A variable provides us with named storage that our programs can manipulate. A variable in R can store an atomic vector, group of atomic vectors or a combination of many R objects. A valid variable name consists of letters, numbers and the dot or underline characters. The variable name starts with a letter or the dot not followed by a number.

Variable Name	Validity	Reason
var_name2.	valid	Has letters, numbers, dot and underscore
var_name%	Invalid	Has the character ‘%’. Only dot(.) and underscore allowed.
2var_name	invalid	Starts with a number
.var_name, var.name	valid	Can start with a dot(.) but the dot(.)should not be followed by a number.
.2var_name	invalid	The starting dot is followed by a number making it invalid.
_var_name	invalid	Starts with _ which is not valid

Variable Assignment

The variables can be assigned values using leftward, rightward and equal to operator. The values of the variables can be printed using print() or cat() function. The cat() function combines multiple items into a continuous print output.

# Assignment using equal operator.
var.1 = c(0,1,2,3)

# Assignment using leftward operator.
var.2 <- c("learn","R")

# Assignment using rightward operator. 
c(TRUE,1) -> var.3

print(var.1)
cat ("var.1 is ", var.1 ,"\n")
cat ("var.2 is ", var.2 ,"\n")
cat ("var.3 is ", var.3 ,"\n")
When we execute the above code, it produces the following result −
[1] 0 1 2 3
var.1 is 0 1 2 3 
var.2 is learn R 
var.3 is 1 1

Note − The vector c(TRUE,1) has a mix of logical and numeric class. So logical class is coerced to numeric class making TRUE as 1.

Data Type of a Variable

In R, a variable itself is not declared of any data type, rather it gets the data type of the R – object assigned to it. So R is called a dynamically typed language, which means that we can change a variable’s data type of the same variable again and again when using it in a program.

var_x <- "Hello"
cat("The class of var_x is ",class(var_x),"\n")

var_x <- 34.5
cat(" Now the class of var_x is ",class(var_x),"\n")

var_x <- 27L
cat(" Next the class of var_x becomes ",class(var_x),"\n")

When we execute the above code, it produces the following result −
The class of var_x is character
Now the class of var_x is numeric
Next the class of var_x becomes integer

Finding Variables

To know all the variables currently available in the workspace we use the ls() function. Also the ls() function can use patterns to match the variable names.

print(ls())
When we execute the above code, it produces the following result −
[1] “my var” “my_new_var” “my_var” “var.1”
[5] “var.2” “var.3” “var.name” “var_name2.”
[9] “var_x” “varname”
Note − It is a sample output depending on what variables are declared in your environment.
The ls() function can use patterns to match the variable names.

# List the variables starting with the pattern “var”.
print(ls(pattern = “var”))
When we execute the above code, it produces the following result −
[1] “my var” “my_new_var” “my_var” “var.1”
[5] “var.2” “var.3” “var.name” “var_name2.”
[9] “var_x” “varname”
The variables starting with dot(.) are hidden, they can be listed using “all.names = TRUE” argument to ls() function.

print(ls(all.name = TRUE))
When we execute the above code, it produces the following result −
[1] “.cars” “.Random.seed” “.var_name” “.varname” “.varname2”
[6] “my var” “my_new_var” “my_var” “var.1” “var.2”
[11]”var.3″ “var.name” “var_name2.” “var_x”

Deleting Variables

Variables can be deleted by using the rm() function. Below we delete the variable var.3. On printing the value of the variable error is thrown.

rm(var.3)
print(var.3)
When we execute the above code, it produces the following result −
[1] “var.3”
Error in print(var.3) : object ‘var.3’ not found
All the variables can be deleted by using the rm() and ls() function together.

rm(list = ls())
print(ls())
When we execute the above code, it produces the following result −
character(0)

R – Operators

An operator is a symbol that tells the compiler to perform specific mathematical or logical manipulations. R language is rich in built-in operators and provides following types of operators.

Types of Operators

We have the following types of operators in R programming −
• Arithmetic Operators
• Relational Operators
• Logical Operators
• Assignment Operators
• Miscellaneous Operators

Arithmetic Operators

Following table shows the arithmetic operators supported by R language. The operators act on each element of the vector.

Operator	Description	Example
+	Adds two vectors	v <- c( 2,5.5,6) t <- c(8, 3, 4) print(v+t) it produces the following result − [1] 10.0 8.5 10.0
−	Subtracts second vector from the first	v <- c( 2,5.5,6) t <- c(8, 3, 4) print(v-t) it produces the following result − [1] -6.0 2.5 2.0
*	Multiplies both vectors	v <- c( 2,5.5,6) t <- c(8, 3, 4) print(v*t) it produces the following result − [1] 16.0 16.5 24.0
/	Divide the first vector with the second	v <- c( 2,5.5,6) t <- c(8, 3, 4) print(v/t) When we execute the above code, it produces the following result − [1] 0.250000 1.833333 1.500000
%%	Give the remainder of the first vector with the second	v <- c( 2,5.5,6) t <- c(8, 3, 4) print(v%%t) it produces the following result − [1] 2.0 2.5 2.0
%/%	The result of division of first vector with second (quotient)	v <- c( 2,5.5,6) t <- c(8, 3, 4) print(v%/%t) it produces the following result − [1] 0 1 1
^	The first vector raised to the exponent of second vector	v <- c( 2,5.5,6) t <- c(8, 3, 4) print(v^t) it produces the following result − [1] 256.000 166.375 1296.000

Relational Operators

Following table shows the relational operators supported by R language. Each element of the first vector is compared with the corresponding element of the second vector. The result of comparison is a Boolean value.

Operator	Description	Example
>	Checks if each element of the first vector is greater than the corresponding element of the second vector.	v <- c(2,5.5,6,9) t <- c(8,2.5,14,9) print(v>t) it produces the following result − [1] FALSE TRUE FALSE FALSE
<	Checks if each element of the first vector is less than the corresponding element of the second vector.	v <- c(2,5.5,6,9) t <- c(8,2.5,14,9) print(v < t) it produces the following result − [1] TRUE FALSE TRUE FALSE
==	Checks if each element of the first vector is equal to the corresponding element of the second vector.	v <- c(2,5.5,6,9) t <- c(8,2.5,14,9) print(v == t) it produces the following result − [1] FALSE FALSE FALSE TRUE
<=	Checks if each element of the first vector is less than or equal to the corresponding element of the second vector.	v <- c(2,5.5,6,9) t <- c(8,2.5,14,9) print(v<=t) it produces the following result − [1] TRUE FALSE TRUE TRUE
>=	Checks if each element of the first vector is greater than or equal to the corresponding element of the second vector.	v <- c(2,5.5,6,9) t <- c(8,2.5,14,9) print(v>=t) it produces the following result − [1] FALSE TRUE FALSE TRUE
!=	Checks if each element of the first vector is unequal to the corresponding element of the second vector.	v <- c(2,5.5,6,9) t <- c(8,2.5,14,9) print(v!=t) it produces the following result − [1] TRUE TRUE TRUE FALSE

Logical Operators

Following table shows the logical operators supported by R language. It is applicable only to vectors of type logical, numeric or complex. All numbers greater than 1 are considered as logical value TRUE.
Each element of the first vector is compared with the corresponding element of the second vector. The result of comparison is a Boolean value.

Operator	Description	Example
&	It is called Element-wise Logical AND operator. It combines each element of the first vector with the corresponding element of the second vector and gives a output TRUE if both the elements are TRUE.	v <- c(3,1,TRUE,2+3i) t <- c(4,1,FALSE,2+3i) print(v&t) it produces the following result − [1] TRUE TRUE FALSE TRUE
\|	It is called Element-wise Logical OR operator. It combines each element of the first vector with the corresponding element of the second vector and gives a output TRUE if one the elements is TRUE.	v <- c(3,0,TRUE,2+2i) t <- c(4,0,FALSE,2+3i) print(v\|t) it produces the following result − [1] TRUE FALSE TRUE TRUE
!	It is called Logical NOT operator. Takes each element of the vector and gives the opposite logical value.	v <- c(3,0,TRUE,2+2i) print(!v) it produces the following result − [1] FALSE TRUE FALSE FALSE

The logical operator && and || considers only the first element of the vectors and give a vector of single element as output.

Operator

Description

Example

Called Logical AND operator. Takes first element of both the vectors and gives the TRUE only if both are TRUE.

v <- c(3,0,TRUE,2+2i)

t <- c(1,3,TRUE,2+3i)

print(v&&t)

it produces the following result −

[1] TRUE

Called Logical OR operator. Takes first element of both the vectors and gives the TRUE if one of them is TRUE.

v <- c(0,0,TRUE,2+2i)

t <- c(0,3,TRUE,2+3i)

print(v||t)

it produces the following result −

[1] FALSE

Assignment Operators

These operators are used to assign values to vectors.

Operator

Description

Example

<−

<<−

Called Left Assignment

v1 <- c(3,1,TRUE,2+3i)

v2 <<- c(3,1,TRUE,2+3i)

v3 = c(3,1,TRUE,2+3i)

print(v1)

print(v2)

print(v3)

it produces the following result −

[1] 3+0i 1+0i 1+0i 2+3i

->>

Called Right Assignment

c(3,1,TRUE,2+3i) -> v1

c(3,1,TRUE,2+3i) ->> v2

print(v1)

print(v2)

it produces the following result −

[1] 3+0i 1+0i 1+0i 2+3i

Miscellaneous Operators

These operators are used to for specific purpose and not general mathematical or logical computation.

Operator	Description	Example
:	Colon operator. It creates the series of numbers in sequence for a vector.	v <- 2:8 print(v) it produces the following result − [1] 2 3 4 5 6 7 8
%in%	This operator is used to identify if an element belongs to a vector.	v1 <- 8 v2 <- 12 t <- 1:10 print(v1 %in% t) print(v2 %in% t) it produces the following result − [1] TRUE [1] FALSE
%*%	This operator is used to multiply a matrix with its transpose.	M = matrix( c(2,6,5,1,10,4), nrow = 2,ncol = 3,byrow = TRUE) t = M %*% t(M) print(t) it produces the following result − [,1] [,2] [1,] 65 82 [2,] 82 117

So, this brings us to the end of blog. This Tecklearn ‘Operators and Variables in R Language’ blog helps you with commonly asked questions if you are looking out for a job in Data Science. If you wish to learn R Language and build a career in Data Science domain, then check out our interactive, Data Science using R Language Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

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