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

Univariate analysis


Univariate analysis is the simplest form of analysis, where we consider only one variable at a time and understand the data. Some of the measures have already been covered in descriptive statistics such as the mean and median of the data.

We will perform one more univariate analysis: the distribution of the data. We will consider the age of the people who had travelled in the Titanic, and we will find out how many people were there in the different age groups:

age <- na.omit(tdata$Age)

First, we read the data to the age data frame by excluding the cases where the age was not present. As we want to get the distribution on a fixed range, we first get the age of the youngest as well as the oldest person who travelled on the ship from the available dataset using the seq function. We set the starting value as 0 and the last value as 80; we also set the interval as 10:

range(age)
breaks = seq(0, 80, by=10)

We created the intervals and stored them in the variable breaks. Using...