R

Apress, 2016. — 217 p. — ISBN: 9781484221778, EISBN: 9781484221785 Code files only! This book explains the broad topic of automated trading, starting with its mathematics and moving to its computation and execution. Readers will gain a unique insight into the mechanics and computational considerations taken in building a backtester, strategy optimizer, and fully functional...

2nd.ed. — Packt, 2017. — 523 p. — ISBN: 978-1787124479. Only sample files! Key Features Analyse your data using the popular R packages with ready-to-use and customizable recipes Find meaningful insights from your data and generate dynamic reports A practical guide to help you put your data analysis skills in R to practical use Book Description This book will show you how you...

Packt, 2019. — 438 p. — ISBN: 1788629159. Code files only! Hands-On Time Series Analysis with R: Perform time series analysis and forecasting using R Build efficient forecasting models using traditional time series models and machine learning algorithms. Key Features Perform time series analysis and forecasting using R packages such as Forecast and h2o Develop models and find...

Код и учебные материалы к книге: Kumar A., Paul A., Mastering Text Mining with R. В архив включены: Код для примеров на R к главам 4, 6, 7. Данные к примерам и тестовые материалы для глав 2, 5, 6.

Packt Publishing, 2017. — 366 p. !Code files only Key Features Use R's popular packages—such as ggplot2, ggvis, ggforce, and more—to create custom, interactive visualization solutions. Create, design, and build interactive dashboards using Shiny A highly practical guide to help you get to grips with the basics of data visualization techniques, and how you can implement them...

Apress, 2017. — 262 p. — ISBN: 978-1-4842-3143-2. Code files only! Get an introduction to functional data structures using R and write more effective code and gain performance for your programs. This book teaches you workarounds because data in functional languages is not mutable: for example you’ll learn how to change variable-value bindings by modifying environments, which...

New York: Apress, 2017. - 103 p. Learn how to manipulate functions and expressions to modify how the R language interprets itself. This book is an introduction to metaprogramming in the R language, so you will write programs to manipulate other programs. Metaprogramming in R shows you how to treat code as data that you can generate, analyze, or modify. R is a very high-level...

In Easy Steps Limited, 2018. — 192 p. — ISBN: 978-1840787955. Only CODE files! The R language is widely used by statisticians for data analysis, and the popularity of R programming has therefore increased substantially in recent years. The emerging Internet of Things (IoT) gathers increasing amounts of data that can be analyzed to gain useful insights into trends. R for Data...

Packt Publishing, 2020. — 317 p. — ISBN: 978-1-78899-683-9. Code files only! Explore and implement deep learning to solve various real-world problems using modern R libraries such as TensorFlow, MXNet, H2O, and Deepnet Deep learning enables efficient and accurate learning from a massive amount of data. This book will help you overcome a number of challenges using various deep...

2nd Ed. — Apress, 2019. — 712 p. in color. — ISBN: 9781484242148, EISBN 9781484242155. Code files only! Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition shows you how to work with a machine-learning algorithm and use it to build a ML model from raw data. You will see how to use R programming...

Packt Publishing, 2018. — 376 p. — ISBN: 978-1788624145. !Only code files Key Features Implement machine learning algorithms to build ensemble-efficient models Explore powerful R packages to create predictive models using ensemble methods Learn to build ensemble models on large datasets using a practical approach Book Description Ensemble techniques are used for combining two...

Apress, 2021. — 404 p. — ISBN 978-1484268308. Source code only! Master the syntax for working with R’s plotting functions in graphics and stats in this easy reference to formatting plots. The approach in Visualizing Data in R 4 toward the application of formatting in ggplot() will follow the structure of the formatting used by the plotting functions in graphics and stats. This...