subset dataframe - r

Question

I have a dataframe with counts of geese at several different sites. The aim was to make monthly counts of geese in
all 8 months between September-April at each site in consecutive winter periods. A winter period is defined as the 8 months between
September-April.
If the method had been carried out as planned, this is what the data would look like:
df <- data.frame(site=c(rep('site 1', 16), rep('site 2', 16), rep('site 3', 16)),
date=dmy(rep(c('01/09/2007', '02/10/2007', '02/11/2007',
'02/12/2007', '02/01/2008', '02/02/2008', '02/03/2008',
'02/04/2008', '01/09/2008', '02/10/2008', '02/11/2008',
'02/12/2008', '02/01/2009', '02/02/2009', '02/03/2009',
'02/04/2009'),3)),
count=sample(1:100, 48))
Its ended up with a situation where some sites have all 8 counts in some September-April periods, but not in other September-April periods. In addition, some sites, never achieved 8 counts in a September-April period. These toy data look like my actual data:
df <- df[-c(11:16, 36:48),]
I need to remove rows from the dataframe which do not form part of 8 consecutive counts in a September-April period. Using the toy data, this is the dataframe I need:
df <- df[-c(9:10, 27:29), ]
I've tried various commands using ddply() from plyr package but without success. Is there a solution to this problem?

Answer 1

One way I could think of is to subtract four months from your date so that, then you could group by year. To get the corresponding date by subtracting by 4 months, I suggest you use mondate package. See here for an excellent answer as to what problem you'd face when you subtract month and how you can overcome it.
require(mondate)
df$grp <- mondate(df$date) - 4
df$year <- year(df$grp)
df$month <- month(df$date)
ddply(df, .(site, year), function(x) {
if (all(c(1:4, 9:12) %in% x$month)) {
return(x)
} else {
return(NULL)
}
})
# site date count grp year month
# 1 site 1 2007-09-01 87 2007-05-02 2007 9
# 2 site 1 2007-10-02 44 2007-06-02 2007 10
# 3 site 1 2007-11-02 50 2007-07-03 2007 11
# 4 site 1 2007-12-02 65 2007-08-02 2007 12
# 5 site 1 2008-01-02 12 2007-09-02 2007 1
# 6 site 1 2008-02-02 2 2007-10-03 2007 2
# 7 site 1 2008-03-02 100 2007-11-02 2007 3
# 8 site 1 2008-04-02 29 2007-12-03 2007 4
# 9 site 2 2007-09-01 3 2007-05-02 2007 9
# 10 site 2 2007-10-02 22 2007-06-02 2007 10
# 11 site 2 2007-11-02 56 2007-07-03 2007 11
# 12 site 2 2007-12-02 5 2007-08-02 2007 12
# 13 site 2 2008-01-02 40 2007-09-02 2007 1
# 14 site 2 2008-02-02 15 2007-10-03 2007 2
# 15 site 2 2008-03-02 10 2007-11-02 2007 3
# 16 site 2 2008-04-02 20 2007-12-03 2007 4
# 17 site 2 2008-09-01 93 2008-05-02 2008 9
# 18 site 2 2008-10-02 13 2008-06-02 2008 10
# 19 site 2 2008-11-02 58 2008-07-03 2008 11
# 20 site 2 2008-12-02 64 2008-08-02 2008 12
# 21 site 2 2009-01-02 92 2008-09-02 2008 1
# 22 site 2 2009-02-02 69 2008-10-03 2008 2
# 23 site 2 2009-03-02 89 2008-11-02 2008 3
# 24 site 2 2009-04-02 27 2008-12-03 2008 4
An alternative solution using data.table:
require(data.table)
require(mondate)
dt <- data.table(df)
dt[, `:=`(year=year(mondate(date)-4), month=month(date))]
dt.out <- dt[, .SD[rep(all(c(1:4,9:12) %in% month), .N)],
by=list(site,year)][, c("year", "month") := NULL]