Every programming language has its data types, and almost all programming languages share some common data types like Integer, String, Character, Array, etc. R has an extensive type of data types, including vectors (numerical, character, logical), matrices, data frames, arrays, factors, and lists.

**R Data Types**

In the R language, the variables are not declared as some data type. The variables are assigned with R-Objects, and the data type of the R-object becomes the data type of the variable, unlike other programming languages.

R lang has various R-objects, which are the following.

- Vector
- List
- Factor
- Matrix
- Array
- DataFrame

Among all the above R-objects, the vector is the simplest data type in R language.

**R Vector Data type**

A vector in R is a series of data items of the same basic type. Members in a vector are called **components**. When you want to create a vector with more than one element, use the **c()** function, combining them into a single vector.

```
x <- c(11, 21, 5.2, 6, -19, 0.46)
y <- c("eleven", "eight", "one")
z <- c(TRUE, FALSE, TRUE, FALSE, TRUE, FALSE)
x
typeof(x)
y
typeof(y)
z
typeof(z)
```

**Output**

```
[1] 11.00 21.00 5.20 6.00 -19.00 0.46
[1] "double"
[1] "eleven" "eight" "one"
[1] "character"
[1] TRUE FALSE TRUE FALSE TRUE FALSE
[1] "logical"
```

We have created three types of vectors using the c() function. To check the R object’s data type, use the typeof() function.

There are six types of vector objects available in R.

- Logical vector
- Numeric vector
- Integer vector
- Complex vector
- Character vector
- Raw vector

**Logical Vector**

A **logical vector** is a **vector** that only contains **TRUE** and **FALSE** values, which means it only contains boolean values.

```
logical_rv <- TRUE
logical_rv
class(logical_rv)
```

**Output**

```
[1] TRUE
[1] "logical"
```

The class() function determines the class of an object or its “**internal**” type.

**Numeric Vector**

The numeric vectors create a real vector of the specified length. The elements of the vector are all equal to 0. The numeric vectors can have values like 11, 21.19, 1.1, etc.

```
numeric_rv <- 11.21
numeric_rv
class(numeric_rv)
```

**Output**

```
[1] 11.21
[1] "numeric"
```

And we get the numeric vector. It has **as.numeric()** method, which is a generic function. To check if it is a numeric value or not, use the **is.numeric() **method.

**Integer Vector**

The integer() method creates or tests for objects of type “**integer**“. The current implementation of **R** uses the 32-bit integers for integer vectors. Integer vectors can have values like 21L, 19L, 0L, etc.

```
integer_rv <- 21L
integer_rv
class(integer_rv)
```

**Output**

```
[1] 21
[1] "integer"
```

The as.integer() function tries to coerce its argument to be of integer type. To check if the value is an integer or not, use the is.integer() function.

**Complex Vector**

The complex vector in R can be specified either by providing its length, its real and imaginary parts, or modulus and argument. The complex vector can have values like 1 + 2i, 2 + 5i, 1 + 0i, etc.

```
complex_rv <- 19 + 21i
complex_rv
class(complex_rv)
```

**Output**

```
[1] 19+21i
[1] "complex"
```

The as.complex() function tries to control its argument to be of complex type. To check if the data type is complex or not, use the is.complex() method. All forms of **NA** and **NAN** are coerced to a complex **NA**, for which both the real and imaginary parts are **NA**.

**Character Vector**

Text in R is represented by character vectors. A character vector is a vector consisting of characters. To create a character vector, use the characters in double or single-quotes. You can have character vector values like ‘**x**‘ , **“data”,** “**TRUE**“, ‘**21.19**‘.

```
character_rv <- "a + b + c"
character_rv
class(character_rv)
```

**Output**

```
[1] "a + b + c"
[1] "character"
```

You can assign a value to a character vector using the assignment operator (<-), the same way you do for all other data types variables. To find out the length of the vector, use the length() function. To check if the variable holds a character vector or not, use the is.character() function.

**Raw Vector**

The raw vector data type is intended to hold raw bytes. A raw vector is printed with each byte separately represented as a pair of hex digits. If you want to see a character representation (with escape sequences for non-printing characters), use the **rawToChar() **function.

The “**Hello**” is stored as 48 65 6c 6c 6f as raw values. Let’s see with examples.

```
raw <- raw(8)
raw
class(raw)
raw_rv <- charToRaw("Database")
raw_rv
class(raw_rv)
```

**Output**

```
[1] 00 00 00 00 00 00 00 00
[1] "raw"
[1] 44 61 74 61 62 61 73 65
[1] "raw"
```

To convert raw to a character in R, use the rawToChar() function.

```
raw_rv <- charToRaw("Database")
raw_rv
class(raw_rv)
char_rv <- rawToChar(raw_rv)
char_rv
class(char_rv)
```

**Output**

```
[1] 44 61 74 61 62 61 73 65
[1] "raw"
[1] "Database"
[1] "character"
```

The **as.raw()** function tries to curb its argument to be of raw type. The answer will be 0 unless the coercion succeeds.

The **is.raw()** function returns **TRUE** if and only if **typeof(x) == “raw”**.

**List in R**

A list is an R-object containing different types of elements inside it like vectors, functions, and even another list inside it. A list is an ordered collection of elements under one name.

```
r_list <- list(c(11, 19, 21), 19.3, cos, TRUE)
r_list
```

**Output**

```
[[1]]
[1] 11 19 21
[[2]]
[1] 19.3
[[3]]
function (x) .Primitive("cos")
[[4]]
[1] TRUE
```

To access list elements one by one, use the [[ ]] brackets. Let’s access the third element of the list. Remember, the list index starts at 1 and not 0.

```
r_list <- list(c(11, 19, 21), 19.3, cos, TRUE)
r_list[[3]]
```

**Output**

`function (x) .Primitive("cos")`

**Factor in R**

The factor stores the nominal values as a vector of integers in the specific range and an internal vector of character strings mapped to these integers.

```
rv <- c(rep("adam", 3), rep("eve", 4))
r_factor <- factor(rv)
r_factor
```

**Output**

```
[1] adam adam adam eve eve eve eve
Levels: adam eve
```

R treat factors as nominal variables and ordered factors as ordinal variables in statistical procedures and graphical analyses.

**Matrix in R**

A matrix is a two-dimensional rectangular data structure that can be created using a vector input to the matrix function. The as.matrix() function attempts to turn its argument into the matrix. The is.matrix() function tests if its argument is a (strict) matrix.

```
mtrx <- matrix(c("a", "b", "c", "d"), nrow = 2, ncol = 2, byrow = TRUE)
mtrx
```

**Output**

```
[,1] [,2]
[1,] "a" "b"
[2,] "c" "d"
```

**Array in R**

Array in R is a **list** or **vector **with two or more dimensions. An array is like a stacked matrix, and a matrix is a two-dimensional array.

```
rv <- c(11, 19, 18)
rv2 <- c(21, 6, 29, 46, 37, 38)
result <- array(c(rv, rv2), dim = c(3, 3, 2))
print(result)
```

**Output**

```
, , 1
[,1] [,2] [,3]
[1,] 11 21 46
[2,] 19 6 37
[3,] 18 29 38
, , 2
[,1] [,2] [,3]
[1,] 11 21 46
[2,] 19 6 37
[3,] 18 29 38
```

**Data Frame in R**

Data Frame in R is a table or two-dimensional array-like structure in which a row contains a set of values, and each column holds values of one variable.

```
streaming <- data.frame(
service_id = c(1:5),
service_name = c("Netflix", "Disney+", "HBOMAX", "Hulu", "Peacock"),
service_price = c(18, 10, 15, 7, 12),
stringsAsFactors = FALSE
)
# Print the data frame.
print(streaming)
```

**Output**

```
service_id service_name service_price
1 1 Netflix 18
2 2 Disney+ 10
3 3 HBOMAX 15
4 4 Hulu 7
5 5 Peacock 12
```

Unlike a matrix, in a data frame, each column can contain different modes of data. Data Frames are created using the **data.frame()** function.

**Useful inbuilt R Functions**

Inbuilt functions are also the data types in R.

```
length(object)
str(object)
class(object)
names(object)
c(object,object,...)
cbind(object, object, ...)
rbind(object, object, ...)
```

That is it for the R Data Types example.

Krunal Lathiya is a Software Engineer with over eight years of experience. He has developed a strong foundation in computer science principles and a passion for problem-solving. In addition, Krunal has excellent knowledge of Data Science and Machine Learning, and he is an expert in R Language.