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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Free Chapter

Getting Started with Data Science and R

Getting Started with Data Science and R

Introduction to data science

Active domains of data science

Solving problems with data science

Using R for data science

Our first R program

Descriptive and Inferential Statistics

Descriptive and Inferential Statistics

Measures of central tendency and dispersion

Statistical hypothesis testing

Data Wrangling with R

Data Wrangling with R

Introduction to data wrangling with R

Data extraction, transformation, and load

Miscellaneous topics

Tutorial – looking at airline flight times data

KDD, Data Mining, and Text Mining

KDD, Data Mining, and Text Mining

Good practices of KDD and data mining

Scraping a dwarf name

Retrieving text from the web

Retrieving tweets from R community

Cleaning and transforming data

Looking for patterns – peeking, visualizing, and clustering data

Data Analysis with R

Data Analysis with R

Preparing data for analysis

Data visualisation

Saving analysis for future work

Machine Learning with R

Machine Learning with R

What is machine learning?

Linear regression with R

Random forests – a collection of trees

Support vector machines

What about regressions?

Hierarchical and k-means clustering

Neural networks

Forecasting and ML App with R

Forecasting and ML App with R

The UI and server

Forecasting machine learning application

Neural Networks and Deep Learning

Neural Networks and Deep Learning

Daily neural nets

Overview – NNs and deep learning

Markovian in R

Markovian-type models

Programming an HMM with R

Visualizing Data

Visualizing Data

Retrieving and cleaning data

Crafting visualizations

Going to Production with R

Going to Production with R

What is R Shiny?

How to build a Shiny app

Building an application inside R

Approach for creating a data product from statistical modeling and web UI

Some advice about Shiny

Large Scale Data Analytics with Hadoop

Large Scale Data Analytics with Hadoop

Installing the package and Spark

Manipulating Spark data using both dplyr and SQL

Filtering and aggregating Spark datasets

Using Spark machine learning or H2O Sparking Water

Providing interfaces to Spark packages

Spark DataFrames within the RStudio IDE

R on Cloud

Cloud computing

Azure registration

Azure Machine Learning Studio

The Road Ahead

Growing your skills

Meeting Stack Overflow

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Cleaning and transforming data

In Chapter 3, Data Wrangling with R, we approached the topic of data cleaning (munging). Data cleaning is so important that the majority of data scientists spend most of their work time cleaning and preparing data. The last session, What is the R community tweeting about?, gave us a DataFrame with 15999 rows and 42 columns. That is raw data. This session will clean and transform it.

Our initial goal was to check which packages the R community is talking about on Twitter. There are three variables we will use to achieve the final goal.

The variable text can be truncated when there is a retweet. When that is the case, check retweet_text, which won't be truncated. The quoted_text variable also brings useful information. To unite all the useful information into a single object, we can use the following code:

quotes <- tweets_dt$is_quote
rts...