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 converting various kinds of data into 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 into the book. We learned how to read all kinds of delimited datasets into R packages using the readr package and also advanced options for reading in Excel data. We then learned how to use the jsonlite package to read JSON in R data structures and learned how to use the httr package to read data into R from web APIs.

At the end of the chapter, we learned how to get data into R by scraping the web using the rvest package, and we also learned how to connect to relational databases from R using the DBI package.

In the next chapter, we will explore how to identify and clean missing and erroneous data. This...