Return melted data for each token in a fixed width file

This function has been superseded in readr and moved to the meltr package.

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
)

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.

Details

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() 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.

Examples

fwf_sample <- readr_example("fwf-sample.txt")
cat(read_lines(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")))
#> Warning: `melt_fwf()` was deprecated in readr 2.0.0.
#> ℹ Please use `meltr::melt_fwf()` instead
#> # 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

Return melted data for each token in a fixed width file

Usage

Arguments

Details

See also

Examples