Return melted data for each token in a fixed width file

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)

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. It must contain at least one new line to be recognised as data (instead of a path) or be a vector of greater than length 1. 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 be trimmed from each field before parsing it?
skip	Number of lines to skip before reading data.
n_max	Maximum number of records to read.
progress	Display a progress bar? By default it will only display in an interactive session and not while knitting a document. The display is updated every 50,000 values and will only display if estimated reading time is 5 seconds or more. 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

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")))
#> # A tibble: 12 x 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 x 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 x 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 x 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 x 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

Arguments

Details

See also

Examples

Contents