Ggplot2：如何将图例按颜色拆分为自定义图例 [英] ggplot2: How to split legend by color into a customized legend

问题描述

我很难让情节的标签看起来像是某种方式。我正在使用ggplot2和tidyverse。

这是我拥有的：

我希望图例有两个标题(=名称)，一个用于单元格类型HCT，一个用于单元格类型RKO。然后对于HCT和RKO，我想要具有各自颜色、线型和形状的试剂的图例。所以基本上，我想把颜色传说分成两个独立的传说。我就是想不通该怎么编码。以下是我想要的图形(对于图形图例，请想象橙色正方形已填好)：

我是否需要更改geom_line和geom_point代码才能获得我想要的图例样式？还是有其他方法可以做到这一点？我试着寻找一种方法来做到这一点，但什么也找不到(也许我只是没有使用正确的术语)。 我已经尝试遵循此处所做的操作：How to merge color, line style and shape legends in ggplot和Combine legends for color and shape into a single legend，但我无法使其工作。(换句话说，我尝试更改Scale_Shape_MANUAL等以满足我的愿望，但没有成功。我还尝试使用Interaction())

注意：我决定不使用Facet_WRAP，因为我想在同一图上显示这两种单元格类型。真实数据的曲线图看起来有点不同，也不是那么压倒性。我使用ggpubr成功绘制了"；facet_Wrap"；图。

注2：我也没有使用STAT_SUMMARY()，因为我需要取相同试剂浓度、试剂和细胞类型的平均值。使用我的数据，我找不到让STAT_SUMMARY工作的方法。

以下是我当前拥有的代码：

mean_mutated <- mutated %>% group_by(Reagent, Reagent.Conc, Cell.type) %>%
  summarise(Avg.Viable.Cells = mean(Mean.Viable.Cells.1, na.rm = TRUE))
mutated_0 = mutated %>% group_by(Reagent, Reagent.Conc, Cell.type) %>% filter(Reagent=="0") %>% 
  summarise(Avg.Viable.Cells = mean(Mean.Viable.Cells.1, na.rm = TRUE))
mutated_1 = mutated %>% group_by(Reagent, Reagent.Conc, Cell.type) %>% filter(Reagent=="1") %>% 
  summarise(Avg.Viable.Cells = mean(Mean.Viable.Cells.1, na.rm = TRUE))
mutated_2 = mutated %>% group_by(Reagent, Reagent.Conc, Cell.type) %>% filter(Reagent=="2") %>% 
  summarise(Avg.Viable.Cells = mean(Mean.Viable.Cells.1, na.rm = TRUE))

#linetype by reagent
ggplot() +  
  #the scatter plot per cell type -> that way I can color them the way I want to, I believe
  #the mean/average line plot 
  geom_point(mean_mutated, mapping= aes(x = as.factor(Reagent.Conc), y = Avg.Viable.Cells, shape=as.factor(Reagent), color=Cell.type)) +
  geom_line(mutated_1, mapping= aes(x = as.factor(Reagent.Conc),y = Avg.Viable.Cells, group=Cell.type, color=Cell.type, linetype = "1"))+
  geom_line(mutated_2, mapping= aes(x = as.factor(Reagent.Conc),y = Avg.Viable.Cells, group=Cell.type, color=Cell.type, linetype = "2"))+
  geom_line(mutated_0, mapping= aes(x = as.factor(Reagent.Conc),y = Avg.Viable.Cells, group=Cell.type, color=Cell.type, linetype = "0"))+
  
  #making the plot look prettier
  scale_colour_manual(values = c("#999999", "#E69F00")) +
  #scale_linetype_manual(values = c("solid", "dashed", "dotted")) + #for whatever reason, when I add this, the dash in the legend is removed...?
  labs(shape = "Reagent", linetype = "Reagent", color="Cell type")+
  scale_shape_manual(values=c(15,16,4), labels=c("0", "1", "2"))+
  #guides(shape = FALSE)+ #this removes the label that you don't want
  
  #Change the look of the plot and change the axes
  xlab("[Reagent] (nM/ml)")+ #change name of x-axis
  ylab("Relative viability")+ #change name of y-axis
  scale_y_continuous(breaks = scales::pretty_breaks(n = 10))+ #adjust the y-axis so that it has more ticks
  expand_limits(y = 0)+
  theme_bw() + #this and the next line are to remove the background grid and make it look more publication-like
  theme(panel.border = element_blank(), panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"))

和dput(df[9：32，c(1，2，3，4，5)])：

生成的我的数据帧的快照

    structure(list(Biological.Replicate = c(1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L), Reagent.Conc = c(10000, 2500, 625, 156.3, 39.1, 9.8, 
2.4, 0.6, 10000, 2500, 625, 156.3, 39.1, 9.8, 2.4, 0.6, 10000, 
2500, 625, 156.3, 39.1, 9.8, 2.4, 0.6), Reagent = c(1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L), Cell.type = c("HCT", "HCT", "HCT", "HCT", 
"HCT", "HCT", "HCT", "HCT", "HCT", "HCT", "HCT", "HCT", "HCT", 
"HCT", "HCT", "HCT", "RKO", "RKO", "RKO", "RKO", "RKO", "RKO", 
"RKO", "RKO"), Mean.Viable.Cells.1 = c(1.014923966, 1.022279854, 
1.00926559, 0.936979842, 0.935565248, 0.966403395, 1.00007073, 
0.978144524, 1.019673384, 0.991595836, 0.977270557, 1.007353643, 
1.111928183, 0.963518289, 0.993028364, 1.027409034, 1.055452733, 
0.953801253, 0.956577449, 0.792568337, 0.797052961, 0.755623576, 
0.838482346, 0.836773918)), row.names = 9:32, class = "data.frame")

注3：尽管有一个列名是"；Mean.Viable.Cells.1"；，但这不是我绘制的平均值，而是前面计算的技术复制的平均值。我取突变_0、突变_1和突变_2中生物复制的平均值来绘制它。

推荐答案

使用ggnewscale包可以这样实现：

1. 在操作数据集之前将Cell.Type和Reagent转换为系数
2. 不需要数据集mutate_0，...您只需要一个摘要数据集，我将其除以Cell.type以简化稍后的代码。
3. 要获得您想要的结果，请分别绘制每个单元格类型的数据。这就是我按单元格类型拆分数据的原因
4. 要分离图例，请使用ggnewscale::new_scale为linetype和shape添加第二个刻度和图例。此外，将color从美学中删除，并将其设置为参数
5. 至少对于您的数据片段，您必须将drop=FALSE添加到两个标度，以保持未使用的因素级别。
6. 最后，为了减少代码重复，我使用帮助器函数为每个Cell.type添加几何图形和比例。

library(ggplot2)
library(dplyr)

mutated <- mutated %>% 
  mutate(Cell.type = factor(Cell.type, levels = c("HCT", "RKO")),
         Reagent = factor(Reagent, levels = c("0", "1", "2"))
  )

mean_mutated <- mutated %>%
  group_by(Reagent, Reagent.Conc, Cell.type) %>%
  summarise(Avg.Viable.Cells = mean(Mean.Viable.Cells.1, na.rm = TRUE)) %>% 
  split(.$Cell.type)
#> `summarise()` has grouped output by 'Reagent', 'Reagent.Conc'. You can override using the `.groups` argument.

layer_geom_scale <- function(cell_type, color) {
  list(
    geom_point(mean_mutated[[cell_type]], mapping = aes(shape = Reagent), color = color),
    geom_line(mean_mutated[[cell_type]], mapping = aes(group = Reagent, linetype = Reagent), color = color),
    scale_linetype_manual(name = cell_type, values = c("solid", "dashed", "dotted"), drop=FALSE),
    scale_shape_manual(name = cell_type, values = c(15, 16, 4), labels = c("0", "1", "2"), drop=FALSE) 
  )
}

# linetype by reagent
ggplot(mapping = aes(
  x = as.factor(Reagent.Conc),
  y = Avg.Viable.Cells
)) +
  layer_geom_scale("HCT", "#999999") +
  ggnewscale::new_scale("linetype") +
  ggnewscale::new_scale("shape") +
  layer_geom_scale("RKO", "#E69F00") +
  scale_y_continuous(breaks = scales::pretty_breaks(n = 10), limits = c(0, NA)) +
  theme_bw() +
  theme(
    panel.border = element_blank(), panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")
  ) +
  labs(shape = "Reagent", 
       linetype = "Reagent", 
       color = "Cell type",
       x = "[Reagent] (nM/ml)",
       y = "Relative viability")

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