twitter - How to check condition for observations that belong to a specific category in R? -

i’m sorry if question confusing, couldn’t find better way express it.

i have dataset tweets, user ids , dates when tweets created.

userid            tweet         date 1132622143        bla bla       2014-04-23 1132622143        bla bla       2014-05-23 1132622143        ...           ... 1132622143 1132622145 

i want make subset of dataset contains data users posted on twitter recently, in may or later, users have @ least 1 tweet date > 2014-05-01 (i want have recent , old tweets these active users in new dataset).

i think need create function this:

for each distinct userid     find rows same userid value , put them in list     each row in list         if there exists row date>2014-05-01     select rows userid 

i'm not sure if correct logic , if yes, how program this. grateful help.

ave of use here.

here's generalization of problem, can extend actual data.

first, sample data. i'm assuming know how convert "date" variable actual time/date variable.

mydf <- data.frame(   id = c(1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 4),   text = c("a", "b", "c", "a", "b", "c", "d", "e", "a", "b", "c", "a"),   time = c(1, 1, 2, 2, 3, 4, 4, 5, 3, 5, 5, 1) ) 

with ave, can create logical vector group. here, check see if "time" greater or equal 4 within each set of "id"s. if there any, returns true rows of "id".

that information can used directly extract relevant rows.

as.logical(with(mydf, ave(time, id, fun = function(x) any(x > 4)))) #  [1] false false false  true  true  true  true  true  true  true  true false mydf[as.logical(with(mydf, ave(time, id, fun = function(x) any(x > 4)))), ] #    id text time # 4   2       2 # 5   2    b    3 # 6   2    c    4 # 7   2    d    4 # 8   2    e    5 # 9   3       3 # 10  3    b    5 # 11  3    c    5