[R] Need to aggregate large dataset by week...
danielrev
revdan20 at gmail.com
Fri Feb 10 13:55:44 CET 2012
Hi all,
I have a large dataset with ~8600 observations that I want to compress to
weekly means. There are 9 variables (columns), and I have already added a
"week" column with 51 weeks. I have been looking at the functions:
aggregate, tapply, apply, etc. and I am just not savvy enough with R to
figure this out on my own, though I'm sure it's fairly easy. I also have the
Dates (month/day/year) for all of the observations, but I figured just
having a week column may be easier. If someone wanted to show me how to
organize this data using a date function and aggregating by month that would
be useful too!
Here's an example of the data set, with only 5 of the variables and 10 of
8600 obs.:
week rainfall windspeed winddir temp oakdepth
1 1 0.20000000 0.89000 245.9200 1.150000 4.400000
2 1 0.00000000 0.84000 292.8800 1.190000 5.300000
3 1 0.20000000 0.74000 258.5400 1.360000 6.000000
4 1 0.00000000 0.93000 3.7000 1.430000 4.400000
5 1 0.20000000 0.69000 37.8200 1.560000 5.200000
6 1 0.00000000 0.80000 17.2900 1.690000 4.400000
7 1 0.20000000 0.70000 28.7300 1.880000 5.000000
8 1 0.20000000 1.12000 294.3700 1.930000 6.000000
9 1 0.00000000 1.21000 274.9700 1.800000 4.400000
10 1 0.00000000 1.31000 279.2400 1.860000 5.800000
...so after about 170 observations it changes to week 2, and so on.
I've tried something like this, but its only one variable's mean, and I
would rather have the rows=weeks and columns= the different variables.
< tapply(metdata$rainfall,metdata$week,FUN=mean)
1 2 3 4 5 6
0.080952381 0.101190476 0.379761905 0.179761905 0.000000000 0.295238095
7 8 9 10 11 12
0.146428571 0.015476190 0.163888889 0.098809524 0.065476190 0.215476190
Hope this is enough information and that I'm not just re-asking an old
question. Thanks so much in advance for any help.
--
View this message in context: http://r.789695.n4.nabble.com/Need-to-aggregate-large-dataset-by-week-tp4376154p4376154.html
Sent from the R help mailing list archive at Nabble.com.
More information about the R-help
mailing list