Title:	Integration to 'Apache' 'Arrow'
Version:	19.0.1
Description:	'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. This package provides an interface to the 'Arrow C++' library.
Depends:	R (≥ 4.0)
License:	Apache License (≥ 2.0)
URL:	https://github.com/apache/arrow/, https://arrow.apache.org/docs/r/
BugReports:	https://github.com/apache/arrow/issues
Encoding:	UTF-8
Language:	en-US
SystemRequirements:	C++17; for AWS S3 support on Linux, libcurl and openssl (optional); cmake >= 3.16 (build-time only, and only for full source build)
Biarch:	true
Imports:	assertthat, bit64 (≥ 0.9-7), glue, methods, purrr, R6, rlang (≥ 1.0.0), stats, tidyselect (≥ 1.0.0), utils, vctrs
RoxygenNote:	7.3.2
Config/testthat/edition:	3
Config/build/bootstrap:	TRUE
Suggests:	blob, curl, cli, DBI, dbplyr, decor, distro, dplyr, duckdb (≥ 0.2.8), hms, jsonlite, knitr, lubridate, pillar, pkgload, reticulate, rmarkdown, stringi, stringr, sys, testthat (≥ 3.1.0), tibble, tzdb, withr
LinkingTo:	cpp11 (≥ 0.4.2)
Collate:	'arrowExports.R' 'enums.R' 'arrow-object.R' 'type.R' 'array-data.R' 'arrow-datum.R' 'array.R' 'arrow-info.R' 'arrow-package.R' 'arrow-tabular.R' 'buffer.R' 'chunked-array.R' 'io.R' 'compression.R' 'scalar.R' 'compute.R' 'config.R' 'csv.R' 'dataset.R' 'dataset-factory.R' 'dataset-format.R' 'dataset-partition.R' 'dataset-scan.R' 'dataset-write.R' 'dictionary.R' 'dplyr-across.R' 'dplyr-arrange.R' 'dplyr-by.R' 'dplyr-collect.R' 'dplyr-count.R' 'dplyr-datetime-helpers.R' 'dplyr-distinct.R' 'dplyr-eval.R' 'dplyr-filter.R' 'dplyr-funcs-agg.R' 'dplyr-funcs-augmented.R' 'dplyr-funcs-conditional.R' 'dplyr-funcs-datetime.R' 'dplyr-funcs-doc.R' 'dplyr-funcs-math.R' 'dplyr-funcs-simple.R' 'dplyr-funcs-string.R' 'dplyr-funcs-type.R' 'expression.R' 'dplyr-funcs.R' 'dplyr-glimpse.R' 'dplyr-group-by.R' 'dplyr-join.R' 'dplyr-mutate.R' 'dplyr-select.R' 'dplyr-slice.R' 'dplyr-summarize.R' 'dplyr-union.R' 'record-batch.R' 'table.R' 'dplyr.R' 'duckdb.R' 'extension.R' 'feather.R' 'field.R' 'filesystem.R' 'flight.R' 'install-arrow.R' 'ipc-stream.R' 'json.R' 'memory-pool.R' 'message.R' 'metadata.R' 'parquet.R' 'python.R' 'query-engine.R' 'record-batch-reader.R' 'record-batch-writer.R' 'reexports-bit64.R' 'reexports-tidyselect.R' 'schema.R' 'udf.R' 'util.R'
NeedsCompilation:	yes
Packaged:	2025-02-24 17:36:41 UTC; bryce
Author:	Neal Richardson [aut], Ian Cook [aut], Nic Crane [aut], Dewey Dunnington [aut], Romain François [aut], Jonathan Keane [aut, cre], Dragoș Moldovan-Grünfeld [aut], Jeroen Ooms [aut], Jacob Wujciak-Jens [aut], Javier Luraschi [ctb], Karl Dunkle Werner [ctb], Jeffrey Wong [ctb], Apache Arrow [aut, cph]
Maintainer:	Jonathan Keane <jkeane@gmail.com>
Repository:	CRAN
Date/Publication:	2025-02-26 08:50:02 UTC

arrow: Integration to 'Apache' 'Arrow'

Description

'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. This package provides an interface to the 'Arrow C++' library.

Author(s)

Maintainer: Jonathan Keane jkeane@gmail.com

Authors:

• Neal Richardson neal.p.richardson@gmail.com

• Ian Cook ianmcook@gmail.com

• Nic Crane thisisnic@gmail.com

• Dewey Dunnington dewey@fishandwhistle.net (ORCID)

• Romain François (ORCID)

• Dragoș Moldovan-Grünfeld dragos.mold@gmail.com

• Jeroen Ooms jeroen@berkeley.edu

• Jacob Wujciak-Jens jacob@wujciak.de

• Apache Arrow dev@arrow.apache.org [copyright holder]

Other contributors:

• Javier Luraschi javier@rstudio.com [contributor]

• Karl Dunkle Werner karldw@users.noreply.github.com (ORCID) [contributor]

• Jeffrey Wong jeffreyw@netflix.com [contributor]

See Also

Useful links:

• https://github.com/apache/arrow/

• https://arrow.apache.org/docs/r/

• Report bugs at https://github.com/apache/arrow/issues

Functions available in Arrow dplyr queries

Description

The arrow package contains methods for 37 dplyr table functions, many of which are "verbs" that do transformations to one or more tables. The package also has mappings of 213 R functions to the corresponding functions in the Arrow compute library. These allow you to write code inside of dplyr methods that call R functions, including many in packages like stringr and lubridate, and they will get translated to Arrow and run on the Arrow query engine (Acero). This document lists all of the mapped functions.

dplyr verbs

Most verb functions return an arrow_dplyr_query object, similar in spirit to a dbplyr::tbl_lazy. This means that the verbs do not eagerly evaluate the query on the data. To run the query, call either compute(), which returns an arrow Table, or collect(), which pulls the resulting Table into an R tibble.

• anti_join(): the copy argument is ignored

• arrange()

• collapse()

• collect()

• compute()

• count()

• distinct(): .keep_all = TRUE returns a non-missing value if present, only returning missing values if all are missing.

• explain()

• filter()

• full_join(): the copy argument is ignored

• glimpse()

• group_by()

• group_by_drop_default()

• group_vars()

• groups()

• inner_join(): the copy argument is ignored

• left_join(): the copy argument is ignored

• mutate()

• pull(): the name argument is not supported; returns an R vector by default but this behavior is deprecated and will return an Arrow ChunkedArray in a future release. Provide as_vector = TRUE/FALSE to control this behavior, or set options(arrow.pull_as_vector) globally.

• relocate()

• rename()

• rename_with()

• right_join(): the copy argument is ignored

• select()

• semi_join(): the copy argument is ignored

• show_query()

• slice_head(): slicing within groups not supported; Arrow datasets do not have row order, so head is non-deterministic; prop only supported on queries where nrow() is knowable without evaluating

• slice_max(): slicing within groups not supported; with_ties = TRUE (dplyr default) is not supported; prop only supported on queries where nrow() is knowable without evaluating

• slice_min(): slicing within groups not supported; with_ties = TRUE (dplyr default) is not supported; prop only supported on queries where nrow() is knowable without evaluating

• slice_sample(): slicing within groups not supported; replace = TRUE and the weight_by argument not supported; n only supported on queries where nrow() is knowable without evaluating

• slice_tail(): slicing within groups not supported; Arrow datasets do not have row order, so tail is non-deterministic; prop only supported on queries where nrow() is knowable without evaluating

• summarise(): window functions not currently supported; arguments .drop = FALSE and .groups = "rowwise" not supported

• tally()

• transmute()

• ungroup()

• union()

• union_all()

Function mappings

In the list below, any differences in behavior or support between Acero and the R function are listed. If no notes follow the function name, then you can assume that the function works in Acero just as it does in R.

Functions can be called either as pkg::fun() or just fun(), i.e. both str_sub() and stringr::str_sub() work.

In addition to these functions, you can call any of Arrow's 271 compute functions directly. Arrow has many functions that don't map to an existing R function. In other cases where there is an R function mapping, you can still call the Arrow function directly if you don't want the adaptations that the R mapping has that make Acero behave like R. These functions are listed in the C++ documentation, and in the function registry in R, they are named with an arrow_ prefix, such as arrow_ascii_is_decimal.

arrow

• add_filename()

• cast()

• one()

base

• !

• !=

• %%

• %/%

• %in%

• &

• *

• +

• -

• /

• <

• <=

• ==

• >

• >=

• ISOdate()

• ISOdatetime()

• ^

• abs()

• acos()

• all()

• any()

• as.Date(): Multiple tryFormats not supported in Arrow. Consider using the lubridate specialised parsing functions ymd(), ymd(), etc.

• as.character()

• as.difftime(): only supports units = "secs" (the default)

• as.double()

• as.integer()

• as.logical()

• as.numeric()

• asin()

• ceiling()

• cos()

• data.frame(): row.names and check.rows arguments not supported; stringsAsFactors must be FALSE

• difftime(): only supports units = "secs" (the default); tz argument not supported

• endsWith()

• exp()

• floor()

• format()

• grepl()

• gsub()

• ifelse()

• is.character()

• is.double()

• is.factor()

• is.finite()

• is.infinite()

• is.integer()

• is.list()

• is.logical()

• is.na()

• is.nan()

• is.numeric()

• log()

• log10()

• log1p()

• log2()

• logb()

• max()

• mean()

• min()

• nchar(): allowNA = TRUE and keepNA = TRUE not supported

• paste(): the collapse argument is not yet supported

• paste0(): the collapse argument is not yet supported

• pmax()

• pmin()

• prod()

• round()

• sign()

• sin()

• sqrt()

• startsWith()

• strftime()

• strptime(): accepts a unit argument not present in the base function. Valid values are "s", "ms" (default), "us", "ns".

• strrep()

• strsplit()

• sub()

• substr(): start and stop must be length 1

• substring()

• sum()

• tan()

• tolower()

• toupper()

• trunc()

• |

bit64

• as.integer64()

• is.integer64()

dplyr

• across()

• between()

• case_when(): .ptype and .size arguments not supported

• coalesce()

• desc()

• if_all()

• if_any()

• if_else()

• n()

• n_distinct()

lubridate

• am()

• as_date()

• as_datetime()

• ceiling_date()

• date()

• date_decimal()

• day()

• ddays()

• decimal_date()

• dhours()

• dmicroseconds()

• dmilliseconds()

• dminutes()

• dmonths()

• dmy(): locale argument not supported

• dmy_h(): locale argument not supported

• dmy_hm(): locale argument not supported

• dmy_hms(): locale argument not supported

• dnanoseconds()

• dpicoseconds(): not supported

• dseconds()

• dst()

• dweeks()

• dyears()

• dym(): locale argument not supported

• epiweek()

• epiyear()

• fast_strptime(): non-default values of lt and cutoff_2000 not supported

• floor_date()

• force_tz(): Timezone conversion from non-UTC timezone not supported; roll_dst values of 'error' and 'boundary' are supported for nonexistent times, roll_dst values of 'error', 'pre', and 'post' are supported for ambiguous times.

• format_ISO8601()

• hour()

• is.Date()

• is.POSIXct()

• is.instant()

• is.timepoint()

• isoweek()

• isoyear()

• leap_year()

• make_date()

• make_datetime(): only supports UTC (default) timezone

• make_difftime(): only supports units = "secs" (the default); providing both num and ... is not supported

• mday()

• mdy(): locale argument not supported

• mdy_h(): locale argument not supported

• mdy_hm(): locale argument not supported

• mdy_hms(): locale argument not supported

• minute()

• month()

• my(): locale argument not supported

• myd(): locale argument not supported

• parse_date_time(): quiet = FALSE is not supported Available formats are H, I, j, M, S, U, w, W, y, Y, R, T. On Linux and OS X additionally a, A, b, B, Om, p, r are available.

• pm()

• qday()

• quarter()

• round_date()

• second()

• semester()

• tz()

• wday()

• week()

• with_tz()

• yday()

• ydm(): locale argument not supported

• ydm_h(): locale argument not supported

• ydm_hm(): locale argument not supported

• ydm_hms(): locale argument not supported

• year()

• ym(): locale argument not supported

• ymd(): locale argument not supported

• ymd_h(): locale argument not supported

• ymd_hm(): locale argument not supported

• ymd_hms(): locale argument not supported

• yq(): locale argument not supported

methods

• is()

rlang

• is_character()

• is_double()

• is_integer()

• is_list()

• is_logical()

stats

• median(): approximate median (t-digest) is computed

• quantile(): probs must be length 1; approximate quantile (t-digest) is computed

• sd()

• var()

stringi

• stri_reverse()

stringr

Pattern modifiers coll() and boundary() are not supported in any functions.

• str_c(): the collapse argument is not yet supported

• str_count(): pattern must be a length 1 character vector

• str_detect()

• str_dup()

• str_ends()

• str_length()

• str_like()

• str_pad()

• str_remove()

• str_remove_all()

• str_replace()

• str_replace_all()

• str_split(): Case-insensitive string splitting and splitting into 0 parts not supported

• str_starts()

• str_sub(): start and end must be length 1

• str_to_lower()

• str_to_title()

• str_to_upper()

• str_trim()

tibble

• tibble()

tidyselect

• all_of()

• contains()

• ends_with()

• everything()

• last_col()

• matches()

• num_range()

• one_of()

• starts_with()

Add the data filename as a column

Description

This function only exists inside arrow dplyr queries, and it only is valid when querying on a FileSystemDataset.

Usage

add_filename()

Details

To use filenames generated by this function in subsequent pipeline steps, you must either call compute() or collect() first. See Examples.

Value

A FieldRef Expression that refers to the filename augmented column.

Examples

## Not run: 
open_dataset("nyc-taxi") %>% mutate(
  file =
    add_filename()
)

# To use a verb like mutate() with add_filename() we need to first call
# compute()
open_dataset("nyc-taxi") %>%
  mutate(file = add_filename()) %>%
  compute() %>%
  mutate(filename_length = nchar(file))

## End(Not run)

Array Classes

Description

An Array is an immutable data array with some logical type and some length. Most logical types are contained in the base Array class; there are also subclasses for DictionaryArray, ListArray, and StructArray.

Factory

The Array$create() factory method instantiates an Array and takes the following arguments:

• x: an R vector, list, or data.frame

• type: an optional data type for x. If omitted, the type will be inferred from the data.

Array$create() will return the appropriate subclass of Array, such as DictionaryArray when given an R factor.

To compose a DictionaryArray directly, call DictionaryArray$create(), which takes two arguments:

• x: an R vector or Array of integers for the dictionary indices

• dict: an R vector or Array of dictionary values (like R factor levels but not limited to strings only)

Usage

a <- Array$create(x)
length(a)

print(a)
a == a

Methods

• ⁠$IsNull(i)⁠: Return true if value at index is null. Does not boundscheck

• ⁠$IsValid(i)⁠: Return true if value at index is valid. Does not boundscheck

• ⁠$length()⁠: Size in the number of elements this array contains

• ⁠$nbytes()⁠: Total number of bytes consumed by the elements of the array

• ⁠$offset⁠: A relative position into another array's data, to enable zero-copy slicing

• ⁠$null_count⁠: The number of null entries in the array

• ⁠$type⁠: logical type of data

• ⁠$type_id()⁠: type id

• ⁠$Equals(other)⁠ : is this array equal to other

• ⁠$ApproxEquals(other)⁠ :

• ⁠$Diff(other)⁠ : return a string expressing the difference between two arrays

• ⁠$data()⁠: return the underlying ArrayData

• ⁠$as_vector()⁠: convert to an R vector

• ⁠$ToString()⁠: string representation of the array

• ⁠$Slice(offset, length = NULL)⁠: Construct a zero-copy slice of the array with the indicated offset and length. If length is NULL, the slice goes until the end of the array.

• ⁠$Take(i)⁠: return an Array with values at positions given by integers (R vector or Array Array) i.

• ⁠$Filter(i, keep_na = TRUE)⁠: return an Array with values at positions where logical vector (or Arrow boolean Array) i is TRUE.

• ⁠$SortIndices(descending = FALSE)⁠: return an Array of integer positions that can be used to rearrange the Array in ascending or descending order

• ⁠$RangeEquals(other, start_idx, end_idx, other_start_idx)⁠ :

• ⁠$cast(target_type, safe = TRUE, options = cast_options(safe))⁠: Alter the data in the array to change its type.

• ⁠$View(type)⁠: Construct a zero-copy view of this array with the given type.

• ⁠$Validate()⁠ : Perform any validation checks to determine obvious inconsistencies within the array's internal data. This can be an expensive check, potentially O(length)

Examples

my_array <- Array$create(1:10)
my_array$type
my_array$cast(int8())

# Check if value is null; zero-indexed
na_array <- Array$create(c(1:5, NA))
na_array$IsNull(0)
na_array$IsNull(5)
na_array$IsValid(5)
na_array$null_count

# zero-copy slicing; the offset of the new Array will be the same as the index passed to $Slice
new_array <- na_array$Slice(5)
new_array$offset

# Compare 2 arrays
na_array2 <- na_array
na_array2 == na_array # element-wise comparison
na_array2$Equals(na_array) # overall comparison

ArrayData class

Description

The ArrayData class allows you to get and inspect the data inside an arrow::Array.

Usage

data <- Array$create(x)$data()

data$type
data$length
data$null_count
data$offset
data$buffers

Methods

...

Create an Arrow Array

Description

Create an Arrow Array

Usage

arrow_array(x, type = NULL)

Arguments

Examples

my_array <- arrow_array(1:10)

# Compare 2 arrays
na_array <- arrow_array(c(1:5, NA))
na_array2 <- na_array
na_array2 == na_array # element-wise comparison

Report information on the package's capabilities

Description

This function summarizes a number of build-time configurations and run-time settings for the Arrow package. It may be useful for diagnostics.

Usage

arrow_info()

arrow_available()

arrow_with_acero()

arrow_with_dataset()

arrow_with_substrait()

arrow_with_parquet()

arrow_with_s3()

arrow_with_gcs()

arrow_with_json()

Value

arrow_info() returns a list including version information, boolean "capabilities", and statistics from Arrow's memory allocator, and also Arrow's run-time information. The ⁠_available()⁠ functions return a logical value whether or not the C++ library was built with support for them.

See Also

If any capabilities are FALSE, see the install guide for guidance on reinstalling the package.

Helpers to raise classed errors

Description

arrow_not_supported() and validation_error() raise classed errors that allow us to distinguish between things that are not supported in Arrow and things that are just invalid input. Additional wrapping in arrow_eval() and try_arrow_dplyr() provide more context and suggestions. Importantly, if arrow_not_supported is raised, then retrying the same code in regular dplyr in R may work. But if validation_error is raised, then we shouldn't recommend retrying with regular dplyr because it will fail there too.

Usage

arrow_not_supported(
  msg,
  .actual_msg = paste(msg, "not supported in Arrow"),
  ...
)

validation_error(msg, ...)

Arguments

Details

Use these in function bindings and in the dplyr methods. Inside of function bindings, you don't need to provide the call argument, as it will be automatically filled in with the expression that caused the error in arrow_eval(). In dplyr methods, you should provide the call argument; rlang::caller_call() often is correct, but you may need to experiment to find how far up the call stack you need to look.

You may provide additional information in the body argument, a named character vector. Use i for additional information about the error and > to indicate potential solutions or workarounds that don't require pulling the data into R. If you have an arrow_not_supported() error with a > suggestion, when the error is ultimately raised by try_error_dplyr(), ⁠Call collect() first to pull data into R⁠ won't be the only suggestion.

You can still use match.arg() and assert_that() for simple input validation inside of the function bindings. arrow_eval() will catch their errors and re-raise them as validation_error.

Create an Arrow Table

Description

Create an Arrow Table

Usage

arrow_table(..., schema = NULL)

Arguments

See Also

Table

Examples

tbl <- arrow_table(name = rownames(mtcars), mtcars)
dim(tbl)
dim(head(tbl))
names(tbl)
tbl$mpg
tbl[["cyl"]]
as.data.frame(tbl[4:8, c("gear", "hp", "wt")])

Convert an object to an Arrow Array

Description

The as_arrow_array() function is identical to Array$create() except that it is an S3 generic, which allows methods to be defined in other packages to convert objects to Array. Array$create() is slightly faster because it tries to convert in C++ before falling back on as_arrow_array().

Usage

as_arrow_array(x, ..., type = NULL)

## S3 method for class 'Array'
as_arrow_array(x, ..., type = NULL)

## S3 method for class 'Scalar'
as_arrow_array(x, ..., type = NULL)

## S3 method for class 'ChunkedArray'
as_arrow_array(x, ..., type = NULL)

Arguments

Value

An Array with type type.

Examples

as_arrow_array(1:5)

Convert an object to an Arrow Table

Description

Whereas arrow_table() constructs a table from one or more columns, as_arrow_table() converts a single object to an Arrow Table.

Usage

as_arrow_table(x, ..., schema = NULL)

## Default S3 method:
as_arrow_table(x, ...)

## S3 method for class 'Table'
as_arrow_table(x, ..., schema = NULL)

## S3 method for class 'RecordBatch'
as_arrow_table(x, ..., schema = NULL)

## S3 method for class 'data.frame'
as_arrow_table(x, ..., schema = NULL)

## S3 method for class 'RecordBatchReader'
as_arrow_table(x, ...)

## S3 method for class 'Dataset'
as_arrow_table(x, ...)

## S3 method for class 'arrow_dplyr_query'
as_arrow_table(x, ...)

## S3 method for class 'Schema'
as_arrow_table(x, ...)

Arguments

Value

A Table

Examples

# use as_arrow_table() for a single object
as_arrow_table(data.frame(col1 = 1, col2 = "two"))

# use arrow_table() to create from columns
arrow_table(col1 = 1, col2 = "two")

Convert an object to an Arrow ChunkedArray

Description

Whereas chunked_array() constructs a ChunkedArray from zero or more Arrays or R vectors, as_chunked_array() converts a single object to a ChunkedArray.

Usage

as_chunked_array(x, ..., type = NULL)

## S3 method for class 'ChunkedArray'
as_chunked_array(x, ..., type = NULL)

## S3 method for class 'Array'
as_chunked_array(x, ..., type = NULL)

Arguments

Value

A ChunkedArray.

Examples

as_chunked_array(1:5)

Convert an object to an Arrow DataType

Description

Convert an object to an Arrow DataType

Usage

as_data_type(x, ...)

## S3 method for class 'DataType'
as_data_type(x, ...)

## S3 method for class 'Field'
as_data_type(x, ...)

## S3 method for class 'Schema'
as_data_type(x, ...)

Arguments

Value

A DataType object.

Examples

as_data_type(int32())

Convert an object to an Arrow RecordBatch

Description

Whereas record_batch() constructs a RecordBatch from one or more columns, as_record_batch() converts a single object to an Arrow RecordBatch.

Usage

as_record_batch(x, ..., schema = NULL)

## S3 method for class 'RecordBatch'
as_record_batch(x, ..., schema = NULL)

## S3 method for class 'Table'
as_record_batch(x, ..., schema = NULL)

## S3 method for class 'arrow_dplyr_query'
as_record_batch(x, ...)

## S3 method for class 'data.frame'
as_record_batch(x, ..., schema = NULL)

Arguments

Value

A RecordBatch

Examples

# use as_record_batch() for a single object
as_record_batch(data.frame(col1 = 1, col2 = "two"))

# use record_batch() to create from columns
record_batch(col1 = 1, col2 = "two")

Convert an object to an Arrow RecordBatchReader

Description

Convert an object to an Arrow RecordBatchReader

Usage

as_record_batch_reader(x, ...)

## S3 method for class 'RecordBatchReader'
as_record_batch_reader(x, ...)

## S3 method for class 'Table'
as_record_batch_reader(x, ...)

## S3 method for class 'RecordBatch'
as_record_batch_reader(x, ...)

## S3 method for class 'data.frame'
as_record_batch_reader(x, ...)

## S3 method for class 'Dataset'
as_record_batch_reader(x, ...)

## S3 method for class ''function''
as_record_batch_reader(x, ..., schema)

## S3 method for class 'arrow_dplyr_query'
as_record_batch_reader(x, ...)

## S3 method for class 'Scanner'
as_record_batch_reader(x, ...)

Arguments

Value

A RecordBatchReader

Examples

reader <- as_record_batch_reader(data.frame(col1 = 1, col2 = "two"))
reader$read_next_batch()

Convert an object to an Arrow Schema

Description

Convert an object to an Arrow Schema

Usage

as_schema(x, ...)

## S3 method for class 'Schema'
as_schema(x, ...)

## S3 method for class 'StructType'
as_schema(x, ...)

Arguments

Value

A Schema object.

Examples

as_schema(schema(col1 = int32()))

Create a Buffer

Description

Create a Buffer

Usage

buffer(x)

Arguments

Value

an instance of Buffer that borrows memory from x

See Also

Buffer

Buffer class

Description

A Buffer is an object containing a pointer to a piece of contiguous memory with a particular size.

Factory

buffer() lets you create an arrow::Buffer from an R object

Methods

• ⁠$is_mutable⁠ : is this buffer mutable?

• ⁠$ZeroPadding()⁠ : zero bytes in padding, i.e. bytes between size and capacity

• ⁠$size⁠ : size in memory, in bytes

• ⁠$capacity⁠: possible capacity, in bytes

Examples

my_buffer <- buffer(c(1, 2, 3, 4))
my_buffer$is_mutable
my_buffer$ZeroPadding()
my_buffer$size
my_buffer$capacity

Call an Arrow compute function

Description

This function provides a lower-level API for calling Arrow functions by their string function name. You won't use it directly for most applications. Many Arrow compute functions are mapped to R methods, and in a dplyr evaluation context, all Arrow functions are callable with an arrow_ prefix.

Usage

call_function(
  function_name,
  ...,
  args = list(...),
  options = empty_named_list()
)

Arguments

Details

When passing indices in ..., args, or options, express them as 0-based integers (consistent with C++).

Value

An Array, ChunkedArray, Scalar, RecordBatch, or Table, whatever the compute function results in.

See Also

Arrow C++ documentation for the functions and their respective options.

Examples

a <- Array$create(c(1L, 2L, 3L, NA, 5L))
s <- Scalar$create(4L)
call_function("coalesce", a, s)

a <- Array$create(rnorm(10000))
call_function("quantile", a, options = list(q = seq(0, 1, 0.25)))

Change the type of an array or column

Description

This is a wrapper around the ⁠$cast()⁠ method that many Arrow objects have. It is more convenient to call inside dplyr pipelines than the method.

Usage

cast(x, to, safe = TRUE, ...)

Arguments

Value

An Expression

See Also

data-type for a list of DataType to be used with to.

Arrow C++ CastOptions documentation # nolint for the list of supported CastOptions.

Examples

## Not run: 
mtcars %>%
  arrow_table() %>%
  mutate(cyl = cast(cyl, string()))

## End(Not run)

Cast options

Description

Cast options

Usage

cast_options(safe = TRUE, ...)

Arguments

Value

A list

Create a Chunked Array

Description

Create a Chunked Array

Usage

chunked_array(..., type = NULL)

Arguments

See Also

ChunkedArray

Examples

# Pass items into chunked_array as separate objects to create chunks
class_scores <- chunked_array(c(87, 88, 89), c(94, 93, 92), c(71, 72, 73))

# If you pass a list into chunked_array, you get a list of length 1
list_scores <- chunked_array(list(c(9.9, 9.6, 9.5), c(8.2, 8.3, 8.4), c(10.0, 9.9, 9.8)))

# When constructing a ChunkedArray, the first chunk is used to infer type.
infer_type(chunked_array(c(1, 2, 3), c(5L, 6L, 7L)))

# Concatenating chunked arrays returns a new chunked array containing all chunks
a <- chunked_array(c(1, 2), 3)
b <- chunked_array(c(4, 5), 6)
c(a, b)

ChunkedArray class

Description

A ChunkedArray is a data structure managing a list of primitive Arrow Arrays logically as one large array. Chunked arrays may be grouped together in a Table.

Factory

The ChunkedArray$create() factory method instantiates the object from various Arrays or R vectors. chunked_array() is an alias for it.

Methods

• ⁠$length()⁠: Size in the number of elements this array contains

• ⁠$chunk(i)⁠: Extract an Array chunk by integer position

• '$nbytes() : Total number of bytes consumed by the elements of the array

• ⁠$as_vector()⁠: convert to an R vector

• ⁠$Slice(offset, length = NULL)⁠: Construct a zero-copy slice of the array with the indicated offset and length. If length is NULL, the slice goes until the end of the array.

• ⁠$Take(i)⁠: return a ChunkedArray with values at positions given by integers i. If i is an Arrow Array or ChunkedArray, it will be coerced to an R vector before taking.

• ⁠$Filter(i, keep_na = TRUE)⁠: return a ChunkedArray with values at positions where logical vector or Arrow boolean-type ⁠(Chunked)Array⁠ i is TRUE.

• ⁠$SortIndices(descending = FALSE)⁠: return an Array of integer positions that can be used to rearrange the ChunkedArray in ascending or descending order

• ⁠$cast(target_type, safe = TRUE, options = cast_options(safe))⁠: Alter the data in the array to change its type.

• ⁠$null_count⁠: The number of null entries in the array

• ⁠$chunks⁠: return a list of Arrays

• ⁠$num_chunks⁠: integer number of chunks in the ChunkedArray

• ⁠$type⁠: logical type of data

• ⁠$View(type)⁠: Construct a zero-copy view of this ChunkedArray with the given type.

• ⁠$Validate()⁠: Perform any validation checks to determine obvious inconsistencies within the array's internal data. This can be an expensive check, potentially O(length)

See Also

Array

Examples

# Pass items into chunked_array as separate objects to create chunks
class_scores <- chunked_array(c(87, 88, 89), c(94, 93, 92), c(71, 72, 73))
class_scores$num_chunks

# When taking a Slice from a chunked_array, chunks are preserved
class_scores$Slice(2, length = 5)

# You can combine Take and SortIndices to return a ChunkedArray with 1 chunk
# containing all values, ordered.
class_scores$Take(class_scores$SortIndices(descending = TRUE))

# If you pass a list into chunked_array, you get a list of length 1
list_scores <- chunked_array(list(c(9.9, 9.6, 9.5), c(8.2, 8.3, 8.4), c(10.0, 9.9, 9.8)))
list_scores$num_chunks

# When constructing a ChunkedArray, the first chunk is used to infer type.
doubles <- chunked_array(c(1, 2, 3), c(5L, 6L, 7L))
doubles$type

# Concatenating chunked arrays returns a new chunked array containing all chunks
a <- chunked_array(c(1, 2), 3)
b <- chunked_array(c(4, 5), 6)
c(a, b)

Compression Codec class

Description

Codecs allow you to create compressed input and output streams.

Factory

The Codec$create() factory method takes the following arguments:

• type: string name of the compression method. Possible values are "uncompressed", "snappy", "gzip", "brotli", "zstd", "lz4", "lzo", or "bz2". type may be upper- or lower-cased. Not all methods may be available; support depends on build-time flags for the C++ library. See codec_is_available(). Most builds support at least "snappy" and "gzip". All support "uncompressed".

• compression_level: compression level, the default value (NA) uses the default compression level for the selected compression type.

Check whether a compression codec is available

Description

Support for compression libraries depends on the build-time settings of the Arrow C++ library. This function lets you know which are available for use.

Usage

codec_is_available(type)

Arguments

Value

Logical: is type available?

Examples

codec_is_available("gzip")

Compressed stream classes

Description

CompressedInputStream and CompressedOutputStream allow you to apply a compression Codec to an input or output stream.

Factory

The CompressedInputStream$create() and CompressedOutputStream$create() factory methods instantiate the object and take the following arguments:

• stream An InputStream or OutputStream, respectively

• codec A Codec, either a Codec instance or a string

• compression_level compression level for when the codec argument is given as a string

Methods

Methods are inherited from InputStream and OutputStream, respectively

Concatenate zero or more Arrays

Description

Concatenates zero or more Array objects into a single array. This operation will make a copy of its input; if you need the behavior of a single Array but don't need a single object, use ChunkedArray.

Usage

concat_arrays(..., type = NULL)

## S3 method for class 'Array'
c(...)

Arguments

Value

A single Array

Examples

concat_arrays(Array$create(1:3), Array$create(4:5))

Concatenate one or more Tables

Description

Concatenate one or more Table objects into a single table. This operation does not copy array data, but instead creates new chunked arrays for each column that point at existing array data.

Usage

concat_tables(..., unify_schemas = TRUE)

Arguments

Examples

tbl <- arrow_table(name = rownames(mtcars), mtcars)
prius <- arrow_table(name = "Prius", mpg = 58, cyl = 4, disp = 1.8)
combined <- concat_tables(tbl, prius)
tail(combined)$to_data_frame()

Does this string contain regex metacharacters?

Description

Does this string contain regex metacharacters?

Usage

contains_regex(string)

Arguments

Value

Logical: does string contain regex metacharacters?

Copy files between FileSystems

Description

Copy files between FileSystems

Usage

copy_files(from, to, chunk_size = 1024L * 1024L)

Arguments

Value

Nothing: called for side effects in the file system

Examples

# Copy an S3 bucket's files to a local directory:
copy_files("s3://your-bucket-name", "local-directory")
# Using a FileSystem object
copy_files(s3_bucket("your-bucket-name"), "local-directory")
# Or go the other way, from local to S3
copy_files("local-directory", s3_bucket("your-bucket-name"))

Manage the global CPU thread pool in libarrow

Description

Manage the global CPU thread pool in libarrow

Usage

cpu_count()

set_cpu_count(num_threads)

Arguments

Create a source bundle that includes all thirdparty dependencies

Description

Create a source bundle that includes all thirdparty dependencies

Usage

create_package_with_all_dependencies(dest_file = NULL, source_file = NULL)

Arguments

Value

The full path to dest_file, invisibly

This function is used for setting up an offline build. If it's possible to download at build time, don't use this function. Instead, let cmake download the required dependencies for you. These downloaded dependencies are only used in the build if ARROW_DEPENDENCY_SOURCE is unset, BUNDLED, or AUTO. https://arrow.apache.org/docs/developers/cpp/building.html#offline-builds

If you're using binary packages you shouldn't need to use this function. You should download the appropriate binary from your package repository, transfer that to the offline computer, and install that. Any OS can create the source bundle, but it cannot be installed on Windows. (Instead, use a standard Windows binary package.)

Note if you're using RStudio Package Manager on Linux: If you still want to make a source bundle with this function, make sure to set the first repo in options("repos") to be a mirror that contains source packages (that is: something other than the RSPM binary mirror URLs).

Steps for an offline install with optional dependencies:

Using a computer with internet access, pre-download the dependencies:

• Install the arrow package or run source("https://raw.githubusercontent.com/apache/arrow/main/r/R/install-arrow.R")

• Run create_package_with_all_dependencies("my_arrow_pkg.tar.gz")

• Copy the newly created my_arrow_pkg.tar.gz to the computer without internet access

On the computer without internet access, install the prepared package:

• Install the arrow package from the copied file

  • install.packages("my_arrow_pkg.tar.gz", dependencies = c("Depends", "Imports", "LinkingTo"))

  • This installation will build from source, so cmake must be available

• Run arrow_info() to check installed capabilities

Examples

## Not run: 
new_pkg <- create_package_with_all_dependencies()
# Note: this works when run in the same R session, but it's meant to be
# copied to a different computer.
install.packages(new_pkg, dependencies = c("Depends", "Imports", "LinkingTo"))

## End(Not run)

CSV Convert Options

Description

CSV Convert Options

Usage

csv_convert_options(
  check_utf8 = TRUE,
  null_values = c("", "NA"),
  true_values = c("T", "true", "TRUE"),
  false_values = c("F", "false", "FALSE"),
  strings_can_be_null = FALSE,
  col_types = NULL,
  auto_dict_encode = FALSE,
  auto_dict_max_cardinality = 50L,
  include_columns = character(),
  include_missing_columns = FALSE,
  timestamp_parsers = NULL,
  decimal_point = "."
)

Arguments

Examples

tf <- tempfile()
on.exit(unlink(tf))
writeLines("x\n1\nNULL\n2\nNA", tf)
read_csv_arrow(tf, convert_options = csv_convert_options(null_values = c("", "NA", "NULL")))
open_csv_dataset(tf, convert_options = csv_convert_options(null_values = c("", "NA", "NULL")))

CSV Parsing Options

Description

CSV Parsing Options

Usage

csv_parse_options(
  delimiter = ",",
  quoting = TRUE,
  quote_char = "\"",
  double_quote = TRUE,
  escaping = FALSE,
  escape_char = "\\",
  newlines_in_values = FALSE,
  ignore_empty_lines = TRUE
)

Arguments

Examples

tf <- tempfile()
on.exit(unlink(tf))
writeLines("x\n1\n\n2", tf)
read_csv_arrow(tf, parse_options = csv_parse_options(ignore_empty_lines = FALSE))
open_csv_dataset(tf, parse_options = csv_parse_options(ignore_empty_lines = FALSE))

CSV Reading Options

Description

CSV Reading Options

Usage

csv_read_options(
  use_threads = option_use_threads(),
  block_size = 1048576L,
  skip_rows = 0L,
  column_names = character(0),
  autogenerate_column_names = FALSE,
  encoding = "UTF-8",
  skip_rows_after_names = 0L
)

Arguments

Examples

tf <- tempfile()
on.exit(unlink(tf))
writeLines("my file has a non-data header\nx\n1\n2", tf)
read_csv_arrow(tf, read_options = csv_read_options(skip_rows = 1))
open_csv_dataset(tf, read_options = csv_read_options(skip_rows = 1))

CSV Writing Options

Description

CSV Writing Options

Usage

csv_write_options(
  include_header = TRUE,
  batch_size = 1024L,
  null_string = "",
  delimiter = ",",
  eol = "\n",
  quoting_style = c("Needed", "AllValid", "None")
)

Arguments

Examples

tf <- tempfile()
on.exit(unlink(tf))
write_csv_arrow(airquality, tf, write_options = csv_write_options(null_string = "-99"))

CSV dataset file format

Description

A CSVFileFormat is a FileFormat subclass which holds information about how to read and parse the files included in a CSV Dataset.

Value

A CsvFileFormat object

Factory

CSVFileFormat$create() can take options in the form of lists passed through as parse_options, read_options, or convert_options parameters. Alternatively, readr-style options can be passed through individually. While it is possible to pass in CSVReadOptions, CSVConvertOptions, and CSVParseOptions objects, this is not recommended as options set in these objects are not validated for compatibility.

See Also

FileFormat

Examples

# Set up directory for examples
tf <- tempfile()
dir.create(tf)
on.exit(unlink(tf))
df <- data.frame(x = c("1", "2", "NULL"))
write.table(df, file.path(tf, "file1.txt"), sep = ",", row.names = FALSE)

# Create CsvFileFormat object with Arrow-style null_values option
format <- CsvFileFormat$create(convert_options = list(null_values = c("", "NA", "NULL")))
open_dataset(tf, format = format)

# Use readr-style options
format <- CsvFileFormat$create(na = c("", "NA", "NULL"))
open_dataset(tf, format = format)

File reader options

Description

CsvReadOptions, CsvParseOptions, CsvConvertOptions, JsonReadOptions, JsonParseOptions, and TimestampParser are containers for various file reading options. See their usage in read_csv_arrow() and read_json_arrow(), respectively.

Factory

The CsvReadOptions$create() and JsonReadOptions$create() factory methods take the following arguments:

• use_threads Whether to use the global CPU thread pool

• block_size Block size we request from the IO layer; also determines the size of chunks when use_threads is TRUE. NB: if FALSE, JSON input must end with an empty line.

CsvReadOptions$create() further accepts these additional arguments:

• skip_rows Number of lines to skip before reading data (default 0).

• column_names Character vector to supply column names. If length-0 (the default), the first non-skipped row will be parsed to generate column names, unless autogenerate_column_names is TRUE.

• autogenerate_column_names Logical: generate column names instead of using the first non-skipped row (the default)? If TRUE, column names will be "f0", "f1", ..., "fN".

• encoding The file encoding. (default "UTF-8")

• skip_rows_after_names Number of lines to skip after the column names (default 0). This number can be larger than the number of rows in one block, and empty rows are counted. The order of application is as follows:

  • skip_rows is applied (if non-zero);

  • column names are read (unless column_names is set);

  • skip_rows_after_names is applied (if non-zero).

CsvParseOptions$create() takes the following arguments:

• delimiter Field delimiting character (default ",")

• quoting Logical: are strings quoted? (default TRUE)

• quote_char Quoting character, if quoting is TRUE (default '"')

• double_quote Logical: are quotes inside values double-quoted? (default TRUE)

• escaping Logical: whether escaping is used (default FALSE)

• escape_char Escaping character, if escaping is TRUE (default "\\")

• newlines_in_values Logical: are values allowed to contain CR (0x0d) and LF (0x0a) characters? (default FALSE)

• ignore_empty_lines Logical: should empty lines be ignored (default) or generate a row of missing values (if FALSE)?

JsonParseOptions$create() accepts only the newlines_in_values argument.

CsvConvertOptions$create() takes the following arguments:

• check_utf8 Logical: check UTF8 validity of string columns? (default TRUE)

• null_values character vector of recognized spellings for null values. Analogous to the na.strings argument to read.csv() or na in readr::read_csv().

• strings_can_be_null Logical: can string / binary columns have null values? Similar to the quoted_na argument to readr::read_csv(). (default FALSE)

• true_values character vector of recognized spellings for TRUE values

• false_values character vector of recognized spellings for FALSE values

• col_types A Schema or NULL to infer types

• auto_dict_encode Logical: Whether to try to automatically dictionary-encode string / binary data (think stringsAsFactors). Default FALSE. This setting is ignored for non-inferred columns (those in col_types).

• auto_dict_max_cardinality If auto_dict_encode, string/binary columns are dictionary-encoded up to this number of unique values (default 50), after which it switches to regular encoding.

• include_columns If non-empty, indicates the names of columns from the CSV file that should be actually read and converted (in the vector's order).

• include_missing_columns Logical: if include_columns is provided, should columns named in it but not found in the data be included as a column of type null()? The default (FALSE) means that the reader will instead raise an error.

• timestamp_parsers User-defined timestamp parsers. If more than one parser is specified, the CSV conversion logic will try parsing values starting from the beginning of this vector. Possible values are (a) NULL, the default, which uses the ISO-8601 parser; (b) a character vector of strptime parse strings; or (c) a list of TimestampParser objects.

• decimal_point Character to use for decimal point in floating point numbers. Default: "."

TimestampParser$create() takes an optional format string argument. See strptime() for example syntax. The default is to use an ISO-8601 format parser.

The CsvWriteOptions$create() factory method takes the following arguments:

• include_header Whether to write an initial header line with column names

• batch_size Maximum number of rows processed at a time. Default is 1024.

• null_string The string to be written for null values. Must not contain quotation marks. Default is an empty string ("").

• eol The end of line character to use for ending rows.

• delimiter Field delimiter

• quoting_style Quoting style: "Needed" (Only enclose values in quotes which need them, because their CSV rendering can contain quotes itself (e.g. strings or binary values)), "AllValid" (Enclose all valid values in quotes), or "None" (Do not enclose any values in quotes).

Active bindings

• column_names: from CsvReadOptions

Arrow CSV and JSON table reader classes

Description

CsvTableReader and JsonTableReader wrap the Arrow C++ CSV and JSON table readers. See their usage in read_csv_arrow() and read_json_arrow(), respectively.

Factory

The CsvTableReader$create() and JsonTableReader$create() factory methods take the following arguments:

• file An Arrow InputStream

• convert_options (CSV only), parse_options, read_options: see CsvReadOptions

• ... additional parameters.

Methods

• ⁠$Read()⁠: returns an Arrow Table.

Create Arrow data types

Description

These functions create type objects corresponding to Arrow types. Use them when defining a schema() or as inputs to other types, like struct. Most of these functions don't take arguments, but a few do.

Usage

int8()

int16()

int32()

int64()

uint8()

uint16()

uint32()

uint64()

float16()

halffloat()

float32()

float()

float64()

boolean()

bool()

utf8()

large_utf8()

binary()

large_binary()

fixed_size_binary(byte_width)

string()

date32()

date64()

time32(unit = c("ms", "s"))

time64(unit = c("ns", "us"))

duration(unit = c("s", "ms", "us", "ns"))

null()

timestamp(unit = c("s", "ms", "us", "ns"), timezone = "")

decimal(precision, scale)

decimal128(precision, scale)

decimal256(precision, scale)

struct(...)

list_of(type)

large_list_of(type)

fixed_size_list_of(type, list_size)

map_of(key_type, item_type, .keys_sorted = FALSE)

Arguments

Details

A few functions have aliases:

• utf8() and string()

• float16() and halffloat()

• float32() and float()

• bool() and boolean()

• When called inside an arrow function, such as schema() or cast(), double() also is supported as a way of creating a float64()

date32() creates a datetime type with a "day" unit, like the R Date class. date64() has a "ms" unit.

uint32 (32 bit unsigned integer), uint64 (64 bit unsigned integer), and int64 (64-bit signed integer) types may contain values that exceed the range of R's integer type (32-bit signed integer). When these arrow objects are translated to R objects, uint32 and uint64 are converted to double ("numeric") and int64 is converted to bit64::integer64. For int64 types, this conversion can be disabled (so that int64 always yields a bit64::integer64 object) by setting options(arrow.int64_downcast = FALSE).

decimal128() creates a Decimal128Type. Arrow decimals are fixed-point decimal numbers encoded as a scalar integer. The precision is the number of significant digits that the decimal type can represent; the scale is the number of digits after the decimal point. For example, the number 1234.567 has a precision of 7 and a scale of 3. Note that scale can be negative.

As an example, decimal128(7, 3) can exactly represent the numbers 1234.567 and -1234.567 (encoded internally as the 128-bit integers 1234567 and -1234567, respectively), but neither 12345.67 nor 123.4567.

decimal128(5, -3) can exactly represent the number 12345000 (encoded internally as the 128-bit integer 12345), but neither 123450000 nor 1234500. The scale can be thought of as an argument that controls rounding. When negative, scale causes the number to be expressed using scientific notation and power of 10.

decimal256() creates a Decimal256Type, which allows for higher maximum precision. For most use cases, the maximum precision offered by Decimal128Type is sufficient, and it will result in a more compact and more efficient encoding.

decimal() creates either a Decimal128Type or a Decimal256Type depending on the value for precision. If precision is greater than 38 a Decimal256Type is returned, otherwise a Decimal128Type.

Use decimal128() or decimal256() as the names are more informative than decimal().

Value

An Arrow type object inheriting from DataType.

See Also

dictionary() for creating a dictionary (factor-like) type.

Examples

bool()
struct(a = int32(), b = double())
timestamp("ms", timezone = "CEST")
time64("ns")

# Use the cast method to change the type of data contained in Arrow objects.
# Please check the documentation of each data object class for details.
my_scalar <- Scalar$create(0L, type = int64()) # int64
my_scalar$cast(timestamp("ns")) # timestamp[ns]

my_array <- Array$create(0L, type = int64()) # int64
my_array$cast(timestamp("s", timezone = "UTC")) # timestamp[s, tz=UTC]

my_chunked_array <- chunked_array(0L, 1L) # int32
my_chunked_array$cast(date32()) # date32[day]

# You can also use `cast()` in an Arrow dplyr query.
if (requireNamespace("dplyr", quietly = TRUE)) {
  library(dplyr, warn.conflicts = FALSE)
  arrow_table(mtcars) %>%
    transmute(
      col1 = cast(cyl, string()),
      col2 = cast(cyl, int8())
    ) %>%
    compute()
}

Multi-file datasets

Description

Arrow Datasets allow you to query against data that has been split across multiple files. This sharding of data may indicate partitioning, which can accelerate queries that only touch some partitions (files).

A Dataset contains one or more Fragments, such as files, of potentially differing type and partitioning.

For Dataset$create(), see open_dataset(), which is an alias for it.

DatasetFactory is used to provide finer control over the creation of Datasets.

Factory

DatasetFactory is used to create a Dataset, inspect the Schema of the fragments contained in it, and declare a partitioning. FileSystemDatasetFactory is a subclass of DatasetFactory for discovering files in the local file system, the only currently supported file system.

For the DatasetFactory$create() factory method, see dataset_factory(), an alias for it. A DatasetFactory has:

• ⁠$Inspect(unify_schemas)⁠: If unify_schemas is TRUE, all fragments will be scanned and a unified Schema will be created from them; if FALSE (default), only the first fragment will be inspected for its schema. Use this fast path when you know and trust that all fragments have an identical schema.

• ⁠$Finish(schema, unify_schemas)⁠: Returns a Dataset. If schema is provided, it will be used for the Dataset; if omitted, a Schema will be created from inspecting the fragments (files) in the dataset, following unify_schemas as described above.

FileSystemDatasetFactory$create() is a lower-level factory method and takes the following arguments:

• filesystem: A FileSystem

• selector: Either a FileSelector or NULL

• paths: Either a character vector of file paths or NULL

• format: A FileFormat

• partitioning: Either Partitioning, PartitioningFactory, or NULL

Methods

A Dataset has the following methods:

• ⁠$NewScan()⁠: Returns a ScannerBuilder for building a query

• ⁠$WithSchema()⁠: Returns a new Dataset with the specified schema. This method currently supports only adding, removing, or reordering fields in the schema: you cannot alter or cast the field types.

• ⁠$schema⁠: Active binding that returns the Schema of the Dataset; you may also replace the dataset's schema by using ds$schema <- new_schema.

FileSystemDataset has the following methods:

• ⁠$files⁠: Active binding, returns the files of the FileSystemDataset

• ⁠$format⁠: Active binding, returns the FileFormat of the FileSystemDataset

UnionDataset has the following methods:

• ⁠$children⁠: Active binding, returns all child Datasets.

See Also

open_dataset() for a simple interface to creating a Dataset

Create a DatasetFactory

Description

A Dataset can constructed using one or more DatasetFactorys. This function helps you construct a DatasetFactory that you can pass to open_dataset().

Usage

dataset_factory(
  x,
  filesystem = NULL,
  format = c("parquet", "arrow", "ipc", "feather", "csv", "tsv", "text", "json"),
  partitioning = NULL,
  hive_style = NA,
  factory_options = list(),
  ...
)

Arguments

Details

If you would only have a single DatasetFactory (for example, you have a single directory containing Parquet files), you can call open_dataset() directly. Use dataset_factory() when you want to combine different directories, file systems, or file formats.

Value

A DatasetFactory object. Pass this to open_dataset(), in a list potentially with other DatasetFactory objects, to create a Dataset.

DataType class

Description

DataType class

R6 Methods

• ⁠$ToString()⁠: String representation of the DataType

• ⁠$Equals(other)⁠: Is the DataType equal to other

• ⁠$fields()⁠: The children fields associated with this type

• ⁠$code(namespace)⁠: Produces an R call of the data type. Use namespace=TRUE to call with ⁠arrow::⁠.

There are also some active bindings:

• ⁠$id⁠: integer Arrow type id.

• ⁠$name⁠: string Arrow type name.

• ⁠$num_fields⁠: number of child fields.

See Also

infer_type()

data-type

Arrow's default MemoryPool

Description

Arrow's default MemoryPool

Usage

default_memory_pool()

Value

the default MemoryPool

Create a dictionary type

Description

Create a dictionary type

Usage

dictionary(index_type = int32(), value_type = utf8(), ordered = FALSE)

Arguments

Value

A DictionaryType

See Also

Other Arrow data types

class DictionaryType

Description

class DictionaryType

Methods

TODO

Arrow enums

Description

Arrow enums

Usage

TimeUnit

DateUnit

Type

StatusCode

FileMode

MessageType

CompressionType

FileType

ParquetVersionType

MetadataVersion

QuantileInterpolation

NullEncodingBehavior

NullHandlingBehavior

RoundMode

JoinType

Format

An object of class TimeUnit::type (inherits from arrow-enum) of length 4.

An object of class DateUnit (inherits from arrow-enum) of length 2.

An object of class Type::type (inherits from arrow-enum) of length 39.

An object of class StatusCode (inherits from arrow-enum) of length 13.

An object of class FileMode (inherits from arrow-enum) of length 3.

An object of class MessageType (inherits from arrow-enum) of length 5.

An object of class Compression::type (inherits from arrow-enum) of length 9.

An object of class FileType (inherits from arrow-enum) of length 4.

An object of class ParquetVersionType (inherits from arrow-enum) of length 4.

An object of class MetadataVersion (inherits from arrow-enum) of length 5.

An object of class QuantileInterpolation (inherits from arrow-enum) of length 5.

An object of class NullEncodingBehavior (inherits from arrow-enum) of length 2.

An object of class NullHandlingBehavior (inherits from arrow-enum) of length 3.

An object of class RoundMode (inherits from arrow-enum) of length 10.

An object of class JoinType (inherits from arrow-enum) of length 8.

Arrow expressions

Description

Expressions are used to define filter logic for passing to a Dataset Scanner.

Expression$scalar(x) constructs an Expression which always evaluates to the provided scalar (length-1) R value.

Expression$field_ref(name) is used to construct an Expression which evaluates to the named column in the Dataset against which it is evaluated.

Expression$create(function_name, ..., options) builds a function-call Expression containing one or more Expressions. Anything in ... that is not already an expression will be wrapped in Expression$scalar().

Expression$op(FUN, ...) is for logical and arithmetic operators. Scalar inputs in ... will be attempted to be cast to the common type of the Expressions in the call so that the types of the columns in the Dataset are preserved and not unnecessarily upcast, which may be expensive.

ExtensionArray class

Description

ExtensionArray class

Methods

The ExtensionArray class inherits from Array, but also provides access to the underlying storage of the extension.

• ⁠$storage()⁠: Returns the underlying Array used to store values.

The ExtensionArray is not intended to be subclassed for extension types.

ExtensionType class

Description

ExtensionType class

Methods

The ExtensionType class inherits from DataType, but also defines extra methods specific to extension types:

• ⁠$storage_type()⁠: Returns the underlying DataType used to store values.

• ⁠$storage_id()⁠: Returns the Type identifier corresponding to the ⁠$storage_type()⁠.

• ⁠$extension_name()⁠: Returns the extension name.

• ⁠$extension_metadata()⁠: Returns the serialized version of the extension metadata as a raw() vector.

• ⁠$extension_metadata_utf8()⁠: Returns the serialized version of the extension metadata as a UTF-8 encoded string.

• ⁠$WrapArray(array)⁠: Wraps a storage Array into an ExtensionArray with this extension type.

In addition, subclasses may override the following methods to customize the behaviour of extension classes.

• ⁠$deserialize_instance()⁠: This method is called when a new ExtensionType is initialized and is responsible for parsing and validating the serialized extension_metadata (a raw() vector) such that its contents can be inspected by fields and/or methods of the R6 ExtensionType subclass. Implementations must also check the storage_type to make sure it is compatible with the extension type.

• ⁠$as_vector(extension_array)⁠: Convert an Array or ChunkedArray to an R vector. This method is called by as.vector() on ExtensionArray objects, when a RecordBatch containing an ExtensionArray is converted to a data.frame(), or when a ChunkedArray (e.g., a column in a Table) is converted to an R vector. The default method returns the converted storage array.

• ⁠$ToString()⁠ Return a string representation that will be printed to the console when this type or an Array of this type is printed.

FeatherReader class

Description

This class enables you to interact with Feather files. Create one to connect to a file or other InputStream, and call Read() on it to make an arrow::Table. See its usage in read_feather().

Factory

The FeatherReader$create() factory method instantiates the object and takes the following argument:

• file an Arrow file connection object inheriting from RandomAccessFile.

Methods

• ⁠$Read(columns)⁠: Returns a Table of the selected columns, a vector of integer indices

• ⁠$column_names⁠: Active binding, returns the column names in the Feather file

• ⁠$schema⁠: Active binding, returns the schema of the Feather file

• ⁠$version⁠: Active binding, returns 1 or 2, according to the Feather file version

Create a Field

Description

Create a Field

Usage

field(name, type, metadata, nullable = TRUE)

Arguments

See Also

Field

Examples

field("x", int32())

Field class

Description

field() lets you create an arrow::Field that maps a DataType to a column name. Fields are contained in Schemas.

Methods

• f$ToString(): convert to a string

• f$Equals(other): test for equality. More naturally called as f == other

Dataset file formats

Description

A FileFormat holds information about how to read and parse the files included in a Dataset. There are subclasses corresponding to the supported file formats (ParquetFileFormat and IpcFileFormat).

Factory

FileFormat$create() takes the following arguments:

• format: A string identifier of the file format. Currently supported values:

  • "parquet"

  • "ipc"/"arrow"/"feather", all aliases for each other; for Feather, note that only version 2 files are supported

  • "csv"/"text", aliases for the same thing (because comma is the default delimiter for text files

  • "tsv", equivalent to passing ⁠format = "text", delimiter = "\t"⁠

• ...: Additional format-specific options

  format = "parquet":

  • dict_columns: Names of columns which should be read as dictionaries.

  • Any Parquet options from FragmentScanOptions.

  format = "text": see CsvParseOptions. Note that you can specify them either with the Arrow C++ library naming ("delimiter", "quoting", etc.) or the readr-style naming used in read_csv_arrow() ("delim", "quote", etc.). Not all readr options are currently supported; please file an issue if you encounter one that arrow should support. Also, the following options are supported. From CsvReadOptions:

  • skip_rows

  • column_names. Note that if a Schema is specified, column_names must match those specified in the schema.

  • autogenerate_column_names From CsvFragmentScanOptions (these values can be overridden at scan time):

  • convert_options: a CsvConvertOptions

  • block_size

It returns the appropriate subclass of FileFormat (e.g. ParquetFileFormat)

Examples

## Semi-colon delimited files
# Set up directory for examples
tf <- tempfile()
dir.create(tf)
on.exit(unlink(tf))
write.table(mtcars, file.path(tf, "file1.txt"), sep = ";", row.names = FALSE)

# Create FileFormat object
format <- FileFormat$create(format = "text", delimiter = ";")

open_dataset(tf, format = format)

FileSystem entry info

Description

FileSystem entry info

Methods

• base_name() : The file base name (component after the last directory separator).

• extension() : The file extension

Active bindings

• ⁠$type⁠: The file type

• ⁠$path⁠: The full file path in the filesystem

• ⁠$size⁠: The size in bytes, if available. Only regular files are guaranteed to have a size.

• ⁠$mtime⁠: The time of last modification, if available.

file selector

Description

file selector

Factory

The ⁠$create()⁠ factory method instantiates a FileSelector given the 3 fields described below.

Fields

• base_dir: The directory in which to select files. If the path exists but doesn't point to a directory, this should be an error.

• allow_not_found: The behavior if base_dir doesn't exist in the filesystem. If FALSE, an error is returned. If TRUE, an empty selection is returned

• recursive: Whether to recurse into subdirectories.

FileSystem classes

Description

FileSystem is an abstract file system API, LocalFileSystem is an implementation accessing files on the local machine. SubTreeFileSystem is an implementation that delegates to another implementation after prepending a fixed base path

Factory

LocalFileSystem$create() returns the object and takes no arguments.

SubTreeFileSystem$create() takes the following arguments:

• base_path, a string path

• base_fs, a FileSystem object

S3FileSystem$create() optionally takes arguments:

• anonymous: logical, default FALSE. If true, will not attempt to look up credentials using standard AWS configuration methods.

• access_key, secret_key: authentication credentials. If one is provided, the other must be as well. If both are provided, they will override any AWS configuration set at the environment level.

• session_token: optional string for authentication along with access_key and secret_key

• role_arn: string AWS ARN of an AccessRole. If provided instead of access_key and secret_key, temporary credentials will be fetched by assuming this role.

• session_name: optional string identifier for the assumed role session.

• external_id: optional unique string identifier that might be required when you assume a role in another account.

• load_frequency: integer, frequency (in seconds) with which temporary credentials from an assumed role session will be refreshed. Default is 900 (i.e. 15 minutes)

• region: AWS region to connect to. If omitted, the AWS library will provide a sensible default based on client configuration, falling back to "us-east-1" if no other alternatives are found.

• endpoint_override: If non-empty, override region with a connect string such as "localhost:9000". This is useful for connecting to file systems that emulate S3.

• scheme: S3 connection transport (default "https")

• proxy_options: optional string, URI of a proxy to use when connecting to S3

• background_writes: logical, whether OutputStream writes will be issued in the background, without blocking (default TRUE)

• allow_bucket_creation: logical, if TRUE, the filesystem will create buckets if ⁠$CreateDir()⁠ is called on the bucket level (default FALSE).

• allow_bucket_deletion: logical, if TRUE, the filesystem will delete buckets if⁠$DeleteDir()⁠ is called on the bucket level (default FALSE).

• request_timeout: Socket read time on Windows and macOS in seconds. If negative, the AWS SDK default (typically 3 seconds).

• connect_timeout: Socket connection timeout in seconds. If negative, AWS SDK default is used (typically 1 second).

GcsFileSystem$create() optionally takes arguments:

• anonymous: logical, default FALSE. If true, will not attempt to look up credentials using standard GCS configuration methods.

• access_token: optional string for authentication. Should be provided along with expiration

• expiration: POSIXct. optional datetime representing point at which access_token will expire.

• json_credentials: optional string for authentication. Either a string containing JSON credentials or a path to their location on the filesystem. If a path to credentials is given, the file should be UTF-8 encoded.

• endpoint_override: if non-empty, will connect to provided host name / port, such as "localhost:9001", instead of default GCS ones. This is primarily useful for testing purposes.

• scheme: connection transport (default "https")

• default_bucket_location: the default location (or "region") to create new buckets in.

• retry_limit_seconds: the maximum amount of time to spend retrying if the filesystem encounters errors. Default is 15 seconds.

• default_metadata: default metadata to write in new objects.

• project_id: the project to use for creating buckets.

Methods

• path(x): Create a SubTreeFileSystem from the current FileSystem rooted at the specified path x.

• cd(x): Create a SubTreeFileSystem from the current FileSystem rooted at the specified path x.

• ls(path, ...): List files or objects at the given path or from the root of the FileSystem if path is not provided. Additional arguments passed to FileSelector$create, see FileSelector.

• ⁠$GetFileInfo(x)⁠: x may be a FileSelector or a character vector of paths. Returns a list of FileInfo

• ⁠$CreateDir(path, recursive = TRUE)⁠: Create a directory and subdirectories.

• ⁠$DeleteDir(path)⁠: Delete a directory and its contents, recursively.

• ⁠$DeleteDirContents(path)⁠: Delete a directory's contents, recursively. Like ⁠$DeleteDir()⁠, but doesn't delete the directory itself. Passing an empty path ("") will wipe the entire filesystem tree.

• ⁠$DeleteFile(path)⁠ : Delete a file.

• ⁠$DeleteFiles(paths)⁠ : Delete many files. The default implementation issues individual delete operations in sequence.

• ⁠$Move(src, dest)⁠: Move / rename a file or directory. If the destination exists: if it is a non-empty directory, an error is returned otherwise, if it has the same type as the source, it is replaced otherwise, behavior is unspecified (implementation-dependent).

• ⁠$CopyFile(src, dest)⁠: Copy a file. If the destination exists and is a directory, an error is returned. Otherwise, it is replaced.

• ⁠$OpenInputStream(path)⁠: Open an input stream for sequential reading.

• ⁠$OpenInputFile(path)⁠: Open an input file for random access reading.

• ⁠$OpenOutputStream(path)⁠: Open an output stream for sequential writing.

• ⁠$OpenAppendStream(path)⁠: Open an output stream for appending.

Active bindings

• ⁠$type_name⁠: string filesystem type name, such as "local", "s3", etc.

• ⁠$region⁠: string AWS region, for S3FileSystem and SubTreeFileSystem containing a S3FileSystem

• ⁠$base_fs⁠: for SubTreeFileSystem, the FileSystem it contains

• ⁠$base_path⁠: for SubTreeFileSystem, the path in ⁠$base_fs⁠ which is considered root in this SubTreeFileSystem.

• ⁠$options⁠: for GcsFileSystem, the options used to create the GcsFileSystem instance as a list

Notes

On S3FileSystem, ⁠$CreateDir()⁠ on a top-level directory creates a new bucket. When S3FileSystem creates new buckets (assuming allow_bucket_creation is TRUE), it does not pass any non-default settings. In AWS S3, the bucket and all objects will be not publicly visible, and will have no bucket policies and no resource tags. To have more control over how buckets are created, use a different API to create them.

On S3FileSystem, output is only produced for fatal errors or when printing return values. For troubleshooting, the log level can be set using the environment variable ARROW_S3_LOG_LEVEL (e.g., Sys.setenv("ARROW_S3_LOG_LEVEL"="DEBUG")). The log level must be set prior to running any code that interacts with S3. Possible values include 'FATAL' (the default), 'ERROR', 'WARN', 'INFO', 'DEBUG' (recommended), 'TRACE', and 'OFF'.

Format-specific write options

Description

A FileWriteOptions holds write options specific to a FileFormat.

FixedWidthType class

Description

FixedWidthType class

Methods

TODO

Connect to a Flight server

Description

Connect to a Flight server

Usage

flight_connect(host = "localhost", port, scheme = "grpc+tcp")

Arguments

Value

A pyarrow.flight.FlightClient.

Explicitly close a Flight client

Description

Explicitly close a Flight client

Usage

flight_disconnect(client)

Arguments

Get data from a Flight server

Description

Get data from a Flight server

Usage

flight_get(client, path)

Arguments

Value

A Table

Send data to a Flight server

Description

Send data to a Flight server

Usage

flight_put(client, data, path, overwrite = TRUE, max_chunksize = NULL)

Arguments

Value

client, invisibly.

Get a string representing a Dataset or RecordBatchReader object's schema

Description

Get a string representing a Dataset or RecordBatchReader object's schema

Usage

format_schema(obj)

Arguments

Value

A string containing a formatted representation of the schema of obj

Format-specific scan options

Description

A FragmentScanOptions holds options specific to a FileFormat and a scan operation.

Factory

FragmentScanOptions$create() takes the following arguments:

• format: A string identifier of the file format. Currently supported values:

  • "parquet"

  • "csv"/"text", aliases for the same format.

• ...: Additional format-specific options

  format = "parquet":

  • use_buffered_stream: Read files through buffered input streams rather than loading entire row groups at once. This may be enabled to reduce memory overhead. Disabled by default.

  • buffer_size: Size of buffered stream, if enabled. Default is 8KB.

  • pre_buffer: Pre-buffer the raw Parquet data. This can improve performance on high-latency filesystems. Disabled by default.

  • thrift_string_size_limit: Maximum string size allocated for decoding thrift strings. May need to be increased in order to read files with especially large headers. Default value 100000000.

  • thrift_container_size_limit: Maximum size of thrift containers. May need to be increased in order to read files with especially large headers. Default value 1000000. format = "text": see CsvConvertOptions. Note that options can only be specified with the Arrow C++ library naming. Also, "block_size" from CsvReadOptions may be given.

It returns the appropriate subclass of FragmentScanOptions (e.g. CsvFragmentScanOptions).

Get stringr pattern options

Description

This function assigns definitions for the stringr pattern modifier functions (fixed(), regex(), etc.) inside itself, and uses them to evaluate the quoted expression pattern, returning a list that is used to control pattern matching behavior in internal arrow functions.

Usage

get_stringr_pattern_options(pattern)

Arguments

Value

List containing elements pattern, fixed, and ignore_case

Connect to a Google Cloud Storage (GCS) bucket

Description

gs_bucket() is a convenience function to create an GcsFileSystem object that holds onto its relative path

Usage

gs_bucket(bucket, ...)

Arguments

Value

A SubTreeFileSystem containing an GcsFileSystem and the bucket's relative path. Note that this function's success does not guarantee that you are authorized to access the bucket's contents.

Examples

bucket <- gs_bucket("voltrondata-labs-datasets")

Construct Hive partitioning

Description

Hive partitioning embeds field names and values in path segments, such as "/year=2019/month=2/data.parquet".

Usage

hive_partition(..., null_fallback = NULL, segment_encoding = "uri")

Arguments

Details

Because fields are named in the path segments, order of fields passed to hive_partition() does not matter.

Value

A HivePartitioning, or a HivePartitioningFactory if calling hive_partition() with no arguments.

Examples

hive_partition(year = int16(), month = int8())

Extract a schema from an object

Description

Extract a schema from an object

Usage

infer_schema(x)

Arguments

Infer the arrow Array type from an R object

Description

type() is deprecated in favor of infer_type().

Usage

infer_type(x, ...)

type(x)

Arguments

Value

An arrow data type

Examples

infer_type(1:10)
infer_type(1L:10L)
infer_type(c(1, 1.5, 2))
infer_type(c("A", "B", "C"))
infer_type(mtcars)
infer_type(Sys.Date())
infer_type(as.POSIXlt(Sys.Date()))
infer_type(vctrs::new_vctr(1:5, class = "my_custom_vctr_class"))

InputStream classes

Description

RandomAccessFile inherits from InputStream and is a base class for: ReadableFile for reading from a file; MemoryMappedFile for the same but with memory mapping; and BufferReader for reading from a buffer. Use these with the various table readers.

Factory

The ⁠$create()⁠ factory methods instantiate the InputStream object and take the following arguments, depending on the subclass:

• path For ReadableFile, a character file name

• x For BufferReader, a Buffer or an object that can be made into a buffer via buffer().

To instantiate a MemoryMappedFile, call mmap_open().

Methods

• ⁠$GetSize()⁠:

• ⁠$supports_zero_copy()⁠: Logical

• ⁠$seek(position)⁠: go to that position in the stream

• ⁠$tell()⁠: return the position in the stream

• ⁠$close()⁠: close the stream

• ⁠$Read(nbytes)⁠: read data from the stream, either a specified nbytes or all, if nbytes is not provided

• ⁠$ReadAt(position, nbytes)⁠: similar to ⁠$seek(position)$Read(nbytes)⁠

• ⁠$Resize(size)⁠: for a MemoryMappedFile that is writeable

Install or upgrade the Arrow library

Description

Use this function to install the latest release of arrow, to switch to or from a nightly development version, or on Linux to try reinstalling with all necessary C++ dependencies.

Usage

install_arrow(
  nightly = FALSE,
  binary = Sys.getenv("LIBARROW_BINARY", TRUE),
  use_system = Sys.getenv("ARROW_USE_PKG_CONFIG", FALSE),
  minimal = Sys.getenv("LIBARROW_MINIMAL", FALSE),
  verbose = Sys.getenv("ARROW_R_DEV", FALSE),
  repos = getOption("repos"),
  ...
)

Arguments

Details

Note that, unlike packages like tensorflow, blogdown, and others that require external dependencies, you do not need to run install_arrow() after a successful arrow installation.

See Also

arrow_info() to see if the package was configured with necessary C++ dependencies. install guide for more ways to tune installation on Linux.

Install pyarrow for use with reticulate

Description

pyarrow is the Python package for Apache Arrow. This function helps with installing it for use with reticulate.

Usage

install_pyarrow(envname = NULL, nightly = FALSE, ...)

Arguments

Manage the global I/O thread pool in libarrow

Description

Manage the global I/O thread pool in libarrow

Usage

io_thread_count()

set_io_thread_count(num_threads)

Arguments

JSON dataset file format

Description

A JsonFileFormat is a FileFormat subclass which holds information about how to read and parse the files included in a JSON Dataset.

Value

A JsonFileFormat object

Factory

JsonFileFormat$create() can take options in the form of lists passed through as parse_options, or read_options parameters.

Available read_options parameters:

• use_threads: Whether to use the global CPU thread pool. Default TRUE. If FALSE, JSON input must end with an empty line.

• block_size: Block size we request from the IO layer; also determines size of chunks when use_threads is TRUE.

Available parse_options parameters:

• newlines_in_values:Logical: are values allowed to contain CR (0x0d or ⁠\r⁠) and LF (0x0a or ⁠\n⁠) characters? (default FALSE)

See Also

FileFormat

Examples

List available Arrow C++ compute functions

Description

This function lists the names of all available Arrow C++ library compute functions. These can be called by passing to call_function(), or they can be called by name with an arrow_ prefix inside a dplyr verb.

Usage

list_compute_functions(pattern = NULL, ...)

Arguments

Details

The resulting list describes the capabilities of your arrow build. Some functions, such as string and regular expression functions, require optional build-time C++ dependencies. If your arrow package was not compiled with those features enabled, those functions will not appear in this list.

Some functions take options that need to be passed when calling them (in a list called options). These options require custom handling in C++; many functions already have that handling set up but not all do. If you encounter one that needs special handling for options, please report an issue.

Note that this list does not enumerate all of the R bindings for these functions. The package includes Arrow methods for many base R functions that can be called directly on Arrow objects, as well as some tidyverse-flavored versions available inside dplyr verbs.

Value

A character vector of available Arrow C++ function names

See Also

acero for R bindings for Arrow functions

Examples

available_funcs <- list_compute_functions()
utf8_funcs <- list_compute_functions(pattern = "^UTF8", ignore.case = TRUE)

See available resources on a Flight server

Description

See available resources on a Flight server

Usage

list_flights(client)

flight_path_exists(client, path)

Arguments

Value

list_flights() returns a character vector of paths. flight_path_exists() returns a logical value, the equivalent of path %in% list_flights()

Load a Python Flight server

Description

Load a Python Flight server

Usage

load_flight_server(name, path = system.file(package = "arrow"))

Arguments

Examples

load_flight_server("demo_flight_server")

Handle a range of possible input sources

Description

Handle a range of possible input sources

Usage

make_readable_file(file, mmap = TRUE, random_access = TRUE)

Arguments

Value

An InputStream or a subclass of one.

Apply a function to a stream of RecordBatches

Description

As an alternative to calling collect() on a Dataset query, you can use this function to access the stream of RecordBatches in the Dataset. This lets you do more complex operations in R that operate on chunks of data without having to hold the entire Dataset in memory at once. You can include map_batches() in a dplyr pipeline and do additional dplyr methods on the stream of data in Arrow after it.

Usage

map_batches(X, FUN, ..., .schema = NULL, .lazy = TRUE, .data.frame = NULL)

Arguments

Details

This is experimental and not recommended for production use. It is also single-threaded and runs in R not C++, so it won't be as fast as core Arrow methods.

Value

An arrow_dplyr_query.

Value matching for Arrow objects

Description

base::match() and ⁠base::%in%⁠ are not generics, so we can't just define Arrow methods for them. These functions expose the analogous functions in the Arrow C++ library.

Usage

match_arrow(x, table, ...)

is_in(x, table, ...)

Arguments

Value

match_arrow() returns an int32-type Arrow object of the same length and type as x with the (0-based) indexes into table. is_in() returns a boolean-type Arrow object of the same length and type as x with values indicating per element of x it it is present in table.

Examples

# note that the returned value is 0-indexed
cars_tbl <- arrow_table(name = rownames(mtcars), mtcars)
match_arrow(Scalar$create("Mazda RX4 Wag"), cars_tbl$name)

is_in(Array$create("Mazda RX4 Wag"), cars_tbl$name)

# Although there are multiple matches, you are returned the index of the first
# match, as with the base R equivalent
match(4, mtcars$cyl) # 1-indexed
match_arrow(Scalar$create(4), cars_tbl$cyl) # 0-indexed

# If `x` contains multiple values, you are returned the indices of the first
# match for each value.
match(c(4, 6, 8), mtcars$cyl)
match_arrow(Array$create(c(4, 6, 8)), cars_tbl$cyl)

# Return type matches type of `x`
is_in(c(4, 6, 8), mtcars$cyl) # returns vector
is_in(Scalar$create(4), mtcars$cyl) # returns Scalar
is_in(Array$create(c(4, 6, 8)), cars_tbl$cyl) # returns Array
is_in(ChunkedArray$create(c(4, 6), 8), cars_tbl$cyl) # returns ChunkedArray

MemoryPool class

Description

MemoryPool class

Methods

• backend_name: one of "jemalloc", "mimalloc", or "system". Alternative memory allocators are optionally enabled at build time. Windows builds generally have mimalloc, and most others have both jemalloc (used by default) and mimalloc. To change memory allocators at runtime, set the environment variable ARROW_DEFAULT_MEMORY_POOL to one of those strings prior to loading the arrow library.

• bytes_allocated

• max_memory

Message class

Description

Message class

Methods

TODO

MessageReader class

Description

MessageReader class

Methods

TODO

Create a new read/write memory mapped file of a given size

Description

Create a new read/write memory mapped file of a given size

Usage

mmap_create(path, size)

Arguments

Value

a arrow::io::MemoryMappedFile

Open a memory mapped file

Description

Open a memory mapped file

Usage

mmap_open(path, mode = c("read", "write", "readwrite"))

Arguments

Extension types

Description

Extension arrays are wrappers around regular Arrow Array objects that provide some customized behaviour and/or storage. A common use-case for extension types is to define a customized conversion between an an Arrow Array and an R object when the default conversion is slow or loses metadata important to the interpretation of values in the array. For most types, the built-in vctrs extension type is probably sufficient.

Usage

new_extension_type(
  storage_type,
  extension_name,
  extension_metadata = raw(),
  type_class = ExtensionType
)

new_extension_array(storage_array, extension_type)

register_extension_type(extension_type)

reregister_extension_type(extension_type)

unregister_extension_type(extension_name)

Arguments

Details

These functions create, register, and unregister ExtensionType and ExtensionArray objects. To use an extension type you will have to:

• Define an R6::R6Class that inherits from ExtensionType and reimplement one or more methods (e.g., deserialize_instance()).

• Make a type constructor function (e.g., my_extension_type()) that calls new_extension_type() to create an R6 instance that can be used as a data type elsewhere in the package.

• Make an array constructor function (e.g., my_extension_array()) that calls new_extension_array() to create an Array instance of your extension type.

• Register a dummy instance of your extension type created using you constructor function using register_extension_type().

If defining an extension type in an R package, you will probably want to use reregister_extension_type() in that package's .onLoad() hook since your package will probably get reloaded in the same R session during its development and register_extension_type() will error if called twice for the same extension_name. For an example of an extension type that uses most of these features, see vctrs_extension_type().

Value

• new_extension_type() returns an ExtensionType instance according to the type_class specified.

• new_extension_array() returns an ExtensionArray whose ⁠$type⁠ corresponds to extension_type.

• register_extension_type(), unregister_extension_type() and reregister_extension_type() return NULL, invisibly.

Examples

# Create the R6 type whose methods control how Array objects are
# converted to R objects, how equality between types is computed,
# and how types are printed.
QuantizedType <- R6::R6Class(
  "QuantizedType",
  inherit = ExtensionType,
  public = list(
    # methods to access the custom metadata fields
    center = function() private$.center,
    scale = function() private$.scale,

    # called when an Array of this type is converted to an R vector
    as_vector = function(extension_array) {
      if (inherits(extension_array, "ExtensionArray")) {
        unquantized_arrow <-
          (extension_array$storage()$cast(float64()) / private$.scale) +
          private$.center

        as.vector(unquantized_arrow)
      } else {
        super$as_vector(extension_array)
      }
    },

    # populate the custom metadata fields from the serialized metadata
    deserialize_instance = function() {
      vals <- as.numeric(strsplit(self$extension_metadata_utf8(), ";")[[1]])
      private$.center <- vals[1]
      private$.scale <- vals[2]
    }
  ),
  private = list(
    .center = NULL,
    .scale = NULL
  )
)

# Create a helper type constructor that calls new_extension_type()
quantized <- function(center = 0, scale = 1, storage_type = int32()) {
  new_extension_type(
    storage_type = storage_type,
    extension_name = "arrow.example.quantized",
    extension_metadata = paste(center, scale, sep = ";"),
    type_class = QuantizedType
  )
}

# Create a helper array constructor that calls new_extension_array()
quantized_array <- function(x, center = 0, scale = 1,
                            storage_type = int32()) {
  type <- quantized(center, scale, storage_type)
  new_extension_array(
    Array$create((x - center) * scale, type = storage_type),
    type
  )
}

# Register the extension type so that Arrow knows what to do when
# it encounters this extension type
reregister_extension_type(quantized())

# Create Array objects and use them!
(vals <- runif(5, min = 19, max = 21))

(array <- quantized_array(
  vals,
  center = 20,
  scale = 2^15 - 1,
  storage_type = int16()
)
)

array$type$center()
array$type$scale()

as.vector(array)

Get one value from each group

Description

Returns one arbitrary value from the input for each group. The function is biased towards non-null values: if there is at least one non-null value for a certain group, that value is returned, and only if all the values are null for the group will the function return null.

Usage

one(...)

Arguments

Examples

## Not run: 
mtcars |>
  arrow_table() |>
  group_by(cyl) |>
  summarize(x = one(disp))

## End(Not run)

Open a multi-file dataset

Description

Arrow Datasets allow you to query against data that has been split across multiple files. This sharding of data may indicate partitioning, which can accelerate queries that only touch some partitions (files). Call open_dataset() to point to a directory of data files and return a Dataset, then use dplyr methods to query it.

Usage

open_dataset(
  sources,
  schema = NULL,
  partitioning = hive_partition(),
  hive_style = NA,
  unify_schemas = NULL,
  format = c("parquet", "arrow", "ipc", "feather", "csv", "tsv", "text", "json"),
  factory_options = list(),
  ...
)

Arguments

Value

A Dataset R6 object. Use dplyr methods on it to query the data, or call $NewScan() to construct a query directly.

Partitioning

Data is often split into multiple files and nested in subdirectories based on the value of one or more columns in the data. It may be a column that is commonly referenced in queries, or it may be time-based, for some examples. Data that is divided this way is "partitioned," and the values for those partitioning columns are encoded into the file path segments. These path segments are effectively virtual columns in the dataset, and because their values are known prior to reading the files themselves, we can greatly speed up filtered queries by skipping some files entirely.

Arrow supports reading partition information from file paths in two forms:

• "Hive-style", deriving from the Apache Hive project and common to some database systems. Partitions are encoded as "key=value" in path segments, such as "year=2019/month=1/file.parquet". While they may be awkward as file names, they have the advantage of being self-describing.

• "Directory" partitioning, which is Hive without the key names, like "2019/01/file.parquet". In order to use these, we need know at least what names to give the virtual columns that come from the path segments.

The default behavior in open_dataset() is to inspect the file paths contained in the provided directory, and if they look like Hive-style, parse them as Hive. If your dataset has Hive-style partitioning in the file paths, you do not need to provide anything in the partitioning argument to open_dataset() to use them. If you do provide a character vector of partition column names, they will be ignored if they match what is detected, and if they don't match, you'll get an error. (If you want to rename partition columns, do that using select() or rename() after opening the dataset.). If you provide a Schema and the names match what is detected, it will use the types defined by the Schema. In the example file path above, you could provide a Schema to specify that "month" should be int8() instead of the int32() it will be parsed as by default.

If your file paths do not appear to be Hive-style, or if you pass hive_style = FALSE, the partitioning argument will be used to create Directory partitioning. A character vector of names is required to create partitions; you may instead provide a Schema to map those names to desired column types, as described above. If neither are provided, no partitioning information will be taken from the file paths.

See Also

datasets article

Examples

# Set up directory for examples
tf <- tempfile()
dir.create(tf)
on.exit(unlink(tf))

write_dataset(mtcars, tf, partitioning = "cyl")

# You can specify a directory containing the files for your dataset and
# open_dataset will scan all files in your directory.
open_dataset(tf)

# You can also supply a vector of paths
open_dataset(c(file.path(tf, "cyl=4/part-0.parquet"), file.path(tf, "cyl=8/part-0.parquet")))

## You must specify the file format if using a format other than parquet.
tf2 <- tempfile()
dir.create(tf2)
on.exit(unlink(tf2))
write_dataset(mtcars, tf2, format = "ipc")
# This line will results in errors when you try to work with the data
## Not run: 
open_dataset(tf2)

## End(Not run)
# This line will work
open_dataset(tf2, format = "ipc")

## You can specify file partitioning to include it as a field in your dataset
# Create a temporary directory and write example dataset
tf3 <- tempfile()
dir.create(tf3)
on.exit(unlink(tf3))
write_dataset(airquality, tf3, partitioning = c("Month", "Day"), hive_style = FALSE)

# View files - you can see the partitioning means that files have been written
# to folders based on Month/Day values
tf3_files <- list.files(tf3, recursive = TRUE)

# With no partitioning specified, dataset contains all files but doesn't include
# directory names as field names
open_dataset(tf3)

# Now that partitioning has been specified, your dataset contains columns for Month and Day
open_dataset(tf3, partitioning = c("Month", "Day"))

# If you want to specify the data types for your fields, you can pass in a Schema
open_dataset(tf3, partitioning = schema(Month = int8(), Day = int8()))

Open a multi-file dataset of CSV or other delimiter-separated format

Description

A wrapper around open_dataset which explicitly includes parameters mirroring read_csv_arrow(), read_delim_arrow(), and read_tsv_arrow() to allow for easy switching between functions for opening single files and functions for opening datasets.

Usage

open_delim_dataset(
  sources,
  schema = NULL,
  partitioning = hive_partition(),
  hive_style = NA,
  unify_schemas = NULL,
  factory_options = list(),
  delim = ",",
  quote = "\"",
  escape_double = TRUE,
  escape_backslash = FALSE,
  col_names = TRUE,
  col_types = NULL,
  na = c("", "NA"),
  skip_empty_rows = TRUE,
  skip = 0L,
  convert_options = NULL,
  read_options = NULL,
  timestamp_parsers = NULL,
  quoted_na = TRUE,
  parse_options = NULL
)

open_csv_dataset(
  sources,
  schema = NULL,
  partitioning = hive_partition(),
  hive_style = NA,
  unify_schemas = NULL,
  factory_options = list(),
  quote = "\"",
  escape_double = TRUE,
  escape_backslash = FALSE,
  col_names = TRUE,
  col_types = NULL,
  na = c("", "NA"),
  skip_empty_rows = TRUE,
  skip = 0L,
  convert_options = NULL,
  read_options = NULL,
  timestamp_parsers = NULL,
  quoted_na = TRUE,
  parse_options = NULL
)

open_tsv_dataset(
  sources,
  schema = NULL,
  partitioning = hive_partition(),
  hive_style = NA,
  unify_schemas = NULL,
  factory_options = list(),
  quote = "\"",
  escape_double = TRUE,
  escape_backslash = FALSE,
  col_names = TRUE,
  col_types = NULL,
  na = c("", "NA"),
  skip_empty_rows = TRUE,
  skip = 0L,
  convert_options = NULL,
  read_options = NULL,
  timestamp_parsers = NULL,
  quoted_na = TRUE,
  parse_options = NULL
)

Arguments

Options currently supported by read_delim_arrow() which are not supported here

• file (instead, please specify files in sources)

• col_select (instead, subset columns after dataset creation)

• as_data_frame (instead, convert to data frame after dataset creation)

• parse_options

See Also

open_dataset()

Examples

# Set up directory for examples
tf <- tempfile()
dir.create(tf)
df <- data.frame(x = c("1", "2", "NULL"))

file_path <- file.path(tf, "file1.txt")
write.table(df, file_path, sep = ",", row.names = FALSE)

read_csv_arrow(file_path, na = c("", "NA", "NULL"), col_names = "y", skip = 1)
open_csv_dataset(file_path, na = c("", "NA", "NULL"), col_names = "y", skip = 1)

unlink(tf)

OutputStream classes

Description

FileOutputStream is for writing to a file; BufferOutputStream writes to a buffer; You can create one and pass it to any of the table writers, for example.

Factory

The ⁠$create()⁠ factory methods instantiate the OutputStream object and take the following arguments, depending on the subclass:

• path For FileOutputStream, a character file name

• initial_capacity For BufferOutputStream, the size in bytes of the buffer.

Methods

• ⁠$tell()⁠: return the position in the stream

• ⁠$close()⁠: close the stream

• ⁠$write(x)⁠: send x to the stream

• ⁠$capacity()⁠: for BufferOutputStream

• ⁠$finish()⁠: for BufferOutputStream

• ⁠$GetExtentBytesWritten()⁠: for MockOutputStream, report how many bytes were sent.

ParquetArrowReaderProperties class

Description

This class holds settings to control how a Parquet file is read by ParquetFileReader.

Factory

The ParquetArrowReaderProperties$create() factory method instantiates the object and takes the following arguments:

• use_threads Logical: whether to use multithreading (default TRUE)

Methods

• ⁠$read_dictionary(column_index)⁠

• ⁠$set_read_dictionary(column_index, read_dict)⁠

• ⁠$use_threads(use_threads)⁠

ParquetFileReader class

Description

This class enables you to interact with Parquet files.

Factory

The ParquetFileReader$create() factory method instantiates the object and takes the following arguments:

• file A character file name, raw vector, or Arrow file connection object (e.g. RandomAccessFile).

• props Optional ParquetArrowReaderProperties

• mmap Logical: whether to memory-map the file (default TRUE)

• reader_props Optional ParquetReaderProperties

• ... Additional arguments, currently ignored

Methods

• ⁠$ReadTable(column_indices)⁠: get an arrow::Table from the file. The optional ⁠column_indices=⁠ argument is a 0-based integer vector indicating which columns to retain.

• ⁠$ReadRowGroup(i, column_indices)⁠: get an arrow::Table by reading the ith row group (0-based). The optional ⁠column_indices=⁠ argument is a 0-based integer vector indicating which columns to retain.

• ⁠$ReadRowGroups(row_groups, column_indices)⁠: get an arrow::Table by reading several row groups (0-based integers). The optional ⁠column_indices=⁠ argument is a 0-based integer vector indicating which columns to retain.

• ⁠$GetSchema()⁠: get the arrow::Schema of the data in the file

• ⁠$ReadColumn(i)⁠: read the ith column (0-based) as a ChunkedArray.

Active bindings

• ⁠$num_rows⁠: number of rows.

• ⁠$num_columns⁠: number of columns.

• ⁠$num_row_groups⁠: number of row groups.

Examples

f <- system.file("v0.7.1.parquet", package = "arrow")
pq <- ParquetFileReader$create(f)
pq$GetSchema()
if (codec_is_available("snappy")) {
  # This file has compressed data columns
  tab <- pq$ReadTable()
  tab$schema
}

ParquetFileWriter class

Description

This class enables you to interact with Parquet files.

Factory

The ParquetFileWriter$create() factory method instantiates the object and takes the following arguments:

• schema A Schema

• sink An arrow::io::OutputStream

• properties An instance of ParquetWriterProperties

• arrow_properties An instance of ParquetArrowWriterProperties

Methods

• WriteTable Write a Table to sink

• WriteBatch Write a RecordBatch to sink

• Close Close the writer. Note: does not close the sink. arrow::io::OutputStream has its own close() method.

ParquetReaderProperties class

Description

This class holds settings to control how a Parquet file is read by ParquetFileReader.

Factory

The ParquetReaderProperties$create() factory method instantiates the object and takes no arguments.

Methods

• ⁠$thrift_string_size_limit()⁠

• ⁠$set_thrift_string_size_limit()⁠

• ⁠$thrift_container_size_limit()⁠

• ⁠$set_thrift_container_size_limit()⁠

ParquetWriterProperties class

Description

This class holds settings to control how a Parquet file is read by ParquetFileWriter.

Details

The parameters compression, compression_level, use_dictionary and write_statistics' support various patterns:

• The default NULL leaves the parameter unspecified, and the C++ library uses an appropriate default for each column (defaults listed above)

• A single, unnamed, value (e.g. a single string for compression) applies to all columns

• An unnamed vector, of the same size as the number of columns, to specify a value for each column, in positional order

• A named vector, to specify the value for the named columns, the default value for the setting is used when not supplied

Unlike the high-level write_parquet, ParquetWriterProperties arguments use the C++ defaults. Currently this means "uncompressed" rather than "snappy" for the compression argument.

Factory

The ParquetWriterProperties$create() factory method instantiates the object and takes the following arguments:

• table: table to write (required)

• version: Parquet version, "1.0" or "2.0". Default "1.0"

• compression: Compression type, algorithm "uncompressed"

• compression_level: Compression level; meaning depends on compression algorithm

• use_dictionary: Specify if we should use dictionary encoding. Default TRUE

• write_statistics: Specify if we should write statistics. Default TRUE

• data_page_size: Set a target threshold for the approximate encoded size of data pages within a column chunk (in bytes). Default 1 MiB.

See Also

write_parquet

Schema for information about schemas and metadata handling.

Define Partitioning for a Dataset

Description

Pass a Partitioning object to a FileSystemDatasetFactory's ⁠$create()⁠ method to indicate how the file's paths should be interpreted to define partitioning.

DirectoryPartitioning describes how to interpret raw path segments, in order. For example, schema(year = int16(), month = int8()) would define partitions for file paths like "2019/01/file.parquet", "2019/02/file.parquet", etc. In this scheme NULL values will be skipped. In the previous example: when writing a dataset if the month was NA (or NULL), the files would be placed in "2019/file.parquet". When reading, the rows in "2019/file.parquet" would return an NA for the month column. An error will be raised if an outer directory is NULL and an inner directory is not.

HivePartitioning is for Hive-style partitioning, which embeds field names and values in path segments, such as "/year=2019/month=2/data.parquet". Because fields are named in the path segments, order does not matter. This partitioning scheme allows NULL values. They will be replaced by a configurable null_fallback which defaults to the string "__HIVE_DEFAULT_PARTITION__" when writing. When reading, the null_fallback string will be replaced with NAs as appropriate.

PartitioningFactory subclasses instruct the DatasetFactory to detect partition features from the file paths.

Factory

Both DirectoryPartitioning$create() and HivePartitioning$create() methods take a Schema as a single input argument. The helper function hive_partition(...) is shorthand for HivePartitioning$create(schema(...)).

With DirectoryPartitioningFactory$create(), you can provide just the names of the path segments (in our example, c("year", "month")), and the DatasetFactory will infer the data types for those partition variables. HivePartitioningFactory$create() takes no arguments: both variable names and their types can be inferred from the file paths. hive_partition() with no arguments returns a HivePartitioningFactory.

Read a CSV or other delimited file with Arrow

Description

These functions uses the Arrow C++ CSV reader to read into a tibble. Arrow C++ options have been mapped to argument names that follow those of readr::read_delim(), and col_select was inspired by vroom::vroom().

Usage

read_delim_arrow(
  file,
  delim = ",",
  quote = "\"",
  escape_double = TRUE,
  escape_backslash = FALSE,
  schema = NULL,
  col_names = TRUE,
  col_types = NULL,
  col_select = NULL,
  na = c("", "NA"),
  quoted_na = TRUE,
  skip_empty_rows = TRUE,
  skip = 0L,
  parse_options = NULL,
  convert_options = NULL,
  read_options = NULL,
  as_data_frame = TRUE,
  timestamp_parsers = NULL,
  decimal_point = "."
)

read_csv_arrow(
  file,
  quote = "\"",
  escape_double = TRUE,
  escape_backslash = FALSE,
  schema = NULL,
  col_names = TRUE,
  col_types = NULL,
  col_select = NULL,
  na = c("", "NA"),
  quoted_na = TRUE,
  skip_empty_rows = TRUE,
  skip = 0L,
  parse_options = NULL,
  convert_options = NULL,
  read_options = NULL,
  as_data_frame = TRUE,
  timestamp_parsers = NULL
)

read_csv2_arrow(
  file,
  quote = "\"",
  escape_double = TRUE,
  escape_backslash = FALSE,
  schema = NULL,
  col_names = TRUE,
  col_types = NULL,
  col_select = NULL,
  na = c("", "NA"),
  quoted_na = TRUE,
  skip_empty_rows = TRUE,
  skip = 0L,
  parse_options = NULL,
  convert_options = NULL,
  read_options = NULL,
  as_data_frame = TRUE,
  timestamp_parsers = NULL
)

read_tsv_arrow(
  file,
  quote = "\"",
  escape_double = TRUE,
  escape_backslash = FALSE,
  schema = NULL,
  col_names = TRUE,
  col_types = NULL,
  col_select = NULL,
  na = c("", "NA"),
  quoted_na = TRUE,
  skip_empty_rows = TRUE,
  skip = 0L,
  parse_options = NULL,
  convert_options = NULL,
  read_options = NULL,
  as_data_frame = TRUE,
  timestamp_parsers = NULL
)

Arguments

Details

read_csv_arrow() and read_tsv_arrow() are wrappers around read_delim_arrow() that specify a delimiter. read_csv2_arrow() uses ⁠;⁠ for the delimiter and ⁠,⁠ for the decimal point.

Note that not all readr options are currently implemented here. Please file an issue if you encounter one that arrow should support.

If you need to control Arrow-specific reader parameters that don't have an equivalent in readr::read_csv(), you can either provide them in the parse_options, convert_options, or read_options arguments, or you can use CsvTableReader directly for lower-level access.

Value

A tibble, or a Table if as_data_frame = FALSE.

Specifying column types and names

By default, the CSV reader will infer the column names and data types from the file, but there are a few ways you can specify them directly.

One way is to provide an Arrow Schema in the schema argument, which is an ordered map of column name to type. When provided, it satisfies both the col_names and col_types arguments. This is good if you know all of this information up front.

You can also pass a Schema to the col_types argument. If you do this, column names will still be inferred from the file unless you also specify col_names. In either case, the column names in the Schema must match the data's column names, whether they are explicitly provided or inferred. That said, this Schema does not have to reference all columns: those omitted will have their types inferred.

Alternatively, you can declare column types by providing the compact string representation that readr uses to the col_types argument. This means you provide a single string, one character per column, where the characters map to Arrow types analogously to the readr type mapping:

• "c": utf8()

• "i": int32()

• "n": float64()

• "d": float64()

• "l": bool()

• "f": dictionary()

• "D": date32()

• "T": timestamp(unit = "ns")

• "t": time32() (The unit arg is set to the default value "ms")

• "_": null()

• "-": null()

• "?": infer the type from the data

If you use the compact string representation for col_types, you must also specify col_names.

Regardless of how types are specified, all columns with a null() type will be dropped.

Note that if you are specifying column names, whether by schema or col_names, and the CSV file has a header row that would otherwise be used to identify column names, you'll need to add skip = 1 to skip that row.

Examples

tf <- tempfile()
on.exit(unlink(tf))
write.csv(mtcars, file = tf)
df <- read_csv_arrow(tf)
dim(df)
# Can select columns
df <- read_csv_arrow(tf, col_select = starts_with("d"))

# Specifying column types and names
write.csv(data.frame(x = c(1, 3), y = c(2, 4)), file = tf, row.names = FALSE)
read_csv_arrow(tf, schema = schema(x = int32(), y = utf8()), skip = 1)
read_csv_arrow(tf, col_types = schema(y = utf8()))
read_csv_arrow(tf, col_types = "ic", col_names = c("x", "y"), skip = 1)

# Note that if a timestamp column contains time zones,
# the string "T" `col_types` specification won't work.
# To parse timestamps with time zones, provide a [Schema] to `col_types`
# and specify the time zone in the type object:
tf <- tempfile()
write.csv(data.frame(x = "1970-01-01T12:00:00+12:00"), file = tf, row.names = FALSE)
read_csv_arrow(
  tf,
  col_types = schema(x = timestamp(unit = "us", timezone = "UTC"))
)

# Read directly from strings with `I()`
read_csv_arrow(I("x,y\n1,2\n3,4"))
read_delim_arrow(I(c("x y", "1 2", "3 4")), delim = " ")

Read a Feather file (an Arrow IPC file)

Description

Feather provides binary columnar serialization for data frames. It is designed to make reading and writing data frames efficient, and to make sharing data across data analysis languages easy. read_feather() can read both the Feather Version 1 (V1), a legacy version available starting in 2016, and the Version 2 (V2), which is the Apache Arrow IPC file format. read_ipc_file() is an alias of read_feather().

Usage

read_feather(file, col_select = NULL, as_data_frame = TRUE, mmap = TRUE)

read_ipc_file(file, col_select = NULL, as_data_frame = TRUE, mmap = TRUE)

Arguments

Value

A tibble if as_data_frame is TRUE (the default), or an Arrow Table otherwise

See Also

FeatherReader and RecordBatchReader for lower-level access to reading Arrow IPC data.

Examples

# We recommend the ".arrow" extension for Arrow IPC files (Feather V2).
tf <- tempfile(fileext = ".arrow")
on.exit(unlink(tf))
write_feather(mtcars, tf)
df <- read_feather(tf)
dim(df)
# Can select columns
df <- read_feather(tf, col_select = starts_with("d"))

Read Arrow IPC stream format

Description

Apache Arrow defines two formats for serializing data for interprocess communication (IPC): a "stream" format and a "file" format, known as Feather. read_ipc_stream() and read_feather() read those formats, respectively.

Usage

read_ipc_stream(file, as_data_frame = TRUE, ...)

Arguments

Value

A tibble if as_data_frame is TRUE (the default), or an Arrow Table otherwise

See Also

write_feather() for writing IPC files. RecordBatchReader for a lower-level interface.

Read a JSON file

Description

Wrapper around JsonTableReader to read a newline-delimited JSON (ndjson) file into a data frame or Arrow Table.

Usage

read_json_arrow(
  file,
  col_select = NULL,
  as_data_frame = TRUE,
  schema = NULL,
  ...
)

Arguments

Details

If passed a path, will detect and handle compression from the file extension (e.g. .json.gz).

If schema is not provided, Arrow data types are inferred from the data:

• JSON null values convert to the null() type, but can fall back to any other type.

• JSON booleans convert to boolean().

• JSON numbers convert to int64(), falling back to float64() if a non-integer is encountered.

• JSON strings of the kind "YYYY-MM-DD" and "YYYY-MM-DD hh:mm:ss" convert to timestamp(unit = "s"), falling back to utf8() if a conversion error occurs.

• JSON arrays convert to a list_of() type, and inference proceeds recursively on the JSON arrays' values.

• Nested JSON objects convert to a struct() type, and inference proceeds recursively on the JSON objects' values.

When as_data_frame = TRUE, Arrow types are further converted to R types.

Value

A tibble, or a Table if as_data_frame = FALSE.

Examples

tf <- tempfile()
on.exit(unlink(tf))
writeLines('
    { "hello": 3.5, "world": false, "yo": "thing" }
    { "hello": 3.25, "world": null }
    { "hello": 0.0, "world": true, "yo": null }
  ', tf, useBytes = TRUE)

read_json_arrow(tf)

# Read directly from strings with `I()`
read_json_arrow(I(c('{"x": 1, "y": 2}', '{"x": 3, "y": 4}')))

Read a Message from a stream

Description

Read a Message from a stream

Usage

read_message(stream)

Arguments

Read a Parquet file

Description

'Parquet' is a columnar storage file format. This function enables you to read Parquet files into R.

Usage

read_parquet(
  file,
  col_select = NULL,
  as_data_frame = TRUE,
  props = ParquetArrowReaderProperties$create(),
  mmap = TRUE,
  ...
)

Arguments

Value

A tibble if as_data_frame is TRUE (the default), or an Arrow Table otherwise.

Examples

tf <- tempfile()
on.exit(unlink(tf))
write_parquet(mtcars, tf)
df <- read_parquet(tf, col_select = starts_with("d"))
head(df)

Read a Schema from a stream

Description

Read a Schema from a stream

Usage

read_schema(stream, ...)

Arguments

Value

A Schema

Create a RecordBatch

Description

Create a RecordBatch

Usage

record_batch(..., schema = NULL)

Arguments

Examples

batch <- record_batch(name = rownames(mtcars), mtcars)
dim(batch)
dim(head(batch))
names(batch)
batch$mpg
batch[["cyl"]]
as.data.frame(batch[4:8, c("gear", "hp", "wt")])

RecordBatch class

Description

A record batch is a collection of equal-length arrays matching a particular Schema. It is a table-like data structure that is semantically a sequence of fields, each a contiguous Arrow Array.

S3 Methods and Usage

Record batches are data-frame-like, and many methods you expect to work on a data.frame are implemented for RecordBatch. This includes [, [[, $, names, dim, nrow, ncol, head, and tail. You can also pull the data from an Arrow record batch into R with as.data.frame(). See the examples.

A caveat about the $ method: because RecordBatch is an R6 object, $ is also used to access the object's methods (see below). Methods take precedence over the table's columns. So, batch$Slice would return the "Slice" method function even if there were a column in the table called "Slice".

R6 Methods

In addition to the more R-friendly S3 methods, a RecordBatch object has the following R6 methods that map onto the underlying C++ methods:

• ⁠$Equals(other)⁠: Returns TRUE if the other record batch is equal

• ⁠$column(i)⁠: Extract an Array by integer position from the batch

• ⁠$column_name(i)⁠: Get a column's name by integer position

• ⁠$names()⁠: Get all column names (called by names(batch))

• ⁠$nbytes()⁠: Total number of bytes consumed by the elements of the record batch

• ⁠$RenameColumns(value)⁠: Set all column names (called by names(batch) <- value)

• ⁠$GetColumnByName(name)⁠: Extract an Array by string name

• ⁠$RemoveColumn(i)⁠: Drops a column from the batch by integer position

• ⁠$SelectColumns(indices)⁠: Return a new record batch with a selection of columns, expressed as 0-based integers.

• ⁠$Slice(offset, length = NULL)⁠: Create a zero-copy view starting at the indicated integer offset and going for the given length, or to the end of the table if NULL, the default.

• ⁠$Take(i)⁠: return an RecordBatch with rows at positions given by integers (R vector or Array Array) i.

• ⁠$Filter(i, keep_na = TRUE)⁠: return an RecordBatch with rows at positions where logical vector (or Arrow boolean Array) i is TRUE.

• ⁠$SortIndices(names, descending = FALSE)⁠: return an Array of integer row positions that can be used to rearrange the RecordBatch in ascending or descending order by the first named column, breaking ties with further named columns. descending can be a logical vector of length one or of the same length as names.

• ⁠$serialize()⁠: Returns a raw vector suitable for interprocess communication

• ⁠$cast(target_schema, safe = TRUE, options = cast_options(safe))⁠: Alter the schema of the record batch.

There are also some active bindings

• ⁠$num_columns⁠

• ⁠$num_rows⁠

• ⁠$schema⁠

• ⁠$metadata⁠: Returns the key-value metadata of the Schema as a named list. Modify or replace by assigning in (batch$metadata <- new_metadata). All list elements are coerced to string. See schema() for more information.

• ⁠$columns⁠: Returns a list of Arrays

RecordBatchReader classes

Description

Apache Arrow defines two formats for serializing data for interprocess communication (IPC): a "stream" format and a "file" format, known as Feather. RecordBatchStreamReader and RecordBatchFileReader are interfaces for accessing record batches from input sources in those formats, respectively.

For guidance on how to use these classes, see the examples section.

Factory

The RecordBatchFileReader$create() and RecordBatchStreamReader$create() factory methods instantiate the object and take a single argument, named according to the class:

• file A character file name, raw vector, or Arrow file connection object (e.g. RandomAccessFile).

• stream A raw vector, Buffer, or InputStream.

Methods

• ⁠$read_next_batch()⁠: Returns a RecordBatch, iterating through the Reader. If there are no further batches in the Reader, it returns NULL.

• ⁠$schema⁠: Returns a Schema (active binding)

• ⁠$batches()⁠: Returns a list of RecordBatches

• ⁠$read_table()⁠: Collects the reader's RecordBatches into a Table

• ⁠$get_batch(i)⁠: For RecordBatchFileReader, return a particular batch by an integer index.

• ⁠$num_record_batches()⁠: For RecordBatchFileReader, see how many batches are in the file.

See Also

read_ipc_stream() and read_feather() provide a much simpler interface for reading data from these formats and are sufficient for many use cases.

Examples

tf <- tempfile()
on.exit(unlink(tf))

batch <- record_batch(chickwts)

# This opens a connection to the file in Arrow
file_obj <- FileOutputStream$create(tf)
# Pass that to a RecordBatchWriter to write data conforming to a schema
writer <- RecordBatchFileWriter$create(file_obj, batch$schema)
writer$write(batch)
# You may write additional batches to the stream, provided that they have
# the same schema.
# Call "close" on the writer to indicate end-of-file/stream
writer$close()
# Then, close the connection--closing the IPC message does not close the file
file_obj$close()

# Now, we have a file we can read from. Same pattern: open file connection,
# then pass it to a RecordBatchReader
read_file_obj <- ReadableFile$create(tf)
reader <- RecordBatchFileReader$create(read_file_obj)
# RecordBatchFileReader knows how many batches it has (StreamReader does not)
reader$num_record_batches
# We could consume the Reader by calling $read_next_batch() until all are,
# consumed, or we can call $read_table() to pull them all into a Table
tab <- reader$read_table()
# Call as.data.frame to turn that Table into an R data.frame
df <- as.data.frame(tab)
# This should be the same data we sent
all.equal(df, chickwts, check.attributes = FALSE)
# Unlike the Writers, we don't have to close RecordBatchReaders,
# but we do still need to close the file connection
read_file_obj$close()

RecordBatchWriter classes

Description

Apache Arrow defines two formats for serializing data for interprocess communication (IPC): a "stream" format and a "file" format, known as Feather. RecordBatchStreamWriter and RecordBatchFileWriter are interfaces for writing record batches to those formats, respectively.

For guidance on how to use these classes, see the examples section.

Factory

The RecordBatchFileWriter$create() and RecordBatchStreamWriter$create() factory methods instantiate the object and take the following arguments:

• sink An OutputStream

• schema A Schema for the data to be written

• use_legacy_format logical: write data formatted so that Arrow libraries versions 0.14 and lower can read it. Default is FALSE. You can also enable this by setting the environment variable ARROW_PRE_0_15_IPC_FORMAT=1.

• metadata_version: A string like "V5" or the equivalent integer indicating the Arrow IPC MetadataVersion. Default (NULL) will use the latest version, unless the environment variable ARROW_PRE_1_0_METADATA_VERSION=1, in which case it will be V4.

Methods

• ⁠$write(x)⁠: Write a RecordBatch, Table, or data.frame, dispatching to the methods below appropriately

• ⁠$write_batch(batch)⁠: Write a RecordBatch to stream

• ⁠$write_table(table)⁠: Write a Table to stream

• ⁠$close()⁠: close stream. Note that this indicates end-of-file or end-of-stream–it does not close the connection to the sink. That needs to be closed separately.

See Also

write_ipc_stream() and write_feather() provide a much simpler interface for writing data to these formats and are sufficient for many use cases. write_to_raw() is a version that serializes data to a buffer.

Examples

tf <- tempfile()
on.exit(unlink(tf))

batch <- record_batch(chickwts)

# This opens a connection to the file in Arrow
file_obj <- FileOutputStream$create(tf)
# Pass that to a RecordBatchWriter to write data conforming to a schema
writer <- RecordBatchFileWriter$create(file_obj, batch$schema)
writer$write(batch)
# You may write additional batches to the stream, provided that they have
# the same schema.
# Call "close" on the writer to indicate end-of-file/stream
writer$close()
# Then, close the connection--closing the IPC message does not close the file
file_obj$close()

# Now, we have a file we can read from. Same pattern: open file connection,
# then pass it to a RecordBatchReader
read_file_obj <- ReadableFile$create(tf)
reader <- RecordBatchFileReader$create(read_file_obj)
# RecordBatchFileReader knows how many batches it has (StreamReader does not)
reader$num_record_batches
# We could consume the Reader by calling $read_next_batch() until all are,
# consumed, or we can call $read_table() to pull them all into a Table
tab <- reader$read_table()
# Call as.data.frame to turn that Table into an R data.frame
df <- as.data.frame(tab)
# This should be the same data we sent
all.equal(df, chickwts, check.attributes = FALSE)
# Unlike the Writers, we don't have to close RecordBatchReaders,
# but we do still need to close the file connection
read_file_obj$close()

Recycle scalar values in a list of arrays

Description

Recycle scalar values in a list of arrays

Usage

recycle_scalars(arrays)

Arguments

Value

List of arrays with any vector/Scalar/Array/ChunkedArray values of length 1 recycled

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

bit64

print.integer64, str.integer64

tidyselect

all_of, contains, ends_with, everything, last_col, matches, num_range, one_of, starts_with

Register compute bindings

Description

register_binding() is used to populate a list of functions that operate on (and return) Expressions. These are the basis for the .data mask inside dplyr methods.

Usage

register_binding(fun_name, fun, notes = character(0))

Arguments

Value

The previously registered binding or NULL if no previously registered function existed.

Writing bindings

• Expression$create() will wrap any non-Expression inputs as Scalar Expressions. If you want to try to coerce scalar inputs to match the type of the Expression(s) in the arguments, call cast_scalars_to_common_type(args) on the args. For example, Expression$create("add", args = list(int16_field, 1)) would result in a float64 type output because 1 is a double in R. To prevent casting all of the data in int16_field to float and to preserve it as int16, do Expression$create("add", args = cast_scalars_to_common_type(list(int16_field, 1)))

• Inside your function, you can call any other binding with call_binding().

Register user-defined functions

Description

These functions support calling R code from query engine execution (i.e., a dplyr::mutate() or dplyr::filter() on a Table or Dataset). Use register_scalar_function() attach Arrow input and output types to an R function and make it available for use in the dplyr interface and/or call_function(). Scalar functions are currently the only type of user-defined function supported. In Arrow, scalar functions must be stateless and return output with the same shape (i.e., the same number of rows) as the input.

Usage

register_scalar_function(name, fun, in_type, out_type, auto_convert = FALSE)

Arguments

Value

NULL, invisibly

Examples

library(dplyr, warn.conflicts = FALSE)

some_model <- lm(mpg ~ disp + cyl, data = mtcars)
register_scalar_function(
  "mtcars_predict_mpg",
  function(context, disp, cyl) {
    predict(some_model, newdata = data.frame(disp, cyl))
  },
  in_type = schema(disp = float64(), cyl = float64()),
  out_type = float64(),
  auto_convert = TRUE
)

as_arrow_table(mtcars) %>%
  transmute(mpg, mpg_predicted = mtcars_predict_mpg(disp, cyl)) %>%
  collect() %>%
  head()

Take an object of length 1 and repeat it.

Description

Take an object of length 1 and repeat it.

Usage

repeat_value_as_array(object, n)

Arguments

Value

Array of length n

Connect to an AWS S3 bucket

Description

s3_bucket() is a convenience function to create an S3FileSystem object that automatically detects the bucket's AWS region and holding onto the its relative path.

Usage

s3_bucket(bucket, ...)

Arguments

Details

By default, s3_bucket and other S3FileSystem functions only produce output for fatal errors or when printing their return values. When troubleshooting problems, it may be useful to increase the log level. See the Notes section in S3FileSystem for more information or see Examples below.

Value

A SubTreeFileSystem containing an S3FileSystem and the bucket's relative path. Note that this function's success does not guarantee that you are authorized to access the bucket's contents.

Examples

bucket <- s3_bucket("voltrondata-labs-datasets")


# Turn on debug logging. The following line of code should be run in a fresh
# R session prior to any calls to `s3_bucket()` (or other S3 functions)
Sys.setenv("ARROW_S3_LOG_LEVEL"="DEBUG")
bucket <- s3_bucket("voltrondata-labs-datasets")

Create an Arrow Scalar

Description

Create an Arrow Scalar

Usage

scalar(x, type = NULL)

Arguments

Examples

scalar(pi)
scalar(404)
# If you pass a vector into scalar(), you get a list containing your items
scalar(c(1, 2, 3))

scalar(9) == scalar(10)

Arrow scalars

Description

A Scalar holds a single value of an Arrow type.

Factory

The Scalar$create() factory method instantiates a Scalar and takes the following arguments:

• x: an R vector, list, or data.frame

• type: an optional data type for x. If omitted, the type will be inferred from the data.

Usage

a <- Scalar$create(x)
length(a)

print(a)
a == a

Methods

• ⁠$ToString()⁠: convert to a string

• ⁠$as_vector()⁠: convert to an R vector

• ⁠$as_array()⁠: convert to an Arrow Array

• ⁠$Equals(other)⁠: is this Scalar equal to other

• ⁠$ApproxEquals(other)⁠: is this Scalar approximately equal to other

• ⁠$is_valid⁠: is this Scalar valid

• ⁠$null_count⁠: number of invalid values - 1 or 0

• ⁠$type⁠: Scalar type

• ⁠$cast(target_type, safe = TRUE, options = cast_options(safe))⁠: cast value to a different type

Examples

Scalar$create(pi)
Scalar$create(404)
# If you pass a vector into Scalar$create, you get a list containing your items
Scalar$create(c(1, 2, 3))

# Comparisons
my_scalar <- Scalar$create(99)
my_scalar$ApproxEquals(Scalar$create(99.00001)) # FALSE
my_scalar$ApproxEquals(Scalar$create(99.000009)) # TRUE
my_scalar$Equals(Scalar$create(99.000009)) # FALSE
my_scalar$Equals(Scalar$create(99L)) # FALSE (types don't match)

my_scalar$ToString()

Scan the contents of a dataset

Description

A Scanner iterates over a Dataset's fragments and returns data according to given row filtering and column projection. A ScannerBuilder can help create one.

Factory

Scanner$create() wraps the ScannerBuilder interface to make a Scanner. It takes the following arguments:

• dataset: A Dataset or arrow_dplyr_query object, as returned by the dplyr methods on Dataset.

• projection: A character vector of column names to select columns or a named list of expressions

• filter: A Expression to filter the scanned rows by, or TRUE (default) to keep all rows.

• use_threads: logical: should scanning use multithreading? Default TRUE

• ...: Additional arguments, currently ignored

Methods

ScannerBuilder has the following methods:

• ⁠$Project(cols)⁠: Indicate that the scan should only return columns given by cols, a character vector of column names or a named list of Expression.

• ⁠$Filter(expr)⁠: Filter rows by an Expression.

• ⁠$UseThreads(threads)⁠: logical: should the scan use multithreading? The method's default input is TRUE, but you must call the method to enable multithreading because the scanner default is FALSE.

• ⁠$BatchSize(batch_size)⁠: integer: Maximum row count of scanned record batches, default is 32K. If scanned record batches are overflowing memory then this method can be called to reduce their size.

• ⁠$schema⁠: Active binding, returns the Schema of the Dataset

• ⁠$Finish()⁠: Returns a Scanner

Scanner currently has a single method, ⁠$ToTable()⁠, which evaluates the query and returns an Arrow Table.

Examples

# Set up directory for examples
tf <- tempfile()
dir.create(tf)
on.exit(unlink(tf))

write_dataset(mtcars, tf, partitioning="cyl")

ds <- open_dataset(tf)

scan_builder <- ds$NewScan()
scan_builder$Filter(Expression$field_ref("hp") > 100)
scan_builder$Project(list(hp_times_ten = 10 * Expression$field_ref("hp")))

# Once configured, call $Finish()
scanner <- scan_builder$Finish()

# Can get results as a table
as.data.frame(scanner$ToTable())

# Or as a RecordBatchReader
scanner$ToRecordBatchReader()

Create a schema or extract one from an object.

Description

Create a schema or extract one from an object.

Usage

schema(...)

Arguments

See Also

Schema for detailed documentation of the Schema R6 object

Examples

# Create schema using pairs of field names and data types
schema(a = int32(), b = float64())

# Create a schema using a list of pairs of field names and data types
schema(list(a = int8(), b = string()))

# Create schema using fields
schema(
  field("b", double()),
  field("c", bool(), nullable = FALSE),
  field("d", string())
)

# Extract schemas from objects
df <- data.frame(col1 = 2:4, col2 = c(0.1, 0.3, 0.5))
tab1 <- arrow_table(df)
schema(tab1)
tab2 <- arrow_table(df, schema = schema(col1 = int8(), col2 = float32()))
schema(tab2)

Schema class

Description

A Schema is an Arrow object containing Fields, which map names to Arrow data types. Create a Schema when you want to convert an R data.frame to Arrow but don't want to rely on the default mapping of R types to Arrow types, such as when you want to choose a specific numeric precision, or when creating a Dataset and you want to ensure a specific schema rather than inferring it from the various files.

Many Arrow objects, including Table and Dataset, have a ⁠$schema⁠ method (active binding) that lets you access their schema.

Methods

• ⁠$ToString()⁠: convert to a string

• ⁠$field(i)⁠: returns the field at index i (0-based)

• ⁠$GetFieldByName(x)⁠: returns the field with name x

• ⁠$WithMetadata(metadata)⁠: returns a new Schema with the key-value metadata set. Note that all list elements in metadata will be coerced to character.

• ⁠$code(namespace)⁠: returns the R code needed to generate this schema. Use namespace=TRUE to call with ⁠arrow::⁠.

Active bindings

• ⁠$names⁠: returns the field names (called in names(Schema))

• ⁠$num_fields⁠: returns the number of fields (called in length(Schema))

• ⁠$fields⁠: returns the list of Fields in the Schema, suitable for iterating over

• ⁠$HasMetadata⁠: logical: does this Schema have extra metadata?

• ⁠$metadata⁠: returns the key-value metadata as a named list. Modify or replace by assigning in (sch$metadata <- new_metadata). All list elements are coerced to string.

R Metadata

When converting a data.frame to an Arrow Table or RecordBatch, attributes from the data.frame are saved alongside tables so that the object can be reconstructed faithfully in R (e.g. with as.data.frame()). This metadata can be both at the top-level of the data.frame (e.g. attributes(df)) or at the column (e.g. attributes(df$col_a)) or for list columns only: element level (e.g. attributes(df[1, "col_a"])). For example, this allows for storing haven columns in a table and being able to faithfully re-create them when pulled back into R. This metadata is separate from the schema (column names and types) which is compatible with other Arrow clients. The R metadata is only read by R and is ignored by other clients (e.g. Pandas has its own custom metadata). This metadata is stored in ⁠$metadata$r⁠.

Since Schema metadata keys and values must be strings, this metadata is saved by serializing R's attribute list structure to a string. If the serialized metadata exceeds 100Kb in size, by default it is compressed starting in version 3.0.0. To disable this compression (e.g. for tables that are compatible with Arrow versions before 3.0.0 and include large amounts of metadata), set the option arrow.compress_metadata to FALSE. Files with compressed metadata are readable by older versions of arrow, but the metadata is dropped.

Show the details of an Arrow Execution Plan

Description

This is a function which gives more details about the logical query plan that will be executed when evaluating an arrow_dplyr_query object. It calls the C++ ExecPlan object's print method. Functionally, it is similar to dplyr::explain(). This function is used as the dplyr::explain() and dplyr::show_query() methods.

Usage

show_exec_plan(x)

Arguments

Value

x, invisibly.

Examples

library(dplyr)
mtcars %>%
  arrow_table() %>%
  filter(mpg > 20) %>%
  mutate(x = gear / carb) %>%
  show_exec_plan()

Table class

Description

A Table is a sequence of chunked arrays. They have a similar interface to record batches, but they can be composed from multiple record batches or chunked arrays.

S3 Methods and Usage

Tables are data-frame-like, and many methods you expect to work on a data.frame are implemented for Table. This includes [, [[, $, names, dim, nrow, ncol, head, and tail. You can also pull the data from an Arrow table into R with as.data.frame(). See the examples.

A caveat about the $ method: because Table is an R6 object, $ is also used to access the object's methods (see below). Methods take precedence over the table's columns. So, tab$Slice would return the "Slice" method function even if there were a column in the table called "Slice".

R6 Methods

In addition to the more R-friendly S3 methods, a Table object has the following R6 methods that map onto the underlying C++ methods:

• ⁠$column(i)⁠: Extract a ChunkedArray by integer position from the table

• ⁠$ColumnNames()⁠: Get all column names (called by names(tab))

• ⁠$nbytes()⁠: Total number of bytes consumed by the elements of the table

• ⁠$RenameColumns(value)⁠: Set all column names (called by names(tab) <- value)

• ⁠$GetColumnByName(name)⁠: Extract a ChunkedArray by string name

• ⁠$field(i)⁠: Extract a Field from the table schema by integer position

• ⁠$SelectColumns(indices)⁠: Return new Table with specified columns, expressed as 0-based integers.

• ⁠$Slice(offset, length = NULL)⁠: Create a zero-copy view starting at the indicated integer offset and going for the given length, or to the end of the table if NULL, the default.

• ⁠$Take(i)⁠: return an Table with rows at positions given by integers i. If i is an Arrow Array or ChunkedArray, it will be coerced to an R vector before taking.

• ⁠$Filter(i, keep_na = TRUE)⁠: return an Table with rows at positions where logical vector or Arrow boolean-type ⁠(Chunked)Array⁠ i is TRUE.

• ⁠$SortIndices(names, descending = FALSE)⁠: return an Array of integer row positions that can be used to rearrange the Table in ascending or descending order by the first named column, breaking ties with further named columns. descending can be a logical vector of length one or of the same length as names.

• ⁠$serialize(output_stream, ...)⁠: Write the table to the given OutputStream

• ⁠$cast(target_schema, safe = TRUE, options = cast_options(safe))⁠: Alter the schema of the record batch.

There are also some active bindings:

• ⁠$num_columns⁠

• ⁠$num_rows⁠

• ⁠$schema⁠

• ⁠$metadata⁠: Returns the key-value metadata of the Schema as a named list. Modify or replace by assigning in (tab$metadata <- new_metadata). All list elements are coerced to string. See schema() for more information.

• ⁠$columns⁠: Returns a list of ChunkedArrays

Create an Arrow object from a DuckDB connection

Description

This can be used in pipelines that pass data back and forth between Arrow and DuckDB.

Usage

to_arrow(.data)

Arguments

Details

Note that you can only call collect() or compute() on the result of this function once. To work around this limitation, you should either only call collect() as the final step in a pipeline or call as_arrow_table() on the result to materialize the entire Table in-memory.

Value

A RecordBatchReader.

Examples

library(dplyr)

ds <- InMemoryDataset$create(mtcars)

ds %>%
  filter(mpg < 30) %>%
  to_duckdb() %>%
  group_by(cyl) %>%
  summarize(mean_mpg = mean(mpg, na.rm = TRUE)) %>%
  to_arrow() %>%
  collect()

Create a (virtual) DuckDB table from an Arrow object

Description

This will do the necessary configuration to create a (virtual) table in DuckDB that is backed by the Arrow object given. No data is copied or modified until collect() or compute() are called or a query is run against the table.

Usage

to_duckdb(
  .data,
  con = arrow_duck_connection(),
  table_name = unique_arrow_tablename(),
  auto_disconnect = TRUE
)

Arguments

Details

The result is a dbplyr-compatible object that can be used in d(b)plyr pipelines.

If auto_disconnect = TRUE, the DuckDB table that is created will be configured to be unregistered when the tbl object is garbage collected. This is helpful if you don't want to have extra table objects in DuckDB after you've finished using them.

Value

A tbl of the new table in DuckDB

Examples

library(dplyr)

ds <- InMemoryDataset$create(mtcars)

ds %>%
  filter(mpg < 30) %>%
  group_by(cyl) %>%
  to_duckdb() %>%
  slice_min(disp)

Combine and harmonize schemas

Description

Combine and harmonize schemas

Usage

unify_schemas(..., schemas = list(...))

Arguments

Value

A Schema with the union of fields contained in the inputs, or NULL if any of schemas is NULL

Examples

a <- schema(b = double(), c = bool())
z <- schema(b = double(), k = utf8())
unify_schemas(a, z)

table for Arrow objects

Description

This function tabulates the values in the array and returns a table of counts.

Usage

value_counts(x)

Arguments

Value

A StructArray containing "values" (same type as x) and "counts" Int64.

Examples

cyl_vals <- Array$create(mtcars$cyl)
counts <- value_counts(cyl_vals)

Extension type for generic typed vectors

Description

Most common R vector types are converted automatically to a suitable Arrow data type without the need for an extension type. For vector types whose conversion is not suitably handled by default, you can create a vctrs_extension_array(), which passes vctrs::vec_data() to Array$create() and calls vctrs::vec_restore() when the Array is converted back into an R vector.

Usage

vctrs_extension_array(x, ptype = vctrs::vec_ptype(x), storage_type = NULL)

vctrs_extension_type(x, storage_type = infer_type(vctrs::vec_data(x)))

Arguments

Value

• vctrs_extension_array() returns an ExtensionArray instance with a vctrs_extension_type().

• vctrs_extension_type() returns an ExtensionType instance for the extension name "arrow.r.vctrs".

Examples

(array <- vctrs_extension_array(as.POSIXlt("2022-01-02 03:45", tz = "UTC")))
array$type
as.vector(array)

temp_feather <- tempfile()
write_feather(arrow_table(col = array), temp_feather)
read_feather(temp_feather)
unlink(temp_feather)

Write CSV file to disk

Description

Write CSV file to disk

Usage

write_csv_arrow(
  x,
  sink,
  file = NULL,
  include_header = TRUE,
  col_names = NULL,
  batch_size = 1024L,
  na = "",
  write_options = NULL,
  ...
)

Arguments

Value

The input x, invisibly. Note that if sink is an OutputStream, the stream will be left open.

Examples

tf <- tempfile()
on.exit(unlink(tf))
write_csv_arrow(mtcars, tf)

Write a dataset

Description

This function allows you to write a dataset. By writing to more efficient binary storage formats, and by specifying relevant partitioning, you can make it much faster to read and query.

Usage

write_dataset(
  dataset,
  path,
  format = c("parquet", "feather", "arrow", "ipc", "csv", "tsv", "txt", "text"),
  partitioning = dplyr::group_vars(dataset),
  basename_template = paste0("part-{i}.", as.character(format)),
  hive_style = TRUE,
  existing_data_behavior = c("overwrite", "error", "delete_matching"),
  max_partitions = 1024L,
  max_open_files = 900L,
  max_rows_per_file = 0L,
  min_rows_per_group = 0L,
  max_rows_per_group = bitwShiftL(1, 20),
  ...
)

Arguments

Value

The input dataset, invisibly

Examples

# You can write datasets partitioned by the values in a column (here: "cyl").
# This creates a structure of the form cyl=X/part-Z.parquet.
one_level_tree <- tempfile()
write_dataset(mtcars, one_level_tree, partitioning = "cyl")
list.files(one_level_tree, recursive = TRUE)

# You can also partition by the values in multiple columns
# (here: "cyl" and "gear").
# This creates a structure of the form cyl=X/gear=Y/part-Z.parquet.
two_levels_tree <- tempfile()
write_dataset(mtcars, two_levels_tree, partitioning = c("cyl", "gear"))
list.files(two_levels_tree, recursive = TRUE)

# In the two previous examples we would have:
# X = {4,6,8}, the number of cylinders.
# Y = {3,4,5}, the number of forward gears.
# Z = {0,1,2}, the number of saved parts, starting from 0.

# You can obtain the same result as as the previous examples using arrow with
# a dplyr pipeline. This will be the same as two_levels_tree above, but the
# output directory will be different.
library(dplyr)
two_levels_tree_2 <- tempfile()
mtcars %>%
  group_by(cyl, gear) %>%
  write_dataset(two_levels_tree_2)
list.files(two_levels_tree_2, recursive = TRUE)

# And you can also turn off the Hive-style directory naming where the column
# name is included with the values by using `hive_style = FALSE`.

# Write a structure X/Y/part-Z.parquet.
two_levels_tree_no_hive <- tempfile()
mtcars %>%
  group_by(cyl, gear) %>%
  write_dataset(two_levels_tree_no_hive, hive_style = FALSE)
list.files(two_levels_tree_no_hive, recursive = TRUE)

Write a dataset into partitioned flat files.

Description

The ⁠write_*_dataset()⁠ are a family of wrappers around write_dataset to allow for easy switching between functions for writing datasets.

Usage

write_delim_dataset(
  dataset,
  path,
  partitioning = dplyr::group_vars(dataset),
  basename_template = "part-{i}.txt",
  hive_style = TRUE,
  existing_data_behavior = c("overwrite", "error", "delete_matching"),
  max_partitions = 1024L,
  max_open_files = 900L,
  max_rows_per_file = 0L,
  min_rows_per_group = 0L,
  max_rows_per_group = bitwShiftL(1, 20),
  col_names = TRUE,
  batch_size = 1024L,
  delim = ",",
  na = "",
  eol = "\n",
  quote = c("needed", "all", "none")
)

write_csv_dataset(
  dataset,
  path,
  partitioning = dplyr::group_vars(dataset),
  basename_template = "part-{i}.csv",
  hive_style = TRUE,
  existing_data_behavior = c("overwrite", "error", "delete_matching"),
  max_partitions = 1024L,
  max_open_files = 900L,
  max_rows_per_file = 0L,
  min_rows_per_group = 0L,
  max_rows_per_group = bitwShiftL(1, 20),
  col_names = TRUE,
  batch_size = 1024L,
  delim = ",",
  na = "",
  eol = "\n",
  quote = c("needed", "all", "none")
)

write_tsv_dataset(
  dataset,
  path,
  partitioning = dplyr::group_vars(dataset),
  basename_template = "part-{i}.tsv",
  hive_style = TRUE,
  existing_data_behavior = c("overwrite", "error", "delete_matching"),
  max_partitions = 1024L,
  max_open_files = 900L,
  max_rows_per_file = 0L,
  min_rows_per_group = 0L,
  max_rows_per_group = bitwShiftL(1, 20),
  col_names = TRUE,
  batch_size = 1024L,
  na = "",
  eol = "\n",
  quote = c("needed", "all", "none")
)

Arguments

Value

The input dataset, invisibly.

See Also

write_dataset()

Write a Feather file (an Arrow IPC file)

Description

Feather provides binary columnar serialization for data frames. It is designed to make reading and writing data frames efficient, and to make sharing data across data analysis languages easy. write_feather() can write both the Feather Version 1 (V1), a legacy version available starting in 2016, and the Version 2 (V2), which is the Apache Arrow IPC file format. The default version is V2. V1 files are distinct from Arrow IPC files and lack many features, such as the ability to store all Arrow data tyeps, and compression support. write_ipc_file() can only write V2 files.

Usage

write_feather(
  x,
  sink,
  version = 2,
  chunk_size = 65536L,
  compression = c("default", "lz4", "lz4_frame", "uncompressed", "zstd"),
  compression_level = NULL
)

write_ipc_file(
  x,
  sink,
  chunk_size = 65536L,
  compression = c("default", "lz4", "lz4_frame", "uncompressed", "zstd"),
  compression_level = NULL
)

Arguments