dplyr 1.0This 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.
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.
dim() method, which sums rows across all files (#6635, @boshek)UnionDataset with the c() methodNA as FALSE, consistent with dplyr::filter()vignette("dataset", package = "arrow") now has correct, executable codeNOT_CRAN=true. See vignette("install", package = "arrow") for details and more options.unify_schemas() to create a Schema containing the union of fields in multiple schemasread_feather() and other reader functions close any file connections they openR.oo package is also loadedFileStats is renamed to FileInfo, and the original spelling has been deprecatedinstall_arrow() now installs the latest release of arrow, including Linux dependencies, either for CRAN releases or for development builds (if nightly = TRUE)LIBARROW_DOWNLOAD or NOT_CRAN environment variable is setwrite_feather(), write_arrow() and write_parquet() now return their input, similar to the write_* functions in the readr package (#6387, @boshek)list and create a ListArray when all list elements are the same type (#6275, @michaelchirico)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.
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.
Tables and RecordBatches also have dplyr methods.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.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$var1 > 5, ] without having to pull everything into R first.write_parquet() now supports compressioncodec_is_available() returns TRUE or FALSE whether the Arrow C++ library was built with support for a given compression library (e.g. gzip, lz4, snappy)character (as R factor levels are required to be) instead of raising an errorClass$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").ARROW_PRE_0_15_IPC_FORMAT=1.as_tibble argument in the read_*() functions has been renamed to as_data_frame (ARROW-6337, @jameslamb)arrow::Column class has been removed, as it was removed from the C++ libraryTable 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.read_csv_arrow() supports more parsing options, including col_names, na, quoted_na, and skipread_parquet() and read_feather() can ingest data from a raw vector (ARROW-6278)~/file.parquet (ARROW-6323)double()), and time types can be created with human-friendly resolution strings (“ms”, “s”, etc.). (ARROW-6338, ARROW-6364)Initial CRAN release of the arrow package. Key features include: