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read_table() is designed to read the type of textual data where each column is separated by one (or more) columns of space.

read_table() is like read.table(), it allows any number of whitespace characters between columns, and the lines can be of different lengths.

spec_table() returns the column specifications rather than a data frame.


  col_names = TRUE,
  col_types = NULL,
  locale = default_locale(),
  na = "NA",
  skip = 0,
  n_max = Inf,
  guess_max = min(n_max, 1000),
  progress = show_progress(),
  comment = "",
  show_col_types = should_show_types(),
  skip_empty_rows = TRUE



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.


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 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).


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.


Number of lines to skip before reading data.


Maximum number of lines to read.


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.


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.


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


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.

See also

read_fwf() to read fixed width files where each column is not separated by whitespace. read_fwf() is also useful for reading tabular data with non-standard formatting.


ws <- readr_example("whitespace-sample.txt")
#> first last state phone
#> John Smith WA 418-Y11-4111
#> Mary Hartford CA 319-Z19-4341
#> Evan Nolan IL 219-532-c301
#> ── Column specification ──────────────────────────────────────────────────
#> cols(
#>   first = col_character(),
#>   last = col_character(),
#>   state = col_character(),
#>   phone = col_character()
#> )
#> # A tibble: 3 × 4
#>   first last     state phone       
#>   <chr> <chr>    <chr> <chr>       
#> 1 John  Smith    WA    418-Y11-4111
#> 2 Mary  Hartford CA    319-Z19-4341
#> 3 Evan  Nolan    IL    219-532-c301