Read a CSV or other delimited file with Arrow — read_delim

These functions uses the Arrow C++ CSV reader to read into a tibble. Arrow C++ options have been mapped to argument names that follow those of readr::read_delim(), and col_select was inspired by vroom::vroom().

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,
  decimal_point = "."
)

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_csv2_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

file

A character file name or URI, connection, literal data (either a single string or a 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.

To be recognised as literal data, the input must be wrapped with I().

delim

Single character used to separate fields within a record.

quote

Single character used to quote strings.

escape_double

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

escape_backslash

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

Schema that describes the table. If provided, it will be used to satisfy both col_names and col_types.

col_names

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.

col_types

A compact string representation of the column types, an Arrow Schema, or NULL (the default) to infer types from the data.

col_select

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().

na

A character vector of strings to interpret as missing values.

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, strings_can_be_null.)

skip_empty_rows

Should blank rows be ignored altogether? If TRUE, blank rows will not be represented at all. If FALSE, they will be filled with missings.

skip

Number of lines to skip before reading data.

parse_options

see CSV parsing options. If given, this overrides any parsing options provided in other arguments (e.g. delim, quote, etc.).

convert_options

see CSV conversion options

read_options

see CSV reading options

as_data_frame

Should the function return a tibble (default) or an Arrow Table?

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:

NULL: the default, which uses the ISO-8601 parser
a character vector of strptime parse strings
a list of TimestampParser objects

decimal_point

Character to use for decimal point in floating point numbers.

Value

A tibble, or a Table if as_data_frame = FALSE.

Details

read_csv_arrow() and read_tsv_arrow() are wrappers around read_delim_arrow() that specify a delimiter. read_csv2_arrow() uses ; for the delimiter and , for the decimal point.

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.

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 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 identify column names, you'll need to add skip = 1 to skip that row.

Examples

tf <- tempfile()
on.exit(unlink(tf))
write.csv(mtcars, file = tf)
df <- read_csv_arrow(tf)
dim(df)
#> [1] 32 12
# 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)
#> # A tibble: 2 x 2
#>       x y    
#>   <int> <chr>
#> 1     1 2    
#> 2     3 4    
read_csv_arrow(tf, col_types = schema(y = utf8()))
#> # A tibble: 2 x 2
#>       x y    
#>   <int> <chr>
#> 1     1 2    
#> 2     3 4    
read_csv_arrow(tf, col_types = "ic", col_names = c("x", "y"), skip = 1)
#> # A tibble: 2 x 2
#>       x y    
#>   <int> <chr>
#> 1     1 2    
#> 2     3 4    

# Note that if a timestamp column contains time zones,
# the string "T" `col_types` specification won't work.
# To parse timestamps with time zones, provide a [Schema] to `col_types`
# and specify the time zone in the type object:
tf <- tempfile()
write.csv(data.frame(x = "1970-01-01T12:00:00+12:00"), file = tf, row.names = FALSE)
read_csv_arrow(
  tf,
  col_types = schema(x = timestamp(unit = "us", timezone = "UTC"))
)
#> # A tibble: 1 x 1
#>   x                  
#>   <dttm>             
#> 1 1970-01-01 00:00:00

# Read directly from strings with `I()`
read_csv_arrow(I("x,y\n1,2\n3,4"))
#> # A tibble: 2 x 2
#>       x     y
#>   <int> <int>
#> 1     1     2
#> 2     3     4
read_delim_arrow(I(c("x y", "1 2", "3 4")), delim = " ")
#> # A tibble: 2 x 2
#>       x     y
#>   <int> <int>
#> 1     1     2
#> 2     3     4