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The R Project for Statistical Computing Getting Started. R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. To download R, please choose your preferred CRAN mirror.
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To find out your R version, look at the message you get whenever R starts up, or run sessionInfo. If you are running a newer version of R than 3.4.4, I would not recommend downgrading unless you’re sure you know what you’re doing! That said, older R binaries are available on CRAN. There are different pages for different platforms.
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Using Bioconductor

The current release of Bioconductor is version3.12; it works with R version4.0.3. Users of older R andBioconductor must update their installation to take advantageof new features and to access packages that have been added toBioconductor since the last release.

Last released version for Mac OS X 10.4 (Tiger) was R 2.10.1, last release for Mac OS X 10.5 (Leopard) was R 2.15.3, last release for Mac OS X 10.11 (El Capitan) was R 3.6.3. Other binaries The following binaries are not maintained or supported by R-core and are provided without any guarantee and for convenience only (Mac OS X 10.4.4 or higher.

The development version of Bioconductor is version3.13; it works with R version4.1.0. More recent ‘devel’versions of R (if available) will be supported during the nextBioconductor release cycle.

Install the latest release of R, then get the latest version ofBioconductor by starting R and entering the commands

It may be possible to change the Bioconductor version of an existinginstallation; see the ‘Changing version’ section of the BiocManagervignette.

Details, including instructions toinstall additional packages and toupdate,find, andtroubleshoot are providedbelow. A devel version ofBioconductor is available. There are goodreasons for using BiocManager::install() formanaging Bioconductor resources.

Install R

Download the most recent version of R. The R FAQs and the RInstallation and Administration Manual contain detailed instructionsfor installing R on various platforms (Linux, OS X, and Windows beingthe main ones).

Start the R program; on Windows and OS X, this will usually meandouble-clicking on the R application, on UNIX-like systems, type“R” at a shell prompt.
As a first step with R, start the R help browser by typinghelp.start() in the R command window. For help on anyfunction, e.g. the “mean” function, type ? mean.

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Install Bioconductor Packages

To install core packages, type the following in an R command window:

Install specific packages, e.g., “GenomicFeatures” and “AnnotationDbi”, with

The install() function (in the BiocManager package) has arguments that changeits default behavior; type ?install for further help.

For a more detailed explanation on using BiocManager and its advanced usage,such as version switching, please refer to theBiocManager vignette.

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Find Bioconductor Packages

Visit the software package listto discover available packages.

To search through available packages programmatically, use the following:

For example, using a “^org” search pattern will show all of the availableorganism annotation packages.

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Update Installed Bioconductor Packages

Bioconductor packages, especially those in the development branch, areupdated fairly regularly. To identify packages requiring update withinyour version of Bioconductor, start a new session of R and enter

Use the argument ask=FALSE to update old packages without beingprompted. Read the help page for ?install for additional details.

Upgrading installed Bioconductor packages

Some versions of R support more than one version of Bioconductor. Touse the latest version of Bioconductor for your version of R, enter

Remember that more recent versions of Bioconductor may be available if yourversion of R is out-of-date.

For more details on Bioconductor approaches to versioning, seethe advanced sectionin the vignette and version numbering in the developer reference section.

Recompiling installed Bioconductor packages

Rarely, underlying changes in the operating system require ALLinstalled packages to be recompiled for source (C or Fortran)compatibility. One way to address this might be to start a new Rsession and enter

As this will reinstall all currently installed packages, it likelyinvolves a significant amount of network bandwidth and compilationtime. All packages are implicitly updated, and the cumulative effectmight introduce wrinkles that disrupt your work flow. It also requiresthat you have the necessary compilers installed.

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Troubleshoot Package Installations

Use the commands

to flag packages that are either out-of-date or too new for yourversion of Bioconductor. The output suggests ways to solve identifiedproblems, and the help page ?valid lists arguments influencingthe behavior of the function.

Troubleshoot BiocManager

One likely reason for BiocManager not working on your system couldbe that your version of R is too old for BiocManager. In orderavoid this issue, please ensure that you have the latest version of Rinstalled in your system. BiocManager supports R versions from 3.5.0and above.

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Why use BiocManager::install()?

BiocManager::install() is the recommended way to install Bioconductorpackages. There are several reasons for preferring this to the‘standard’ way in which R pacakges are installed viainstall.packages().

Bioconductor has a repository and release schedule that differs from R(Bioconductor has a ‘devel’ branch to which new packages and updatesare introduced, and a stable ‘release’ branch emitted once every 6months to which bug fixes but not new features are introduced).

A consequence of the mismatch between R and Bioconductor releaseschedules is that the Bioconductor version identified byinstall.packages() is sometimes not the most recent ‘release’available. For instance, an R minor version may be introduced somemonths before the next Bioc release. After the Bioc release the usersof the R minor version will be pointed to an out-of-date version ofBioconductor.

A consequence of the distinct ‘devel’ branch is thatinstall.packages() sometimes points only to the ‘release’repository, whereas Bioconductor developers and users wantingleading-edge features wish to access the Bioconductor ‘devel’repository. For instance, the Bioconductor 3.0 release is availablefor R.3.1.x, so Bioconductor developers and leading-edge users need tobe able to install the devel version of Bioconductor packages into thesame version (though perhaps different instance or at least librarylocation) of R that supports version 2.14 of Bioconductor.

An indirect consequence of Bioconductor’s structured release is thatpackages generally have more extensive dependencies with one another,both explicitly via the usual package mechan