Kirjahaku

Over 50 practical and useful recipes to help you perform data analysis with R by unleashing every native RStudio feature About This Book • 54 useful and practical tasks to improve working systems • Includes optimizing performance and reliability or uptime, reporting, system management tools, interfacing to standard data ports, and so on • Offers 10-15 real-life, practical improvements for each user type Who This Book Is For This book is targeted at R statisticians, data scientists, and R programmers. Readers with R experience who are looking to take the plunge into statistical computing will find this Cookbook particularly indispensable. What You Will Learn • Familiarize yourself with the latest advanced R console features • Create advanced and interactive graphics • Manage your R project and project files effectively • Perform reproducible statistical analyses in your R projects • Use RStudio to design predictive models for a specific domain-based application • Use RStudio to effectively communicate your analyses results and even publish them to a blog • Put yourself on the frontiers of data science and data monetization in R with all the tools that are needed to effectively communicate your results and even transform your work into a data product In Detail The requirement of handling complex datasets, performing unprecedented statistical analysis, and providing real-time visualizations to businesses has concerned statisticians and analysts across the globe. RStudio is a useful and powerful tool for statistical analysis that harnesses the power of R for computational statistics, visualization, and data science, in an integrated development environment. This book is a collection of recipes that will help you learn and understand RStudio features so that you can effectively perform statistical analysis and reporting, code editing, and R development. The first few chapters will teach you how to set up your own data analysis project in RStudio, acquire data from different data sources, and manipulate and clean data for analysis and visualization purposes. You'll get hands-on with various data visualization methods using ggplot2, and you will create interactive and multidimensional visualizations with D3.js. Additional recipes will help you optimize your code; implement various statistical models to manage large datasets; perform text analysis and predictive analysis; and master time series analysis, machine learning, forecasting; and so on. In the final few chapters, you'll learn how to create reports from your analytical application with the full range of static and dynamic reporting tools that are available in RStudio so that you can effectively communicate results and even transform them into interactive web applications. Style and approach RStudio is an open source Integrated Development Environment (IDE) for the R platform. The R programming language is used for statistical computing and graphics, which RStudio facilitates and enhances through its integrated environment. This Cookbook will help you learn to write better R code using the advanced features of the R programming language using RStudio. Readers will learn advanced R techniques to compute the language and control object evaluation within R functions. Some of the contents are: • Accessing an API with R • Substituting missing values by interpolation • Performing data filtering activities • R Statistical implementation for Geospatial data • Developing shiny add-ins to expand RStudio functionalities • Using GitHub with RStudio • Modelling a recommendation engine with R • Using R Markdown for static and dynamic reporting • Curating a blog through RStudio • Advanced statistical modelling with R and RStudio

Mine valuable insights from your data using popular tools and techniques in R About This Book • Understand the basics of data mining and why R is a perfect tool for it. • Manipulate your data using popular R packages such as ggplot2, dplyr, and so on to gather valuable business insights from it. • Apply effective data mining models to perform regression and classification tasks. Who This Book Is For If you are a budding data scientist, or a data analyst with a basic knowledge of R, and want to get into the intricacies of data mining in a practical manner, this is the book for you. No previous experience of data mining is required. What You Will Learn • Master relevant packages such as dplyr, ggplot2 and so on for data mining • Learn how to effectively organize a data mining project through the CRISP-DM methodology • Implement data cleaning and validation tasks to get your data ready for data mining activities • Execute Exploratory Data Analysis both the numerical and the graphical way • Develop simple and multiple regression models along with logistic regression • Apply basic ensemble learning techniques to join together results from different data mining models • Perform text mining analysis from unstructured pdf files and textual data • Produce reports to effectively communicate objectives, methods, and insights of your analyses In Detail R is widely used to leverage data mining techniques across many different industries, including finance, medicine, scientific research, and more. This book will empower you to produce and present impressive analyses from data, by selecting and implementing the appropriate data mining techniques in R. It will let you gain these powerful skills while immersing in a one of a kind data mining crime case, where you will be requested to help resolving a real fraud case affecting a commercial company, by the mean of both basic and advanced data mining techniques. While moving along the plot of the story you will effectively learn and practice on real data the various R packages commonly employed for this kind of tasks. You will also get the chance of apply some of the most popular and effective data mining models and algos, from the basic multiple linear regression to the most advanced Support Vector Machines. Unlike other data mining learning instruments, this book will effectively expose you the theory behind these models, their relevant assumptions and when they can be applied to the data you are facing. By the end of the book you will hold a new and powerful toolbox of instruments, exactly knowing when and how to employ each of them to solve your data mining problems and get the most out of your data. Finally, to let you maximize the exposure to the concepts described and the learning process, the book comes packed with a reproducible bundle of commented R scripts and a practical set of data mining models cheat sheets. Style and approach This book takes a practical, step-by-step approach to explain the concepts of data mining. Practical use-cases involving real-world datasets are used throughout the book to clearly explain theoretical concepts.