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.
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(...)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.
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.
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.
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.
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().
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.
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.
fwf_sample <- meltr_example("fwf-sample.txt")
writeLines(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
melt_fwf(fwf_sample, fwf_empty(fwf_sample, col_names = c("first", "last", "state", "ssn")))
#> # A tibble: 12 × 4
#> row col data_type value
#> <dbl> <dbl> <chr> <chr>
#> 1 1 1 character John
#> 2 1 2 character Smith
#> 3 1 3 character WA
#> 4 1 4 character 418-Y11-4111
#> 5 2 1 character Mary
#> 6 2 2 character Hartford
#> 7 2 3 character CA
#> 8 2 4 character 319-Z19-4341
#> 9 3 1 character Evan
#> 10 3 2 character Nolan
#> 11 3 3 character IL
#> 12 3 4 character 219-532-c301
# 2. A vector of field widths
melt_fwf(fwf_sample, fwf_widths(c(20, 10, 12), c("name", "state", "ssn")))
#> # A tibble: 9 × 4
#> row col data_type value
#> <dbl> <dbl> <chr> <chr>
#> 1 1 1 character John Smith
#> 2 1 2 character WA
#> 3 1 3 character 418-Y11-4111
#> 4 2 1 character Mary Hartford
#> 5 2 2 character CA
#> 6 2 3 character 319-Z19-4341
#> 7 3 1 character Evan Nolan
#> 8 3 2 character IL
#> 9 3 3 character 219-532-c301
# 3. Paired vectors of start and end positions
melt_fwf(fwf_sample, fwf_positions(c(1, 30), c(10, 42), c("name", "ssn")))
#> # A tibble: 6 × 4
#> row col data_type value
#> <dbl> <dbl> <chr> <chr>
#> 1 1 1 character John Smith
#> 2 1 2 character 418-Y11-4111
#> 3 2 1 character Mary Hartf
#> 4 2 2 character 319-Z19-4341
#> 5 3 1 character Evan Nolan
#> 6 3 2 character 219-532-c301
# 4. Named arguments with start and end positions
melt_fwf(fwf_sample, fwf_cols(name = c(1, 10), ssn = c(30, 42)))
#> # A tibble: 6 × 4
#> row col data_type value
#> <dbl> <dbl> <chr> <chr>
#> 1 1 1 character John Smith
#> 2 1 2 character 418-Y11-4111
#> 3 2 1 character Mary Hartf
#> 4 2 2 character 319-Z19-4341
#> 5 3 1 character Evan Nolan
#> 6 3 2 character 219-532-c301
# 5. Named arguments with column widths
melt_fwf(fwf_sample, fwf_cols(name = 20, state = 10, ssn = 12))
#> # A tibble: 9 × 4
#> row col data_type value
#> <dbl> <dbl> <chr> <chr>
#> 1 1 1 character John Smith
#> 2 1 2 character WA
#> 3 1 3 character 418-Y11-4111
#> 4 2 1 character Mary Hartford
#> 5 2 2 character CA
#> 6 2 3 character 319-Z19-4341
#> 7 3 1 character Evan Nolan
#> 8 3 2 character IL
#> 9 3 3 character 219-532-c301