将两个ggplots合二为一

Question

我面临一些问题，即从单独的数据帧中绘制一个而不是两个。我在下面解释了情况。数据框如下所示：

df1 <- structure(list(value = c(9921L, 21583L, 11822L, 1054L, 13832L, 
16238L, 13838L, 20801L, 20204L, 13881L, 19935L, 13829L, 14012L, 
20654L, 13862L, 21191L, 3777L, 15552L, 13817L, 20428L, 16850L, 
21003L, 11072L, 22477L, 12321L, 12856L, 16295L, 11431L, 13469L, 
14680L, 10552L, 15272L, 9132L, 9374L, 15123L, 22754L, 10363L, 
12160L, 13729L, 11151L, 11451L, 11272L, 14900L, 14688L, 17133L, 
7315L, 7268L, 6262L, 72769L, 7650L, 16389L, 13027L, 7134L, 6465L, 
6490L, 15183L, 7201L, 14070L, 11210L, 10146L), limit = structure(c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("1Mbit", 
"5Mbit", "10Mbit"), class = "factor")), class = "data.frame", row.names = c(NA, 
-60L))

df2 <- structure(list(value = c(37262L, 39881L, 30914L, 32976L, 28657L, 
39364L, 39915L, 30115L, 29326L, 36199L, 37976L, 36694L, 33718L, 
36945L, 33182L, 35866L, 34188L, 33426L, 32804L, 34986L, 29355L, 
30470L, 37420L, 26465L, 28975L, 29144L, 27491L, 30507L, 27146L, 
26257L, 31231L, 30521L, 30370L, 31683L, 33774L, 35654L, 34172L, 
38554L, 38030L, 33439L, 34817L, 31278L, 33579L, 31175L, 31001L, 
29908L, 31658L, 33381L, 28709L, 34794L, 34154L, 30157L, 33362L, 
30363L, 31097L, 29116L, 27703L, 31229L, 30196L, 30077L), limit = structure(c(3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("180ms", 
"190ms", "200ms"), class = "factor")), class = "data.frame", row.names = c(NA, 
-60L))

从上面的数据框中，我有这些图：

limit_bw <- factor(df1$limit, levels = c("1Mbit", "5Mbit", "10Mbit"))
limit_lt <- factor(df2$limit, levels = c("200ms", "190ms", "180ms"))

（按顺序使用）

bw_line <- ggplot(df1, aes(x = limit_bw, y = value, group=1)) + geom_quantile(method = "loess")
lt_line <- ggplot(df2, aes(x = limit_lt, y = value, group=1)) + geom_quantile(method = "loess")

（我其实有很多数据所以我用geom_quantile()）

还有两个使用rbind/cbind的网格中的图（这不是我现在想要的）：

grid.draw(rbind(ggplotGrob(ggplot(df1, aes(limit_bw,value,group=1)) + geom_quantile(method = "loess") + labs(title = "value vs bw",x="bandwidth",y="value")),
ggplotGrob(ggplot(df2, aes(limit_lt, value, group = 1)) + geom_quantile(method="loess") + labs(title="value vs latency", x="latency", y="value")), size = "last"))

我正在寻求您的帮助，将它们合并成一个图（将 bw_line 和 lt_line 放在同一个图中）在顶部和底部显示两个 x 轴或两个底部的轴提到他们的标题。请注意，value 对每个数据集都有不同的范围。但是，我需要为每个数据帧的不同范围显示两个 y 轴，或者可能是一个 y 轴，显示两个数据帧中的所有值（最小值到最大值）。

我实际上从@RichieCotton 看到了一个非常接近的解决方案here，但由于我有一些factors 而不是整数值，所以无法找出我的数据。

非常感谢您的帮助。谢谢。

Answer 1

我认为通过首先将数据组合到一个数据帧中来解决这个问题可能是最简单的。在这里，我制作了组合的 x 值并将您的数据映射到这些值。然后我们像往常一样映射，添加一个辅助 y 轴。

library(tidyverse); library(forcats)

# Create shared x axis and combine data frames
limit_combo <- data.frame(level_num = 1:3, 
                          level = as_factor(c("1Mbit\n200ms",
                                              "5Mbit\n190ms",
                                              "10Mbit\n180ms"))) 
df1b <- df1 %>%
  mutate(level_num = limit %>% as.numeric) %>%
  left_join(limit_combo)
df2b <- df2 %>%
  mutate(level_num = 4 - (limit %>% as.numeric)) %>%
  left_join(limit_combo)
df3 <- bind_rows(df1b, df2b, .id = "plot") %>%
  mutate(plot = if_else(plot == "1", "bw", "lt"))

# plot with adjusted y values and second axis for reference
ggplot(df3, aes(x = level, 
                y = value * if_else(plot == "lt", 0.44, 1), 
                group=plot, color = plot)) + 
  geom_quantile(method = "loess") +
  scale_y_continuous("value", sec.axis = sec_axis(~./0.44)) +
  theme(axis.text.y.left  = element_text(color = "#F8766D"),
        axis.text.y.right = element_text(color = "#00BFC4"))

Answer 2

这是一种从两个数据集创建单个图的不同方法，它避免将两个数据集合并为一个并处理limit 的因素。 df1、df2、limit_bw 和 limit_lt 按 OP 的规定使用。

情节分三步细化。

1。常用x轴，常用y坐标

library(ggplot2)
ggplot() + aes(y = value) +
  geom_quantile(aes(x = as.integer(limit_bw), colour = "bw"), df1, method = "loess") + 
  geom_quantile(aes(x = as.integer(limit_lt), colour = "lt"), df2, method = "loess") +
  scale_x_continuous("limit",
    breaks = 1:nlevels(limit_bw), 
    labels = paste(levels(limit_bw), levels(limit_lt), sep = "\n")) +
  scale_colour_discrete(NULL)

2。单独的x轴，常用的y标度

library(ggplot2)
ggplot() + aes(y = value) +
  geom_quantile(aes(x = as.integer(limit_bw), colour = "bw"), df1, method = "loess") + 
  geom_quantile(aes(x = as.integer(limit_lt), colour = "lt"), df2, method = "loess") +
  scale_x_continuous("limit",
                     breaks = 1:nlevels(limit_bw), 
                     labels = levels(limit_bw), 
                     sec.axis = dup_axis(labels = levels(limit_lt))) +
  scale_colour_manual(NULL, values = c(bw = "blue", lt = "red")) +
  theme(axis.text.x.bottom = element_text(color = "blue"),
        axis.text.x.top    = element_text(color = "red"))

3。分开 x 轴，分开 y 轴

在这里，第二个数据集的 y 值被缩放，使得两个数据集的最小值和最大值重合。

# compute scaling factor and offset
library(magrittr)   # used to improve readability
bw_rng <- loess(df1$value ~ as.integer(limit_bw)) %>% fitted() %>% range()
lt_rng <- loess(df2$value ~ as.integer(limit_lt)) %>% fitted() %>% range()
scl <- diff(bw_rng) / diff(lt_rng)
ofs <- bw_rng[1] - scl * lt_rng[1]
library(ggplot2)
ggplot() +
  geom_quantile(aes(x = as.integer(limit_bw), y = value, colour = "bw"), 
                df1, method = "loess") + 
  geom_quantile(aes(x = as.integer(limit_lt), y = scl * value + ofs, colour = "lt"), 
                df2, method = "loess") +
  scale_x_continuous("limit",
                     breaks = 1:nlevels(limit_bw), 
                     labels = levels(limit_bw), 
                     sec.axis = dup_axis(labels = levels(limit_lt))) +
  scale_y_continuous(sec.axis = sec_axis(~ (. - ofs) / scl)) +
  scale_colour_manual(NULL, values = c(bw = "blue", lt = "red")) +
  theme(axis.text.x.bottom = element_text(color = "blue"),
        axis.text.x.top    = element_text(color = "red"),
        axis.text.y.left   = element_text(color = "blue"),
        axis.text.y.right  = element_text(color = "red"))