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

Mapping and understanding the structure

This section involves understanding the depth of each and every attribute that is considered to be important for the previously mentioned dataset.

The following steps are used to map the underlying structure of our dataset:

  1. Try to get a feel for the data as per the attribute structure:
> class(longley)
[1] "data.frame"

The output shows that the dataset includes a tabular format of rows and columns that is well defined in dimensions.

  1. Check the dimensions of the dataset:
> dim(longley)
[1] 16 7

This means that the dataset is comprised of 858 rows and 36 columns. The column structure is as discussed in the first section.

  1. View the column names of the dataset to check whether they match the records included in the Excel file:
> colnames(longley)
[1] "GNP Deflator" "GNP" "Unemployed" "Armed...