Data Science. TileDB. Open Source. Quant Research. R. C++. Debian. Linux. Adjunct Clinical Professor, University of Illinois. Lots of coffee. And some running.
It's been a while since I looked at it but I am fairly certain that `caret` and/or `mlr3` already cover it for #rstats.
It is a not uncommon task. Here is a tweet from just yesterday doing it for #rspatial data too:
New version of #rstats#rspatial package CAST allows visualizing whether training data for #MachineLearning have representative coverage of the prediction area and whether CV folds are appropriately chosen. Tutorial: hannameyer.github.io/CAST/ar…
@MLdwig @edzerpebesma @carles_milagarc
Hey, look, round number at CRAN!
A big, big thank you to the CRAN maintainers who are volunteers putting together an unparallel repository with unmatched quality guarantees, year in and out. Very much appreciated!
(Even if #RStats package authors like myself grumble at times.)
#rstats package tinytest now used to unit-test 200+ packages on CRAN! Thanks to all users for your trust in the package, and for all your valuable suggestions!
M-x R
M-x rename-buffer *R:projA*
and repeat for several buffers to give multiple (long-running) R sessions within Emacs. Which of course runs in daemon mode so that you can access it at the workstation or remotely ssh'ed in _accessing the same R sessions_.
#rstats#emacs
S, of course.
Which dates back to May 5, 1976, at Bell Labs and what followed. You may find the excellent article on "S, #RStats, and Data Science" by John Chambers in the ACM HOPL issue interesting if all this new to you:
doi.org/10.1145/3386334
If you're getting sliced and diced by the bleeding edge, #rocker to the rescue with a container for #rstats with gcc-12. Thanks to @eddelbuettel.
New linter in gcc-12 identified a real problem in my code (first picked up by CRAN, verified with r-edge).
github.com/rocker-org/rocker…
It's a NOTE.
Not a WARNING or ERROR. There a lots of packages with larger installed footprints.
So you can proceed. But you can consider it as hint to maybe reduce same sample data or documentation.
With daughter #1 in town for her first big grad school conference (hello to the APS meeting at McCormick Place) I fired up a family favourite recipe @bittman's HTCE: crispy pork with orange and black beans.
The quote below is from the @duckdb documentation, but holds in general: don't use `insert` for bulk operations.
Rather look at the _specific_ documentation for _your_ SQL backend and see what it recommends for bulk.
Or else just be very patient.
dtts 0.1.0 on CRAN: New Package
data.table time series ops at nanosecond resolution
dirk.eddelbuettel.com/blog/2…#rstats#rcpp
Think `xts` but at nanosecond resolution. All the immense power of `data.table` at the hightest time resolution.
The @Debian package for #RStats 4.1.3 released today was updated a few hours ago, used to update the #RockerProject container, and PRed as usual into @Docker itself to update the official r-base container. @marutterstat will follow-up as usual with @Ubuntu binaries.