R function that works on previous observation -
i have collection of user's ip addresses , associated time hit particular website. trying change in time between each ip address change. make easier, i've assigned each row label whether or not represents change previous row, , i've done on per-user basis.
sample data:
user.nm ip.addr.txt login.sessn.ts change.label b c 2/18/2013 16:08 false b c 2/18/2013 16:08 false b c 2/28/2013 13:37 false b c 2/28/2013 16:10 false b c 2/28/2013 16:20 false b c 3/5/2013 9:29 false b c 3/6/2013 11:42 false b c 3/11/2013 13:55 false <- b b 6/25/2013 13:22 true <- b d 6/25/2013 13:22 false <- b b 8/12/2013 13:18 true <- b c 8/12/2013 13:18 false b c 8/20/2013 15:13 false b c 8/20/2013 15:13 false b c 9/23/2013 14:08 false b c 9/23/2013 14:09 false b c 9/25/2013 11:00 false b c 10/18/2013 16:54 false b c 10/18/2013 16:54 false b c 10/30/2013 14:33 false b c 11/8/2013 15:03 false b c 11/18/2013 11:30 false b c 11/18/2013 11:33 false b c 11/20/2013 16:08 false b c 11/21/2013 11:51 false b c 11/21/2013 11:52 false b c 11/21/2013 15:18 false b c 11/21/2013 16:40 false b c 11/21/2013 16:44 false b c 11/21/2013 16:45 false b c 11/21/2013 16:45 false b c 11/29/2013 15:41 false b c 11/29/2013 15:41 false 1/9/2013 15:32 false 1/9/2013 15:32 false 1/9/2013 15:32 false 1/9/2013 15:32 false 1/10/2013 10:39 false 1/10/2013 10:39 false 1/10/2013 10:39 false 1/11/2013 10:31 false 1/11/2013 10:31 false 1/18/2013 12:30 false 2/22/2013 10:54 false <- b 3/6/2013 12:27 true <- dput:
sample.data=structure(list(user.nm = c("b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a"), ip.addr.txt = c("c", "c", "c", "c", "c", "c", "c", "c", "b", "c", "b", "c", "c", "c", "c", "c", "c", "c", "c", "c", "c", "c", "c", "c", "c", "c", "c", "c", "c", "c", "c", "c", "c", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "b" ), login.sessn.ts = structure(c(1361221680, 1361221680, 1362076620, 1362085800, 1362086400, 1362493740, 1362588120, 1363024500, 1372180920, 1372180920, 1376327880, 1376327880, 1377025980, 1377025980, 1379959680, 1379959740, 1380121200, 1382129640, 1382129640, 1383157980, 1383940980, 1384792200, 1384792380, 1384981680, 1385052660, 1385052720, 1385065080, 1385070000, 1385070240, 1385070300, 1385070300, 1385757660, 1385757660, 1357763520, 1357763520, 1357763520, 1357763520, 1357832340, 1357832340, 1357832340, 1357918260, 1357918260, 1358530200, 1361548440, 1362590820), class = c("posixct", "posixt"), tzone = ""), change.label = c(false, false, false, false, false, false, false, false, true, false, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true)), .names = c("user.nm", "ip.addr.txt", "login.sessn.ts", "change.label"), row.names = c(na, -45l), class = "data.frame") i attempting write ddply summarize statement give me difference in time between each ip change per user (among other things). normally, subset df observations have label true , use ddply dataframe. however, need difference between pairs of rows false followed true.
ideally, output dataframe this:
user.nm change count min.change.tme max.change.time 2 10 sec 4 hours b 1 1 hour 1 hour i hoping use kind of index lookup function match, i'm not sure how translate function.
is there kind of "look-behind" function in r this?
my code getting number of ip changes works far, , below:
did.change<-function(vec){ #consumes vector #returns p-1 boolean vector of instances element not directly repeated (duplicated) b.vec=head(vec, -1)==tail(vec, -1) return(!b.vec) } ###this function works on entire list of entries per user, broad time.changes<-function(vec){ a=head(vec-1)-tail(vec,-1) return(abs(a)) } user.changes=ddply(sample.data, c("user.nm"), summarize, change.count=sum(did.change(ip.addr.txt))) #max.change.time=max(time.changes(login.sessn.ts)), #min.change.time=min(time.changes(login.sessn.ts)))
short answer: yes, , called diff!
long answer:
is_diff <- which(diff(sample.data$change.label)==1) ss <- do.call(c,lapply(is_diff,function(x) c(x,x+1))) sample.data[ss,] user.nm ip.addr.txt login.sessn.ts change.label 8 b c 2013-03-11 10:55:00 false 9 b b 2013-06-25 10:22:00 true 10 b c 2013-06-25 10:22:00 false 11 b b 2013-08-12 10:18:00 true 44 2013-02-22 07:54:00 false 45 b 2013-03-06 09:27:00 true here 1 way calculate changes in login times:
ss_list <- lapply(is_diff,function(x) c(x,x+1)) logins <- lapply(ss_list,function(x) sample.data[x,"login.sessn.ts"]) library(lubridate) lapply(logins,function(x) diff(ymd_hms(x))) if want break down user.nm, try using dplyr:
library(dplyr) sample.data %>% mutate(rownum = 1:nrow(sample.data)) %>% filter(rownum %in% ss) %>% group_by(user.nm) %>% mutate(change = login.sessn.ts - lag(login.sessn.ts)) user.nm ip.addr.txt login.sessn.ts change.label rownum change 1 b c 2013-03-11 10:55:00 false 8 na days 2 b b 2013-06-25 10:22:00 true 9 9.156420e+06 days 3 b c 2013-06-25 10:22:00 false 10 0.000000e+00 days 4 b b 2013-08-12 10:18:00 true 11 4.146960e+06 days 5 2013-02-22 07:54:00 false 44 na days 6 b 2013-03-06 09:27:00 true 45 1.206458e+01 days
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