Hands-On Programming with R

Hands-On Programming with R

Author: Garrett Grolemund

Publisher: "O'Reilly Media, Inc."

ISBN: 9781449359119

Category: Computers

Page: 250

View: 779

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 based on casino games Store, retrieve, and change data values in your computer’s memory Write programs and simulations that outperform those written by typical R users Use R programming tools such as if else statements, for loops, and S3 classes Learn how to write lightning-fast vectorized R code Take advantage of R’s package system and debugging tools Practice and apply R programming concepts as you learn them

R Programming for Beginners

R Programming for Beginners

Author: Nathan Metzler

Publisher:

ISBN: 171037411X

Category:

Page: 166

View: 549

Master the programming skills you need to turn raw, unfiltered data into deep insights and get ready for a data science and analytics career with this definitive guide to R Programming for Beginners! Do you want to get started learning how to program, but don't know where to begin? Are you interested in moving beyond Excel sheets and learning one of the most powerful programming language used in cutting edge research such as machine learning? If you answered yes to any of these questions, then this book might just be what you need. R can be a royal pain in the neck sometimes. Even seasoned programmers and data analysts still struggle with it. But it doesn't have to be you. In this guide, you're going to learn everything you need to do heavy data wrangling in R, with graded exercises and examples at the end to help you reinforce what you've learned. Here's a preview of what you're going to discover in R Programming for Beginners Step-by-step instructions to help you set up and install the R Environment with photos How to properly Execute R Scripts with your favorite code editor Everything you need to know about the R syntax-statements, blocks, comments, and keywords Steps to help you write your very first R script and begin your programming journey The 6 data types supported by the R programming language How to name variables and assign values to them Steps to help you write well-defined user functions effectively How to control program flow with decision making control structures and loops How to visualize data with R programming ...and lots more! Whether you're completely new to programming and have never written a single line of code before, or you're an intermediate or experienced R programmer looking to brush up on the basics, this book has everything you need to master R completely. Scroll to the top of the page and click the "Add to Cart" button to get started today!

Building a Platform for Data-Driven Pandemic Prediction

Building a Platform for Data-Driven Pandemic Prediction

Author: Dani Gamerman

Publisher: CRC Press

ISBN: 9781000457193

Category: Medical

Page: 382

View: 560

This book is about building platforms for pandemic prediction. It provides an overview of probabilistic prediction for pandemic modeling based on a data-driven approach. It also provides guidance on building platforms with currently available technology using tools such as R, Shiny, and interactive plotting programs. The focus is on the integration of statistics and computing tools rather than on an in-depth analysis of all possibilities on each side. Readers can follow different reading paths through the book, depending on their needs. The book is meant as a basis for further investigation of statistical modelling, implementation tools, monitoring aspects, and software functionalities. Features: A general but parsimonious class of models to perform statistical prediction for epidemics, using a Bayesian approach Implementation of automated routines to obtain daily prediction results How to interactively visualize the model results Strategies for monitoring the performance of the predictions and identifying potential issues in the results Discusses the many decisions required to develop and publish online platforms Supplemented by an R package and its specific functionalities to model epidemic outbreaks The book is geared towards practitioners with an interest in the development and presentation of results in an online platform of statistical analysis of epidemiological data. The primary audience includes applied statisticians, biostatisticians, computer scientists, epidemiologists, and professionals interested in learning more about epidemic modelling in general, including the COVID-19 pandemic, and platform building. The authors are professors at the Statistics Department at Universidade Federal de Minas Gerais. Their research records exhibit contributions applied to a number of areas of Science, including Epidemiology. Their research activities include books published with Chapman and Hall/CRC and papers in high quality journals. They have also been involved with academic management of graduate programs in Statistics and one of them is currently the President of the Brazilian Statistical Association.

R Programming By Example

R Programming By Example

Author: Omar Trejo

Publisher: Packt Publishing Ltd

ISBN: 9781788291361

Category: Computers

Page: 470

View: 581

This step-by-step guide demonstrates how to build simple-to-advanced applications through examples in R using modern tools. About This Book Get a firm hold on the fundamentals of R through practical hands-on examples Get started with good R programming fundamentals for data science Exploit the different libraries of R to build interesting applications in R Who This Book Is For This books is for aspiring data science professionals or statisticians who would like to learn about the R programming language in a practical manner. Basic programming knowledge is assumed. What You Will Learn Discover techniques to leverage R's features, and work with packages Perform a descriptive analysis and work with statistical models using R Work efficiently with objects without using loops Create diverse visualizations to gain better understanding of the data Understand ways to produce good visualizations and create reports for the results Read and write data from relational databases and REST APIs, both packaged and unpackaged Improve performance by writing better code, delegating that code to a more efficient programming language, or making it parallel In Detail R is a high-level statistical language and is widely used among statisticians and data miners to develop analytical applications. Often, data analysis people with great analytical skills lack solid programming knowledge and are unfamiliar with the correct ways to use R. Based on the version 3.4, this book will help you develop strong fundamentals when working with R by taking you through a series of full representative examples, giving you a holistic view of R. We begin with the basic installation and configuration of the R environment. As you progress through the exercises, you'll become thoroughly acquainted with R's features and its packages. With this book, you will learn about the basic concepts of R programming, work efficiently with graphs, create publication-ready and interactive 3D graphs, and gain a better understanding of the data at hand. The detailed step-by-step instructions will enable you to get a clean set of data, produce good visualizations, and create reports for the results. It also teaches you various methods to perform code profiling and performance enhancement with good programming practices, delegation, and parallelization. By the end of this book, you will know how to efficiently work with data, create quality visualizations and reports, and develop code that is modular, expressive, and maintainable. Style and Approach This is an easy-to-understand guide filled with real-world examples, giving you a holistic view of R and practical, hands-on experience.

Hands on Programming with Python

Hands on Programming with Python

Author: Jose María Alvarez Rodríguez

Publisher:

ISBN: 9798656766715

Category:

Page: 436

View: 157

In this book, programming concepts are theoretically introduced and explained. Then, their application is presented in the context of a programming language (Python) to finally introduce examples of use. The learning methodology is based on a four-stage method (problem statement, concept, Python implementation and examples of use) helping to solve the 5 W's + How questions when learning a new programming language from scratch. In terms of learning methodology, the first ten chapters corresponds to the theoretical concepts that are complemented with more than 200 examples and exercises in the Lab-x chapters. Furthermore, all contents are also publicly available as Jupyter notebooks and other complementary materials such as Kahoot! Quizzes have also been designed to establish an active learning methodology encouraging a blended and self-paced learning process.

R Programming by Example

R Programming by Example

Author: Omar Trejo Navarro

Publisher:

ISBN: 1788292545

Category: Computers

Page: 470

View: 531

This step-by-step guide demonstrates how to build simple-to-advanced applications through examples in R using modern tools. Key Features Get a firm hold on the fundamentals of R through practical hands-on examples Get started with good R programming fundamentals for data science Exploit the different libraries of R to build interesting applications in R Book Description R is a high-level statistical language and is widely used among statisticians and data miners to develop analytical applications. Often, data analysis people with great analytical skills lack solid programming knowledge and are unfamiliar with the correct ways to use R. Based on the version 3.4, this book will help you develop strong fundamentals when working with R by taking you through a series of full representative examples, giving you a holistic view of R. We begin with the basic installation and configuration of the R environment. As you progress through the exercises, you'll become thoroughly acquainted with R's features and its packages. With this book, you will learn about the basic concepts of R programming, work efficiently with graphs, create publication-ready and interactive 3D graphs, and gain a better understanding of the data at hand. The detailed step-by-step instructions will enable you to get a clean set of data, produce good visualizations, and create reports for the results. It also teaches you various methods to perform code profiling and performance enhancement with good programming practices, delegation, and parallelization. By the end of this book, you will know how to efficiently work with data, create quality visualizations and reports, and develop code that is modular, expressive, and maintainable. What you will learn Discover techniques to leverage R's features, and work with packages Perform a descriptive analysis and work with statistical models using R Work efficiently with objects without using loops Create diverse visualizations to gain better understanding of the data Understand ways to produce good visualizations and create reports for the results Read and write data from relational databases and REST APIs, both packaged and unpackaged Improve performance by writing better code, delegating that code to a more efficient programming language, or making it parallel Who this book is for This books is for aspiring data science professionals or statisticians who would like to learn about the R programming language in a practical manner. Basic programming knowledge is assumed.

Introduction to Shiny

Introduction to Shiny

Author: Garrett Grolemund

Publisher:

ISBN: OCLC:1137159606

Category:

Page:

View: 760

"Data scientists who work with R look to Shiny as the web framework of choice for moving analytical power into the hands of their bosses, clients, and the public at large. The reason? Shiny apps let the non-coders of the world control the visualization of complex data sets so they can explore, analyze and model on their own. Taught by RStudio master instructor Garrett Grolemund, this video details how Shiny combines the computational power of R and the interactivity of the web to produce highly interactive reports and visualizations. Part one offers a detailed description of Shiny and how to use it build an app. Part two covers reactive programming and why it differs from functional programming, the paradigm that guides most of R. Part three outlines the Shiny UI and the toolsets it offers to customize the appearance of a Shiny app. This video is optimized for the intermediate level R coder."--Resource description page.

Expert Data Wrangling with R

Expert Data Wrangling with R

Author: Garrett Grolemund

Publisher:

ISBN: OCLC:1137163886

Category:

Page:

View: 537

"Analysts often spend 50-80% of their time preparing and transforming data sets before they begin more formal analysis work. This video tutorial shows you how to streamline your code-and your thinking-by introducing a set of principles and R packages that make this work much faster and easier. Garrett Grolemund, Data Scientist and Master Instructor at RStudio, demonstrates how R and its packages help you tackle three main issues. Data Manipulation. Data sets contain more information than they display. By transforming your data, you can reveal a wealth of descriptive statistics, group level observations, and hidden variables. R's dplyr package provides optimized functions to help you transform data, as well as a pipe syntax that makes R code more concise and intuitive. Data Tidying. Data sets come in many formats, but R prefers just one. R runs quickly and intuitively when your data is stored in the tidy format, a layout that allows vectorized programming. R's tidyr package reshapes the layout of your data sets, making them tidy while preserving the relationships they contain. Data Visualization. The structure of data visualizations parallels the structure of data sets. Once your data is tidy, visualizations become straightforward: each observation in your dataset becomes a mark on a graph, each variable becomes a visual property of the marks. The result is a grammar of graphics that lets you create thousands of graphs. R's ggvis package implements the grammar, providing a system of data visualization for R."--Resource description page.

Programming with Microsoft? AFC

Programming with Microsoft? AFC

Author: Eric Swildens

Publisher: Wiley

ISBN: 0471248916

Category: Computers

Page: 573

View: 947

Quickly master Microsoft(r) AFCTM and design cutting-edge GUIs for your Java programs. This is definitely not just another AFC reference! It's a hands-on programming guide for Java developers who want to add great-looking user interfaces and stylish graphics to their Java applets and applications. Microsoft's answer to all of the well-known problems associated with JavaSoft's AWT—AFC—is quickly becoming the first choice among Java programmers for designing GUIs and working with 2D graphics. Now here's your chance to quickly master the knowledge and skills you need to use AFC for: Creating hot user-interface components for your programs Laying out your program's user interface in an intuitive and easy-to-use fashion Making optimal use of color, graphics, and fonts Distributing your AFC applets on the Internet Using advanced drawing features, and more. And, just to make your life that much easier, Eric Swildens and Selena Sol have provided complete style guidelines for each AFC class! On the CD-ROM, you'll find: Nearly 100 Java AFC programs and ready-to-use GUI components Trial version of Neuron Data's Elements Presenter/JTM

Reproducible Research and Reports with R Markdown

Reproducible Research and Reports with R Markdown

Author: Garrett Grolemund

Publisher:

ISBN: OCLC:1137159498

Category:

Page:

View: 235

"R Markdown does three main things pretty close to magic. First, it lets you make a completely reproducible, parameter-set and automatable R report. Second, it lets you export that report into a multitude of formats (HTML, Word, .js slide show, interactive web app, etc.). Third, it does the first two things really fast. Wishing for a way to document your code so it still makes sense to you or somebody else six months down the road? Presto! R Markdown does that. Hoping for a button you could click to reproduce your entire analysis with a new data set or parameter? Shazaam! R Markdown does that. Sick of having to copy and paste your results? Poof! R Markdown takes the pain away. If you're an analyst, scientist, actuary, statistician, or a programmer familiar with R, you should add this package to your bag of tricks."--Resource description page.