Read a fixed width file into a tibble

  col_positions = fwf_empty(file, skip, n = guess_max),
  col_types = NULL,
  col_select = NULL,
  id = NULL,
  locale = default_locale(),
  na = c("", "NA"),
  comment = "",
  trim_ws = TRUE,
  skip = 0,
  n_max = Inf,
  guess_max = 100,
  altrep = TRUE,
  altrep_opts = deprecated(),
  num_threads = vroom_threads(),
  progress = vroom_progress(),
  .name_repair = "unique"

fwf_empty(file, skip = 0, col_names = NULL, comment = "", n = 100L)

fwf_widths(widths, col_names = NULL)

fwf_positions(start, end = NULL, col_names = NULL)




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.


Column positions, as created by fwf_empty(), fwf_widths() or fwf_positions(). To read in only selected fields, use fwf_positions(). If the width of the last column is variable (a ragged fwf file), supply the last end position as NA.


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().


One or more selection expressions, like in dplyr::select(). Use c() or list() to use more than one expression. See ?dplyr::select for details on available selection options.


Either a string or 'NULL'. If a string, the output will contain a variable with that name with the filename(s) as the value. If 'NULL', the default, no variable will be 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.


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 read.


Maximum number of records to use for guessing column types.


Control which column types use Altrep representations, either a character vector of types, TRUE or FALSE. See vroom_altrep() for for full details.


Deprecated lifecycle


Number of threads to use when reading and materializing vectors. If your data contains newlines within fields the parser will automatically be forced to use a single thread only.


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.


Handling of column names. By default, vroom ensures column names are not empty and unique. See .name_repair as documented in tibble::tibble() for additional options including supplying user defined name repair functions.


Either NULL, or a character vector column names.


Number of lines the tokenizer will read to determine file structure. By default it is set to 100.


Width of each field. Use NA as width of last field when reading a ragged fwf file.

start, end

Starting and ending (inclusive) positions of each field. Use NA as last end field when reading a ragged fwf file.


If the first element is a data frame, then it must have all numeric columns and either one or two rows. The column names are the variable names. The column values are the variable widths if a length one vector, and if length two, variable start and end positions. The elements of ... are used to construct a data frame with or or two rows as above.


fwf_sample <- vroom_example("fwf-sample.txt") cat(readLines(fwf_sample))
#> John Smith WA 418-Y11-4111 Mary Hartford CA 319-Z19-4341 Evan Nolan IL 219-532-c301
# You can specify column positions in several ways: # 1. Guess based on position of empty columns vroom_fwf(fwf_sample, fwf_empty(fwf_sample, col_names = c("first", "last", "state", "ssn")))
#> # A tibble: 3 x 4 #> first last state ssn #> <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
# 2. A vector of field widths vroom_fwf(fwf_sample, fwf_widths(c(20, 10, 12), c("name", "state", "ssn")))
#> # A tibble: 3 x 3 #> name state ssn #> <chr> <chr> <chr> #> 1 John Smith WA 418-Y11-4111 #> 2 Mary Hartford CA 319-Z19-4341 #> 3 Evan Nolan IL 219-532-c301
# 3. Paired vectors of start and end positions vroom_fwf(fwf_sample, fwf_positions(c(1, 30), c(20, 42), c("name", "ssn")))
#> # A tibble: 3 x 2 #> name ssn #> <chr> <chr> #> 1 John Smith 418-Y11-4111 #> 2 Mary Hartford 319-Z19-4341 #> 3 Evan Nolan 219-532-c301
# 4. Named arguments with start and end positions vroom_fwf(fwf_sample, fwf_cols(name = c(1, 20), ssn = c(30, 42)))
#> # A tibble: 3 x 2 #> name ssn #> <chr> <chr> #> 1 John Smith 418-Y11-4111 #> 2 Mary Hartford 319-Z19-4341 #> 3 Evan Nolan 219-532-c301
# 5. Named arguments with column widths vroom_fwf(fwf_sample, fwf_cols(name = 20, state = 10, ssn = 12))
#> # A tibble: 3 x 3 #> name state ssn #> <chr> <chr> <chr> #> 1 John Smith WA 418-Y11-4111 #> 2 Mary Hartford CA 319-Z19-4341 #> 3 Evan Nolan IL 219-532-c301