Table class

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.

Factory

The Table$create() function takes the following arguments:

• ... arrays, chunked arrays, or R vectors, with names; alternatively, an unnamed series of record batches may also be provided, which will be stacked as rows in the table.

• schema a Schema, or NULL (the default) to infer the schema from the data in ...

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))

• $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.

• $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.

• $columns: Returns a list of ChunkedArrays

Examples

# \donttest{
tab <- Table$create(name = rownames(mtcars), mtcars)
dim(tab)
#> [1] 32 12
dim(head(tab))
#> [1]  6 12
names(tab)
#>  [1] "name" "mpg"  "cyl"  "disp" "hp"   "drat" "wt"   "qsec" "vs"   "am"  
#> [11] "gear" "carb"
tab$mpg
#> ChunkedArray
#> [
#>   [
#>     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
#>   ]
#> ]
tab[["cyl"]]
#> ChunkedArray
#> [
#>   [
#>     6,
#>     6,
#>     4,
#>     6,
#>     8,
#>     6,
#>     8,
#>     4,
#>     4,
#>     6,
#>     ...
#>     8,
#>     8,
#>     8,
#>     4,
#>     4,
#>     4,
#>     8,
#>     6,
#>     8,
#>     4
#>   ]
#> ]
as.data.frame(tab[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
# }