#### 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

## Converting data types

If we do not specify a data type during the import phase, R will automatically assign a type to the imported dataset. However, if the data type assigned is different to the actual type, we may face difficulties in further data manipulation. Thus, data type conversion is an essential step during the preprocessing phase.

Complete the previous recipe and import both `employees.csv` and `salaries.csv` into an R session. You must also specify column names for these two datasets to be able to perform the following steps.

### How to do it…

Perform the following steps to convert the data type:

1. First, examine the data type of each attribute using the `class` function:

```> class(employees\$birth_date)
[1] "factor"
```
2. You can also examine types of all attributes using the `str` function:

```> str(employees)

'data.frame': 10 obs. of  6 variables:
\$ emp_no    : int  10001 10002 10003 10004 10005 10006 10007 10008 10009 10010
\$ birth_date: Factor w/ 10 levels "1952-04-19","1953-04-20...```