Dear Iñaki and Joris, thank you for the positive feedback! I had attached a code file to the post, but apparently it was removed. I will attach it again to this e-mail, otherwise both vignette and code can be downloaded from the following link: https://www.dropbox.com/sh/s0k1tiz7el55g1q/AACpri-nruXjcMwUnNcHoycKa?dl=0 Best, Sebastian
On Wed, 27 Feb 2019 at 11:14, Joris Meys <jorism...@gmail.com> wrote: > Dear Sebastian, > > Initially I was a bit hesitant to think about yet another way to summarize > data, but your illustrations convinced me this is actually a great addition > to the toolset currently available in different R packages. Many of us have > written custom functions to get the required tables for specific data sets, > but this would reduce that effort to simply using the right collap() call. > > Like Inaki, I'm very interested in trying it out if you have the code > available somewhere. > > Cheers > Joris > > > > > > On Wed, Feb 27, 2019 at 9:01 AM Sebastian Martin Krantz < > sebastian.kra...@graduateinstitute.ch> wrote: > >> Dear Developers, >> >> Having spent time developing and thinking about how data aggregation and >> summary statistics can be enhanced in R, I would like to present my >> ideas/efforts in the form of two commands: >> >> The first, which for now I called 'collap', is an upgrade of aggregate >> that >> accommodates and extends the functionality of aggregate in various >> respects, most importantly to work with multilevel and multi-type data, >> multiple function calls, highly customized aggregation tasks, a much >> greater flexibility in the passing of inputs and tidy output. >> >> The second function, 'qsu', is an advanced and flexible summary command >> for >> cross-sectional and multilevel (panel) data (i.e. it can provide overall, >> between and within entities statistics, and allows for grouping, custom >> functions and transformations). It also provides a quick method to compute >> and output within-transformed data. >> >> Both commands are efficiently built from core R, but provide for optional >> integration with data.table, which renders them extremely fast on large >> datasets. An explanation of the syntax, a demonstration and benchmark >> results are provided in the attached vignette. >> >> Since both commands accommodate existing functionality while adding >> significant basic functionality, I though that their addition to the stats >> package would be a worthwhile consideration. I am happy for your feedback. >> >> Best regards, >> >> Sebastian Krantz >> ______________________________________________ >> R-devel@r-project.org mailing list >> https://stat.ethz.ch/mailman/listinfo/r-devel >> > > > -- > Joris Meys > Statistical consultant > > Department of Data Analysis and Mathematical Modelling > Ghent University > Coupure Links 653, B-9000 Gent (Belgium) > > <https://maps.google.com/?q=Coupure+links+653,%C2%A0B-9000+Gent,%C2%A0Belgium&entry=gmail&source=g> > > ----------- > Biowiskundedagen 2018-2019 > http://www.biowiskundedagen.ugent.be/ > > ------------------------------- > Disclaimer : http://helpdesk.ugent.be/e-maildisclaimer.php > ______________________________________________ R-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-devel