Match text across multiple rows in R - r

Question

My data.frame(Networks) contains the following:
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)
MY QUESTION
I wish to include a new column Transactions$Count in a new data.frame Transactions that sums the exchanges between each Instructor and Operator for every Location
EXPECTED OUTPUT
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)
For example, there would be a total of 2 exchanges between Bob and Lee at the Supermarket. It does not matter if one person is a instructor or operator, I am interested in their exchange. In the expected output, the two exchanges between Bob and Lee at the Supermarket are noted. There is one exchange for every other combination at the other locations.
WHAT I HAVE TRIED
I thought grepl may be of use, but I wish to iterate across 1300 rows of this data, so it may be computationally expensive.
Thank you.

Answer 1

You can consider using "data.table" and use pmin and pmax in your "by" argument.
Example:
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
Based on your update, it sounds like this might be more appropriate for you:
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

Answer 2

You may try
library(dplyr)
Networks %>%
group_by(Location, Person1=pmin(Instructor,Operator),
Person2= pmax(Instructor,Operator)) %>%
summarise(Count=n())
# Location Person1 Person2 Count
#1 Conference Louise Sally 1
#2 Farm Bob Lee 1
#3 Farm Julie Louise 1
#4 Supermarket Bob Lee 2
#5 Supermarket Jeff Louise 1
Or using base R
d1 <-cbind(Location=Networks[,1],
data.frame(setNames(Map(do.call, c('pmin', 'pmax'),
list(Networks[-1])), c('Person1', 'Person2'))))
aggregate(cbind(Count=1:nrow(d1))~., d1, FUN=length)
# Location Person1 Person2 Count
#1 Farm Bob Lee 1
#2 Supermarket Bob Lee 2
#3 Supermarket Jeff Louise 1
#4 Farm Julie Louise 1
#5 Conference Louise Sally 1
data
Networks <- data.frame(Location, Instructor, Operator,
stringsAsFactors=FALSE)