使用 pmap 遍历 tibble 的行 [英] Using pmap to iterate over rows of a tibble

问题描述

我有一个非常简单的 tibble，我想遍历它的行以使用 pmap 函数应用一个函数.我想我可能误解了 pmap 函数的一些要点，但我在选择参数时遇到了困难.所以我想知道在这种情况下我是否应该将 rowwise 函数与 pmap 一起使用.不过我没见过案例.另一个问题是使用列表或 select 函数选择要迭代的变量:

I have a very simple tibble and I would like to iterate over its rows to apply a function using pmap function. I think I may have misinterpreted some points on pmap function but I mostly have difficulty selecting arguments. So I would like to know whether I should use rowwise function in this case with pmap or not. However I haven't seen a case. The other problem is the selection of variables to iterate over using list or select function:

# Here is my tibble
# Imagine I would like to apply a `n_distinct` function with pmap on it every rows

df <-  tibble(id = c("01", "02", "03","04","05","06"),
                  A = c("Jan", "Mar", "Jan","Jan","Jan","Mar"),
                  B = c("Feb", "Mar", "Jan","Jan","Mar","Mar"),
                  C = c("Feb", "Mar", "Feb","Jan","Feb","Feb")
)

# It is perfectly achievable with `rowwise` and `mutate` and results in my desired output

df %>%
  rowwise() %>%
  mutate(overal = n_distinct(c_across(A:C)))

# A tibble: 6 x 5
# Rowwise: 
  id    A     B     C     overal
  <chr> <chr> <chr> <chr>  <int>
1 01    Jan   Feb   Feb        2
2 02    Mar   Mar   Mar        1
3 03    Jan   Jan   Feb        2
4 04    Jan   Jan   Jan        1
5 05    Jan   Mar   Feb        3
6 06    Mar   Mar   Feb        2

# But with `pmap` it won't. 


df %>%
  select(-id) %>%
  mutate(overal = pmap_dbl(list(A, B, C), n_distinct))


# A tibble: 6 x 4
  A     B     C     overal
  <chr> <chr> <chr>  <dbl>
1 Jan   Feb   Feb        1
2 Mar   Mar   Mar        1
3 Jan   Jan   Feb        1
4 Jan   Jan   Jan        1
5 Jan   Mar   Feb        1
6 Mar   Mar   Feb        1

我只需要对 pmap 在 tibbles 上按行迭代的应用做一些解释，所以我非常感谢您提前提供的帮助，谢谢.

I just need a little bit of explanation on the application of pmap for rowwise iteration on tibbles, so I highly appreciate any help in advance, thank you.

推荐答案

我能够追踪到问题，但在这里无法确定它是错误还是功能.关键是 pmap 中的 n_distinct() 将给定的输入处理为具有 3 列的数据框.将 n_distinct() 应用于数据帧时，它计算不同行的数量，因此每行中的 1

I was able to track down the issue yet cannot say whether it's a bug or a feature here. The point is that n_distinct() inside pmap handles the given input as a data frame with 3 columns. When applying n_distinct() to a data frame it counts the number of distinct rows, hence the 1 in each row

n_distinct(tibble(a = c(1, 2, 2),
                  b = 3))
#> [1] 2

诀窍是先将输入转换为向量，然后将其传递给 n_distinct

The trick is to convert the input to a vector first and then pass it to n_distinct

df %>%
  select(-id) %>%
  mutate(overal = pmap_dbl(list(A, B, C), ~ n_distinct(c(...))))
#> # A tibble: 6 x 4
#>   A     B     C     overal
#>   <chr> <chr> <chr>  <dbl>
#> 1 Jan   Feb   Feb        2
#> 2 Mar   Mar   Mar        1
#> 3 Jan   Jan   Feb        2
#> 4 Jan   Jan   Jan        1
#> 5 Jan   Mar   Feb        3
#> 6 Mar   Mar   Feb        2

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