将 dplyr 连接语法转换为纯 data.table 语法 [英] convert dplyr join syntax into pure data.table syntax

问题描述

我正在学习 data.table.我很难转换 dplyr 连接语法.您能否为以下测试用例推荐 data.table 等效项?

I am learning data.table. I have difficulty converting the dplyr join syntax. Can you please recommend the data.table equivalence for the following test cases?

library(data.table)
library(dplyr)

dtProduct <- data.table(
    ProductID  = c(6, 33, 17, 88, 44, 51),
    ProductName= c("Shirt", "Helmet", "Gloves", "Towel", "Chair", "Detergent"),
    Price= c(25, 60, 10, 7.5, 135, 16),
    key = 'ProductID'
)

set.seed(20141216)
dtOrder <- data.table(
    OrderID    = sample(1001:9999, 12),
    CustomerID = sample(271:279, 12, replace=TRUE),
    # NOTE: some non-existent ProductID intentionally introduced
    ProductID  = sample(c(dtProduct[, ProductID], 155, 439), 12, replace=TRUE),
    Qty = sample(1:3, 12, replace=TRUE),
    key = 'OrderID'
)

> tables()
     NAME      NROW NCOL MB COLS                             KEY      
[1,] dtOrder     12    4  1 OrderID,CustomerID,ProductID,Qty OrderID  
[2,] dtProduct    6    3  1 ProductID,ProductName,Price      ProductID

> dtProduct
   ProductID ProductName Price
1:         6       Shirt  25.0
2:        17      Gloves  10.0
3:        33      Helmet  60.0
4:        44       Chair 135.0
5:        51   Detergent  16.0
6:        88       Towel   7.5
> dtOrder
    OrderID CustomerID ProductID Qty
 1:    1651        275         6   3
 2:    2726        272        88   2
 3:    3079        275        88   2
 4:    3168        274        17   1
 5:    4816        277        88   1
 6:    4931        278        51   1
 7:    5134        274       439   2
 8:    5265        272        33   3
 9:    7702        275        33   2
10:    7727        279       155   2
11:    8412        273        88   2
12:    9130        271        17   3

案例 1:显示订单详情，不匹配的 ProductID 被隐藏

dtOrder %>%
    inner_join(dtProduct, by="ProductID") %>%
    transmute(OrderID, ProductID, ProductName, Qty, Price, ExtPrice=Qty*Price)

   OrderID ProductID ProductName Qty Price ExtPrice
1     1651         6       Shirt   3  25.0     75.0
2     3168        17      Gloves   1  10.0     10.0
3     9130        17      Gloves   3  10.0     30.0
4     5265        33      Helmet   3  60.0    180.0
5     7702        33      Helmet   2  60.0    120.0
6     4931        51   Detergent   1  16.0     16.0
7     2726        88       Towel   2   7.5     15.0
8     3079        88       Towel   2   7.5     15.0
9     4816        88       Towel   1   7.5      7.5
10    8412        88       Towel   2   7.5     15.0

案例 2:显示订单详情，包括不匹配的 ProductID

dtOrder %>%
    left_join(dtProduct, by="ProductID") %>%
    transmute(OrderID, ProductID, ProductName, Qty, Price, ExtPrice=Qty*Price)

   OrderID ProductID ProductName Qty Price ExtPrice
1     1651         6       Shirt   3  25.0     75.0
2     3168        17      Gloves   1  10.0     10.0
3     9130        17      Gloves   3  10.0     30.0
4     5265        33      Helmet   3  60.0    180.0
5     7702        33      Helmet   2  60.0    120.0
6     4931        51   Detergent   1  16.0     16.0
7     2726        88       Towel   2   7.5     15.0
8     3079        88       Towel   2   7.5     15.0
9     4816        88       Towel   1   7.5      7.5
10    8412        88       Towel   2   7.5     15.0
11    7727       155          NA   2    NA       NA
12    5134       439          NA   2    NA       NA

案例 3:显示订单错误(仅 ProductID 不匹配)

dtOrder %>%
    left_join(dtProduct, by="ProductID") %>%
    filter(is.na(ProductName)) %>%
    select(OrderID, ProductID, ProductName, Qty)

  OrderID ProductID ProductName Qty
1    7727       155          NA   2
2    5134       439          NA   2

案例 4:按 ProductID 的各种聚合，按 TotalSales 降序排序结果

dtOrder %>%
    inner_join(dtProduct, by="ProductID") %>%
    group_by(ProductID) %>%
    summarize(OrderCount=n(), TotalQty=sum(Qty), TotalSales=sum(Qty*Price)) %>%
    arrange(desc(TotalSales))

  ProductID OrderCount TotalQty TotalSales
1        33          2        5      300.0
2         6          1        3       75.0
3        88          4        7       52.5
4        17          2        4       40.0
5        51          1        1       16.0

案例 5:按 ProductID 的各种聚合，按 TotalSales 降序排序结果

• 注意 1:这一次，ProductName 与 ProductID 一起显示

• 注意 2:按降序排序 TotalSales 不再起作用(BUG?)

• NOTE1: This time, ProductName is displayed along with ProductID

• NOTE2: sort by descending TotalSales no longer working (BUG?)

dtOrder %>%
   inner_join(dtProduct, by="ProductID") %>%
   group_by(ProductID, ProductName) %>%
   summarize(OrderCount=n(), TotalQty=sum(Qty), TotalSales=sum(Qty*Price)) %>%
   arrange(desc(TotalSales))

  ProductID ProductName OrderCount TotalQty TotalSales
1         6       Shirt          1        3       75.0
2        17      Gloves          2        4       40.0
3        33      Helmet          2        5      300.0
4        51   Detergent          1        1       16.0
5        88       Towel          4        7       52.5

非常感谢您的任何帮助.

Thank you very much in advance for any help.

推荐答案

setkey(dtOrder, ProductID)

(1-2)

# this will be literally what you wrote

dtProduct[dtOrder,
          list(OrderID, ProductID, ProductName, Qty, Price, ExtPrice=Qty*Price),
          nomatch = 0 # or omit this to get (2)
         ]

# but I think you'd be better off with this
dtProduct[dtOrder][, ExtPrice := Qty*Price][]

(3)

# you can again take the literal direction:
dtProduct[dtOrder][!is.na(ProductName)][,
          list(OrderID, ProductID, ProductName, Qty)]

# but again I think you'd be better off with
dtOrder[!dtProduct]

(4-5)

dtProduct[dtOrder, nomatch = 0][,
          list(OrderCount=.N, TotalQty=sum(Qty), TotalSales=sum(Qty*Price)),
          by = list(ProductID, ProductName)][
          order(-TotalSales)]

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