Significance of R in Research

G. Sateesh*, B. Padmaja**, M. V. Bhuvaneswari***
*-*** Department of Computer Science Engineering, Lendi College of Engineering, Andhra Pradesh, India.
Periodicity:June - August'2019
DOI : https://doi.org/10.26634/jcom.7.2.15639

Abstract

R is a great degree adaptable factual programming language and condition that is open source and unreservedly accessible for all standard working frameworks. The aim of this paper is to bring significance of R to the allied fields of Data science and development of R in different innovations like Data Analysis, Image Processing, Big Data Analytics and Machine Learning everything under data science advancements. R studio contributes numerous packages, which are valuable in their respective environment and projects effortlessly. Its short syntax structure to quicken tasks on the data, loading and storing information for both nearby and over web, an extensive rundown of long list of tools for data analysis pulls in clients to work with R. R demonstrates that imperative strategies not accessible somewhere else can be actualized in R effectively.

Keywords

Big Data, R Studio, Image Processing, Machine learning.

How to Cite this Article?

Satheesh, G., Padmaja, B., Bhuvaneswari, M. V.(2019). Significance of R in Research, i-manager's Journal on Computer Science, 7(2), 1-7. https://doi.org/10.26634/jcom.7.2.15639

References

[1]. Babu, K. N. (2016). A novel approach on image processing using R Studio. International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), 5(11), 399-402.
[2]. Babu, T. G., & Babu, G. A. (2016). A survey on data science technologies & big data analytics. International Journal of Advanced Research in Computer Science and Software Engineering, 6(2), 322-327.
[3]. Beaujean, A. A. (2013). Factor analysis using R. Practical Assessment, Research & Evaluation, 18(4), 1-11.
[4]. Dancik, G. (2018). Automated grading of templatebased R programming assignments using swirl-tbp. Journal of Computing Sciences in Colleges, 33(6), 179- 180.
[5]. Henao, J. D. V., & Bedoya, J. W. B. (2012). Examples in the classroom: Pattern classification using the R language. Dyna, 79(173), 81-88.
[6]. Huang, R., Xu, W., Liverani, S., Hiltbrand, D., & Stapleton, A. E. (2018, April). A case study of r performance analysis and optimization. In Proceedings of the Practice and Experience on Advanced Research Computing (p. 33). ACM.
[7]. Jatain, A., & Ranja, A. (2017). A review study on big data anaylsis using R Studio. International Journal of Computer Science and Mobile Computing, 6(6), 8-13.
[8]. Kelley, K., Lai, K., & Wu, P. J. (2008). 34 using R for Data Analysis -a Best Practice for Research (pp. 535-572). Retrieved from http://citeseerx.ist.psu.edu/viewdoc/ summary?doi=10.1.1.163.6787
[9]. Kolaczyk, E. D., & Csárdi, G. (2014). Statistical Analysis of Network Data with R (Vol. 65). New York: Springer.
[10]. Matloff, N. (2011). The Art of R programming: A Tour of Statistical Software Design. No Starch Press.
[11]. Pandey, R. (2015, December). Elective Recommendation Support through K-Means Clustering using R-Tool. In 2015 International Conference on Computational Intelligence and Communication Networks (CICN) (pp. 851-856). IEEE.
[12]. Ramalakshmi, E., & Kompala, N. (2017, February). Multi-threading image processing in single-core and multi-core CPU using R language. In 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT) (pp. 1-5). IEEE.
[13]. Shinde, P. P., Oza, K. S., & Kamat, R. K. (2017, February). Big data predictive analysis: Using R analytical tool. In 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) (pp. 839- 842). IEEE.
[14]. Siddiqui, T., Alkadri, M., & Khan, N. A. (2017). Review of programming languages and tools for Big Data Analytics. International Journal of Advanced Research in Computer Science, 8(5), 1112-1118.
[15]. Xu, W., Huang, R., Zhang, H., El-Khamra, Y., & Walling, D. (2016). Empowering R with high performance computing resources for big data analytics. In Conquering Big Data with High Performance Computing (pp. 191-217). Springer, Cham.
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