shea shea - 22 days ago 10
R Question

Get monthly means from dataframe of several years of daily temps

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 Source

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