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

Reshaping and tidying up erroneous data

Erroneous data is regarded as data that falls outside of what is accepted and what should be rejected by the system. In this section, we will focus on two major activities: reshaping and tidying up erroneous data. With the R programming language, this process can be achieved with the tidyr package. This package is designed specifically for data tidying and works well with manipulated data. It is important that you install this package if you have newly installed the R environment.

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

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

From this, we get the following output:

  1. Now, it is important to include this package in your workspace (R environment). By including it, we can call the necessary...