使用Tidyverse重组Ggplot2组合数据。分组和条形图条形图 [英] Restructuring Data for Ggplot2 Combination Grouped and Stacked Barchart Using Tidyverse

问题描述

  library（tidyverse）
 library（ggplot2）
  

我试图在下面创建条形图，但我在重构数​​据时遇到了麻烦。我提供了一些示例数据，我在其中创建了一些快速的数据，所以结果可能很奇怪，但我更关心如何使用Tidyverse工具来设置数据。

  Q1_Sat <-c（星期六，星期六，星期六，其他，其他， （VSat），其他，其他，其他，其他，其他）
 Q1_VSat <-c（VSat，其他，其他，VSat，VSat，VSat，VSat ）
 Q1_M <-c（SatVSat，SatVSat，SatVSat，SatVSat，其他，其他，SatVSat，SatVSat）
 Q2_Sat < c（Sat，Other，Sat，Other，Sat，Sat，Other，Other）
 Q2_VSat <-c（VSat ，VSat，其他，VSat，VSat，VSat，VSat）
 Q2_M <-c（SatVSat，SatVSat，SatVSat，SatVSat， SatVSat，SatVSat，SatVSat，其他）
 Q3_Sat <-c（星期六，其他，星期六，其他，星期六， （Sat，Sat）
 Q3_VSat <-c（VSat，其他，VSat，其他，其他，其他，其他，VSat）
 Q3_M <-c（SatVSat，SatVSat，SatVSat，其他，其他，其他，其他，其他）
 
 Q4_Sat < - c（Sat，Other，Other，Other，Other，Other，Other，Other）
 Q4_VSat <-c（VSat ，VSat，VSat，VSat，VSat，VSat，VSat）
 Q4_M <-c（SatVSat，其他，其他，其他， 其他， 其他， SatVSat，S atatat）
 
 Q20 <-c（Nat，Internat，Nat，Nat，Internat，Internat，Nat，Nat）
 Calc_Sat <-c（Sat，Sat，Sat，Other，Other，Other，Sat，Sat）
 Calc_VSat< -c（Other ，其他，VSat，VSat，VSat，VSat，其他，VSat）
 PCode <-c（C11，C11，H12 ，F33，F33，C11，S33，F33）
 CCode <-c（Dept，Camit，Camit CCT，TTT，CCT）
 Data_data_frame（Q1_Sat，Q1_VSat，Q1_M，Q2_Sat，Q2_VSat，Q2_M，Q3_Sat，Q3_VSat，Q3_M，Q4_Sat，Q4_VSat，Q4_M，Q20，PCode，CCode ，Calc_Sat，Calc_VSat）
  

以下是我迄今为止开发的代码，这一点，并不知道如何纳入彩色分组酒吧Q20变量。我想用Tidyverse和ggplot2来实现这一点。关于如何使我的代码更优雅和紧凑的任何其他反馈也将不胜感激。

 数据％>％
 select（-CCode，-Q1_M，-Q2_M，-Q3_M，-Q4_M） ％>％
 collect（key，value，-PCode，-Q20）％>％
 filter（PCode ==C11）％>％
 count（Q20，key ，value）％>％
 mutate（perc = round（n / sum（n），2））％>％
 separate（key，c（Question，SatLevel （aes（x = Question，y = perc，fill = SatLevel））+ geom_col（％= 
 filter（value！=Other）％>％
 ggplot （）
  

$ b $一般来说， ggplot 需要长格式的表格，而且您的数据看起来很宽。也就是说，最后你的数据应该是这样的：

  Q bar颜色barShade Y 
 1灰色光55 
 1灰暗20 
 1蓝色光57 
 1蓝色黑色21 
 2灰色光23 
 ... 
  

，这样你可以用 aes（color = barColor，y = c）调用 ggplot Y）等。

我会说 tidyr :: gather 应该关注大部分重组，但请参阅对于其他

library(tidyverse)
library(ggplot2)

I am attempting to create the bar chart below but I'm having trouble restructuring the data. I provided some sample data below which I created kind of fast, so the results may be strange, but I'm more interested in how to use tidyverse tools to set up the data.

Q1_Sat<-c("Sat","Sat","Sat","Other","Other","Other","Other","Other")
Q1_VSat<-c("VSat","Other","Other","VSat","VSat","VSat","VSat","VSat")
Q1_M<-c("SatVSat","SatVSat","SatVSat","SatVSat","Other","Other","SatVSat","SatVSat")
Q2_Sat<-c("Sat","Other","Sat","Other","Sat","Sat","Other","Other")
Q2_VSat<-c("VSat","Other","VSat","Other","VSat","VSat","VSat","VSat")
Q2_M<-c("SatVSat","SatVSat","SatVSat","SatVSat","SatVSat","SatVSat","SatVSat","Other")
Q3_Sat<-c("Sat","Other","Sat","Other","Sat","Sat","Sat","Sat")
Q3_VSat<-c("VSat","Other","VSat","Other","Other","Other","Other","VSat")
Q3_M<-c ("SatVSat","SatVSat","SatVSat","Other","Other","Other","Other","Other")

Q4_Sat<-c("Sat","Other","Other","Other","Other","Other","Other","Other")
Q4_VSat<-c("VSat","VSat","VSat","VSat","VSat","VSat","VSat","VSat")
Q4_M<-c("SatVSat","Other","Other","Other","Other","Other","SatVSat","SatVSat")

Q20<-c("Nat","Internat","Nat","Nat","Internat","Internat","Nat","Nat")
Calc_Sat<-c("Sat","Sat","Sat","Other","Other","Other","Sat","Sat")
Calc_VSat<-c("Other","Other","VSat","VSat","VSat","VSat","Other","VSat")
PCode<-c("C11","C11","H12","F33","F33","C11","S33","F33")
CCode<-c("Dept","Camit","Camit","CCT","Dept","CCT","TTT","CCT")
Data<-data_frame(Q1_Sat,Q1_VSat,Q1_M,Q2_Sat,Q2_VSat,Q2_M,Q3_Sat,Q3_VSat,Q3_M,Q4_Sat,Q4_VSat,Q4_M,Q20,PCode,CCode,Calc_Sat,Calc_VSat)

Below is the code I've developed so far but I'm stuck at this point and not sure how to incorporate the Q20 variable for the coloured grouped bars. I would like to use Tidyverse and ggplot2 to achieve this. Any other feedback about how to make my code more elegant and compact would also be greatly appreciated.

Data%>%
select(-CCode,-Q1_M,-Q2_M,-Q3_M,-Q4_M)%>%
gather(key,value,-PCode,-Q20)%>%
filter(PCode=="C11")%>%
count(Q20,key,value)%>%
mutate(perc=round(n/sum(n),2))%>%
separate(key,c("Question","SatLevel"),sep="_")%>%
filter(value != "Other")%>%
ggplot(aes(x=Question,y=perc,fill=SatLevel))+geom_col()

解决方案

Generally, ggplot requires long-format tables, and your data seems to be wide. I.e., in the end your data should look something like:

Q barColor barShade Y
1 grey light 55
1 grey dark 20
1 blue light 57
1 blue dark 21
2 grey light 23
...

so that you could call ggplot with aes(color=barColor, y=Y) etc.
I'd say tidyr::gather should take care of most of the restructuring, but see also this great cheatsheet for other helpful tools.

---

Edit: possible solution for stacked+grouped barplots, without using facet_wrap:

df = Data%>%
    select(-CCode,-Q1_M,-Q2_M,-Q3_M,-Q4_M)%>%
    gather(key,value,-PCode,-Q20)%>%
    filter(PCode=="C11")%>%
    count(Q20,key,value)%>%
    mutate(perc=round(n/sum(n),2))%>%
    separate(key,c("Question","SatLevel"),sep="_")%>%
    filter(value != "Other") df$Question = c(14, 14, 1, 1, 4, 4, 7, 10,
                15, 2, 2, 5, 5, 8, 8, 11, 11)

ggplot(df, aes(x=Question,y=perc,fill=SatLevel)) + geom_col() +
    theme_bw() +
    scale_x_continuous(breaks=c(1.5, 4.5, 7.5, 10.5, 14.5),
                       labels=c("Q1", "Q2", "Q3", "Q4", "Calc"))

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