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.

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Table of Contents (16 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
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
Summary
Quiz
2
Descriptive and Inferential Statistics
Descriptive and Inferential Statistics
Measures of central tendency and dispersion
Statistical hypothesis testing
Summary
Quiz
3
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
Summary
Quiz
4
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
Summary
Quiz
5
Data Analysis with R
Data Analysis with R
Preparing data for analysis
Data visualisation
Saving analysis for future work
Summary
Quiz
6
Machine Learning with R
Machine Learning with R
What is machine learning?
Linear regression with R
Tree models
Random forests – a collection of trees
Support vector machines
What about regressions?
Hierarchical and k-means clustering
Neural networks
Summary
Quiz
7
Forecasting and ML App with R
Forecasting and ML App with R
The UI and server
Forecasting machine learning application
Summary
Quiz
8
Neural Networks and Deep Learning
Neural Networks and Deep Learning
Daily neural nets
Overview – NNs and deep learning
NNs with Keras
Summary
Quiz
9
Markovian in R
Markovian in R
Markovian-type models
Programming an HMM with R
Summary
Quiz
10
Visualizing Data
Visualizing Data
Retrieving and cleaning data
Crafting visualizations
Summary
Quiz
11
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
Summary
Quiz
12
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
Summary
Quiz
13
R on Cloud
R on Cloud
Cloud computing
Azure registration
Azure Machine Learning Studio
Summary
Quiz
14
The Road Ahead
The Road Ahead
Growing your skills
Meeting Stack Overflow
15
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think

Miscellaneous topics

In this section, we will look at a few other data-related topics. The following will be covered:

  • Checking data quality
  • Reading binary files: Excel, SAS, and other data sources
  • On-disk formats
  • Working with web data

Checking data quality

There are several inbuilt functions as well as packages for checking the quality of data in R. The most commonly used among them is the summary function in base R:

## Packages Used: 
## psych, pastecs, dataMaid, daff 
 
# install.packages(c("psych","pastecs","dataMaid","daff")) 
 
state <- data.frame(state.x77) 
state$State <- row.names(state) 
state 
 
 
summary(state)

The output of the preceding code is as follows:

library...
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