#### Overview of this book

This cookbook offers a range of data analysis samples in simple and straightforward R code, providing step-by-step resources and time-saving methods to help you solve data problems efficiently. The first section deals with how to create R functions to avoid the unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation is provided, illustrating how to use the “dplyr” and “data.table” packages to efficiently process larger data structures. We also focus on “ggplot2” and show you how to create advanced figures for data exploration. In addition, you will learn how to build an interactive report using the “ggvis” package. Later chapters offer insight into time series analysis on financial data, while there is detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction. By the end of this book, you will understand how to resolve issues and will be able to comfortably offer solutions to problems encountered while performing data analysis.
R for Data Science Cookbook
Credits
www.PacktPub.com
Preface
Free Chapter
Functions in R
Data Preprocessing and Preparation
Visualizing Data with ggplot2
Making Interactive Reports
Simulation from Probability Distributions
Statistical Inference in R
Time Series Mining with R
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.

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:

`> employees...`