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Mastering Rstudio Develop Communicate And Collaborate With R

Author: Julian Hillebrand
Publisher: Packt Publishing Ltd
ISBN: 1783982551
Size: 65.20 MB
Format: PDF, Docs
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Harness the power of RStudio to create web applications, R packages, markdown reports and pretty data visualizations About This Book Discover the multi-functional use of RStudio to support your daily work with R code Learn to create stunning, meaningful, and interactive graphs and learn to embed them into easy communicable reports using multiple R packages Develop your own R packages and Shiny web apps to share your knowledge and collaborate with others. Who This Book Is For This book is aimed at R developers and analysts who wish to do R statistical development while taking advantage of RStudio's functionality to ease their development efforts. R programming experience is assumed as well as being comfortable with R's basic structures and a number of functions. What You Will Learn Discover the RStudio IDE and details about the user interface Communicate your insights with R Markdown in static and interactive ways Learn how to use different graphic systems to visualize your data Build interactive web applications with the Shiny framework to present and share your results Understand the process of package development and assemble your own R packages Easily collaborate with other people on your projects by using Git and GitHub Manage the R environment for your organization with RStudio and Shiny server Apply your obtained knowledge about RStudio and R development to create a real-world dashboard solution In Detail RStudio helps you to manage small to large projects by giving you a multi-functional integrated development environment, combined with the power and flexibility of the R programming language, which is becoming the bridge language of data science for developers and analyst worldwide. Mastering the use of RStudio will help you to solve real-world data problems. This book begins by guiding you through the installation of RStudio and explaining the user interface step by step. From there, the next logical step is to use this knowledge to improve your data analysis workflow. We will do this by building up our toolbox to create interactive reports and graphs or even web applications with Shiny. To collaborate with others, we will explore how to use Git and GitHub with RStudio and how to build your own packages to ensure top quality results. Finally, we put it all together in an interactive dashboard written with R. Style and approach An easy-to-follow guide full of hands-on examples to master RStudio. Beginning from explaining the basics, each topic is explained with a lot of details for every feature.

Unsupervised Learning With R

Author: Erik Rodriguez Pacheco
Publisher: Packt Publishing Ltd
ISBN: 1785885812
Size: 35.39 MB
Format: PDF, ePub, Mobi
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Work with over 40 packages to draw inferences from complex datasets and find hidden patterns in raw unstructured data About This Book Unlock and discover how to tackle clusters of raw data through practical examples in R Explore your data and create your own models from scratch Analyze the main aspects of unsupervised learning with this comprehensive, practical step-by-step guide Who This Book Is For This book is intended for professionals who are interested in data analysis using unsupervised learning techniques, as well as data analysts, statisticians, and data scientists seeking to learn to use R to apply data mining techniques. Knowledge of R, machine learning, and mathematics would help, but are not a strict requirement. What You Will Learn Load, manipulate, and explore your data in R using techniques for exploratory data analysis such as summarization, manipulation, correlation, and data visualization Transform your data by using approaches such as scaling, re-centering, scale [0-1], median/MAD, natural log, and imputation data Build and interpret clustering models using K-Means algorithms in R Build and interpret clustering models by Hierarchical Clustering Algorithm's in R Understand and apply dimensionality reduction techniques Create and use learning association rules models, such as recommendation algorithms Use and learn about the techniques of feature selection Install and use end-user tools as an alternative to programming directly in the R console In Detail The R Project for Statistical Computing provides an excellent platform to tackle data processing, data manipulation, modeling, and presentation. The capabilities of this language, its freedom of use, and a very active community of users makes R one of the best tools to learn and implement unsupervised learning. If you are new to R or want to learn about unsupervised learning, this book is for you. Packed with critical information, this book will guide you through a conceptual explanation and practical examples programmed directly into the R console. Starting from the beginning, this book introduces you to unsupervised learning and provides a high-level introduction to the topic. We quickly move on to discuss the application of key concepts and techniques for exploratory data analysis. The book then teaches you to identify groups with the help of clustering methods or building association rules. Finally, it provides alternatives for the treatment of high-dimensional datasets, as well as using dimensionality reduction techniques and feature selection techniques. By the end of this book, you will be able to implement unsupervised learning and various approaches associated with it in real-world projects. Style and approach This book takes a step-by-step approach to unsupervised learning concepts and tools, explained in a conversational and easy-to-follow style. Each topic is explained sequentially, explaining the theory and then putting it into practice by using specialized R packages for each topic.

The Practice Of Enterprise Modeling

Author: Geert Poels
Publisher: Springer
ISBN: 3319702416
Size: 49.19 MB
Format: PDF, Kindle
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This volume constitutes the proceedings of the 10th IFIP WG 8.1 Conference on the Practice of Enterprise Modeling held in November 2017 in Leuven, Belgium. The conference was created by the International Federation for Information Processing (IFIP) Working Group 8.1 to offer a forum for knowledge transfer and experience sharing between the academic and practitioner communities. The 20 full papers and 4 short papers accepted were carefully reviewed and selected from 70 submissions. They include research results, practitioner/experience reports and work-in-progress papers and were presented in 8 sessions covering diverse topics related to enterprise modelling and its application in practice.