r - prop.table 中的折叠列

Question

我有这个 prop.table(用 questionr::lprop 创建):

    COMSAL
orig      1      2      3      4      5      6      7      8      9     10  Total
  10  38.31  24.11   6.85   6.45   2.58   3.98   8.72   3.98   1.69   3.32 100.00
  11  34.20  30.22   6.01   9.64   2.36   3.08   2.73   4.44   2.36   4.95 100.00
  14  37.15  30.12   4.89   8.53   3.11   3.05   2.99   3.54   3.22   3.41 100.00
  15  39.67  30.77   6.00   4.54   1.72   1.86   3.09   5.46   2.41   4.48 100.00
  21  36.52  27.27  10.12   3.71   3.65   2.10   6.92   2.96   3.21   3.54 100.00
  22  34.63  27.77   8.99   5.11   2.89   2.78   5.75   7.21   2.36   2.50 100.00

我想从第 3 列到第 10 列求和以获得所需的输出:

    COMSAL
orig      1      2    SUM   Total
  10  38.31  24.11   37.6   100.00
  11  34.20  30.22   35.6   100.00
  14  37.15  30.12   32.7   100.00
  15  39.67  30.77   29.6   100.00
  21  36.52  27.27   36.2   100.00
  22  34.63  27.77   37.6   100.00

目前我使用某种与 dplyr 的组合，但它真的很长，我想知道是否有一个内置的包/函数可以做到这一点。

数据

structure(c(38.31, 34.2, 37.15, 39.67, 36.52, 34.63, 24.11, 30.22, 
30.12, 30.77, 27.27, 27.77, 6.85, 6.01, 4.89, 6, 10.12, 8.99, 
6.45, 9.64, 8.53, 4.54, 3.71, 5.11, 2.58, 2.36, 3.11, 1.72, 3.65, 
2.89, 3.98, 3.08, 3.05, 1.86, 2.1, 2.78, 8.72, 2.73, 2.99, 3.09, 
6.92, 5.75, 3.98, 4.44, 3.54, 5.46, 2.96, 7.21, 1.69, 2.36, 3.22, 
2.41, 3.21, 2.36, 3.32, 4.95, 3.41, 4.48, 3.54, 2.5, 100, 100, 
100, 100, 100, 100), .Dim = c(6L, 11L), .Dimnames = list(orig = c("10", 
"11", "14", "15", "21", "22"), COMSAL = c("1", "2", "3", "4", 
"5", "6", "7", "8", "9", "10", "Total")), class = "table")

Answer 1

1。 base R 解决方案

cbind(x[, 1:2], rowSums(x[, 3:10]), x[, 11])

---

2。 dplyr 解决方案

library(dplyr)

as.data.frame.matrix(x) %>%
  rowwise() %>%
  mutate(SUM = sum(c_across(`3`:`10`)), .keep = "unused") %>%
  ungroup()

# # A tibble: 6 x 4
#     `1`   `2` Total   SUM
#   <dbl> <dbl> <dbl> <dbl>
# 1  38.3  24.1   100  37.6
# 2  34.2  30.2   100  35.6
# 3  37.2  30.1   100  32.7
# 4  39.7  30.8   100  29.6
# 5  36.5  27.3   100  36.2
# 6  34.6  27.8   100  37.6