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. It must contain at least one new line to be recognised as data (instead of a path) or be a vector of greater than length 1.

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

A callback function to call on each chunk

The number of rows to include in each chunk

Single character used to separate fields within a record.

Single character used to quote strings.

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.

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

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 X1, X2 etc. Duplicate column names will generate a warning and be made unique with a numeric suffix.

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

If NULL, all column types will be imputed from the first 1000 rows on the input. This is convenient (and fast), but not robust. If the imputation fails, you'll need to supply the correct types yourself.

If a column specification created by cols(), it 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, use col_types = cols().

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.

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

Should missing values inside quotes be treated as missing values (the default) or strings.

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

Should leading and trailing whitespace be trimmed from each field before parsing it?

Number of lines to skip before reading data.

Maximum number of records to use for guessing column types.

Display a progress bar? By default it will only display in an interactive session and not while knitting a document. The display is updated every 50,000 values and will only display if estimated reading time is 5 seconds or more. The automatic progress bar can be disabled by setting option readr.show_progress to FALSE.

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.