合并堆积和分组的图表ggplot2

Question

我有一张桌子：

Number  Type    Correction  Adjust  Origin
1061    60-15   Corrected   yes     Small RNA-seq
204     60-15   Corrected   no      Small RNA-seq
0       60-15   Native      yes     Small RNA-seq
540     60-15   Native      no      Small RNA-seq
0       60-30   Corrected   yes     Small RNA-seq
315     60-30   Corrected   no      Small RNA-seq
0       60-30   Native      yes     Small RNA-seq
58      60-30   Native      no      Small RNA-seq
0       70-15   Corrected   yes     Small RNA-seq
200     70-15   Corrected   no      Small RNA-seq
0       70-15   Native      yes     Small RNA-seq
61      70-15   Native      no      Small RNA-seq
0       70-30   Corrected   yes     Small RNA-seq
259     70-30   Corrected   no      Small RNA-seq
0       70-30   Native      yes     Small RNA-seq
42      70-30   Native      no      Small RNA-seq
0       80-15   Corrected   yes     Small RNA-seq
166     80-15   Corrected   no      Small RNA-seq
0       80-15   Native      yes     Small RNA-seq
76      80-15   Native      no      Small RNA-seq
0       80-30   Corrected   yes     Small RNA-seq
182     80-30   Corrected   no      Small RNA-seq
0       80-30   Native      yes     Small RNA-seq
13      80-30   Native      no      Small RNA-seq

并且我已经在ggplot2中生成了以下图，这几乎就是我想要的：

ggplot(Table, aes(fill=Correction, x=Type, y=Number)) +
       geom_bar(position="dodge", stat="identity") +
       scale_fill_brewer(palette = "Set1") + labs(x="", y="") + 
       theme(legend.title=element_blank()) + facet_wrap(~Origin)

它会生成如下图：

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问题是我希望60-15的第一个条形图被一分为二(1061和204)，就像它是一个堆叠的条形图一样。其余的条形图没有这种特殊性，将保持不变。我试图通过添加具有零数值的行来解决这个问题，但是我找不到正确的代码来解决这个问题。

有人能帮帮忙吗？

谢谢

Answer 1

您可以尝试在此之前计算总和。然后使用geom_rect自行添加缺少的条形图部分

library(tidyverse)
df %>% 
  group_by(Type, Correction) %>% 
   summarise(count=sum(Number))
# A tibble: 12 x 3
# Groups:   Type [6]
Type  Correction count
<fct> <fct>      <int>
1 60-15 Corrected   1265
2 60-15 Native       540
3 60-30 Corrected    315
4 60-30 Native        58
5 70-15 Corrected    200
6 70-15 Native        61
7 70-30 Corrected    259
8 70-30 Native        42
9 80-15 Corrected    166
10 80-15 Native        76
11 80-30 Corrected    182
12 80-30 Native        13

df %>% 
  group_by(Type, Correction) %>% 
  summarise(count=sum(Number)) %>% 
  mutate(Correction=factor(Correction, levels = c(levels(Correction), "new_level"))) %>%  
  ggplot(aes(Type, count, fill=Correction)) + 
   geom_col(position = "dodge") +
   geom_rect(aes(xmin=.55, xmax=1, ymin=1061, ymax=1061+204), fill="#619CFF") + 
   scale_fill_discrete(drop=FALSE)

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