melt_table {meltr}

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

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.

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. 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.

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

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. readr::read_table() is the conventional way to read tabular data from whitespace-separated files.

Examples

# One corner from http://www.masseyratings.com/cf/compare.htm
massey <- meltr_example("massey-rating.txt")
cat(readLines(massey))
melt_table(massey)

# Sample of 1978 fuel economy data from
# http://www.fueleconomy.gov/feg/epadata/78data.zip
epa <- meltr_example("epa78.txt")
writeLines(readLines(epa))
melt_table(epa)