Indeed, while you can directly create **integers** in R by appending `L` to a number, converting existing values to integers requires a different approach. Simply appending `L` will cause an error, as demonstrated in the example below:

# Some number
num <- 20
numL # Trying using L for converting

Certainly, that did not achieve the intended result.

Luckily, R simplifies the conversion process with straightforward functions like `as.double()`, `as.integer()`, and `as.complex()`. The function names are quite self-explanatory!

Numerical types follow a sort of 'hierarchy' where each `integer` can be converted to a `double` or a `complex` number, and each `double` can become a `complex` number. However, converting a `double` to an `integer` will result in **truncation**, not rounding, of the number to its integer component.

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.

Type Conversion

Solution