## Installation

### 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. NOTE: If your R version is not smaller than 3.5.0, you should set DUSE_R35=ON in cmake options when build precompiled dll/lib.

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);
"

## 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.