It is the same command as regular chisq.test().
The chiSq test here would tell you whether the sample is independent of the data. You do not want this happening sine, this would mean that the sample is not representative. Hence, a p value greater than 0.05 is desirable - suggesting that the sample is not significantly different than the data.
membersVec <- c("annual", "bargain", "life", "notYetRetired", "outOfArea", "outOfAreaBargain")
membersDf <- sample(membersVec, size = 2000, prob = c(0.4013,0.4402,0.0914, 0.0141, 0.022, 0.0308), replace = T)
membersTbl <- table(membersDf)
## Sample
membersSample <- sample(membersDf, size = 200, replace = T)
membersSampleTbl <- table(membersSample)
## ChiSq test
chisq.test(membersTbl, membersSampleTbl, simulate.p.value = T, B = 1000)