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使用dplyr将一个data.frame输出到另一个

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我有两个data.frames - 一个查询表告诉我组中包含的一组产品 . 每组至少有一种1型和2型产品 .

第二个data.frame告诉我有关交易的详细信息 . 每笔交易都可以使用以下产品之一：

a）仅来自其中一个组的类型1的产品 s

b）仅来自其中一个组的类型2的产品 s

c）来自同一组的1型和2型产品

对于我的分析，我有兴趣发现上面的c），即有多少交易产品的类型1和类型2（来自同一组） . 如果在同一交易中出售的不同组中的类型1的产品和类型2的产品，我们将完全忽略该交易 .

因此，类型1或类型2的每个产品必须属于同一组 .

这是我的查找表：

> P_Lookup
   Group ProductID1 ProductID2
  Group1          A          1
  Group1          B          2
  Group1          B          3
  Group2          C          4
  Group2          C          5
  Group2          C          6
  Group3          D          7
  Group3          C          8
  Group3          C          9
  Group4          E         10
  Group4          F         11
  Group4          G         12
  Group5          H         13
  Group5          H         14
  Group5          H         15

例如，我不会在一次交易中拥有产品G和产品15，因为它们属于不同的组 .

以下是交易：

TransactionID ProductID ProductType
             a1         A           1
             a1         B           1
             a1         1           2
             a2         C           1
             a2         4           2
             a2         5           2
             a3         D           1
             a3         C           1
             a3         7           2
             a3         8           2
             a4         H           1
             a5         1           2
             a5         2           2
             a5         3           2
             a5         3           2
             a5         1           2
             a6         H           1
             a6        15           2

My Code:

现在，我能够使用 dplyr 编写代码，用于从一个组中筛选交易 . 但是，我不确定如何为所有组矢量化我的代码 .

这是我的代码：

P_Groups<-unique(P_Lookup$Group)
Chosen_Group<-P_Groups[5]

P_Group_Ind <- P_Trans %>%
group_by(TransactionID)%>%
dplyr::filter((ProductID %in% unique(P_Lookup[P_Lookup$Group==Chosen_Group,]$ProductID1)) | 
(ProductID %in% unique(P_Lookup[P_Lookup$Group==Chosen_Group,]$ProductID2)) ) %>%
mutate(No_of_PIDs = n_distinct(ProductType)) %>%
mutate(Group_Name = Chosen_Group)

P_Group_Ind<-P_Group_Ind[P_Group_Ind$No_of_PIDs>1,]

只要我手动选择每个组，即设置 Chosen_Group ，这就可以正常工作 . 但是，我不确定如何自动化这个 . 一种方式，我想是使用for循环，但我知道R的美丽是矢量化，所以我想远离使用for循环 .

我真诚地感谢任何帮助 . 我花了差不多两天时间 . 我看了using dplyr in for loop in r，但似乎这个主题正在讨论一个不同的问题 .

DATA: 这是 dput for P_Trans ：

structure(list(TransactionID = c("a1", "a1", "a1", "a2", "a2", 
"a2", "a3", "a3", "a3", "a3", "a4", "a5", "a5", "a5", "a5", "a5", 
"a6", "a6"), ProductID = c("A", "B", "1", "C", "4", "5", "D", 
"C", "7", "8", "H", "1", "2", "3", "3", "1", "H", "15"), ProductType = c(1, 
1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2)), .Names = c("TransactionID", 
"ProductID", "ProductType"), row.names = c(NA, 18L), class = "data.frame")

这是 dput for P_Lookup ：

structure(list(Group = c("Group1", "Group1", "Group1", "Group2", 
"Group2", "Group2", "Group3", "Group3", "Group3", "Group4", "Group4", 
"Group4", "Group5", "Group5", "Group5"), ProductID1 = c("A", 
"B", "B", "C", "C", "C", "D", "C", "C", "E", "F", "G", "H", "H", 
"H"), ProductID2 = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 
14, 15)), .Names = c("Group", "ProductID1", "ProductID2"), row.names = c(NA, 
15L), class = "data.frame")

将查询表中不存在的产品添加到P_Trans后，这是 dput() ：

structure(list(TransactionID = c("a1", "a1", "a1", "a2", "a2", 
"a2", "a3", "a3", "a3", "a3", "a4", "a5", "a5", "a5", "a5", "a5", 
"a6", "a6", "a7"), ProductID = c("A", "B", "1", "C", "4", "5", 
"D", "C", "7", "8", "H", "1", "2", "3", "3", "1", "H", "15", 
"22"), ProductType = c(1, 1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2, 2, 
2, 2, 2, 1, 2, 3)), .Names = c("TransactionID", "ProductID", 
"ProductType"), row.names = c(NA, 19L), class = "data.frame")
r nested dplyr

2 回答

  • 2

    下面是一个tidyverse（dplyr，tidyr和purrr）解决方案，我希望能帮到你 .

    请注意，在最后一行中使用 map_df 会将所有结果作为数据框返回 . 如果您希望它是每个组的列表对象，那么只需使用 map .

    library(dplyr)
library(tidyr)
library(purrr)

# Save unique groups for later use
P_Groups <- unique(P_Lookup$Group)

# Convert lookup table to product IDs and Groups
P_Lookup <- P_Lookup %>% 
              gather(ProductIDn, ProductID, ProductID1, ProductID2) %>% 
              select(ProductID, Group) %>% 
              distinct() %>% 
              nest(-ProductID, .key = Group)

# Bind Group information to transactions
# and group for next analysis
P_Trans <- P_Trans %>%
             left_join(P_Lookup) %>%
             filter(!map_lgl(Group, is.null)) %>%  
             unnest(Group) %>% 
             group_by(TransactionID)

# Iterate through Groups to produce results
map(P_Groups, ~ filter(P_Trans, Group == .)) %>% 
  map(~ mutate(., No_of_PIDs = n_distinct(ProductType))) %>% 
  map_df(~ filter(., No_of_PIDs > 1))
#> Source: local data frame [12 x 5]
#> Groups: TransactionID [4]
#> 
#>    TransactionID ProductID ProductType  Group No_of_PIDs
#>            <chr>     <chr>       <dbl>  <chr>      <int>
#> 1             a1         A           1 Group1          2
#> 2             a1         B           1 Group1          2
#> 3             a1         1           2 Group1          2
#> 4             a2         C           1 Group2          2
#> 5             a2         4           2 Group2          2
#> 6             a2         5           2 Group2          2
#> 7             a3         D           1 Group3          2
#> 8             a3         C           1 Group3          2
#> 9             a3         7           2 Group3          2
#> 10            a3         8           2 Group3          2
#> 11            a6         H           1 Group5          2
#> 12            a6        15           2 Group5          2
    回复于
  • 4

    这是一个单一的管道 dplyr 解决方案：

    P_DualGroupTransactionsCount <- 
    P_Lookup %>% # data needing single column map of Keys
    gather(IDnum, ProductID, ProductID1:ProductID2) %>% # produce long single map of Keys for GroupID (tidyr::)
    right_join(P_trans) %>% # join transactions to groupID info
    group_by(TransactionID, Group) %>% # organize for same transaction & same group
    mutate(DualGroup = ifelse(n_distinct(ProductType)==2, T, F)) %>% # flag groups with both groups in a single transaction
    filter(DualGroup == T) %>% # choose only doubles
    select(TransactionID, Group) %>% # remove excess columns
    distinct %>%  # remove excess rows
    nrow # count of unique transaction ID's

# P_DualGroupTransactions
# Source: local data frame [4 x 2]
# Groups: TransactionID, Group [4]
#     
# TransactionID  Group
#           <chr>  <chr>
# 1            a1 Group1
# 2            a2 Group2
# 3            a3 Group3
# 4            a6 Group5


# P_DualGroupTransactionsCount
 [1] 4
    回复于

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