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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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Toc

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

Mapping and understanding structure

This section involves understanding each and every attribute in depth, which is considered to be important for the dataset specified.

We need to carry out the following steps to understand the data structure and mapping attributes, if any:

  1. Try to get a feel for the data as per the attribute structure:
> class(AirQualityUCI) 
[1] "tbl_df"     "tbl"        "data.frame" 

The output shows that the dataset is merely a tabular format of a data frame.

  1. Check the dimensions of the dataset:
> dim(AirQualityUCI) 
[1] 9357 15

This shows that the dataset comprises 9357 rows and 15 columns. The column structure has already been discussed in the first section.

  1. View the column names of the dataset. We need to check whether these correspond to the records included in the Excel file:
> colnames(AirQualityUCI) 
[1...
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