Data Science. TileDB. Open Source. Quant Research. R. C++. Debian. Linux. Adjunct Clinical Professor, University of Illinois. Lots of coffee. And some running.

Chicago, IL, USA
Joined March 2007
The first (and more introductory) of the two new vignettes in package `RcppRedis` is now on arXiv at, respectively, arxiv.org/abs/2203.06559 and doi.org/10.48550/arXiv.2203.…. #rcpp #rstats
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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.
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Replying to @superboreen
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.
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RcppGSL 0.3.11 on CRAN: Small Maintenance Easier GNU GSL use from R dirk.eddelbuettel.com/blog/2… #rcpp #rstats
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Rcpp 1.0.8.2 on CRAN: Hot-fix to silence compiler nags Seamless R and C++ Integration dirk.eddelbuettel.com/blog/2… #rcpp #rstats
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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.
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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.
#rstats 4.1.3 "One Push-Up" (source version) has been released. Binaries will follow in due course.
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RcppRedis 0.2.0 on CRAN: Major Updates Performant R interface to Redis dirk.eddelbuettel.com/blog/2… #rstats #rcpp With lovely new code for pub/sub with @Redisinc, two examples for pub/sub for (financial) market monitoring, and two new vignettes.
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Which has the added advantage of already working today with the released version of #RStats 😀
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Nice indeed. But then lm(mpg ~ disp, data = mtcars, subset = cyl==4) is still shorter, and quite possibly more readable. 😉 #rstats
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