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

What to learn next

In the previous chapters, we experienced a variety of implementations with various datasets with the main focus on performing various statistical approaches with the R tool. The R programming language can be tricky for data scientists with limited programming experience. Therefore, it is considered important to create a learning path for R.

Creating this learning path for R was a continuous trade-off between being pragmatic and exhaustive. Therefore, we decided on creating the outline for the learning path.

The outline for the learning path is as follows:

  • Why R?
  • Environmental setup.
  • Gist to R syntax and its pattern.
  • Primary packages.
  • Understanding the help system in R.
  • The data analysis workflow:
    • Data import
    • Manipulating data
    • Visualizing data
    • Presenting the results
    • Standout as R wizard