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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/csv.R
\name{csv_convert_options}
\alias{csv_convert_options}
\title{CSV Convert Options}
\usage{
csv_convert_options(
check_utf8 = TRUE,
null_values = c("", "NA"),
true_values = c("T", "true", "TRUE"),
false_values = c("F", "false", "FALSE"),
strings_can_be_null = FALSE,
col_types = NULL,
auto_dict_encode = FALSE,
auto_dict_max_cardinality = 50L,
include_columns = character(),
include_missing_columns = FALSE,
timestamp_parsers = NULL,
decimal_point = "."
)
}
\arguments{
\item{check_utf8}{Logical: check UTF8 validity of string columns?}
\item{null_values}{Character vector of recognized spellings for null values.
Analogous to the \code{na.strings} argument to
\code{\link[utils:read.table]{read.csv()}} or \code{na} in \code{\link[readr:read_delim]{readr::read_csv()}}.}
\item{true_values}{Character vector of recognized spellings for \code{TRUE} values}
\item{false_values}{Character vector of recognized spellings for \code{FALSE} values}
\item{strings_can_be_null}{Logical: can string / binary columns have
null values? Similar to the \code{quoted_na} argument to \code{\link[readr:read_delim]{readr::read_csv()}}}
\item{col_types}{A \code{Schema} or \code{NULL} to infer types}
\item{auto_dict_encode}{Logical: Whether to try to automatically
dictionary-encode string / binary data (think \code{stringsAsFactors}).
This setting is ignored for non-inferred columns (those in \code{col_types}).}
\item{auto_dict_max_cardinality}{If \code{auto_dict_encode}, string/binary columns
are dictionary-encoded up to this number of unique values (default 50),
after which it switches to regular encoding.}
\item{include_columns}{If non-empty, indicates the names of columns from the
CSV file that should be actually read and converted (in the vector's order).}
\item{include_missing_columns}{Logical: if \code{include_columns} is provided, should
columns named in it but not found in the data be included as a column of
type \code{null()}? The default (\code{FALSE}) means that the reader will instead
raise an error.}
\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
(a) \code{NULL}, the default, which uses the ISO-8601 parser;
(b) a character vector of \link[base:strptime]{strptime} parse strings; or
(c) a list of \link{TimestampParser} objects.}
\item{decimal_point}{Character to use for decimal point in floating point numbers.}
}
\description{
CSV Convert Options
}
\examples{
\dontshow{if (arrow_with_dataset()) withAutoprint(\{ # examplesIf}
tf <- tempfile()
on.exit(unlink(tf))
writeLines("x\n1\nNULL\n2\nNA", tf)
read_csv_arrow(tf, convert_options = csv_convert_options(null_values = c("", "NA", "NULL")))
open_csv_dataset(tf, convert_options = csv_convert_options(null_values = c("", "NA", "NULL")))
\dontshow{\}) # examplesIf}
}
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