subsetting by multi-column index/key in dplyr (have data.table soln)

I'm looking to find a way to subset (or rethink how I handle the task) the following situation to stay in dplyr rather than "resort" to data.table as much of my analysis before/after this chunk is done in dplyr. Situation: given a simulated dataset with multiple replications I would like to subset/dplyr::filter based on a two column key (ID and REP). ``` libs <- c("dplyr", "data.table") lapply(libs, require, character.only = T) # minimally reproducible example # dataset dat <- expand.grid(ID = 1:3, REP = 1:5, TIME = 1:3) dat <- dat[order(dat$REP, dat$ID, dat$TIME),] dat$CONC <- runif(nrow(dat), 1, 10) # key/index set.seed(1235) ID_sample <- sample(unique(dat$ID), size = 5, replace = TRUE) REP_sample <- sample(unique(dat$REP), size = 5, replace = TRUE) key <- data.frame(ID = ID_sample, REP = REP_sample) # data table solution dt <- data.table(dat) setkey(dt, ID, REP) dt_subset <- dt[J(key)] ``` The data.table solution results in the following: initial data structure: ``` ID REP TIME CONC 1 1 1 1 1.310819 2 1 1 2 2.371361 3 1 1 3 7.621165 4 2 1 1 1.010229 5 2 1 2 4.520830 6 2 1 3 5.162452 ... 40 2 5 1 6.629885 41 2 5 2 9.680233 42 2 5 3 8.445726 43 3 5 1 3.835254 44 3 5 2 2.917229 45 3 5 3 7.592465 ``` generated key and resulting subset: ``` > key ID REP 1 1 3 2 2 3 3 1 4 4 3 3 5 3 2 > dt[J(key)] ID REP TIME CONC 1: 1 3 1 3.038205 2: 1 3 2 5.361020 3: 1 3 3 8.137065 4: 2 3 1 1.053889 5: 2 3 2 2.689412 6: 2 3 3 7.136503 7: 1 4 1 9.137392 8: 1 4 2 6.556821 9: 1 4 3 2.206285 10: 3 3 1 4.330937 11: 3 3 2 4.254630 12: 3 3 3 8.819154 13: 3 2 1 4.508456 14: 3 2 2 7.286893 15: 3 2 3 5.896521 ``` Is there a way of using this multi-column index to filter in dplyr? The only 'solution' I've thought of so far is is to create a new column like so: ``` dat <- transform(dat, ID_REP = paste0(ID, '_', REP)) KEY <- paste0(ID_sample, '_', REP_sample) filter(dat, ID_REP %in% KEY) ``` which works: ``` ID REP TIME CONC ID_REP 1 3 2 1 4.029622 3_2 2 3 2 2 5.786582 3_2 3 3 2 3 2.846836 3_2 4 1 3 1 4.968823 1_3 5 1 3 2 6.940782 1_3 6 1 3 3 5.017697 1_3 7 2 3 1 7.571442 2_3 8 2 3 2 6.350095 2_3 9 2 3 3 3.924427 2_3 10 3 3 1 6.360991 3_3 11 3 3 2 3.273693 3_3 12 3 3 3 4.029781 3_3 13 1 4 1 6.617855 1_4 14 1 4 2 1.910202 1_4 15 1 4 3 5.496817 1_4 ``` but is inelegant and does not provide an easily extensible solution.