ISBN: 978-81-964364-4-5
Authors: Dr. Lakshmana Rao Agatamudi, Dr. Ganapathi Swamy Chintada, Mr. N. Lakshmana Rao, Dr. Santhikumar Rajamahanthi
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Book Description
Basically, R was initiated by Professors ROSS IHAKA and ROBERT GENTLEMAN as a programming language in 1990’s from the inspiration of S programming language. R can be downloaded from CRAN website i.e.,https://www.R-project.org/ . R is a statistical computer program, made available through the Internet under the General Public License (GPL). That is, it is supplied with a license that allows you to use it freely, distribute it, or even sell it, if the receiver has the same rights, and the source code is freely available. R is an open source, interpreted programming language and interactive development for high performance statistical computing and effective data visualization. It is like other statistical packages like the S language that was originally developed by Bell Labs, USA. Nowadays, it is a widely accepted open-source solution for high dimensional data analytics supported by a dynamic and vibrant research community. The differences are not all that big, but it would be silly not to take advantage of the improvements in a book at this level, so, the book might be used as an introduction to S-PLUS as well as R, the reader is urged to use R while working through it.R implements a dialect of the S language. There are some differences, but in everyday use the two are very similar. However, some functions do differ, often because the R version tries to simplify things for the user. Many of the data analysts and data scientists around the globe utilize R programming to tackle challenging issues in fields ranging from computational science to business marketing. R programming has become the most common programming language of the data science community and for various finance – and analytics – driven business organizations such as Google, Facebook, and Linkedin.
R is designed in such a way that it is always possible to do further computations on the results of a statistical procedure. Furthermore, the design for graphical presentation of data allows both no-nonsense methods, for example plot(x,y), and the possibility of fine-grained control of the output appearance. R and its libraries include support for different statistical and graphics related functions along with linear and non – linear modelling, time – sequence analysis, and data mining techniques such as clustering and classifications. R can also be used as an extension service with other packages like Hadoop. Further, the open-source community has developed a number of plugs – ins and extensions to a variety of applications ranging from health care to business intelligence. R allows application developers to select the algorithms of their choice and develop packages of their own. For computationally intensive asks, other programming language (e.g., C, C++, and Python) codes can be linked with R in run – time. Users can write C, C++, Java, NET, or Python code to control R objects. Further, R has more than 10000 packages including libraries and functions that support various focused applications such as cosmology, physical sciences, genomics, drug advancement, finance, health care, advertisement, and many others. Hence, it is very easy for the applications developers to start building applications using R. The fact that R is based on a formal computer language gives it tremendous flexibility. Other systems present simpler interfaces in terms of menus and forms, but often the apparent userfriendliness turns into a hindrance in the longer run. Although elementary statistics is often presented as a collection of fixed procedures, analysis of moderately complex data requires ad-hoc statistical model building, which makes the added flexibility of R highly desirable.
The book is based upon a set of notes developed for the course in Probability and Statistics for Engineers.This course has a primary target of students for Engineering degree in Science and Technology. This book is not a manual for R. The idea is to introduce several basic concepts and techniques that should allow the reader to get started with practical statistics. In terms of the practical methods, the book covers a reasonable curriculum for first-year students of theoretical statistics as well as for engineering students. These groups will eventually need to go further and study more complex models as well as general techniques involving actual programming in the R language.The book is thus intended to be useful for several groups, but I will not pretend that it can stand alone for any of them. I have included brief theoretical sections in connection with the various methods, but more than as teaching material, these should serve as reminders or perhaps as appetizers for readers who are new to the world of statistics.