# arrow 1.0.0 2020-07-25

## Arrow format conversion

• vignette("arrow", package = "arrow") includes tables that explain how R types are converted to Arrow types and vice versa.
• Support added for converting to/from more Arrow types: uint64, binary, fixed_size_binary, large_binary, large_utf8, large_list, list of structs.
• character vectors that exceed 2GB are converted to Arrow large_utf8 type
• POSIXlt objects can now be converted to Arrow (struct)
• R attributes() are preserved in Arrow metadata when converting to Arrow RecordBatch and table and are restored when converting from Arrow. This means that custom subclasses, such as haven::labelled, are preserved in round trip through Arrow.
• Schema metadata is now exposed as a named list, and it can be modified by assignment like batch$metadata$new_key <- "new value"
• Arrow types int64, uint32, and uint64 now are converted to R integer if all values fit in bounds
• Arrow date32 is now converted to R Date with double underlying storage. Even though the data values themselves are integers, this provides more strict round-trip fidelity
• When converting to R factor, dictionary ChunkedArrays that do not have identical dictionaries are properly unified
• In the 1.0 release, the Arrow IPC metadata version is increased from V4 to V5. By default, RecordBatch{File,Stream}Writer will write V5, but you can specify an alternate metadata_version. For convenience, if you know the consumer you’re writing to cannot read V5, you can set the environment variable ARROW_PRE_1_0_METADATA_VERSION=1 to write V4 without changing any other code.

## Datasets

• CSV and other text-delimited datasets are now supported
• With a custom C++ build, it is possible to read datasets directly on S3 by passing a URL like ds <- open_dataset("s3://..."). Note that this currently requires a special C++ library build with additional dependencies–this is not yet available in CRAN releases or in nightly packages.
• When reading individual CSV and JSON files, compression is automatically detected from the file extension

## Other enhancements

• Initial support for C++ aggregation methods: sum() and mean() are implemented for Array and ChunkedArray
• Tables and RecordBatches have additional data.frame-like methods, including dimnames() and as.list()
• Tables and ChunkedArrays can now be moved to/from Python via reticulate

## Bug fixes and deprecations

• Non-UTF-8 strings (common on Windows) are correctly coerced to UTF-8 when passing to Arrow memory and appropriately re-localized when converting to R
• The coerce_timestamps option to write_parquet() is now correctly implemented.
• Creating a Dictionary array respects the type definition if provided by the user
• read_arrow and write_arrow are now deprecated; use the read/write_feather() and read/write_ipc_stream() functions depending on whether you’re working with the Arrow IPC file or stream format, respectively.
• Previously deprecated FileStats, read_record_batch, and read_table have been removed.

## Installation and packaging

• For improved performance in memory allocation, macOS and Linux binaries now have jemalloc included, and Windows packages use mimalloc
• Linux installation: some tweaks to OS detection for binaries, some updates to known installation issues in the vignette
• The bundled libarrow is built with the same CC and CXX values that R uses
• Failure to build the bundled libarrow yields a clear message
• Various streamlining efforts to reduce library size and compile time

# arrow 0.17.1 2020-05-19

• Updates for compatibility with dplyr 1.0
• reticulate::r_to_py() conversion now correctly works automatically, without having to call the method yourself
• Assorted bug fixes in the C++ library around Parquet reading

# arrow 0.17.0 2020-04-21

## Feather v2

This release includes support for version 2 of the Feather file format. Feather v2 features full support for all Arrow data types, fixes the 2GB per-column limitation for large amounts of string data, and it allows files to be compressed using either lz4 or zstd. write_feather() can write either version 2 or version 1 Feather files, and read_feather() automatically detects which file version it is reading.

Related to this change, several functions around reading and writing data have been reworked. read_ipc_stream() and write_ipc_stream() have been added to facilitate writing data to the Arrow IPC stream format, which is slightly different from the IPC file format (Feather v2 is the IPC file format).

Behavior has been standardized: all read_<format>() return an R data.frame (default) or a Table if the argument as_data_frame = FALSE; all write_<format>() functions return the data object, invisibly. To facilitate some workflows, a special write_to_raw() function is added to wrap write_ipc_stream() and return the raw vector containing the buffer that was written.

To achieve this standardization, read_table(), read_record_batch(), read_arrow(), and write_arrow() have been deprecated.

## Python interoperability

The 0.17 Apache Arrow release includes a C data interface that allows exchanging Arrow data in-process at the C level without copying and without libraries having a build or runtime dependency on each other. This enables us to use reticulate to share data between R and Python (pyarrow) efficiently.

See vignette("python", package = "arrow") for details.

## Datasets

• Dataset reading benefits from many speedups and fixes in the C++ library
• Datasets have a dim() method, which sums rows across all files (#6635, @boshek)
• Combine multiple datasets into a single queryable UnionDataset with the c() method
• Dataset filtering now treats NA as FALSE, consistent with dplyr::filter()
• Dataset filtering is now correctly supported for all Arrow date/time/timestamp column types
• vignette("dataset", package = "arrow") now has correct, executable code

## Installation

• Installation on Linux now builds C++ the library from source by default, with some compression libraries disabled. For a faster, richer build, set the environment variable NOT_CRAN=true. See vignette("install", package = "arrow") for details and more options.
• Source installation is faster and more reliable on more Linux distributions.

## Other bug fixes and enhancements

• unify_schemas() to create a Schema containing the union of fields in multiple schemas
• Timezones are faithfully preserved in roundtrip between R and Arrow
• read_feather() and other reader functions close any file connections they open
• Arrow R6 objects no longer have namespace collisions when the R.oo package is also loaded
• FileStats is renamed to FileInfo, and the original spelling has been deprecated

# arrow 0.16.0.2 2020-02-14

• install_arrow() now installs the latest release of arrow, including Linux dependencies, either for CRAN releases or for development builds (if nightly = TRUE)
• Package installation on Linux no longer downloads C++ dependencies unless the LIBARROW_DOWNLOAD or NOT_CRAN environment variable is set
• write_feather(), write_arrow() and write_parquet() now return their input, similar to the write_* functions in the readr package (#6387, @boshek)
• Can now infer the type of an R list and create a ListArray when all list elements are the same type (#6275, @michaelchirico)

# arrow 0.16.0 2020-02-09

## Multi-file datasets

This release includes a dplyr interface to Arrow Datasets, which let you work efficiently with large, multi-file datasets as a single entity. Explore a directory of data files with open_dataset() and then use dplyr methods to select(), filter(), etc. Work will be done where possible in Arrow memory. When necessary, data is pulled into R for further computation. dplyr methods are conditionally loaded if you have dplyr available; it is not a hard dependency.

See vignette("dataset", package = "arrow") for details.

## Linux installation

A source package installation (as from CRAN) will now handle its C++ dependencies automatically. For common Linux distributions and versions, installation will retrieve a prebuilt static C++ library for inclusion in the package; where this binary is not available, the package executes a bundled script that should build the Arrow C++ library with no system dependencies beyond what R requires.

See vignette("install", package = "arrow") for details.

## Data exploration

• Tables and RecordBatches also have dplyr methods.
• For exploration without dplyr, [ methods for Tables, RecordBatches, Arrays, and ChunkedArrays now support natural row extraction operations. These use the C++ Filter, Slice, and Take methods for efficient access, depending on the type of selection vector.
• An experimental, lazily evaluated array_expression class has also been added, enabling among other things the ability to filter a Table with some function of Arrays, such as arrow_table[arrow_table$var1 > 5, ] without having to pull everything into R first. ## Compression • write_parquet() now supports compression • codec_is_available() returns TRUE or FALSE whether the Arrow C++ library was built with support for a given compression library (e.g. gzip, lz4, snappy) • Windows builds now include support for zstd and lz4 compression (#5814, @gnguy) ## Other fixes and improvements • Arrow null type is now supported • Factor types are now preserved in round trip through Parquet format (#6135, @yutannihilation) • Reading an Arrow dictionary type coerces dictionary values to character (as R factor levels are required to be) instead of raising an error • Many improvements to Parquet function documentation (@karldw, @khughitt) # arrow 0.15.1 2019-11-04 • This patch release includes bugfixes in the C++ library around dictionary types and Parquet reading. # arrow 0.15.0 2019-10-07 ## Breaking changes • The R6 classes that wrap the C++ classes are now documented and exported and have been renamed to be more R-friendly. Users of the high-level R interface in this package are not affected. Those who want to interact with the Arrow C++ API more directly should work with these objects and methods. As part of this change, many functions that instantiated these R6 objects have been removed in favor of Class$create() methods. Notably, arrow::array() and arrow::table() have been removed in favor of Array$create() and Table$create(), eliminating the package startup message about masking base functions. For more information, see the new vignette("arrow").
• Due to a subtle change in the Arrow message format, data written by the 0.15 version libraries may not be readable by older versions. If you need to send data to a process that uses an older version of Arrow (for example, an Apache Spark server that hasn’t yet updated to Arrow 0.15), you can set the environment variable ARROW_PRE_0_15_IPC_FORMAT=1.
• The as_tibble argument in the read_*() functions has been renamed to as_data_frame (ARROW-6337, @jameslamb)
• The arrow::Column class has been removed, as it was removed from the C++ library

## New features

• Table and RecordBatch objects have S3 methods that enable you to work with them more like data.frames. Extract columns, subset, and so on. See ?Table and ?RecordBatch for examples.
• Initial implementation of bindings for the C++ File System API. (ARROW-6348)
• Compressed streams are now supported on Windows (ARROW-6360), and you can also specify a compression level (ARROW-6533)

• Parquet file reading is much, much faster, thanks to improvements in the Arrow C++ library.
• read_csv_arrow() supports more parsing options, including col_names, na, quoted_na, and skip
• read_parquet() and read_feather() can ingest data from a raw vector (ARROW-6278)
• File readers now properly handle paths that need expanding, such as ~/file.parquet (ARROW-6323)
• Improved support for creating types in a schema: the types’ printed names (e.g. “double”) are guaranteed to be valid to use in instantiating a schema (e.g. double()), and time types can be created with human-friendly resolution strings (“ms”, “s”, etc.). (ARROW-6338, ARROW-6364)

# arrow 0.14.1 2019-08-05

Initial CRAN release of the arrow package. Key features include:

• Read and write support for various file formats, including Parquet, Feather/Arrow, CSV, and JSON.
• API bindings to the C++ library for Arrow data types and objects, as well as mapping between Arrow types and R data types.
• Tools for helping with C++ library configuration and installation.