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)

Retrieving and cleaning data

First things first, we must get data. We also need to clean it before doing any drawing. The wbstats package will be used to get data. It retrieves data from the World Bank Data API. This section will demonstrate how to use wbstats. Data obtained and cleaned through this section are going to be later used to make plots.

Worldwide data about inequality, education, and population will be searched. All of these can be retrieved from the World Bank Database. Start by downloading wbstats if don't have it yet. If you are not sure whether you have it, simply run the following code:

if(!require('wbstats')){install.packages('wbstats')}

Load the wbstats library and enter wbcache() to download an updated list of available countries, indicators, and source information:

library(wbstats)
update_cache <- wbcache()

One can investigate the...