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# arrow 6.0.1

There are now two ways to query Arrow data:

## 1. Expanded Arrow-native queries: aggregation and joins

`dplyr::summarize()`, both grouped and ungrouped, is now implemented for Arrow Datasets, Tables, and RecordBatches. Because data is scanned in chunks, you can aggregate over larger-than-memory datasets backed by many files. Supported aggregation functions include `n()`, `n_distinct()`, `min(),` `max()`, `sum()`, `mean()`, `var()`, `sd()`, `any()`, and `all()`. `median()` and `quantile()` with one probability are also supported and currently return approximate results using the t-digest algorithm.

Along with `summarize()`, you can also call `count()`, `tally()`, and `distinct()`, which effectively wrap `summarize()`.

This enhancement does change the behavior of `summarize()` and `collect()` in some cases: see "Breaking changes" below for details.

In addition to `summarize()`, mutating and filtering equality joins (`inner_join()`, `left_join()`, `right_join()`, `full_join()`, `semi_join()`, and `anti_join()`) with are also supported natively in Arrow.

Grouped aggregation and (especially) joins should be considered somewhat experimental in this release. We expect them to work, but they may not be well optimized for all workloads. To help us focus our efforts on improving them in the next release, please let us know if you encounter unexpected behavior or poor performance.

New non-aggregating compute functions include string functions like `str_to_title()` and `strftime()` as well as compute functions for extracting date parts (e.g. `year()`, `month()`) from dates. This is not a complete list of additional compute functions; for an exhaustive list of available compute functions see `list_compute_functions()`.

We've also worked to fill in support for all data types, such as `Decimal`, for functions added in previous releases. All type limitations mentioned in previous release notes should be no longer valid, and if you find a function that is not implemented for a certain data type, please [report an issue](https://issues.apache.org/jira/projects/ARROW/issues).

## 2. DuckDB integration

If you have the [duckdb package](https://CRAN.R-project.org/package=duckdb) installed, you can hand off an Arrow Dataset or query object to [DuckDB](https://duckdb.org/) for further querying using the `to_duckdb()` function. This allows you to use duckdb's `dbplyr` methods, as well as its SQL interface, to aggregate data. Filtering and column projection done before `to_duckdb()` is evaluated in Arrow, and duckdb can push down some predicates to Arrow as well. This handoff *does not* copy the data, instead it uses Arrow's C-interface (just like passing arrow data between R and Python). This means there is no serialization or data copying costs are incurred.

You can also take a duckdb `tbl` and call `to_arrow()` to stream data to Arrow's query engine. This means that in a single dplyr pipeline, you could start with an Arrow Dataset, evaluate some steps in DuckDB, then evaluate the rest in Arrow.

## Breaking changes

* Row order of data from a Dataset query is no longer deterministic. If you need a stable sort order, you should explicitly `arrange()` the query result. For calls to `summarize()`, you can set `options(arrow.summarise.sort = TRUE)` to match the current `dplyr` behavior of sorting on the grouping columns.
* `dplyr::summarize()` on an in-memory Arrow Table or RecordBatch no longer eagerly evaluates. Call `compute()` or `collect()` to evaluate the query.
* `head()` and `tail()` also no longer eagerly evaluate, both for in-memory data and for Datasets. Also, because row order is no longer deterministic, they will effectively give you a random slice of data from somewhere in the dataset unless you `arrange()` to specify sorting.
* Simple Feature (SF) columns no longer save all of their metadata when converting to Arrow tables (and thus when saving to Parquet or Feather). This also includes any dataframe column that has attributes on each element (in other words: row-level metadata). Our previous approach to saving this metadata is both (computationally) inefficient and unreliable with Arrow queries + datasets. This will most impact saving SF columns. For saving these columns we recommend either converting the columns to well-known binary representations (using `sf::st_as_binary(col)`) or using the [sfarrow package](https://CRAN.R-project.org/package=sfarrow) which handles some of the intricacies of this conversion process. We have plans to improve this and re-enable custom metadata like this in the future when we can implement the saving in a safe and efficient way. If you need to preserve the pre-6.0.0 behavior of saving this metadata, you can set `options(arrow.preserve_row_level_metadata = TRUE)`. We will be removing this option in a coming release. We strongly recommend avoiding using this workaround if possible since the results will not be supported in the future and can lead to surprising and inaccurate results. If you run into a custom class besides sf columns that are impacted by this please [report an issue](https://issues.apache.org/jira/projects/ARROW/issues).
* Datasets are officially no longer supported on 32-bit Windows on R < 4.0 (Rtools 3.5). 32-bit Windows users should upgrade to a newer version of R in order to use datasets.


## Installation on Linux

* Package installation now fails if the Arrow C++ library does not compile. In previous versions, if the C++ library failed to compile, you would get a successful R package installation that wouldn't do much useful.
* You can disable all optional C++ components when building from source by setting the environment variable `LIBARROW_MINIMAL=true`. This will have the core Arrow/Feather components but excludes Parquet, Datasets, compression libraries, and other optional features.
* Source packages now bundle the Arrow C++ source code, so it does not have to be downloaded in order to build the package. Because the source is included, it is now possible to build the package on an offline/airgapped system. By default, the offline build will be minimal because it cannot download third-party C++ dependencies required to support all features. To allow a fully featured offline build, the included `create_package_with_all_dependencies()` function (also available on GitHub without installing the arrow package) will download all third-party C++ dependencies and bundle them inside the R source package. Run this function on a system connected to the network to produce the "fat" source package, then copy that .tar.gz package to your offline machine and install. Special thanks to @karldw for the huge amount of work on this. 
* Source builds can make use of system dependencies (such as `libz`) by setting `ARROW_DEPENDENCY_SOURCE=AUTO`. This is not the default in this release (`BUNDLED`, i.e. download and build all dependencies) but may become the default in the future.
* The JSON library components (`read_json_arrow()`) are now optional and still on by default; set `ARROW_JSON=OFF` before building to disable them.

## Other enhancements and fixes

* More Arrow data types use ALTREP when converting to and from R. This speeds up some workflows significantly, while for others it merely delays conversion from Arrow to R. ALTREP is used by default, but to disable it, set `options(arrow.use_altrep = FALSE)`
* `Field` objects can now be created as non-nullable, and `schema()` now optionally accepts a list of `Field`s
* Numeric division by zero now matches R's behavior and no longer raises an error
* `write_parquet()` no longer errors when used with a grouped data.frame
* `case_when()` now errors cleanly if an expression is not supported in Arrow
* `open_dataset()` now works on CSVs without header rows
* Fixed a minor issue where the short readr-style types `T` and `t` were reversed in `read_csv_arrow()`
* Bindings for `log(..., base = b)` where b is something other than 2, e, or 10
* A number of updates and expansions to our vignettes
* Fix segfaults in converting length-0 ChunkedArrays to R vectors
* `Table$create()` now has alias `arrow_table()`

## Internals

* We now use testthat 3rd edition as our default
* A number of large test reorganizations
* Style changes to conform with the tidyverse style guide + using lintr

# arrow 5.0.0.2

This patch version contains fixes for some sanitizer and compiler warnings.

# arrow 5.0.0

## More dplyr

* There are now more than 250 compute functions available for use in `dplyr::filter()`, `mutate()`, etc. Additions in this release include:

  * String operations: `strsplit()` and `str_split()`; `strptime()`; `paste()`, `paste0()`, and `str_c()`; `substr()` and `str_sub()`; `str_like()`; `str_pad()`; `stri_reverse()`
  * Date/time operations: `lubridate` methods such as `year()`, `month()`, `wday()`, and so on
  * Math: logarithms (`log()` et al.); trigonometry (`sin()`, `cos()`, et al.); `abs()`; `sign()`; `pmin()` and `pmax()`; `ceiling()`, `floor()`, and `trunc()`
  * Conditional functions, with some limitations on input type in this release: `ifelse()` and `if_else()` for all but `Decimal` types; `case_when()` for logical, numeric, and temporal types only; `coalesce()` for all but lists/structs. Note also that in this release, factors/dictionaries are converted to strings in these functions.
  * `is.*` functions are supported and can be used inside `relocate()`

* The print method for `arrow_dplyr_query` now includes the expression and the resulting type of columns derived by `mutate()`.
* `transmute()` now errors if passed arguments `.keep`, `.before`, or `.after`, for consistency with the behavior of `dplyr` on `data.frame`s.

## CSV writing

* `write_csv_arrow()` to use Arrow to write a data.frame to a single CSV file
* `write_dataset(format = "csv", ...)` to write a Dataset to CSVs, including with partitioning

## C interface

* Added bindings for the remainder of C data interface: Type, Field, and RecordBatchReader (from the experimental C stream interface). These also have `reticulate::py_to_r()` and `r_to_py()` methods. Along with the addition of the `Scanner$ToRecordBatchReader()` method, you can now build up a Dataset query in R and pass the resulting stream of batches to another tool in process.
* C interface methods are exposed on Arrow objects (e.g. `Array$export_to_c()`, `RecordBatch$import_from_c()`), similar to how they are in `pyarrow`. This facilitates their use in other packages. See the `py_to_r()` and `r_to_py()` methods for usage examples.

## Other enhancements

* Converting an R `data.frame` to an Arrow `Table` uses multithreading across columns
* Some Arrow array types now use ALTREP when converting to R. To disable this, set `options(arrow.use_altrep = FALSE)`
* `is.na()` now evaluates to `TRUE` on `NaN` values in floating point number fields, for consistency with base R.
* `is.nan()` now evaluates to `FALSE` on `NA` values in floating point number fields and `FALSE` on all values in non-floating point fields, for consistency with base R.
* Additional methods for `Array`, `ChunkedArray`, `RecordBatch`, and `Table`: `na.omit()` and friends, `any()`/`all()`
* Scalar inputs to `RecordBatch$create()` and `Table$create()` are recycled
* `arrow_info()` includes details on the C++ build, such as compiler version
* `match_arrow()` now converts `x` into an `Array` if it is not a `Scalar`, `Array` or `ChunkedArray` and no longer dispatches `base::match()`.
* Row-level metadata is now restricted to reading/writing single parquet or feather files. Row-level metadata with datasets is ignored (with a warning) if the dataset contains row-level metadata. Writing a dataset with row-level metadata will also be ignored (with a warning). We are working on a more robust implementation to support row-level metadata (and other complex types) --- stay tuned. For working with {sf} objects, [{sfarrow}](https://CRAN.R-project.org/package=sfarrow) is helpful for serializing sf columns and sharing them with geopandas.

# arrow 4.0.1

* Resolved a few bugs in new string compute kernels (ARROW-12774, ARROW-12670)

# arrow 4.0.0.1

 * The mimalloc memory allocator is the default memory allocator when using a static source build of the package on Linux. This is because it has better behavior under valgrind than jemalloc does. A full-featured build (installed with `LIBARROW_MINIMAL=false`) includes both jemalloc and mimalloc, and it has still has jemalloc as default, though this is configurable at runtime with the `ARROW_DEFAULT_MEMORY_POOL` environment variable.
 * Environment variables `LIBARROW_MINIMAL`, `LIBARROW_DOWNLOAD`, and `NOT_CRAN` are now case-insensitive in the Linux build script.
 * A build configuration issue in the macOS binary package has been resolved.

# arrow 4.0.0

## dplyr methods

Many more `dplyr` verbs are supported on Arrow objects:

* `dplyr::mutate()` is now supported in Arrow for many applications. For queries on `Table` and `RecordBatch` that are not yet supported in Arrow, the implementation falls back to pulling data into an in-memory R `data.frame` first, as in the previous release. For queries on `Dataset` (which can be larger than memory), it raises an error if the function is not implemented. The main `mutate()` features that cannot yet be called on Arrow objects are (1) `mutate()` after `group_by()` (which is typically used in combination with aggregation) and (2) queries that use `dplyr::across()`.
* `dplyr::transmute()` (which calls `mutate()`)
* `dplyr::group_by()` now preserves the `.drop` argument and supports on-the-fly definition of columns
* `dplyr::relocate()` to reorder columns
* `dplyr::arrange()` to sort rows
* `dplyr::compute()` to evaluate the lazy expressions and return an Arrow Table. This is equivalent to `dplyr::collect(as_data_frame = FALSE)`, which was added in 2.0.0.

Over 100 functions can now be called on Arrow objects inside a `dplyr` verb:

* String functions `nchar()`, `tolower()`, and `toupper()`, along with their `stringr` spellings `str_length()`, `str_to_lower()`, and `str_to_upper()`, are supported in Arrow `dplyr` calls. `str_trim()` is also supported.
* Regular expression functions `sub()`, `gsub()`, and `grepl()`, along with `str_replace()`, `str_replace_all()`, and `str_detect()`, are supported.
* `cast(x, type)` and `dictionary_encode()` allow changing the type of columns in Arrow objects; `as.numeric()`, `as.character()`, etc. are exposed as similar type-altering conveniences
* `dplyr::between()`; the Arrow version also allows the `left` and `right` arguments to be columns in the data and not just scalars
* Additionally, any Arrow C++ compute function can be called inside a `dplyr` verb. This enables you to access Arrow functions that don't have a direct R mapping. See `list_compute_functions()` for all available functions, which are available in `dplyr` prefixed by `arrow_`.
* Arrow C++ compute functions now do more systematic type promotion when called on data with different types (e.g. int32 and float64). Previously, Scalars in an expressions were always cast to match the type of the corresponding Array, so this new type promotion enables, among other things, operations on two columns (Arrays) in a dataset. As a side effect, some comparisons that worked in prior versions are no longer supported: for example, `dplyr::filter(arrow_dataset, string_column == 3)` will error with a message about the type mismatch between the numeric `3` and the string type of `string_column`.

## Datasets

* `open_dataset()` now accepts a vector of file paths (or even a single file path). Among other things, this enables you to open a single very large file and use `write_dataset()` to partition it without having to read the whole file into memory.
* Datasets can now detect and read a directory of compressed CSVs
* `write_dataset()` now defaults to `format = "parquet"` and better validates the `format` argument
* Invalid input for `schema` in `open_dataset()` is now correctly handled
* Collecting 0 columns from a Dataset now no longer returns all of the columns
* The `Scanner$Scan()` method has been removed; use `Scanner$ScanBatches()`

## Other improvements

* `value_counts()` to tabulate values in an `Array` or `ChunkedArray`, similar to `base::table()`.
* `StructArray` objects gain data.frame-like methods, including `names()`, `$`, `[[`, and `dim()`.
* RecordBatch columns can now be added, replaced, or removed by assigning (`<-`) with either `$` or `[[`
* Similarly, `Schema` can now be edited by assigning in new types. This enables using the CSV reader to detect the schema of a file, modify the `Schema` object for any columns that you want to read in as a different type, and then use that `Schema` to read the data.
* Better validation when creating a `Table` with a schema, with columns of different lengths, and with scalar value recycling
* Reading Parquet files in Japanese or other multi-byte locales on Windows no longer hangs (workaround for a [bug in libstdc++](https://gcc.gnu.org/bugzilla/show_bug.cgi?id=98723); thanks @yutannihilation for the persistence in discovering this!)
* If you attempt to read string data that has embedded nul (`\0`) characters, the error message now informs you that you can set `options(arrow.skip_nul = TRUE)` to strip them out. It is not recommended to set this option by default since this code path is significantly slower, and most string data does not contain nuls.
* `read_json_arrow()` now accepts a schema: `read_json_arrow("file.json", schema = schema(col_a = float64(), col_b = string()))`

## Installation and configuration

* The R package can now support working with an Arrow C++ library that has additional features (such as dataset, parquet, string libraries) disabled, and the bundled build script enables setting environment variables to disable them. See `vignette("install", package = "arrow")` for details. This allows a faster, smaller package build in cases where that is useful, and it enables a minimal, functioning R package build on Solaris.
* On macOS, it is now possible to use the same bundled C++ build that is used by default on Linux, along with all of its customization parameters, by setting the environment variable `FORCE_BUNDLED_BUILD=true`.
* `arrow` now uses the `mimalloc` memory allocator by default on macOS, if available (as it is in CRAN binaries), instead of `jemalloc`. There are [configuration issues](https://issues.apache.org/jira/browse/ARROW-6994) with `jemalloc` on macOS, and [benchmark analysis](https://ursalabs.org/blog/2021-r-benchmarks-part-1/) shows that this has negative effects on performance, especially on memory-intensive workflows. `jemalloc` remains the default on Linux; `mimalloc` is default on Windows.
* Setting the `ARROW_DEFAULT_MEMORY_POOL` environment variable to switch memory allocators now works correctly when the Arrow C++ library has been statically linked (as is usually the case when installing from CRAN).
* The `arrow_info()` function now reports on the additional optional features, as well as the detected SIMD level. If key features or compression libraries are not enabled in the build, `arrow_info()` will refer to the installation vignette for guidance on how to install a more complete build, if desired.
* If you attempt to read a file that was compressed with a codec that your Arrow build does not contain support for, the error message now will tell you how to reinstall Arrow with that feature enabled.
* A new vignette about developer environment setup `vignette("developing", package = "arrow")`.
* When building from source, you can use the environment variable `ARROW_HOME` to point to a specific directory where the Arrow libraries are. This is similar to passing `INCLUDE_DIR` and `LIB_DIR`.

# arrow 3.0.0

## Python and Flight

* Flight methods `flight_get()` and `flight_put()` (renamed from `push_data()` in this release) can handle both Tables and RecordBatches
* `flight_put()` gains an `overwrite` argument to optionally check for the existence of a resource with the the same name
* `list_flights()` and `flight_path_exists()` enable you to see available resources on a Flight server
* `Schema` objects now have `r_to_py` and `py_to_r` methods
* Schema metadata is correctly preserved when converting Tables to/from Python

## Enhancements

* Arithmetic operations (`+`, `*`, etc.) are supported on Arrays and ChunkedArrays and can be used in filter expressions in Arrow `dplyr` pipelines
* Table columns can now be added, replaced, or removed by assigning (`<-`) with either `$` or `[[`
* Column names of Tables and RecordBatches can be renamed by assigning `names()`
* Large string types can now be written to Parquet files
* The [pronouns `.data` and `.env`](https://rlang.r-lib.org/reference/tidyeval-data.html) are now fully supported in Arrow `dplyr` pipelines.
* Option `arrow.skip_nul` (default `FALSE`, as in `base::scan()`) allows conversion of Arrow string (`utf8()`) type data containing embedded nul `\0` characters to R. If set to `TRUE`, nuls will be stripped and a warning is emitted if any are found.
* `arrow_info()` for an overview of various run-time and build-time Arrow configurations, useful for debugging
* Set environment variable `ARROW_DEFAULT_MEMORY_POOL` before loading the Arrow package to change memory allocators. Windows packages are built with `mimalloc`; most others are built with both `jemalloc` (used by default) and `mimalloc`. These alternative memory allocators are generally much faster than the system memory allocator, so they are used by default when available, but sometimes it is useful to turn them off for debugging purposes. To disable them, set `ARROW_DEFAULT_MEMORY_POOL=system`.
* List columns that have attributes on each element are now also included with the metadata that is saved when creating Arrow tables. This allows `sf` tibbles to faithfully preserved and roundtripped (ARROW-10386).
* R metadata that exceeds 100Kb is now compressed before being written to a table; see `schema()` for more details.

## Bug fixes

* Fixed a performance regression in converting Arrow string types to R that was present in the 2.0.0 release
* C++ functions now trigger garbage collection when needed
* `write_parquet()` can now write RecordBatches
* Reading a Table from a RecordBatchStreamReader containing 0 batches no longer crashes
* `readr`'s `problems` attribute is removed when converting to Arrow RecordBatch and table to prevent large amounts of metadata from accumulating inadvertently (ARROW-10624)
* Fixed reading of compressed Feather files written with Arrow 0.17 (ARROW-10850)
* `SubTreeFileSystem` gains a useful print method and no longer errors when printing

## Packaging and installation

* Nightly development versions of the conda `r-arrow` package are available with `conda install -c arrow-nightlies -c conda-forge --strict-channel-priority r-arrow`
* Linux installation now safely supports older `cmake` versions
* Compiler version checking for enabling S3 support correctly identifies the active compiler
* Updated guidance and troubleshooting in `vignette("install", package = "arrow")`, especially for known CentOS issues
* Operating system detection on Linux uses the [`distro`](https://enpiar.com/distro/) package. If your OS isn't correctly identified, please report an issue there.

# arrow 2.0.0

## Datasets

* `write_dataset()` to Feather or Parquet files with partitioning. See the end of `vignette("dataset", package = "arrow")` for discussion and examples.
* Datasets now have `head()`, `tail()`, and take (`[`) methods. `head()` is optimized but the others  may not be performant.
* `collect()` gains an `as_data_frame` argument, default `TRUE` but when `FALSE` allows you to evaluate the accumulated `select` and `filter` query but keep the result in Arrow, not an R `data.frame`
* `read_csv_arrow()` supports specifying column types, both with a `Schema` and with the compact string representation for types used in the `readr` package. It also has gained a `timestamp_parsers` argument that lets you express a set of `strptime` parse strings that will be tried to convert columns designated as `Timestamp` type.

## AWS S3 support

* S3 support is now enabled in binary macOS and Windows (Rtools40 only, i.e. R >= 4.0) packages. To enable it on Linux, you need the additional system dependencies `libcurl` and `openssl`, as well as a sufficiently modern compiler. See `vignette("install", package = "arrow")` for details.
* File readers and writers (`read_parquet()`, `write_feather()`, et al.), as well as `open_dataset()` and `write_dataset()`, allow you to access resources on S3 (or on file systems that emulate S3) either by providing an `s3://` URI or by providing a `FileSystem$path()`. See `vignette("fs", package = "arrow")` for examples.
* `copy_files()` allows you to recursively copy directories of files from one file system to another, such as from S3 to your local machine.

## Flight RPC

[Flight](https://arrow.apache.org/blog/2019/10/13/introducing-arrow-flight/)
is a general-purpose client-server framework for high performance
transport of large datasets over network interfaces.
The `arrow` R package now provides methods for connecting to Flight RPC servers
to send and receive data. See `vignette("flight", package = "arrow")` for an overview.

## Computation

* Comparison (`==`, `>`, etc.) and boolean (`&`, `|`, `!`) operations, along with `is.na`, `%in%` and `match` (called `match_arrow()`), on Arrow Arrays and ChunkedArrays are now implemented in the C++ library.
* Aggregation methods `min()`, `max()`, and `unique()` are implemented for Arrays and ChunkedArrays.
* `dplyr` filter expressions on Arrow Tables and RecordBatches are now evaluated in the C++ library, rather than by pulling data into R and evaluating. This yields significant performance improvements.
* `dim()` (`nrow`) for dplyr queries on Table/RecordBatch is now supported

## Packaging and installation

* `arrow` now depends on [`cpp11`](https://cpp11.r-lib.org/), which brings more robust UTF-8 handling and faster compilation
* The Linux build script now succeeds on older versions of R
* MacOS binary packages now ship with zstandard compression enabled

## Bug fixes and other enhancements

* Automatic conversion of Arrow `Int64` type when all values fit with an R 32-bit integer now correctly inspects all chunks in a ChunkedArray, and this conversion can be disabled (so that `Int64` always yields a `bit64::integer64` vector) by setting `options(arrow.int64_downcast = FALSE)`.
* In addition to the data.frame column metadata preserved in round trip, added in 1.0.0, now attributes of the data.frame itself are also preserved in Arrow schema metadata.
* File writers now respect the system umask setting
* `ParquetFileReader` has additional methods for accessing individual columns or row groups from the file
* Various segfaults fixed: invalid input in `ParquetFileWriter`; invalid `ArrowObject` pointer from a saved R object; converting deeply nested structs from Arrow to R
* The `properties` and `arrow_properties` arguments to `write_parquet()` are deprecated

# 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, @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)
* Compressed streams are now supported on Windows (ARROW-6360), and you can also specify a compression level (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)
* 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

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.
