如何更有效地使用 map_dfr() 过滤数据集

我有一个单词对列表:

library(tidyverse)

word_pairs <- structure(list(V1 = c("cup", "cup", "cup"), V2 = c("kilo", "slice","bacon")), row.names = c(NA, -3L), class = "data.frame")

我有以下数据:

data <- structure(list(keyword_pair = c("cup-bacon", "cup-kilo", "cup-slice"
), kwe_1 = c("cup", "cup", "cup"), kwe_2 = c("bacon", "kilo", "slice"), cup = c(2L, 2L, 2L), kilo = c(7L, 7L, 7L), lot = c(3L,3L, 3L), pound = c(5L, 5L, 5L), slice = c(7L, 7L, 7L), bacon = c(4L,4L, 4L), bowl = c(3L, 3L, 3L), box = c(2L, 2L, 2L), fruit = c(2L, 2L, 2L), plate = c(4L, 4L, 4L), bag = c(2L, 2L, 2L), bunch = c(3L, 3L, 3L), chop = c(3L, 3L, 3L), ground = c(2L, 2L, 2L), lettuc = c(2L,2L, 2L), lean = c(2L, 2L, 2L), appl = c(4L, 4L, 4L), barbel = c(2L,2L, 2L), potato = c(2L, 2L, 2L), shoulder = c(2L, 2L, 2L), carrot = c(2L,2L, 2L), mango = c(2L, 2L, 2L), chicken = c(4L, 4L, 4L), press = c(3L,3L, 3L), strawberri = c(3L, 3L, 3L), pint = c(3L, 3L, 3L), sausag = c(2L,2L, 2L), orang = c(2L, 2L, 2L), up = c(2L, 2L, 2L), breast = c(2L,2L, 2L), head = c(2L, 2L, 2L), frozen = c(2L, 2L, 2L), peach = c(2L,2L, 2L), berri = c(2L, 2L, 2L), cherri = c(2L, 2L, 2L), flower = c(2L, 2L, 2L), tomato = c(2L, 2L, 2L), egg = c(2L, 2L, 2L)), row.names = c(NA, -3L), class = "data.frame")

我想从匹配词对的数据中提取每行(频率)的数值.

以下功能将执行此操作:

my_function <- function(x) {
 data %>%
  filter(kwe_1  == word_pairs[x,1] & kwe_2 == word_pairs[x,2]) %>%
  select(keyword_pair:kwe_2,
         starts_with(word_pairs[x,1]),
         starts_with(word_pairs[x,2])) %>%
  rename(freq_kwe_1 = 4,
         freq_kwe_2 = 5)
}

如果我把这个函数插入到map_dfr()中，它会生成我想要的，但是如果数据集很长，运行时间就很长.

我希望有人能回答两个问题:

1. 我怎样才能加快速度？
2. 我需要学习什么样的原则，这样我才能自己解决这个问题？

1:nrow(word_pairs) %>%
 map_dfr(
  my_function)
#>   keyword_pair kwe_1 kwe_2 freq_kwe_1 freq_kwe_2
#> 1     cup-kilo   cup  kilo          2          7
#> 2    cup-slice   cup slice          2          7
#> 3    cup-bacon   cup bacon          2          4

^{由reprex package(v2.0.1)于2022年4月29日创建}