2

使用从这里生成的数据，我想让 ggplot2 创建一个类似于下面的图。

带有不良标签 ( facet_wrap) 的理想绘图面板：

带有不良标签的理想绘图面板（facet_wrap）

此图中的目的是允许在第一列 (Approach1) 的分布平均值与每列的密度平均值之间进行视觉比较。创建密度图的脚本如下：

ggplot(surg_df, aes(x=op_tm, col=color_hex)) +
  geom_density(aes(fill=color_hex), alpha=0.3) +
  geom_density(data = base_comp_df, col='#a269ff', alpha=1, size=0.5) +
  geom_vline(data=avg_surg_df, aes(xintercept= avg_op_tm), 
             size=1, col=avg_surg_df$color_hex, linetype="dashed") +
  geom_vline(data=avg_comp_df, aes(xintercept= avg_op_tm+2), 
             size=1, colour='#a269ff', linetype="dashed") +
  annotate(geom= "text",
           label=paste("mean diff: ", 
                       as.character(floor(avg_comp_df$avg_op_tm-avg_surg_df$avg_op_tm)), 
                       sep=""), 
           col='black', size=4, x=100, y=0.006) +
  geom_segment(aes(x = avg_op_tm, y=0.006, xend = avg_surg_df$avg_op_tm, 
                   yend = 0.006, colour = "red") , 
               size=1, data = avg_comp_df) +    
  facet_wrap(~surg_grp) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  scale_colour_identity(guide="none", breaks=base_surg_df$color_hex) +
  scale_fill_identity(guide="legend", breaks=base_surg_df$color_hex,
                      name="Surgical Approaches", 
                      labels=base_surg_df$surg_apprch)

最好使用facet_wrap()with grouping by variable创建上图surg_grp。但是当我决定利用标签的优势让它看起来不那么凌乱时facet_grid()，事情就失控了。到目前为止，我设法创建的facet_grid()是以下糟糕的密度图集合，每个面板都有 3 条可笑的avg_op_tm垂直avg_surg_df线.

不受欢迎的绘图面板，理想的标签（facet_grid）：

不受欢迎的绘图面板，理想的标签（facet_grid）

正如您在下面的脚本中注意到的那样，与上一个图不同，只有一个 geom_vline 并且每个面板上的三行来自该单行：

ggplot(surg_df)+
  geom_density(aes(x=op_tm, col=color_hex, fill=color_hex), alpha=0.3) +
  scale_fill_identity("Approaches", guide="legend", breaks=base_surg_df$color_hex, 
                      labels=base_surg_df$surg_apprch,
                      aesthetics = "fill")+    
  scale_colour_identity(guide="none",breaks=base_surg_df$color_hex)+
  geom_density(data = base_comp_df, aes(x=op_tm), alpha=1, col='#a269ff', size=0.5) +
  geom_vline(data=avg_surg_df, aes(xintercept= avg_op_tm), size=1,
             linetype="dashed")+
  annotate(geom= "text",
           label=paste("mean diff: ", 
                       as.character(floor(avg_surg_df$avg_op_tm)), sep=""), 
           col='black', size=4, x=100, y=0.006)+
  facet_grid(rows = vars(condition_grp), cols=vars(surg_apprch), scales = 'free')

社区有很多类似的问答，facet_wrap() + geom_vline()但关于facet_grid() + geom_vline(). 如何geom_vline()使用已输入facet_wrap(condition_grp和surg_apprch) 的两个分组参数并使其正确映射数据？我没有学到什么教育点导致我的方法facet_grid()失败了？

非常感谢任何帮助。

更新

> head(avg_comp_df)
  surg_grp avg_op_tm Cnt surg_apprch color_hex
1       A1  309.5494  74   Approach1   #a269ff
2       A2  309.5494  74   Approach2   #00CC00
3       A3  309.5494  74   Approach3   #FFAA93
4       A4  309.5494  74   Approach4   #5DD1FF
5       B1  263.0835  71   Approach1   #a269ff
6       B2  263.0835  71   Approach2   #00CC00

> head(surg_df) #used to create 12 different curves
  surg_grp surg_apprch condition_grp    op_tm color_hex
1       A1   Approach1      Benign-1 287.2103   #a269ff
2       A1   Approach1      Benign-1 261.2655   #a269ff
3       A1   Approach1      Benign-1 308.9267   #a269ff
4       A1   Approach1      Benign-1 257.9060   #a269ff
5       A1   Approach1      Benign-1 408.0310   #a269ff
6       A1   Approach1      Benign-1 405.4334   #a269ff

> head(avg_surg_df)
  surg_grp avg_op_tm Cnt surg_apprch color_hex
1       A1  309.5494  74   Approach1   #a269ff
2       A2  378.4466 113   Approach2   #00CC00
3       A3  242.9890 101   Approach3   #FFAA93
4       A4  273.0774  71   Approach4   #5DD1FF
5       B1  263.0835  71   Approach1   #a269ff
6       B2  243.1910  85   Approach2   #00CC00

> head(base_comp_df)  #to create similar orchid control distributions in each row 
  surg_grp surg_apprch condition_grp    op_tm color_hex
1       A1   Approach1      Benign-1 287.2103   #a269ff
2       A1   Approach1      Benign-1 261.2655   #a269ff
3       A1   Approach1      Benign-1 308.9267   #a269ff
4       A1   Approach1      Benign-1 257.9060   #a269ff
5       A1   Approach1      Benign-1 408.0310   #a269ff
6       A1   Approach1      Benign-1 405.4334   #a269ff

> head(base_surg_df) #to make the legend
  surg_apprch condition_grp surg_grp color_hex
1   Approach1      Benign-1       A1   #a269ff
2   Approach2      Benign-1       A2   #00CC00
3   Approach3      Benign-1       A3   #FFAA93
4   Approach4      Benign-1       A4   #5DD1FF
5   Approach1      Benign-2       B1   #a269ff
6   Approach2      Benign-2       B2   #00CC00
4

1 回答 1

0

您需要在一个单独的数据框中预先计算方法，该数据框中复制您facet_grid在其组中的结构。一个粗糙而肮脏的例子mtcars：

library(dplyr)
library(ggplot2)

mean_df <- mtcars %>% 
  group_by(am, vs) %>% 
  summarise(mean_val = mean(mpg))

ggplot(mtcars) +
  geom_freqpoly(aes(x = mpg)) +
  geom_vline(data = mean_df, aes(xintercept = mean_val), colour = "red") +
  facet_grid(am ~ vs)

于 2021-01-21T23:20:40.303 回答