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Return melted data for each token in a whitespace-separated file

Source: R/melt_table.R
melt_table.Rd

[Superseded] This function has been superseded in readr and moved to the meltr package.

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_table() and melt_table2() are designed to read the type of textual data where each column is separated by one (or more) columns of space.

melt_table2() allows any number of whitespace characters between columns, and the lines can be of different lengths.

melt_table() is more strict, each line must be the same length, and each field is in the same position in every line. It first finds empty columns and then parses like a fixed width file.

Usage

melt_table(
  file,
  locale = default_locale(),
  na = "NA",
  skip = 0,
  n_max = Inf,
  guess_max = min(n_max, 1000),
  progress = show_progress(),
  comment = "",
  skip_empty_rows = FALSE
)

melt_table2(
  file,
  locale = default_locale(),
  na = "NA",
  skip = 0,
  n_max = Inf,
  progress = show_progress(),
  comment = "",
  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.

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.

skip

Number of lines to skip before reading data.

n_max

Maximum number of lines to read.

guess_max

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.

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.

comment

A string used to identify comments. Any text after the comment characters will be silently ignored.

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.

See also

melt_fwf() to melt fixed width files where each column is not separated by whitespace. melt_fwf() is also useful for reading tabular data with non-standard formatting. read_table() is the conventional way to read tabular data from whitespace-separated files.

Examples

fwf <- readr_example("fwf-sample.txt")
writeLines(read_lines(fwf))
#> John Smith          WA        418-Y11-4111
#> Mary Hartford       CA        319-Z19-4341
#> Evan Nolan          IL        219-532-c301
melt_table(fwf)
#> Warning: `melt_table()` was deprecated in readr 2.0.0.
#> ℹ Please use `meltr::melt_table()` 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

ws <- readr_example("whitespace-sample.txt")
writeLines(read_lines(ws))
#> first last state phone
#> John Smith WA 418-Y11-4111
#> Mary Hartford CA 319-Z19-4341
#> Evan Nolan IL 219-532-c301
melt_table2(ws)
#> Warning: `melt_table2()` was deprecated in readr 2.0.0.
#> ℹ Please use `meltr::melt_table2()` instead
#> # A tibble: 16 × 4
#>      row   col data_type value       
#>    <dbl> <dbl> <chr>     <chr>       
#>  1     1     1 character first       
#>  2     1     2 character last        
#>  3     1     3 character state       
#>  4     1     4 character phone       
#>  5     2     1 character John        
#>  6     2     2 character Smith       
#>  7     2     3 character WA          
#>  8     2     4 character 418-Y11-4111
#>  9     3     1 character Mary        
#> 10     3     2 character Hartford    
#> 11     3     3 character CA          
#> 12     3     4 character 319-Z19-4341
#> 13     4     1 character Evan        
#> 14     4     2 character Nolan       
#> 15     4     3 character IL          
#> 16     4     4 character 219-532-c301