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textTinyR

The textTinyR package consists of text processing functions for small or big data files. More details on the functionality of textTinyR can be found in the package Documentation and Vignettes. The R package can be installed, in the following Operating Systems: Linux, Mac and Windows. However, there is one limitation : chinese, japanese, korean, thai or languages with ambiguous word boundaries are not supported.

UPDATE 01-04-2018 : boost-locale is no longer a system requirement for the textTinyR package.

Installation of the textTinyR package (CRAN, Github)

To install the package from CRAN use,


install.packages('textTinyR')

and to download the latest version from Github use the install_github function of the devtools package, 


devtools::install_github(repo = 'mlampros/textTinyR')

https://github.com/mlampros/textTinyR/issues

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Version

Install

install.packages('textTinyR')

Monthly Downloads

1,142

Version

1.1.2

License

GPL-3

Issues

0

Pull Requests

0

Stars

38

Forks

8

Maintainer

Lampros Mouselimis

Last Published

July 25th, 2018

Functions in textTinyR (1.1.2)

COS_TEXT

Cosine similarity for text documents
token_stats

token statistics
tokenize_transform_text

String tokenization and transformation ( character string or path to a file )
text_file_parser

text file parser
text_intersect

intersection of words or letters in tokenized text
TEXT_DOC_DISSIM

Dissimilarity calculation of text documents
big_tokenize_transform

String tokenization and transformation for big data sets
cosine_distance

cosine distance of two character strings (each string consists of more than one words)
dense_2sparse

convert a dense matrix to a sparse matrix
matrix_sparsity

sparsity percentage of a sparse matrix
read_characters

read a specific number of characters from a text file
select_predictors

Exclude highly correlated predictors
sparse_Means

RowMens and colMeans for a sparse matrix
vocabulary_parser

returns the vocabulary counts for small or medium ( xml and not only ) files
Doc2Vec

Conversion of text documents to word-vector-representation features ( Doc2Vec )
JACCARD_DICE

Jaccard or Dice similarity for text documents
bytes_converter

bytes converter of a text file ( KB, MB or GB )
dice_distance

dice similarity of words using n-grams
cluster_frequency

Frequencies of an existing cluster object
read_rows

read a specific number of rows from a text file
dims_of_word_vecs

dimensions of a word vectors file
save_sparse_binary

save a sparse matrix in binary format
sparse_Sums

RowSums and colSums for a sparse matrix
sparse_term_matrix

Term matrices and statistics ( document-term-matrix, term-document-matrix)
tokenize_transform_vec_docs

String tokenization and transformation ( vector of documents )
utf_locale

utf-locale for the available languages
Count_Rows

Number of rows of a file
levenshtein_distance

levenshtein distance of two words
load_sparse_binary

load a sparse matrix in binary format