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.
arrow_table(..., schema = NULL)
| ... | A |
|---|---|
| schema | a Schema, or |
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".
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))
$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.
$columns: Returns a list of ChunkedArrays
tbl <- arrow_table(name = rownames(mtcars), mtcars) dim(tbl) #> [1] 32 12 dim(head(tbl)) #> [1] 6 12 names(tbl) #> [1] "name" "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" #> [11] "gear" "carb" tbl$mpg #> ChunkedArray #> [ #> [ #> 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 #> [ #> [ #> 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