R data science essentials : learn the essence of data science and visualization using R in no time at all / Raja B. Koushik, Sharan Kumar Ravindran.

Online resource; title from PDF title page (EBSCO, viewed February 5, 2016)

Includes index.

Learn the essence of data science and visualization using R in no time at allAbout This Book Become a pro at making stunning visualizations and dashboards quickly and without hassle For better decision making in business, apply the R programming language with the help of useful statistical techniques. From seasoned authors comes a book that offers you a plethora of fast-paced techniques to detect and analyze data patternsWho This Book Is ForIf you are an aspiring data scientist or analyst who has a basic understanding of data science and has basic hands-on experience in R or any other analytics tool, then R Data Science Essentials is the book for you.What You Will Learn Perform data preprocessing and basic operations on data Implement visual and non-visual implementation data exploration techniques Mine patterns from data using affinity and sequential analysis Use different clustering algorithms and visualize them Implement logistic and linear regression and find out how to evaluate and improve the performance of an algorithm Extract patterns through visualization and build a forecasting algorithm Build a recommendation engine using different collaborative filtering algorithms Make a stunning visualization and dashboard using ggplot and R shinyIn DetailWith organizations increasingly embedding data science across their enterprise and with management becoming more data-driven it is an urgent requirement for analysts and managers to understand the key concept of data science. The data science concepts discussed in this book will help you make key decisions and solve the complex problems you will inevitably face in this new world.R Data Science Essentials will introduce you to various important concepts in the field of data science using R. We start by reading data from multiple sources, then move on to processing the data, extracting hidden patterns, building predictive and forecasting models, building a recommendation engine, and communicating to the user through stunning visualizations and dashboards.By the end of this book, you will have an understanding of some very important techniques in data science, be able to implement them using R, understand and interpret the outcomes, and know how they helps businesses make a decision.Style and approachThis easy-to-follow guide contains hands-on examples of the concepts of data science using R.

Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started with R; Reading data from different sources; Reading data from a database; Data types in R; Variable data types; Data preprocessing techniques; Performing data operations; Arithmetic operations on the data; String operations on the data; Aggregation operations on the data; Mean; Median; Sum; Maximum and minimum; Standard deviation; Control structures in R; Control structures -- if and else; Control structures -- for; Control structures -- while

Control structures -- repeat and breakControl structures -- next and return; Bringing data to a usable format; Summary; Chapter 2: Exploratory Data Analysis; The Titanic dataset; Descriptive statistics; Box plot; Exercise; Inferential statistics; Univariate analysis; Bivariate analysis; Multivariate analysis; Cross-tabulation analysis; Graphical analysis; Summary; Chapter 3: Pattern Discovery; Transactional datasets; Using the built-in dataset; Building the dataset; Apriori analysis; Support, confidence, and lift; Support; Confidence; Lift; Generating filtering rules; Plotting; Dataset; Rules

Sequential datasetApriori sequence analysis; Understanding the results; Reference; Business cases; Summary; Chapter 4: Segmentation Using Clustering; Datasets; Reading and formatting the dataset in R; Centroid-based clustering and an ideal number of clusters; Implementation using K-means; Visualizing the clusters; Connectivity-based clustering; Visualizing the connectivity; Business use cases; Summary; Chapter 5: Developing Regression Models; Datasets; Sampling the dataset; Logistic regression; Evaluating logistic regression; Linear regression; Evaluating linear regression

Methods to improve the accuracyEnsemble models; Replacing NA with mean or median; Removing the highly correlated values; Removing outliers; Summary; Chapter 6: Time Series Forecasting; Datasets; Extracting patterns; Forecasting using ARIMA; Forecasting using Holt-Winters; Methods to improve accuracy; Summary; Chapter 7: Recommendation Engine; Dataset and transformation; Recommendations using user-based CF; Recommendations using item-based CF; Challenges and enhancements; Summary; Chapter 8: Communicating Data Analysis; Dataset; Plotting using the googleVis package

Creating an interactive dashboard using ShinySummary; Index

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