Calculate daily means from 5-minute interval data

d$POSIXct <-

d

with(d[order(d$POSIXct),], plot(temp)) # monotonic temperature

Regardless of whether you use the lower-level split function, or the
higher-level aggregate function, or the tidyverse group_by function, the
key is learning how to create the column that is the same for all records
corresponding to the time interval of interest.

If you convert the sampdate to POSIXct, the tz IS important, because most
of us use local timezones that respect daylight savings time, and a naive
conversion of standard time will run into trouble if R is assuming daylight
savings time applies. The lubridate package gets around this by always
assuming UTC and giving you a function to "fix" the timezone after the
conversion. I prefer to always be specific about timezones, at least by
using so something like

    Sys.setenv( TZ = "Etc/GMT+8" )

which does not respect daylight savings.

Regarding using character data for identifying the month, in order to have
clean plots of the data I prefer to use the trunc function but it returns a
POSIXlt so I convert it to POSIXct:

    discharge$sampmonthbegin <- as.POSIXct( trunc( discharge$sampdate,
units = "months" ) )

Then any of various ways can be used to aggregate the records by that
column.

On September 2, 2021 12:10:15 PM PDT, Andrew Simmons <akwsimmo at gmail.com>
wrote:

You could use 'split' to create a list of data frames, and then apply a
function to each to get the means and sds.


cols <- "cfs"  # add more as necessary
S <- split(discharge[cols], format(discharge$sampdate, format = "%Y-%m"))
means <- do.call("rbind", lapply(S, colMeans, na.rm = TRUE))
sds   <- do.call("rbind", lapply(S, function(xx) sapply(xx, sd, na.rm =
TRUE)))

On Thu, Sep 2, 2021 at 3:01 PM Rich Shepard <rshepard at appl-ecosys.com>
wrote:

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

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