R packages

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R package management

Challenges in Package Management

Challenges in Package Management

Packages loaded at startup


How to install a new package


  • By default, install.packages() will check versions and install uninstalled packages shown in 'Depends', 'Imports' , and 'LinkingTo' (DIL) fields. See R-exts manual.
  • Take advantage of Ncpus parameter in install.packages()
  • If we want to install packages listed in 'Suggests' field, we should specify it explicitly by using dependencies argument:
install.packages(XXXX, dependencies = c("Depends", "Imports", "Suggests", "LinkingTo"))
# OR
install.packages(XXXX, dependencies = TRUE)
For example, if I use a plain install.packages() command to install downloader package, it only installs 'digest' and 'downloader' packages. If I use
install.packages("downloader", dependencies=TRUE)
it will also install 'testhat' package.
  • Even a warning is given when some imports package is not available, it does not stop the installation (trying to install plotROC package on mac R 3.5.3). See also Warning: dependency ‘XXX’ is not available.
    > install.packages("plotROC")
    --- Please select a CRAN mirror for use in this session ---
    Warning: dependency ‘gridSVG’ is not available
    trying URL 'https://cloud.r-project.org/bin/macosx/el-capitan/contrib/3.5/plotROC_2.2.1.tgz'
    Content type 'application/x-gzip' length 1288370 bytes (1.2 MB)
    downloaded 1.2 MB
    The downloaded binary packages are in
    > library(gridSVG)
    Error in library(gridSVG) : there is no package called ‘gridSVG’
    > install.packages("gridSVG", type = "source")
    Warning message:
    package ‘gridSVG’ is not available (for R version 3.5.3) 
    > ap <- available.packages()
    > "gridSVG" %in% rownames(ap) # [1] FALSE
    > dim(ap)  # [1] 14220    17
    > library(plotROC)
    Loading required package: ggplot2
Look at CRAN, we see the latest version (1.7-0) of gridSVG depends on the latest version of R (3.6.0). But why even the source is removed from the old version of R?
  • The install.packages function source code can be found in R -> src -> library -> utils -> R -> packages2.R file from Github repository (put 'install.packages' in the search box).


local({r <- getOption("repos")
       r["CRAN"] <- "http://cran.r-project.org" 


On my mac,

  • install.packages() will install the packages to User Library '/Users/USERNAME/Library/R/3.6/library'
  • RStudio GUI method will install the packages to System Library '/Library/Frameworks/R.framework/Versions/3.6/Resources/library'. So if we want to remove a package, we need to specify the lib parameter in remove.packages() or clicking on the adjacent X icon to remove it.

utils:::menuInstallLocal() (Windows OS only)

Packages -> Install package(s) from local files...

It works on tarball file.

pacman: Install and load the package at the same time, installing temporarily

p_load() function from the pacman package.

An example of installing the purrr package.

Also used by BBC Visual and Data Journalism cookbook for R graphics

Also used by Biowulf/NIH

For bioconductor, use, for example,


PS. Brute force method

mypackages<-c("ggplot2", "dplyr")
for (p in mypackages){
  cond <- suppressWarnings(!require(p, character.only = TRUE))
    try(install.packages(p), silent = TRUE)
    library(p, character.only = TRUE)


devtools depends on 92 (non-base) packages and remotes depends on none.

Download and install R packages stored in 'GitHub', 'BitBucket', or plain 'subversion' or 'git' repositories. This package is a lightweight replacement of the 'install_*' functions in 'devtools'.

To install a package from a local machines with all dependency, run remotes::install_local(path = "MyPackage.tar.gz", dependencies=TRUE) or devtools::install() though the later requires to untar the source first.

remotes::install_github() vs devtools::install_github()

See the answers. remotes is a lighter weight package for those who don't need/want all the other functionality in devtools. The remotes package says it copied most of its functionality from devtools, so the functions are likely the same.






pkgman: A fresh approach to package installation - Gábor Csárdi

ccache: Faster R package installation

Faster R package installation


Posit Package Manager/RStudio Package Manager/PPM

  • https://packagemanager.posit.co/client/#/ -> Repository:Bioconductor -> Setup.
    • On Ubuntu (Linux), there is no binary format of Bioconductor packages. P3M only has binary format of CRAN packages.
    • On Windows, I have to install Rtools. After Rtools has been installed, R will recognize it. Go to https://packagemanager.posit.co/client/#/. Select Bioconductor and click "SETUP". Pick Bioconductor version. Copy/paste options() statements into R or put it in the ~/.Rprofile.
    • On macOS, similar to Windows.
  • PPM Admin Guide. It only supports Ubuntu (no Debian), RedHat, OpenSUSE.
  • R Configuration Steps (Linux) -> Using Linux Binary Packages -> Configuring the R User Agent Header from Admin Guide. The step of configuring http user agent header should not be skipped.
  • Posit Package Manager for Linux R Binaries. Put the following in the <.Rprofile> file. If inside the RStudio, we need to quit the current R session first.
    options(HTTPUserAgent = sprintf(
      "R/%s R (%s)", 
    # adjust this for your Linux distribution
    .ppm <- "https://packagemanager.posit.co/cran/__linux__/noble/latest"
    options(repos = c(CRAN = .ppm))
    # verify
  • It provides pre-compiled binary packages for Linux and Windows OS, but not macOS. When enabled, RStudio Package Manager will serve the appropriate CRAN binaries to R users instead of the source packages, saving R users significant installation time.
  • It is similar to MRAN as it allows users to use particular a snapshot of CRAN as their active repositories within their R session
  • Once configured, users can access RStudio Package Manager through all their familiar tools including base R’s install.packages(), packrat, and devtools.
  • Announcing Public Package Manager and v1.1.6
  • Package Manager allows linux users to install pre-built binaries of the packages which will make install quicker. See an example here.
    # Freeze to Apr 29, 2021 8:00 PM
    options(repos = c(REPO_NAME = "https://packagemanager.posit.co/all/2639103"))
    # Using Linux Binary Packages
  • Bring Your Own Binary Packages with RSPM
  • Docker images
    • r-base, rocker/r-base don't use PPM (as of R 4.3.3).
    • rocker/r-ver (eg. rocker/r-ver:4.3.3) uses PPM. See the table of images on The versioned stack.
    • Bioconductor (source on github) does take advantage of PPM. In Bioc 3.17/R 4.3.1, Posit's PPM hosts the binaries. As of Bioc 3.18/R 4.3.3, Bioconductor hosts the binaries.
    RStudio will be available on your web browser at http://localhost:8787. The username is "rstudio".
    docker run \
     	-e PASSWORD=bioc \
     	-p 8787:8787 \
    R will be available on the terminal
    docker run -it --rm \
        --user rstudio \
        bioconductor/bioconductor_docker:RELEASE_3_18 R
    system.time(BiocManager::install("DESeq2", ask = FALSE))
    # Replacement repositories:
    #     CRAN: https://p3m.dev/cran/__linux__/jammy/latest
    # ...
    # trying URL 'https://bioconductor.org/packages/3.18/container-binaries/bioconductor_docker/src/contrib/bitops_1.0-7_R_x86_64-pc-linux-gnu.tar.gz'
    Content type 'application/gzip' length 24831 bytes (24 KB)
    # ...
    # 52 seconds

install a package as it existed on a specific date/snapshot: mran repository

Look at https://cran.microsoft.com/snapshot/, it seems the snapshot went back 2014 for R 3.1.1.

For example,

options(repos = c(CRAN = "https://mran.microsoft.com/snapshot/2022-10-01"))

install.packages("xgboost", repos="https://mran.microsoft.com/snapshot/2022-10-01/")

This trick works great when I try to install an R package (glmnet 3.0-2) on an old version of R. The current glmnet requires R 3.6.0. So even the source package is not available for older versions of R. If I install a package that does not require a very new version of R, that it works. The same problem happens on Windows OS. (today is 2020-05-05)

$ docker run --rm -it r-base:3.5.3
> install.packages("glmnet")
Installing package into ‘/usr/local/lib/R/site-library’
(as ‘lib’ is unspecified)
Warning message:
package ‘glmnet’ is not available (for R version 3.5.3)
> install.packages("glmnet", repos="https://mran.microsoft.com/snapshot/2019-09-20/")
* DONE (glmnet)

The downloaded source packages are in
> packageVersion("glmnet")
[1] ‘2.0.18’
> options()$repos
> install.packages('knitr') # OK.

install a package on an old version of R

  • Currently R is 4.2.2
  • I have build R 4.1.3 and R 4.0.5 from source on Ubuntu 22.04. P.S. We need to include/enable "Source code" URIs Error :: You must put some 'source' URIs in your sources.list.
    sudo apt update
    sudo apt-get build-dep r-base
    tar -xzvf R-4.1.3.tar.gz
    cd R-4.1.3
    time make -j2
  • I'm trying to install DESEq2 on R 4.1.3 and R 4.0.5
  • On R 4.1.3, the installation is successful.
    packageVersion("DESeq2")  # 1.34.0
  • On R 4.0.5, the installation of "locfit" (current version 1.5-9.7) requires R >= 4.1.0. Check on CRAN, we see the locfit version is 1.5-6 for R 4.0.5 (2021-3-31). So I try this version first.
    remotes::install_version("locfit", "1.5-6")
    # Error: a 'NAMESPACE' file is required
    # Try the next version 1.5-7
    remotes::install_version("locfit", "1.5-7") # Works
    BiocManager::install("DESeq2")   # Works too
    packageVersion("DESeq2")  # 1.30.1
  • Now it is a good opportunity to try renv. So I create a new folder 'testProject' and create a new file 'test.R'. This R file contains only one line: library(DESeq2).
    > setwd("testProj")
    > install.packages("renv")
    > renv::init()
    * Initializing project ...
    * Discovering package dependencies ... Done!
    * Copying packages into the cache ... [79/79] Done!
    One or more Bioconductor packages are used in your project,
    but the BiocManager package is not available.
    Consider installing BiocManager before snapshot.
    The following package(s) will be updated in the lockfile:
    The version of R recorded in the lockfile will be updated:
    - R                      [*] -> [4.0.5]
    * Lockfile written to '~/Downloads/testProject/renv.lock'.
    * Project '~/Downloads/testProject' loaded. [renv 0.16.0]
    * renv activated -- please restart the R session.
    > renv::install("BiocManager")  # NOT SURE IF THIS IS NECESSARY
    Retrieving 'https://cloud.r-project.org/src/contrib/BiocManager_1.30.19.tar.gz' ...
    	OK [file is up to date]
    Installing BiocManager [1.30.19] ...
    	OK [built from source]
    Moving BiocManager [1.30.19] into the cache ...
    	OK [moved to cache in 20 milliseconds]
    > q()

    Now DESeq2 is in "renv.lock" file!

    Note if we accidentally start a different version of R on an renv-ed project based on an older version of R, we shall see some special message informing us The project library is out of sync with the lockfile. At this time, the "renv.lock" is not touched yet.

    # Bootstrapping renv 0.16.0 --------------------------------------------------
    * Downloading renv 0.16.0 ... OK (downloaded source)
    * Installing renv 0.16.0 ... Done!
    * Successfully installed and loaded renv 0.16.0.
    ! Using R 4.2.2 (lockfile was generated with R 4.0.5)
    * Project '~/Downloads/testProject' loaded. [renv 0.16.0]
    * The project library is out of sync with the lockfile.
    * Use `renv::restore()` to install packages recorded in the lockfile.
  • What happened if we start with the same version of R but have a different version of renv in the global environment (vs renv.lock)?
    remotes::install_version("renv", "0.15.5")
    # Start the same R from another directory and update renv to the latest version 0.16.0
    # Now go back to the project directory and start R to see what will happen?
    # Nothing....  This is because the same project has the old & same version of "renv" library 
    # in the "$PROJECT/renv" directory

    If we delete the "renv" subdirectory, ".Rprofile" file but keep "renv.lock" file, and start R (remember the 'renv' package version in the global R is different from the version in "renv.lock".

    > renv::restore()
    This project has not yet been activated.
    Activating this project will ensure the project library is used during restore.
    Please see `?renv::activate` for more details.
    Would you like to activate this project before restore? [Y/n]: y
    Retrieving 'https://cloud.r-project.org/src/contrib/BiocManager_1.30.19.tar.gz' ...
    	OK [file is up to date]
    Installing BiocManager [1.30.19] ...
    	OK [built from source]
    Moving BiocManager [1.30.19] into the cache ...
    	OK [moved to cache in 27 milliseconds]
    * Project '~/Downloads/testProject' loaded. [renv 0.16.0]
    * The project may be out of sync -- use `renv::status()` for more details.
    The following package(s) will be updated:
    # Bioconductor =======================
    - AnnotationDbi          [* -> 1.56.2]
    # CRAN ===============================
    - renv                   [0.16.0 -> 0.15.5]
    Do you want to proceed? [y/N]: y
    Retrieving 'https://cloud.r-project.org/src/contrib/Archive/renv/renv_0.15.5.tar.gz' ...
    	OK [downloaded 961.3 Kb in 0.2 secs]
    Installing renv [0.15.5] ...
    	OK [built from source]
    Moving renv [0.15.5] into the cache ...
    	OK [moved to cache in 6.8 milliseconds]
    The following package(s) have been updated:
    	renv [installed version 0.15.5 != loaded version 0.16.0]
    Consider restarting the R session and loading the newly-installed packages.

    All the R packages are restored in the $PROJECT/renv directory.

Check installed Bioconductor version

Following this post, use tools:::.BioC_version_associated_with_R_version().

Mind the '.' in front of the 'BioC'. It may be possible for some installed packages to have been sourced from a different BioC version.

tools:::.BioC_version_associated_with_R_version() # `3.6'
tools:::.BioC_version_associated_with_R_version() == '3.6'  # TRUE

CRAN Package Depends on Bioconductor Package

For example, if I run install.packages("NanoStringNorm") to install the package from CRAN, I may get

ERROR: dependency ‘vsn’ is not available for package ‘NanoStringNorm’

This is because the NanoStringNorm package depends on the vsn package which is on Bioconductor.

Another example is CRAN's tidyHeatmap that imports ComplexHeatmap in Bioconductor.

Another instance is CRAN's biospear (actually plsRcox) that depends on Bioc's 'survcomp' & 'mixOmics'.

One solution is to run a line setRepositories(ind=1:2). See this post or this one. Note that the default repository list can be found at (Ubuntu) /usr/lib/R/etc/repositories file.

options("repos") # display the available repositories (only CRAN)
options("repos") # CRAN and bioc are included
#                                        CRAN
#                "https://cloud.r-project.org"
# "https://bioconductor.org/packages/3.6/bioc"

install.packages("biospear") # it will prompt to select CRAN

install.packages("biospear", repos = "http://cran.rstudio.com") # NOT work since bioc repos is erased

This will also install the BiocInstaller package if it has not been installed before. See also Install Bioconductor Packages.

Bioconductor packages depend on CRAN

For example cowplot shows breakpointR from Bioconductor depends on it.


update.packages(ask="graphics") can open a graphical window to select packages.

only upgrade to binary package versions

Try setting options(pkgType = "binary") before running update.packages(). To make this always be the default behavior, you can add that line to your .Rprofile file.

How about options(install.packages.check.source = "no"), options(install.packages.compile.from.source = "never")?

Binary packages only for two versions of R

Check What Repos You are Using.

CRAN only provides binaries for one version of R prior to the current one. So when CRAN moves to post-3.6.* R most non version-stuck mirrors will not have 3.5.* binary versions of packages.

Install a tar.gz (e.g. an archived package) from a local directory

R CMD INSTALL <package-name>.tar.gz

Or in R:

# Method 1: cannot install dependencies 
install.packages(<pathtopackage>, repos = NULL)
# These 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.)

# Method 2: take care of dependencies from CRAN
devtools::install(<directory to package>, dependencies = TRUE) 
                         # this will use 'R CMD INSTALL' to install the package.
                         # It will try to install dependencies of the package from CRAN,
                         # if they're not already installed.

The installation process can be nasty due to the dependency issue. Consider the 'biospear' package

biospear - plsRcox (archived) - plsRglm (archived) - bipartite
                              - lars
                              - pls
                              - kernlab
                              - mixOmics (CRAN->Bioconductor)
                              - risksetROC
                              - survcomp (Bioconductor)
                              - rms

So in order to install the 'plsRcox' package, we need to do the following steps. Note: plsRcox package is back on 6/2/2018.

# For curl
system("apt update")
system("apt install curl libcurl4-openssl-dev libssl-dev")

# For X11
system("apt install libcgal-dev libglu1-mesa-dev libglu1-mesa-dev")
system("apt install libfreetype6-dev") # https://stackoverflow.com/questions/31820865/error-in-installing-rgl-package
biocLite("survcomp") # this has to be run before the next command of installing a bunch of packages from CRAN

                 repos = NULL, type="source")
# ERROR: dependencies ‘pkgconfig’, ‘cobs’, ‘corpcor’, ‘devtools’, ‘glmnet’, ‘grplasso’, ‘mboost’, ‘plsRcox’, 
# ‘pROC’, ‘PRROC’, ‘RCurl’, ‘survAUC’ are not available for package ‘biospear’
install.packages(c("pkgconfig", "cobs", "corpcor", "devtools", "glmnet", "grplasso", "mboost", 
                   "plsRcox", "pROC", "PRROC", "RCurl", "survAUC"))
# optional: install.packages(c("doRNG", "mvnfast"))
                 repos = NULL, type="source")
# OR
# devtools::install_github("cran/biospear")
library(biospear) # verify

To install the (deprecated, bioc) packages 'inSilicoMerging',

biocLite(c('rjson', 'Biobase', 'RCurl'))

# destination directory is required
# download.file("http://www.bioconductor.org/packages/3.3/bioc/src/contrib/inSilicoDb_2.7.0.tar.gz", 
#               "~/Downloads/inSilicoDb_2.7.0.tar.gz")
# download.file("http://www.bioconductor.org/packages/3.3/bioc/src/contrib/inSilicoMerging_1.15.0.tar.gz", 
#               "~/Downloads/inSilicoMerging_1.15.0.tar.gz")
# ~/Downloads or $HOME/Downloads won't work in untar()
# untar("~/Downloads/inSilicoDb_2.7.0.tar.gz", exdir="/home/brb/Downloads") 
# untar("~/Downloads/inSilicoMerging_1.15.0.tar.gz", exdir="/home/brb/Downloads") 
# install.packages("~/Downloads/inSilicoDb", repos = NULL)
# install.packages("~/Downloads/inSilicoMerging", repos = NULL)
                 repos = NULL, type = "source")
                 repos = NULL, type = "source")

R CMD INSTALL -l LIB/--library=LIB option

Install a package to a custom location

$ R CMD INSTALL -l /usr/me/localR/library myRPackage.tar.gz

Use a package installed in a custom location

R> library("myRPackage", lib.loc="/usr/me/local/R/library")

# OR include below in .bashrc file
$ export R_LIBS=/usr/me/local/R/library
R> .libPaths() # check
R> library("myRPackage")

Install a specific version of R/Bioconductor package

For packages from CRAN, use something like remotes::install_version("dplyr", "1.0.2")

For packages from Bioconductor, see the two solutions R how to install a specified version of a bioconductor package?

Install multiple/different versions of the same R package


install.packages("~/Downloads/foo_0.1.1.tar.gz", lib = "/tmp", repos = NULL)
# a new folder "/tmp/foo" will be created
library(foo, lib.loc="/tmp") # Or use 'lib' to be consistent with install.packages()      

library(foo, lib.loc="~/dev/foo/v1")    ## loads v1
# and
library(foo, lib.loc="~/dev/foo/v2")    ## loads v2

packageVersion("foo", lib.loc = "/tmp")
help(package = "foo", lib.loc = "/tmp")

remove.packages("foo", lib = "/tmp")

The same works for install.packages(). help(install.packages)

The install_version() from devtools and remotes will overwrite the existing installation.

Query an R package installed locally


Query an R package (from CRAN) basic information: available.packages()

packageStatus() # Summarize information about installed packages

available.packages() # List Available Packages at CRAN-like Repositories
                     # Even I use an old version of R, it still return the latest version of the packages
                     # The 'problem' happens on install.packages() too.

The available.packages() command is useful for understanding package dependency. Use setRepositories() or 'RGUI -> Packages -> select repositories' to select repositories and options()$repos to check or change the repositories.

The return result of available.packages() depends on the R version.

The number of packages returned from available.packages() is smaller than the number of packages obtained from rsync CRAN Mirror HOWTO/FAQ. For example, 19473 < 25000. Some archived packages are still available via rsync.

Also the packageStatus() is another useful function for query how many packages are in the repositories, how many have been installed, and individual package status (installed or not, needs to be upgraded or not).

> options()$repos 

> packageStatus() 
Number of installed packages:
                                      ok upgrade unavailable
  C:/Program Files/R/R-3.0.1/library 110       0           1

Number of available packages (each package counted only once):
                                                                                    installed not installed
  http://watson.nci.nih.gov/cran_mirror/bin/windows/contrib/3.0                            76          4563
  http://www.stats.ox.ac.uk/pub/RWin/bin/windows/contrib/3.0                                0             5
  http://www.bioconductor.org/packages/2.12/bioc/bin/windows/contrib/3.0                   16           625
  http://www.bioconductor.org/packages/2.12/data/annotation/bin/windows/contrib/3.0         4           686
> tmp <- available.packages()
> str(tmp)
 chr [1:5975, 1:17] "A3" "ABCExtremes" "ABCp2" "ACCLMA" "ACD" "ACNE" "ADGofTest" "ADM3" "AER" ...
 - attr(*, "dimnames")=List of 2
  ..$ : chr [1:5975] "A3" "ABCExtremes" "ABCp2" "ACCLMA" ...
  ..$ : chr [1:17] "Package" "Version" "Priority" "Depends" ...
> tmp[1:3,]
            Package       Version Priority Depends                     Imports LinkingTo Suggests             
A3          "A3"          "0.9.2" NA       "xtable, pbapply"           NA      NA        "randomForest, e1071"
ABCExtremes "ABCExtremes" "1.0"   NA       "SpatialExtremes, combinat" NA      NA        NA                   
ABCp2       "ABCp2"       "1.1"   NA       "MASS"                      NA      NA        NA                   
            Enhances License      License_is_FOSS License_restricts_use OS_type Archs MD5sum NeedsCompilation File
A3          NA       "GPL (>= 2)" NA              NA                    NA      NA    NA     NA               NA  
ABCExtremes NA       "GPL-2"      NA              NA                    NA      NA    NA     NA               NA  
ABCp2       NA       "GPL-2"      NA              NA                    NA      NA    NA     NA               NA  
A3          "http://watson.nci.nih.gov/cran_mirror/bin/windows/contrib/3.0"
ABCExtremes "http://watson.nci.nih.gov/cran_mirror/bin/windows/contrib/3.0"
ABCp2       "http://watson.nci.nih.gov/cran_mirror/bin/windows/contrib/3.0"

And the following commands find which package depends on Rcpp and also which are from bioconductor repository.

> pkgName <- "Rcpp"
> rownames(tmp)[grep(pkgName, tmp[,"Depends"])]
> tmp[grep("Rcpp", tmp[,"Depends"]), "Depends"]

> ind <- intersect(grep(pkgName, tmp[,"Depends"]), grep("bioconductor", tmp[, "Repository"]))
> rownames(grep)[ind]
> rownames(tmp)[ind]
 [1] "ddgraph"            "DESeq2"             "GeneNetworkBuilder" "GOSemSim"           "GRENITS"           
 [6] "mosaics"            "mzR"                "pcaMethods"         "Rdisop"             "Risa"              
[11] "rTANDEM"

CRAN vs Bioconductor packages

> R.version # 3.4.3
> x <- available.packages()
> dim(x)
[1] 12581    17

# Bioconductor Soft
> biocinstallRepos()
> y <- available.packages(repos = biocinstallRepos()[1])
> dim(y)
[1] 1477   17
> intersect(x[, "Package"], y[, "Package"])
# Bioconductor Annotation
> dim(available.packages(repos = biocinstallRepos()[2]))
[1] 909  17
# Bioconductor Experiment
> dim(available.packages(repos = biocinstallRepos()[3]))
[1] 326  17

# CRAN + All Bioconductor
> z <- available.packages(repos = biocinstallRepos())
> dim(z)
[1] 15292    17

Downloading Bioconductor package with an old R

When I try to download the GenomicDataCommons package using R 3.4.4 with Bioc 3.6 (the current R version is 3.5.0), it was found it can only install version 1.2.0 instead the latest version 1.4.1.

It does not work by running biocLite("BiocUpgrade") to upgrade Bioc from 3.6 to 3.7.

# Error: Bioconductor version 3.6 cannot be upgraded with R version 3.4.4

See some instruction on RStudio package manager website.

Analyzing data on CRAN packages

R package location when they are installed by root


Customizing your package/library location

Add a personal directory to .libPaths()

.libPaths( c( .libPaths(), "~/userLibrary") )

No need to use the assignment operator.

Install personal R packages after upgrade R, .libPaths(), Rprofile.site, R_LIBS_USER

File Example
Rprofile.site/.Rprofile .libPaths(c("/usr/lib/R/site-library",
Renviron.site/.Renviron R_LIB_SITE="/usr/lib/R/site-library:/usr/lib/R/library"

Scenario: We already have installed many R packages under R 3.1.X in the user's directory. Now we upgrade R to a new version (3.2.X). We like to have these packages available in R 3.2.X.

For Windows OS, refer to R for Windows FAQ

The follow method works on Linux and Windows.

Make sure only one instance of R is running

# Step 1. update R's built-in packages and install them on my personal directory
update.packages(ask=FALSE, checkBuilt = TRUE, repos="http://cran.rstudio.com")

# Step 2. update Bioconductor packages
.libPaths() # The first one is my personal directory
# [1] "/home/brb/R/x86_64-pc-linux-gnu-library/3.2"
# [2] "/usr/local/lib/R/site-library"
# [3] "/usr/lib/R/site-library"
# [4] "/usr/lib/R/library"

Sys.getenv("R_LIBS_USER") # may or may not equivalent to .libPaths()[1]
ul <- unlist(strsplit(Sys.getenv("R_LIBS_USER"), "/"))
src <- file.path(paste(ul[1:(length(ul)-1)], collapse="/"), "3.1") 
des <- file.path(paste(ul[1:(length(ul)-1)], collapse="/"), "3.2") 
pkg <- dir(src, full.names = TRUE)
if (!file.exists(des)) dir.create(des)  # If 3.2 subdirectory does not exist yet!
file.copy(pkg, des, overwrite=FALSE, recursive = TRUE)
biocLite(ask = FALSE)

From Robert Kabacoff (R in Action)

  • If you have a customized Rprofile.site file (see appendix B), save a copy outside of R.
  • Launch your current version of R and issue the following statements
oldip <- installed.packages()[,1]
save(oldip, file="path/installedPackages.Rdata")

where path is a directory outside of R.

  • Download and install the newer version of R.
  • If you saved a customized version of the Rprofile.site file in step 1, copy it into the new installation.
  • Launch the new version of R, and issue the following statements
newip <- installed.packages()[,1]
for(i in setdiff(oldip, newip))

where path is the location specified in step 2.

  • Delete the old installation (optional).

This approach will install only packages that are available from the CRAN. It won’t find packages obtained from other locations. In fact, the process will display a list of packages that can’t be installed For example for packages obtained from Bioconductor, use the following method to update packages


Persistent config and data for R packages with .Rprofile and .Renviron

Persistent config and data for R packages. startup, rappdirs, hoardr, keyring.

Would you like to use a personal library instead?

Some posts from internet

  • Setting R_LIBS & avoiding “Would you like to use a personal library instead?”. Note: I try to create ~/.Renviron to add my personal folder in it. But update.packages() still asks me if I like to use a personal library instead (tested on Ubuntu + R 3.4).
  • automatically create personal library in R. Using suppressUpdates + specify lib in biocLite() or update.packages(Sys.getenv("R_LIBS_USER"), ask = F)
    # create local user library path (not present by default)
    dir.create(path = Sys.getenv("R_LIBS_USER"), showWarnings = FALSE, recursive = TRUE)
    # install to local user library path
    install.packages(p, lib = Sys.getenv("R_LIBS_USER"), repos = "https://cran.rstudio.com/")
    # Bioconductor version
    biocLite(p, suppressUpdates = TRUE, lib = Sys.getenv("R_LIBS_USER"))

The problem can happen if the R was installed to the C:\Program Files\R folder by users but then some main packages want to be upgraded. R will always pops a message 'Would you like to use a personal library instead?'.

To suppress the message and use the personal library always,

Actually the following hints will help us to create a convenient function UpdateMainLibrary() which will install updated main packages in the user's Documents directory without a warning dialog.

  • .libPaths() only returns 1 string "C:/Program Files/R/R-x.y.z/library" on the machines that does not have this problem
  • .libPaths() returns two strings "C:/Users/USERNAME/Documents/R/win-library/x.y" & "C:/Program Files/R/R-x.y.z/library" on machines with the problem.
UpdateMainLibrary <- function() {
  # Update main/site packages
  # The function is used to fix the problem 'Would you like to use a personal library instead?'  
  if (length(.libPaths()) == 1) return()
  ind_mloc <- grep("Program", .libPaths()) # main library e.g. 2
  ind_ploc <- grep("Documents", .libPaths()) # personal library e.g. 1
  if (length(ind_mloc) > 0L && length(ind_ploc) > 0L)
     # search outdated main packages
	 old_mloc <- ! old.packages(.libPaths()[ind_mloc])[, "Package"] %in% 
	               installed.packages(.libPaths()[ind_ploc])[, "Package"]
     oldpac <- old.packages(.libPaths()[ind_mloc])[old_mloc, "Package"]
	 if (length(oldpac) > 0L)
        install.packages(oldpac, .libPaths()[ind_ploc])  

On Linux,

> update.packages()
The downloaded source packages are in
Warning in install.packages(update[instlib == l, "Package"], l, contriburl = contriburl,  :
                              'lib = "/opt/R/3.5.0/lib/R/library"' is not writable
Would you like to use a personal library instead? (yes/No/cancel) yes
> system("ls -lt /home/brb/R/x86_64-pc-linux-gnu-library/3.5 | head")
total 224
drwxrwxr-x  9 brb brb 4096 Oct  3 09:30 survival
drwxrwxr-x  9 brb brb 4096 Oct  3 09:29 mgcv
drwxrwxr-x 10 brb brb 4096 Oct  3 09:29 MASS
drwxrwxr-x  9 brb brb 4096 Oct  3 09:29 foreign

# So new versions of survival, mgc, MASS, foreign are installed in the personal directory
# The update.packages() will issue warnings if we try to run it again. 
# It's OK to ignore these warnings.
> update.packages()
Warning: package 'foreign' in library '/opt/R/3.5.0/lib/R/library' will not be updated
Warning: package 'MASS' in library '/opt/R/3.5.0/lib/R/library' will not be updated
Warning: package 'mgcv' in library '/opt/R/3.5.0/lib/R/library' will not be updated
Warning: package 'survival' in library '/opt/R/3.5.0/lib/R/library' will not be updated

R_LIBS_USER is empty in R 3.4.1

See install.package() error, R_LIBS_USER is empty in R 3.4.1.

List vignettes from a package


List data from a package



List all functions of a package

Assume a package is already loaded. Then


Getting a list of functions and objects in a package. This also assumes the package in loaded. In addition to functions (separated by primitive and non-primitive), it can show constants and objects.

List installed packages and versions

ip <- as.data.frame(installed.packages()[,c(1,3:4)])
rownames(ip) <- NULL
# [1] <NA>        base        recommended
# Levels: base recommended
ip <- ip[is.na(ip$Priority),1:2,drop=FALSE]
print(ip, row.names=FALSE)

Query the names of outdated packages

psi <- packageStatus()$inst
subset(psi, Status == "upgrade", drop = FALSE)
#                     Package                                  LibPath     Version    Priority                Depends
# RcppArmadillo RcppArmadillo C:/Users/brb/Documents/R/win-library/3.2        <NA>                   <NA>
# Matrix               Matrix       C:/Program Files/R/R-3.2.0/library       1.2-0 recommended R (>= 2.15.2), methods
#                                             Imports LinkingTo                 Suggests
# RcppArmadillo                      Rcpp (>= 0.11.0)      Rcpp RUnit, Matrix, pkgKitten
# Matrix        graphics, grid, stats, utils, lattice      <NA>               expm, MASS
#                                            Enhances    License License_is_FOSS License_restricts_use OS_type MD5sum
# RcppArmadillo                                  <NA> GPL (>= 2)            <NA>                  <NA>    <NA>   <NA>
# Matrix        MatrixModels, graph, SparseM, sfsmisc GPL (>= 2)            <NA>                  <NA>    <NA>   <NA>
#               NeedsCompilation Built  Status
# RcppArmadillo              yes 3.2.0 upgrade
# Matrix                     yes 3.2.0 upgrade

The above output does not show the package version from the latest packages on CRAN. So the following snippet does that.

psi <- packageStatus()$inst
pl <- unname(psi$Package[psi$Status == "upgrade"])  # List package names
ap <- as.data.frame(available.packages()[, c(1,2,3)], stringsAsFactors = FALSE)
out <- cbind(subset(psi, Status == "upgrade")[, c("Package", "Version")], ap[match(pl, ap$Package), "Version"])
colnames(out)[2:3] <- c("OldVersion", "NewVersion")
rownames(out) <- NULL
#         Package  OldVersion  NewVersion
# 1 RcppArmadillo
# 2        Matrix       1.2-0       1.2-1

To consider also the packages from Bioconductor, we have the following code. Note that "3.1" means the Bioconductor version and "3.2" is the R version. See Bioconductor release versions page.

psic <- packageStatus(repos = c(contrib.url(getOption("repos")),
subset(psic, Status == "upgrade", drop = FALSE)
pl <- unname(psic$Package[psic$Status == "upgrade"])

ap   <- as.data.frame(available.packages(c(contrib.url(getOption("repos")),
                                "http://www.bioconductor.org/packages/3.1/data/annotation/bin/windows/contrib/3.2"))[, c(1:3)], 
                      stringAsFactors = FALSE)

out <- cbind(subset(psic, Status == "upgrade")[, c("Package", "Version")], ap[match(pl, ap$Package), "Version"])
colnames(out)[2:3] <- c("OldVersion", "NewVersion")
rownames(out) <- NULL
#         Package  OldVersion  NewVersion
# 1         limma      3.24.5      3.24.9
# 2 RcppArmadillo
# 3        Matrix       1.2-0       1.2-1

Searching for packages in CRAN

METACRAN (www.r-pkg.org) - Search and browse all CRAN/R packages

cranly visualisations and summaries for R packages

Exploring R packages with cranly

Query top downloaded packages, download statistics


cranlogs package - Download Logs from the RStudio CRAN Mirror. Suitable on R console.

out <- cran_top_downloads("last-week", 100) # 100 is the maximum limit


packageRank package: Computing and Visualizing CRAN Downloads. Suitable to run on RStudio cloud. Include both CRAN and Bioconductor.

> plot(cranDownloads(packages = c("packageRank", "limma"), when = "last-month"))
> plot(cranDownloads(packages = c("shiny", "glmnet"), when = "last-month"))
> plot(cranDownloads(packages = c("shiny", "glmnet"), from = "2019", to ="2019"))
> plot(cranDownloads(packages = c("shiny", "glmnet"), from = "2019-12", to ="2019-12"))
> plot(bioconductorDownloads(packages = c("edgeR", "DESeq2", "Rsubread", "limma"), when = "last-year"))


For Bioconductor packages, try BiocPkgTools. See the paper.


dlstats. Monthly download stats of 'CRAN' and 'Bioconductor' packages.

installation path not writeable from running biocLite()

When I ran biocLite() to install a new package, I got a message (the Bioc packages are installed successfully anyway)

* DONE (curatedOvarianData)

The downloaded source packages are in
installation path not writeable, unable to update packages: rgl, rJava,
  codetools, foreign, lattice, MASS, spatial, survival

However, if I uses install.package() it can update the package

> packageVersion("survival")
[1] ‘2.42.3’
> update.packages("survival")  # Not working though no error message
> packageVersion("survival")
[1] ‘2.42.3’
> install.packages("survival")
Installing package into ‘/home/brb/R/x86_64-pc-linux-gnu-library/3.4’
* DONE (survival)

The downloaded source packages are in
> packageVersion("survival")
[1] ‘2.42.6’
> library(survival)
> sessionInfo() # show survival package 2.42-6 was attached

It makes sense to always use personal directory when we install packages. See .libPaths().

Warning: cannot remove prior installation of package


Instance 1.

# Install the latest hgu133plus2cdf package
# Remove/Uninstall hgu133plus2.db package
# Put/Install an old version of IRanges (eg version 1.18.2 while currently it is version 1.18.3)
# Test on R 3.0.1
library(hgu133plus2cdf) # hgu133pluscdf does not depend or import IRanges
biocLite("hgu133plus2.db", ask=FALSE) # hgu133plus2.db imports IRanges
# Warning:cannot remove prior installation of package 'IRanges'
# Open Windows Explorer and check IRanges folder. Only see libs subfolder.


  • In the above example, all packages were installed under C:\Program Files\R\R-3.0.1\library\.
  • In another instance where I cannot reproduce the problem, new R packages were installed under C:\Users\xxx\Documents\R\win-library\3.0\. The different thing is IRanges package CAN be updated but if I use packageVersion("IRanges") command in R, it still shows the old version.
  • The above were tested on a desktop.

Instance 2.

# On a fresh R 3.2.0, I install Bioconductor's depPkgTools & lumi packages. Then I close R, re-open it, 
# and install depPkgTools package again.
> source("http://bioconductor.org/biocLite.R")
Bioconductor version 3.1 (BiocInstaller 1.18.2), ?biocLite for help
> biocLite("pkgDepTools")
BioC_mirror: http://bioconductor.org
Using Bioconductor version 3.1 (BiocInstaller 1.18.2), R version 3.2.0.
Installing package(s) ‘pkgDepTools’
trying URL 'http://bioconductor.org/packages/3.1/bioc/bin/windows/contrib/3.2/pkgDepTools_1.34.0.zip'
Content type 'application/zip' length 390579 bytes (381 KB)
downloaded 381 KB

package ‘pkgDepTools’ successfully unpacked and MD5 sums checked
Warning: cannot remove prior installation of package ‘pkgDepTools’

The downloaded binary packages are in
> library(pkgDepTools)
Error in library(pkgDepTools) : there is no package called ‘pkgDepTools’

The pkgDepTools library folder appears in C:\Users\brb\Documents\R\win-library\3.2, but it is empty. The weird thing is if I try the above steps again, I cannot reproduce the problem.

Warning: dependency ‘XXX’ is not available

How should I deal with “package 'xxx' is not available (for R version x.y.z)” warning?

Error: there is no package called XXX

The error happened when I try to run library() command on a package that was just installed. R 3.6.0. 'biospear' version is 1.0.2. macOS.

> library(biospear)
Loading required package: pkgconfig
Error: package or namespace load failed for ‘biospear’ in loadNamespace(i, c(lib.loc, .libPaths()), versionCheck = vIi):
 there is no package called ‘mixOmics’


  • The package mixOmics was removed from CRAN. It is now available on Bioconductor.
  • Tested to install on a docker container: docker run --net=host -it --rm r-base
    ERROR: dependency ‘car’ is not available for package ‘plsRglm’
    * removing ‘/usr/local/lib/R/site-library/plsRglm’
    ERROR: dependencies ‘plsRglm’, ‘mixOmics’, ‘survcomp’ are not available for package ‘plsRcox’
    * removing ‘/usr/local/lib/R/site-library/plsRcox’
    ERROR: dependencies ‘devtools’, ‘plsRcox’, ‘RCurl’ are not available for package ‘biospear’
    * removing ‘/usr/local/lib/R/site-library/biospear’
    The car package looks OK on CRAN. survcomp was moved from CRAN to Bioconductor too.
  • As we can see above, the official r-base image does not contain libraries enough to install RCurl/devtools packages. Consider the tidyverse image (based on RStudio image) from the rocker project.
    docker pull rocker/tidyverse:3.6.0
    docker run --net=host -it --rm -e PASSWORD=password -p 8787:8787 rocker/tidyverse:3.6.0
    # the default username is 'rstudio'
    # Open a browser, log in. Run 'install.packages("RCurl")'. It works.
  • Testing on Mint linux also shows errors about dependencies of mixOmics and survcomp.
  • The best practice to install a package that may depend on packages located/moved to Bioconductor: Run setRepositories(ind=1:2) before calling install.packages(). However, it does not remedy the situation that the 1st level imports package (eg plsRcox) was installed before but the 2nd level imports package (eg mixOmics) was not installed.
  • dependsOnPkgs(): Find Reverse Dependencies. It seems it only return packages that have been installed locally. For example, tools::dependsOnPkgs("RcppEigen", "LinkingTo")

Warning: Unable to move temporary installation

The problem seems to happen only on virtual machines (Virtualbox).

  • Warning: unable to move temporary installation `C:\Users\brb\Documents\R\win-library\3.0\fileed8270978f5\quadprog` to `C:\Users\brb\Documents\R\win-library\3.0\quadprog` when I try to run 'install.packages("forecast").
  • Warning: unable to move temporary installation ‘C:\Users\brb\Documents\R\win-library\3.2\file5e0104b5b49\plyr’ to ‘C:\Users\brb\Documents\R\win-library\3.2\plyr’ when I try to run 'biocLite("lumi")'. The other dependency packages look fine although I am not sure if any unknown problem can happen (it does, see below).

Here is a note of my trouble shooting.

  1. If I try to ignore the warning and load the lumi package. I will get an error.
  2. If I try to run biocLite("lumi") again, it will only download & install lumi without checking missing 'plyr' package. Therefore, when I try to load the lumi package, it will give me an error again.
  3. Even I install the plyr package manually, library(lumi) gives another error - missing mclust package.
> biocLite("lumi")
trying URL 'http://bioconductor.org/packages/3.1/bioc/bin/windows/contrib/3.2/BiocInstaller_1.18.2.zip'
Content type 'application/zip' length 114097 bytes (111 KB)
downloaded 111 KB
package ‘lumi’ successfully unpacked and MD5 sums checked

The downloaded binary packages are in
Old packages: 'BiocParallel', 'Biostrings', 'caret', 'DESeq2', 'gdata', 'GenomicFeatures', 'gplots', 'Hmisc', 'Rcpp', 'RcppArmadillo', 'rgl',
Update all/some/none? [a/s/n]: a
also installing the dependencies ‘Rsamtools’, ‘GenomicAlignments’, ‘plyr’, ‘rtracklayer’, ‘gridExtra’, ‘stringi’, ‘magrittr’

trying URL 'http://bioconductor.org/packages/3.1/bioc/bin/windows/contrib/3.2/Rsamtools_1.20.1.zip'
Content type 'application/zip' length 8138197 bytes (7.8 MB)
downloaded 7.8 MB
package ‘Rsamtools’ successfully unpacked and MD5 sums checked
package ‘GenomicAlignments’ successfully unpacked and MD5 sums checked
package ‘plyr’ successfully unpacked and MD5 sums checked
Warning: unable to move temporary installation ‘C:\Users\brb\Documents\R\win-library\3.2\file5e0104b5b49\plyr’ 
         to ‘C:\Users\brb\Documents\R\win-library\3.2\plyr’
package ‘rtracklayer’ successfully unpacked and MD5 sums checked
package ‘gridExtra’ successfully unpacked and MD5 sums checked
package ‘stringi’ successfully unpacked and MD5 sums checked
package ‘magrittr’ successfully unpacked and MD5 sums checked
package ‘BiocParallel’ successfully unpacked and MD5 sums checked
package ‘Biostrings’ successfully unpacked and MD5 sums checked
Warning: cannot remove prior installation of package ‘Biostrings’
package ‘caret’ successfully unpacked and MD5 sums checked
package ‘DESeq2’ successfully unpacked and MD5 sums checked
package ‘gdata’ successfully unpacked and MD5 sums checked
package ‘GenomicFeatures’ successfully unpacked and MD5 sums checked
package ‘gplots’ successfully unpacked and MD5 sums checked
package ‘Hmisc’ successfully unpacked and MD5 sums checked
package ‘Rcpp’ successfully unpacked and MD5 sums checked
package ‘RcppArmadillo’ successfully unpacked and MD5 sums checked
package ‘rgl’ successfully unpacked and MD5 sums checked
package ‘stringr’ successfully unpacked and MD5 sums checked

The downloaded binary packages are in
> library(lumi)
Error in loadNamespace(i, c(lib.loc, .libPaths()), versionCheck = vIi) : 
  there is no package called ‘plyr’
Error: package or namespace load failed for ‘lumi’
> search()
 [1] ".GlobalEnv"            "package:BiocInstaller" "package:Biobase"       "package:BiocGenerics"  "package:parallel"      "package:stats"        
 [7] "package:graphics"      "package:grDevices"     "package:utils"         "package:datasets"      "package:methods"       "Autoloads"            
[13] "package:base"         
> biocLite("lumi")
BioC_mirror: http://bioconductor.org
Using Bioconductor version 3.1 (BiocInstaller 1.18.2), R version 3.2.0.
Installing package(s) ‘lumi’
trying URL 'http://bioconductor.org/packages/3.1/bioc/bin/windows/contrib/3.2/lumi_2.20.1.zip'
Content type 'application/zip' length 18185326 bytes (17.3 MB)
downloaded 17.3 MB

package ‘lumi’ successfully unpacked and MD5 sums checked

The downloaded binary packages are in
> search()
 [1] ".GlobalEnv"            "package:BiocInstaller" "package:Biobase"       "package:BiocGenerics"  "package:parallel"      "package:stats"        
 [7] "package:graphics"      "package:grDevices"     "package:utils"         "package:datasets"      "package:methods"       "Autoloads"            
[13] "package:base"         
> library(lumi)
Error in loadNamespace(i, c(lib.loc, .libPaths()), versionCheck = vIi) : 
  there is no package called ‘plyr’
Error: package or namespace load failed for ‘lumi’
> biocLite("plyr")
BioC_mirror: http://bioconductor.org
Using Bioconductor version 3.1 (BiocInstaller 1.18.2), R version 3.2.0.
Installing package(s) ‘plyr’
trying URL 'http://cran.rstudio.com/bin/windows/contrib/3.2/plyr_1.8.2.zip'
Content type 'application/zip' length 1128621 bytes (1.1 MB)
downloaded 1.1 MB

package ‘plyr’ successfully unpacked and MD5 sums checked

The downloaded binary packages are in

> library(lumi)
Error in loadNamespace(j <- i1L, c(lib.loc, .libPaths()), versionCheck = vIj) : 
  there is no package called ‘mclust’
Error: package or namespace load failed for ‘lumi’

> ?biocLite
Warning messages:
1: In read.dcf(file.path(p, "DESCRIPTION"), c("Package", "Version")) :
  cannot open compressed file 'C:/Users/brb/Documents/R/win-library/3.2/Biostrings/DESCRIPTION', probable reason 'No such file or directory'
2: In find.package(if (is.null(package)) loadedNamespaces() else package,  :
  there is no package called ‘Biostrings’
> library(lumi)
Error in loadNamespace(j <- i1L, c(lib.loc, .libPaths()), versionCheck = vIj) : 
  there is no package called ‘mclust’
In addition: Warning messages:
1: In read.dcf(file.path(p, "DESCRIPTION"), c("Package", "Version")) :
  cannot open compressed file 'C:/Users/brb/Documents/R/win-library/3.2/Biostrings/DESCRIPTION', probable reason 'No such file or directory'
2: In find.package(if (is.null(package)) loadedNamespaces() else package,  :
  there is no package called ‘Biostrings’
Error: package or namespace load failed for ‘lumi’

Other people also have the similar problem. The possible cause is the virus scanner locks the file and R cannot move them.

Some possible solutions:

  1. Delete ALL folders under R/library (e.g. C:/Progra~1/R/R-3.2.0/library) folder and install the main package again using install.packages() or biocLite().
  2. For specific package like 'lumi' from Bioconductor, we can find out all dependency packages and then install them one by one.
  3. Find out and install the top level package which misses dependency packages.
    1. This is based on the fact that install.packages() or biocLite() sometimes just checks & installs the 'Depends' and 'Imports' packages and won't install all packages recursively
    2. we can do a small experiment by removing a package which is not directly dependent/imported by another package; e.g. 'iterators' is not dependent/imported by 'glment' directly but indirectly. So if we run remove.packages("iterators"); install.packages("glmnet"), then the 'iterator' package is still missing.
    3. A real example is if the missing packages are 'Biostrings', 'limma', 'mclust' (these are packages that 'minfi' directly depends/imports although they should be installed when I run biocLite("lumi") command), then I should just run the command remove.packages("minfi"); biocLite("minfi"). If we just run biocLite("lumi") or biocLite("methylumi"), the missing packages won't be installed.

Error in download.file(url, destfile, method, mode = "wb", ...)

HTTP status was '404 Not Found'

Tested on an existing R-3.2.0 session. Note that VariantAnnotation 1.14.4 was just uploaded to Bioc.

> biocLite("COSMIC.67")
BioC_mirror: http://bioconductor.org
Using Bioconductor version 3.1 (BiocInstaller 1.18.3), R version 3.2.0.
Installing package(s) ‘COSMIC.67’
also installing the dependency ‘VariantAnnotation’

trying URL 'http://bioconductor.org/packages/3.1/bioc/bin/windows/contrib/3.2/VariantAnnotation_1.14.3.zip'
Error in download.file(url, destfile, method, mode = "wb", ...) : 
  cannot open URL 'http://bioconductor.org/packages/3.1/bioc/bin/windows/contrib/3.2/VariantAnnotation_1.14.3.zip'
In addition: Warning message:
In download.file(url, destfile, method, mode = "wb", ...) :
  cannot open: HTTP status was '404 Not Found'
Warning in download.packages(pkgs, destdir = tmpd, available = available,  :
  download of package ‘VariantAnnotation’ failed
installing the source package ‘COSMIC.67’

trying URL 'http://bioconductor.org/packages/3.1/data/experiment/src/contrib/COSMIC.67_1.4.0.tar.gz'
Content type 'application/x-gzip' length 40999037 bytes (39.1 MB)

However, when I tested on a new R-3.2.0 (just installed in VM), the COSMIC package installation is successful. That VariantAnnotation version 1.14.4 was installed (this version was just updated today from Bioconductor).

The cause of the error is the available.package() function will read the rds file first from cache in a tempdir (C:\Users\XXXX\AppData\Local\Temp\RtmpYYYYYY). See lines 51-55 of <packages.R>.

 dest <- file.path(tempdir(),
                   paste0("repos_", URLencode(repos, TRUE), ".rds"))
 if(file.exists(dest)) {
    res0 <- readRDS(dest)
 } else {

Since my R was opened 1 week ago, the rds file it reads today contains old information. Note that Bioconductor does not hold the source code or binary code for the old version of packages. This explains why biocLite() function broke. When I restart R, the original problem is gone.

If we look at the source code of available.packages(), we will see we could use cacheOK option in download.file() function.

download.file(url, destfile, method, cacheOK = FALSE, quiet = TRUE, mode ="wb")

Another case: Error in download.file(url, destfile, method, mode = "wb", ...)

> install.packages("quantreg")

  There is a binary version available but the source version is later:
         binary source needs_compilation
quantreg   5.33   5.34              TRUE

Do you want to install from sources the package which needs compilation?
y/n: n
trying URL 'https://cran.rstudio.com/bin/macosx/el-capitan/contrib/3.4/quantreg_5.33.tgz'
Warning in install.packages :
  cannot open URL 'https://cran.rstudio.com/bin/macosx/el-capitan/contrib/3.4/quantreg_5.33.tgz': HTTP status was '404 Not Found'
Error in download.file(url, destfile, method, mode = "wb", ...) : 
  cannot open URL 'https://cran.rstudio.com/bin/macosx/el-capitan/contrib/3.4/quantreg_5.33.tgz'
Warning in install.packages :
  download of package ‘quantreg’ failed

It seems the binary package cannot be found on the mirror. So the solution here is to download the package from the R main server. Note that after I have successfully installed the binary package from the main R server, I remove the package in R and try to install the binary package from rstudio.com server agin and it works this time.

> install.packages("quantreg", repos = "https://cran.r-project.org")
trying URL 'https://cran.r-project.org/bin/macosx/el-capitan/contrib/3.4/quantreg_5.34.tgz'
Content type 'application/x-gzip' length 1863561 bytes (1.8 MB)
downloaded 1.8 MB

Another case: Error in download.file() on Windows 7

For some reason, IE 8 cannot interpret https://ftp.ncbi.nlm.nih.gov though it understands ftp://ftp.ncbi.nlm.nih.gov.

This is tested using R 3.4.3.

> download.file("https://ftp.ncbi.nlm.nih.gov/geo/series/GSE7nnn/GSE7848/soft/GSE7848_family.soft.gz", "test.soft.gz")
trying URL 'https://ftp.ncbi.nlm.nih.gov/geo/series/GSE7nnn/GSE7848/soft/GSE7848_family.soft.gz'
Error in download.file("https://ftp.ncbi.nlm.nih.gov/geo/series/GSE7nnn/GSE7848/soft/GSE7848_family.soft.gz",  : 
  cannot open URL 'https://ftp.ncbi.nlm.nih.gov/geo/series/GSE7nnn/GSE7848/soft/GSE7848_family.soft.gz'
In addition: Warning message:
In download.file("https://ftp.ncbi.nlm.nih.gov/geo/series/GSE7nnn/GSE7848/soft/GSE7848_family.soft.gz",  :
  InternetOpenUrl failed: 'An error occurred in the secure channel support'

> download.file("ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE7nnn/GSE7848/soft/GSE7848_family.soft.gz", "test.soft.gz")
trying URL 'ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE7nnn/GSE7848/soft/GSE7848_family.soft.gz'
downloaded 9.1 MB

ERROR: failed to lock directory

Follow the suggestion to remove the LOCK file. See the post.

It could happened in calling install.packages(), biocLite() or devtools::install_github(), and so on.

Error in unloadNamespace(package)

> d3heatmap(mtcars, scale = "column", colors = "Blues")
Error: 'col_numeric' is not an exported object from 'namespace:scales'
> packageVersion("scales")
[1] ‘0.2.5’
> library(scales)
Error in unloadNamespace(package) : 
  namespace ‘scales’ is imported by ‘ggplot2’ so cannot be unloaded
In addition: Warning message:
package ‘scales’ was built under R version 3.2.1 
Error in library(scales) : 
  Package ‘scales’ version 0.2.4 cannot be unloaded
> search()
 [1] ".GlobalEnv"             "package:d3heatmap"      "package:ggplot2"       
 [4] "package:microbenchmark" "package:COSMIC.67"      "package:BiocInstaller" 
 [7] "package:stats"          "package:graphics"       "package:grDevices"     
[10] "package:utils"          "package:datasets"       "package:methods"       
[13] "Autoloads"              "package:base" 

If I open a new R session, the above error will not happen!

The problem occurred because the 'scales' package version required by the d3heatmap package/function is old. See this post. And when I upgraded the 'scales' package, it was locked by the package was imported by the ggplot2 package.

Unload a package

Add unload = TRUE option to unload the namespace. See detach().

detach(package:splines, unload=TRUE)


https://crantastic.org/. A community site for R packages where you can search for, review and tag CRAN packages.


New R packages as reported by CRANberries


# Build a vextor of the directories of interest
year <- c("2013","2014","2015")
month <- c("01","02","03","04","05","06","07","08","09","10","11","12")
span <-c(rep(month,2),month[1:7])
dir <- "http://dirk.eddelbuettel.com/cranberries"

url2013 <- file.path(dir,"2013",month)
url2014 <- file.path(dir,"2014",month)
url2015 <- file.path(dir,"2015",month[1:7])
url <- c(url2013,url2014,url2015)

# Read each directory and count the new packages
new_p <- vector()
for(i in url){
  raw.data <- readLines(i)
  new_p[i] <- length(grep("New package",raw.data,value=TRUE))

# Plot
time <- seq(as.Date("2013-01-01"), as.Date("2015-07-01"), by="months")
new_pkgs <- data.frame(time,new_p)

ggplot(new_pkgs, aes(time,y=new_p)) +
  geom_line() + xlab("") + ylab("Number of new packages") + 
  geom_smooth(method='lm') + ggtitle("New R packages as reported by CRANberries") 

R packages being removed

Top new packages in 2015

keep.source.pkgs option

State of R packages in your library. The original code formatting and commenting being removed by default, unless one changes some options for installing packages.

  • options(keep.source.pkgs = TRUE)
  • install.packages("rhub", INSTALL_opts = "--with-keep.source", type = "source")
  • R CMD install --with-keep.source

Package installation speed for packages installed with ‘keep.source’ has been improved. 2021-12-2.

Speeding up package installation

An efficient way to install and load R packages

An efficient way to install and load R packages

# Package names
packages <- c("ggplot2", "readxl", "dplyr", "tidyr", "ggfortify", "DT", "reshape2", "knitr", "lubridate", "pwr", "psy", "car", "doBy", "imputeMissings", "RcmdrMisc", "questionr", "vcd", "multcomp", "KappaGUI", "rcompanion", "FactoMineR", "factoextra", "corrplot", "ltm", "goeveg", "corrplot", "FSA", "MASS", "scales", "nlme", "psych", "ordinal", "lmtest", "ggpubr", "dslabs", "stringr", "assist", "ggstatsplot", "forcats", "styler", "remedy", "snakecaser", "addinslist", "esquisse", "here", "summarytools", "magrittr", "tidyverse", "funModeling", "pander", "cluster", "abind")

# Install packages not yet installed
installed_packages <- packages %in% rownames(installed.packages())
if (any(installed_packages == FALSE)) {

# Packages loading
invisible(lapply(packages, library, character.only = TRUE))

Alternatively use the pacman package.

library( , exclude, include.only)

See ?library

library(MASS, exclude='select')
library(thepackage, include.only="thefunction")

package ‘XXX’ was installed by an R version with different internals

it needs to be reinstalled for use with this R version. The problem seems to be specific to R 3.5.1 in Ubuntu 16.04. I got this message when I try to install the keras and tidyverse packages. The 'XXX' package includes nlme for installing "tidyverse" and Matrix for installing "reticulate". I have already logged in as root. I need to manually install these packages again though it seems I did not see a version change for these packages.

Same error Error: package was installed by an R version with different internals; it needs to be reinstalled for use with this R version.

Today it also happened when I tried to install "pec" which broke when it was installing "Hmisc". The error message is "Error : package ‘rpart’ was installed by an R version with different internals; it needs to be reinstalled for use with this R version". I am using R 3.5.2. rpart version is ‘4.1.13’. The solution is I install rpart again (under my account is enough) though rpart does not have a new version. Then I can install "Hmisc".

packrat and renv

See Reproducible → packrat/renv

R package dependencies

Depends, Imports, Suggests, Enhances, LinkingTo

See Writing R Extensions and install.packages().

  • Depends: list of package names which this package depends on. Those packages will be attached (so it is better to use Imports instead of Depends as much as you can) before the current package when library or require is called. The ‘Depends’ field can also specify a dependence on a certain version of R.
  • Imports: lists packages whose namespaces are imported from (as specified in the NAMESPACE file) but which do not need to be attached.
  • Suggests: lists packages that are not necessarily needed. This includes packages used only in examples, tests or vignettes, and packages loaded in the body of functions.
  • Enhances: lists packages “enhanced” by the package at hand, e.g., by providing methods for classes from these packages, or ways to handle objects from these packages.
  • LinkingTo: A package that wishes to make use of header files in other packages needs to declare them as a comma-separated list in the field ‘LinkingTo’ in the DESCRIPTION file.

Package related functions from package 'utils'

  1. inst - a data frame with columns as the matrix returned by installed.packages plus "Status", a factor with levels c("ok", "upgrade"). Note: the manual does not mention "unavailable" case (but I do get it) in R 3.2.0?
  2. avail - a data frame with columns as the matrix returned by available.packages plus "Status", a factor with levels c("installed", "not installed", "unavailable"). Note: I don't get the "unavailable" case in R 3.2.0?
> x <- packageStatus()
> names(x)
[1] "inst"  "avail"
> dim(x'inst')
[1] 225  17
> x'inst'[1:3, ]
              Package                            LibPath Version Priority               Depends Imports
acepack       acepack C:/Program Files/R/R-3.1.2/library 1.3-3.3     <NA>                  <NA>    <NA>
adabag         adabag C:/Program Files/R/R-3.1.2/library     4.0     <NA> rpart, mlbench, caret    <NA>
affxparser affxparser C:/Program Files/R/R-3.1.2/library  1.38.0     <NA>          R (>= 2.6.0)    <NA>
           LinkingTo                                                        Suggests Enhances
acepack         <NA>                                                            <NA>     <NA>
adabag          <NA>                                                            <NA>     <NA>
affxparser      <NA> R.oo (>= 1.18.0), R.utils (>= 1.32.4),\nAffymetrixDataTestFiles     <NA>
                      License License_is_FOSS License_restricts_use OS_type MD5sum NeedsCompilation Built
acepack    MIT + file LICENSE            <NA>                  <NA>    <NA>   <NA>              yes 3.1.2
adabag             GPL (>= 2)            <NA>                  <NA>    <NA>   <NA>               no 3.1.2
affxparser        LGPL (>= 2)            <NA>                  <NA>    <NA>   <NA>             <NA> 3.1.1
acepack             ok
adabag              ok
affxparser unavailable
> dim(x'avail')
[1] 6538   18
> x'avail'[1:3, ]
                Package Version Priority                        Depends        Imports LinkingTo
A3                   A3   0.9.2     <NA> R (>= 2.15.0), xtable, pbapply           <NA>      <NA>
ABCExtremes ABCExtremes     1.0     <NA>      SpatialExtremes, combinat           <NA>      <NA>
ABCanalysis ABCanalysis   1.0.1     <NA>                    R (>= 2.10) Hmisc, plotrix      <NA>
                       Suggests Enhances    License License_is_FOSS License_restricts_use OS_type Archs
A3          randomForest, e1071     <NA> GPL (>= 2)            <NA>                  <NA>    <NA>  <NA>
ABCExtremes                <NA>     <NA>      GPL-2            <NA>                  <NA>    <NA>  <NA>
ABCanalysis                <NA>     <NA>      GPL-3            <NA>                  <NA>    <NA>  <NA>
            MD5sum NeedsCompilation File                                      Repository        Status
A3            <NA>             <NA> <NA> http://cran.rstudio.com/bin/windows/contrib/3.1 not installed
ABCExtremes   <NA>             <NA> <NA> http://cran.rstudio.com/bin/windows/contrib/3.1 not installed
ABCanalysis   <NA>             <NA> <NA> http://cran.rstudio.com/bin/windows/contrib/3.1 not installed

download.packages() for source package

Consider the esimate package hosted on r-forge.

download.packages("estimate", destdir = "~/Downloads", 
   repos = "https://R-Forge.R-project.org")

tools package

db <- tools::CRAN_package_db()
nRcpp <- length(tools::dependsOnPkgs("Rcpp", recursive=FALSE, installed=db) )
nCompiled <- table(db[, "NeedsCompilation"])[["yes"]]
propRcpp <- nRcpp / nCompiled * 100
  • package.dependencies(), pkgDepends(), etc are deprecated now, mostly in favor of package_dependencies() which is both more flexible and efficient. See R 3.3.0 News. For example, tools::package_dependencies(c("remotes", "devtools"), recursive=TRUE) shows remotes has only a few dependencies while devtools has a lot.

crandep package

https://cran.r-project.org/web/packages/crandep/index.html. Useful to find reverse dependencies. ?get_dep. Consider the abc package:

get_dep("abc", "depends") # abc depends on these packages
                          # note my computer does not have 'abc' installed 
#   from       to    type reverse
# 1  abc abc.data depends   FALSE
# 2  abc     nnet depends   FALSE
# 3  abc quantreg depends   FALSE
# 4  abc     MASS depends   FALSE
# 5  abc   locfit depends   FALSE

get_dep("abc", "reverse_depends")
#    from       to    type reverse
# 1  abc abctools depends    TRUE
# 2  abc  EasyABC depends    TRUE

x <- get_dep("RcppEigen", c("reverse linking to")) 
# [1] 331   4
head(x, 3)
#        from      to       type reverse
# 1 RcppEigen   abess linking to    TRUE
# 2 RcppEigen    acrt linking to    TRUE
# 3 RcppEigen ADMMnet linking to    TRUE

How does this package depend on this other package

How does this package depend on this other package?, pak::pkg_deps_explain()



remotes::local_package_deps(dependencies=TRUE) will find and return all dependent packages based on the "DESCRIPTION" file. See an example here.

Bioconductor's pkgDepTools package

The is an example of querying the dependencies of the notorious 'lumi' package which often broke the installation script. I am using R 3.2.0 and Bioconductor 3.1.

The getInstallOrder function is useful to get a list of all (recursive) dependency packages.

if (!require(pkgDepTools)) {
  biocLite("pkgDepTools", ask = FALSE)
MkPlot <- FALSE

biocUrl <- biocinstallRepos()["BioCsoft"]
biocDeps <- makeDepGraph(biocUrl, type="source", dosize=FALSE) # pkgDepTools defines its makeDepGraph()

PKG <- "lumi"
if (MkPlot) {
  if (!require(Biobase))  {
    biocLite("Biobase", ask = FALSE)
  if (!require(Rgraphviz))  {
    biocLite("Rgraphviz", ask = FALSE) 
  categoryNodes <- c(PKG, names(acc(biocDeps, PKG)1))  
  categoryGraph <- subGraph(categoryNodes, biocDeps) 
  nn <- makeNodeAttrs(categoryGraph, shape="ellipse") 
  plot(categoryGraph, nodeAttrs=nn)   # Complete but plot is too complicated & font is too small.

system.time(allDeps <- makeDepGraph(biocinstallRepos(), type="source",
                           keep.builtin=TRUE, dosize=FALSE)) # takes a little while
#    user  system elapsed 
# 175.737  10.994 186.875 
# Warning messages:
# 1: In .local(from, to, graph) : edges replaced: ‘SNPRelate|gdsfmt’
# 2: In .local(from, to, graph) :
#   edges replaced: ‘RCurl|methods’, ‘NA|bitops’

# When needed.only=TRUE, only those dependencies not currently installed are included in the list.
x1 <- sort(getInstallOrder(PKG, allDeps, needed.only=TRUE)$packages); x1
 [1] "affy"                              "affyio"                           
 [3] "annotate"                          "AnnotationDbi"                    
 [5] "base64"                            "beanplot"                         
 [7] "Biobase"                           "BiocParallel"                     
 [9] "biomaRt"                           "Biostrings"                       
[11] "bitops"                            "bumphunter"                       
[13] "colorspace"                        "DBI"                              
[15] "dichromat"                         "digest"                           
[17] "doRNG"                             "FDb.InfiniumMethylation.hg19"     
[19] "foreach"                           "futile.logger"                    
[21] "futile.options"                    "genefilter"                       
[23] "GenomeInfoDb"                      "GenomicAlignments"                
[25] "GenomicFeatures"                   "GenomicRanges"                    
[27] "GEOquery"                          "ggplot2"                          
[29] "gtable"                            "illuminaio"                       
[31] "IRanges"                           "iterators"                        
[33] "labeling"                          "lambda.r"                         
[35] "limma"                             "locfit"                           
[37] "lumi"                              "magrittr"                         
[39] "matrixStats"                       "mclust"                           
[41] "methylumi"                         "minfi"                            
[43] "multtest"                          "munsell"                          
[45] "nleqslv"                           "nor1mix"                          
[47] "org.Hs.eg.db"                      "pkgmaker"                         
[49] "plyr"                              "preprocessCore"                   
[51] "proto"                             "quadprog"                         
[53] "RColorBrewer"                      "Rcpp"                             
[55] "RCurl"                             "registry"                         
[57] "reshape"                           "reshape2"                         
[59] "rngtools"                          "Rsamtools"                        
[61] "RSQLite"                           "rtracklayer"                      
[63] "S4Vectors"                         "scales"                           
[65] "siggenes"                          "snow"                             
[67] "stringi"                           "stringr"                          
[69] "TxDb.Hsapiens.UCSC.hg19.knownGene" "XML"                              
[71] "xtable"                            "XVector"                          
[73] "zlibbioc"                         

# When needed.only=FALSE the complete list of dependencies is given regardless of the set of currently installed packages.
x2 <- sort(getInstallOrder(PKG, allDeps, needed.only=FALSE)$packages); x2
 [1] "affy"                              "affyio"                            "annotate"                         
 [4] "AnnotationDbi"                     "base64"                            "beanplot"                         
 [7] "Biobase"                           "BiocGenerics"                      "BiocInstaller"                    
[10] "BiocParallel"                      "biomaRt"                           "Biostrings"                       
[13] "bitops"                            "bumphunter"                        "codetools"                        
[16] "colorspace"                        "DBI"                               "dichromat"                        
[19] "digest"                            "doRNG"                             "FDb.InfiniumMethylation.hg19"     
[22] "foreach"                           "futile.logger"                     "futile.options"                   
[25] "genefilter"                        "GenomeInfoDb"                      "GenomicAlignments"                
[28] "GenomicFeatures"                   "GenomicRanges"                     "GEOquery"                         
[31] "ggplot2"                           "graphics"                          "grDevices"                        
[34] "grid"                              "gtable"                            "illuminaio"                       
[37] "IRanges"                           "iterators"                         "KernSmooth"                       
[40] "labeling"                          "lambda.r"                          "lattice"                          
[43] "limma"                             "locfit"                            "lumi"                             
[46] "magrittr"                          "MASS"                              "Matrix"                           
[49] "matrixStats"                       "mclust"                            "methods"                          
[52] "methylumi"                         "mgcv"                              "minfi"                            
[55] "multtest"                          "munsell"                           "nleqslv"                          
[58] "nlme"                              "nor1mix"                           "org.Hs.eg.db"                     
[61] "parallel"                          "pkgmaker"                          "plyr"                             
[64] "preprocessCore"                    "proto"                             "quadprog"                         
[67] "RColorBrewer"                      "Rcpp"                              "RCurl"                            
[70] "registry"                          "reshape"                           "reshape2"                         
[73] "rngtools"                          "Rsamtools"                         "RSQLite"                          
[76] "rtracklayer"                       "S4Vectors"                         "scales"                           
[79] "siggenes"                          "snow"                              "splines"                          
[82] "stats"                             "stats4"                            "stringi"                          
[85] "stringr"                           "survival"                          "tools"                            
[88] "TxDb.Hsapiens.UCSC.hg19.knownGene" "utils"                             "XML"                              
[91] "xtable"                            "XVector"                           "zlibbioc" 

> sort(setdiff(x2, x1)) # Not all R's base packages are included; e.g. 'base', 'boot', ...
 [1] "BiocGenerics"  "BiocInstaller" "codetools"     "graphics"      "grDevices"    
 [6] "grid"          "KernSmooth"    "lattice"       "MASS"          "Matrix"       
[11] "methods"       "mgcv"          "nlme"          "parallel"      "splines"      
[16] "stats"         "stats4"        "survival"      "tools"         "utils"  

Lumi rgraphviz.svg

Bioconductor BiocPkgTools

Collection of simple tools for learning about Bioc Packages. Functionality includes access to :

  • Download statistics
  • General package listing
  • Build reports
  • Package dependency graphs
  • Vignettes

Overview of BiocPkgTools & Dependency graphs

BiocPkgTools: Toolkit for Mining the Bioconductor Package Ecosystem in biorxiv.org.

miniCRAN package

miniCRAN package can be used to identify package dependencies or create a local CRAN repository. It can be used on repositories other than CRAN, such as Bioconductor.

Before we go into R, we need to install some packages from Ubuntu terminal. See here.

# Consider glmnet package (today is 4/29/2015)
# Version:	2.0-2
# Depends:	Matrix (≥ 1.0-6), utils, foreach
# Suggests:	survival, knitr, lars
if (!require("miniCRAN"))  {
  install.packages("miniCRAN", dependencies = TRUE, repos="http://cran.rstudio.com") # include 'igraph' in Suggests.
if (!"igraph" %in% installed.packages()[,1]) install.packages("igraph")

tags <- "glmnet"
pkgDep(tags, suggests=TRUE, enhances=TRUE) # same as pkgDep(tags)
#  [1] "glmnet"    "Matrix"    "foreach"   "codetools" "iterators" "lattice"   "evaluate"  "digest"   
#  [9] "formatR"   "highr"     "markdown"  "stringr"   "yaml"      "mime"      "survival"  "knitr"    
# [17] "lars"   

dg <- makeDepGraph(tags, suggests=TRUE, enhances=TRUE) # miniCRAN defines its makeDepGraph()
plot(dg, legendPosition = c(-1, 1), vertex.size=20)

MiniCRAN dep.svg PkgDepTools dep.svg Glmnet dep.svg

We can also display the dependence for a package from the Bioconductor repository.

tags <- "DESeq2"
# Depends	S4Vectors, IRanges, GenomicRanges, Rcpp (>= 0.10.1), RcppArmadillo (>=
# Imports	BiocGenerics(>= 0.7.5), Biobase, BiocParallel, genefilter, methods, locfit, geneplotter, ggplot2, Hmisc
# Suggests	RUnit, gplots, knitr, RColorBrewer, BiocStyle, airway,\npasilla (>= 0.2.10), DESeq, vsn
# LinkingTo     Rcpp, RcppArmadillo
index <- function(url, type="source", filters=NULL, head=5, cols=c("Package", "Version")){
  contribUrl <- contrib.url(url, type=type)
  available.packages(contribUrl, type=type, filters=filters)

bioc <- local({
  env <- new.env()
  evalq(source("http://bioconductor.org/biocLite.R", local=TRUE), env)
  biocinstallRepos() # return URLs

#                                               BioCsoft 
#            "http://bioconductor.org/packages/3.0/bioc" 
#                                                BioCann 
# "http://bioconductor.org/packages/3.0/data/annotation" 
#                                                BioCexp 
# "http://bioconductor.org/packages/3.0/data/experiment" 
#                                              BioCextra 
#           "http://bioconductor.org/packages/3.0/extra" 
#                                                   CRAN 
#                                "http://cran.fhcrc.org" 
#                                              CRANextra 
#                   "http://www.stats.ox.ac.uk/pub/RWin" 
str(index(bioc["BioCsoft"])) # similar to cranJuly2014 object 

system.time(dg <- makeDepGraph(tags, suggests=TRUE, enhances=TRUE, availPkgs = index(bioc["BioCsoft"]))) # Very quick!
plot(dg, legendPosition = c(-1, 1), vertex.size=20)

Deseq2 dep.svg Lumi dep.svg

The dependencies of GenomicFeature and GenomicAlignments are more complicated. So we turn the 'suggests' option to FALSE.

tags <- "GenomicAlignments"
dg <- makeDepGraph(tags, suggests=FALSE, enhances=FALSE, availPkgs = index(bioc["BioCsoft"]))
plot(dg, legendPosition = c(-1, 1), vertex.size=20)

Genomicfeature dep dep.svg Genomicalignments dep.svg

Github repository

Submitting R package to CRAN



cranlike keeps the package data in a SQLite database, in addition to the PACKAGES* files. This database is the canonical source of the package data. It can be updated quickly, to add and remove packages. The PACKAGES* files are generated from the database.

MRAN (CRAN only) & checkpoint package

According to the snapsot list here, the oldest version is 2014-08-18 which corresponds to R 3.1.0.

checkpoint package


Note the Bioconductor packages have no similar solution.

groundhog package

  • groundhog: Version-Control for CRAN, GitHub, and GitLab Packages (Bioconductor?)
  • It seems groundhog is like checkpoint but groundhog does not depend on MRAN and it will figure out the package dependencies by itself.
  • MRAN is getting shutdown - what else is there for reproducibility with R, or why reproducibility is on a continuum? The author provides an example where we can integrate "groundhog" in the Dockerfile for reproducibility. Pay attention to the sentence (appear 2 times) “why use Docker at all? Since it’s easy to install older versions of R on Windows and macOS, wouldn’t an renv.lock file suffice? Or even just {groundhog} which is arguably even easier to use?”
  • MRAN is getting shutdown #593 related to Rocker project. Rocker now uses RSPM (PPM now) from RStudio.
  • groundhog.library() differs from the library() function: 1) it installed and loaded packages in one step, 2) it allowed to install multiple packages.

Note I use the docker's R since that's the easiest way to use an old version of R (e.g. for some old R script) in Linux. Note that it will create a new folder R_groundhog folder in the working directory (see the message below).

$ docker run --rm -it -v $(pwd):/home/docker \
    -w /home/docker -u docker r-base:4.0.2 R

> install.packages("groundhog")
> library(groundhog")
groundhog needs authorization to save files to '/home/docker/R_groundhog'
Enter 'OK' to provide authorization
The groundhog folder path is now:
Loaded 'groundhog' (version:2.1.0) using R-4.0.2
Tips and troubleshooting: https://groundhogR.com

> groundhog.library("
> library()
# Packages in library ‘/usr/local/lib/R/site-library’:
> packageVersion("ggplot2")
[1] ‘3.3.2’   # the latest version is 3.4.0 on R 4.2.2
> library(ggplot2)

(Current groundhog is v3.1.0) If I don't specify a user in docker run, I'll need to call groundhog.library() twice in order to install packages (eg. "DT" package). In this case I can use groundhog:::save.cookie("copy_instead_of_renaming") before calling groundhog.library().

$ docker run -it --rm rocker/verse:4.3.0 bash
root@58978695ec12:/# R
R version 4.3.0 (2023-04-21) -- "Already Tomorrow"
> install.packages("groundhog"); library(groundhog)

> .libPaths()
[1] "/usr/local/lib/R/site-library" "/usr/local/lib/R/library"
> get.groundhog.folder()
[1] "/root/R_groundhog/groundhog_library/"

> groundhog:::save.cookie("copy_instead_of_renaming") 
groundhog needs authorization to save files to '/root/R_groundhog/'
Enter 'OK' to provide authorization, and 'NO' not to.
|   >OK

Groundhog folder set to: '/root/R_groundhog/groundhog_library/

--- You may change it with`set.groundhog.folder(<path>)`---
Downloading database with information for all CRAN packages ever published
trying URL 'http://s3.wasabisys.com/groundhog/cran.toc.rds'
Content type 'application/octet-stream' length 2010504 bytes (1.9 MB)
downloaded 1.9 MB

Downloading database with installation times for all source packages on CRAN
trying URL 'http://s3.wasabisys.com/groundhog/cran.times.rds'
Content type 'application/octet-stream' length 803912 bytes (785 KB)
downloaded 785 KB
> groundhog.library("DT", "2023-4-23")
> find.package("DT")


rang: make ancient R code run again


R package dependence trees


tmp = session_info("sessioninfo")
dim(tmp$packages) # [1]  7 11

tmp = session_info("tidyverse")
dim(tmp$packages) # [1] 95 11

Reverse dependence

Install packages offline


Install a packages locally and its dependencies

It's impossible to install the dependencies if you want to install a package locally. See Windows-GUI: "Install Packages from local zip files" and dependencies

A minimal R package (for testing purpose)

An R package that does not require others during install.packages()

Create a new R package, namespace, documentation

Package structure

http://r-pkgs.had.co.nz/package.html. On Linux/macOS, use tree -d DIRNAME to show the directories only. At a minimum, we will have R and man directories.

  • ChangeLog
  • MD5
  • R/
    • zzz.R
  • build/
    • Package.pdf (eg dplyr)
    • vignette.rds
  • data/
  • demo/
  • inst/
    • extdata/
    • doc/
      • FileName.R
      • FileName.Rmd
      • FileName.html
    • include/
    • othersYouInclude/
    • tinytest/
  • man/
    • figures/
  • src/
  • tests/
    • testthat
  • vignettes/


Namespace dependencies not required. If you use import or importFrom in your NAMESPACE file, you should have an entry for that package in the Imports section of your DESCRIPTION file (unless there is a reason that you need to use Depends in which case the package should have an entry in Depends, and not Imports).


Install software for PDF output

Windows: Rtools

  • (Old) Installing RTools for Compiled Code via Rcpp. Just remember to check the option to include some paths in the PATH environment variable.
  • Using Rtools4 on Windows. This version gives more details about the story in Rtools4X versions.
  • Testing Rtools43 on R 4.3.3. According to the information on the website: When using R installed by the installer, no further setup is necessary after installing Rtools43 to build R packages from source. I can testify that by running install.packages("jsonlite", type = "source") . We can also verify that make can be found.
    ## "C:\\rtools40\\usr\\bin\\make.exe"

Screenshots of installation of Rtools44



R Installation and Administration

  • R CMD build XXX. Note this will not create pdf files for vignettes. The output is a tarball.
  • R CMD SHLIB files. For example, "Rcmd shlib *.f *.c -o surv.dll" on Windows.
  • R CMD make
  • R CMD check XXX. Useful if we want to create reference manual (PDF file). See R create reference manual with R CMD check.
  • R CMD javareconf

usethis package

Github Actions

To use the Github Actions for continuous integration/CI checks,

  • I first follow this to run usethis::use_github_action_check_release(). Once I commit and push the files to Github, Github Actions are kicked off. The R console also tell me to copy and paste a line to add a workflow's badge to README.md.
  • Then I modify the yaml file to become this to run a standard check. This took 3m24s to run.
  • Then I further delete the 'devel' line (line 23) to reduce one more platform to check. This took 3m to check. My example is on Github (rtoy).

I also try to follow Github Actions with R and create a pkgdown/package documentation web page.

  • usethis::use_github_actions("pkgdown") Now go to Github repo's Settings -> Options and scroll down until you see Github Pages. For Source, the page site should be set to being built from the root folder of the gh-pages.
  • I have used Jekyll to create a gh-page. I don't need to delete anything for this new gh-pages. I just need to go to the repository setting and (scroll down until we see Github Pages) change the Source of Github Pages to 'gh-pages branch' from 'master branch'.
  • The files on the gh-pages branch are generated by Github Actions; these files are not available on my local machine. My location machine only has .github/workflows/pkgdown.yaml file.


  • The workflow file specifies R version and OS platform.
  • Right now the workflow file (like pkgdown) is using "r-lib/actions/setup-r@master" that has an "action.yml" file. The r-version is '3.x' only. What about if R 4.0.0 is released?

Packages, webpages and Github

Tutorials by Lisa DeBruine


biocthis, slides

R package depends vs imports

In the namespace era Depends is never really needed. All modern packages have no technical need for Depends anymore. Loosely speaking the only purpose of Depends today is to expose other package's functions to the user without re-exporting them.

load = functions exported in myPkg are available to interested parties as myPkg::foo or via direct imports - essentially this means the package can now be used

attach = the namespace (and thus all exported functions) is attached to the search path - the only effect is that you have now added the exported functions to the global pool of functions - sort of like dumping them in the workspace (for all practical purposes, not technically)

import a function into a package = make sure that this function works in my package regardless of the search path (so I can write fn1 instead of pkg1::fn1 and still know it will come from pkg1 and not someone's workspace or other package that chose the same name)

The distinction is between "loading" and "attaching" a package. Loading it (which would be done if you had MASS::loglm, or imported it) guarantees that the package is initialized and in memory, but doesn't make it visible to the user without the explicit MASS:: prefix. Attaching it first loads it, then modifies the user's search list so the user can see it.

Loading is less intrusive, so it's preferred over attaching. Both library() and require() would attach it.

import() and importFrom()

If our package depends on a package, we need to made some changes. Below we assume the package glmnet in our new package.

  • DESCRIPTION: Imports: glmnet
  • NAMESPACE: either import(glmnet) to import all functions from glmnet or importFrom(glmnet, cv.glmnet) to import 'cv.glmnet' only
  • hello.R: nothing needs to be added

For more resource, see

R package suggests

stringr has suggested htmlwidgets. An error will come out if the suggested packages are not available.

> library(stringr)
> str_view(c("abc", "a.c", "bef"), "a\\.c")
Error in loadNamespace(name) : there is no package called ‘htmlwidgets’

Useful functions for accessing files in packages

> system.file(package = "batr")
[1] "f:/batr"
> system.file("extdata", "logo.png", package = "cowplot") # Mac
[1] "/Library/Frameworks/R.framework/Versions/4.0/Resources/library/cowplot/extdata/logo.png"

> path.package("batr")
[1] "f:\\batr"
> path.package("ggplot2") # Mac
[1] "/Library/Frameworks/R.framework/Versions/4.0/Resources/library/ggplot2"

# sometimes it returns the forward slash format for some reason; C:/Program Files/R/R-3.4.0/library/batr
# so it is best to add normalizePath().
> normalizePath(path.package("batr"))

> file.path("f:", "git", "surveyor")
[1] "f:/git/surveyor"

Internal functions

RStudio shortcuts

RStudio Build.png

available package

available. Check if a given package name is available to use. It checks the name's validity. Checks if it is used on 'GitHub', 'CRAN' and 'Bioconductor'.

Create an R package

Your first R package in 1 hour

Using usethis

New R Package 'foo' -- Updated

  1. setwd()
  2. usethis::create_package("foo")
  3. usethis::use_git(); usethis_github()
  4. usethis::use_mit_license("Your name")
  5. usethis::use_r("foo_function")
  6. usethis::use_package("dplyr") # specify import dependency, e.g. dplyr package
  7. usethis::use_testthat()
  8. usethis::use_test("firsttest")
  9. setwd("..")
  10. roxygen2::roxygenise()

Using devtools and roxygen2

How to use devtools::load_all("FolderName"). load_all() loads any modified R files, and recompile and reload any modified C or Fortran files.

# Step 1

# Step 2
dir.create(file.path("MyCode", "R"), recursive = TRUE)
cat("foo=function(x){x*2}", file = file.path("MyCode", "R", "foo.R"))
write.dcf(list(Package = "MyCode", Title = "My Code for this project", Description = "To tackle this problem", 
    Version = "0.0", License = "For my eyes only", Author = "First Last <[email protected]>", 
    Maintainer = "First Last <[email protected]>"), file = file.path("MyCode", "DESCRIPTION"))
# OR
# create("path/to/package/pkgname")
# create() will create R/ directory, DESCRIPTION and NAMESPACE files.

# Step 3 (C/Fortran code, optional)
dir.create(file.path("MyCode", "src"))
cat("void cfoo(double *a, double *b, double *c){*c=*a+*b;}\n", file = file.path("MyCode", 
    "src", "cfoo.c"))
cat("useDynLib(MyCode)\n", file = file.path("MyCode", "NAMESPACE"))

# Step 4 

# Step 5
# Modify R/C/Fortran code and run load_all("MyCode")

# Step 6 (Automatically generate the documentation from R source code, 
#         in the man/ folder and export the function in NAMESPACE file.
#         optional, repeat if any function's help has changed)

# Step 7 (check the package, optional)

# Step 8 (Deployment, create a tarball, 
#         optional, repeat if necessary)

# Step 9 (Install the package, optional)


  1. load_all("FolderName") will make the FolderName to become like a package to be loaded into the current R session so the 2nd item returned from search() will be "package:FolderName". However, the FolderName does not exist under Program Files/R/R-X.Y.Z/library nor Documents/R/win-library/X.Y/ (Windows OS).
  2. build("FolderName") will create a tarball in the current directory. User can install the new package for example using Packages -> Install packages from local files on Windows OS. This will build/run vignettes so it may take some time. The tarball will contain a build folder containing 'vignette.rds' file. It'll also create a new folder inst/doc containing 3 files (MyPkg.html, MyPkg.Rmd and MyPkg.R). The vignettes may contain a new folder MyPkg_cache if we use chunk = TRUE option in Rmd file. Note install.packages() will not run the R code in vignettes.
  3. For the simplest R package, the source code only contains a file <DESCRIPTION> and a folder <R> with individual R files in the text format.

Using RStudio

  • Youtube
  • How to Create and Distribute an R Package.
    • The goal of the article to set up an easily installable R package on Github for others to use via remotes::install_github().
    • The main tools required are RStudio along with the packages roxygen2 and usethis.
    • How to take care of Bioconductor and Github Dependencies
    • How to publish your package to Github
  • Building a Corporate R Package for Pleasure and Profit
  • Toy example*
    1. In RStudio, click "File" - "New Project" - "New Directory" - "R package". Package name = "a1" (this naming will guarantee this package will be shown on top of all R packages in RStudio). Press 'Create Project'. This new project folder will have necessary files for an R package including DESCRIPTION, NAMESPACE, R/hello.R, man/hello.Rd. The "hello.R" file will be opened in RStudio automatically.
    2. Create a new R file under a1/R folder. An example of this R file <add.R> containing roxygen comments can be found under here. Pressing Ctrl/Cmd + Shift + D (or running devtools::document()) will generate a man/add.Rd.
    3. In R, type usethis::use_vignette("my-vignette") to create a new vignette. The new vignette "my-vignette.Rmd" will be saved under "a1/vignettes" subfolder. We can modify the Rmd file as we need.
    4. In RStudio, click "Build" - "Build Source Package". You will see some messages on the "Build" tab of the top-right panel. Eventually, a tarball "/home/$USERNAME/a1_0.1.0.tar.gz" is created (I create the project under /home/$USERNAME directory).
    5. We can install the package in R install.packages("~/a1_0.1.0.tar.gz", repos= NULL, type = "source") if we have already created the tarball. Another method is to use RStudio "Build" - "Install and Restart" (Ctrl + Shift + B).
    6. In RStudio, type help(package = "a1") or click "Packages" tab on the bottom-right panel and click "a1" package. It will show a line "User guides, package vignettes and other documentation". The vignette we just created will be available in HTML, source and R code format.

Using RStudio.cloud

There is a problem. We can use devtools::create("/cloud/project") to create a new package. When it builds the source package, the package file will be located in the root directory of the package. However, if we use a local RStudio to create a source package, the source package will be located in the upper directory.

Binary packages

  • No .R files in the R/ directory. There are 3 files that store the parsed functions in an efficient file format. This is the result of loading all the R code and then saving the functions with save().
  • A Meta/ directory contains a number of Rds files. These files contain cached metadata about the package, like what topics the help files cover and parsed version of the DESCRIPTION file.
  • An html/ directory.
  • libs/ directory if you have any code in the src/' directory
  • The contents of inst/ are moved to the top-level directory.

Building the tarball

  • No matter we uses devtools::build() or the terminal R CMD build MyPkg it will uses run the R code in vignette. Be cautious on the extra time and storage the process incurred.
  • If 'cache = TRUE' is used in vignettes, it will create a new subfolder called MyuPkg_cache under the vignettes folder. This takes a lot of space (eg 1GB in some case).

Building the binary

R CMD INSTALL --build MyPkg.tar.gz
# OR
R CMD INSTALL --build Full_Path_Of_MyPkg 

The binary (on Windows) can be installed by install.packages("Mypkg.zip",repos=NULL)

If the installation is successful, it will overwrite any existing installation of the same package. To prevent changes to the present working installation or to provide an install location with write access, create a suitably located directory with write access and use the -l option to build the package in the chosen location.

R CMD INSTALL -l location --build pkg

R folder

See an example from DuoClustering2018.

#' @importFrom utils read.csv
.onLoad <- function(libname, pkgname) {
  fl <- system.file("extdata", "metadata.csv", package = "DuoClustering2018")
  titles <- utils::read.csv(fl, stringsAsFactors = FALSE)$Title
  ExperimentHub::createHubAccessors(pkgname, titles)

Note that the environment of a function from the DuoClustering2018 package is not the package name.

<environment: 0x7fe01dbe7dd0>

Q: where is the definition of DuoClustering2018::clustering_summary_filteredExpr10_TrapnellTCC_v2()? A: createHubAccessors() - Creating A Hub Package: ExperimentHub or AnnotationHub.



Three ways to include data in your package.

  • If you want to store binary data and make it available to the user, put it in data/. This is the best place to put example datasets.
  • If you want to store parsed data, but not make it available to the user, put it in R/sysdata.rda. This is the best place to put data that your functions need.
  • If you want to store raw data, put it in inst/extdata. See External data. An example from tximportData package.
    # grep -r extdata /home/brb/R/x86_64-pc-linux-gnu-library/4.0
    logo_file <- system.file("extdata", "logo.png", package = "cowplot")
    orgDBLoc = system.file("extdata", "org.Hs.eg.sqlite", package="org.Hs.eg.db")
    # grep -r readRDS /home/brb/R/x86_64-pc-linux-gnu-library/4.0
    patient.data  <- readRDS("assets/coxnet.RDS")

How to distribute data with your R package

Rd file

Using Mathjax in Rd Files


Long execution for R code in vignette


Why and how maintain a NEWS file for your R package?

README.Rmd & README.md files

See Releasing a package from R packages by Hadley Wickham.

How to convert .Rmd into .md in R studio?

Example: ggplot2 repository at Github

It seems RStudio cannot create TOC for *.md files. glmnet package creates TOC of its vignette by itself. Visual Studio Code has an extension to do that.


badger: Badge for R Package

tests folder and testthat package


Non-standard files/directories, Rbuildignore and inst

URL checker

What is a library?

A library is simply a directory containing installed packages.

You can use .libPaths() to see which libraries are currently active.


lapply(.libPaths(), dir)

Object names

  • Variable and function names should be lower case.
  • Use an underscore (_) to separate words within a name (reserve . for S3 methods).
  • Camel case is a legitimate alternative, but be consistent! For example, preProcess(), twoClassData, createDataPartition(), trainingRows, trainPredictors, testPredictors, trainClasses, testClasses have been used in Applied Predictive Modeling by Kuhn & Johnson.
  • Generally, variable names should be nouns and function names should be verb.


  • Add a space around the operators +, -, \ and *.
  • Include a space around the assignment operators, <- and =.
  • Add a space around any comparison operators such as == and <.


  • Use two spaces to indent code.
  • Never mix tabs and spaces.
  • RStudio can automatically convert the tab character to spaces (see Tools -> Global options -> Code).


formatR and lintr package

Use formatR package to clean up poorly formatted code


Another way is to use the lintr package (lint).



Thirteen Simple Steps for Creating An R Package with an External C++ Library

C library

Using R — Packaging a C library in 15 minutes

Data package

Chapter 12 Create a data package from rstudio4edu

Minimal R package for submission

https://stat.ethz.ch/pipermail/r-devel/2013-August/067257.html and CRAN Repository Policy.

Create R Windows Binary on non-Windows OS

r-hub/rhub package: the R package builder service

https://github.com/r-hub/proposal, R-hub v2 2024/4.

# Today 1/1/2020
$ git clone https://github.com/arraytools/rtoy.git
$ rm -rf rtoy/.git
$ rm rtoy/.gitignore rtoy/_config.yml
$ R
> install.packages("devtools", repos = "https://cran.rstudio.com")
> install.packages("rhub")
> devtools::install_github("r-hub/sysreqs") #needed before calling local_check_linux()

> pkg_path <- "~/Downloads/rtoy"
> chk <- local_check_linux(pkg_path, image = "rhub/debian-gcc-release")

─  Building package

Container name: 6dca434d-84f9-42e2-ab83-b8c364594476-2
It will _not_ be removed after the check.

R-hub Linux builder script v0.10.0 (c) R Consortium, 2018-2019

Package: /tmp/RtmpviOCT1/file470972a3c4ab/rtoy_0.1.0.tar.gz
Docker image: rhub/debian-gcc-release
Env vars:
R CMD check arguments:
Unable to find image 'rhub/debian-gcc-release:latest' locally
latest: Pulling from rhub/debian-gcc-release
Digest: sha256:a9e01ca57bfd44f20eb6719f0bfecdd8cf0f59610984342598a53f11555b515d
Status: Downloaded newer image for rhub/debian-gcc-release:latest
Sysreqs platform: linux-x86_64-debian-gcc
No system requirements

>>>>>==================== Installing system requirements

>>>>>==================== Starting Docker container
ls: cannot access '/opt/R-*': No such file or directory
> source('https://bioconductor.org/biocLite.R')
Error: With R version 3.5 or greater, install Bioconductor packages using BiocManager; see https://bioconductor.org/install
Execution halted
Error in run(bash, c(file.path(wd, "rhub-linux.sh"), args), echo = TRUE,  :
  System command error

Moreover, it create a new Docker image and a new Docker container. We need to manually clean them:-(

Running a check on its own (remote) server works

> check_on_linux("rtoy") # will run remotely. 
     # We need to verify the email and enter a token.
     # We will get a report and a full build log.
     # The report includes both the linux command and the log from the server.

Now I go back to the original method.

$ R
> install.packages("tinytex")
> tinytex::install_tinytex()
> q()
$ exit

$ sudo apt-get install texinfo
$ R CMD build rtoy
$ R CMD check rtoy_0.1.0.tar.gz

# Install pandoc
$ wget https://github.com/jgm/pandoc/releases/download/2.9.1/pandoc-2.9.1-1-amd64.deb
$ sudo dpkg -i pandoc-2.9.1-1-amd64.deb
$ R CMD check --as-cran rtoy_0.1.0.tar.gz
# Ignore a 'Note' https://stackoverflow.com/a/23831508


Run R CMD check from R and Capture Results

The rcmdcheck package was used by Github Actions for R language from r-lib/R infrastructure.

CRAN check API

Continuous Integration

Travis-CI (Linux, Mac)

Continuous Integration: Appveyor (Windows)

Github Actions


Submit packages to cran


Everything you should know about WinBuilder

Other tips/advice



For example pnorm5() was used by survS.h by survival package (old version) .

Packages includes Fortran

On mac, gfortran (6.1) can be downloaded from CRAN. It will be installed onto /usr/local/gfortran. Note that the binary will not be present in PATH. So we need to run the following command to make gfortran avaiialbe.

sudo ln -s /usr/local/gfortran/bin/gfortran /usr/local/bin/gfortran

A useful tool to find R packages containing Fortran code is pkgsearch package. Note

  • The result is not a comprehensive list of packages containing Fortran code.
  • It seems the result is the same as I got from https://www.r-pkg.org
> pkg_search("Fortran")
- "Fortran" ------------------------------------ 69 packages in 0.009 seconds -
  #     package      version by                    @ title
  1 100 covr         3.3.2   Jim Hester          12d Test Coverage for Packages
  2  91 inline       0.3.15  Dirk Eddelbuettel    1y Functions to Inline C, ...
  3  43 randomForest 4.6.14  Andy Liaw            2y Breiman and Cutler's Ra...
  4  39 deSolve      1.24    Thomas Petzoldt      4M Solvers for Initial Val...
  5  27 mnormt       1.5.5   Adelchi Azzalini     3y The Multivariate Normal...
  6  26 minqa        1.2.4   Katharine M. Mullen  5y Derivative-free optimiz...
  7  24 rgcvpack     0.1.4   Xianhong Xie         6y R Interface for GCVPACK...
  8  22 leaps        3.0     Thomas Lumley        3y Regression Subset Selec...
  9  21 akima        0.6.2   Albrecht Gebhardt    3y Interpolation of Irregu...
 10  20 rootSolve    1.7     Karline Soetaert     3y Nonlinear Root Finding,...

> more()
- "Fortran" ------------------------------------ 69 packages in 0.009 seconds -
  #    package    version   by                    @ title
 11 15 BB         2019.10.1 Paul Gilbert        11d Solving and Optimizing L...
 12 15 limSolve   Karline Soetaert     2y Solving Linear Inverse M...
 13 14 insideRODE 2.0       YUZHUO PAN           7y insideRODE includes buil...
 14 13 earth      5.1.1     Stephen Milborrow    7M Multivariate Adaptive Re...
 15 13 cluster    2.1.0     Martin Maechler      4M "Finding Groups in Data"...
 16 13 spam   ORPHANED            11h SPArse Matrix
 17 12 diptest    0.75.7    Martin Maechler      4y Hartigan's Dip Test Stat...
 18 10 pbivnorm   0.6.0     Brenton Kenkel       5y Vectorized Bivariate Nor...
 19  7 optmatch   0.9.12    Mark M. Fredrickson 17d Functions for Optimal Ma...
 20  7 lsei       1.2.0     Yong Wang            2y Solving Least Squares or...

Another way is to run rsync to download src/contrib directory from CRAN and then use grep to find these packages. Note: the source packages takes about 8GB space (2019-10-28).

mkdir ~/Downloads/cran
rsync -avz --delete cran.r-project.org::CRAN/src/contrib/*.tar.gz ~/Downloads/cran/
rsync -avz --delete cran.r-project.org::CRAN/src/contrib/PACKAGES ~/Downloads/cran/
rsync -avz --delete cran.r-project.org::CRAN/src/contrib/PACKAGES.gz ~/Downloads/cran/
cd ~/Downloads/cran
find . -xtype l -delete # remove broken symbolic links

touch tmp
for f in *.gz;
  tar -tzvf $f | grep -E "(\.f|\.f90|\.f95)$"  |& tee -a tmp

to check if the tarball contains Fortran 77/90 code.

Now to see all packages names we can process it in R

x <- read.table("~/Downloads/tmp", stringsAsFactors = F)
strsplit(x$V6, "/") %>% sapply(function(x) x[1]) %>% unique() # 415 packages
strsplit(x$V6, "/") %>% sapply(function(x) x[1]) %>% table() %>% sort() # how many Fortran files in each package

str_subset(x$V6, "\\.f90$") %>% strsplit("/") %>% sapply(function(x) x[1]) %>% unique() # Fortran 90 only packages
str_subset(x$V6, "\\.f95$") %>% strsplit("/") %>% sapply(function(x) x[1]) %>% unique() # Fortran 95 only packages

str_subset(x$V6, "BayesFM") # f95 


Datasets in R packages


Turn your analysis into a package

Organise your own analysis

Build R package faster using multicore


The idea is edit the /lib64/R/etc/Renviron file (where /lib64/R/etc/ is the result to a call to the R.home() function in R) and set:

MAKE='make -j 8' # submit 8 jobs at once

Then build R package as regular, for example,

$ time R CMD INSTALL ~/R/stringi --preclean --configure-args='--disable-pkg-config'

suppressPackageStartupMessages() and .onAttach()

KernSmooth package example.

It is Time for CRAN to Ban Package Ads


fusen package

Identifying R Functions & Packages Used in GitHub Repos

Identifying R Functions & Packages Used in GitHub Repos: funspotr package




Successful R-based Test Package Submitted to FDA