R 在两个数据集之间匹配列表中的值

我有两个数据集，我正在工作。第一个是：

data_1 <- tribble(
  ~shop_name, ~sub_category,
  "A",        "Blu-ray, DVDs, CD",
  "B",        "Sneakers, Make-up, Blu-ray",         
  "C",        "Camera, Optic, DVDs",
  "D",        "Flower, Notebooks, Make-up", 
)

第二个是：

data_2 <- tribble(
  ~sub_category, ~main_category,
  "Blu-ray",      "Electronic",
  "DVDs",         "Electronic",        
  "CD",           "Electronic",
  "Sneakers",     "Fashion",
  "Make-up",      "Fashion", 
  "Camera",       "Electronic",
  "Optic",        "Health", 
  "Flower",       "Home",
)

现在，我想执行左连接以在data_1中添加主类别。最终数据应如下所示：

merged_data <- tribble(
  ~shop_name, ~sub_category,                 ~main_category,
  "A",        "Blu-ray, DVDs, CD",            "Electronic,  Electronic,  Electronic",
  "B",        "Sneakers, Make-up, Blu-ray",   "Fashion,  Fashion,  Electronic",      
  "C",        "Camera, Optic",                "Electronic, Health",
  "D",        "Flower",                       "Home"
)

我的代码如下所示：

data3 <- left_join(data_1, data_2, by = "sub_category")

---

但不知何故，主_类别返回了NA。有人能帮我吗？提前感谢。

您基本上需要从数据_1中拆分子类别字符串，然后加入，即

data_1 %>% 
 separate_rows(sub_category, sep = ', ') %>% 
 left_join(data_2, by = 'sub_category') %>% 
 group_by(shop_name) %>% 
 summarise_all(funs(toString))

这就给了,

如果您有更多的列，则需要将Summary_all替换为Summary_atvarscontains'category'，funstoString

以下是两个数据表解决方案，以供记录：

代码

您可以直接将数据_1的子类别中的每个字符串与其对应的数据_2的主类别相匹配：

或者，将数据_1转换为长格式，并与子_类别上的数据_2合并：

结果

---

谢谢你的评论。关于代码，只有一个问题。除了这两个类别，我有不同的专栏。在这种情况下，summary_allfunstoString不起作用。有没有办法只看两栏？编辑了我的答案。现在请看一下。您可以在contains中更改模式以适应您的情况

# A tibble: 4 x 3
  shop_name sub_category               main_category                     
  <chr>     <chr>                      <chr>                             
1 A         Blu-ray, DVDs, CD          Electronic, Electronic, Electronic
2 B         Sneakers, Make-up, Blu-ray Fashion, Fashion, Electronic      
3 C         Camera, Optic, DVDs        Electronic, Health, Electronic    
4 D         Flower, Notebooks, Make-up Home, NA, Fashion

require(data.table); setDT(data_1); setDT(data_2)

data_1[, main_category := sapply(sub_category, function(x){

  str = unlist(strsplit(x, ', '))
  match = as.numeric(sapply(str, function(x) data_2[, which(x == sub_category)]))
  data_2[match, paste(main_category, collapse = ', ')]

})]

data_1 = data_1[, .(sub_category = unlist(strsplit(sub_category, ', '))), keyby = shop_name] # data_1 to long format
dt_final = merge(data_1, data_2, by = 'sub_category', all = T) # Join data_1 and data_2 on sub_category
dt_final = dt_final[, lapply(.SD, function(x) paste(x, collapse = ', ')), keyby = shop_name]

> data_1
   shop_name               sub_category                      main_category
1:         A          Blu-ray, DVDs, CD Electronic, Electronic, Electronic
2:         B Sneakers, Make-up, Blu-ray       Fashion, Fashion, Electronic
3:         C        Camera, Optic, DVDs     Electronic, Health, Electronic
4:         D Flower, Notebooks, Make-up                  Home, NA, Fashion

> dt_final
   shop_name               sub_category                      main_category
1:         A          Blu-ray, CD, DVDs Electronic, Electronic, Electronic
2:         B Blu-ray, Make-up, Sneakers       Electronic, Fashion, Fashion
3:         C        Camera, DVDs, Optic     Electronic, Electronic, Health
4:         D Flower, Make-up, Notebooks                  Home, Fashion, NA