Publisher's Synopsis
Growing demand for data analytics in industrial houses and service organizations coupled with ever growing competition brings challenges to take right decision at right time. So, there is a need for data analytics tool, which can be met through R programming This handles analytics related tasks in mathematics and statistics.
In this direction, Prof.R. Panneerselvam with his rich experience of teaching and research at Anna University main campus and Pondicherry University main campus for four decades, in specific teaching research methodology and data analytics related courses and publishing 16 well established text books in PHI Learning, Cengage and Amazon, as an extension, the professor feels happy in publishing this text, 'R Programming Made Simple; to serve B.Tech/B.E (Data Analytics Stream), M.B.A/ M.Sc. (Data Science/ Data Analytics), MBA students, and B.Sc. and M.Sc.(Mathematics and Statistics). This text is useful to practitioners in Core industries and IT industry also..
The text begins with a chapter on installation of R programming software. Chapter 2 introduces basic R statements. In Chapter 3, basic data types are illustrated using suitable R programs. It is followed by a chapter on vector, which presents vector with integer data type, vector with character data type, vector with logical data, vector arithmetic, recycling rule applied to vector operations, and vector indexing.
Chapter 5 gives comprehensive treatments of matrix operations using R programming. The next chapter presents R programming applied to list. Chapter 7 presents data frame, which includes data frame column vector, data frame column slicing, and data frame row slicing. In Chapter 8, a complete coverage of quantitative data using R programming is given, which includes frequency distribution, histogram, cumulative frequency distribution and cumulative frequency graph.
Chapter 9 gives a comprehensive treatment of numerical measures using R programming, which includes, mean, median, quartile, percentile, range, interquartile range, box plot, variance, standard deviation, covariance, correlation coefficient, skewness, and kurtosis. Next chapter presents different probability distributions, viz. binomial distribution, Poisson distribution, exponential distribution, uniform distribution, normal distribution, student t distribution, Chi-square distribution and F distribution. Chapter 11 presents interval estimation using R programming. Next chapter gives a comprehensive analysis of hypothesis testing using R programming. Chapter 13 presents chi-square test using R programming. In Chapter 14, the use of R programming applied to different types of ANOVA, viz. completely randomized design, randomized block design and factorial design. The next chapter 15 gives R program for different non-parametric tests. The last chapter is on regression analysis using R programming, which presents simple regression analysis and multiple regression analysis.
This text is written in easy-to-read style. Each and every chapter contains numerous examples. I express my deep gratitude to all my academic colleagues and teachers of other universities and colleges for having suggested me to come out with this text. Further, I take this opportunity to thank Amazon, which provided a platform to publish this book.
Any constructive suggestion for further improvement of the book is most welcome.
R. Panneerselvam, Ph. D