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.
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(...)
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.
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 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).
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.
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.
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.
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.
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.
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.
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.
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().
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.
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.
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.
Comments are no longer looked for anywhere in the file. They are now only ignored at the start of a line.
read_table() to read fixed width files where each
column is separated by whitespace.
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))
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