Rtools Install In Rstudio

[Tina Larzelere]

Thisis an old thread, but I had a similar issue with rstudio asking me to install rtools every time I ran a stan model, whereas with an older R version it was working just fine.

I ended up fixing the problem by running:

options(buildtools.check = function(action) TRUE )

Hope this may help others bumping into this problem.

You most likely will not want an R shortcut on your desktop, as you will almost certainly use RStudio as an interface. You can still have one if you would like. Otherwise, accept the defaults and hit Next >.

Over time updates to R and R Studio will necessitate and update. Packages you use may not update with R! If you rely heavily on a package for a project, it is highly recommended you save your current configuration of R and R Studio before updating. If there was a major version change, you will also need to re-install all of your packages. Upgrading R and R Studio follows the same process as installing it in the first place. You can simply install the new versions on your machine. As long as you do not remove the old instances of R as part of this process, you would be able to revert to a previous version.

If you are installing R/RStudio on a Mac, there is no extra configuration. Simply go the download pages for R and RStudio and choose the latest version for mac. Run the installer package when it is finished downloading. If you receive an error regarding the app being from an unidentified developer, please follow the instructions here.

Once you have installed both R and RStudio, you should be able to run RStudio on your machine. You can verify your install is working by opening RStudio and typing paste("Hello World!") into the console as shown below. If the code runs you should see a response that says [1] Hello World!. If that works you are all set!

R packages are community made functions that automate or expand the things you can do in the R language. The process for installing them is largely the same for both Windows and Mac. There are three main methods for installing R packages. You can install them from the Comprehensive R Archive Network (CRAN) from within R, from another online source like Github, or from files on your local machine. We will cover each of these methods here.

The vast majority to packages in R can be installed from CRAN. You will need the name of the package you want to install. Once you have the name (case sensitive!), you can download it from within R using the install.packages() function in the R console.

For example, if you wanted to install the skimr package, you would enter install.packages("skimr"). Note that you do need to put the package name in quotes, and that it is case sensitive. You can install multiple packages at a time by passing a vector of package names to install.packages(), for example: install.packages(c("skimr", "corrplot")).

Not all packages are available on CRAN, especially very new or very old packages. For these you will most likely need to install them from an online repository, the most common of which is Github. There are some R packages that make this process easy. We will use the remotes package.

R Tools is a bundle of programs on Windows that allows R to build packages from local source files, rather than installing through CRAN. The vast majority of the time this is unnecessary, but some circumstances require it. Mac users do not need to install R Tools.

To verify R tools was installed successfully, first restart R to assure you are in a clean environment. You can do this by closing and re-opening your R or RStudio window. Afterwards, type Sys.which("make") (case sensitive!) into the console. If you see "C:\\rtools40\\usr\\bin\\make.exe" as a result you are all good!

Please download and install Rtools custom from RTools: Toolchains for building R and R packages from source on Windows.

Skipping install of 'qiime2R' from a github remote, the SHA1 (4c2abcd1) has not changed since last install.

Use force = TRUE to force installation

It would appear that rtools is not a r package but a separate piece of software that can be installed outside of R. This post may be helpful for using rtools3.5 with r 3.6.1: -in-rtool-installations/35689

In this post I will share a few tips and tricks about installing CmdStanR on Windows. I write these down to not forget them myself, but maybe someone will find them useful as well. This page might be subject to change. It is not an official installation guide.

Unfortunately, I recently ran into a few problems installing CmdStanR. All of them could eventually be solved with the help of the awesome Stan devs at discourse.mc-stan.org. The following post is a collection of notes, mainly to myself, on how to install CmdStanR on Windows. I hope that some of you might find these helpful and more of you are inclined to try CmdStanR.

to you PATH (see these instructions, if you are not sure how to add something to your PATH). You might have to change these lines to point to the correct location of your RTools 4.0 installation. Another thing to look out for is any Anaconda installation you might have on your system. Usually, it is sufficient to put the path to RTools before the path to Anaconda in your PATH.

When I upgraded to R 4.0 with RTools 4.0 and tried to install CmdStanR, I got an error message telling me that mingw32-make could not be found. You actually have to install mingw32-make manually via RTools Bash. To open RTools Bash, which comes with RTools 4.0, just hit Windows Key, type rtools bash and hit Enter. A new console window will pop up. Now, just type

to your PATH (check whether these point to the correct directories and adjust accordingly). Another thing to look out for is any Anaconda installation you might have on your system. Usually, it is sufficient to put the path to RTools before the path to Anaconda in your PATH.

With Windows 10 came the possibility to run a full fledged Linux distribution within Windows via the Windows Subsystem for Linux (WSL). The installation process is fairly easy, but before we get into that, why would you want to run CmdStanR through WSL? One reason is that installing and running CmdStan (through CmdStanR) is generally easier and more robust compared to Windows once you have set everything up in Linux. Another advantage of running CmdStan on Linux is that it is likely faster than on Windows.

I found the installation of WSL to be surprisingly easy. This is party due to the fact that the process is relatively easy, but also due to the great Installation Guide, which I want to refer to here. I basically just followed these instructions and installed WSL1. I chose Ubuntu 18.04 LTS and would recommend you do the same (unless you know what you are doing, but in this case you probably should not have to read this anyways). Note that you do not need a Microsoft account when downloading Ubuntu from the store. You can simply skip when being asked to sign in.

You are now ready to go. RStudio Server should already be running. In Windows, open your browser and type localhost:8787. Sign in using your Linux credentials (user name and password, which you specified earlier) and you should be good to go.

The v2 tag is a sliding tag. It is not fixed to a certain version, but we regularly update it with (non-breaking) improvements and fixes. If it is absolutely crucial that your workflow runs the same way, use one of the fixed tags, e.g. v2.2.2 is the most recent one.

As of today, usethis v2.1.6 defaults to configuring workflows from the v2 tag. But use_github_action() accepts a ref argument, which allows you specify a different tag (such as v2.2.2) or even a branch name or specific SHA.

Encoding issues are not uncommon in snapshot tests across platforms. To make these easier to debug, check-r-package@v2 will now upload snapshot output as artifacts if you set the upload-snapshots parameter to true:

Rtools42 is the new version of the Rtools compiler bundle, which will be the default for latest R 4.2.0. You can now optionally install Rtools42 with the setup-r action. By default setup-r uses Rtools40 because it is pre-installed on the CI machines, and it is fully compatible with Rtools42. To select Rtools42, set the rtools-version parameter to 42:

The Linux system requirements look-up is more robust now, and uses SystemRequirements fields from all local, GitHub or URL remotes, and it also uses the package installation plan, instead of only relying on the dependency tress of CRAN packages.

R 4.4.0, to be released tomorrow, comes with experimental native support for64-bit ARM Windows machines (aarch64, arm64). Rtools44 with native supportfor the platform has been released at the beginning of March.

R for Windows/aarch64 is distributed separately from the primarydistribution for x86_64 (64-bit Intel machines). There is a specialinstaller of R for Windows/aarch64. From the user perspective it is almostidentical to the installer for x86_64. Windows 11 is required by R onaarch64.

The installer can be built natively on Windows/aarch64 from R sources thesame way that an installer for x86_64 can be build on x86_64. Theinstallers provided via the URL above are cross-compiled on existing x86_64infrastructure: the native code is cross-compiled using Rtools44 onLinux/x86_64, the rest including the installer is built on Windows/x86_64.

Rtools44 includes an installer for Windows/aarch64 and toolchain/librariestarballs (full, base, cross-compiler, Tcl/Tk bundle), mirroring the usualcomponents provided for Windows/x86_64. They are available fromCRAN.Later versions of Rtools43 already included unreleased temporary builds foraarch64 for testing with the development version of R, which were availablefrom a temporary location.

The Rtools44 installer is an x86_64 application, which runs onWindows/aarch64 in the emulator. Rtools44 is installed by default tortools44-aarch64 (unlike rtools44 with an x86_64 build) as to allow thetwo installations to coexist on Windows/aarch64. Having an x86_64 versionmay be useful for package developers with aarch64 as their main platform.The R installer build will use the correct Rtools automatically: R foraarch64 installed via the installer would use Rtools for aarch64, R forx86_64 will use Rtools for x86_64.

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