Time aggregation in R - r

Question

I have dataset with data of gamesessions(id,count of session,averege seconds of session and date of session for each id)
here sample of mydat:
mydat=read.csv("C:/Users/Admin/desktop/rty.csv", sep=";",dec=",")
structure(list(udid = c(74385162L, 79599601L, 79599601L, 91475825L,
91475825L, 91492531L, 92137561L, 96308016L, 96308016L, 96308016L,
96308016L, 96308016L, 96495076L, 97135620L, 97135620L, 97135620L,
97135620L, 97135620L, 97135620L, 97135620L, 97135620L, 97135620L,
97135620L, 97165942L), count = c(1L, 1L, 1L, 1L, 3L, 1L, 1L,
2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L), avg_duration = c(39L, 1216L, 568L, 5L, 6L, 79L, 9L, 426L,
78L, 884L, 785L, 785L, 22L, 302L, 738L, 280L, 2782L, 5L, 2284L,
144L, 234L, 231L, 539L, 450L), date = structure(c(13L, 3L, 3L,
1L, 1L, 14L, 2L, 11L, 11L, 11L, 12L, 12L, 9L, 7L, 4L, 4L, 5L,
6L, 8L, 8L, 8L, 8L, 8L, 10L), .Label = c("11.10.16", "12.12.16",
"15.11.16", "15.12.16", "16.12.16", "17.12.16", "18.10.16", "18.12.16",
"21.10.16", "26.10.16", "28.11.16", "29.11.16", "31.10.16", "8.10.16"
), class = "factor")), .Names = c("udid", "count", "avg_duration",
"date"), class = "data.frame", row.names = c(NA, -24L))
I need calculating the time difference between the first date of the player's appearance and the last date when he was seen.
For example uid 97135620 the first time when he started play was 18.10.2016 and last time he was seen at 18.12.2016, it is mean that the difference between first and last day = 60,9 days,
meanwhile uid74385162 started at 31.10.2016 and after he didn't play(i.e he played one time), it is mean the difference between first data and last data = 0.
id79599601 has two count of session in 1 day(i.e for one day I played 2 times), so the the difference =1
In output i expect this format only with last date and the value of the difference between the last day and the first day.
udid count avg_duration date datediff
74385162 1 39 31.10.2016 0
79599601 1 568 15.11.2016 1
91475825 1 5 11.10.2016 1
91492531 1 79 08.10.2016 0
92137561 1 9 12.12.2016 0
96308016 1 785 29.11.2016 1
96495076 1 22 21.10.2016 0
97135620 1 539 18.12.2016 61
97165942 1 450 26.10.2016 0
How do that?

Answer 1

This function calculates the difference between first and last session, and only returns the date of the last session:
get_datediff <- function (x) {
dates <- as.Date(as.character(x$date), "%d.%m.%y")
x <- x[order(dates), ]
if (length(x$date)==1) {
x$datediff <- 0
} else {
x$datediff <- max(1, diff(range(dates)))
}
x[nrow(x), ]
}
This can then be applied to data for each user, making use of dplyr and magrittr packages:
group_by(mydat, udid) %>% do(get_datediff(.))
# A tibble: 9 x 5
# Groups: udid [9]
udid count avg_duration date datediff
<int> <int> <int> <fctr> <dbl>
1 74385162 1 39 31.10.16 0
2 79599601 1 568 15.11.16 1
3 91475825 3 6 11.10.16 1
4 91492531 1 79 8.10.16 0
5 92137561 1 9 12.12.16 0
6 96308016 1 785 29.11.16 1
7 96495076 1 22 21.10.16 0
8 97135620 1 539 18.12.16 61
9 97165942 1 450 26.10.16 0

Answer 2

The way you describe how your metrics are calculated are confusing, but following what you wrote as closely as possible, I ended up with the following:
dplyr solution:
timeData%>%
mutate(dateFormat = as.Date(date, format = "%d.%m.%y"))%>%
group_by(udid)%>%
arrange(udid,dateFormat)%>%
summarise(dateBetween = difftime(last(dateFormat), first(dateFormat), units = "days"), mean(avg_duration))%>%
left_join((timeData%>%
mutate(dateFormat = as.Date(date, format = "%d.%m.%y"))%>%
select(udid, count,dateFormat)%>%
group_by(udid)%>%
slice(which.min(dateFormat))))
Result:
# A tibble: 9 x 5
udid dateBetween `mean(avg_duration)` count dateFormat
<int> <time> <dbl> <int> <date>
1 74385162 0 days 39.0 1 2016-10-31
2 79599601 0 days 892.0 1 2016-11-15
3 91475825 0 days 5.5 1 2016-10-11
4 91492531 0 days 79.0 1 2016-10-08
5 92137561 0 days 9.0 1 2016-12-12
6 96308016 1 days 591.6 1 2016-11-29
7 96495076 0 days 22.0 1 2016-10-21
8 97135620 61 days 753.9 1 2016-12-18
9 97165942 0 days 450.0 1 2016-10-26