使用多个匹配模式标记/分类字符串列

Question

我有一个数据框，其中有一列字符串需要根据另一个数据框进行分类，该数据框在一列中具有类别标签，在另一列中具有匹配的术语/模式。

有 50 多个类别，每个字符串可以匹配多个类别，而其他字符串则不匹配。如何使用类别标签有效地标记这些字符串？

下面是一个简单的示例数据集和我希望得到的输出。如果有什么不同的话，真实数据集中的字符串比这些样本字符串长得多，而且有几十万个。

recipes <- c('fresh asparagus', 'a bunch of bananas', 'one pound pork', 'no fruits, no veggies, no nothing', 'broccoli or spinach','I like apples, asparagus, and pork', 'meats like lamb', 'venison sausage and fried eggs', 'spinach and arugula salad', 'scrambled or poached eggs', 'sourdough english muffins')
recipes_df <- data.frame(recipes, stringsAsFactors = FALSE)

category <- c('vegetable', 'fruit', 'meat','bread','dairy')
items <- c('arugula|asparagus|broccoli|peas|spinach', 'apples|bananas|blueberries|oranges', 'lamb|pork|turkey|venison', 'sourdough', 'buttermilk|butter|cream|eggs')
category_df <- data.frame(category, items)

这是我希望得到的输出：

                          recipes            recipes_category
1                     fresh asparagus              vegetable
2                  a bunch of bananas                  fruit
3                      one pound pork                   meat
4   no fruits, no veggies, no nothing                   <NA>
5                 broccoli or spinach              vegetable
6  I like apples, asparagus, and pork fruit, vegetable, meat
7                     meats like lamb                   meat
8      venison sausage and fried eggs            meat, dairy
9           spinach and arugula salad              vegetable
10          scrambled or poached eggs                  dairy
11          sourdough english muffins                 breads

我相信 grepl 和 for 循环或 apply 版本的某种组合是必要的，但我在下面尝试的示例确实暴露了我对 R 的了解程度。例如使用 sapply 给出了我期望的结果，@987654325 @但我不确定如何将这些结果转换为我需要的简单列。

如果我使用找到here的categorize函数，它只匹配一个类别到每个字符串：

categorize_food <- function(df, searchString, category) {
  df$category <- "OTHER"
  for(i in seq_along(searchString)) {
    list <- grep(searchString[i], df[,1], ignore.case=TRUE) 
    if (length(list) > 0) {
  df$category[list] <- category[i]
    }
  }
  df
}
recipes_cat <- categorize_food(recipes_df, category_df$items, category_df$category)

同样，找到的函数here 与我正在寻找的最接近，但我不明白为什么类别编号映射它们的方式。我希望蔬菜类别是 1 而不是 2，乳制品是 5 而不是 3。

vec = category_df$items
recipes_df$category = apply(recipes_df, 1, function(u){
  bool = sapply(vec, function(x) grepl(x, u[['recipes']]))
  if(any(bool)) vec[bool] else NA
})

Answer 1

对于大型数据集来说，接近尾声的聚合有点慢，所以也许寻找一种更快的方法（data.table？）将行转换为字符串，但这通常应该有效：

tmplist <- strsplit(items, "|", fixed=TRUE)
#Removes horrid '|' separated values into neat rows
searchterms <- data.frame(category=rep(category, sapply(tmplist, length)),
           items=unlist(tmplist), stringsAsFactors=FALSE)
#Recreates data frame, neatly
res <- lapply(searchterms$items, grep, x=recipes, value=TRUE)
#throws an lapply on the neat data pattern against recipes

matched_times <- sapply(res, length)
df_matched <- data.frame( category = rep(searchterms$category[matched_times!=0],
                                 matched_times[matched_times != 0]),
                  recipes = unlist(res))
# Combines category names the correct nr of times with grep
#results (recipe names), to create a tidy result 

df_ummatched <- data.frame( category = NA, recipes = recipes[!recipes %in% unlist(res)])
df <- rbind(df_matched, df_ummatched)
#gets the nonmatched, plops it in with NA values. 

final  <- aggregate(category~recipes, data=df, paste, sep=",", na.action=na.pass)
#makes the data untidy, as you asked. 

但这仍然给我们留下了重复的vegetable, vegetable 条目。不能这样：

SplitFunction <- function(x) {
  b <- unlist(strsplit(x, ','))
  c <- b[!duplicated(b)]
  return(paste(c, collapse=", "))
}
SplitFunctionV <- Vectorize(SplitFunction)
final$category <- SplitFunctionV(final$category)

结果：

final
                              recipes               category
1                  a bunch of bananas                  fruit
2                 broccoli or spinach              vegetable
3                     fresh asparagus              vegetable
4  I like apples, asparagus, and pork vegetable, fruit, meat
5                     meats like lamb                   meat
6                      one pound pork                   meat
7           scrambled or poached eggs                  dairy
8           sourdough english muffins                  bread
9           spinach and arugula salad              vegetable
10     venison sausage and fried eggs            meat, dairy
11  no fruits, no veggies, no nothing                     NA

Answer 2

这是一个非常简单的tidyverse 选项：

library(tidyverse)

# reformat category data frame so each item has its own line: 
category_df <- 
category_df %>% 
  mutate(items = str_split(items, "\\|")) %>%
  unnest()

# then use string_extract_all() to find every item in each recipe string:
recipes_df %>% 
  mutate(recipe_category = str_extract_all(recipes, paste(category_df$items, collapse = '|')))