利用dplyr中cross()中的函数来处理成对的列 [英] Utilizing functions within across() in dplyr to work with paired-columns
问题描述
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))
假设我的数据看起来像这样.我希望为每个广告运行一个函数,并与下划线的数字配对,即AD1fun,AD2fun,AD3fun.
Suppose I have data that looks like this. I wish to run a function for each AD, paired with underscored number, i.e., 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),
...)
查找配对的差异-使用dplyr 的列显示
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,如果我们要执行非操作功能,
However, if we were to make non-operational function,
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"))
由于引用,它将产生一个错误
it will produce an error, since to quote,
此过程实质上意味着您要比较两个数据集:一个变量以_1结尾,一个以_2结尾.因此,是相同的as dat%>%select(ends_with("_ 1"))-dat%>%select(ends_with("_ 2"))).而且,由于这些是列表,因此您无法通过这种方式进行比较
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,怎么做才能使用cross()包含一个函数?
If so, what can be done to include a function using across()?
推荐答案
我们可以循环遍历跨
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
的值并将其用作 fun
表示当前列以及 _2
We could loop across
the columns with names that ends_with
"_1", then use cur_column()
to extract the column name, replace the suffix part with _2
, get
the value and use that as argument to the fun
for the current column and the corresponding pair from _2
library(dplyr)
library(stringr)
x %>%
mutate(across(ends_with("_1"), ~
fun(., get(str_replace(cur_column(), "_1$", "_2"))), .names = "{.col}_case"))
-输出
# A tibble: 4 x 10
# Measure AD1_1 AD1_2 AD2_1 AD2_2 AD3_1 AD3_2 AD1_1_case AD2_1_case AD3_1_case
# <chr> <int> <int> <int> <int> <int> <int> <chr> <chr> <chr>
#1 Height 6 10 10 3 12 8 Disagree Disagree Disagree
#2 Weight 8 9 13 6 14 7 Agree Disagree Disagree
#3 Width 10 9 11 5 12 8 Agree Disagree Disagree
#4 Length 8 9 8 7 8 13 Agree Agree Disagree
或另一个选项是 split.default/map
.在这里,我们将数据集划分为 data.frame
的 list
,每个数据集都具有与列名相同的前缀,然后在每个 fun
> list 元素和 map/reduce
,然后使用 bind_cols
Or another option is split.default/map
. Here, we split the datasets into list
of data.frame
each having the same prefix as column name, then apply the fun
on each list
element with map/reduce
and bind the output back to the original dataset with bind_cols
library(purrr)
x %>%
select(-Measure) %>%
split.default(str_remove(names(.), "_\\d+$")) %>%
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 Vectorize
d.如果这样做,它可以应用于多个成对的列
Regarding the OP's approach, the fun
is not Vectorize
d. If we do that, it can be applied to multiple pairwise columns
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
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