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Hands-On Exploratory Data Analysis with R

You're reading from   Hands-On Exploratory Data Analysis with R Become an expert in exploratory data analysis using R packages

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781789804379
Length 266 pages
Edition 1st Edition
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Authors (2):
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Radhika Datar Radhika Datar
Author Profile Icon Radhika Datar
Radhika Datar
Harish Garg Harish Garg
Author Profile Icon Harish Garg
Harish Garg
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Table of Contents (17) Chapters Close

Preface 1. Section 1: Setting Up Data Analysis Environment
2. Setting Up Our Data Analysis Environment FREE CHAPTER 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

Density plots

The major shortcoming of histograms (which we discussed in an earlier section) is that they are quite sensitive to the choice of bin margins and the number of bins. The best alternative to this is to use density plots, which are regarded as a smoothed version of a histogram. We will implement the following steps to create density plots for an iris dataset:

  1. Include the library within the R workspace. This is considered a mandatory step:
> library(ggplot2)
Attaching package: 'ggplot2'
The following object is masked _by_ '.GlobalEnv':
mpg
Warning message:
package 'ggplot2' was built under R version 3.5.3
Ignore the warning messages if there are any. The warning messages are usually determined with respect to system configuration.
  1. Have a look at the parameters to determine which plot should be used for density plot creation:

We will consider...

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