Book Image

R Data Analysis Projects

Book Image

R Data Analysis Projects

Overview of this book

R offers a large variety of packages and libraries for fast and accurate data analysis and visualization. As a result, it’s one of the most popularly used languages by data scientists and analysts, or anyone who wants to perform data analysis. This book will demonstrate how you can put to use your existing knowledge of data analysis in R to build highly efficient, end-to-end data analysis pipelines without any hassle. You’ll start by building a content-based recommendation system, followed by building a project on sentiment analysis with tweets. You’ll implement time-series modeling for anomaly detection, and understand cluster analysis of streaming data. You’ll work through projects on performing efficient market data research, building recommendation systems, and analyzing networks accurately, all provided with easy to follow codes. With the help of these real-world projects, you’ll get a better understanding of the challenges faced when building data analysis pipelines, and see how you can overcome them without compromising on the efficiency or accuracy of your systems. The book covers some popularly used R packages such as dplyr, ggplot2, RShiny, and others, and includes tips on using them effectively. By the end of this book, you’ll have a better understanding of data analysis with R, and be able to put your knowledge to practical use without any hassle.
Table of Contents (9 chapters)

Introduction to the MXNet R package


We will use the package MXNet R to build our neural networks. It implements state-of-the-art deep learning algorithms and enables efficient GPU computing. We can work in our familiar R environment and at the same time harness the power of the GPUs (though access to GPU is available through the Python API now, we still need to wait for it to be available for R). It will be useful to give you a small overview about the basic building blocks of MXNet before we start using it for our time series predictions.

Note

Refer to the https://github.com/apache/incubator-mxnet/tree/master/R package for more details on the MXNet R package.

In MXNet, NDArray is the basic operation unit. It's a vectorized operation unit for matrix and tensor computations. All operations on this operation unit can be run on either the CPU or GPUs. The most important point is that all these operations are parallel. It's the basic data structure for manipulating and playing around with data...