如何使用 map_dfr() 更有效地过滤 () 数据集？

Question

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")

我想从匹配单词对的数据中提取每一行（频率）的数值。

以下函数将执行此操作：

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一个map_dfr()，它会生成我要找的东西，但是如果数据集很长，运行时间就很长。

我希望有人能够回答的两个问题：

我怎样才能加快速度？
我需要学习什么原理才能自己弄清楚如何做到这一点？

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 2022-04-29 创建

Answer 1

也许我遗漏了什么，但你可能根本不需要使用 purrr:

data %>%
    rowwise() %>%
    transmute(keyword_pair,
              kwe_1,
              kwe_2,
              across(c(kwe_1, kwe_2), ~ get(.), .names = "freq_{.col}"))

  keyword_pair kwe_1 kwe_2 freq_kwe_1 freq_kwe_2
  <chr>        <chr> <chr>      <int>      <int>
1 cup-bacon    cup   bacon          2          4
2 cup-kilo     cup   kilo           2          7
3 cup-slice    cup   slice          2          7

如何使用 map_dfr() 更有效地过滤 () 数据集？

How do I filter() over a dataset with map_dfr() more efficiently?

r

dplyr

purrr