Pull subset of rows of dataframe based on conditions from other columns - r

Question

I have a dataframe like the one below:
x <- data.table(Tickers=c("A","A","A","B","B","B","B","D","D","D","D"),
Type=c("put","call","put","call","call","put","call","put","call","put","call"),
Strike=c(35,37.5,37.5,10,11,11,12,40,40,42,42),
Other=sample(20,11))
Tickers Type Strike Other
1: A put 35.0 6
2: A call 37.5 5
3: A put 37.5 13
4: B call 10.0 15
5: B call 11.0 12
6: B put 11.0 4
7: B call 12.0 20
8: D put 40.0 7
9: D call 40.0 11
10: D put 42.0 10
11: D call 42.0 1
I am trying to analyze a subset of the data. The subset I would like to take is data where the ticker and strike are the same. But I also only want to grab this data if both a put and a call exists under type. With the data above for example, I would like to return the following result:
x[c(2,3,5,6,8:11),]
Tickers Type Strike Other
1: A call 37.5 5
2: A put 37.5 13
3: B call 11.0 12
4: B put 11.0 4
5: D put 40.0 7
6: D call 40.0 11
7: D put 42.0 10
8: D call 42.0 1
I'm not sure what the best way to go about doing this. My thought process is that I should create another column vector like
x$id <- paste(x$Tickers,x$Strike,sep="_")
Then use this vector to only pull values where there are multiple ids.
x[x$id %in% x$id[duplicated(x$id)],]
Tickers Type Strike Other id
1: A call 37.5 5 A_37.5
2: A put 37.5 13 A_37.5
3: B call 11.0 12 B_11
4: B put 11.0 4 B_11
5: D put 40.0 7 D_40
6: D call 40.0 11 D_40
7: D put 42.0 10 D_42
8: D call 42.0 1 D_42
I'm not sure how efficient this is, as my actual data consists of a lot more rows.
Also, this solution does not check for the type condition of there being one put and one call.
also the wording of the title could be a lot better, I apologize
EDIT::: having checked out this post Finding ALL duplicate rows, including "elements with smaller subscripts"
I could also use this solution:
x$id <- paste(x$Tickers,x$Strike,sep="_")
x[duplicated(x$id) | duplicated(x$id,fromLast=T),]

Answer 1

You could try something like:
x[, select := (.N >= 2 & all(c("put", "call") %in% unique(Type))), by = .(Tickers, Strike)][which(select)]
# Tickers Type Strike Other select
#1: A call 37.5 17 TRUE
#2: A put 37.5 16 TRUE
#3: B call 11.0 11 TRUE
#4: B put 11.0 20 TRUE
#5: D put 40.0 1 TRUE
#6: D call 40.0 12 TRUE
#7: D put 42.0 6 TRUE
#8: D call 42.0 2 TRUE
Another idea might be a merge:
x[x, on = .(Tickers, Strike), select := (length(Type) >= 2 & all(c("put", "call") %in% Type)),by = .EACHI][which(select)]
I'm not entirely sure how to get around the group-by operations since you want to make sure for each group they have both "call" and "put". I was thinking about using keys, but haven't been able to incorporate the "call"/"put" aspect.

Answer 2

An edit to your data to give a case where both put and call does not exist (I changed the very last "call" to "put"):
x <- data.table(Tickers=c("A","A","A","B","B","B","B","D","D","D","D"),
Type=c("put","call","put","call","call","put","call","put","call","put","put"),
Strike=c(35,37.5,37.5,10,11,11,12,40,40,42,42),
Other=sample(20,11))
Since you are using data.table, you can use the built in counter .N along with by variables to count groups and subset with that. If by counting Type you can reliably determine there is both put and call, this could work:
x[, `:=`(n = .N, types = uniqueN(Type)), by = c('Tickers', 'Strike')][n > 1 & types == 2]
The part enclosed in the first set of [] does the counting, and then the [n > 1 & types == 2] does the subsetting.

Answer 3

I am not a user of package data.table so this code is base R only.
agg <- aggregate(Type ~ Tickers + Strike, data = x, length)
result <- merge(x, subset(agg, Type > 1)[1:2], by = c("Tickers", "Strike"))[, c(1, 3, 2, 4)]
result
# Tickers Type Strike Other
#1: A call 37.5 17
#2: A put 37.5 7
#3: B call 11.0 14
#4: B put 11.0 20
#5: D put 40.0 15
#6: D call 40.0 2
#7: D put 42.0 8
#8: D call 42.0 1
rm(agg) # final clean up