R: Read a fixed width file into a tibble

read_fwf {readr}

R Documentation

Read a fixed width file into a tibble

Description

A fixed width file can be a very compact representation of numeric data. It's also very fast to parse, because every field is in the same place in every line. Unfortunately, it's painful to parse because you need to describe the length of every field. Readr aims to make it as easy as possible by providing a number of different ways to describe the field structure.

fwf_empty() - Guesses based on the positions of empty columns.
fwf_widths() - Supply the widths of the columns.
fwf_positions() - Supply paired vectors of start and end positions.
fwf_cols() - Supply named arguments of paired start and end positions or column widths.

Usage

read_fwf(
  file,
  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 = min(n_max, 1000),
  progress = show_progress(),
  name_repair = "unique",
  num_threads = readr_threads(),
  show_col_types = should_show_types(),
  lazy = should_read_lazy(),
  skip_empty_rows = TRUE
)

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

fwf_widths(widths, col_names = NULL)

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

fwf_cols(...)

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.
`col_positions`	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.
`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)`.
`col_select`	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()` 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.
`id`	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.
`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.
`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.
`n_max`	Maximum number of lines to read.
`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`.
`name_repair`	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`. `"unique_quiet"`: Repair with the `unique` strategy, quietly. `"universal"`: Make the names `unique` and syntactic. `"universal_quiet"`: Repair with the `universal` strategy, quietly. 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.
`num_threads`	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.
`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.
`lazy`	Read values lazily? By default, this is `FALSE`, because there are special considerations when reading a file lazily that have tripped up some users. Specifically, things get tricky when reading and then writing back into the same file. But, in general, lazy reading (`lazy = TRUE`) has many benefits, especially for interactive use and when your downstream work only involves a subset of the rows or columns. Learn more in `should_read_lazy()` and in the documentation for the `altrep` argument of `vroom::vroom()`.
`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.
`col_names`	Either NULL, or a character vector column names.
`n`	Number of lines the tokenizer will read to determine file structure. By default it is set to 100.
`widths`	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.

Second edition changes

Comments are no longer looked for anywhere in the file. They are now only ignored at the start of a line.

Examples

fwf_sample <- readr_example("fwf-sample.txt")
writeLines(read_lines(fwf_sample))

# You can specify column positions in several ways:
# 1. Guess based on position of empty columns
read_fwf(fwf_sample, fwf_empty(fwf_sample, col_names = c("first", "last", "state", "ssn")))
# 2. A vector of field widths
read_fwf(fwf_sample, fwf_widths(c(20, 10, 12), c("name", "state", "ssn")))
# 3. Paired vectors of start and end positions
read_fwf(fwf_sample, fwf_positions(c(1, 30), c(20, 42), c("name", "ssn")))
# 4. Named arguments with start and end positions
read_fwf(fwf_sample, fwf_cols(name = c(1, 20), ssn = c(30, 42)))
# 5. Named arguments with column widths
read_fwf(fwf_sample, fwf_cols(name = 20, state = 10, ssn = 12))