In the R programming language, there are several basic data types that are used to define the kind of data that can be stored in a variable, each with its own set of properties and operations:

## Data Types in R programming

### Numeric Data Type:

Represents a set of real numbers, including decimal values. In R, real numbers with a decimal point are represented using this numeric data type.

Example:

`x <- 3.14`

class(x) # Output: "numeric"

typeof(x) # Output: "double"

### Integer Data Type:

- Represents a set of all integers.
- Integers are denoted by adding “L” at the end of the value.

Example:

`y <- 42L`

class(y) # Output: "integer"

### Logical Data Type:

- Represents logical (Boolean) values, which can be either TRUE or FALSE.

Example:

```
z <- TRUE
class(z) # Output: "logical"
```

### Complex Data Type:

- Represents a set of complex numbers with real and imaginary parts.

Example:

`w <- 1 + 2i`

class(w) # Output: "complex"

### Character Data Type:

- Represents a set of characters, including letters, digits, and special symbols, enclosed in double or single quotes.

Example:

`str <- "Hello, World"`

class(str) # Output: "character"

### Raw Data Type:

- Represents a single-byte memory containing raw data as bytes.

Example:

`raw_value <- as.raw(255)`

class(raw_value) # Output: "raw"

Each of these data types in R has its own specific operations and memory requirements, and they are crucial for effective memory consumption and precise computation in R programming.