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R for Data Science Cookbook (n)

You're reading from   R for Data Science Cookbook (n) Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniques

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Product type Paperback
Published in Jul 2016
Publisher
ISBN-13 9781784390815
Length 452 pages
Edition 1st Edition
Languages
Tools
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Author (1):
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Yu-Wei, Chiu (David Chiu) Yu-Wei, Chiu (David Chiu)
Author Profile Icon Yu-Wei, Chiu (David Chiu)
Yu-Wei, Chiu (David Chiu)
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Toc

Table of Contents (14) Chapters Close

Preface 1. Functions in R FREE CHAPTER 2. Data Extracting, Transforming, and Loading 3. Data Preprocessing and Preparation 4. Data Manipulation 5. Visualizing Data with ggplot2 6. Making Interactive Reports 7. Simulation from Probability Distributions 8. Statistical Inference in R 9. Rule and Pattern Mining with R 10. Time Series Mining with R 11. Supervised Machine Learning 12. Unsupervised Machine Learning Index

Working with the date format

After we have converted each data attribute to the proper data type, we may determine that some attributes in employees and salaries are in the date format. Thus, we can calculate the number of years between the employees' date of birth and current year to estimate the age of each employee. Here, we will show you how to use some built-in date functions and the lubridate package to manipulate date format data.

Getting ready

Refer to the previous recipe and convert each attribute of imported data into the correct data type. Also, you have to rename the columns of the employees and salaries datasets by following the steps from the Renaming the data variable recipe.

How to do it…

Perform the following steps to work with the date format in employees and salaries:

  1. We can add or subtract days on the date format attribute using the following:
    > employees$hire_date + 30
    
  2. We can obtain time differences in days between hire_date and birth_date using the following...
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