Book Image

Hands-On Exploratory Data Analysis with R

By : Radhika Datar, Harish Garg
Book Image

Hands-On Exploratory Data Analysis with R

By: Radhika Datar, Harish Garg

Overview of this book

Hands-On Exploratory Data Analysis with R will help you build a strong foundation in data analysis and get well-versed with elementary ways to analyze data. You will learn how to understand your data and summarize its characteristics. You'll also study the structure of your data, and you'll explore graphical and numerical techniques using the R language. This book covers the entire exploratory data analysis (EDA) process—data collection, generating statistics, distribution, and invalidating the hypothesis. As you progress through the book, you will set up a data analysis environment with tools such as ggplot2, knitr, and R Markdown, using DOE Scatter Plot and SML2010 for multifactor, optimization, and regression data problems. By the end of this book, you will be able to successfully carry out a preliminary investigation on any dataset, uncover hidden insights, and present your results in a business context.
Table of Contents (17 chapters)
Free Chapter
1
Section 1: Setting Up Data Analysis Environment
7
Section 2: Univariate, Time Series, and Multivariate Data
11
Section 3: Multifactor, Optimization, and Regression Data Problems
14
Section 4: Conclusions

Manipulating and mutating data

In this section, we will focus on the way in which the dplyr package works, which helps the manipulation and mutation of data. This package primarily provides a flexible grammar for data manipulation. It is the next iteration of the plyr package, which focuses on tools for working with data frames.

The following steps are implemented to include this package in the R environment:

  1. Use the install.packages command to install the dplyr package in its entirety:
> install.packages("dplyr")  

If the package is already in use, it will be reinstalled or will prompt with the following message:

  1. Now, it is important to include this package in your workspace (R environment). By including it, we can call the necessary libraries and functions associated with this package in the R workspace:
> library(dplyr)  

The command execution in the R...