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Table class

Source: R/table.R
Table.Rd

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

Usage

arrow_table(..., schema = NULL)

Arguments

...

A data.frame or a named set of Arrays or vectors. If given a mixture of data.frames and named vectors, the inputs will be autospliced together (see examples). Alternatively, you can provide a single Arrow IPC InputStream, Message, Buffer, or R raw object containing a Buffer.

schema

a Schema, or NULL (the default) to infer the schema from the data in .... When providing an Arrow IPC buffer, schema is required.

S3 Methods and Usage

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

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

R6 Methods

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

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

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

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

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

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

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

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

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

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

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

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

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

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

There are also some active bindings:

  • $num_columns

  • $num_rows

  • $schema

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

  • $columns: Returns a list of ChunkedArrays

Examples

tbl <- arrow_table(name = rownames(mtcars), mtcars)
dim(tbl)
#> [1] 32 12
dim(head(tbl))
#> [1]  6 12
names(tbl)
#>  [1] "name" "mpg"  "cyl"  "disp" "hp"   "drat" "wt"   "qsec" "vs"   "am"  
#> [11] "gear" "carb"
tbl$mpg
#> ChunkedArray
#> <double>
#> [
#>   [
#>     21,
#>     21,
#>     22.8,
#>     21.4,
#>     18.7,
#>     18.1,
#>     14.3,
#>     24.4,
#>     22.8,
#>     19.2,
#>     ...
#>     15.2,
#>     13.3,
#>     19.2,
#>     27.3,
#>     26,
#>     30.4,
#>     15.8,
#>     19.7,
#>     15,
#>     21.4
#>   ]
#> ]
tbl[["cyl"]]
#> ChunkedArray
#> <double>
#> [
#>   [
#>     6,
#>     6,
#>     4,
#>     6,
#>     8,
#>     6,
#>     8,
#>     4,
#>     4,
#>     6,
#>     ...
#>     8,
#>     8,
#>     8,
#>     4,
#>     4,
#>     4,
#>     8,
#>     6,
#>     8,
#>     4
#>   ]
#> ]
as.data.frame(tbl[4:8, c("gear", "hp", "wt")])
#> # A tibble: 5 x 3
#>    gear    hp    wt
#>   <dbl> <dbl> <dbl>
#> 1     3   110  3.22
#> 2     3   175  3.44
#> 3     3   105  3.46
#> 4     3   245  3.57
#> 5     4    62  3.19