Get monthly means from dataframe of several years of daily temps - r

Question

I have daily temperature values for several years, 1949-2010. I would like to calculate monthly means. Here is an example of the data:
head(tmeasmax)
TIMESTEP MEAN.C. MINIMUM.C. MAXIMUM.C. VARIANCE.C.2. STD_DEV.C. SUM COUNT
1949-01-01 6.836547 6.65 7.33 0.02850574 0.1688364 1.426652 6
1949-01-02 10.533371 10.24 10.74 0.06140426 0.2477988 1.426652 6
1949-01-03 18.746729 18.02 19.78 0.18507860 0.4302076 1.426652 6
1949-01-04 21.244562 20.09 22.40 0.76106980 0.8723931 1.426652 6
1949-01-05 3.826716 3.11 5.37 0.52706647 0.7259935 1.426652 6
1949-01-06 9.127782 8.46 10.26 0.20236358 0.4498484 1.426652 6
str(tmeasmax)
'data.frame': 22645 obs. of 8 variables:
$ TIMESTEP : Date, format: "1949-01-01" "1949-01-02" ...
$ MEAN.C. : num 6.84 10.53 18.75 21.24 3.83 ...
$ MINIMUM.C. : num 6.65 10.24 18.02 20.09 3.11 ...
$ MAXIMUM.C. : num 7.33 10.74 19.78 22.4 5.37 ...
$ VARIANCE.C.2.: num 0.0285 0.0614 0.1851 0.7611 0.5271 ...
$ STD_DEV.C. : num 0.169 0.248 0.43 0.872 0.726 ...
$ SUM : num 1.43 1.43 1.43 1.43 1.43 ...
$ COUNT : int 6 6 6 6 6 6 6 6 6 6 ...
There is a previous question that I couldn't make heads or tails of. I imagine I can probably use aggregate, but I don't know how to break up the dates into the years and months and then approach the nesting of the months inside the years. I tried a loop inside of a loop, but I can never get nested loops to work.
EDIT to reply to comments/questions:
I was looking for the mean of "MEAN.C."

Answer 1

Here's a quick data.table solution. I assuming you want the means of MEAN.C. (?)
library(data.table)
setDT(tmeasmax)[, .(MontlyMeans = mean(MEAN.C.)), by = .(year(TIMESTEP), month(TIMESTEP))]
# year month MontlyMeans
# 1: 1949 1 11.71928
You can also do this for all the columns at once if you want
tmeasmax[, lapply(.SD, mean), by = .(year(TIMESTEP), month(TIMESTEP))]
# year month MEAN.C. MINIMUM.C. MAXIMUM.C. VARIANCE.C.2. STD_DEV.C. SUM COUNT
# 1: 1949 1 11.71928 11.095 12.64667 0.2942481 0.482513 1.426652 6

Answer 2

Here's a way to do it with the dplyr package:
library(dplyr)
library(lubridate)
tmeasmax$TIMESTEP = ymd(tmeasmax$TIMESTEP)
tmeasmax %>%
group_by(Year=year(TIMESTEP), Month=month(TIMESTEP)) %>%
summarise(meanDailyMin=mean(MINIMUM.C.),
meanDailyMean=mean(MEAN.C.))
Year Month meanDailyMin meanDailyMean
1 1949 1 11.095 11.71928
You can summarise any other column by month in a similar way.

Answer 3

You can use the lubridate package to create a new factor variable consisting of the year-month combinations, then use aggregate.
library('lubridate')
tmeasmax2 <- within(tmeasmax, {
monthlies <- paste(year(TIMESTEP),
month(TIMESTEP))
})
aggregate(tmeasmax2, list(monthlies), mean, na.rm = TRUE)