Using Textual and Binary Formats for Storing Data in R-Programming
N.V.Neeraja
, V.Rajyalakshmi , D.Sowjanya , B.Rajesh
CSV, Knowledge Extraction, Machine Learning, Text Mining
The aim of this study is to develop an answer for text mining scientific articles exploitation the R language within the “Knowledge Extraction and Machine Learning” course. Automatic text outline of papers could be a difficult downside whose approach would permit researchers to browse massive article collections and quickly read highlights and drill down for details. In proposed system, there are a variety of ways that data can be stored, including structured text files like CSV or tabdelimited, or more complex binary formats. However, there's associate degree intermediate format that's matter, but not as simple as something like CSV. The format is native to R and is somewhat legible as a result ofits matter nature. One will produce a additional descriptive illustration of associate degree R object by exploitation the dput() or dump() functions. The dump() and dput() functions are useful because the resulting textual format is editable, and in the case of corruption, potentially recoverable. By using dump() and dput(), we can easily mine the text. Unlike writing out a table or CSV file, dump() and dput() preserve the information (sacrificing some readability), so another user doesn’t got to specify it everywhere once more.
"Using Textual and Binary Formats for Storing Data in R-Programming", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.3, Issue 11, page no.237 - 240, December-2018, Available :https://ijsdr.org/papers/IJSDR1812038.pdf
Volume 3
Issue 11,
December-2018
Pages : 237 - 240
Paper Reg. ID: IJSDR_180911
Published Paper Id: IJSDR1812038
Downloads: 000347185
Research Area: Engineering
Country: palamaner, chittor(district), Andhra Pradesh, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
