stylo: Stylometric multidimensional analyses
In stylo: Stylometric Multivariate Analyses

Description Usage Arguments Details Value Author(s) References See Also Examples

It is quite a long story what this function does. Basically, it is an all-in-one tool for a variety of experiments in computational stylistics. For a more detailed description, refer to HOWTO available at: https://sites.google.com/site/computationalstylistics/

stylo(gui = TRUE, frequencies = NULL, parsed.corpus = NULL,
      features = NULL, path = NULL, metadata = NULL, 
      filename.column = "filename", grouping.column = "author",
      corpus.dir = "corpus", ...)

`gui`	an optional argument; if switched on, a simple yet effective graphical interface (GUI) will appear. Default value is `TRUE`.
`frequencies`	using this optional argument, one can load a custom table containing frequencies/counts for several variables, e.g. most frequent words, across a number of text samples. It can be either an R object (matrix or data frame), or a filename containing tab-delimited data. If you use an R object, make sure that the rows contain samples, and the columns – variables (words). If you use an external file, the variables should go vertically (i.e. in rows): this is because files containing vertically-oriented tables are far more flexible and easily editable using, say, Excel or any text editor. To flip your table horizontally/vertically use the generic function t().
`parsed.corpus`	another option is to pass a pre-processed corpus as an argument. It is assumed that this object is a list, each element of which is a vector containing one tokenized sample. The example shown below will give you some hints how to prepare such a corpus.
`features`	usually, a number of the most frequent features (words, word n-grams, character n-grams) are extracted automatically from the corpus, and they are used as variables for further analysis. However, in some cases it makes sense to use a set of tailored features, e.g. the words that are associated with emotions or, say, a specific subset of function words. This optional argument allows to pass either a filename containing your custom list of features, or a vector (R object) of features to be assessed.
`path`	if not specified, the current directory will be used for input/output procedures (reading files, outputting the results).
`corpus.dir`	the subdirectory (within the current working directory) that contains the corpus text files. If not specified, the default subdirectory `corpus` will be used. This option is immaterial when an external corpus and/or external table with frequencies is loaded.
`metadata`	if not specified, colors for plotting will be assigned accoding to file names after the usual author_ducument.txt pattern. But users can also specify a grouping variable, i.e. a vector of a length equal to the number of texts in the corpus, or a csv file, conventionally named "metadata.csv" containg matadata for the corpus. This metadata file should contain one row per document, a column with the file names in alphabetical order, and a calumn containing the grouping varible.
`filename.column`	the column in the metadata.csv containg the file names of the documents in alphabetical order.
`grouping.column`	the column in the metadata.csv containg the grouping variable.
`...`	any variable produced by `stylo.default.settings` can be set here, in order to overwrite the default values. An example of such a variable is `network = TRUE` (switched off as default) for producing stylometric bootstrap consensus networks (Eder, forthcoming); the function saves a csv file, containing a list of nodes that can be loaded into, say, Gephi.

If no additional argument is passed, then the function tries to load text files from the default subdirectory corpus. There are a lot of additional options that should be passed to this function; they are all loaded when stylo.default.settings is executed (which is typically called automatically from inside the stylo function).

The function returns an object of the class stylo.results: a list of variables, including a table of word frequencies, vector of features used, a distance table and some more stuff. Additionally, depending on which options have been chosen, the function produces a number of files containing results, plots, tables of distances, etc.

Maciej Eder, Jan Rybicki, Mike Kestemont, Steffen Pielström

Eder, M., Rybicki, J. and Kestemont, M. (2016). Stylometry with R: a package for computational text analysis. "R Journal", 8(1): 107-21.

Eder, M. (2017). Visualization in stylometry: cluster analysis using networks. "Digital Scholarship in the Humanities", 32(1): 50-64.

classify, oppose, rolling.classify

## Not run: 
# standard usage (it builds a corpus from a set of text files):
stylo()

# loading word frequencies from a tab-delimited file:
stylo(frequencies = "my_frequencies.txt")

# using an existing corpus (a list containing tokenized texts):
txt1 = c("to", "be", "or", "not", "to", "be")
txt2 = c("now", "i", "am", "alone", "o", "what", "a", "slave", "am", "i")
txt3 = c("though", "this", "be", "madness", "yet", "there", "is", "method")
custom.txt.collection = list(txt1, txt2, txt3)
  names(custom.txt.collection) = c("hamlet_A", "hamlet_B", "polonius_A")
stylo(parsed.corpus = custom.txt.collection)

# using a custom set of features (words, n-grams) to be analyzed:
my.selection.of.function.words = c("the", "and", "of", "in", "if", "into",
                                   "within", "on", "upon", "since")
stylo(features = my.selection.of.function.words)

# loading a custom set of features (words, n-grams) from a file:
stylo(features = "wordlist.txt")

# batch mode, custom name of corpus directory:
my.test = stylo(gui = FALSE, corpus.dir = "ShakespeareCanon")
summary(my.test)

# batch mode, character 3-grams requested:
stylo(gui = FALSE, analyzed.features = "c", ngram.size = 3)


## End(Not run)