Название: Exploring Operations Research with R
Автор: Jim Duggаn
Издательство: CRC Press
Год: 2024
Страниц: 396
Язык: английский
Формат: pdf (true)
Размер: 52.1 MB

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. The central idea behind this book is that R is a valuable computational tool that can be applied to the field of operations research. R provides excellent features such as data representation, data manipulation, and data analysis. You will learn how to program and manipulate data in R, how to harness the power of R’s tidyverse, and observe how R can be used to support problem solving within the field of operations research. The book is primarily aimed at post-graduate students in operations research. With its coverage of R, the tidyverse, and applications in agent-based simulation and system dynamics, the book also supports continuing professional development for operations research practitioners. As R is presented from scratch, there are no prerequisites, although prior experience of a programming language would provide useful contextual knowledge.

Название: R for the Rest of Us: A Statistics-Free Introduction
Автор: Dаvid Кеуеs
Издательство: No Starch Press
Год: 2024
Страниц: 306
Язык: английский
Формат: pdf, epub
Размер: 31.8 MB

The R programming language is a remarkably powerful tool for data analysis and visualization, but its steep learning curve can be intimidating for some. If you just want to automate repetitive tasks or visualize your data, without the need for complex math, R for the Rest of Us is for you. Inside you’ll find a crash course in R, a quick tour of the RStudio programming environment, and a collection of real-word applications that you can put to use right away. You’ll learn how to create informative visualizations, streamline report generation, and develop interactive websites—whether you’re a seasoned R user or have never written a line of R code. Many people think of R as simply a tool for hardcore statistical analysis, but it can do much more than manipulate numerical values. After all, every R user must illuminate their findings and communicate their results somehow, whether that’s via data visualizations, reports, websites, or presentations. Also, the more you use R, the more you’ll find yourself wanting to automate tasks you currently do manually. No matter your background, using R can transform your work. This book is for you if you’re either a current R user keen to explore its uses for visualization and communication or a non-R user wondering if R is right for you. I’ve written R for the Rest of Us so that it should make sense whether or not you’ve ever written a line of R code. But even if you’ve written entire R programs, the book should help you learn plenty of new techniques to up your game. R is a great tool for anyone who works with data.

Название: Data Science Fundamentals with R, Python, and Open Data
Автор: Маrсо Сrеmоnini
Издательство: Wiley
Год: 2024
Страниц: 461
Язык: английский
Формат: epub
Размер: 10.1 MB

Introduction to essential concepts and techniques of the fundamentals of R and Python needed to start Data Science projects. Organized with a strong focus on open data, Data Science Fundamentals with R, Python, and Open Data discusses concepts, techniques, tools, and first steps to carry out Data Science projects, with a focus on Python and RStudio, reflecting a clear industry trend emerging towards the integration of the two. The text examines intricacies and inconsistencies often found in real data, explaining how to recognize them and guiding readers through possible solutions, and enables readers to handle real data confidently and apply transformations to reorganize, indexing, aggregate, and elaborate. This book is full of reader interactivity, with a companion website hosting supplementary material including datasets used in the examples and complete running code (R scripts and Jupyter notebooks) of all examples. Exam-style questions are implemented and multiple choice questions to support the readers’ active learning. Each chapter presents one or more case studies. Data Science Fundamentals with R, Python, and Open Data is a highly accessible learning resource for students from heterogeneous disciplines where Data Science and quantitative, computational methods are gaining popularity, along with hard sciences not closely related to Computer Science, and medical fields using stochastic and quantitative models.

Название: Learn R: As a Language, 2nd Edition
Автор: Реdrо J. Арhаlо
Издательство: CRC Press
Серия: The R Series
Год: 2024
Страниц: 466
Язык: английский
Формат: pdf (true)
Размер: 11.2 MB

Learning a computer language like R can be either frustrating, fun or boring. Having fun requires challenges that wake up the learner’s curiosity but also provide an emotional reward for overcoming them. The book is designed so that it includes smaller and bigger challenges, in what I call playgrounds, in the hope that all readers will enjoy their path to R fluency. Fluency in the use of a language is a skill that is acquired through practice and exploration. For students and professionals in the biological sciences, humanities and many applied fields, recognizing the parallels between R and natural languages should help them feel at home with R. The approach I use is similar to that of a travel guide, encouraging exploration and describing the available alternatives and how to reach them. The intention is to guide the reader through the R landscape of 2024 and beyond. In R, like in most “rich” languages, there are multiple ways of coding the same operations. I have included code examples that aim to strike a balance between execution speed and readability. One could write equivalent R books using substantially different code examples. Keep this in mind when reading the book and using R. Keep also in mind that it is impossible to remember everything about R, and as a user you will frequently need to consult the documentation, even while doing the exercises in this book. The R language, in a broad sense, is vast because it can be expanded with independently developed packages. Learning to use R mainly consists of learning the basics plus developing the skill of finding your way in R, its documentation and on-line question-and-answer forums.

Название: Statistical Analysis with R Essentials For Dummies
Автор: Jоsерh Sсhmullеr
Издательство: For Dummies
Год: 2024
Страниц: 192
Язык: английский
Формат: pdf, epub (true), mobi
Размер: 10.1 MB

The easy way to get started coding and analyzing data in the R programming language. Statistical Analysis with R Essentials For Dummies is your reference to all the core concepts about R―the widely used, open-source programming language and data analysis tool. This no-nonsense book gets right to the point, eliminating review material, wordy explanations, and fluff. Understand all you need to know about the foundations of R, swiftly and clearly. Perfect for a brush-up on the basics or as an everyday desk reference on the job, this is the reliable little book you can always turn to for answers. As the title indicates, this book covers the essentials of statistics and R. Although it’s designed to get you up and running in a hurry, and to quickly answer your questions, it’s not just a cookbook. Before I tell you about one of R’s features, I give you the statistical foundation it’s based on. My goal is that you understand that feature when you use it — and that you use it effectively. In the proper context, R can be a great tool for learning statistics and for refreshing what you already know. I’ve tried to supply that context in this book. Although the development of statistics concepts proceeds in a logical way, I organized this book so you can open it up in any chapter and start reading. The idea is for you to quickly find what you’re looking for and use it immediately — whether it’s a statistical concept or an R feature.

Название: Data Analytics for Business: AI-ML-PBI-SQL-R
Автор: Wоlfgаng Gаrn
Издательство: Routledge
Год: 2024
Страниц: 283
Язык: английский
Формат: pdf (true)
Размер: 29.2 MB

We are drowning in data but are starved for knowledge. Data Analytics is the discipline of extracting actionable insights by structuring, processing, analysing and visualising data using methods and software tools. Hence, we gain knowledge by understanding the data. A roadmap to achieve this is encapsulated in the knowledge discovery in databases (KDD) process. Databases help us store data in a structured way. The structure query language (SQL) allows us to gain first insights about business opportunities. Visualising the data using business intelligence tools and Data Science languages deepens our understanding of the key performance indicators and business characteristics. This can be used to create relevant classification and prediction models; for instance, to provide customers with the appropriate products or predict the eruption time of geysers. Machine Learning algorithms help us in this endeavour. Moreover, we can create new classes using unsupervised learning methods, which can be used to define new market segments or group customers with similar characteristics. Finally, Artificial Intelligence (AI) allows us to reason under uncertainty and find optimal solutions for business challenges. The material was especially designed for Business Analytics degrees with a focus on Data Science and can also be used for Machine Learning or Artificial Intelligence classes. Data Science languages such as R provide a platform to access the state-of-the-art methods for analysing business data.

Название: Research Software Engineering: A Guide to the Open Source Ecosystem
Автор: Маtthiаs Ваnnеrt
Издательство: CRC Press
Серия: Data Science Series
Год: 2024
Страниц: 201
Язык: английский
Формат: pdf (true)
Размер: 14.0 MB

Research Software Engineering: A Guide to the Open Source Ecosystem strives to give a big-picture overview and an understanding of the opportunities of programming as an approach to analytics and statistics. The book argues that a solid "programming" skill level is not only well within reach for many but also worth pursuing for researchers and business analysts. The ability to write a program leverages field-specific expertise and fosters interdisciplinary collaboration as source code continues to become an important communication channel. Given the pace of the development in data science, many senior researchers and mentors, alongside non-computer science curricula lack a basic software engineering component. This book fills the gap by providing a dedicated programming-with-data resource to both academic scholars and practitioners. Even though Research Software Engineering aims to be inclusive and open to a broad audience with different starting points, several prerequisites exist to get the most out of this book. I recommend you have made your first experience with an interpreted programming language like R or Python.

Название: Data Analytics & Visualization All-in-One For Dummies
Автор: Jасk Нуmаn, Luса Маssаrоn, Раul МсFеdriеs
Издательство: For Dummies
Год: 2024
Страниц: 835
Язык: английский
Формат: pdf (true), epub (true)
Размер: 39.8 MB, 27.3 MB

Install data analytics into your brain with this comprehensive introduction. Data Analytics & Visualization All-in-One For Dummies collects the essential information on mining, organizing, and communicating data, all in one place. Clocking in at around 850 pages, this tome of a reference delivers eight books in one, so you can build a solid foundation of knowledge in data wrangling. Data analytics professionals are highly sought after these days, and this book will put you on the path to becoming one. You'll learn all about sources of data like data lakes, and you'll discover how to extract data using tools like Microsoft Power BI, organize the data in Microsoft Excel, and visually present the data in a way that makes sense using a Tableau. You'll even get an intro to the Python, R, and SQL coding needed to take your data skills to a new level. With this Dummies guide, you'll be well on your way to becoming a priceless data jockey. Data Science is the person behind the partition in the experience of the wonderment of technology. Python is uniquely suited to making it easier to work with Data science. For one thing, Python provides an incredible number of math-related libraries that help you perform tasks with a less-than-perfect understanding of precisely what is going on.

Название: Computer Science in Sport: Modeling, Simulation, Data Analysis and Visualization of Sports-Related Data
Автор: Dаniеl Меmmеrt
Издательство: Springer
Год: 2024
Страниц: 247
Язык: английский
Формат: pdf (true), epub
Размер: 17.7 MB

In recent years, computer science in sport has grown extremely, mainly because more and more new data has become available. Computer Science tools in sports, whether used for opponent preparation, competition, or scientific analysis, have become indispensable across various levels of expertise nowadays. A completely new market has emerged through the utilization of these tools in the four major fields of application: clubs and associations, business, science, and the media. This market is progressively gaining importance within university research and educational activities. This textbook aims to live up to the now broad diversity of Computer Science in sport by having more than 30 authors report from their special field and concisely summarise the latest findings. The book is divided into four main sections: data sets, modelling, simulation and data analysis. In addition to background information on programming languages (R and Python) and visualisation, the textbook is framed by history and an outlook. Python is highly popular in the community of data scientists in general and sports analysts in particular because it is a open-source, dynamic, object-oriented, high-level programming language, which provides highly flexible and up-to-date functionalities due to its available modules and libraries.

Название: Modern Data Visualization with R
Автор: Rоbеrt Каbасоff
Издательство: CRC Press
Серия: The R Series
Год: 2024
Страниц: 272
Язык: английский
Формат: pdf (true)
Размер: 33.4 MB

Modern Data Visualization with R describes the many ways that raw and summary data can be turned into visualizations that convey meaningful insights. R is an amazing platform for data analysis, capable of creating almost any type of graph. This book helps you create the most popular visualizations–from quick and dirty plots to publication-ready graphs. The text relies heavily on the ggplot2 package for graphics, but other approaches are covered as well. It starts with basic graphs such as bar charts, scatter plots, and line charts, but progresses to less well-known visualizations such as tree maps, alluvial plots, radar charts, mosaic plots, effects plots, correlation plots, biplots, and the mapping of geographic data. Both static and interactive graphics are described and the use of color, shape, shading, grouping, annotation, and animations are covered in detail. The book moves from a default look and feel for graphs, to graphs with customized colors, fonts, legends, annotations, and organizational themes. The book is written for those new to data analysis as well as the seasoned data scientist. The reader should have some basic coding experience, but expertise in R is not required.