R programming is an efficient tool for statistical analysis of data. Data science has become critical to Each field and The popularity of R is skyrocketing. Organization as large and diverse as Google, face book, Microsoft, Bank of America, Ford motor company, Mozilla, Thomas cook, The new York Times, The National weather service, Twitter and Bank, Uber, Airbnb etc. Have turned to R for reporting, analyzing and visualisations of data, This book is for students and professionals of Mathematics, statistics, Physics, Chemistry, Biology, social Science and medicine, business, engineering, software, information Technology, sales, bio Informatics, pharmacy and any one, where data needs to be analysed and represented graphically. salient features of The bookshelf;/strong> best self-study material and reference guide. and Lt; teaches how to apply various statistical methods to data science. and Lt; help statistician and professional to leverage strength by combining their domain expertise with data analysis capability of R. and Lt; more than 300 solved and tested programmes provided. and Lt; a comprehensive book covering all areas of R in detail. table of content chapter 1 - Introduction; Chapter 2 - Data types operations Chapter 3 - flow control Chapter 4 - functions package; Chapter 5 - charts and graphs; Chapter 6 - connecting R to external interface; Chapter 7 - elementary statistics; Chapter 8 - tests of hypotheses Chapter 9 - Non parametric tests Chapter 10 - analysis of variance; Chapter 11 - and NBE; basic multivariate analysis; Chapter 12 - Advanced multivariate analysis; Chapter 13 - Advanced graphs.