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# arrow 1.0.1

## Bug fixes

* Filtering a Dataset that has multiple partition keys using an `%in%` expression now faithfully returns all relevant rows
* Datasets can now have path segments in the root directory that start with `.` or `_`; files and subdirectories starting with those prefixes are still ignored
* `open_dataset("~/path")` now correctly expands the path
* The `version` option to `write_parquet()` is now correctly implemented
* An UBSAN failure in the `parquet-cpp` library has been fixed
* For bundled Linux builds, the logic for finding `cmake` is more robust, and you can now specify a `/path/to/cmake` by setting the `CMAKE` environment variable

# arrow 1.0.0

## 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

* 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

## 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](https://github.com/wesm/feather) 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

* `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

## 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

* `Table`s and `RecordBatch`es 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

* This patch release includes bugfixes in the C++ library around dictionary types and Parquet reading.

# arrow 0.15.0

## 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](https://issues.apache.org/jira/browse/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.frame`s. Extract columns, subset, and so on. See `?Table` and `?RecordBatch` for examples.
* Initial implementation of bindings for the C++ File System API. ([ARROW-6348](https://issues.apache.org/jira/browse/ARROW-6348))
* Compressed streams are now supported on Windows ([ARROW-6360](https://issues.apache.org/jira/browse/ARROW-6360)), and you can also specify a compression level ([ARROW-6533](https://issues.apache.org/jira/browse/ARROW-6533))

## Other upgrades

* 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](https://issues.apache.org/jira/browse/ARROW-6278))
* File readers now properly handle paths that need expanding, such as `~/file.parquet` ([ARROW-6323](https://issues.apache.org/jira/browse/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](https://issues.apache.org/jira/browse/ARROW-6338), [ARROW-6364](https://issues.apache.org/jira/browse/ARROW-6364))


# arrow 0.14.1

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.
