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Hands-On Exploratory Data Analysis with R

You're reading from  Hands-On Exploratory Data Analysis with R

Product type Book
Published in May 2019
Publisher Packt
ISBN-13 9781789804379
Pages 266 pages
Edition 1st Edition
Languages
Authors (2):
Radhika Datar Radhika Datar
Profile icon Radhika Datar
Harish Garg Harish Garg
Profile icon Harish Garg
View More author details
Toc

Table of Contents (17) Chapters close

Preface 1. Section 1: Setting Up Data Analysis Environment
2. Setting Up Our Data Analysis Environment 3. Importing Diverse Datasets 4. Examining, Cleaning, and Filtering 5. Visualizing Data Graphically with ggplot2 6. Creating Aesthetically Pleasing Reports with knitr and R Markdown 7. Section 2: Univariate, Time Series, and Multivariate Data
8. Univariate and Control Datasets 9. Time Series Datasets 10. Multivariate Datasets 11. Section 3: Multifactor, Optimization, and Regression Data Problems
12. Multi-Factor Datasets 13. Handling Optimization and Regression Data Problems 14. Section 4: Conclusions
15. Next Steps 16. Other Books You May Enjoy

Introducing and reading a dataset

In this chapter, we will focus on a macroeconomic dataset that is found in a built-in library called dataset in R. It provides a well-known example of a highly collinear regression.

The data frame includes seven economical variables, observed yearly from 1947 to 1962 (n=16):

> library(pls)
> data(longley)

As discussed in previous chapters, we will carry out the following steps to read the data structure:

  1. After including the necessary libraries, you can have a look at the attribute structure:
> longley
GNP.deflator GNP Unemployed Armed.Forces Population Year Employed

1947 83.0 234.289 235.6 159.0 107.608 1947 60.323
1948 88.5 259.426 232.5 145.6 108.632 1948 61.122
1949 88.2 258.054 368.2 161.6 109.773 1949 60.171
1950 89.5 284.599 335.1 165.0 110.929 1950 61.187
1951 96.2 328.975 209.9 309.9 112.075 1951 63.221
1952 98.1 346.999 193.2 359.4...
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