Installation

Installing the CRAN package

As of this writing, LightGBM’s R package is not available on CRAN. However, start with LightGBM 3.0.0, you can install a released source distribution. This is the same type of package that you’d install from CRAN. It does not require CMake, Visual Studio, or anything else outside the CRAN toolchain.

To install this package on any operating system:

  1. Choose a release from the “Releases” page
  2. Look for the artifact with a name like lightgbm-{VERSION}-r-cran.tar.gz. Right-click it and choose “copy link address”.
  3. Copy that link into PKG_URL in the code below and run it.
PKG_URL <- "https://github.com/microsoft/LightGBM/releases/download/v3.0.0/lightgbm-3.0.0-r-cran.tar.gz"

remotes::install_url(PKG_URL)

Installing Precompiled Binaries

Starting with LightGBM 3.0.0, precompiled binaries for the R package are created for each release. These packages do not require compilation, so they will be faster and easier to install than packages that are built from source. These packages are created with R 4.0 and are not guaranteed to work with other R versions.

Binaries are available for Windows, Mac, and Linux systems. They are not guaranteed to work with all variants and versions of these operating systems. Please open an issue if you encounter any problems.

To install a binary for the R package:

  1. Choose a release from the “Releases” page.
  2. Choose a file based on your operating system. Right-click it and choose “copy link address”.
    • Linux: lightgbm-{VERSION}-r40-linux.tgz
    • Mac: lightgbm-{VERSION}-r40-macos.tgz
    • Windows: lightgbm-{VERSION}-r40-windows.zip
  3. Copy that link into PKG_URL in the code below and run it.

This sample code installs version 3.0.0-1 of the R package on Mac.

PKG_URL <- "https://github.com/microsoft/LightGBM/releases/download/v3.0.0rc1/lightgbm-3.0.0-1-r40-macos.tgz"

local_file <- paste0("lightgbm.", tools::file_ext(PKG_URL))

download.file(
    url = PKG_URL
    , destfile = local_file
)
install.packages(
    pkgs = local_file
    , type = "binary"
    , repos = NULL
)

Preparation

You need to install git and CMake first.

Note: 32-bit (i386) R/Rtools is currently not supported.

Windows Preparation

Installing a 64-bit version of Rtools is mandatory.

After installing Rtools and CMake, be sure the following paths are added to the environment variable PATH. These may have been automatically added when installing other software.

  • Rtools
    • If you have Rtools 3.x, example:
      • C:\Rtools\mingw_64\bin
    • If you have Rtools 4.0, example:
      • C:\rtools40\mingw64\bin
      • C:\rtools40\usr\bin
  • CMake
    • example: C:\Program Files\CMake\bin
  • R
    • example: C:\Program Files\R\R-3.6.1\bin

NOTE: Two Rtools paths are required from Rtools 4.0 onwards because paths and the list of included software was changed in Rtools 4.0.

Windows Toolchain Options

A “toolchain” refers to the collection of software used to build the library. The R package can be built with three different toolchains.

Warning for Windows users: it is recommended to use Visual Studio for its better multi-threading efficiency in Windows for many core systems. For very simple systems (dual core computers or worse), MinGW64 is recommended for maximum performance. If you do not know what to choose, it is recommended to use Visual Studio, the default compiler. Do not try using MinGW in Windows on many core systems. It may result in 10x slower results than Visual Studio.

Visual Studio (default)

By default, the package will be built with Visual Studio Build Tools.

MinGW (R 3.x)

If you are using R 3.x and installation fails with Visual Studio, LightGBM will fall back to using MinGW bundled with Rtools.

If you want to force LightGBM to use MinGW (for any R version), open R-package/src/install.libs.R and change use_mingw:

use_mingw <- TRUE

MSYS2 (R 4.x)

If you are using R 4.x and installation fails with Visual Studio, LightGBM will fall back to using MSYS2. This should work with the tools already bundled in Rtools 4.0.

If you want to force LightGBM to use MSYS2 (for any R version), open R-package/src/install.libs.R and change use_msys2:

use_msys2 <- TRUE

Mac OS Preparation

You can perform installation either with Apple Clang or gcc. In case you prefer Apple Clang, you should install OpenMP (details for installation can be found in Installation Guide) first and CMake version 3.16 or higher is required. In case you prefer gcc, you need to install it (details for installation can be found in Installation Guide) and set some environment variables to tell R to use gcc and g++. If you install these from Homebrew, your versions of g++ and gcc are most likely in /usr/local/bin, as shown below.

# replace 8 with version of gcc installed on your machine
export CXX=/usr/local/bin/g++-8 CC=/usr/local/bin/gcc-8

Install

Build and install R-package with the following commands:

git clone --recursive https://github.com/microsoft/LightGBM
cd LightGBM
Rscript build_r.R

The build_r.R script builds the package in a temporary directory called lightgbm_r. It will destroy and recreate that directory each time you run the script.

Note: for the build with Visual Studio/VS Build Tools in Windows, you should use the Windows CMD or Powershell.

Windows users may need to run with administrator rights (either R or the command prompt, depending on the way you are installing this package). Linux users might require the appropriate user write permissions for packages.

Set use_gpu to TRUE in R-package/src/install.libs.R to enable the build with GPU support. You will need to install Boost and OpenCL first: details for installation can be found in Installation-Guide.

If you are using a precompiled dll/lib locally, you can move the dll/lib into LightGBM root folder, modify LightGBM/R-package/src/install.libs.R’s 2nd line (change use_precompile <- FALSE to use_precompile <- TRUE), and install R-package as usual.

When your package installation is done, you can check quickly if your LightGBM R-package is working by running the following:

library(lightgbm)
data(agaricus.train, package='lightgbm')
train <- agaricus.train
dtrain <- lgb.Dataset(train$data, label=train$label)
params <- list(objective="regression", metric="l2")
model <- lgb.cv(params, dtrain, 10, nfold=5, min_data=1, learning_rate=1, early_stopping_rounds=10)

Testing

The R package’s unit tests are run automatically on every commit, via integrations like Travis CI and Azure DevOps. Adding new tests in R-package/tests/testthat is a valuable way to improve the reliability of the R package.

When adding tests, you may want to use test coverage to identify untested areas and to check if the tests you’ve added are covering all branches of the intended code.

The example below shows how to generate code coverage for the R package on a macOS or Linux setup, using gcc-8 to compile LightGBM. To adjust for your environment, swap out the ‘Install’ step with the relevant code from the instructions above.

# Install
export CXX=/usr/local/bin/g++-8
export CC=/usr/local/bin/gcc-8
Rscript build_r.R --skip-install

# Get coverage
Rscript -e " \
    coverage  <- covr::package_coverage('./lightgbm_r', quiet=FALSE);
    print(coverage);
    covr::report(coverage, file = file.path(getwd(), 'coverage.html'), browse = TRUE);
    "

Preparing a CRAN Package and Installing It

This section is primarily for maintainers, but may help users and contributors to understand the structure of the R package.

Most of LightGBM uses CMake to handle tasks like setting compiler and linker flags, including header file locations, and linking to other libraries. Because CRAN packages typically do not assume the presence of CMake, the R package uses an alternative method that is in the CRAN-supported toolchain for building R packages with C++ code: Autoconf.

For more information on this approach, see “Writing R Extensions”.

Build a CRAN Package

From the root of the repository, run the following.

sh build-cran-package.sh

This will create a file lightgbm_${VERSION}.tar.gz, where VERSION is the version of LightGBM.

Standard Installation from CRAN Package

After building the package, install it with a command like the following:

R CMD install lightgbm_*.tar.gz

Custom Installation (Linux, Mac)

To change the compiler used when installing the package, you can create a file ~/.R/Makevars which overrides CC (C compiler) and CXX (C++ compiler). For example, to use gcc instead of clang on Mac, you could use something like the following:

# ~/.R/Makevars
CC=gcc-8
CXX=g++-8
CXX11=g++-8

Changing the CRAN Package

A lot of details are handled automatically by R CMD build and R CMD install, so it can be difficult to understand how the files in the R package are related to each other. An extensive treatment of those details is available in “Writing R Extensions”.

This section briefly explains the key files for building a CRAN package. To update the package, edit the files relevant to your change and re-run the steps in Build a CRAN Package.

Linux or Mac

At build time, configure will be run and used to create a file Makevars, using Makevars.in as a template.

  1. Edit configure.ac

  2. Create configure with autoconf. Do not edit it by hand. This file must be generated on Ubuntu 18.04.

    If you have an Ubuntu 18.04 environment available, run the provided script from the root of the LightGBM repository.

    ./R-package/recreate-configure.sh

    If you do not have easy access to an Ubuntu 18.04 environment, the configure script can be generated using Docker by running the code below from the root of this repo.

    docker run \
        -v $(pwd):/opt/LightGBM \
        -t ubuntu:18.04 \
        /bin/bash -c "cd /opt/LightGBM && ./R-package/recreate-configure.sh"

    The version of autoconf used by this project is stored in R-package/AUTOCONF_UBUNTU_VERSION. To update that version, update that file and run the commands above. To see available versions, see https://packages.ubuntu.com/search?keywords=autoconf.

  3. Edit src/Makevars.in

Configuring for Windows

At build time, configure.win will be run and used to create a file Makevars.win, using Makevars.win.in as a template.

  1. Edit configure.win directly
  2. Edit src/Makevars.win.in

Build Precompiled Binaries of the CRAN Package

This section is mainly for maintainers. As long as the R package is not available on CRAN (which will build precompiled binaries automatically) you may want to build precompiled versions of the R package manually, since these will be easier for users to install.

For more details, see “Writing R Extensions”.

Packages built like this will only work for the minor version of R used to build them. They may or may not work across different versions of operating systems.

Mac

Binary produced: lightgbm-${VERSION}-r40-macos.tgz.

LGB_VERSION="3.0.0-1"
sh build-cran-package.sh
R CMD INSTALL --build lightgbm_${LGB_VERSION}.tar.gz
mv \
    lightgbm_${LGB_VERSION}.tgz \
    lightgbm-${LGB_VERSION}-r40-macos.tgz

Linux

Binary produced: lightgbm-${VERSION}-r40-linux.tgz.

You can access a Linux system that has R and its build toolchain installed with the rocker Docker images.

R_VERSION=4.0.2

docker run \
    -v $(pwd):/opt/LightGBM \
    -it rocker/verse:${R_VERSION} \
        /bin/bash

From inside that container, the commands to create a precompiled binary are very similar.

cd /opt/LightGBM
LGB_VERSION="3.0.0-1"
sh build-cran-package.sh
R CMD INSTALL --build lightgbm_${LGB_VERSION}.tar.gz
mv \
    lightgbm_${LGB_VERSION}_R_*-linux-gnu.tar.gz \
    lightgbm-${LGB_VERSION}-r40-linux.tgz

Exit the container, and the binary package should still be there on the host system.

exit

Windows

Binary produced: lightgbm-${VERSION}-r40-windows.zip.

LGB_VERSION="3.0.0-1"
sh build-cran-package.sh
R CMD INSTALL --build lightgbm_${LGB_VERSION}.tar.gz
mv \
    lightgbm_${LGB_VERSION}.tgz \
    lightgbm-${LGB_VERSION}-r40-windows.zip

External (Unofficial) Repositories

Projects listed here are not maintained or endorsed by the LightGBM development team, but may offer some features currently missing from the main R package.

  • lightgbm.py: This R package offers a wrapper built with reticulate, a package used to call Python code from R. If you are comfortable with the added installation complexity of installing lightgbm’s Python package and the performance cost of passing data between R and Python, you might find that this package offers some features that are not yet available in the native lightgbm R package.

Known Issues

For information about known issues with the R package, see the R-package section of LightGBM’s main FAQ page.