R Data Science Essentials

R Data Science Essentials

Overview of this book

With organizations increasingly embedding data science across their enterprise and with management becoming more data-driven it is an urgent requirement for analysts and managers to understand the key concept of data science. The data science concepts discussed in this book will help you make key decisions and solve the complex problems you will inevitably face in this new world. R Data Science Essentials will introduce you to various important concepts in the field of data science using R. We start by reading data from multiple sources, then move on to processing the data, extracting hidden patterns, building predictive and forecasting models, building a recommendation engine, and communicating to the user through stunning visualizations and dashboards. By the end of this book, you will have an understanding of some very important techniques in data science, be able to implement them using R, understand and interpret the outcomes, and know how they helps businesses make a decision.

R Data Science Essentials

Credits

About the Authors

About the Reviewers

www.PacktPub.com

Preface

Free Chapter

Getting Started with R

Reading data from different sources

Reading data from a database

Data types in R

Data preprocessing techniques

Performing data operations

Control structures in R

Bringing data to a usable format

Summary

Exploratory Data Analysis

The Titanic dataset

Descriptive statistics

Inferential statistics

Univariate analysis

Bivariate analysis

Multivariate analysis

Summary

Pattern Discovery

Transactional datasets

Apriori analysis

Support, confidence, and lift

Generating filtering rules

Plotting

Sequential dataset

Apriori sequence analysis

Understanding the results

Business cases

Summary

Segmentation Using Clustering

Datasets

Centroid-based clustering and an ideal number of clusters

Implementation using K-means

Visualizing the clusters

Connectivity-based clustering

Visualizing the connectivity

Business use cases

Summary

Developing Regression Models

Datasets

Sampling the dataset

Logistic regression

Evaluating logistic regression

Linear regression

Evaluating linear regression

Methods to improve the accuracy

Ensemble models

Summary

Time Series Forecasting

Datasets

Extracting patterns

Forecasting using ARIMA

Forecasting using Holt-Winters

Methods to improve accuracy

Summary

Recommendation Engine

Dataset and transformation

Recommendations using user-based CF

Recommendations using item-based CF

Challenges and enhancements

Summary

Communicating Data Analysis

Dataset

Plotting using the googleVis package

Creating an interactive dashboard using Shiny

Summary

Index

Customer Reviews

5 star

4 star

3 star

2 star

1 star

Linear regression

Building a linear regression model is very similar to building a logistic regression model. In simple linear regression, we predict the value of a dependent variable based on the value of other independent variables. In case of multiple linear regression, we will predict the dependent variable based on two or more independent variables.

Let's learn the implementation of linear regression using R. First, we need to divide the dataset into training and testing data. The code that is used to split the dataset is very similar to the code explained in the Sampling the dataset section. You can use the following code on the dataset that was created to explore the linear regression:

# divide into sample
training_positions<- sample(nrow(wdata), size=floor((nrow(wdata)*0.7)))
# Split into train and test based on the sample size
traindata<-wdata[training_positions,]
testdata<-wdata[-training_positions,]
nrow(traindata)
nrow(testdata)

The preceding code splits the dataset into...

R Data Science Essentials

R Data Science Essentials

Overview of this book

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R Data Science Essentials

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