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   salini         


        

Answer 1

I'd add the day, month and year into the data frame and then use aggregate().

First convert your date column into a POSIXct objet:

d$timestamp <- as.POSIXct(d$dates,format = "%m/%d/%Y %H:%M",tz ="GMT")

Then get the date (e.g. 12/03/2012) into a column called Date, try this:

d$Date <- format(d$timestamp,"%y-%m-%d",tz = "GMT")

Next, aggregate by the date:

aggregate(cbind("temperature.mean" = temperature,
                "salinity.mean" = salinity) ~ Date,
          data = d,
          FUN = mean)

Similarly, you can get the month into a column (let's call it M for month), and then...

d$M <- format(d$timestamp,"%B",tz = "GMT")

aggregate(cbind("temperature.mean" = temperature,
                "salinity.mean" = salinity) ~ M,
          data = d,
          FUN = mean)

or if you want year-month

d$YM <- format(d$timestamp,"%y-%B",tz = "GMT")

aggregate(cbind("temperature.mean" = temperature,
                "salinity.mean" = salinity) ~ YM,
          data = d,
          FUN = mean)

If you have any NA values in your data, you may need to account for those:

aggregate(cbind("temperature.mean" = temperature,
                "salinity.mean" = salinity) ~ YM,
          data = d,
          function(x) mean(x,na.rm = TRUE))

Finally, if you want to average by week, you can do that as well. First generate the week number, and then use aggregate() again.

d$W <- format(d$timestamp,"%W",tz = "GMT")

aggregate(cbind("temperature.mean" = temperature,
                "salinity.mean" = salinity) ~ W,
          data = d,
          function(x) mean(x,na.rm = TRUE))

This version of week number defines week 1 as being the week with the first Monday of the year. The weeks are from Monday to Sunday.