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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
Languages
Tools
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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

Cleaning the dataset

Data cleaning is the process of converting the raw data into a specific format that includes consistent data designed in a simpler manner. R includes a set of comprehensive tools, that are designed specially to clean the data in an effective manner. We will try to focus on cleaning the dataset here in a specific way and will carry out the following steps to this end:

  1. Include the libraries that are needed to clean and tidy up the dataset as follows:
> library(dplyr)
> library(tidyr)
  1. Analyze the summary of our dataset as shown in the following code. This will help us to focus on which attributes are important:
>  summary(Autompg)
mpg cylinders displacement horsepower weight acceleration

Min. : 9.00 Min. :3.000 Min. : 68.0 150 : 22 Min. :1613 Min. : 8.00

1st Qu.:17.50 1st Qu.:4.000 1st Qu.:104.2 90 : 20 1st Qu.:2224...
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