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

Summary

In this chapter, we listed some of the various packages that are available for reading in various kinds of attributes within the mentioned dataset in R. There are a lot of different options, and even the options we have listed have a wide functionality, which we are going to cover and use as we go further in the book. We learned how to reshape and tidy the erroneous data, along with manipulating and mutating it. By the end of the chapter, we learned how to clean the time series data.

In the next chapter, we will learn to visualize data graphically using the ggplot2 package. We will also demonstrate how to draw different kinds of plots and charts, such as scatter plots, histograms, probability plots, residual plots, box plots, and block plots.