Book Image

Hands-On Data Science with R

By : Vitor Bianchi Lanzetta, Doug Ortiz, Nataraj Dasgupta, Ricardo Anjoleto Farias
Book Image

Hands-On Data Science with R

By: Vitor Bianchi Lanzetta, Doug Ortiz, Nataraj Dasgupta, Ricardo Anjoleto Farias

Overview of this book

R is the most widely used programming language, and when used in association with data science, this powerful combination will solve the complexities involved with unstructured datasets in the real world. This book covers the entire data science ecosystem for aspiring data scientists, right from zero to a level where you are confident enough to get hands-on with real-world data science problems. The book starts with an introduction to data science and introduces readers to popular R libraries for executing data science routine tasks. This book covers all the important processes in data science such as data gathering, cleaning data, and then uncovering patterns from it. You will explore algorithms such as machine learning algorithms, predictive analytical models, and finally deep learning algorithms. You will learn to run the most powerful visualization packages available in R so as to ensure that you can easily derive insights from your data. Towards the end, you will also learn how to integrate R with Spark and Hadoop and perform large-scale data analytics without much complexity.
Table of Contents (16 chapters)

Summary

In this chapter, we looked at the various ways in which data.table and dplyr can be used. We covered the basics of loading data from various data sources, performing basic subsetting, grouping, pivoting, and other operations from both the data.table and dplyr perspective. We saw that both packages offer a high level of versatilitydata.table is much faster than dplyr and is extremely useful for large-scale datasets but it comes at the expense of learning a new syntax. dplyr, on the other hand, is relatively slower than data.table but it provides a high level of simplicity and ease of downstream analysis.

In the next chapter, we will discuss data mining techniques for both structured data that conform to a clearly defined schema and unstructured data that exists in the form of natural language text. Specific topics include pattern discovery, clustering, text retrieval...