Here is my approximation:
# Creation of the temporal variables
DF$year <- as.numeric(format(DF$date, format = "%Y"))
DF$month <- as.numeric(format(DF$date, format = "%m"))
# For years with data from 2006 to 2008
DF_type1 <- DF [ - which (year == 2006 & month ==1 | year == 2006 & month ==
2 | year == 2008 & month == 12), ]
# For years with data from 2007 to 2011
DF_type2 <- DF [ - which (year == 2007 & month ==1 | year == 2007 & month ==
2 | year == 2011 & month == 12), ]
# Including the Season as a factor
DF$season <- factor ( with ( ifelse (( month == 1 | nonth == 2 | month == 3
), "Win",
ifelse ((month == 4 | nonth == 5 | month == 6 )
, "Spr",
ifelse ((month == 6 | nonth == 7 | month == 8 ) , "Sum", "Aut")))))
# To get the mean per year and season
library (plyr)
ddply ( DF, . (year, season), summarize, mean_season = mean (data))
--
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