R: Return melted data for each token in a delimited file...

melt_delim {readr} | R Documentation

## Return melted data for each token in a delimited file (including csv & tsv)

### 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_delim(file, delim, quote = "\"", escape_backslash = FALSE,
      escape_double = TRUE, locale = default_locale(), na = c("", "NA"),
      quoted_na = TRUE, comment = "", trim_ws = FALSE, skip = 0,
      n_max = Inf, progress = show_progress(), skip_empty_rows = FALSE)

    melt_csv(file, locale = default_locale(), na = c("", "NA"),
      quoted_na = TRUE, quote = "\"", comment = "", trim_ws = TRUE,
      skip = 0, n_max = Inf, progress = show_progress(),
      skip_empty_rows = FALSE)

    melt_csv2(file, locale = default_locale(), na = c("", "NA"),
      quoted_na = TRUE, quote = "\"", comment = "", trim_ws = TRUE,
      skip = 0, n_max = Inf, progress = show_progress(),
      skip_empty_rows = FALSE)

    melt_tsv(file, locale = default_locale(), na = c("", "NA"),
      quoted_na = TRUE, quote = "\"", 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.
`delim` | Single character used to separate fields within a record.
`quote` | Single character used to quote strings.
`escape_backslash` | Does the file use backslashes to escape special characters? This is more general than `escape_double` as backslashes can be used to escape the delimiter character, the quote character, or to add special characters like `\n`.
`escape_double` | Does the file escape quotes by doubling them? i.e. If this option is `TRUE`, the value `""""` represents a single quote, `\"`.
`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.
`quoted_na` | Should missing values inside quotes be treated as missing values (the default) or strings.
`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_csv()` and `melt_tsv()` are special cases of the general `melt_delim()`. They're useful for reading the most common types of flat file data, comma separated values and tab separated values, respectively. `melt_csv2()` uses `;` for the field separator and `,` for the decimal point. This is common in some European countries.

### 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

`read_delim()` for the conventional way to read rectangular data from delimited files.

### Examples

    # Input sources -------------------------------------------------------------
    # Read from a path
    melt_csv(readr_example("mtcars.csv"))
    melt_csv(readr_example("mtcars.csv.zip"))
    melt_csv(readr_example("mtcars.csv.bz2"))
    ## Not run:
    melt_csv("https://github.com/tidyverse/readr/raw/master/inst/extdata/mtcars.csv")

    ## End(Not run)

    # Or directly from a string (must contain a newline)
    melt_csv("x,y\n1,2\n3,4")

    # To import empty cells as 'empty' rather than `NA`
    melt_csv("x,y\n,NA,\"\",''", na = "NA")

    # File types ----------------------------------------------------------------
    melt_csv("a,b\n1.0,2.0")
    melt_csv2("a;b\n1,0;2,0")
    melt_tsv("a\tb\n1.0\t2.0")
    melt_delim("a|b\n1.0|2.0", delim = "|")

[Package readr version 1.3.1 Index]