r - 在 R 中跨多行匹配文本

Question

我的 data.frame(Networks)包含以下内容:

Location <- c("Farm", "Supermarket", "Farm", "Conference", 
         "Supermarket", "Supermarket")
Instructor <- c("Bob", "Bob", "Louise", "Sally", "Lee", "Jeff")
Operator <- c("Lee", "Lee", "Julie", "Louise", "Bob", "Louise")

Networks <- data.frame(Location, Instructor, Operator, stringsAsFactors=FALSE)

我的问题

我想加入一个新专栏 Transactions$Count在一个新的数据框 Transactions总结每个 Instructor 之间的交换和 Operator每 Location 预期输出

Location <- c("Farm", "Supermarket", "Farm", "Conference", "Supermarket")
Person1 <- c("Bob", "Louise", "Sally", "Jeff")
Person2 < - c("Lee", "Julie", "Louise", "Louise")
Count < - c(1, 2, 1, 1, 1)
Transactions <- data.frame(Location, Person1, Person2, Count, 
            stringsAsFactors=FALSE) 

例如，Bob 和 Lee 在超市总共进行了 2 次交易。不管一个人是讲师还是运算符(operator)，我对他们的交流很感兴趣。在预期输出中，注意到了 Bob 和 Lee 在超市的两次交流。在其他位置，每个其他组合都有一个交换。
我试过的
我以为 grepl可能有用，但我希望遍历此数据的 1300 行，因此计算成本可能很高。
谢谢你。

Answer 1

您可以考虑使用“data.table”并使用 pmin和 pmax在你的“by”论点中。

例子:

Networks <- data.frame(Location, Instructor, Operator, stringsAsFactors = FALSE)
library(data.table)

as.data.table(Networks)[
  , TransCount := .N, 
  by = list(Location, 
            pmin(Instructor, Operator), 
            pmax(Instructor, Operator))][]
#       Location Instructor Operator TransCount
# 1:        Farm        Bob      Lee          1
# 2: Supermarket        Bob      Lee          2
# 3:        Farm     Louise    Julie          1
# 4:  Conference      Sally   Louise          1
# 5: Supermarket        Lee      Bob          2
# 6: Supermarket       Jeff   Louise          1

根据您的更新，这听起来可能更适合您:

as.data.table(Networks)[
  , c("Person1", "Person2") := list(
    pmin(Instructor, Operator), 
    pmax(Instructor, Operator)), 
  by = 1:nrow(Networks)
][
  , list(TransCount = .N), 
  by = .(Location, Person1, Person2)
]
#       Location Person1 Person2 TransCount
# 1:        Farm     Bob     Lee          1
# 2: Supermarket     Bob     Lee          2
# 3:        Farm   Julie  Louise          1
# 4:  Conference  Louise   Sally          1
# 5: Supermarket    Jeff  Louise          1