[R] Correctly applying aggregate.ts() [RESOLVED]

Rich Shepard r@hep@rd @end|ng |rom @pp|-eco@y@@com
Sat Sep 8 00:39:36 CEST 2018

On Fri, 7 Sep 2018, Bert Gunter wrote:

> 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.


   I saw the four varieties of aggregate and thought the time series
appropriate for the data frame of sequential dates. As I wrote, I had
difficulties understanding which flavor to use.

> 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.)

   Ah so. All the examples I saw used single column data frames.

> 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> ?

   Yes. Alpine broke the line so I added a newline to the first part.

> 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)

   Thanks for making the syntax so clear.

> 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

   I looked at the formula method without appreciating how to apply it.

   Now I can work with the multiple of daily data sets I have and properly
condense them for presentation to readers of the report. And I'm much better
armed to understand how to apply aggregate() to various data sets.

Very much appreciated,


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