These functions uses the Arrow C++ CSV reader to read into a data.frame.
Arrow C++ options have been mapped to argument names that follow those of
readr::read_delim(), and col_select was inspired by vroom::vroom().
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
)
A character file name or URI, raw vector, an Arrow input stream,
or a FileSystem with path (SubTreeFileSystem).
If a file name, a memory-mapped Arrow 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.
Single character used to separate fields within a record.
Single character used to quote strings.
Does the file escape quotes by doubling them?
i.e. If this option is TRUE, the value """" represents
a single quote, \".
Does the file use backslashes to escape special
characters? This is more general than escape_double as backslashes
can be used to escape the delimiter character, the quote character, or
to add special characters like \\n.
Schema that describes the table. If provided, it will be
used to satisfy both col_names and col_types.
If TRUE, the first row of the input will be used as the
column names and will not be included in the data frame. If FALSE, column
names will be generated by Arrow, starting with "f0", "f1", ..., "fN".
Alternatively, you can specify a character vector of column names.
A compact string representation of the column types, or
NULL (the default) to infer types from the data.
A character vector of column names to keep, as in the
"select" argument to data.table::fread(), or a
tidy selection specification
of columns, as used in dplyr::select().
A character vector of strings to interpret as missing values.
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,
strings_can_be_null.)
Should blank rows be ignored altogether? If
TRUE, blank rows will not be represented at all. If FALSE, they will be
filled with missings.
Number of lines to skip before reading data.
see file reader options.
If given, this overrides any
parsing options provided in other arguments (e.g. delim, quote, etc.).
Should the function return a data.frame (default) or
an Arrow Table?
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:
NULL: the default, which uses the ISO-8601 parser
a character vector of strptime parse strings
a list of TimestampParser objects
A data.frame, or a Table if as_data_frame = FALSE.
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 Schema in the schema argument,
which is an ordered map of column name to type.
When provided, it satisfies both the col_names and col_types arguments.
This is good if you know all of this information up front.
You can also pass a Schema to the col_types argument. If you do this,
column names will still be inferred from the file unless you also specify
col_names. In either case, the column names in the Schema must match the
data's column names, whether they are explicitly provided or inferred. That
said, this 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 readr uses to the col_types argument. This means you provide a
single string, one character per column, where the characters map to Arrow
types analogously to the readr type mapping:
"c": utf8()
"i": int32()
"n": float64()
"d": float64()
"l": bool()
"f": dictionary()
"D": date32()
"T": timestamp(unit = "ns")
"t": time32() (The unit arg is set to the default value "ms")
"_": null()
"-": null()
"?": infer the type from the data
If you use the compact string representation for col_types, you must also
specify col_names.
Regardless of how types are specified, all columns with a null() type will
be dropped.
Note that if you are specifying column names, whether by schema or
col_names, and the CSV file has a header row that would otherwise be used
to idenfity column names, you'll need to add skip = 1 to skip that row.
read_csv_arrow() and read_tsv_arrow() are wrappers around
read_delim_arrow() that specify a delimiter.
Note that not all readr options are currently implemented here. Please file
an issue if you encounter one that arrow should support.
If you need to control Arrow-specific reader parameters that don't have an
equivalent in readr::read_csv(), you can either provide them in the
parse_options, convert_options, or read_options arguments, or you can
use CsvTableReader directly for lower-level access.
# NOT RUN {
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"))
# Specifying column types and names
write.csv(data.frame(x = c(1, 3), y = c(2, 4)), file = tf, row.names = FALSE)
read_csv_arrow(tf, schema = schema(x = int32(), y = utf8()), skip = 1)
read_csv_arrow(tf, col_types = schema(y = utf8()))
read_csv_arrow(tf, col_types = "ic", col_names = c("x", "y"), skip = 1)
# }
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