Calculate daily means from 5-minute interval data

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