Introduction

To use R most efficiently and effectively, you must understand the data types and data structures of R, because the Machine Learning algorithms such as Support Vector Machine (SVM), decision tree, and even linear regression and logistic regression have optimization components. The use of the correct data types is the key part of the performance of optimization. Also, to perform matrix operations, the user must use matrix data types. Moreover, the user needs to know the use of correct variables into the statistical model, and understanding R data types is the important aspect in this area. Here are the data types that will help you store and manage your data correctly and efficiently:

• Character
• Numeric
• Logical
• Integer
• Complex

The data structure of R is the way to organize the dataset for data storage and analytics purposes. Each of the different data structures can contain certain types of data. The following are the most popular and primary structures in R:

• Vector: This can contain only one type of data
• Matrix: This contains only one type of data but in two-dimensional representation
• Array: This contains only one type of data and it can store data with more than two dimensional structure
• Data frame: This can contain mixed types of data, and it is also a two-dimensional representation
• List: This can contain heterogeneous data, including vector, matrix, data frames, and even the list itself

By going through each of the recipes in this chapter, you will learn to create, modify, and access the properties, and perform certain operations on various types of data.