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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

Reading in Excel data with the readxl R package

The readxl R package makes it very easy and straightforward to read data from Excel files into R:

We start by loading the module into R:

library(readxl)

Reading Excel data with readxl is simple; we just need to call the read_excel function and pass in the file path, as follows:

read_excel("data.xls")

readxl supports both the old XLS and the new XLSX format. It guesses the format from the extension of the file being read:

read_excel("data.xlsx")

Excel files sometimes have multiple sheets. For example, the following Excel file has multiple sheets:

You can easily handle them with the readxl package. To see the number of sheets in the Excel document, use the excel_sheets command:

excel_sheets("data.xlsx")
#1> [i] "sheet1" "sheet2"

We can also specify which sheet should be accessed...

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