tidyverse: grouped summaries (with summerize)

As Eric has pointed out, perhaps Rich is not thinking pipelined. Summarize() takes a first argument as:
	summarise(.data=whatever, ...)

But in a pipeline, you OMIT the first argument and let the pipeline supply an argument silently.

What I think summarize saw was something like:

summarize(. , disc_by_month, vol = mean(cfs, na.rm = TRUE))

There is now a superfluous SECOND argument in a place it expected not a data.frame type of variable but the name of a column in the hidden data.frame-like object it was passed. You do not have a column called disc_by_month and presumably some weird logic made it suggest it was replacing that by the first column or something.

I hope this makes sense. You do not cobble a pipeline together from parts without carefully making sure all first arguments otherwise used are NOT used.

And, just FYI, the subject line should not use a word that some see as the opposite companion of "winterize" ...

-----Original Message-----
From: R-help <r-help-bounces at r-project.org> On Behalf Of Rich Shepard
Sent: Monday, September 13, 2021 5:51 PM
To: r-help at r-project.org
Subject: Re: [R] tidyverse: grouped summaries (with summerize)

That's what I thought I did. I'll rewrite the script and work toward 
the output I need.

Still not the correct syntax. Command is now:
disc_by_month %>%
     group_by(year, month) %>%
     summarize(disc_by_month, vol = mean(cfs, na.rm = TRUE))

and results are:

source('disc.R')

`summarise()` has grouped output by 'year', 'month'. You can override using the `.groups` argument.

disc_by_month

# A tibble: 590,940 ? 6
# Groups:   year, month [66]
     year month   day  hour   min    cfs
    <int> <int> <int> <int> <int>  <dbl>
  1  2016     3     3    12     0 149000
  2  2016     3     3    12    10 150000
  3  2016     3     3    12    20 151000
  4  2016     3     3    12    30 156000
  5  2016     3     3    12    40 154000
  6  2016     3     3    12    50 150000
  7  2016     3     3    13     0 153000
  8  2016     3     3    13    10 156000
  9  2016     3     3    13    20 154000
10  2016     3     3    13    30 155000
# ? with 590,930 more rows

The grouping is still not right. I expected to see a mean value for each month of each year in the data set, not for each minute.

Rich

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