1 2.935000
# 3 4 1 0 2.140000
# 4 4 1 1 2.028286
# 5 6 0 0 0.000000
# 6 6 0 1 3.388750
# 7 6 1 0 2.755000
# 8 6 1 1 0.000000
# 9 8 0 0 4.104083
# 10 8 0 1 0.000000
# 11 8 1 0 3.370000
# 12 8 1 1 0.000000
我正试图用这些点来绘制...
ggplot(data = d0,
mapping = aes(x = am, y = ave_wt, colour = vs, group = cyl)) +
geom_point(position = position_dodge(0.5))
... 加入每个 vs - am 组合，如下所示，但线条更直(此处未使用 ggplot 绘制线条)。
到目前为止我所有的尝试都没有奏效......
ggplot(data = d0,
mapping = aes(x = am, y = ave_wt, colour = vs, group = cyl)) +
geom_point(position = position_dodge(0.5)) +
geom_line()
# geom_line(mapping = aes(group = interaction(vs, am)))
# geom_line(mapping = aes(group = interaction(vs, am)),
# position = position_dodge(0.5))
# geom_line(position = position_dodge(0.5))
# geom_path(position = position_dodge(0.5),
# mapping = aes(group = interaction(vs, am)))
最佳答案
这是适合您的一种方法。这个想法是根据您的输出创建最终输出。我使用了您图形后面的数据框。它包含我们需要的所有信息。我对数据进行了排序并将数据点分为四组，这是在 mutate() 中完成的。然后，我画了下图。
library(dplyr)
library(ggplot2)
foo <- ggplot(data = d0,
aes(x = am, y = ave_wt, colour = vs, group = cyl)) +
geom_point(position = position_dodge(0.5))
temp <- as.data.frame(ggplot_build(foo)$data[1])
arrange(temp, colour, x) %>%
mutate(whatever = rep(1:4, each = 3)) %>%
ggplot(aes(x = x, y = y, color = factor(colour, levels = c("#F8766D", "#00BFC4")),
group = whatever)) +
geom_line() +
geom_point() +
scale_x_continuous(breaks = c(1,2), minor_breaks = NULL, labels = c(0, 1)) +
labs(x = "am", y = "ave_wt") +
scale_color_discrete(name = "vs", labels = c(0, 1))
关于r - 从 geom_point 连接 position_dodge 点，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/49830313/