[R] Correctly applying aggregate.ts()

[Bert Gunter]

Well, let's see:
"monthly.rain <- aggregate.ts(x = dp['sampdate','prcp'], by = list(month = \
substr(dp$sampdate, 1, 7)), FUN = sum, na.rm = TRUE)"

1. x is a data frame, so why are you using the time series method?
Perhaps you need to study S3 method usage in R.

2. You have improperly subscripted the data frame: it should be dp[,
c('sampdate','prcp')] . Perhaps you need to read about how
subscripting in R. However, in this case, no subscripting is needed
(see 3.)

3. As you should be using the data frame method, and the month is
obtained as a substring of sampdate, you should use dp[,'prcp'] as
your data frame so that sum() is not applied to the sampdate column.

4. I assume the "\" indicates <Return> ?

Anyway, once you have corrected all that, here's the call:

> monthly.rain <- aggregate(dp[, 'prcp'],
+                           list(substr(dp$sampdate,1,7)),
+                           FUN = sum, na.rm = TRUE)
> ## yielding
> monthly.rain
  Group.1    x
1 2005-01 4.88
2 2005-02 2.27
3 2005-03 0.06

It's perhaps also worth noting that the formula method (for data
frames) is somewhat more convenient, especially with several grouping
factors in the list:

> monthly.rain <- aggregate(prcp ~ substr(sampdate,1,7), data = dp, FUN = sum, na.rm = TRUE)
> ##yielding
> monthly.rain
  substr(sampdate, 1, 7) prcp
1                2005-01 4.88
2                2005-02 2.27
3                2005-03 0.06

Cheers,

Bert Gunter

"The trouble with having an open mind is that people keep coming along
and sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Fri, Sep 7, 2018 at 2:19 PM Rich Shepard <rshepard using appl-ecosys.com> wrote:
>
>    I've read ?aggregate and several blog posts on using aggregate() yet I
> still haven't applied it correctly to my dataframe. The sample data are:
>
> structure(list(sampdate = c("2005-01-01", "2005-01-02", "2005-01-03",
> "2005-01-04", "2005-01-05", "2005-01-06", "2005-01-07", "2005-01-08",
> "2005-01-09", "2005-01-10", "2005-01-11", "2005-01-12", "2005-01-13",
> "2005-01-14", "2005-01-15", "2005-01-16", "2005-01-17", "2005-01-18",
> "2005-01-19", "2005-01-20", "2005-01-21", "2005-01-22", "2005-01-23",
> "2005-01-24", "2005-01-25", "2005-01-26", "2005-01-27", "2005-01-28",
> "2005-01-29", "2005-01-30", "2005-01-31", "2005-02-01", "2005-02-02",
> "2005-02-03", "2005-02-04", "2005-02-05", "2005-02-06", "2005-02-07",
> "2005-02-08", "2005-02-09", "2005-02-10", "2005-02-11", "2005-02-12",
> "2005-02-13", "2005-02-14", "2005-02-15", "2005-02-16", "2005-02-17",
> "2005-02-18", "2005-02-19", "2005-02-20", "2005-02-21", "2005-02-22",
> "2005-02-23", "2005-02-24", "2005-02-25", "2005-02-26", "2005-02-27",
> "2005-02-28", "2005-03-01", "2005-03-02", "2005-03-03"), prcp = c(0.59,
> 0.08, 0.1, 0, 0, 0.02, 0.05, 0.1, 0, 0.02, 0, 0.05, 0.2, 0, 0,
> 0.5, 0.41, 0.84, 0.01, 0.1, 0.01, 0, 0, 0, 0, 0.21, 0.24, 0.13,
> 1.12, 0.01, 0.09, 0, 0, 0, 0.35, 0.18, 0.65, 0.16, 0, 0, 0, 0,
> 0.55, 0.21, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.17, 0.05,
> 0.01, 0)), row.names = c(NA, 62L), class = "data.frame")
>
>    What I need to learn how to do is to calculate monthly sum, median, and
> maximum rainfall amounts from the full data set which has daily rainfall
> amounts. My most current effort to calculate monthly sums uses this syntax:
>
> monthly.rain <- aggregate.ts(x = dp['sampdate','prcp'], by = list(month = \
> substr(dp$sampdate, 1, 7)), FUN = sum, na.rm = TRUE)
>
> (entered on a single line) which produces this result:
>
> head(monthly.rain)
> [1] NA
>
>    The sample data has 62 of the 113K rows in the dataframe. A larger set can
> be provided if needed.
>
>    An explanation of what I've missed is needed.
>
> Regards,
>
> Rich
>
> ______________________________________________
> R-help using r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.