Learn R Programming

utils (version 3.6.2)

install.packages: Install Packages from Repositories or Local Files

Description

Download and install packages from CRAN-like repositories or from local files.

Usage

install.packages(pkgs, lib, repos = getOption("repos"),
                 contriburl = contrib.url(repos, type),
                 method, available = NULL, destdir = NULL,
                 dependencies = NA, type = getOption("pkgType"),
                 configure.args = getOption("configure.args"),
                 configure.vars = getOption("configure.vars"),
                 clean = FALSE, Ncpus = getOption("Ncpus", 1L),
                 verbose = getOption("verbose"),
                 libs_only = FALSE, INSTALL_opts, quiet = FALSE,
                 keep_outputs = FALSE, …)

Arguments

pkgs

character vector of the names of packages whose current versions should be downloaded from the repositories.

If repos = NULL, a character vector of file paths,

on windows,

file paths of .zip files containing binary builds of packages. (http:// and file:// URLs are also accepted and the files will be downloaded and installed from local copies.) Source directories or file paths or URLs of archives may be specified with type = "source", but some packages need suitable tools installed (see the ‘Details’ section).

On Unix-alikes,

these file paths can be source directories or archives or binary package archive files (as created by R CMD build --binary). (http:// and file:// URLs are also accepted and the files will be downloaded and installed from local copies.) On a CRAN build of R for macOS these can be .tgz files containing binary package archives. Tilde-expansion will be done on file paths.

If this is missing, a listbox of available packages is presented where possible in an interactive R session.

lib

character vector giving the library directories where to install the packages. Recycled as needed. If missing, defaults to the first element of .libPaths().

repos

character vector, the base URL(s) of the repositories to use, e.g., the URL of a CRAN mirror such as "https://cloud.r-project.org". For more details on supported URL schemes see url.

Can be NULL to install from local files, directories or URLs: this will be inferred by extension from pkgs if of length one.

contriburl

URL(s) of the contrib sections of the repositories. Use this argument if your repository mirror is incomplete, e.g., because you burned only the contrib section on a CD, or only have binary packages. Overrides argument repos. Incompatible with type = "both".

method

download method, see download.file. Unused if a non-NULL available is supplied.

available

a matrix as returned by available.packages listing packages available at the repositories, or NULL when the function makes an internal call to available.packages. Incompatible with type = "both".

destdir

directory where downloaded packages are stored. If it is NULL (the default) a subdirectory downloaded_packages of the session temporary directory will be used (and the files will be deleted at the end of the session).

dependencies

logical indicating whether to also install uninstalled packages which these packages depend on/link to/import/suggest (and so on recursively). Not used if repos = NULL. Can also be a character vector, a subset of c("Depends", "Imports", "LinkingTo", "Suggests", "Enhances").

Only supported if lib is of length one (or missing), so it is unambiguous where to install the dependent packages. If this is not the case it is ignored, with a warning.

The default, NA, means c("Depends", "Imports", "LinkingTo").

TRUE means to use c("Depends", "Imports", "LinkingTo", "Suggests") for pkgs and c("Depends", "Imports", "LinkingTo") for added dependencies: this installs all the packages needed to run pkgs, their examples, tests and vignettes (if the package author specified them correctly).

In all of these, "LinkingTo" is omitted for binary packages.

type

character, indicating the type of package to download and install. Will be "source" except on Windows and some macOS builds: see the section on ‘Binary packages’ for those.

configure.args

(Used only for source installs.) A character vector or a named list. If a character vector with no names is supplied, the elements are concatenated into a single string (separated by a space) and used as the value for the --configure-args flag in the call to R CMD INSTALL. If the character vector has names these are assumed to identify values for --configure-args for individual packages. This allows one to specify settings for an entire collection of packages which will be used if any of those packages are to be installed. (These settings can therefore be re-used and act as default settings.)

A named list can be used also to the same effect, and that allows multi-element character strings for each package which are concatenated to a single string to be used as the value for --configure-args.

configure.vars

(Used only for source installs.) Analogous to configure.args for flag --configure-vars, which is used to set environment variables for the configure run.

clean

a logical value indicating whether to add the --clean flag to the call to R CMD INSTALL. This is sometimes used to perform additional operations at the end of the package installation in addition to removing intermediate files.

Ncpus

the number of parallel processes to use for a parallel install of more than one source package. Values greater than one are supported if the make command specified by Sys.getenv("MAKE", "make") accepts argument -k -j Ncpus.

verbose

a logical indicating if some “progress report” should be given.

libs_only

a logical value: should the --libs-only option be used to install only additional sub-architectures for source installs? (See also INSTALL_opts.) This can also be used on Windows to install just the DLL(s) from a binary package, e.g.to add 64-bit DLLs to a 32-bit install.

INSTALL_opts

an optional character vector of additional option(s) to be passed to R CMD INSTALL for a source package install. E.g., c("--html", "--no-multiarch", "--no-test-load").

Can also be a named list of character vectors to be used as additional options, with names the respective package names.

quiet

logical: if true, reduce the amount of output.

keep_outputs

a logical: if true, keep the outputs from installing source packages in the current working directory, with the names of the output files the package names with .out appended. Alternatively, a character string giving the directory in which to save the outputs. Ignored when installing from local files.

Arguments to be passed to download.file or to the functions for binary installs on macOS and Windows (which accept an argument "lock": see the section on ‘Locking’).

Value

Invisible NULL.

Binary packages

This section applies only to platforms where binary packages are available: Windows and CRAN builds for macOS.

R packages are primarily distributed as source packages, but binary packages (a packaging up of the installed package) are also supported, and the type most commonly used on Windows and by the CRAN builds for macOS. This function can install either type, either by downloading a file from a repository or from a local file.

Possible values of type are (currently) "source", "mac.binary", "mac.binary.el-capitan" and "win.binary": the appropriate binary type where supported can also be selected as "binary".

For a binary install from a repository, the function checks for the availability of a source package on the same repository, and reports if the source package has a later version, or is available but no binary version is. This check can be suppressed by using

    options(install.packages.check.source = "no")

and should be if there is a partial repository containing only binary files.

An alternative (and the current default) is "both" which means ‘use binary if available and current, otherwise try source’. The action if there are source packages which are preferred but may contain code which needs to be compiled is controlled by getOption("install.packages.compile.from.source"). type = "both" will be silently changed to "binary" if either contriburl or available is specified.

Using packages with type = "source" always works provided the package contains no C/C++/Fortran code that needs compilation. Otherwise,

on Windows

you will need to have installed the Rtools collection as described in the ‘R for Windows FAQ’ and you must have the PATH environment variable set up as required by Rtools.

For a 32/64-bit installation of R on Windows, a small minority of packages with compiled code need either INSTALL_opts = "--force-biarch" or INSTALL_opts = "--merge-multiarch" for a source installation. (It is safe to always set the latter when installing from a repository or tarballs, although it will be a little slower.)

When installing a binary package, install.packages will abort the install if it detects that the package is already installed and is currently in use. In some circumstances (e.g., multiple instances of R running at the same time and sharing a library) it will not detect a problem, but the installation may fail as Windows locks files in use.

On Unix-alikes,

when the package contains C/C++/Fortran code that needs compilation, on macOS you need to have installed the ‘Command-line tools for Xcode’ (see the ‘R Installation and Administration Manual’) and if needed by the package a Fortran compiler, and have them in your path.

Locking

There are various options for locking: these differ between source and binary installs.

By default for a source install, the library directory is ‘locked’ by creating a directory 00LOCK within it. This has two purposes: it prevents any other process installing into that library concurrently, and is used to store any previous version of the package to restore on error. A finer-grained locking is provided by the option --pkglock which creates a separate lock for each package: this allows enough freedom for parallel installation. Per-package locking is the default when installing a single package, and for multiple packages when Ncpus > 1L. Finally locking (and restoration on error) can be suppressed by --no-lock.

For a macOS binary install, no locking is done by default. Setting argument lock to TRUE (it defaults to the value of getOption("install.lock", FALSE)) will use per-directory locking as described for source installs. For Windows binary install, per-directory locking is used by default (lock defaults to the value of getOption("install.lock", TRUE)). If the value is "pkglock" per-package locking will be used.

If package locking is used on Windows with libs_only = TRUE and the installation fails, the package will be restored to its previous state.

Note that it is possible for the package installation to fail so badly that the lock directory is not removed: this inhibits any further installs to the library directory (or for --pkglock, of the package) until the lock directory is removed manually.

Parallel installs

Parallel installs are attempted if pkgs has length greater than one and Ncpus > 1. It makes use of a parallel make, so the make specified (default make) when R was built must be capable of supporting make -j n: GNU make, dmake and pmake do, but Solaris make and older FreeBSD make do not: if necessary environment variable MAKE can be set for the current session to select a suitable make.

install.packages needs to be able to compute all the dependencies of pkgs from available, including if one element of pkgs depends indirectly on another. This means that if for example you are installing CRAN packages which depend on Bioconductor packages which in turn depend on CRAN packages, available needs to cover both CRAN and Bioconductor packages.

Timeouts

A limit on the elapsed time for each call to R CMD INSTALL (so for source installs) can be set via environment variable _R_INSTALL_PACKAGES_ELAPSED_TIMEOUT_: in seconds (or in minutes or hours with optional suffix m or h, suffix s being allowed for the default seconds) with 0 meaning no limit.

For non-parallel installs this is implemented via the timeout argument of system2: for parallel installs via the OS's timeout command. (The one tested is from GNU coreutils, commonly available on Linux but not other Unix-alikes. If no such command is available the timeout request is ignored, with a warning.) For parallel installs a Error 124 message from make indicates that timeout occurred.

Timeouts during installation might leave lock directories behind and not restore previous versions.

Details

This is the main function to install packages. It takes a vector of names and a destination library, downloads the packages from the repositories and installs them. (If the library is omitted it defaults to the first directory in .libPaths(), with a message if there is more than one.) If lib is omitted or is of length one and is not a (group) writable directory, in interactive use the code offers to create a personal library tree (the first element of Sys.getenv("R_LIBS_USER")) and install there.

Detection of a writable directory is problematic on Windows: see the ‘Note’ section.

For installs from a repository an attempt is made to install the packages in an order that respects their dependencies. This does assume that all the entries in lib are on the default library path for installs (set by environment variable R_LIBS).

You are advised to run update.packages before install.packages to ensure that any already installed dependencies have their latest versions.

See Also

update.packages, available.packages, download.packages, installed.packages, contrib.url.

See download.file for how to handle proxies and other options to monitor file transfers.

untar for manually unpacking source package tarballs.

INSTALL, REMOVE, remove.packages, library, .packages, read.dcf

The ‘R Installation and Administration’ manual for how to set up a repository.

Examples

Run this code
# NOT RUN {
## A Linux example for Fedora's layout of udunits2 headers.
install.packages(c("ncdf4", "RNetCDF"),
  configure.args = c(RNetCDF = "--with-netcdf-include=/usr/include/udunits2"))
# }

Run the code above in your browser using DataLab

Continue Improving Your R Skills

R Fundamentals

Level-up your R programming skills! Learn how to work with common data structures, optimize code, and write your own functions.

Big Data with R

Work with big data in R via parallel programming, interfacing with Spark, writing scalable & efficient R code, and learn ways to visualize big data.

Machine Learning with R

A machine learning scientist researches new approaches and builds machine learning models.