Summary  
This chapter explains logical data types (TRUE/FALSE), how to evaluate conditions and expressions, and the rules for converting between logical, numeric, and string representations.  

General domain of usage  
Conditional evaluation and control flow.

Let's now turn our attention to logical data types.


For instance, if you evaluate `2 > 1`, it will return `TRUE` because `2` is indeed greater than `1`. This principle will be highly relevant in later chapters.

**Numbers** and **strings** (text) can also be converted to logical types. Any number except `0` will be converted to `TRUE` (`0` converts to `FALSE`), and the strings `'F'`, `'false'`, `'False'`, and `'FALSE'` will convert to the logical `FALSE`. The same principle applies for logical `TRUE`.

When converting logical values to numbers, `FALSE` becomes `0`, and `TRUE` becomes `1`.

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.

Logical Types

Oplossing

Awesome!

Logical Types

Oplossing

Awesome!