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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
Concepts
Authors (2):
Radhika Datar Radhika Datar
Profile icon Radhika Datar
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Radhika Datar has more than 5 years' experience in software development and content writing. She is well versed in frameworks such as Python, PHP, and Java, and regularly provides training on them. She has been working with Educba and Eduonix as a training consultant since June 2016, while also working as a freelance academic writer in data science and data analytics. She obtained her master's degree from the Symbiosis Institute of Computer Studies and Research and her bachelor's degree from K. J. Somaiya College of Science and Commerce.
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Harish Garg Harish Garg
Profile icon Harish Garg
LinkedIn
Harish Garg is a Principal Software Developer, author, and co-founder of a software development and training company, Bignumworks. Harish has more than 19 years of experience in a wide variety of technologies, including blockchain, data science and enterprise software. During this time, he has worked for companies such as McAfee, Intel, etc.
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Table of Contents (17) Chapters

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

Data visualization

In this section, we will focus on the creation of the following plots:

  • Autocorrelation
  • Spectrum
  • Phase

Autocorrelation plots

Autocorrelation plots are regarded as plots for creating randomness in a particular dataset. This randomness is very powerful regarding autocorrelations of data values with varying time lags. It is mandatory that autocorrelations for any dataset should be near zero, for any and all time-lag separations.

The Acf function computes (and, by default, plots) an estimate of the autocorrelation function of a (possibly multivariate) time series. The syntax is as follows:

> Acf(x, lag.max = NULL, type = c("correlation", "covariance","partial"), plot = TRUE...
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Authors (2)

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Profile icon Radhika Datar
LinkedIn
Radhika Datar has more than 5 years' experience in software development and content writing. She is well versed in frameworks such as Python, PHP, and Java, and regularly provides training on them. She has been working with Educba and Eduonix as a training consultant since June 2016, while also working as a freelance academic writer in data science and data analytics. She obtained her master's degree from the Symbiosis Institute of Computer Studies and Research and her bachelor's degree from K. J. Somaiya College of Science and Commerce.
Read more
See other products by Radhika Datar
Profile icon Harish Garg
LinkedIn
Harish Garg is a Principal Software Developer, author, and co-founder of a software development and training company, Bignumworks. Harish has more than 19 years of experience in a wide variety of technologies, including blockchain, data science and enterprise software. During this time, he has worked for companies such as McAfee, Intel, etc.
Read more
See other products by Harish Garg
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