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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 FREE CHAPTER
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

Probability plots

This section describes the creation of probability plots in R that can be used for didactic purposes and, predominantly, for the purpose of data analysis. The following functions are available for each distribution of probability plots in the format specified:

Name

Description

dnorm()

Density or probability function

pnorm()

Cumulative density function

qnorm()

Quantile function

Rnorm()

Random deviates

We will be creating probability plots for the bank dataset with reference to the age and balance parameters, which are regarded as the crucial parameters in establishing loan eligibility using the following steps:

  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...
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