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Turn your R code into packages that others can easily download and use. This practical book shows you how to bundle reusable R functions, sample data, and documentation together by applying author Hadley Wickhamâ??s package development philosophy. In the process, youâ??ll work with devtools, roxygen, and testthat, a set of R packages that automate common development tasks. Devtools encapsulates best practices that Hadley has learned from years of working with this programming language.
Ideal for developers, data scientists, and programmers with various backgrounds, this book starts you with the basics and shows you how to improve your...
Learn how to program by diving into the R language, and then use your newfound skills to solve practical data science problems. With this book, you’ll learn how to load data, assemble and disassemble data objects, navigate R’s environment system, write your own functions, and use all of R’s programming tools.
RStudio Master Instructor Garrett Grolemund not only teaches you how to program, but also shows you how to get more from R than just visualizing and modeling data. You’ll gain valuable programming skills and support your work as a data scientist at the same time.
Work hands-on with three practical data analysis projects...
Get going with tidymodels, a collection of R packages for modeling and machine learning. Whether you're just starting out or have years of experience with modeling, this practical introduction shows data analysts, business analysts, and data scientists how the tidymodels framework offers a consistent, flexible approach for your work.
RStudio engineers Max Kuhn and Julia Silge demonstrate ways to create models by focusing on an R dialect called the tidyverse. Software that adopts tidyverse principles shares both a high-level design philosophy and low-level grammar and data structures, so learning one piece of the ecosystem makes it...
The world of R has evolved substantially since its early days as a statistical computing language. As the field of data science has rocketed to the forefront of all areas of scientific and industry work, R has become the centerpiece language for doing data science. Through the contributions of a vibrant and highly active developer community, R has evolved to the point where it can be considered a software development language for developing robust, modular, and highly reusable software tools.
We begin by providing a rigorous introduction to the R language, and quickly move on to more advanced aspects like functional programming...
Master the Shiny web framework—and take your R skills to a whole new level. By letting you move beyond static reports, Shiny helps you create fully interactive web apps for data analyses. Users will be able to jump between datasets, explore different subsets or facets of the data, run models with parameter values of their choosing, customize visualizations, and much more.
Hadley Wickham from RStudio shows data scientists, data analysts, statisticians, and scientific researchers with no knowledge of HTML, CSS, or JavaScript how to create rich web apps from R. This in-depth guide provides a learning path that you can follow with...
"[This book] contains an excellent blend of both Shiny-specific topics … and practical advice from software development that fits in nicely with Shiny apps. You will find many nuggets of wisdom sprinkled throughout these chapters…."
Eric Nantz, Host of the R-Podcast and the Shiny Developer Series (from the Foreword)
"[This] book is a gradual and pleasant invitation to the production-ready shiny apps world. It …exposes a comprehensive and robust workflow powered by the {golem} package. [It] fills the not yet covered gap between shiny app development and deployment in such a thrilling way that it may be read in one...
Use R to turn data into insight, knowledge, and understanding. With this practical book, aspiring data scientists will learn how to do data science with R and RStudio, along with the tidyverseâ??a collection of R packages designed to work together to make data science fast, fluent, and fun. Even if you have no programming experience, this updated edition will have you doing data science quickly.
You'll learn how to import, transform, and visualize your data and communicate the results. And you'll get a complete, big-picture understanding of the data science cycle and the basic tools you need to manage the details. Updated for the...
People Analytics (also known as HR Analytics or Workforce Analytics) refers to the analysis of data from human resources (human capital) in conjunction with other company data. People Analytics is based on research fields such as social psychology, motivational psychology and behavioral science as well as business intelligence, predictive analytics and big data.
This open access book prepares current and aspiring analytics professionals to effectively address this need by curating key concepts spanning the entire analytics lifecycle, along with step-by-step instructions for their applications to real-world problems, using ubiquitous...
The book offers an introduction to statistical data analysis applying the free statistical software R, probably the most powerful statistical software today. The analyses are performed and discussed using real data.
This book is an introduction to geographic data science using R, covers the necessary skills in basic programming, data wrangling and reproducible research to tackle sophisticated but non-spatial data analyses.
Census data is widely used by practitioners to understand demographic change, allocate resources, address inequalities, and make sound business decisions. Until recently, projects using US Census data have required proficiency with multiple web interfaces and software platforms to prepare, map, and present data products. This book introduces readers to tools in the R programming language for accessing and analyzing Census data and shows how to carry out demographic analyses in a single computing environment.
Chapters in this book cover the following key topics:
• Rapidly acquiring data from the decennial US Census and American...
This short publication attempts to provide practical insights into using the sparklyr interface to gain the benefits of Apache Spark while still retaining the ability to use R code organized in custom-built functions and packages.
If you’re like most R users, you have deep knowledge and love for statistics. But as your organization continues to collect huge amounts of data, adding tools such as Apache Spark makes a lot of sense. With this practical book, data scientists and professionals working with large-scale data applications will learn how to use Spark from R to tackle big data and big compute problems.
Authors Javier Luraschi, Kevin Kuo, and Edgar Ruiz show you how to use R with Spark to solve different data analysis problems. This book covers relevant data science topics, cluster computing, and issues that should interest even the most advanced users.
This new book written by the developers of R Markdown is an essential reference that will help users learn and make full use of the software.
Those new to R Markdown will appreciate the short, practical examples that address the most common issues users encounter. Frequent users will also benefit from the wide ranging tips and tricks that expose ‘hidden’ features, support customization and demonstrate the many new and varied applications of the software.
After reading this book users will learn how to:
Enhance your R Markdown content with diagrams, citations, and dynamically generated text
Beyond Multiple Linear Regression: Applied Generalized Linear Models and Multilevel Models in R is designed for undergraduate students who have successfully completed a multiple linear regression course, helping them develop an expanded modeling toolkit that includes non-normal responses and correlated structure. Even though there is no mathematical prerequisite, the authors still introduce fairly sophisticated topics such as likelihood theory, zero-inflated Poisson, and parametric bootstrapping in an intuitive and applied manner. The case studies and exercises feature real data and real research questions; thus, most of the data in the...
This approachable introduction to doing data science in R provides step-by-step advice on using the tools and statistical methods to carry out data analysis. Introducing the fundamentals of data science and R before moving into more advanced topics like Multilevel Models and Probabilistic Modelling with Stan, it builds knowledge and skills gradually.
This book:
Focuses on providing practical guidance for all aspects, helping readers get to grips with the tools, software, and statistical methods needed to provide the right type and level of analysis their data requires
Explores the foundations of data science and breaks...
This latest edition of The Definitive Guide to Django is updated for Django 1.1, and, with the forward–compatibility guarantee that Django now provides, should serve as the ultimate tutorial and reference for this popular framework for years to come.
Django, the Python–based equivalent to Ruby's Rails web development framework, is one of the hottest topics in web development today. Lead developer Jacob Kaplan–Moss and Django creator Adrian Holovaty show you how they use this framework to create award–winning web sites by guiding you through the creation of a web application reminiscent of ChicagoCrime.org.
Django is a web framework for perfectionists with deadlines, designed to help you build manageable medium and large web projects in a short time span. This fourth edition of the Django Web Development Cookbook is updated with Django 3's latest features to guide you effectively through the development process. This Django book starts by helping you create a virtual environment and project structure for building Python web apps. You'll learn how to build models, views, forms, and templates for your web apps and then integrate JavaScript in your Django apps to add more features. As you advance, you'll create responsive multilingual...
Concepts of discrete mathematics can help clarify a programmer's thinking about software design problems and can make many solutions obvious. Starting at an elementary level, this book teaches about fundamental structures of discrete mathematics and many simple but powerful programming techniques using those structures.
Python Essential Reference is the definitive reference guide to the Python programming language — the one authoritative handbook that reliably untangles and explains both the core Python language and the most essential parts of the Python library.
Designed for the professional programmer, the book is concise, to the point, and highly accessible. It also includes detailed information on the Python library and many advanced subjects that is not available in either the official Python documentation or any other single reference source.
Thoroughly updated to reflect the significant new programming language features and library...
If you want a basic understanding of computer vision’s underlying theory and algorithms, this hands-on introduction is the ideal place to start. You’ll learn techniques for object recognition, 3D reconstruction, stereo imaging, augmented reality, and other computer vision applications as you follow clear examples written in Python.
Programming Computer Vision with Python explains computer vision in broad terms that won’t bog you down in theory. You get complete code samples with explanations on how to reproduce and build upon each example, along with exercises to help you apply what you’ve learned. This book is ideal for students...
This is the old edition! The new edition is under the title "Cracking Codes with Python" by Al Sweigart * * *Hacking Secret Ciphers with Python not only teaches you how to write in secret ciphers with paper and pencil. This book teaches you how to write your own cipher programs and also the hacking programs that can break the encrypted messages from these ciphers. Unfortunately, the programs in this book wont get the reader in trouble with the law (or rather, fortunately) but it is a guide on the basics of both cryptography and the Python programming language. Instead of presenting a dull laundry list of concepts, this book provides the...
A hands-on approach to understanding and building compilers using the programming language Python.
Compilers are notoriously difficult programs to teach and understand. Most books about compilers dedicate one chapter to each progressive stage, a structure that hides how language features motivate design choices. By contrast, this innovative textbook provides an incremental approach that allows students to write every single line of code themselves. Jeremy Siek guides the reader in constructing their own compiler in the powerful object-oriented programming language Python, adding complex language features as the book progresses...
Python is an amazing language with a strong and friendly community of programmers. However, there is a lack of documentation on what to learn after getting the basics of Python down your throat. Through this book I aim to solve this problem. I would give you bits of information about some interesting topics which you can further explore.
This book provides a reader with a holistic and in-depth knowledge of the Python language. It explains how methods and functions are related, how sequences can be created elegantly, the tools for functional programming, how user defined objects can be no different from Python objects and how one can control almost everything from import statements to class creation using meta-classes and decorators etc.
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