% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dataset.R
\name{open_dataset}
\alias{open_dataset}
\title{Open a multi-file dataset}
\usage{
open_dataset(
  sources,
  schema = NULL,
  partitioning = hive_partition(),
  unify_schemas = NULL,
  format = c("parquet", "arrow", "ipc", "feather", "csv", "tsv", "text"),
  ...
)
}
\arguments{
\item{sources}{One of:
\itemize{
\item a string path or URI to a directory containing data files
\item a string path or URI to a single file
\item a character vector of paths or URIs to individual data files
\item a list of \code{Dataset} objects as created by this function
\item a list of \code{DatasetFactory} objects as created by \code{\link[=dataset_factory]{dataset_factory()}}.
}

When \code{sources} is a vector of file URIs, they must all use the same protocol
and point to files located in the same file system and having the same
format.}

\item{schema}{\link{Schema} for the \code{Dataset}. If \code{NULL} (the default), the schema
will be inferred from the data sources.}

\item{partitioning}{When \code{sources} is a directory path/URI, one of:
\itemize{
\item a \code{Schema}, in which case the file paths relative to \code{sources} will be
parsed, and path segments will be matched with the schema fields. For
example, \code{schema(year = int16(), month = int8())} would create partitions
for file paths like \code{"2019/01/file.parquet"}, \code{"2019/02/file.parquet"},
etc.
\item a character vector that defines the field names corresponding to those
path segments (that is, you're providing the names that would correspond
to a \code{Schema} but the types will be autodetected)
\item a \code{HivePartitioning} or \code{HivePartitioningFactory}, as returned
by \code{\link[=hive_partition]{hive_partition()}} which parses explicit or autodetected fields from
Hive-style path segments
\item \code{NULL} for no partitioning
}

The default is to autodetect Hive-style partitions. When \code{sources} is not a
directory path/URI, \code{partitioning} is ignored.}

\item{unify_schemas}{logical: should all data fragments (files, \code{Dataset}s)
be scanned in order to create a unified schema from them? If \code{FALSE}, 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.
The default is \code{FALSE} when creating a dataset from a directory path/URI or
vector of file paths/URIs (because there may be many files and scanning may
be slow) but \code{TRUE} when \code{sources} is a list of \code{Dataset}s (because there
should be few \code{Dataset}s in the list and their \code{Schema}s are already in
memory).}

\item{format}{A \link{FileFormat} object, or a string identifier of the format of
the files in \code{x}. This argument is ignored when \code{sources} is a list of \code{Dataset} objects.
Currently supported values:
\itemize{
\item "parquet"
\item "ipc"/"arrow"/"feather", all aliases for each other; for Feather, note that
only version 2 files are supported
\item "csv"/"text", aliases for the same thing (because comma is the default
delimiter for text files
\item "tsv", equivalent to passing \verb{format = "text", delimiter = "\\t"}
}

Default is "parquet", unless a \code{delimiter} is also specified, in which case
it is assumed to be "text".}

\item{...}{additional arguments passed to \code{dataset_factory()} when \code{sources}
is a directory path/URI or vector of file paths/URIs, otherwise ignored.
These may include \code{format} to indicate the file format, or other
format-specific options.}
}
\value{
A \link{Dataset} R6 object. Use \code{dplyr} methods on it to query the data,
or call \code{\link[=Scanner]{$NewScan()}} to construct a query directly.
}
\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
\code{open_dataset()} to point to a directory of data files and return a
\code{Dataset}, then use \code{dplyr} methods to query it.
}
\examples{
\dontshow{if (arrow_with_dataset() & arrow_with_parquet() ) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
# Set up directory for examples
tf <- tempfile()
dir.create(tf)
on.exit(unlink(tf))

data <- dplyr::group_by(mtcars, cyl)
write_dataset(data, tf)

# 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-1.parquet"), file.path(tf,"cyl=8/part-2.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(data, tf2, format = "ipc")
# This line will results in errors when you try to work with the data
\dontrun{open_dataset(tf2)}
# 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
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()))
\dontshow{\}) # examplesIf}
}
\seealso{
\code{vignette("dataset", package = "arrow")}
}
