按类别分组并按行R计算

Question

我有这个数据帧：

> set.seed(100)
> df <- data.frame(X1 = sample(c(1:7, NA), 10, replace=TRUE),
                 X2 = sample(c(1:7, NA), 10, replace=TRUE),
                 X3 = sample(c(1:7, NA), 10, replace=TRUE),
                 YY = sample(c("a","b"), 10, replace=TRUE),
                 stringsAsFactors = FALSE)

> df
   X1 X2 X3 YY
1   3  5  5  a
2   3 NA  6  b
3   5  3  5  a
4   1  4  6  b
5   4  7  4  b
6   4  6  2  b
7   7  2  7  a
8   3  3 NA  b
9   5  3  5  b
10  2  6  3  a

最终输出是这样的：

YY   XX
 a  -0.17
 b  -0.38

每个百分比的公式为：

（counts of c(6,7) - counts of c(1,2,3,4)）/ counts of c(1,2,3,4,5,6,7)。例如，要获得-0.17的a：

Where the columns are all (`X1, X2, X3`) and `YY = a`, then:
prom = counts of c(6,7) = 3
detr = counts of c(1,2,3,4) = 5 
total = counts of c(1,2,3,4,5,6,7) = 12 
The percentage is (prom - detr) / total = (2-3)/ 9 = -0.17

但是，当我使用summarize_all()时，我只能按列计算：

df %>%
  group_by(YY) %>%
  summarize_all(~ (sum(.x %in% 6:7) - sum(.x %in% 1:4)) / sum(.x %in% 1:7))

  YY        X1     X2     X3
  <chr>  <dbl>  <dbl>  <dbl>
1 a     -0.333 -1      0.333
2 b      0.167 -0.714 -0.667

当我想要计算YY中给定类别的所有列时，而不是每列（如上面所需输出中所示）。

Answer 1

可以尝试：

library(tidyverse)

df %>%
  gather(key, val, -YY) %>%
  group_by(YY) %>%
  summarise(
    XX = ( sum(val %in% 6:7) - sum(val %in% 1:4) ) / sum(val %in% 1:7)
  ) 

输出：

# A tibble: 2 x 2
  YY        XX
  <chr>  <dbl>
1 a     -0.167
2 b     -0.375

Answer 2

试试melt

library(reshape2) 
library(dplyr) 

melt(df,'YY')%>%
    group_by(YY)%>%
   summarise(XX=(sum(value %in% 6:7) - sum(value %in% 1:4)) / sum(value%in% 1:7))
# A tibble: 2 x 2
     YY                 XX
  <chr>              <dbl>
1     a -0.714285714285714
2     b  0.105263157894737