Book Image

R Data Science Essentials

Book Image

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.
Table of Contents (15 chapters)
R Data Science Essentials
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

The Titanic dataset


In this chapter, let's use the Titanic dataset, which is available on the Internet and also hosted on GitHub, to implement various techniques. Place the dataset in the current working directory in R; before this, first set the working directory accordingly using the setwd() command. The setwd() function is used to specify the location that should be considered as the current working directory. Now, read the data using the read.csv function and store it in a data frame. In this book, we have named the data frame tdata. The various details that are present in the dataset, which is hosted on GitHub, are as follows:

tdata<- read.csv("titanic.csv")
names(tdata)

The output of the preceding command is as follows:

These are the various columns captured in the dataset. The explanation of these variables is given. For more detailed understanding about the dataset, visit https://www.kaggle.com/c/titanic/data. We have used the file named train.csv for our learning purpose.

The variable...