How it works...

Generally, data for collection may be in multiple files and different formats. To exchange data between files and RData, R provides many built-in functions, such as save, load, read.csv, read.table, write.csv, and write.table.

This example first demonstrates how to load the built-in dataset iris into an R session.
The iris dataset is the most famous and commonly used dataset in the field of machine learning. Here, we use the iris dataset as an example. The recipe shows how to save RData and load it with the save and load functions. Furthermore, the example explains how to use read.table, write.table, read.csv, and write.csv to exchange data from files to a DataFrame. The use of the R I/O function to read and write data is very important as most of the data sources are external. Therefore, you have to use these functions to load data into an R session.

You need to install the package for reading from the database. For all database, you can find the package, after installing the steps mostly remains the same for reading the data from the database.