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.

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 ...1, ...2 etc. Duplicate column names will generate a warning and be made unique, see name_repair to control how this is done.

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

If NULL, all column types will be imputed from guess_max rows on the input interspersed throughout the file. This is convenient (and fast), but not robust. If the imputation fails, you'll need to increase the 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).

Columns to include in the results. You can use the same mini-language as dplyr::select() to refer to the columns by name. Use c() or list() to use more than one selection expression. Although this usage is less common, col_select also accepts a numeric column index. See ?tidyselect::language for full details on the selection language.

The name of a column in which to store the file path. This is useful when reading multiple input files and there is data in the file paths, such as the data collection date. If NULL (the default) no extra column is created.

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. This parameter is soft deprecated as of readr 2.0.0.

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

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

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

Maximum number of lines to read.

Maximum number of lines to use for guessing column types. See vignette("column-types", package = "readr") for more details.

Handling of column names. The default behaviour is to ensure column names are "unique". Various repair strategies are supported:

• "minimal": No name repair or checks, beyond basic existence of names.

• "unique" (default value): Make sure names are unique and not empty.

• "check_unique": no name repair, but check they are unique.

• "universal": Make the names unique and syntactic.

• A function: apply custom name repair (e.g., name_repair = make.names for names in the style of base R).

• A purrr-style anonymous function, see rlang::as_function().

This argument is passed on as repair to vctrs::vec_as_names(). See there for more details on these terms and the strategies used to enforce them.

The number of processing threads to use for initial parsing and lazy reading of data. If your data contains newlines within fields the parser should automatically detect this and fall back to using one thread only. However if you know your file has newlines within quoted fields it is safest to set num_threads = 1 explicitly.

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.

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.

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.

Read values lazily? By default the file is initially only indexed and the values are read lazily when accessed. Lazy reading is useful interactively, particularly if you are only interested in a subset of the full dataset. Note, if you later write to the same file you read from you need to set lazy = FALSE. On Windows the file will be locked and on other systems the memory map will become invalid.