使用 pmap 迭代 tibble 的行

Question

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 函数的某些要点，但我在选择参数方面大多有困难。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。但是我还没有看到一个案例。另一个问题是选择要使用列表或 select 函数迭代的变量：

# 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

我只需要一点关于 pmap 在 tibble 上的行迭代的应用的解释，所以我非常感谢提前提供的任何帮助，谢谢。

Answer 1

我能够找到这个问题，但不能说它是错误还是这里的功能。关键是 pmap 内的 n_distinct() 将给定的输入处理为具有 3 列的数据框。将 n_distinct() 应用于数据框时，它会计算不同行的数量，因此每行中的 1

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

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

使用 pmap 迭代 tibble 的行

Using pmap to iterate over rows of a tibble

r

dplyr

purrr

rowwise