read_delim_chunked: Read a delimited file by chunks

Description

Read a delimited file by chunks

Usage

read_delim_chunked(
  file,
  callback,
  delim = NULL,
  chunk_size = 10000,
  quote = "\"",
  escape_backslash = FALSE,
  escape_double = TRUE,
  col_names = TRUE,
  col_types = NULL,
  locale = default_locale(),
  na = c("", "NA"),
  quoted_na = TRUE,
  comment = "",
  trim_ws = FALSE,
  skip = 0,
  guess_max = chunk_size,
  progress = show_progress(),
  show_col_types = should_show_types(),
  skip_empty_rows = TRUE
)
read_csv_chunked(
  file,
  callback,
  chunk_size = 10000,
  col_names = TRUE,
  col_types = NULL,
  locale = default_locale(),
  na = c("", "NA"),
  quoted_na = TRUE,
  quote = "\"",
  comment = "",
  trim_ws = TRUE,
  skip = 0,
  guess_max = chunk_size,
  progress = show_progress(),
  show_col_types = should_show_types(),
  skip_empty_rows = TRUE
)
read_csv2_chunked(
  file,
  callback,
  chunk_size = 10000,
  col_names = TRUE,
  col_types = NULL,
  locale = default_locale(),
  na = c("", "NA"),
  quoted_na = TRUE,
  quote = "\"",
  comment = "",
  trim_ws = TRUE,
  skip = 0,
  guess_max = chunk_size,
  progress = show_progress(),
  show_col_types = should_show_types(),
  skip_empty_rows = TRUE
)
read_tsv_chunked(
  file,
  callback,
  chunk_size = 10000,
  col_names = TRUE,
  col_types = NULL,
  locale = default_locale(),
  na = c("", "NA"),
  quoted_na = TRUE,
  quote = "\"",
  comment = "",
  trim_ws = TRUE,
  skip = 0,
  guess_max = chunk_size,
  progress = show_progress(),
  show_col_types = should_show_types(),
  skip_empty_rows = TRUE
)

Arguments

file

Either a path to a file, a connection, or literal data (either a single string or a raw vector).

Files ending in .gz, .bz2, .xz, or .zip will be automatically uncompressed. Files starting with http://, https://, ftp://, or ftps:// will be automatically downloaded. Remote gz files can also be automatically downloaded and decompressed.

Literal data is most useful for examples and tests. To be recognised as literal data, the input must be either wrapped with I(), be a string containing at least one new line, or be a vector containing at least one string with a new line.

Using a value of clipboard() will read from the system clipboard.

callback

A callback function to call on each chunk

delim

Single character used to separate fields within a record.

chunk_size

The number of rows to include in each chunk

quote

Single character used to quote strings.

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.

escape_double

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

col_names

Either TRUE, FALSE or a character vector of column 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 automatically: X1, X2, X3 etc.

If col_names is a character vector, the values will be used as the names of the columns, and the first row of the input will be read into the first row of the output data frame.

Missing (NA) column names will generate a warning, and be filled in with dummy names ...1, ...2 etc. Duplicate column names will generate a warning and be made unique, see name_repair to control how this is done.

col_types

One of NULL, a cols() specification, or a string. See vignette("readr") for more details.

If NULL, all column types will be inferred from guess_max rows of the input, interspersed throughout the file. This is convenient (and fast), but not robust. If the guessed types are wrong, you'll need to increase guess_max or supply the correct types yourself.

Column specifications created by list() or cols() must contain one column specification for each column. If you only want to read a subset of the columns, use cols_only().

Alternatively, you can use a compact string representation where each character represents one column:

c = character
i = integer
n = number
d = double
l = logical
f = factor
D = date
T = date time
t = time
? = guess
_ or - = skip

By default, reading a file without a column specification will print a message showing what readr guessed they were. To remove this message, set show_col_types = FALSE or set options(readr.show_col_types = FALSE).

locale

The locale controls defaults that vary from place to place. The default locale is US-centric (like R), but you can use locale() to create your own locale that controls things like the default time zone, encoding, decimal mark, big mark, and day/month names.

na

Character vector of strings to interpret as missing values. Set this option to character() to indicate no missing values.

quoted_na

Should missing values inside quotes be treated as missing values (the default) or strings. This parameter is soft deprecated as of readr 2.0.0.

comment

A string used to identify comments. Any text after the comment characters will be silently ignored.

trim_ws

Should leading and trailing whitespace (ASCII spaces and tabs) be trimmed from each field before parsing it?

skip

Number of lines to skip before reading data. If comment is supplied any commented lines are ignored after skipping.

guess_max

Maximum number of lines to use for guessing column types. Will never use more than the number of lines read. See vignette("column-types", package = "readr") for more details.

progress

Display a progress bar? By default it will only display in an interactive session and not while knitting a document. The automatic progress bar can be disabled by setting option readr.show_progress to FALSE.

show_col_types

If FALSE, do not show the guessed column types. If TRUE always show the column types, even if they are supplied. If NULL (the default) only show the column types if they are not explicitly supplied by the col_types argument.

skip_empty_rows

Should blank rows be ignored altogether? i.e. If this option is TRUE then blank rows will not be represented at all. If it is FALSE then they will be represented by NA values in all the columns.

Details

The number of lines in file can exceed the maximum integer value in R (~2 billion).

Examples

Run this code

# Cars with 3 gears
f <- function(x, pos) subset(x, gear == 3)
read_csv_chunked(readr_example("mtcars.csv"), DataFrameCallback$new(f), chunk_size = 5)

Run the code above in your browser using DataLab

Description

Usage

Arguments

Details

See Also

Examples