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
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Thanks all: I am (of course) aware of `ymd()`, the point of the tweet was that `anydate()` avoids all this automagically.
Plus, as @shabbychef reminded us, base #Rstats was there first (for this value) as `as.Date("2022-03-21") + 7` also works. Peace out.
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I also use it but it a) supports fewer formats that anydate(), b) does not deal with as many input types and c) still insists on the re-repeating for 1e5s time what "origin" is (as if there ever was a doubt) so anydate() wins.
Apart from that it's great and zero-dependency.
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> lubridate::dmy("2022-03-21") + lubridate::days(7)
[1] NA
Warning message:
All formats failed to parse. No formats found.
>
> anytime::anydate("2022-03-21") + 7
[1] "2022-03-28"
>
Always happy to help another #rstats user 😉
𝚍𝚖𝚢("𝟸𝟶𝟸𝟸-𝟶𝟹-𝟸𝟷") + 𝚍𝚊𝚢𝚜(𝟽)
That's right, for folks planning to submit your talk for consideration at 𝚛𝚜𝚝𝚞𝚍𝚒𝚘::𝚌𝚘𝚗𝚏(𝟸𝟶𝟸𝟸), we’re extending the deadline to March 28.
#rstudioconf #rstudioconf2022
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In one week today: @NISS_DataSci seminar on #Rcpp for C++ and #rstats intgegration.
Inexpensive registration and more details at niss.org/events/rcpp-r-and-c…
If you want to learn about #Rcpp for C++ and #Rstats integration, I will be giving a one-hour introduction on March 25 thanks to a kind invitation by @NISS_DataSci via Kevin Lee. Registration should be opening soon, more details at the link below.
niss.org/events/rcpp-r-and-c…
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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
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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.)
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My name is Dirk, and I endorse this package.
As I did a year ago in a talk at the @Bioconductor developer forum; video at youtube.com/watch?v=nmY1jIhY… and slides at dirk.eddelbuettel.com/papers… #rstats
#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!
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The second (and more advanced, covering 'live' (financial) market monitoring via pub/sub) of the two new vignettes in package `RcppRedis` is now also on arXiv at, respectively, arxiv.org/abs/2203.08323, and doi.org/10.48550/arXiv.2203.…. #rcpp #rstats
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Rcpp 1.0.8.3 on CRAN: Hot-Fix to a Hot-Fix
Seamless R and C++ Integration
dirk.eddelbuettel.com/blog/2…
#rcpp #rstats
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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
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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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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.
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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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An underappreciated #rstats simulation option is simple mixtures: eg 80% from N(0,1) and 20% from N(0,5) giving fatter tails "cheaply":
> set.seed(42)
> X <- c(rnorm(10000,0,1), rnorm(2500,0,5))
> e1071::skewness(X)
[1] -0.00247981
> e1071::kurtosis(X)
[1] 7.92564
>
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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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