使用 dplyr：：mutate() 修改任意列属性

我们可以使用dplyr

library(dplyr)
v1 <- v1 %>%
    mutate(across(everything(), 
     ~ {attr(.x, "source") <- "Collected at v1"
    .x} ))

-输出

> str(v1)
tibble [5 × 4] (S3: tbl_df/tbl/data.frame)
 $ id   : int [1:5] 1 2 3 4 5
  ..- attr(*, "source")= chr "Collected at v1"
 $ visit: num [1:5] 1 1 1 1 1
  ..- attr(*, "source")= chr "Collected at v1"
 $ x    : int [1:5] 1 2 3 4 5
  ..- attr(*, "source")= chr "Collected at v1"
 $ y    : num [1:5] 0 0 0 1 1
  ..- attr(*, "source")= chr "Collected at v1"

或者，如果我们想要使用Access，则使用cur_column()并使用<<-赋值

v1 %>%
  mutate(across(everything(), 
  ~ {attr(v1[[cur_column()]], "source") <<- "Collected at v1"
   .x})) 

-输出

> str(v1)
tibble [5 × 4] (S3: tbl_df/tbl/data.frame)
 $ id   : int [1:5] 1 2 3 4 5
  ..- attr(*, "source")= chr "Collected at v1"
 $ visit: num [1:5] 1 1 1 1 1
  ..- attr(*, "source")= chr "Collected at v1"
 $ x    : int [1:5] 1 2 3 4 5
  ..- attr(*, "source")= chr "Collected at v1"
 $ y    : num [1:5] 0 0 0 1 1
  ..- attr(*, "source")= chr "Collected at v1"

或者，如果我们想要复制相同的行为，即使用原始数据对象名称及其列名称，就像在for循环中一样，使用reduce

library(purrr)
v1 <- reduce(names(v1), ~ {
         attr(.x[[.y]], "source") <- "Collected at v1"
             .x}, .init = v1)

-输出

> str(v1)
tibble [5 × 4] (S3: tbl_df/tbl/data.frame)
 $ id   : int [1:5] 1 2 3 4 5
  ..- attr(*, "source")= chr "Collected at v1"
 $ visit: num [1:5] 1 1 1 1 1
  ..- attr(*, "source")= chr "Collected at v1"
 $ x    : int [1:5] 1 2 3 4 5
  ..- attr(*, "source")= chr "Collected at v1"
 $ y    : num [1:5] 0 0 0 1 1
  ..- attr(*, "source")= chr "Collected at v1"