Articles by JottR on R

future 1.15.0 – Lazy Futures are Now Launched if Queried

November 9, 2019 | 0 Comments

No dogs were harmed while making this release future 1.15.0 is now on CRAN, accompanied by a recent, related update of future.callr 0.5.0. The main update is a change to the Future API: resolved() will now also launch lazy futures Although this change does not look much to the world, I’...
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useR! 2019 Slides on Futures

July 12, 2019 | 0 Comments

Below are the slides for my Future: Simple Parallel and Distributed Processing in R that I presented at the useR! 2019 conference in Toulouse, France on July 9-12, 2019. My talk (25 slides; ~15+3 minutes): Title: Future: Simple Parallel and Dist...
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startup – run R startup files once per hour, day, week, …

May 26, 2019 | 0 Comments

New release: startup 0.12.0 is now on CRAN. This version introduces support for processing some of the R startup files with a certain frequency, e.g. once per day, once per week, or once per month. See below for two examples. startup::startup() is cross platform. The startup package makes it ...
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SatRday LA 2019 Slides on Futures

May 16, 2019 | 0 Comments

A bit late but here are my slides on Future: Friendly Parallel Processing in R for Everyone that I presented at the satRday LA 2019 conference in Los Angeles, CA, USA on April 6, 2019. My talk (33 slides; ~45 minutes): Title: : Friendly Parallel...
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SatRday Paris 2019 Slides on Futures

March 7, 2019 | 0 Comments

Below are links to my slides from my talk on Future: Friendly Parallel Processing in R for Everyone that I presented last month at the satRday Paris 2019 conference in Paris, France (February 23, 2019). My talk (32 slides; ~40 minutes): Title: F...
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Maintenance Updates of Future Backends and doFuture

January 6, 2019 | 0 Comments

New versions of the following future backends are available on CRAN: future.callr - parallelization via callr, i.e. on the local machine future.batchtools - parallelization via batchtools, i.e. on a compute cluster with job schedulers (SLURM, SGE, Torque/PBS, etc.) but also on the local machine future....
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future 1.9.0 – Output from The Future

July 22, 2018 | 0 Comments

future 1.9.0 - Unified Parallel and Distributed Processing in R for Everyone - is on CRAN. This is a milestone release: Standard output is now relayed from futures back to the master R session - regardless of where the futures are processed! Disclaimer: A future’s output is relayed only after ...
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R.devices – Into the Void

July 20, 2018 | 0 Comments

R.devices 2.16.0 - Unified Handling of Graphics Devices - is on CRAN. With this release, you can now easily suppress unwanted graphics, e.g. graphics produced by one of those do-everything-in-one-call functions that we all bump into once in a while. To suppress graphics, the R.devices package provides graphics ...
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future.apply – Parallelize Any Base R Apply Function

June 22, 2018 | 0 Comments

Got compute? future.apply 1.0.0 - Apply Function to Elements in Parallel using Futures - is on CRAN. With this milestone release, all* base R apply functions now have corresponding futurized implementations. This makes it easier than ever before to parallelize your existing apply(), lapply(), mapply(), … code - just prepend future_ ...
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Delayed Future(Slides from eRum 2018)

June 17, 2018 | 0 Comments

As promised - though a bit delayed - below are links to my slides and the video of my talk on Future: Parallel & Distributed Processing in R for Everyone that I presented last month at the eRum 2018 conference in Budapest, Hungary (May 14-16, 2...
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future 1.8.0: Preparing for a Shiny Future

April 11, 2018 | 0 Comments

future 1.8.0 is available on CRAN. This release lays the foundation for being able to capture outputs from futures, perform automated timing and memory benchmarking (profiling) on futures, and more. These features are not yet available out of the b...
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Performance: Avoid Coercing Indices To Doubles

April 1, 2018 | 0 Comments

x[idxs + 1] or x[idxs + 1L]? That is the question. Assume that we have a vector $x$ of $n = 100,000$ random values, e.g. __ n x idxs y y typeof(idxs) [1] "integer" __ typeof(idxs + 1) [1] "double" __ typeof(1) [1] "double" Note also that doubles (aka “numerics” in R) take up twice the amount of ...
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Startup with Secrets – A Poor Man’s Approach

March 29, 2018 | 0 Comments

New release: startup 0.10.0 is now on CRAN. If your R startup files (.Renviron and .Rprofile) get long and windy, or if you want to make parts of them public and other parts private, then you can use the startup package to split them up in separate files and directories under ....
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The Many-Faced Future

June 4, 2017 | 0 Comments

The future package defines the Future API, which is a unified, generic, friendly API for parallel processing. The Future API follows the principle of write code once and run anywhere - the developer chooses what to parallelize and the user how and where. The nature of a future is such ...
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The R-help Community was Started on This Day 20 Years Ago

March 31, 2017 | 0 Comments

Today, its been 20 years since Martin Mächler started the R-help community list. The first post was written by Ross Ihaka on 1997-04-01: Screenshot of the very first post to the R-help mailing list. This is a post about R’s memory model. We’re talking R v0.50 beta. ...
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doFuture: A Universal Foreach Adaptor Ready to be Used by 1,000+ Packages

March 17, 2017 | 0 Comments

doFuture 0.4.0 is available on CRAN. The doFuture package provides a universal foreach adaptor enabling any future backend to be used with the foreach() %dopar% { ... } construct. As shown below, this will allow foreach() to parallelize on not only multiple cores, multiple background R sessions, and ad-hoc clusters, but also cloud-based clusters ...
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future 1.3.0: Reproducible RNGs, future_lapply() and More

February 18, 2017 | 0 Comments

future 1.3.0 is available on CRAN. With futures, it is easy to write R code once, which the user can choose to evaluate in parallel using whatever resources s/he has available, e.g. a local machine, a set of local machines, a set of remote machines, a high-end compute cluster (...
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