Data types

Arrow has a rich data type system that includes direct analogs of many R data types, and many data types that do not have a counterpart in R. This article describes the Arrow type system, compares it to R data types, and outlines the default mappings used when data are transferred from Arrow to R. At the end of the article there are two lookup tables: one describing the default “R to Arrow” type mappings and the other describing the “Arrow to R” mappings.

Motivating example

To illustrate the conversion that needs to take place, consider the differences between the output when obtain we use dplyr::glimpse() to inspect the starwars data in its original format – as a data frame in R – and the output we obtain when we convert it to an Arrow Table first by calling arrow_table():

library(dplyr, warn.conflicts = FALSE)
library(arrow, warn.conflicts = FALSE)

glimpse(starwars)

## Rows: 87
## Columns: 14
## $ name       <chr> "Luke Skywalker", "C-3PO", "R2-D2", "Darth Vader", "Leia Or~
## $ height     <int> 172, 167, 96, 202, 150, 178, 165, 97, 183, 182, 188, 180, 2~
## $ mass       <dbl> 77.0, 75.0, 32.0, 136.0, 49.0, 120.0, 75.0, 32.0, 84.0, 77.~
## $ hair_color <chr> "blond", NA, NA, "none", "brown", "brown, grey", "brown", N~
## $ skin_color <chr> "fair", "gold", "white, blue", "white", "light", "light", "~
## $ eye_color  <chr> "blue", "yellow", "red", "yellow", "brown", "blue", "blue",~
## $ birth_year <dbl> 19.0, 112.0, 33.0, 41.9, 19.0, 52.0, 47.0, NA, 24.0, 57.0, ~
## $ sex        <chr> "male", "none", "none", "male", "female", "male", "female",~
## $ gender     <chr> "masculine", "masculine", "masculine", "masculine", "femini~
## $ homeworld  <chr> "Tatooine", "Tatooine", "Naboo", "Tatooine", "Alderaan", "T~
## $ species    <chr> "Human", "Droid", "Droid", "Human", "Human", "Human", "Huma~
## $ films      <list> <"The Empire Strikes Back", "Revenge of the Sith", "Return~
## $ vehicles   <list> <"Snowspeeder", "Imperial Speeder Bike">, <>, <>, <>, "Imp~
## $ starships  <list> <"X-wing", "Imperial shuttle">, <>, <>, "TIE Advanced x1",~

glimpse(arrow_table(starwars))

## Table
## 87 rows x 14 columns
## $ name         <string> "Luke Skywalker", "C-3PO", "R2-D2", "Darth Vader", "Leia~
## $ height        <int32> 172, 167, 96, 202, 150, 178, 165, 97, 183, 182, 188, 180~
## $ mass         <double> 77.0, 75.0, 32.0, 136.0, 49.0, 120.0, 75.0, 32.0, 84.0, ~
## $ hair_color   <string> "blond", NA, NA, "none", "brown", "brown, grey", "brown"~
## $ skin_color   <string> "fair", "gold", "white, blue", "white", "light", "light"~
## $ eye_color    <string> "blue", "yellow", "red", "yellow", "brown", "blue", "blu~
## $ birth_year   <double> 19.0, 112.0, 33.0, 41.9, 19.0, 52.0, 47.0, NA, 24.0, 57.~
## $ sex          <string> "male", "none", "none", "male", "female", "male", "femal~
## $ gender       <string> "masculine", "masculine", "masculine", "masculine", "fem~
## $ homeworld    <string> "Tatooine", "Tatooine", "Naboo", "Tatooine", "Alderaan",~
## $ species      <string> "Human", "Droid", "Droid", "Human", "Human", "Human", "H~
## $ films     <list<...>> <"The Empire Strikes Back", "Revenge of the Sith", "Retu~
## $ vehicles  <list<...>> <"Snowspeeder", "Imperial Speeder Bike">, <>, <>, <>, "I~
## $ starships <list<...>> <"X-wing", "Imperial shuttle">, <>, <>, "TIE Advanced x1~
## Call `print()` for full schema details

The data represented are essentially the same, but the descriptions of the data types for the columns have changed. For example:

name is labelled <chr> (character vector) in the data frame; it is labelled <string> (a string type, also referred to as utf8 type) in the Arrow Table
height is labelled <int> (integer vector) in the data frame; it is labelled <int32> (32 bit signed integer) in the Arrow Table
mass is labelled <dbl> (numeric vector) in the data frame; it is labelled <double> (64 bit floating point number) in the Arrow Table

Some of these differences are purely cosmetic: integers in R are in fact 32 bit signed integers, so the underlying data types in Arrow and R are direct analogs of one another. In other cases the differences are purely about the implementation: Arrow and R have different ways to store a vector of strings, but at a high level of abstraction the R character type and the Arrow string type can be viewed as direct analogs. In some cases, however, there are no clear analogs: while Arrow has an analog of POSIXct (the timestamp type) it does not have an analog of POSIXlt; conversely, while R can represent 32 bit signed integers, it does not have an equivalent of a 64 bit unsigned integer.

When the arrow package converts between R data and Arrow data, it will first check to see if a Schema has been provided – see schema() for more information – and if none is available it will attempt to guess the appropriate type by following the default mappings. A complete listing of these mappings is provided at the end of the article, but the most common cases are depicted in the illustration below:

In this image, black boxes refer to R data types and light blue boxes refer to Arrow data types. Directional arrows specify conversions (e.g., the bidirectional arrow between the logical R type and the boolean Arrow type means that R logicals convert to Arrow booleans and vice versa). Solid lines indicate that the this conversion rule is always the default; dashed lines mean that it only sometimes applies (the rules and special cases are described below).

Logical/boolean types

Arrow and R both use three-valued logic. In R, logical values can be TRUE or FALSE, with NA used to represent missing data. In Arrow, the corresponding boolean type can take values true, false, or null, as shown below:

chunked_array(c(TRUE, FALSE, NA), type = boolean()) # default

## ChunkedArray
## <bool>
## [
##   [
##     true,
##     false,
##     null
##   ]
## ]

It is not strictly necessary to set type = boolean() in this example because the default behavior in arrow is to translate R logical vectors to Arrow booleans and vice versa. However, for the sake of clarity we will specify the data types explicitly throughout this article. We will likewise use chunked_array() to create Arrow data from R objects and as.vector() to create R data from Arrow objects, but similar results are obtained if we use other methods.

Integer types

Base R natively supports only one type of integer, using 32 bits to represent signed numbers between -2147483648 and 2147483647, though R can also support 64 bit integers via the bit64 package. Arrow inherits signed and unsigned integer types from C++ in 8 bit, 16 bit, 32 bit, and 64 bit versions:

Description	Data Type Function	Smallest Value	Largest Value
8 bit unsigned	`uint8()`	0	255
16 bit unsigned	`uint16()`	0	65535
32 bit unsigned	`uint32()`	0	4294967295
64 bit unsigned	`uint64()`	0	18446744073709551615
8 bit signed	`int8()`	-128	127
16 bit signed	`int16()`	-32768	32767
32 bit signed	`int32()`	-2147483648	2147483647
64 bit signed	`int64()`	-9223372036854775808	9223372036854775807

By default, arrow translates R integers to the int32 type in Arrow, but you can override this by explicitly specifying another integer type:

chunked_array(c(10L, 3L, 200L), type = int32()) # default

## ChunkedArray
## <int32>
## [
##   [
##     10,
##     3,
##     200
##   ]
## ]

chunked_array(c(10L, 3L, 200L), type = int64())

## ChunkedArray
## <int64>
## [
##   [
##     10,
##     3,
##     200
##   ]
## ]

If the value in R does not fall within the permissible range for the corresponding Arrow type, arrow throws an error:

chunked_array(c(10L, 3L, 200L), type = int8())

## Error: Invalid: value outside of range

When translating from Arrow to R, integer types alway translate to R integers unless one of the following exceptions applies:

If the value of an Arrow uint32 or uint64 falls outside the range allowed for R integers, the result will be a numeric vector in R
If the value of an Arrow int64 variable falls outside the range allowed for R integers, the result will be a bit64::integer64 vector in R
If the user sets options(arrow.int64_downcast = FALSE), the Arrow int64 type always yields a bit64::integer64 vector in R regardless of the value

Floating point numeric types

R has one double-precision (64 bit) numeric type, which translates to the Arrow 64 bit floating point type by default. Arrow supports both single-precision (32 bit) and double-precision (64 bit) floating point numbers, specified using the float32() and float64() data type functions. Both of these are translated to doubles in R. Examples are shown below:

chunked_array(c(0.1, 0.2, 0.3), type = float64()) # default

## ChunkedArray
## <double>
## [
##   [
##     0.1,
##     0.2,
##     0.3
##   ]
## ]

chunked_array(c(0.1, 0.2, 0.3), type = float32())

## ChunkedArray
## <float>
## [
##   [
##     0.1,
##     0.2,
##     0.3
##   ]
## ]

arrow_double <- chunked_array(c(0.1, 0.2, 0.3), type = float64())
as.vector(arrow_double)

## [1] 0.1 0.2 0.3

Note that the Arrow specification also permits half-precision (16 bit) floating point numbers, but these have not yet been implemented.

Fixed point decimal types

Arrow also contains decimal() data types, in which numeric values are specified in decimal format rather than binary. Decimals in Arrow come in two varieties, a 128 bit version and a 256 bit version, but in most cases users should be able to use the more general decimal() data type function rather than the specific decimal128() and decimal256() functions.

The decimal types in Arrow are fixed-precision numbers (rather than floating-point), which means it is necessary to explicitly specify the precision and scale arguments:

precision specifies the number of significant digits to store.
scale specifies the number of digits that should be stored after the decimal point. If you set scale = 2, exactly two digits will be stored after the decimal point. If you set scale = 0, values will be rounded to the nearest whole number. Negative scales are also permitted (handy when dealing with extremely large numbers), so scale = -2 stores the value to the nearest 100.

Because R does not have any way to create decimal types natively, the example below is a little circuitous. First we create some floating point numbers as Chunked Arrays, and then explicitly cast these to decimal types within Arrow. This is possible because Chunked Array objects possess a cast() method:

arrow_floating <- chunked_array(c(.01, .1, 1, 10, 100))
arrow_decimals <- arrow_floating$cast(decimal(precision = 5, scale = 2))
arrow_decimals

## ChunkedArray
## <decimal128(5, 2)>
## [
##   [
##     0.01,
##     0.10,
##     1.00,
##     10.00,
##     100.00
##   ]
## ]

Though not natively used in R, decimal types can be useful in situations where it is especially important to avoid problems that arise in floating point arithmetic.

String/character types

R uses a single character type to represent strings whereas Arrow has two types. In the Arrow C++ library these types are referred to as strings and large_strings, but to avoid ambiguity in the arrow R package they are defined using the utf8() and large_utf8() data type functions. The distinction between these two Arrow types is unlikely to be important for R users, though the difference is discussed in the article on data object layout.

The default behavior is to translate R character vectors to the utf8/string type, and to translate both Arrow types to R character vectors:

strings <- chunked_array(c("oh", "well", "whatever"))
strings

## ChunkedArray
## <string>
## [
##   [
##     "oh",
##     "well",
##     "whatever"
##   ]
## ]

as.vector(strings)

## [1] "oh"       "well"     "whatever"

Factor/dictionary types

The analog of R factors in Arrow is the dictionary type. Factors translate to dictionaries and vice versa. To illustrate this, let’s create a small factor object in R:

fct <- factor(c("cat", "dog", "pig", "dog"))
fct

## [1] cat dog pig dog
## Levels: cat dog pig

When translated to Arrow, this is the dictionary that results:

dict <- chunked_array(fct, type = dictionary())
dict

## ChunkedArray
## <dictionary<values=string, indices=int32>>
## [
## 
##   -- dictionary:
##     [
##       "cat",
##       "dog",
##       "pig"
##     ]
##   -- indices:
##     [
##       0,
##       1,
##       2,
##       1
##     ]
## ]

When translated back to R, we recover the original factor:

as.vector(dict)

## [1] cat dog pig dog
## Levels: cat dog pig

Arrow dictionaries are slightly more flexible than R factors: values in a dictionary do not necessarily have to be strings, but labels in a factor do. As a consequence, non-string values in an Arrow dictionary are coerced to strings when translated to R.

Date types

In R, dates are typically represented using the Date class. Internally a Date object is a numeric type whose value counts the number of days since the beginning of the Unix epoch (1 January 1970). Arrow supplies two data types that can be used to represent dates: the date32 type and the date64 type. The date32 type is similar to the Date class in R: internally it stores a 32 bit integer that counts the number of days since 1 January 1970. The default in arrow is to translate R Date objects to Arrow date32 types:

nirvana_album_dates <- as.Date(c("1989-06-15", "1991-09-24", "1993-09-13"))
nirvana_album_dates

## [1] "1989-06-15" "1991-09-24" "1993-09-13"

nirvana_32 <- chunked_array(nirvana_album_dates, type = date32()) # default
nirvana_32

## ChunkedArray
## <date32[day]>
## [
##   [
##     1989-06-15,
##     1991-09-24,
##     1993-09-13
##   ]
## ]

Arrow also supplies a higher-precision date64 type, in which the date is represented as a 64 bit integer that encodes the number of milliseconds since 1970-01-01 00:00 UTC:

nirvana_64 <- chunked_array(nirvana_album_dates, type = date64())
nirvana_64

## ChunkedArray
## <date64[ms]>
## [
##   [
##     1989-06-15,
##     1991-09-24,
##     1993-09-13
##   ]
## ]

The translation from Arrow to R differs. Internally the date32 type is very similar to an R Date, so these objects are translated to R as Dates:

class(as.vector(nirvana_32))

## [1] "Date"

However, because date64 types are specified to millisecond-level precision, they are translated to R as POSIXct times to avoid the possibility of losing relevant information:

class(as.vector(nirvana_64))

## [1] "POSIXct" "POSIXt"

Temporal/timestamp types

In R there are two classes used to represent date and time information, POSIXct and POSIXlt. Arrow only has one: the timestamp type. Arrow timestamps are loosely analogous to the POSIXct class. Internally, a POSIXct object represents the date with as a numeric variable that stores the number of seconds since 1970-01-01 00:00 UTC. Internally, an Arrow timestamp is a 64 bit integer counting the number of milliseconds since 1970-01-01 00:00 UTC.

Arrow and R both support timezone information, but display it differently in the printed object. In R, local time is printed with the timezone name adjacent to it:

sydney_newyear <- as.POSIXct("2000-01-01 00:01", tz = "Australia/Sydney")
sydney_newyear

## [1] "2000-01-01 00:01:00 AEDT"

When translated to Arrow, this POSIXct object becomes an Arrow timestamp object. When printed, however, the temporal instant is always displayed in UTC rather than local time:

sydney_newyear_arrow <- chunked_array(sydney_newyear, type = timestamp())
sydney_newyear_arrow

## ChunkedArray
## <timestamp[s]>
## [
##   [
##     1999-12-31 13:01:00
##   ]
## ]

The timezone information is not lost, however, which we can easily see by translating the sydney_newyear_arrow object back to an R POSIXct object:

as.vector(sydney_newyear_arrow)

## [1] "1999-12-31 13:01:00 UTC"

For POSIXlt objects the behaviour is different. Internally a POSIXlt object is a list specifying the “local time” in terms of a variety of human-relevant fields. There is no analogous class to this in Arrow, so the default behaviour is to translate it to an Arrow list.

Time of day types

Base R does not have a class to represent the time of day independent of the date (i.e., it is not possible to specify “3pm” without referring to a specific day), but it can be done with the help of the hms package. Internally, hms objects are always stored as the number of seconds since 00:00:00.

Arrow has two data types for this purposes. For time32 types, data are stored as a 32 bit integer that is interpreted either as the number of seconds or the number of milliseconds since 00:00:00. Note the difference between the following:

time_of_day <- hms::hms(56, 34, 12)
chunked_array(time_of_day, type = time32(unit = "s"))

## ChunkedArray
## <time32[s]>
## [
##   [
##     12:34:56
##   ]
## ]

chunked_array(time_of_day, type = time32(unit = "ms"))

## ChunkedArray
## <time32[ms]>
## [
##   [
##     12:34:56.000
##   ]
## ]

A time64 object is similar, but stores the time of day using a 64 bit integer and can represent the time at higher precision. It is possible to choose microseconds (unit = "us") or nanoseconds (unit = "ns"), as shown below:

chunked_array(time_of_day, type = time64(unit = "us"))

## ChunkedArray
## <time64[us]>
## [
##   [
##     12:34:56.000000
##   ]
## ]

chunked_array(time_of_day, type = time64(unit = "ns"))

## ChunkedArray
## <time64[ns]>
## [
##   [
##     12:34:56.000000000
##   ]
## ]

All versions of time32 and time64 objects in Arrow translate to hms times in R.

Duration types

Lengths of time are represented as difftime objects in R. The analogous data type in Arrow is the duration type. A duration type is stored as a 64 bit integer, which can represent the number of seconds (the default, unit = "s"), milliseconds (unit = "ms"), microseconds (unit = "us"), or nanoseconds (unit = "ns"). To illustrate this we’ll create a difftime in R corresponding to 278 seconds:

len <- as.difftime(278, unit = "secs")
len

## Time difference of 278 secs

The translation to Arrow looks like this:

chunked_array(len, type = duration(unit = "s")) # default

## ChunkedArray
## <duration[s]>
## [
##   [
##     278
##   ]
## ]

chunked_array(len, type = duration(unit = "ns"))

## ChunkedArray
## <duration[ns]>
## [
##   [
##     278000000000
##   ]
## ]

Regardless of the underlying unit, duration objects in Arrow translate to difftime objects in R.

List of default translations

The discussion above covers the most common cases. The two tables in this section provide a more complete list of how arrow translates between R data types and Arrow data types. In these table, entries with a - are not currently implemented.

Translations from R to Arrow

Original R type	Arrow type after translation
logical	boolean
integer	int32
double (“numeric”)	float64 ¹
character	utf8 ²
factor	dictionary
raw	uint8
Date	date32
POSIXct	timestamp
POSIXlt	struct
data.frame	struct
list ³	list
bit64::integer64	int64
hms::hms	time32
difftime	duration
vctrs::vctrs_unspecified	null

¹: float64 and double are the same concept and data type in Arrow C++; however, only float64() is used in arrow as the function double() already exists in base R

²: If the character vector exceeds 2GB of strings, it will be converted to a large_utf8 Arrow type

³: Only lists where all elements are the same type are able to be translated to Arrow list type (which is a “list of” some type).

Translations from Arrow to R

Original Arrow type	R type after translation
boolean	logical
int8	integer
int16	integer
int32	integer
int64	integer ¹
uint8	integer
uint16	integer
uint32	integer ¹
uint64	integer ¹
float16	- ²
float32	double
float64	double
utf8	character
large_utf8	character
binary	arrow_binary ³
large_binary	arrow_large_binary ³
fixed_size_binary	arrow_fixed_size_binary ³
date32	Date
date64	POSIXct
time32	hms::hms
time64	hms::hms
timestamp	POSIXct
duration	difftime
decimal	double
dictionary	factor ⁴
list	arrow_list ⁵
large_list	arrow_large_list ⁵
fixed_size_list	arrow_fixed_size_list ⁵
struct	data.frame
null	vctrs::vctrs_unspecified
map	arrow_list ⁵
union	- ²

¹: These integer types may contain values that exceed the range of R’s integer type (32 bit signed integer). When they do, uint32 and uint64 are converted to double (“numeric”) and int64 is converted to bit64::integer64. This conversion can be disabled (so that int64 always yields a bit64::integer64 vector) by setting options(arrow.int64_downcast = FALSE).

²: Some Arrow data types do not currently have an R equivalent and will raise an error if cast to or mapped to via a schema.

³: arrow*_binary classes are implemented as lists of raw vectors.

⁴: Due to the limitation of R factors, Arrow dictionary values are coerced to string when translated to R if they are not already strings.

⁵: arrow*_list classes are implemented as subclasses of vctrs_list_of with a ptype attribute set to what an empty Array of the value type converts to.