使用表格在r中重现嵌套的Excel Pivot表

Question

我试图在r中重现以下excel数据透视表：

使用表格：

library(vcd)
library(tables)
tabular(Sex*(Treatment+1)+1~(count=ID + Percent("col")), data=Arthritis)

产地：

              count        
 Sex    Treatment ID    Percent
 Female Placebo   32     38.10 
        Treated   27     32.14 
        All       59     70.24 
 Male   Placebo   11     13.10 
        Treated   14     16.67 
        All       25     29.76 
        All       84    100.00 

有没有办法让每个性别的治疗百分比加起来像excel数据透视表一样？

Answer 1

除最终All行之外的所有内容都可以通过以下方式完成。

library(dplyr)
library(tidyr)

   df <- Arthritis %>%
  group_by(Sex, Treatment) %>%
  summarise(cnt = n()) %>%
  ungroup() %>%
  spread(Treatment, cnt) %>%
  mutate(All = Placebo + Treated) %>%
  gather(Treatment, ID , -Sex) %>%
  group_by(Sex) %>%
  mutate(percent = ID / (sum(ID) / 2)) %>%
  arrange(Sex, desc(Treatment)) #forces "Treated" to top of Treatment column for each group

    > df
Source: local data frame [6 x 4]
Groups: Sex [2]

     Sex Treatment    ID   percent
  <fctr>     <chr> <int>     <dbl>
1 Female   Treated    27 0.4576271
2 Female   Placebo    32 0.5423729
3 Female       All    59 1.0000000
4   Male   Treated    14 0.5600000
5   Male   Placebo    11 0.4400000
6   Male       All    25 1.0000000

如果你想要一个总行，你可以使用以下，但它不是很漂亮。

grand_total <- data.frame(Sex = "Total" , "Treatment" = "All", 
                          ID = nrow(Arthritis), percent = 1, 
                          stringsAsFactors = FALSE)

df_final <- bind_rows(df, grand_total)

现在，如果您要删除Sex列以外的第一个事件，那么您可以这样做。由于我们在Treatment列上按降序排序，因此我们知道Treated它是每个组的顶部。因此，当Sex列不等于Treatment时，我们只需将Treated列替换为空白。我们也不会删除我们创建的All。

df_final$Sex[df_final$Treatment != "Treated" & 
df_final$Sex %in% c("Female", "Male")] <- ""

Source: local data frame [7 x 4]
Groups: Sex [3]

     Sex Treatment    ID   percent
   <chr>     <chr> <int>     <dbl>
1 Female   Treated    27 0.4576271
2          Placebo    32 0.5423729
3              All    59 1.0000000
4   Male   Treated    14 0.5600000
5          Placebo    11 0.4400000
6              All    25 1.0000000
7  Total       All    84 1.0000000