There are many excellent R resources for visualization, data science, and package development. Hundreds of scattered vignettes, web pages, and forums explain how to use R in particular domains. But little has been written on how to simply make R work effectively—until now. This hands-on book teaches novices and experienced R users how to write efficient R code. Drawing on years of experience teaching R courses, authors Colin Gillespie and Robin Lovelace provide practical advice on a range of topics—from optimizing the set-up of RStudio to leveraging C++—that make this book a useful addition to any R user’s bookshelf. Academics, business users, and programmers from a wide range of backgrounds stand to benefit from the guidance in Efficient R Programming. Get advice for setting up an R programming environment Explore general programming concepts and R coding techniques Understand the ingredients of an efficient R workflow Learn how to efficiently read and write data in R Dive into data carpentry—the vital skill for cleaning raw data Optimize your code with profiling, standard tricks, and other methods Determine your hardware capabilities for handling R computation Maximize the benefits of collaborative R programming Accelerate your transition from R hacker to R programmer

Dr.K.Varada Rajkumar, Associate Professor, Department of Computer Science and Engineering (AIML), MLR Institute of Technology, Hyderabad, Telangana, India. Vikram Pasupuleti, Software Developer, MS in Computer Technology, School of Technology, Eastern Illinois University, Charleston, Illinois, USA. Bharadwaj Thuraka, Software Developer, Master of Science, Information Systems, Northwest Missouri State University, Maryville, Missouri, USA. Dr.Saiteja Malisetty, Ph.D in Computing & Information Science, College of Information Science and Technology, University of Nebraska, Omaha, Nebraska, USA. Chandra Shikhi Kodete, Software Engineer, MS in Computer Technology, School of Technology, Eastern Illinois University, Charleston, Illinois, USA.

The only how-to guide offering a unified, systemic approach to acquiring, cleaning, and managing data in R Every experienced practitioner knows that preparing data for modeling is a painstaking, time-consuming process. Adding to the difficulty is that most modelers learn the steps involved in cleaning and managing data piecemeal, often on the fly, or they develop their own ad hoc methods. This book helps simplify their task by providing a unified, systematic approach to acquiring, modeling, manipulating, cleaning, and maintaining data in R. Starting with the very basics, data scientists Samuel E. Buttrey and Lyn R. Whitaker walk readers through the entire process. From what data looks like and what it should look like, they progress through all the steps involved in getting data ready for modeling. They describe best practices for acquiring data from numerous sources; explore key issues in data handling, including text/regular expressions, big data, parallel processing, merging, matching, and checking for duplicates; and outline highly efficient and reliable techniques for documenting data and recordkeeping, including audit trails, getting data back out of R, and more. The only single-source guide to R data and its preparation, it describes best practices for acquiring, manipulating, cleaning, and maintaining data Begins with the basics and walks readers through all the steps necessary to get data ready for the modeling process Provides expert guidance on how to document the processes described so that they are reproducible Written by seasoned professionals, it provides both introductory and advanced techniques Features case studies with supporting data and R code, hosted on a companion website A Data Scientist's Guide to Acquiring, Cleaning and Managing Data in R is a valuable working resource/bench manual for practitioners who collect and analyze data, lab scientists and research associates of all levels of experience, and graduate-level data mining students.

Discover the intricate nature of a company's production function and the comprehensive principles of planning operations in this book. Through practical applications and enriched by numerical examples, readers gain essential knowledge of elementary mathematical methods in operations planning. The inclusion of the powerful R programming language, accompanied by code scripts and real-world examples, enhances the learning experience. Blending theory with practice, this resource equips readers with the tools necessary to optimize production systems, make informed decisions, and gain a competitive edge in today's dynamic business landscape.

Special edition of the Federal Register, containing a codification of documents of general applicability and future effect ... with ancillaries.

The Code of Federal Regulations is the codification of the general and permanent rules published in the Federal Register by the executive departments and agencies of the Federal Government.

Almost two decades ago, by using random coding bound arguments, it was proved that considerable progress in the power efficiency of digital communication could be obtained by treating coding and modulation as a single entity. These ideas were carried further in the manifestation of Trellis Coded Modulation (TCM) techniques. Sine then, the idea of TCM has expanded greatly beyond the type of schemes originally presented, and has given way to a considerable amount of research activity, both theoretical and applied, and to the implementation of several TCM techniques in new generations of modems. Block-Coded Modulation (BCM) was discovered at about the same time as TCM, and is currently attracting considerable interest as a possible alternative to TCM for some applications. A thorough overview on both kinds of coded modulation is discussed in this volume. It assesses the status and the prospects of coded modulation and of bandwidth-efficient transmission, shedding light on the future avenue of theory and applications in these exciting areas.