r - 根据dplyr中每列中的数据组合数据框

Question

假设我有一些网络数据，如下所示:

col_a <- c("A","B","C")
col_b <- c("B","A","A")
val <- c(1,3,7)
df <- data.frame(col_a, col_b, val)
df

  col_a col_b val
1     A     B   1
2     B     A   3
3     C     A   7

这可能是一个网络，而 val 可能是两者之间边的权重。但是，我想在 A 和 B 以及 B 和 A 之间添加权重以获得以下结果:

new_col_a <- c("A", "A")
new_col_b <- c("B", "C")
new_val <- c(4,7)
want_df <- data.frame(new_col_a, new_col_b, new_val)
want_df

  new_col_a new_col_b new_val
1         A         B       4
2         A         C       7

有没有办法在 dplyr 中做到这一点？

Answer 1

一个 dplyr 可能是:

df %>%
 mutate_if(is.factor, as.character) %>%
 group_by(grp = paste(pmin(col_a, col_b), pmax(col_a, col_b), sep = "_")) %>%
 summarise(val = sum(val))

  grp     val
  <chr> <dbl>
1 A_B       4
2 A_C       7

或者使用 tidyverse，使用与@Sonny 类似的想法:

df %>%
 mutate_if(is.factor, as.character) %>%
 nest(col_a, col_b) %>%
 group_by(grp = unlist(map(data, function(x) paste(sort(x), collapse = "_")))) %>%
 summarise(val = sum(val))

如果你也想把它分成两列(这一步也需要 tidyr):

df %>%
 mutate_if(is.factor, as.character) %>%
 group_by(grp = paste(pmin(col_a, col_b), pmax(col_a, col_b), sep = "_")) %>%
 summarise(val = sum(val)) %>%
 separate(grp, c("new_col_a", "new_col_b"), sep = "_")

  new_col_a new_col_b   val
  <chr>     <chr>     <dbl>
1 A         B             4
2 A         C             7

或者在第二种可能性的情况下:

df %>%
 mutate_if(is.factor, as.character) %>%
 nest(col_a, col_b) %>%
 group_by(grp = unlist(map(data, function(x) paste(sort(x), collapse = "_")))) %>%
 summarise(val = sum(val)) %>%
 separate(grp, c("new_col_a", "new_col_b"), sep = "_")