It's time to explore how to store multiple values within a single variable, which can be efficiently done using **vectors**.

It's essential to understand that **a vector must contain data of the same type**; this means you cannot mix integers, complex numbers, and logical values in a single vector.

Creating a vector in R is straightforward: just list all the values separated by **commas** inside the `c()` function. Here's how you do it:

vec <- c(1, 2, 3) # Create a vector
vec # Output its value

When you input different data types within the `c()` function, R will default to **one type for all elements**. For instance, in a vector like `c(1, 2, 'some text')`, all numeric values will be converted to **text**. The reasoning is straightforward: if you attempt to construct a vector with varying types, R will select the one type to which all values can be **converted**.

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.

Vectors

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