Annual, monthly or daily mean for irregular time series

Question

I am a new user of "R", and I couldn't find a good solution to solve it. I got a timeseries in the following format:

>dates  temperature depth   salinity
>12/03/2012 11:26   9.7533  0.48073 37.607
>12/03/2012 11:56   9.6673  0.33281 37.662
>12/03/2012 12:26   9.6673  0.33281 37.672

I have an irregular frequency for variable measurements, done every 15 or every 30 minutes depending on the period. I would like to calculate annual, monthly and daily averages for each of my variables, whatever the number of data in a day/month/year is. I read a lot of things about the packages zoo, timeseries, xts, etc. but I can't get a clear vision of what I nead (maybe cause I'm not skilled enough with R...).

I hope my post is clear, don't hesitate to tell me if it's not.

Answer 1

Convert your data to an xts object, then use apply.daily et al to calculate whatever values you want.

library(xts)
d <- structure(list(dates = c("12/03/2012 11:26", "12/03/2012 11:56", 
"12/03/2012 12:26"), temperature = c(9.7533, 9.6673, 9.6673), 
    depth = c(0.48073, 0.33281, 0.33281), salinity = c(37.607, 
    37.662, 37.672)), .Names = c("dates", "temperature", "depth", 
"salinity"), row.names = c(NA, -3L), class = "data.frame")
x <- xts(d[,-1], as.POSIXct(d[,1], format="%m/%d/%Y %H:%M"))
apply.daily(x, colMeans)
#                     temperature     depth salinity
# 2012-12-03 12:26:00    9.695967 0.3821167   37.647