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Exploring Operations Research with R shows how the R programming language can be a valuable tool – and way of thinking – which can be successfully applied to the field of operations research (OR). This approach is centred on the idea of the future OR professional as someone who can combine knowledge of key OR techniques (e.g., simulation, linear programming, data science, and network science) with an understanding of R, including tools for data representation, manipulation, and analysis. The core aim of the book is to provide a self-contained introduction to R (both Base R and the tidyverse) and show how this knowledge can be applied to a range of OR challenges in the domains of public health, infectious disease, and energy generation, and thus provide a platform to develop actionable insights to support decision making. Features Can serve as a primary textbook for a comprehensive course in R, with applications in OR Suitable for post-graduate students in OR and data science, with a focus on the computational perspective of OR The text will also be of interest to professional OR practitioners as part of their continuing professional development Linked to a Github repository including code, solutions, data sets, and other ancillary material

If you’re curious about how things work, this fun and intriguing guide will help you find real answers to everyday problems. By using fundamental math and doing simple programming with the Ruby and R languages, you’ll learn how to model a problem and work toward a solution. All you need is a basic understanding of programming. After a quick introduction to Ruby and R, you’ll explore a wide range of questions by learning how to assemble, process, simulate, and analyze the available data. You’ll learn to see everyday things in a different perspective through simple programs and common sense logic. Once you finish this book, you can begin your own journey of exploration and discovery. Here are some of the questions you’ll explore: Determine how many restroom stalls can accommodate an office with 70 employees Mine your email to understand your particular emailing habits Use simple audio and video recording devices to calculate your heart rate Create an artificial society—and analyze its behavioral patterns to learn how specific factors affect our real society

Learn exploratory data analysis concepts using powerful R packages to enhance your R data analysis skills Key FeaturesSpeed up your data analysis projects using powerful R packages and techniquesCreate multiple hands-on data analysis projects using real-world dataDiscover and practice graphical exploratory analysis techniques across domainsBook Description Hands-On Exploratory Data Analysis with R will help you build not just a foundation but also expertise in the elementary ways to analyze data. You will learn how to understand your data and summarize its main characteristics. You'll also uncover the structure of your data, and you'll learn graphical and numerical techniques using the R language. This book covers the entire exploratory data analysis (EDA) process—data collection, generating statistics, distribution, and invalidating the hypothesis. As you progress through the book, you will learn how to set up a data analysis environment with tools such as ggplot2, knitr, and R Markdown, using tools such as DOE Scatter Plot and SML2010 for multifactor, optimization, and regression data problems. By the end of this book, you will be able to successfully carry out a preliminary investigation on any dataset, identify hidden insights, and present your results in a business context. What you will learnLearn powerful R techniques to speed up your data analysis projectsImport, clean, and explore data using powerful R packagesPractice graphical exploratory analysis techniquesCreate informative data analysis reports using ggplot2Identify and clean missing and erroneous dataExplore data analysis techniques to analyze multi-factor datasetsWho this book is for Hands-On Exploratory Data Analysis with R is for data enthusiasts who want to build a strong foundation for data analysis. If you are a data analyst, data engineer, software engineer, or product manager, this book will sharpen your skills in the complete workflow of exploratory data analysis.

For more than 70 years, memorable automobiles have rolled out of Bayerische Motor Werke. This sprawling photographic history spans the entire range, from the 1927 Dixi 3/51 PS to the James Bond Z8 roadster. The story of BMW's genesis in the aircraft industry is followed by complete series and model histories and overviews of BMW forays into motorsport. Gorgeously illustrated with rare archival imagery and modern color photos, this lavish treatment features classics like the mystically elegant pre-war 328, post-war 502 luxury saloons, the curious single-cylinder Isetta, hand-built 507 sports cars, the revolutionary 2002 Turbo, the M1 supercar, the Z3 roadster and much more.

Provides both rich theory and powerful applications Figures are accompanied by code required to produce them Full color figures

Every professional needs to perform statistical analysis in some form of the other. In order to perform this task various software tools are available. Majority of them are paid software. R programming which is an open source tool can be used to perform statistical analysis. Since it is an open source tool many front end GUI’s are available to make the job easier for the user. In this book the most popular GUI RStudio is used. RStudio is a most powerful GUI front end for R programming which has been designed to use all the features of this language with ease. This book has been authored with a novice user in mind. Various steps in statistical analysis have been explained in detail using a large number of screenshots. Codes used have been clearly illustrated. The book has been structured in such a manner to ensure that basic concepts have been clearly explained with the help of screenshots before taking on challenging analytical problems. Towards the end of the book the reader is provided with an additional resource which gives out all the codes used in this book as well as those additional ones that have not found their place in the book. Learning R coding is not difficult provided the reader spends time practicing the same. The reader is encouraged to execute all the codes provided in the R_code manual which has been provided at the end of the book. R programming can be compared to that of SPSS (the popular statistical analytical tool) as far as its ability to perform statistical analysis. One tip the author wishes to provide to the reader who is attempting to make data entry within the RStudio environment. It is always better to import data into RStudio for performing data analysis. Data can be imported from Excel , google spread sheets etc. The reader is encouraged to download the install the software and libraries that have been described in the book and to try them out. Advantages of R Programming : 1. It is a powerful statistical tool 2. It is open source and hence it is free 3. It is an excellent tool that can be used to perform visual analysis of a dataset. It can created different types of charts and graphs, thereby facilitating accurate analysis of data