% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/csv.R
\name{read_delim_arrow}
\alias{read_delim_arrow}
\alias{read_csv_arrow}
\alias{read_tsv_arrow}
\title{Read a CSV or other delimited file with Arrow}
\usage{
read_delim_arrow(
  file,
  delim = ",",
  quote = "\\"",
  escape_double = TRUE,
  escape_backslash = FALSE,
  schema = NULL,
  col_names = TRUE,
  col_types = NULL,
  col_select = NULL,
  na = c("", "NA"),
  quoted_na = TRUE,
  skip_empty_rows = TRUE,
  skip = 0L,
  parse_options = NULL,
  convert_options = NULL,
  read_options = NULL,
  as_data_frame = TRUE,
  timestamp_parsers = NULL
)

read_csv_arrow(
  file,
  quote = "\\"",
  escape_double = TRUE,
  escape_backslash = FALSE,
  schema = NULL,
  col_names = TRUE,
  col_types = NULL,
  col_select = NULL,
  na = c("", "NA"),
  quoted_na = TRUE,
  skip_empty_rows = TRUE,
  skip = 0L,
  parse_options = NULL,
  convert_options = NULL,
  read_options = NULL,
  as_data_frame = TRUE,
  timestamp_parsers = NULL
)

read_tsv_arrow(
  file,
  quote = "\\"",
  escape_double = TRUE,
  escape_backslash = FALSE,
  schema = NULL,
  col_names = TRUE,
  col_types = NULL,
  col_select = NULL,
  na = c("", "NA"),
  quoted_na = TRUE,
  skip_empty_rows = TRUE,
  skip = 0L,
  parse_options = NULL,
  convert_options = NULL,
  read_options = NULL,
  as_data_frame = TRUE,
  timestamp_parsers = NULL
)
}
\arguments{
\item{file}{A character file name or URI, \code{raw} vector, an Arrow input stream,
or a \code{FileSystem} with path (\code{SubTreeFileSystem}).
If a file name, a memory-mapped Arrow \link{InputStream} will be opened and
closed when finished; compression will be detected from the file extension
and handled automatically. If an input stream is provided, it will be left
open.}

\item{delim}{Single character used to separate fields within a record.}

\item{quote}{Single character used to quote strings.}

\item{escape_double}{Does the file escape quotes by doubling them?
i.e. If this option is \code{TRUE}, the value \verb{""""} represents
a single quote, \verb{\\"}.}

\item{escape_backslash}{Does the file use backslashes to escape special
characters? This is more general than \code{escape_double} as backslashes
can be used to escape the delimiter character, the quote character, or
to add special characters like \verb{\\\\n}.}

\item{schema}{\link{Schema} that describes the table. If provided, it will be
used to satisfy both \code{col_names} and \code{col_types}.}

\item{col_names}{If \code{TRUE}, the first row of the input will be used as the
column names and will not be included in the data frame. If \code{FALSE}, column
names will be generated by Arrow, starting with "f0", "f1", ..., "fN".
Alternatively, you can specify a character vector of column names.}

\item{col_types}{A compact string representation of the column types, or
\code{NULL} (the default) to infer types from the data.}

\item{col_select}{A character vector of column names to keep, as in the
"select" argument to \code{data.table::fread()}, or a
\link[tidyselect:vars_select]{tidy selection specification}
of columns, as used in \code{dplyr::select()}.}

\item{na}{A character vector of strings to interpret as missing values.}

\item{quoted_na}{Should missing values inside quotes be treated as missing
values (the default) or strings. (Note that this is different from the
the Arrow C++ default for the corresponding convert option,
\code{strings_can_be_null}.)}

\item{skip_empty_rows}{Should blank rows be ignored altogether? If
\code{TRUE}, blank rows will not be represented at all. If \code{FALSE}, they will be
filled with missings.}

\item{skip}{Number of lines to skip before reading data.}

\item{parse_options}{see \link[=CsvReadOptions]{file reader options}.
If given, this overrides any
parsing options provided in other arguments (e.g. \code{delim}, \code{quote}, etc.).}

\item{convert_options}{see \link[=CsvReadOptions]{file reader options}}

\item{read_options}{see \link[=CsvReadOptions]{file reader options}}

\item{as_data_frame}{Should the function return a \code{data.frame} (default) or
an Arrow \link{Table}?}

\item{timestamp_parsers}{User-defined timestamp parsers. If more than one
parser is specified, the CSV conversion logic will try parsing values
starting from the beginning of this vector. Possible values are:
\itemize{
\item \code{NULL}: the default, which uses the ISO-8601 parser
\item a character vector of \link[base:strptime]{strptime} parse strings
\item a list of \link{TimestampParser} objects
}}
}
\value{
A \code{data.frame}, or a Table if \code{as_data_frame = FALSE}.
}
\description{
These functions uses the Arrow C++ CSV reader to read into a \code{data.frame}.
Arrow C++ options have been mapped to argument names that follow those of
\code{readr::read_delim()}, and \code{col_select} was inspired by \code{vroom::vroom()}.
}
\details{
\code{read_csv_arrow()} and \code{read_tsv_arrow()} are wrappers around
\code{read_delim_arrow()} that specify a delimiter.

Note that not all \code{readr} options are currently implemented here. Please file
an issue if you encounter one that \code{arrow} should support.

If you need to control Arrow-specific reader parameters that don't have an
equivalent in \code{readr::read_csv()}, you can either provide them in the
\code{parse_options}, \code{convert_options}, or \code{read_options} arguments, or you can
use \link{CsvTableReader} directly for lower-level access.
}
\section{Specifying column types and names}{


By default, the CSV reader will infer the column names and data types from the file, but there
are a few ways you can specify them directly.

One way is to provide an Arrow \link{Schema} in the \code{schema} argument,
which is an ordered map of column name to type.
When provided, it satisfies both the \code{col_names} and \code{col_types} arguments.
This is good if you know all of this information up front.

You can also pass a \code{Schema} to the \code{col_types} argument. If you do this,
column names will still be inferred from the file unless you also specify
\code{col_names}. In either case, the column names in the \code{Schema} must match the
data's column names, whether they are explicitly provided or inferred. That
said, this \code{Schema} does not have to reference all columns: those omitted
will have their types inferred.

Alternatively, you can declare column types by providing the compact string representation
that \code{readr} uses to the \code{col_types} argument. This means you provide a
single string, one character per column, where the characters map to Arrow
types analogously to the \code{readr} type mapping:
\itemize{
\item "c": \code{utf8()}
\item "i": \code{int32()}
\item "n": \code{float64()}
\item "d": \code{float64()}
\item "l": \code{bool()}
\item "f": \code{dictionary()}
\item "D": \code{date32()}
\item "T": \code{timestamp()}
\item "t": \code{time32()}
\item "_": \code{null()}
\item "-": \code{null()}
\item "?": infer the type from the data
}

If you use the compact string representation for \code{col_types}, you must also
specify \code{col_names}.

Regardless of how types are specified, all columns with a \code{null()} type will
be dropped.

Note that if you are specifying column names, whether by \code{schema} or
\code{col_names}, and the CSV file has a header row that would otherwise be used
to idenfity column names, you'll need to add \code{skip = 1} to skip that row.
}

\examples{
\dontshow{if (arrow_available()) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
tf <- tempfile()
on.exit(unlink(tf))
write.csv(mtcars, file = tf)
df <- read_csv_arrow(tf)
dim(df)
# Can select columns
df <- read_csv_arrow(tf, col_select = starts_with("d"))
\dontshow{\}) # examplesIf}
}