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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Yup, rings a bell. Might be historical compatibility. There are add-on packages with column-wise variance or sd. Because this is a cludge -- but been-there-done-that:
> library(palmerpenguins)
> sapply(penguins, function(x) if (is.numeric(x)) sd(na.omit(x)) else NULL)
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Provide a MCVE or it doesn't count :)
> sd(1:10)
[1] 3.02765
> set.seed(123); sd(rnorm(10))
[1] 0.953784
>
Is you 'x' a list or data.frame? Well yes in that case you need to loop over columns.
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Maybe you were you thinking of this absolute gem by @VincentAB listing all #RStats package data sets
vincentarelbundock.github.io…
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tidyCpp 0.0.5 on CRAN: More Protect'ion
Tidy C++ wrapping of the C API for R
dirk.eddelbuettel.com/blog/2…
#rstats
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It is more complicated as `paste0(rep('a', 5000), collapse="")` easily creates a longer one.
What you found is an (arbitrary but high) limit on a (single) input line in the REPL. If this really mattered to you could probably recompile with a larger constant.
#rstats
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The xrprof tool by @unconj1 was built for this:
github.com/atheriel/xrprof
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And if you want to really go for pole position:
library(data.table)
df.all <- rbindlist(lapply(v.filename, fread))
print(df.all)
/cc @rdatatable
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Thanks so much to the @zhaw organisers of the 6th European COST Conference on #AI in Industry and Finance for having me present yesterday. Slides of my short talk on #RStats, #Rcpp and #MachineLearning are on my talks page.
dirk.eddelbuettel.com/presen…
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RcppSMC 0.2.5 on CRAN: Build Fix
Sequential Monte Carlo / Particle Filters for R
dirk.eddelbuettel.com/blog/2…
#rcpp #rstats
/cc @LeahFSouth @IZ89557597
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Or if you *really* want to drive the point home:
> letters |> toupper() |> tail() |> substitute() |> deparse()
[1] "tail(toupper(letters))"
>
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And the best part, by a mile, is what @groundwalkergmb mentioned: internally it just plain regular code:
> deparse(substitute(letters |> toupper() |> tail()))
[1] "tail(toupper(letters))"
>
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RcppSimdJson 0.1.6 on CRAN: New Upstream 1.0.0
Highest-performance JSON Parsing via Modern C++ from R
dirk.eddelbuettel.com/blog/2…
#rcpp #rstats
/cc @knapply_ @lemire
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Well I do have blog posts and more for easier installations of stan and friends too. Especially if you pick one of the OSs that support ... the awesome automated installed from binaries. See for example dirk.eddelbuettel.com/blog/2… but the r^4 series is full of posts on this.
tidyCpp 0.0.4 on CRAN: Adding a Numeric Vector Class
Tidy C++ wrapping of the C API for R
dirk.eddelbuettel.com/blog/2…
#rstats
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You may be looking for the `conflicted` package. (And you meant MASS::select here.)
You can also use selective attaching, either via NAMESPACE in a package, or via library() explicitly choose (or exclude) certain identifiers.
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RQuantLib 0.4.13 on CRAN: Routine Update
Quantitative Finance library bindings for R
dirk.eddelbuettel.com/blog/2…
#rstats #rcpp
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RcppSMC 0.2.4 on CRAN: Even More GSoC !!
Sequential Monte Carlo / Particle Filters for R
dirk.eddelbuettel.com/blog/2…
#rstats #rcpp
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