利用 dplyr 中的 carrow() 中的函数处理成对列

Question

set.seed(3)
library(dplyr)
x <- tibble(Measure = c("Height","Weight","Width","Length"),
        AD1_1= rpois(4,10),
        AD1_2= rpois(4,9),
        AD2_1= rpois(4,10),
        AD2_2= rpois(4,9),
        AD3_1= rpois(4,10),
        AD3_2= rpois(4,9))

假设我有这样的数据。我希望运行每个 AD 的函数，与带下划线的数字配对，即 AD1fun、AD2fun、AD3fun。

而不是写作，

fun <- function(x,y){x-y}
dat %>%
mutate(AD1fun = fun(AD1_1,AD1_2),
       AD2fun = fun(AD2_1,AD2_2),
...)

表示

x_minus <- x %>%
  mutate(fun(across(ends_with("_1"), .names = "{col}_minus"), across(ends_with("_2")))) %>%
  rename_with(~ sub("_\d+", "", .), ends_with("_minus"))

可用于生产

# A tibble: 4 x 10
  Measure AD1_1 AD1_2 AD2_1 AD2_2 AD3_1 AD3_2 AD1_minus AD2_minus AD3_minus
  <chr>   <int> <int> <int> <int> <int> <int>     <int>     <int>     <int>
1 Height      6    10    10     3    12     8        -4         7         4
2 Weight      8     9    13     6    14     7        -1         7         7
3 Width      10     9    11     5    12     8         1         6         4
4 Length      8     9     8     7     8    13        -1         1        -5

但是，如果我们要制作非操作函数，

fun <- function(x,y){
  case <- case_when(
    x == y ~ "Agree",
    x == 0 & y != 0 ~ "Disagreement",
    x != 0 & y == 0 ~ "Disagreement",
    x-y <= 1 & x-y >= -1 ~ "Agree",
    TRUE ~ "Disagree"
  )
  return(case)
}

x_case <- x %>%
  mutate(fun(across(ends_with("_1"), .names = "{col}_case"), across(ends_with("_2")))) %>%
  rename_with(~ sub("_\d+", "", .), ends_with("_case"))

会报错，因为要引用，

This procedure essentially means that you compare two datasets: one with variables ending with _1 and one with _2. It is, thus, the same as dat %>% select(ends_with("_1")) - dat %>% select(ends_with("_2")). And as these are lists, you cannot compare them that way

如果是这样，如何使用 across() 包含一个函数？

Answer 1

我们可以循环 across 名称为 ends_with "_1" 的列，然后使用 cur_column() 提取列名，将后缀部分替换为 _2 , get 值并将其用作当前列的 fun 和 _2

中的对应对的参数

library(dplyr)
library(stringr)
x %>% 
   mutate(across(ends_with("_1"), ~
     fun(., get(str_replace(cur_column(), "_1$", "_2"))), .names = "{.col}_case"))

-输出

# A tibble: 4 x 10
#  Measure AD1_1 AD1_2 AD2_1 AD2_2 AD3_1 AD3_2 AD1_1_case AD2_1_case AD3_1_case
#  <chr>   <int> <int> <int> <int> <int> <int> <chr>      <chr>      <chr>     
#1 Height      6    10    10     3    12     8 Disagree   Disagree   Disagree  
#2 Weight      8     9    13     6    14     7 Agree      Disagree   Disagree  
#3 Width      10     9    11     5    12     8 Agree      Disagree   Disagree  
#4 Length      8     9     8     7     8    13 Agree      Agree      Disagree

或者另一个选项是 split.default/map。在这里，我们将数据集拆分为 list 和 data.frame，每个都具有与列名相同的前缀，然后将 fun 应用于每个 list 元素 map/reduce 和使用 bind_cols

将输出绑定回原始数据集

library(purrr)
x %>% 
  select(-Measure) %>% 
  split.default(str_remove(names(.), "_\d+$")) %>%
  map_dfr(reduce, fun) %>% 
  rename_all(~ str_c(., "_case")) %>%
  bind_cols(x, .)

-输出

# A tibble: 4 x 10
#  Measure AD1_1 AD1_2 AD2_1 AD2_2 AD3_1 AD3_2 AD1_case AD2_case AD3_case
#  <chr>   <int> <int> <int> <int> <int> <int> <chr>    <chr>    <chr>   
#1 Height      6    10    10     3    12     8 Disagree Disagree Disagree
#2 Weight      8     9    13     6    14     7 Agree    Disagree Disagree
#3 Width      10     9    11     5    12     8 Agree    Disagree Disagree
#4 Length      8     9     8     7     8    13 Agree    Agree    Disagree

关于 OP 的方法，fun 不是 Vectorized。如果我们这样做，它可以应用于多个成对列

x %>%
  mutate(Vectorize(fun)(across(ends_with("_1"), 
         .names = "{col}_minus"), across(ends_with("_2"))))%>%
   do.call(data.frame, .) %>% 
   rename_at(vars(contains('minus')),
         ~ str_extract(., 'AD\d+_\d+_minus'))
#  Measure AD1_1 AD1_2 AD2_1 AD2_2 AD3_1 AD3_2 AD1_1_minus AD2_1_minus AD3_1_minus
#1  Height     6    10    10     3    12     8    Disagree    Disagree    Disagree
#2  Weight     8     9    13     6    14     7       Agree    Disagree    Disagree
#3   Width    10     9    11     5    12     8       Agree    Disagree    Disagree
#4  Length     8     9     8     7     8    13       Agree       Agree    Disagree

利用 dplyr 中的 carrow() 中的函数处理成对列

Utilizing functions within across() in dplyr to work with paired-columns

r

function

dplyr

across