df2vw: Create a VW data file from a R data.frame object
In ivan-pavlov/rvwgsoc: R Interface for Vowpal Wabbit

Description Usage Arguments

Create a VW data file from a R data.frame object

df2vw(data, file_path, namespaces = NULL, keep_space = NULL,
  fixed = NULL, targets = NULL, probabilities = NULL,
  weight = NULL, base = NULL, tag = NULL, multiline = NULL,
  append = FALSE)

`data`	[data.frame] data.frame object to be converted
`file_path`	[string] file name of the resulting data in VW-friendly format
`namespaces`	[list or yaml file] name of each namespace and each variable for each namespace can be a R list, or a YAML file example namespace with the IRIS database: namespaces = list(sepal = list('Sepal.Length', 'Sepal.Width'), petal = list('Petal.Length', 'Petal.Width') this creates 2 namespaces (sepal and petal) containing the features defined by elements of this lists.
`keep_space`	[string vector] keep spaces for this features Example:"FERRARI 4Si" With `keep_space` will be "FERRARI 4Si" and will be treated as two features Without `keep_space` will be "FERRARI_4Si" and will be treated as one feature
`fixed`	[string vector] fixed parsing for this features Similar to `keep_space`, but parse features exactly without replacement of special characters ("(", ")", "\|", ":", "'"). Can be used for LDA ("word_1:2 word_2:3" will stay the same), but should be used carefully, because special characters can ruin final VW format file.
`targets`	[string or string vector] If `[string]` then will be treated as vector with real number labels for regular VW input format. If `[string vector]` then will be treated as vectors with class costs for wap and csoaa multi-class classification algorithms or as vectors with actions for Contextual Bandit algorithm.
`probabilities`	[string vector] vectors with action probabilities for Contextual Bandit algorithm.
`weight`	[string] weight (importance) of each line of the dataset.
`base`	[string] base of each line of the dataset. Used for residual regression.
`tag`	[string] tag of each line of the dataset.
`multiline`	[integer] number of labels (separate lines) for multilines example
`append`	[bool] data to be appended to the result file