dplyr:mutate 中的自定义函数。使用全矩阵而不是块?

dplyr: custum function in mutate. Uses full matrix instead of chunks?

考虑这个例子:

library(dplyr)
library(magrittr)

set.seed(123)
grp_s <- round(runif(4, 1, 10))
group <- rep(1:length(grp_s), grp_s)
dataF <- data.frame(grouping = group, var_a = runif(length(group)), var_b = runif(length(group)), var_c = runif(length(group)))

compute_it <- function(var_a, var_b){
    sum(var_a[var_b > .5], na.rm = TRUE)
}

dataF %<>%
        group_by(grouping) %>%
        mutate(fix_it = compute_it(var_a, var_b))

到目前为止一切顺利。现在代替 compute_it 作为参数 列名,我想使用一个函数作为参数 数据块(grouping 的每个值一个块)。

使用此函数的列表:

compute_it_2 <- function(Data){
    sum(Data$var_a[Data$var_b > .5], na.rm = TRUE)
}

上面用的是compute_it。怎么做?

同时使用 tidyrpurrr 我们可以先使用 donest:

library(tidyverse)

dataF %>%
  group_by(grouping) %>%
  do(fix_it = compute_it_2(.)) %>% 
  unnest()

给予:

# A tibble: 4 × 2
  grouping    fix_it
     <int>     <dbl>
1        1 2.4065483
2        2 0.9568333
3        3 0.0000000
4        4 1.8274955

或者嵌套方式:

dataF %>% 
  group_by(grouping) %>% 
  nest() %>% 
  mutate(fix_it = map_dbl(data, compute_it_2))
# A tibble: 4 × 3
  grouping             data    fix_it
     <int>           <list>     <dbl>
1        1 <tibble [4 × 3]> 2.4065483
2        2 <tibble [8 × 3]> 0.9568333
3        3 <tibble [5 × 3]> 0.0000000
4        4 <tibble [9 × 3]> 1.8274955

如果你unnest()第二个选项你会得到原来的框架:

# A tibble: 26 × 5
   grouping    fix_it     var_a      var_b      var_c
      <int>     <dbl>     <dbl>      <dbl>      <dbl>
1         1 2.4065483 0.9404673 0.96302423 0.12753165
2         1 2.4065483 0.0455565 0.90229905 0.75330786
3         1 2.4065483 0.5281055 0.69070528 0.89504536
4         1 2.4065483 0.8924190 0.79546742 0.37446278
5         2 0.9568333 0.5514350 0.02461368 0.66511519
6         2 0.9568333 0.4566147 0.47779597 0.09484066
7         2 0.9568333 0.9568333 0.75845954 0.38396964
8         2 0.9568333 0.4533342 0.21640794 0.27438364
9         2 0.9568333 0.6775706 0.31818101 0.81464004
10        2 0.9568333 0.5726334 0.23162579 0.44851634
# ... with 16 more rows