You may find something useful on handling timestamp data here: https://jdnewmil.github.io/
On August 29, 2021 9:23:31 AM PDT, Jeff Newmiller <jdnew...@dcn.davis.ca.us> wrote: >The general idea is to create a "grouping" column with repeated values for >each day, and then to use aggregate to compute your combined results. The >dplyr package's group_by/summarise functions can also do this, and there are >also proponents of the data.table package which is high performance but tends >to depend on altering data in-place unlike most other R data handling >functions. > >Also pay attention to missing data... if you have any then you will need to >consider whether you want the strictness of na.rm=FALSE or permissiveness of >na.rm=TRUE for your aggregation functions. > >On August 29, 2021 8:08:58 AM PDT, Rich Shepard <rshep...@appl-ecosys.com> >wrote: >>I have a year's hydraulic data (discharge, stage height, velocity, etc.) >>from a USGS monitoring gauge recording values every 5 minutes. The data >>files contain 90K-93K lines and plotting all these data would produce a >>solid block of color. >> >>What I want are the daily means and standard deviation from these data. >> >>As an occasional R user (depending on project needs) I've no idea what >>packages could be applied to these data frames. There likely are multiple >>paths to extracting these daily values so summary statistics can be >>calculated and plotted. I'd appreciate suggestions on where to start to >>learn how I can do this. >> >>TIA, >> >>Rich >> >>______________________________________________ >>R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see >>https://stat.ethz.ch/mailman/listinfo/r-help >>PLEASE do read the posting guide http://www.R-project.org/posting-guide.html >>and provide commented, minimal, self-contained, reproducible code. > -- Sent from my phone. Please excuse my brevity. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
