使用 case_when 进行字符串匹配的多种模式

Question

我正在尝试使用 str_detect 和 case_when 根据多种模式重新编码字符串，并将每次出现的重新编码值粘贴到一个新列中。正确的列是我想要实现的输出。

这类似于this question 和this question 如果不能用case_when（我认为仅限于一种模式）完成，是否有更好的方法仍然可以使用tidyverse 来实现？

Fruit=c("Apples","apples, maybe bananas","Oranges","grapes w apples","pears")
Num=c(1,2,3,4,5)
data=data.frame(Num,Fruit)

df= data %>% mutate(Incorrect=
paste(case_when(
  str_detect(Fruit, regex("apples", ignore_case=TRUE)) ~ "good",
  str_detect(Fruit, regex("bananas", ignore_case=TRUE)) ~ "gross",
  str_detect(Fruit, regex("grapes | oranges", ignore_case=TRUE)) ~ "ok",
  str_detect(Fruit, regex("lemon", ignore_case=TRUE)) ~ "sour",
  TRUE ~ "other"
),sep=","))

  Num                 Fruit Incorrect
  1                Apples      good
  2 apples, maybe bananas      good
  3               Oranges      other
  4       grapes w apples      good
  5                pears       other

 Num                 Fruit    Correct
   1                Apples       good
   2 apples, maybe bananas good,gross
   3               Oranges         ok
   4       grapes w apples    ok,good
   5                pears       other

Answer 1

在case_when 中，如果满足某一行的条件，它将停在那里并且不再检查任何其他条件。因此，通常在这种情况下，最好将每个条目放在单独的行中，以便更容易分配值，然后将 summarise 全部放在一起。但是，在这种情况下，Fruit 列没有明确的分隔符，一些水果用逗号分隔（,），一些带有空格，并且它们之间还有额外的单词。为了处理所有此类情况，我们将NA 分配给不匹配的单词，然后在汇总期间将其删除。

library(dplyr)
library(stringr)

data %>%
  tidyr::separate_rows(Fruit, sep = ",|\\s+") %>%
   mutate(Correct = case_when(
      str_detect(Fruit, regex("apples", ignore_case=TRUE)) ~ "good",
      str_detect(Fruit, regex("bananas", ignore_case=TRUE)) ~ "gross",
      str_detect(Fruit, regex("grapes|oranges", ignore_case=TRUE)) ~ "ok",
      str_detect(Fruit, regex("lemon", ignore_case=TRUE)) ~ "sour",
      TRUE ~ NA_character_)) %>% 
   group_by(Num) %>%
   summarise(Correct = toString(na.omit(Correct))) %>%
   left_join(data)

#   Num Correct     Fruit                
#  <dbl> <chr>       <fct>                
#1     1 good        Apples               
#2     2 good, gross apples, maybe bananas
#3     3 ok          Oranges              
#4     4 ok, good    grapes w apples      
#5     5 sour        Lemons               

---

对于更新后的数据，我们可以去掉出现的多余单词并做

data %>%
  mutate(Fruit = gsub("maybe|w", "", Fruit)) %>%
  tidyr::separate_rows(Fruit, sep = ",\\s+|\\s+") %>%
  mutate(Correct = case_when(
     str_detect(Fruit, regex("apples", ignore_case=TRUE)) ~ "good",
     str_detect(Fruit, regex("bananas", ignore_case=TRUE)) ~ "gross",
     str_detect(Fruit, regex("grapes|oranges", ignore_case=TRUE)) ~ "ok",
     str_detect(Fruit, regex("lemon", ignore_case=TRUE)) ~ "sour",
     TRUE ~ "other")) %>% 
  group_by(Num) %>%
  summarise(Correct = toString(na.omit(Correct))) %>%
  left_join(data)

#    Num Correct     Fruit                
#  <dbl> <chr>       <fct>                
#1     1 good        Apples               
#2     2 good, gross apples, maybe bananas
#3     3 ok          Oranges              
#4     4 ok, good    grapes w apples      
#5     5 other       pears