Chapter 3 - Basic R Programming Concepts

Introduction

Welcome to the third chapter of Data Analytics with R 101! In this chapter, we will dive into the fundamental programming concepts that make R a powerful tool for data analysis. Whether you're manipulating datasets or performing complex calculations, understanding the basics of R programming will set you up for success.

We’ll cover:

• R syntax essentials
• Key data types including vectors, lists, matrices, and data frames
• Basic operations and calculations in R

By the end of this chapter, you’ll have a solid foundation to build upon as you continue your journey into R programming!

R Syntax Essentials

R programming language has a specific syntax that you’ll need to get comfortable with. Here’s a breakdown of the fundamental elements:

Comments

• Use the # symbol for comments. Anything after this symbol on the same line will be ignored by R.

r

Functions

• Functions in R have a specific format: function_name(arguments).

r

Assigning Values

• Use the <- operator (or =) for assignment.

r

Data Types in R

R has several data types that are fundamental to data manipulation. Understanding these types will help you know how to handle your data effectively.

Vectors

• A vector is a one-dimensional array that can hold numeric, character, or logical data.

r

Lists

• A list can contain elements of different types, including other lists.

r

Matrices

• A matrix is a two-dimensional array where all elements must be of the same type.

r

Data Frames

• A data frame is similar to a table in a database and can hold different types of data.

r

Basic Operations and Calculations in R

Once you're familiar with data types, you'll want to perform operations on them. R provides powerful capabilities for math and logical operations.

Arithmetic Operations

• R supports basic arithmetic operations like addition, subtraction, multiplication, and division.

r

Logical Operations

• Logical operations are essential for comparison and control flow in data analysis.

r

Practical Exercises

Now that you have a grasp of R’s basic programming concepts, let's solidify your understanding with some exercises.

1. Create a Vector: Create a vector named my_vector containing the numbers 1 through 5. Print the vector.

2. Construct a List: Create a list named student_info, including the fields for name, age, and whether the student is a graduate (TRUE/FALSE).

3. Matrix Operations: Create a 3x2 matrix filled with the numbers 1–6 and print it. Access and print the element in the second row and first column.

4. Basic Calculations:

   • Set two numeric variables, first_num = 15 and second_num = 4.
   • Compute and print their addition, subtraction, multiplication, and division.

Chapter Summary

In this chapter, we walked through the fundamental programming concepts in R that lay the groundwork for data analysis. We explored R syntax, key data types, and how to perform basic arithmetic and logical operations. These concepts will be essential as you work with real datasets in later chapters.

By solidifying your understanding of these basics, you’ll be prepared to tackle more advanced data manipulation and analysis tasks in R. Now it’s time to continue your journey and experiment with R on your own!