Package 'arrow'

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 212 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 not supported

• 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 262 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()

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()

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

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

...

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

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.

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")])

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)

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")

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)

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())

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")

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()

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()))

Description

Create a Buffer

Usage

buffer(x)

Arguments

Value

an instance of Buffer that borrows memory from x

See Also

Buffer

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

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)))

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)

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)

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.

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")

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

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))

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()

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"))

Description

Manage the global CPU thread pool in libarrow

Usage

cpu_count()

set_cpu_count(num_threads)

Arguments

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)

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")))

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))

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))

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"))

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)

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

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.

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()
}

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

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.

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

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

Description

class DictionaryType

Methods

TODO

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.

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.

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.

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

Description

Create a Field

Usage

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

Arguments

See Also

Field

Examples

field("x", int32())

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

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)

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.

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.

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'.

Description

A FileWriteOptions holds write options specific to a FileFormat.

Description

FixedWidthType class

Methods

TODO

Description

Connect to a Flight server

Usage

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

Arguments

Value

A pyarrow.flight.FlightClient.

Description

Explicitly close a Flight client

Usage

flight_disconnect(client)

Arguments

Description

Get data from a Flight server

Usage

flight_get(client, path)

Arguments

Value

A Table

Description

Send data to a Flight server

Usage

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

Arguments

Value

client, invisibly.

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).

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")

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())

Description

Extract a schema from an object

Usage

infer_schema(x)

Arguments

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"))

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

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.

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

Description

Manage the global I/O thread pool in libarrow

Usage

io_thread_count()

set_io_thread_count(num_threads)

Arguments

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

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)

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()

Description

Load a Python Flight server

Usage

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

Arguments

Examples

load_flight_server("demo_flight_server")

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.

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

Description

Message class

Methods

TODO

Description

MessageReader class

Methods

TODO

Description

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

Usage

mmap_create(path, size)

Arguments

Value

a arrow::io::MemoryMappedFile

Description

Open a memory mapped file

Usage

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

Arguments

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)

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()))

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)

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.