melt_fwf: Return melted data for each token in a fixed width file

In meltr: Read Non-Rectangular Text Data

Return melted data for each token in a fixed width file

Description

For certain non-rectangular data formats, it can be useful to parse the data into a melted format where each row represents a single token.

Usage

melt_fwf(
  file,
  col_positions,
  locale = default_locale(),
  na = c("", "NA"),
  comment = "",
  trim_ws = TRUE,
  skip = 0,
  n_max = Inf,
  progress = show_progress(),
  skip_empty_rows = FALSE
)

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.
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.
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.
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.

Details

melt_fwf() parses each token of a fixed width file into a single row, but it still requires that each field is in the same in every row of the source file.

Value

A tibble() of four columns:

• row, the row that the token comes from in the original file

• col, the column that the token comes from in the original file

• data_type, the data type of the token, e.g. "integer", "character", "date", guessed in a similar way to the guess_parser() function.

• value, the token itself as a character string, unchanged from its representation in the original file.

If there are parsing problems, a warning tells you how many, and you can retrieve the details with problems().

See Also

melt_table() to melt fixed width files where each column is separated by whitespace, and melt_fwf() for the conventional way to read rectangular data from fixed width files.

Examples

fwf_sample <- meltr_example("fwf-sample.txt")
writeLines(readLines(fwf_sample))

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

meltr documentation built on Sept. 11, 2022, 1:07 a.m.