---
description: In this Operators in R tutorial, you will learn R Arithmetic Operators, R Logical Operators, and R basic data types are character, numeric, integer, complex, and logical.
title: Data Types in R with Example
image: https://www.guru99.com/images/r_programming/032918_1328_RDataTypes1.png
---

 

[Skip to content](#main) 

## What are the Data Types in R?

Following are the Data Types or Data Structures in R Programming:

* Scalars
* Vectors (numerical, character, logical)
* Matrices
* [Data frames](https://www.guru99.com/r-data-frames.html)
* Lists

### Basics types

* 4.5 is a decimal value called **numerics**.
* 4 is a natural value called **integers**. Integers are also numerics.
* TRUE or FALSE is a Boolean value called **logical** binary operators in R.
* The value inside ” ” or ‘ ‘ are text (string). They are called **characters**.

We can check the type of a variable with the class function

### Example 1

# Declare variables of different types
# Numeric
x <- 28
class(x)

Output:

## [1] "numeric"

### Example 2

# String
y <- "R is Fantastic"
class(y)

Output:

## [1] "character"

### Example 3

# Boolean
z <- TRUE
class(z)

Output:

## [1] "logical"

## Variables

Variables are one of the basic data types in R that store values and are an important component in [R programming](https://www.guru99.com/r-programming-introduction-basics.html), especially for a [data scientist](https://www.guru99.com/data-science-tutorial.html). A variable in R data types can store a number, an object, a statistical result, vector, dataset, a model prediction basically anything R outputs. We can use that variable later simply by calling the name of the variable.

To declare variable data structures in R, we need to assign a variable name. The name should not have space. We can use \_ to connect to words.

To add a value to the variable in data types in R programming, use <- or =.

Here is the syntax:

# First way to declare a variable:  use the `<-`
name_of_variable <- value
# Second way to declare a variable:  use the `=`
name_of_variable = value

In the command line, we can write the following codes to see what happens:

### Example 1

# Print variable x
x <- 42
x

Output:

## [1] 42

### Example 2

y  <- 10
y

Output:

## [1] 10

### Example 3

# We call x and y and apply a subtraction
x-y

Output:

## [1] 32

### RELATED ARTICLES

* [R Data Frame: How to Create, Append, Select & Subset ](https://www.guru99.com/r-data-frames.html "R Data Frame: How to Create, Append, Select & Subset")
* [Import Data in R: Read CSV, Excel, SPSS, Stata, SAS Files ](https://www.guru99.com/r-import-data.html "Import Data in R: Read CSV, Excel, SPSS, Stata, SAS Files")
* [R Stepwise & Multiple Linear Regression \[Step by Step Example\] ](https://www.guru99.com/r-simple-multiple-linear-regression.html "R Stepwise & Multiple Linear Regression [Step by Step Example]")
* [Histogram vs Bar Graph – Difference Between Them ](https://www.guru99.com/histogram-vs-bar-chart.html "Histogram vs Bar Graph – Difference Between Them")

## Vectors

A vector is a one-dimensional array. We can create a vector with all the basic R data types we learnt before. The simplest way to build vector data structures in R, is to use the c command.

### Example 1

# Numerical
vec_num <- c(1, 10, 49)
vec_num

Output:

## [1]  1 10 49

### Example 2

# Character 
vec_chr <- c("a", "b", "c")
vec_chr

Output:

## [1] "a" "b" "c"

### Example 3

# Boolean 
vec_bool <-  c(TRUE, FALSE, TRUE)
vec_bool

Output:

##[1] TRUE FALSE TRUE

We can do arithmetic calculations on vector binary operators in R.

### Example 4

# Create the vectors
vect_1 <- c(1, 3, 5)
vect_2 <- c(2, 4, 6)
# Take the sum of A_vector and B_vector
sum_vect <- vect_1 + vect_2
# Print out total_vector
sum_vect

Output:

[1]  3  7 11

### Example 5

In R, it is possible to slice a vector. In some occasion, we are interested in only the first five rows of a vector. We can use the \[1:5\] command to extract the value 1 to 5.

# Slice the first five rows of the vector
slice_vector <- c(1,2,3,4,5,6,7,8,9,10)
slice_vector[1:5]

Output:

## [1] 1 2 3 4 5

### Example 6

The shortest way to create a range of values is to use the: between two numbers. For instance, from the above example, we can write c(1:10) to create a vector of value from one to ten.

# Faster way to create adjacent values
c(1:10)

Output:

## [1]  1  2  3  4  5  6  7  8  9 10

## R Arithmetic Operators

We will first see the basic arithmetic operators in R data types. Following are the arithmetic and boolean operators in R programming which stand for:

| Operator  | Description    |
| --------- | -------------- |
| +         | Addition       |
| –         | Subtraction    |
| \*        | Multiplication |
| /         | Division       |
| ^ or \*\* | Exponentiation |

### Example 1

# An addition
3 + 4

Output:

## [1] 7

You can easily copy and paste the above R code into Rstudio Console. The **output** is displayed after the character #. For instance, we write the code print(‘Guru99’) the output will be ##\[1\] Guru99.

The ## means we print output and the number in the square bracket (\[1\]) is the number of the display

The sentences starting with # **annotation**. We can use # inside an R script to add any comment we want. R won’t read it during the running time.

### Example 2

# A multiplication
3*5

Output:

## [1] 15

### Example 3

# A division
(5+5)/2

Output:

## [1] 5

### Example 4

# Exponentiation
2^5

Output:

### Example 5

## [1] 32

# Modulo
28%%6

Output:

## [1] 4

## R Logical Operators

With logical operators, we want to return values inside the vector based on logical conditions. Following is a detailed list of logical operators of data types in R programming

[](https://www.guru99.com/images/r%5Fprogramming/032918%5F1328%5FRDataTypes1.png)

Logical Operators in R

The logical statements in R are wrapped inside the \[\]. We can add as many conditional statements as we like but we need to include them in a parenthesis. We can follow this structure to create a conditional statement:

variable_name[(conditional_statement)]

With variable\_name referring to the variable, we want to use for the statement. We create the logical statement i.e. variable\_name > 0\. Finally, we use the square bracket to finalize the logical statement. Below, an example of a logical statement.

### Example 1

# Create a vector from 1 to 10
logical_vector <- c(1:10)
logical_vector>5

Output:

## [1]FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE

In the output above, R reads each value and compares it to the statement logical\_vector>5\. If the value is strictly superior to five, then the condition is TRUE, otherwise FALSE. R returns a vector of TRUE and FALSE.

### Example 2

In the example below, we want to extract the values that only meet the condition ‘is strictly superior to five’. For that, we can wrap the condition inside a square bracket precede by the vector containing the values.

# Print value strictly above 5
logical_vector[(logical_vector>5)]

Output:

## [1]  6  7  8  9 10

### Example 3

# Print 5 and 6
logical_vector <- c(1:10)
logical_vector[(logical_vector>4) & (logical_vector<7)]

Output:

## [1] 5 6

#### Summarize this post with:

ChatGPT Perplexity Grok Google AI 

**Stay Updated on AI** **Get Weekly AI Skills, Trends, Actionable Advice.** 

##### Sign up for the newsletter

Subscribe for Free 

You have successfully subscribed.  
Please check your inbox. 

![AI-Newsletter]() Chosen by over **350,000+** professionals 

[Scroll to top ](#wrapper)Scroll to top 

× 

Toggle Menu Close 

Search for: 

Search

```json
{"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://www.guru99.com/#organization","name":"Guru99","sameAs":["https://www.facebook.com/Guru99Official","https://twitter.com/guru99com"],"logo":{"@type":"ImageObject","@id":"https://www.guru99.com/#logo","url":"https://www.guru99.com/images/guru99-logo-v1-150x59.png","contentUrl":"https://www.guru99.com/images/guru99-logo-v1-150x59.png","caption":"Guru99","inLanguage":"en-US"}},{"@type":"WebSite","@id":"https://www.guru99.com/#website","url":"https://www.guru99.com","name":"Guru99","publisher":{"@id":"https://www.guru99.com/#organization"},"inLanguage":"en-US"},{"@type":"ImageObject","@id":"https://www.guru99.com/images/r-data-types-operator.png","url":"https://www.guru99.com/images/r-data-types-operator.png","width":"249","height":"150","inLanguage":"en-US"},{"@type":"BreadcrumbList","@id":"https://www.guru99.com/r-data-types-operator.html#breadcrumb","itemListElement":[{"@type":"ListItem","position":"1","item":{"@id":"https://www.guru99.com","name":"Home"}},{"@type":"ListItem","position":"2","item":{"@id":"https://www.guru99.com/r-programming","name":"R Programming"}},{"@type":"ListItem","position":"3","item":{"@id":"https://www.guru99.com/r-data-types-operator.html","name":"Data Types in R with Example"}}]},{"@type":"WebPage","@id":"https://www.guru99.com/r-data-types-operator.html#webpage","url":"https://www.guru99.com/r-data-types-operator.html","name":"Data Types in R with Example","dateModified":"2024-06-12T19:11:40+05:30","isPartOf":{"@id":"https://www.guru99.com/#website"},"primaryImageOfPage":{"@id":"https://www.guru99.com/images/r-data-types-operator.png"},"inLanguage":"en-US","breadcrumb":{"@id":"https://www.guru99.com/r-data-types-operator.html#breadcrumb"}},{"@type":"Person","@id":"https://www.guru99.com/author/evelyn","name":"Evelyn Clarke","description":"I'm Evelyn Clarke, an AI Research Scientist specializing in machine learning and natural language processing, dedicated to advancing the field responsibly.","url":"https://www.guru99.com/author/evelyn","image":{"@type":"ImageObject","@id":"https://www.guru99.com/images/evelyn-clarke-author-120x120.png","url":"https://www.guru99.com/images/evelyn-clarke-author-120x120.png","caption":"Evelyn Clarke","inLanguage":"en-US"},"worksFor":{"@id":"https://www.guru99.com/#organization"}},{"@type":"NewsArticle","headline":"Data Types in R with Example","keywords":"R, Programming","dateModified":"2024-06-12T19:11:40+05:30","articleSection":"R Programming","author":{"@id":"https://www.guru99.com/author/evelyn","name":"Evelyn Clarke"},"publisher":{"@id":"https://www.guru99.com/#organization"},"description":"In this Operators in R tutorial, you will learn R Arithmetic Operators, R Logical Operators, and R basic data types are character, numeric, integer, complex, and logical.","copyrightYear":"2024","copyrightHolder":{"@id":"https://www.guru99.com/#organization"},"name":"Data Types in R with Example","@id":"https://www.guru99.com/r-data-types-operator.html#richSnippet","isPartOf":{"@id":"https://www.guru99.com/r-data-types-operator.html#webpage"},"image":{"@id":"https://www.guru99.com/images/r-data-types-operator.png"},"inLanguage":"en-US","mainEntityOfPage":{"@id":"https://www.guru99.com/r-data-types-operator.html#webpage"}}]}
```