In this tutorial, you will learn:

- What are the Data Types in R?
- Variables
- Vectors
- Arithmetic Operators
- Logical Operators

## 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
- 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, especially for a data scientist. 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

## 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 value 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

## 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 an 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

## 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

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