Calculate daily means from 5-minute interval data

On Thu, 2 Sep 2021, Rich Shepard wrote:

If I correctly understand the output of as.POSIXlt each date and time
element is separate, so input such as 2016-03-03 12:00 would now be 2016

03

03 12 00 (I've not read how the elements are separated). (The TZ is not
important because all data are either PST or PDT.)

Using this script:
discharge <- read.csv('../data/water/discharge.dat', header = TRUE, sep =
',', stringsAsFactors = FALSE)
discharge$sampdate <- as.POSIXlt(discharge$sampdate, tz = "",
                                  format = '%Y-%m-%d %H:%M',
                                  optional = 'logical')
discharge$cfs <- as.numeric(discharge$cfs, length = 6)

I get this result:

head(discharge)

              sampdate    cfs
1 2016-03-03 12:00:00 149000
2 2016-03-03 12:10:00 150000
3 2016-03-03 12:20:00 151000
4 2016-03-03 12:30:00 156000
5 2016-03-03 12:40:00 154000
6 2016-03-03 12:50:00 150000

I'm completely open to suggestions on using this output to calculate
monthly
means and sds.

If dplyr:summarize() will do so please show me how to modify this command:
disc_monthly <- ( discharge
         %>% group_by(sampdate)
         %>% summarize(exp_value = mean(cfs, na.rm = TRUE))
because it produces daily means, not monthly means.

TIA,

Rich