Blog Archives

Startup with Secrets – A Poor Man’s Approach

March 29, 2018
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Startup with Secrets – A Poor Man’s Approach

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 .Renviron.d/ and .Rprofile.d/. For instance, the .Rprofile.d/repos.R file...

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The Many-Faced Future

June 4, 2017
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The Many-Faced Future

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 that it lends itself to be used with several...

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The R-help Community was Started on This Day 20 Years Ago

March 31, 2017
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The R-help Community was Started on This Day 20 Years Ago

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. I think that the paragraph at the end provides a nice anecdote on the...

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doFuture: A Universal Foreach Adaptor Ready to be Used by 1,000+ Packages

March 17, 2017
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doFuture: A Universal Foreach Adaptor Ready to be Used by 1,000+ Packages

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 and high...

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future 1.3.0: Reproducible RNGs, future_lapply() and More

February 18, 2017
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future 1.3.0: Reproducible RNGs, future_lapply() and More

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 (via future.BatchJobs and soon also future.batchtools), or in...

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