将具有多变量函数列表变量的数据框应用于具有函数参数的数据框 [英] Apply data frame with list-variable of multivariable functions to a data frame with function arguments

问题描述

这个数据框包含我称之为数据"的内容:

图书馆(tidyverse)df_d <- data_frame(key = c("cat", "cat", "dog", "dog"),value_1 = c(1,2,3,4),value_2 = c(2,4,6,8))

这是一个数据框，我打算将其用作函数查找表之类的东西.f 是单变量函数，f2 是多变量函数:

df_f <- data_frame(key = c("cat", "dog"),f = c(function(x) x^2, function(x) sqrt(x)),f2 = c(function(x) (x[1]+x[2])^2,function(x) sqrt(x[1]+x[2])))

我可以轻松制作一个数据帧，以便任何 cat 行获得 cat 函数，而任何 dog 行获得 dog 函数:

df_both <- left_join(df_d, df_f)

我能够弄清楚如何将每个 f 函数应用于例如 value_1 列以获取:

df_both %>% mutate(result = invoke_map_dbl(f, value_1))#># 小块:4 x 6#>key value_1 value_2 f f2 结果#><chr><dbl><dbl><列表><列表><dbl>#>1 猫 1.00 2.00 <fn><fn>1.00#>2 猫 2.00 4.00 <fn><fn>4.00#>3 狗 3.00 6.00 <fn><fn>1.73#>4 狗 4.00 8.00 <fn><fn>2.00

我的问题是:我怎样才能创建一个列 result2，它接受 f2 中的每个函数并用作它的输入 c(value_1, value_2).如果将 f2 中的函数重新定义为两个变量的显式函数会使事情变得容易得多，那也很好.

所需的输出:

#># 小费:4 x 7#>key value_1 value_2 f f2 result result2#><chr><dbl><dbl><列表><列表><dbl><dbl>#>1 猫 1.00 2.00 <fn><fn>1.00 9.00#>2 猫 2.00 4.00 <fn><fn>4.00 36.0#>3 狗 3.00 6.00 <fn><fn>1.73 3.00#>4 狗 4.00 8.00 <fn><fn>2.00 3.46

(这个问题是由今天早些时候一个不幸的自我删除问题引发的.)

解决方案

如果将 f2 中的函数重新定义为两个变量的显式函数会使事情变得更容易，那也很好."

是的，我认为这是一种更自然的情况.否则数据将按行存储，并且可能需要重新整形.

重新定义你的功能:

df_f <- data_frame(key = c("cat", "dog"),f = c(function(x) x^2, function(x) sqrt(x)),f2 = c(function(x, y) (x + y)^2, function(x, y) sqrt(x + y)))df_both <- left_join(df_d, df_f)

现在您再次使用 map_invoke，将 .x 作为列表传递，尽管您需要使用 transpose 将列表翻转过来:

变异(df_both，结果 = invoke_map_dbl(f, value_1),result2 = invoke_map_dbl(f2, transpose(list(value_1, value_2))))

<块引用>

# tibble: 4 x 7key value_1 value_2 f f2 result result2<chr><dbl><dbl><列表><列表><dbl><dbl>1 猫 1. 2. <fn><fn>1.00 9.002 猫 2. 4. <fn><fn>4.00 36.03 狗 3. 6. <fn><fn>1.73 3.004 狗 4. 8. <fn><fn>2.00 3.46

一组三个参数函数将简单地扩展到 invoke_map_dbl(f3, transpose(list(value_1, value_2, value_3))

请注意，这种方法不适用于大型数据集，因为您没有使用矢量化.

一个更具可扩展性的替代方案可能涉及嵌套，您至少在每个组中应用每个函数一次:

df_both %>%group_by(key) %>%嵌套()%>%变异(数据=地图(数据，~mutate(., result = first(f)(value_1), result2 = first(f2)(value_1, value_2)))) %＞%取消嵌套()

得到相同的结果.

This dataframe contains what I'll call the "data":

library(tidyverse)
df_d <- data_frame(key = c("cat", "cat", "dog", "dog"), 
               value_1 = c(1,2,3,4), 
               value_2 = c(2,4,6,8))

Here is a dataframe that I intend to use as something like a function look-up table. f is a single variable function and f2 is a multivariable function:

df_f <- data_frame(key = c("cat", "dog"),
               f = c(function(x) x^2, function(x) sqrt(x)),
               f2 = c(function(x) (x[1]+x[2])^2, function(x) sqrt(x[1]+x[2])))

I can easily make a dataframe so that any cat row gets the cat functions and any dog row gets the dog functions:

df_both <- left_join(df_d, df_f)

I was able to figure out how to apply each of the f functions to, say, the value_1 column to get:

df_both %>% mutate(result = invoke_map_dbl(f, value_1))        
#> # A tibble: 4 x 6
#>   key   value_1 value_2 f      f2     result
#>   <chr>   <dbl>   <dbl> <list> <list>  <dbl>
#> 1 cat      1.00    2.00 <fn>   <fn>     1.00
#> 2 cat      2.00    4.00 <fn>   <fn>     4.00
#> 3 dog      3.00    6.00 <fn>   <fn>     1.73
#> 4 dog      4.00    8.00 <fn>   <fn>     2.00

My question is: how can I create a columns result2 that takes each function in f2 and uses as its input c(value_1, value_2). If re-defining the functions in f2 to be explicitly functions of two variables makes things much easier, that's fine too.

Desired output:

#> # A tibble: 4 x 7
#>   key   value_1 value_2 f      f2     result result2
#>   <chr>   <dbl>   <dbl> <list> <list>  <dbl>   <dbl>
#> 1 cat      1.00    2.00 <fn>   <fn>     1.00    9.00
#> 2 cat      2.00    4.00 <fn>   <fn>     4.00   36.0 
#> 3 dog      3.00    6.00 <fn>   <fn>     1.73    3.00
#> 4 dog      4.00    8.00 <fn>   <fn>     2.00    3.46

(Question motivated by an unfortunately self-deleted question from earlier today.)

解决方案

"If re-defining the functions in f2 to be explicitly functions of two variables makes things much easier, that's fine too."

Yes, that would be a more natural situation here, I think. Otherwise data is stored rowwise, and should possibly be reshaped.

Redefining your functions:

df_f <- data_frame(key = c("cat", "dog"),
                   f = c(function(x) x^2, function(x) sqrt(x)),
                   f2 = c(function(x, y) (x + y)^2, function(x, y) sqrt(x + y)))
df_both <- left_join(df_d, df_f)

Now you again use map_invoke, passing .x as a list, although you need to turn the lists inside out using transpose:

mutate(
  df_both,
  result  = invoke_map_dbl(f, value_1),
  result2 = invoke_map_dbl(f2, transpose(list(value_1, value_2)))
)

# A tibble: 4 x 7
  key   value_1 value_2 f      f2     result result2
  <chr>   <dbl>   <dbl> <list> <list>  <dbl>   <dbl>
1 cat        1.      2. <fn>   <fn>     1.00    9.00
2 cat        2.      4. <fn>   <fn>     4.00   36.0 
3 dog        3.      6. <fn>   <fn>     1.73    3.00
4 dog        4.      8. <fn>   <fn>     2.00    3.46

A set of three argument functions would then simply extend to invoke_map_dbl(f3, transpose(list(value_1, value_2, value_3))

Note that this kind of approach will not work well on large datasets, since you aren't using vectorization.

A more scalable alternative may involve nesting, where you at least apply each function once within each group:

df_both %>% 
  group_by(key) %>% 
  nest() %>% 
  mutate(data = map(
    data, 
    ~mutate(., result = first(f)(value_1), result2 = first(f2)(value_1, value_2))
    )) %>% 
  unnest()

Which gives the same result.

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