r/README.Rmd

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# arrow

[![cran](https://www.r-pkg.org/badges/version-last-release/arrow)](https://cran.r-project.org/package=arrow) [![conda-forge](https://img.shields.io/conda/vn/conda-forge/r-arrow.svg)](https://anaconda.org/conda-forge/r-arrow) [![Nightly macOS Build Status](https://travis-ci.org/ursa-labs/arrow-r-nightly.png?branch=master)](https://travis-ci.org/ursa-labs/arrow-r-nightly) [![Nightly Windows Build Status](https://ci.appveyor.com/api/projects/status/ume8udm5r26u2c9l/branch/master?svg=true)](https://ci.appveyor.com/project/nealrichardson/arrow-r-nightly-yxl55/branch/master) [![codecov](https://codecov.io/gh/ursa-labs/arrow-r-nightly/branch/master/graph/badge.svg)](https://codecov.io/gh/ursa-labs/arrow-r-nightly)

[Apache Arrow](https://arrow.apache.org/) is a cross-language development platform for in-memory data. It specifies a standardized language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware. It also provides computational libraries and zero-copy streaming messaging and interprocess communication.

The `arrow` package exposes an interface to the Arrow C++ library to access many of its features in R. This includes support for analyzing large, multi-file datasets (`open_dataset()`), working with individual Parquet (`read_parquet()`, `write_parquet()`) and Feather (`read_feather()`, `write_feather()`) files, as well as lower-level access to Arrow memory and messages.

## Installation

Install the latest release of `arrow` from CRAN with

```r
install.packages("arrow")
```

Conda users on Linux and macOS can install `arrow` from conda-forge with

```
conda install -c conda-forge r-arrow
```

Installing a released version of the `arrow` package should require no
additional system dependencies.
For macOS and Windows, CRAN hosts binary packages that contain the
Arrow C++ library. On Linux, source package installation
will download necessary C++ dependencies automatically on most
common Linux distributions. See `vignette("install", package = "arrow")`
for details.

If you install the `arrow` package from source and the C++ library is not found,
the R package functions will notify you that Arrow is not available. Call

```r
arrow::install_arrow()
```

for version- and platform-specific guidance on installing the Arrow C++ library.

## Example

```{r}
library(arrow, warn.conflicts = FALSE)
set.seed(24)

tab <- Table$create(
  x = 1:10,
  y = rnorm(10),
  z = as.factor(rep(c("b", "c"), 5))
)
tab
tab$x
as.data.frame(tab)
```

## Installing a development version

Binary R packages for macOS and Windows are built daily and hosted at https://dl.bintray.com/ursalabs/arrow-r/. To install from there:

```r
install.packages("arrow", repos = "https://dl.bintray.com/ursalabs/arrow-r")
```

These daily package builds are not official Apache releases and are not recommended for production use. They may be useful for testing bug fixes and new features under active development.

## Developing

Windows and macOS users who wish to contribute to the R package and don't need to alter the Arrow C++ library may be able to obtain a recent version of the library without building from source. On macOS, you may install the C++ library using [Homebrew](https://brew.sh/):

```shell
# For the released version:
brew install apache-arrow
# Or for a development version, you can try:
brew install apache-arrow --HEAD
```

On Windows, you can download a .zip file with the arrow dependencies from the [rwinlib](https://github.com/rwinlib/arrow/releases) project, and then set the `RWINLIB_LOCAL` environment variable to point to that zip file before installing the `arrow` R package. That project contains released versions of the C++ library; for a development version, Windows users may be able to find a binary by going to the [Apache Arrow project's Appveyor](https://ci.appveyor.com/project/ApacheSoftwareFoundation/arrow), selecting an R job from a recent build, and downloading the `build\arrow-*.zip` file from the "Artifacts" tab.

If you need to alter both the Arrow C++ library and the R package code, or if
you can't get a binary version of the latest C++ library elsewhere, you'll need
to build it from source too.

First, install the C++ library. See the [developer
guide](https://arrow.apache.org/docs/developers/index.html) for details.

Note that after any change to the C++ library, you must reinstall it and run
`make clean` or `git clean -fdx .` to remove any cached object code in the `r/src/`
directory before reinstalling the R package. This is only necessary if you make changes to the C++ library source; you do not need to manually purge object files if you are only editing R or Rcpp code inside `r/`.

Once you've built the C++ library, you can install the R package and its
dependencies, along with additional dev dependencies, from the git checkout:

```shell
cd ../../r
R -e 'install.packages(c("devtools", "roxygen2", "pkgdown", "covr")); devtools::install_dev_deps()'
R CMD INSTALL .
```

If you need to set any compilation flags while building the Rcpp extensions,
you can use the `ARROW_R_CXXFLAGS` environment variable. For example, if you
are using `perf` to profile the R extensions, you may need to set

```shell
export ARROW_R_CXXFLAGS=-fno-omit-frame-pointer
```

If the package fails to install/load with an error like this:

```
** testing if installed package can be loaded from temporary location
Error: package or namespace load failed for 'arrow' in dyn.load(file, DLLpath = DLLpath, ...):
unable to load shared object '/Users/you/R/00LOCK-r/00new/arrow/libs/arrow.so':
dlopen(/Users/you/R/00LOCK-r/00new/arrow/libs/arrow.so, 6): Library not loaded: @rpath/libarrow.14.dylib
```

try setting the environment variable `R_LD_LIBRARY_PATH` to wherever Arrow C++
was put in `make install`, e.g. `export R_LD_LIBRARY_PATH=/usr/local/lib`, and
retry installing the R package.

When installing from source, if the R and C++ library versions do not match,
installation may fail. If you've previously installed the libraries
and want to upgrade the R package, you'll need to update the Arrow C++ library first.

For any other build/configuration challenges, see the [C++ developer
guide](https://arrow.apache.org/docs/developers/cpp.html#building)
and `vignette("install", package = "arrow")`.

### Editing Rcpp code

The `arrow` package uses some customized tools on top of `Rcpp` to prepare its
C++ code in `src/`. If you change C++ code in the R package, you will need to
set the `ARROW_R_DEV` environment variable to `TRUE` (optionally, add it to
your`~/.Renviron` file to persist across sessions) so that the
`data-raw/codegen.R` file is used for code generation.

The codegen.R script has these additional dependencies:

```r
remotes::install_github("romainfrancois/decor")
install.packages("glue")
```

We use Google C++ style in our C++ code. Check for style errors with

```
./lint.sh
```

Fix any style issues before committing with

```
./lint.sh --fix
```

The lint script requires Python 3 and `clang-format-7`. If the command isn't
found, you can explicitly provide the path to it like
`CLANG_FORMAT=$(which clang-format-7) ./lint.sh`. On macOS,
you can get this by installing LLVM via Homebrew and running the script as
`CLANG_FORMAT=$(brew --prefix llvm@7)/bin/clang-format ./lint.sh`

### Useful functions

Within an R session, these can help with package development:

```r
devtools::load_all() # Load the dev package
devtools::test(filter="^regexp$") # Run the test suite, optionally filtering file names
devtools::document() # Update roxygen documentation
rmarkdown::render("README.Rmd") # To rebuild README.md
pkgdown::build_site() # To preview the documentation website
devtools::check() # All package checks; see also below
covr::package_coverage() # See test coverage statistics
```

Any of those can be run from the command line by wrapping them in `R -e
'$COMMAND'`. There's also a `Makefile` to help with some common tasks from the
command line (`make test`, `make doc`, `make clean`, etc.)

### Full package validation

```shell
R CMD build .
R CMD check arrow_*.tar.gz --as-cran
```