Summary  
This chapter explains how to use comments (marked with #) to annotate code for clarity or deactivate lines for debugging, and discusses best practices to avoid over‐commenting.  

General domain of usage  
Scientific research code maintenance

When you've written an extensive script for research and need to revisit it later for updates or improvements, remembering the **purpose of each line of code** can be challenging. That’s where annotations within the code, known as **comments**, become invaluable.


For instance, we can add comments in two ways: as a **standalone line** and **alongside a computation**:

# Some calculation
11 + 3 # eleven plus three

The console will only show the result of `11 + 3` and **ignore** all the comments.

Comments are useful not just for **clarification**, but also for **debugging purposes**, allowing you to deactivate certain lines of code without deleting them.

R is one of the most popular programming languages among data analysts and data scientists. R has a wide community that will always help you with any problem. Also, R has tons of different packages designed to solve various problems. The language can be easily applied to statistics and data analytics problems. In the first part, you will be introduced to the very basics of R and will deal with single 1-D values of different types.

In the initial section, you’ll dive into the foundational elements of R, such as generating output and performing elementary operations. This section will conclude with an exploration of the theory behind variables.

You’ll become familiar with the vector class in R, which also handles individual values. Here, you’ll delve into the primary data types for singular values and learn how to manage multiple values of the same type simultaneously.

Factors, also known as categorical variables, are prevalent in the real world. Examples include blood types, currencies, nationalities, and brands. Unlike numerical variables, factors have a limited set of possible values.

Comments in R

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