Cleaning Origin and Destination data with duplicates but different factor level - r

Question

I have some GIS data with origins and destinations (OD) and an information about the time of the day of the OD. I intending to make a map of this, and to color the ODs by the time of day information.
One thing is that some ODs are in the data set with both day and night and maybe with a different order. I would like to mark those differntly, e.g. "Day/Night"
Is there an easy way to do this? MY MWE is just one OD but I would need to identify it among several others. I can manage to find the duplicates regardless of the order, but I dont know how to find out wether or not there are both time cases there and how to replace them with "Day/Night"
library(data.table)
Origin<-c("London", "Paris", "Lisbon", "Madrid", "Berlin", "London")
Destination<-c("Paris", "London", "Berlin","Lisbon", "Lisbon", "Paris")
Time=factor(c("Day", "Night", "Day", "Day/Night","Day", "Day/Night"))
dt<-data.table(Origin=Origin, Destination=Destination, Time=Time)
#duplicates regardless of order
dat.sort = t(apply(dt[,.(Origin,Destination)], 1, sort))
dt[duplicated(dat.sort) | duplicated(dat.sort, fromLast=TRUE),]

Answer 1

You can do that using dplyr package as follows;
Feel free to change the conditions to what fits your need.
library(data.table)
library(dplyr)
# Creating data
dt <-
data.table(
Origin = c("London", "Paris", "Italy", "Spain", "Portugal", "Poland"),
Destination = c("Paris", "London", "Norway", "Portugal", "Spain", "Spain"),
Time = c("Day", "Night", "Day", NA_character_, NA_character_, NA_character_)
)
dt
# Origin Destination Time
# London Paris Day
# Paris London Night
# Italy Norway Day
# Spain Portugal <NA>
# Portugal Spain <NA>
# Poland Spain <NA>
dt %>%
# pmin and pmax are used to sort the 2 columns
# in order to group by them regardless to their order
group_by(Origin2 = pmin(Origin, Destination),
Destination2 = pmax(Origin, Destination)) %>%
mutate(count = n(), # To check if Origin/destination are repeated or not
row = row_number(), # Place holder to know if it was first to repeat or second
# If not repeated then make Time = Day
# If repeated and first occurance then Time = Day
# If repeated and second occurance then Time = Night
Time = case_when(count == 1 ~ "Day",
count == 2 & row == 1 ~ "Day",
count == 2 & row == 2 ~ "Night")) %>%
ungroup() %>%
select(Origin, Destination, Time)
# Origin Destination Time
# <chr> <chr> <chr>
# 1 London Paris Day
# 2 Paris London Night
# 3 Italy Norway Day
# 4 Spain Portugal Day
# 5 Portugal Spain Night
# 6 Poland Spain Day

Answer 2

Thanks for the dplyr solution by #Nareman Darwisch that gave me the inspiration for my solution with data.table
I am creating a new variable as a unique ID for each Origin Destination
dat.sort = t(apply(dt[,.(Origin,Destination)], 1, sort))
dt.temp<-data.table(dat.sort)
dt.temp[,unique.name:=paste(V1,V2)]
dt$unique.name<-factor(dt.temp$unique.name)
Then I can either calculate the length of the unique occurences of the factor by group or if they match more than once with any of the 3 levels. Based on this I can recode the labels with the "Day/Night" level whenever the length is > 1 or the other condition is TRUE
dt[,No.levels:=length(unique(c(Time))), by=unique.name]
dt[,No.levels.logi:=sum(c(Time) %in% c(1:3))>1 , by=unique.name]
The thing I would like to understand how I could use a logical condition in the spirit of looking at the levels by group and compares those with the cases I want.
dt[,No.levels.logi:=sum(levels(Time) %in% c("Day", "Night"))>1 , by=unique.name]
But I guess the levels command always gives me all three levels.

Answer 3

If I understand correctly, the OP wants to
identify city pairs regardless of the order of origin and destination, e.g. London-Paris belongs to the same city pair as Paris-London
collapse separate rows if a city pair is operated Day and Night or Day/Night
or update the original dataset
This is what I would do:
library(data.table)
dt <- data.table(Origin, Destination, Time)
# add city pair as unique grouping variable
dt[, Pair := paste(pmin(Origin, Destination), pmax(Origin, Destination), sep = "-")][]
# identify city pairs which are operated day and night
pairs_DN <- dt[, all(c("Day", "Night") %in% Time) | "Day/Night" %in% Time, by = Pair][(V1), .(Pair)]
# update original dataset by an update join
dt[pairs_DN, on = "Pair", Time := "Day/Night"][]
Origin Destination Time Pair
1: London Paris Day/Night London-Paris
2: Paris London Day/Night London-Paris
3: Lisbon Berlin Day Berlin-Lisbon
4: Madrid Lisbon Day/Night Lisbon-Madrid
5: Berlin Lisbon Day Berlin-Lisbon
6: London Paris Day/Night London-Paris
The key point is to identify the city pairs which fullfil the second requirement:
dt[, all(c("Day", "Night") %in% Time) | "Day/Night" %in% Time, by = Pair]
Pair V1
1: London-Paris TRUE
2: Berlin-Lisbon FALSE
3: Lisbon-Madrid TRUE
So, there is no need to deal with factor levels. BTW, factor levels are an attribute of the whole column and do not change when subsetting or grouping. What does change is which of the levels are used in a subset or group.
pairs_DN contains the unique key of those city pairs
Pair
1: London-Paris
2: Lisbon-Madrid