assignment operators in r

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Assignment Operators in R (3 Examples) | Comparing = vs. ＜- vs. ＜＜-

On this page you’ll learn how to apply the different assignment operators in the R programming language .

The content of the article is structured as follows:

Let’s dive right into the exemplifying R syntax!

Example 1: Why You Should Use ＜- Instead of = in R

Generally speaking, there is a preference in the R programming community to use an arrow (i.e. ＜-) instead of an equal sign (i.e. =) for assignment.

In my opinion, it makes a lot of sense to stick to this convention to produce scripts that are easy to read for other R programmers.

However, you should also take care about the spacing when assigning in R. False spacing can even lead to error messages .

For instance, the following R code checks whether x is smaller than minus five due to the false blank between ＜ and -:

A properly working assignment could look as follows:

However, this code is hard to read, since the missing space makes it difficult to differentiate between the different symbols and numbers.

In my opinion, the best way to assign in R is to put a blank before and after the assignment arrow:

As mentioned before, the difference between ＜- and = is mainly due to programming style . However, the following R code using an equal sign would also work:

In the following example, I’ll show a situation where ＜- and = do not lead to the same result. So keep on reading!

Example 2: When ＜- is Really Different Compared to =

In this Example, I’ll illustrate some substantial differences between assignment arrows and equal signs.

Let’s assume that we want to compute the mean of a vector ranging from 1 to 5. Then, we could use the following R code:

However, if we want to have a look at the vector x that we have used within the mean function, we get an error message:

Let’s compare this to exactly the same R code but with assignment arrow instead of an equal sign:

The output of the mean function is the same. However, the assignment arrow also stored the values in a new data object x:

This example shows a meaningful difference between = and ＜-. While the equal sign doesn’t store the used values outside of a function, the assignment arrow saves them in a new data object that can be used outside the function.

Example 3: The Difference Between ＜- and ＜＜-

So far, we have only compared ＜- and =. However, there is another assignment method we have to discuss: The double assignment arrow ＜＜- (also called scoping assignment).

The following code illustrates the difference between ＜- and ＜＜- in R. This difference mainly gets visible when applying user-defined functions .

Let’s manually create a function that contains a single assignment arrow:

Now, let’s apply this function in R:

The data object x_fun1, to which we have assigned the value 5 within the function, does not exist:

Let’s do the same with a double assignment arrow:

Let’s apply the function:

And now let’s return the data object x_fun2:

As you can see based on the previous output of the RStudio console, the assignment via ＜＜- saved the data object in the global environment outside of the user-defined function.

Video & Further Resources

I have recently released a video on my YouTube channel , which explains the R syntax of this tutorial. You can find the video below:

The YouTube video will be added soon.

In addition to the video, I can recommend to have a look at the other articles on this website.

• R Programming Examples

In summary: You learned on this page how to use assignment operators in the R programming language. If you have further questions, please let me know in the comments.

assignment-operators-in-r How to use different assignment operators in R – 3 R programming examples – R programming language tutorial – Actionable R programming syntax in RStudio

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In this article, you will learn about different R operators with the help of examples.

R has many operators to carry out different mathematical and logical operations. Operators perform tasks including arithmetic, logical and bitwise operations.

• Type of operators in R

Operators in R can mainly be classified into the following categories:

• Arithmetic Operators
• Relational Operators
• Logical Operators
• Assignment Operators
• R Arithmetic Operators

These operators are used to carry out mathematical operations like addition and multiplication. Here is a list of arithmetic operators available in R.

Let's look at an example illustrating the use of the above operators:

• R Relational Operators

Relational operators are used to compare between values. Here is a list of relational operators available in R.

Let's see an example for this:

• Operation on Vectors

The above mentioned operators work on vectors . The variables used above were in fact single element vectors.

We can use the function c() (as in concatenate) to make vectors in R.

All operations are carried out in element-wise fashion. Here is an example.

When there is a mismatch in length (number of elements) of operand vectors, the elements in the shorter one are recycled in a cyclic manner to match the length of the longer one.

R will issue a warning if the length of the longer vector is not an integral multiple of the shorter vector.

• R Logical Operators

Logical operators are used to carry out Boolean operations like AND , OR etc.

Operators & and | perform element-wise operation producing result having length of the longer operand.

But && and || examines only the first element of the operands resulting in a single length logical vector.

Zero is considered FALSE and non-zero numbers are taken as TRUE . Let's see an example for this:

• R Assignment Operators

These operators are used to assign values to variables.

The operators <- and = can be used, almost interchangeably, to assign to variables in the same environment.

The <<- operator is used for assigning to variables in the parent environments (more like global assignments). The rightward assignments, although available, are rarely used.

Check out these examples to learn more:

• Add Two Vectors
• Take Input From User
• R Multiplication Table

Table of Contents

• Introduction

Sorry about that.

R Tutorials

Programming

Assignment Operators

Description.

Assign a value to a name.

There are three different assignment operators: two of them have leftwards and rightwards forms.

The operators <- and = assign into the environment in which they are evaluated. The operator <- can be used anywhere, whereas the operator = is only allowed at the top level (e.g., in the complete expression typed at the command prompt) or as one of the subexpressions in a braced list of expressions.

The operators <<- and ->> are normally only used in functions, and cause a search to be made through parent environments for an existing definition of the variable being assigned. If such a variable is found (and its binding is not locked) then its value is redefined, otherwise assignment takes place in the global environment. Note that their semantics differ from that in the S language, but are useful in conjunction with the scoping rules of R . See ‘The R Language Definition’ manual for further details and examples.

In all the assignment operator expressions, x can be a name or an expression defining a part of an object to be replaced (e.g., z[[1]] ). A syntactic name does not need to be quoted, though it can be (preferably by backtick s).

The leftwards forms of assignment <- = <<- group right to left, the other from left to right.

value . Thus one can use a <- b <- c <- 6 .

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language . Wadsworth & Brooks/Cole.

Chambers, J. M. (1998) Programming with Data. A Guide to the S Language . Springer (for = ).

assign (and its inverse get ), for “subassignment” such as x[i] <- v , see [<- ; further, environment .

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R operators

There are several operators in R, such that arithmetic operators for math calculations, logical, relational or assignment operators or even the popular pipe operator. In this tutorial we will show you the R operators divided into operator types. In addition, we will show examples of use of every operator.

Arithmetic operators

The R arithmetic operators allows us to do math operations , like sums, divisions or multiplications, among others. The following table summarizes all base R arithmetic operators.

In the next block of code you will find examples of basic calculations with arithmetic operations with integers.

You can also use the basic operations with R vectors of the same length . Note that the result of these operations will be a vector with element-wise operation results.

Furthermore, you can use those arithmetic operators with matrix objects, besides the ones designed for this type of object (matrix multiplication types). Check our tutorial about matrix operations to learn more.

Logical / boolean operators

In addition, boolean or logical operators in R are used to specify multiple conditions between objects. These comparisons return TRUE and FALSE values.

Relational / comparison operators in R

Comparison or relational operators are designed to compare objects and the output of these comparisons are of type boolean. To clarify, the following table summarizes the R relational operators.

For example, you can compare integer values with these operators as follows.

If you compare vectors the output will be other vector of the same length and each element will contain the boolean corresponding to the comparison of the corresponding elements (the first element of the first vector with the first element of the second vector and so on). Moreover, you can compare each element of a matrix against other.

Assignment operators in R

The assignment operators in R allows you to assign data to a named object in order to store the data .

Note that in almost scripting programming languages you can just use the equal (=) operator. However, in R it is recommended to use the arrow assignment ( <- ) and use the equal sign only to set arguments.

The arrow assignment can be used as left or right assignment, but the right assignment is not generally used. In addition, you can use the double arrow assignment, known as scoping assignment, but we won’t enter in more detail in this tutorial, as it is for advanced users. You can know more about this assignment operator in our post about functions in R .

In the following code block you will find some examples of these operators.

If you need to use the right assignment remember that the object you want to store needs to be at the left, or an error will arise.

There are some rules when naming variables. For instance, you can use letters, numbers, dots and underscores in the variable name, but underscores can’t be the first character of the variable name.

Reserved words

There are also reserved words you can’t use, like TRUE , FALSE , NULL , among others. You can see the full list of R reserved words typing help(Reserved) or ?Reserved .

However, if for some reason you need to name your variable with a reserved word or starting with an underscore you will need to use backticks:

Miscellaneous R operators

Miscellaneous operators in R are operators used for specific purposes , as accessing data, functions, creating sequences or specifying a formula of a model. To clarify, the next table contains all the available miscellaneous operators in R.

In addition, in the following block of code we show several examples of these operators:

Infix operator

You can call an operator as a function . This is known as infix operators. Note that this type of operators are not generally used or needed.

Pipe operator in R

The pipe operator is an operator you can find in several libraries, like dplyr . The operator can be read as ‘AND THEN’ and its purpose is to simplify the syntax when writing R code. As an example, you could subset the cars dataset and then create a summary of the subset with the following code:

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assignOps: Assignment Operators

Assignment operators, description.

Assign a value to a name.

There are three different assignment operators: two of them have leftwards and rightwards forms.

The operators <- and = assign into the environment in which they are evaluated. The operator <- can be used anywhere, whereas the operator = is only allowed at the top level (e.g., in the complete expression typed at the command prompt) or as one of the subexpressions in a braced list of expressions.

The operators <<- and ->> are normally only used in functions, and cause a search to be made through parent environments for an existing definition of the variable being assigned. If such a variable is found (and its binding is not locked) then its value is redefined, otherwise assignment takes place in the global environment. Note that their semantics differ from that in the S language, but are useful in conjunction with the scoping rules of R . See ‘The R Language Definition’ manual for further details and examples.

In all the assignment operator expressions, x can be a name or an expression defining a part of an object to be replaced (e.g., z[[1]] ). A syntactic name does not need to be quoted, though it can be (preferably by backticks).

The leftwards forms of assignment <- = <<- group right to left, the other from left to right.

value . Thus one can use a <- b <- c <- 6 .

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language . Wadsworth & Brooks/Cole.

Chambers, J. M. (1998) Programming with Data. A Guide to the S Language . Springer (for = ).

assign (and its inverse get ), for “subassignment” such as x[i] <- v , see [<- ; further, environment .

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Assignment Operators

Description.

Assign a value to a name.

There are three different assignment operators: two of them have leftwards and rightwards forms.

The operators <- and = assign into the environment in which they are evaluated. The operator <- can be used anywhere, whereas the operator = is only allowed at the top level (e.g., in the complete expression typed at the command prompt) or as one of the subexpressions in a braced list of expressions.

The operators <<- and ->> cause a search to made through the environment for an existing definition of the variable being assigned. If such a variable is found then its value is redefined, otherwise assignment takes place globally. Note that their semantics differ from that in the S language, but are useful in conjunction with the scoping rules of R . See ‘The R Language Definition’ manual for further details and examples.

In all the assignment operator expressions, x can be a name or an expression defining a part of an object to be replaced (e.g., z[[1]] ). The name does not need to be quoted, though it can be.

The leftwards forms of assignment <- = <<- group right to left, the other from left to right.

value . Thus one can use a <- b <- c <- 6 .

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language . Wadsworth & Brooks/Cole.

Chamber, J. M. (1998) Programming with Data. A Guide to the S Language . Springer (for = ).

assign , environment .

UC Business Analytics R Programming Guide

Assignment & evaluation.

The first operator you’ll run into is the assignment operator. The assignment operator is used to assign a value. For instance we can assign the value 3 to the variable x using the <- assignment operator. We can then evaluate the variable by simply typing x at the command line which will return the value of x . Note that prior to the value returned you’ll see ## [1] in the command line. This simply implies that the output returned is the first output. Note that you can type any comments in your code by preceding the comment with the hashtag ( # ) symbol. Any values, symbols, and texts following # will not be evaluated.

Interestingly, R actually allows for five assignment operators:

The original assignment operator in R was <- and has continued to be the preferred among R users. The = assignment operator was added in 2001 primarily because it is the accepted assignment operator in many other languages and beginners to R coming from other languages were so prone to use it. However, R uses = to associate function arguments with values (i.e. f(x = 3) explicitly means to call function f and set the argument x to 3. Consequently, most R programmers prefer to keep = reserved for argument association and use <- for assignment.

The operators <<- is normally only used in functions which we will not get into the details. And the rightward assignment operators perform the same as their leftward counterparts, they just assign the value in an opposite direction.

Overwhelmed yet? Don’t be. This is just meant to show you that there are options and you will likely come across them sooner or later. My suggestion is to stick with the tried and true <- operator. This is the most conventional assignment operator used and is what you will find in all the base R source code…which means it should be good enough for you.

Lastly, note that R is a case sensitive programming language. Meaning all variables, functions, and objects must be called by their exact spelling:

R Data Structures

R statistics, r operators.

Operators are used to perform operations on variables and values.

In the example below, we use the + operator to add together two values:

R divides the operators in the following groups:

• Arithmetic operators
• Assignment operators
• Comparison operators
• Logical operators
• Miscellaneous operators

R Arithmetic Operators

Arithmetic operators are used with numeric values to perform common mathematical operations:

R Assignment Operators

Assignment operators are used to assign values to variables:

Note: <<- is a global assigner. You will learn more about this in the Global Variable chapter .

It is also possible to turn the direction of the assignment operator.

x <- 3 is equal to 3 -> x

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R Comparison Operators

Comparison operators are used to compare two values:

R Logical Operators

Logical operators are used to combine conditional statements:

R Miscellaneous Operators

Miscellaneous operators are used to manipulate data:

Note: You will learn more about Matrix multiplication and matrices in a later chapter.

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Assignment Operators in R – Which One to Use and Where

Assignment Operators

R has five common assignment operators:

Many style guides and traditionalists prefer the left arrow operator, <- . Why use that when it’s an extra keystroke?  <- always means assignment. The equal sign is overloaded a bit taking on the roles of an assignment operator, function argument binding, or depending on the context, case statement.

Equal or “arrow” as an Assignment Operator?

In R, both the equal and arrow symbols work to assign values. Therefore, the following statements have the same effect of assigning a value on the right to the variable on the left:

There is also a right arrow, -> which assigns the value on the left, to a variable on the right:

All three assign the  value of forty-two to the  variable   x .

So what’s the difference? Are these assignment operators interchangeable? Mostly, yes. The difference comes into play, however, when working with functions.

The equal sign can also work as an operator for function parameters.

x <- 42 y <- 18 function(value = x-y)

History of the <- Operator

The S language also didn’t have == for equality testing, so that was left to the single equal sign. Therefore, variable assignment needed to be accomplished with a different symbol, and the arrow was chosen.

There are some differences of opinion as to which assignment operator to use when it comes to = vs <-. Some believe that = is more clear. The <- operator maintains backward compatibility with S.  Google’s R Style Guide recommends using the <- assignment operator, which seems to be a pretty decent reason as well. When all is said and done, though, it is like many things in programming, it depends on what your team does.

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R Operators – Arithmetic, Relational, Logical, Assignment

R Tutorial – We shall learn about R Operators – Arithmetic, Relational, Logical, Assignment and some of the Miscellaneous Operators that R programming language provides.

There are four main categories of Operators in R programming language. They are shown in the following picture :

We shall learn about these operators in detail with Example R programs.

R Arithmetic Operators

Arithmetic Operators are used to accomplish arithmetic operations. They can be operated on the basic data types Numericals, Integers, Complex Numbers. Vectors with these basic data types can also participate in arithmetic operations, during which the operation is performed on one to one element basis.

An example for each of the arithmetic operator on Numerical values is provided below.

r_op_arithmetic.R

An example for each of the arithmetic operator on Vectors is provided below.

r_op_arithmetic_vector.R

R Relational Operators

Relational Operators are those that find out relation between the two operands provided to them. Following are the six relational operations R programming language supports.The output is boolean (TRUE or FALSE) for all of the Relational Operators in R programming language.

An example for each of the relational operator on Numberical values is provided below.

r_op_relational.R

An example for each of the relational operator on Vectors is provided below.

r_op_relational_vector.R

R Logical Operators

Logical Operators in R programming language work only for the basic data types logical, numeric and complex and vectors of these basic data types.

An example for each of the logical operators on Numerical values is provided below.

r_op_logical.R 

An example for each of the logical operators on Vectors is provided below.

r_op_logical_vector.R

R Assignment Operators

Assignment Operators are those that help in assigning a value to the variable.

An example for each of the assignment operators is provided below.

r_op_assignment.R

R Miscellaneous Operators

These operators does not fall into any of the categories mentioned above, but are significantly important during R programming for manipulating data.

An example for each of the Miscellaneous operators is provided below.

r_op_misc.R

In this  R Tutorial , we have learnt about R Operators – R Arithmetic Operators, R Relational Operators, R Logical Operators, R Assignment Operators, R Miscellaneous Operators with example R commands and R script files.

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Introduction to R

Assignment operators.

You can assign values or functions to R objects using <- operator.

Assignment Operators in R

R provides two operators for assignment: <- and = .

Understanding their proper use is crucial for writing clear and readable R code.

Using the <- Operator

For assignments.

The <- operator is the preferred choice for assigning values to variables in R.

It clearly distinguishes assignment from argument specification in function calls.

Readability and Tradition

• This usage aligns with R’s tradition and enhances code readability.

Using the = Operator

The = operator is commonly used to explicitly specify named arguments in function calls.

It helps in distinguishing argument assignment from variable assignment.

Assignment Capability

• While = can also be used for assignment, this practice is less common and not recommended for clarity.

Mixing Up Operators

Potential confusion.

Using = for general assignments can lead to confusion, especially when reading or debugging code.

Mixing operators inconsistently can obscure the distinction between assignment and function argument specification.

• In the example above, x = 10 might be mistaken for a function argument rather than an assignment.

Best Practices Recap

Consistency and clarity.

Use <- for variable assignments to maintain consistency and clarity.

Reserve = for specifying named arguments in function calls.

Avoiding Common Mistakes

Be mindful of the context in which you use each operator to prevent misunderstandings.

Consistently using the operators as recommended helps make your code more readable and maintainable.

Quiz: Assignment Operator Best Practices

Which of the following examples demonstrates the recommended use of assignment operators in R?

• my_var = 5; mean(x = my_var)
• my_var <- 5; mean(x <- my_var)
• my_var <- 5; mean(x = my_var)
• my_var = 5; mean(x <- my_var)
• The correct answer is 3 . my_var <- 5; mean(x = my_var) correctly uses <- for variable assignment and = for specifying a named argument in a function call.

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• Lexical Scoping in R Programming

Input/Output

• Taking Input from User in R Programming
• Printing Output of an R Program
• Print the Argument to the Screen in R Programming - print() Function

Control Flow

• Control Statements in R Programming
• Decision Making in R Programming - if, if-else, if-else-if ladder, nested if-else, and switch
• Switch case in R
• For loop in R
• R - while loop
• R - Repeat loop
• goto statement in R Programming
• Break and Next statements in R
• Functions in R Programming
• Function Arguments in R Programming
• Types of Functions in R Programming
• Recursive Functions in R Programming
• Conversion Functions in R Programming

Data Structures

• Data Structures in R Programming
• R - Matrices
• R - Data Frames

Object Oriented Programming

• R - Object Oriented Programming
• Classes in R Programming
• R - Objects
• Encapsulation in R Programming
• Polymorphism in R Programming
• R - Inheritance
• Abstraction in R Programming
• Looping over Objects in R Programming
• S3 class in R Programming
• Explicit Coercion in R Programming

Error Handling

• Handling Errors in R Programming
• Condition Handling in R Programming
• Debugging in R Programming

File Handling

• File Handling in R Programming
• Reading Files in R Programming
• Writing to Files in R Programming
• Working with Binary Files in R Programming
• Packages in R Programming
• Data visualization with R and ggplot2
• dplyr Package in R Programming
• Grid and Lattice Packages in R Programming
• Shiny Package in R Programming
• tidyr Package in R Programming
• What Are the Tidyverse Packages in R Language?
• Data Munging in R Programming

Data Interfaces

• Data Handling in R Programming
• Importing Data in R Script
• Exporting Data from scripts in R Programming
• Working with CSV files in R Programming
• Working with XML Files in R Programming
• Working with Excel Files in R Programming
• Working with JSON Files in R Programming
• Working with Databases in R Programming
• Data Visualization in R
• R - Line Graphs
• R - Bar Charts
• Histograms in R language
• Scatter plots in R Language
• R - Pie Charts
• Boxplots in R Language
• R - Statistics
• Mean, Median and Mode in R Programming
• Calculate the Average, Variance and Standard Deviation in R Programming
• Descriptive Analysis in R Programming
• Normal Distribution in R
• Binomial Distribution in R Programming
• ANOVA (Analysis of Variance) Test in R Programming
• Covariance and Correlation in R Programming
• Skewness and Kurtosis in R Programming
• Hypothesis Testing in R Programming
• Bootstrapping in R Programming
• Time Series Analysis in R

Machine Learning

• Introduction to Machine Learning in R
• Setting up Environment for Machine Learning with R Programming
• Supervised and Unsupervised Learning in R Programming
• Regression and its Types in R Programming
• Classification in R Programming
• Naive Bayes Classifier in R Programming
• KNN Classifier in R Programming
• Clustering in R Programming
• Decision Tree in R Programming
• Random Forest Approach in R Programming
• Hierarchical Clustering in R Programming
• DBScan Clustering in R Programming
• Deep Learning in R Programming

Operators are the symbols directing the compiler to perform various kinds of operations between the operands. Operators simulate the various mathematical, logical, and decision operations performed on a set of Complex Numbers, Integers, and Numericals as input operands. 

R Operators 

R supports majorly four kinds of binary operators between a set of operands. In this article, we will see various types of operators in R Programming language and their usage.

Types of the operator in R language

Arithmetic Operators

Logical operators, relational operators, assignment operators, miscellaneous operators.

Arithmetic Operators modulo using the specified operator between operands, which may be either scalar values, complex numbers, or vectors. The R operators are performed element-wise at the corresponding positions of the vectors. 

Addition operator (+)

The values at the corresponding positions of both operands are added. Consider the following R operator snippet to add two vectors:

a <- c ( 1 , 0.1 ) b <- c ( 2.33 , 4 ) print ( a + b )

Output : 3.33 4.10

Subtraction Operator (-)

The second operand values are subtracted from the first. Consider the following R operator snippet to subtract two variables:

a <- 6 b <- 8.4 print ( a - b )

Output : -2.4

Multiplication Operator (*)  

The multiplication of corresponding elements of vectors and Integers are multiplied with the use of the ‘*’ operator.

B = c ( 4 , 4 ) C = c ( 5 , 5 ) print ( B * C )

Output : 20 20

Division Operator (/)  

The first operand is divided by the second operand with the use of the ‘/’ operator.

a <- 10 b <- 5 print ( a / b )

Power Operator (^)

The first operand is raised to the power of the second operand.

a <- 4 b <- 5 print ( a ^ b )

Output : 1024

Modulo Operator (%%)

The remainder of the first operand divided by the second operand is returned.

list1 <- c ( 2 , 22 ) list2 <- c ( 2 , 4 ) print ( list1 %% list2 )

Output : 0 2

The following R code illustrates the usage of all Arithmetic R operators.

# R program to illustrate # the use of Arithmetic operators vec1 <- c ( 0 , 2 ) vec2 <- c ( 2 , 3 ) # Performing operations on Operands cat ( "Addition of vectors :" , vec1 + vec2 , "\n" ) cat ( "Subtraction of vectors :" , vec1 - vec2 , "\n" ) cat ( "Multiplication of vectors :" , vec1 * vec2 , "\n" ) cat ( "Division of vectors :" , vec1 / vec2 , "\n" ) cat ( "Modulo of vectors :" , vec1 %% vec2 , "\n" ) cat ( "Power operator :" , vec1 ^ vec2 )

Output  

Addition of vectors : 2 5 Subtraction of vectors : -2 -1 Multiplication of vectors : 0 6 Division of vectors : 0 0.6666667 Modulo of vectors : 0 2 Power operator : 0 8

Logical Operators in R simulate element-wise decision operations, based on the specified operator between the operands, which are then evaluated to either a True or False boolean value. Any non-zero integer value is considered as a TRUE value, be it a complex or real number. 

Element-wise Logical AND operator (&)

Returns True if both the operands are True.

list1 <- c ( TRUE , 0.1 ) list2 <- c ( 0 , 4+3i ) print ( list1 & list2 )

Output : FALSE TRUE Any non zero integer value is considered as a TRUE value, be it complex or real number.

Element-wise Logical OR operator (|)

Returns True if either of the operands is True.

list1 <- c ( TRUE , 0.1 ) list2 <- c ( 0 , 4+3i ) print ( list1 | list2 )

Output : TRUE TRUE

NOT operator (!)

A unary operator that negates the status of the elements of the operand.

list1 <- c ( 0 , FALSE ) print ( ! list1 )

Logical AND operator (&&)

Returns True if both the first elements of the operands are True.

list1 <- c ( TRUE , 0.1 ) list2 <- c ( 0 , 4+3i ) print ( list1 [ 1 ] && list2 [ 1 ])

Output : FALSE Compares just the first elements of both the lists.

Logical OR operator (||)

Returns True if either of the first elements of the operands is True.

list1 <- c ( TRUE , 0.1 ) list2 <- c ( 0 , 4+3i ) print ( list1 [ 1 ] || list2 [ 1 ])

Output : TRUE

The following R code illustrates the usage of all Logical Operators in R:  

# R program to illustrate # the use of Logical operators vec1 <- c ( 0 , 2 ) vec2 <- c ( TRUE , FALSE ) # Performing operations on Operands cat ( "Element wise AND :" , vec1 & vec2 , "\n" ) cat ( "Element wise OR :" , vec1 | vec2 , "\n" ) cat ( "Logical AND :" , vec1 [ 1 ] && vec2 [ 1 ], "\n" ) cat ( "Logical OR :" , vec1 [ 1 ] || vec2 [ 1 ], "\n" ) cat ( "Negation :" , ! vec1 )

Element wise AND : FALSE FALSE Element wise OR : TRUE TRUE Logical AND : FALSE Logical OR : TRUE Negation : TRUE FALSE

The Relational Operators in R carry out comparison operations between the corresponding elements of the operands. Returns a boolean TRUE value if the first operand satisfies the relation compared to the second. A TRUE value is always considered to be greater than the FALSE. 

Less than (<)

Returns TRUE if the corresponding element of the first operand is less than that of the second operand. Else returns FALSE.

list1 <- c ( TRUE , 0.1 , "apple" ) list2 <- c ( 0 , 0.1 , "bat" ) print ( list1 < list2 )

Output : FALSE FALSE TRUE

Less than equal to (<=)

Returns TRUE if the corresponding element of the first operand is less than or equal to that of the second operand. Else returns FALSE.

list1 <- c ( TRUE , 0.1 , "apple" ) list2 <- c ( TRUE , 0.1 , "bat" ) # Convert lists to character strings list1_char <- as.character ( list1 ) list2_char <- as.character ( list2 ) # Compare character strings print ( list1_char <= list2_char )

Output : TRUE TRUE TRUE

Greater than (>)

Returns TRUE if the corresponding element of the first operand is greater than that of the second operand. Else returns FALSE.

list1 <- c ( TRUE , 0.1 , "apple" ) list2 <- c ( TRUE , 0.1 , "bat" ) print ( list1_char > list2_char )

Output : FALSE FALSE FALSE

Greater than equal to (>=)

Returns TRUE if the corresponding element of the first operand is greater or equal to that of the second operand. Else returns FALSE.

list1 <- c ( TRUE , 0.1 , "apple" ) list2 <- c ( TRUE , 0.1 , "bat" ) print ( list1_char >= list2_char )

Output : TRUE TRUE FALSE

Not equal to (!=)  

Returns TRUE if the corresponding element of the first operand is not equal to the second operand. Else returns FALSE.

list1 <- c ( TRUE , 0.1 , 'apple' ) list2 <- c ( 0 , 0.1 , "bat" ) print ( list1 != list2 )

Output : TRUE FALSE TRUE

The following R code illustrates the usage of all Relational Operators in R:

# R program to illustrate # the use of Relational operators vec1 <- c ( 0 , 2 ) vec2 <- c ( 2 , 3 ) # Performing operations on Operands cat ( "Vector1 less than Vector2 :" , vec1 < vec2 , "\n" ) cat ( "Vector1 less than equal to Vector2 :" , vec1 <= vec2 , "\n" ) cat ( "Vector1 greater than Vector2 :" , vec1 > vec2 , "\n" ) cat ( "Vector1 greater than equal to Vector2 :" , vec1 >= vec2 , "\n" ) cat ( "Vector1 not equal to Vector2 :" , vec1 != vec2 , "\n" )

Vector1 less than Vector2 : TRUE TRUE Vector1 less than equal to Vector2 : TRUE TRUE Vector1 greater than Vector2 : FALSE FALSE Vector1 greater than equal to Vector2 : FALSE FALSE Vector1 not equal to Vector2 : TRUE TRUE

Assignment Operators in R are used to assigning values to various data objects in R. The objects may be integers, vectors, or functions. These values are then stored by the assigned variable names. There are two kinds of assignment operators: Left and Right

Left Assignment (<- or <<- or =)

Assigns a value to a vector.

vec1 = c ( "ab" , TRUE ) print ( vec1 )

Output : "ab" "TRUE"

Right Assignment (-> or ->>)

Assigns value to a vector.

c ( "ab" , TRUE ) ->> vec1 print ( vec1 )

# R program to illustrate # the use of Assignment operators vec1 <- c ( 2 : 5 ) c ( 2 : 5 ) ->> vec2 vec3 <<- c ( 2 : 5 ) vec4 = c ( 2 : 5 ) c ( 2 : 5 ) -> vec5 # Performing operations on Operands cat ( "vector 1 :" , vec1 , "\n" ) cat ( "vector 2 :" , vec2 , "\n" ) cat ( "vector 3 :" , vec3 , "\n" ) cat ( "vector 4 :" , vec4 , "\n" ) cat ( "vector 5 :" , vec5 )

vector 1 : 2 3 4 5 vector 2 : 2 3 4 5 vector 3 : 2 3 4 5 vector 4 : 2 3 4 5 vector 5 : 2 3 4 5

Miscellaneous Operator are the mixed operators in R that simulate the printing of sequences and assignment of vectors, either left or right-handed. 

%in% Operator  

Checks if an element belongs to a list and returns a boolean value TRUE if the value is present  else FALSE.

val <- 0.1 list1 <- c ( TRUE , 0.1 , "apple" ) print ( val %in% list1 )

Output : TRUE Checks for the value 0.1 in the specified list. It exists, therefore, prints TRUE.

%*% Operator

This operator is used to multiply a matrix with its transpose. Transpose of the matrix is obtained by interchanging the rows to columns and columns to rows. The number of columns of the first matrix must be equal to the number of rows of the second matrix. Multiplication of the matrix A with its transpose, B, produces a square matrix.  [Tex]A_{r*c} x B_c*r -> P_{r*r}  [/Tex]

mat = matrix ( c ( 1 , 2 , 3 , 4 , 5 , 6 ), nrow = 2 , ncol = 3 ) print ( mat ) print ( t ( mat )) pro = mat %*% t ( mat ) print ( pro )

Input : Output :[,1] [,2] [,3] #original matrix of order 2x3 [1,] 1 3 5 [2,] 2 4 6 [,1] [,2] #transposed matrix of order 3x2 [1,] 1 2 [2,] 3 4 [3,] 5 6 [,1] [,2] #product matrix of order 2x2 [1,] 35 44 [2,] 44 56

The following R code illustrates the usage of all Miscellaneous Operators in R:

# R program to illustrate # the use of Miscellaneous operators mat <- matrix ( 1 : 4 , nrow = 1 , ncol = 4 ) print ( "Matrix elements using : " ) print ( mat ) product = mat %*% t ( mat ) print ( "Product of matrices" ) print ( product ,) cat ( "does 1 exist in prod matrix :" , "1" %in% product )

[1] "Matrix elements using : " [,1] [,2] [,3] [,4] [1,] 1 2 3 4 [1] "Product of matrices" [,1] [1,] 30 does 1 exist in prod matrix : FALSE

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Multiple assignment operators

Description.

Assign values to name(s).

%<-% and %->% invisibly return value .

These operators are used primarily for their assignment side-effect. %<-% and %->% assign into the environment in which they are evaluated.

Name Structure

At its simplest, the name structure may be a single variable name, in which case %<-% and %->% perform regular assignment, x %<-% list(1, 2, 3) or list(1, 2, 3) %->% x .

To specify multiple variable names use a call to c() , for example c(x, y, z) %<-% c(1, 2, 3) .

When value is neither an atomic vector nor a list, %<-% and %->% will try to destructure value into a list before assigning variables, see destructure() .

object parts

Like assigning a variable, one may also assign part of an object, c(x, x[[1]]) %<-% list(list(), 1) .

nested names

One can also nest calls to c() when needed, c(x, c(y, z)) . This nested structure is used to unpack nested values, c(x, c(y, z)) %<-% list(1, list(2, 3)) .

collector variables

To gather extra values from the beginning, middle, or end of value use a collector variable. Collector variables are indicated with a ... prefix, c(...start, z) %<-% list(1, 2, 3, 4) .

skipping values

Use . in place of a variable name to skip a value without raising an error or assigning the value, c(x, ., z) %<-% list(1, 2, 3) .

Use ... to skip multiple values without raising an error or assigning the values, c(w, ..., z) %<-% list(1, NA, NA, 4) .

default values

Use = to specify a default value for a variable, c(x, y = NULL) %<-% tail(1, 2) .

When assigning part of an object a default value may not be specified because of the syntax enforced by R . The following would raise an "unexpected '=' ..." error, c(x, x[[1]] = 1) %<-% list(list()) .

For more on unpacking custom objects please refer to destructure() .

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Assignment operators in r: ‘=’ vs. ‘<-’.

Posted on November 16, 2010 by csgillespie in R bloggers , Uncategorized | 0 Comments

[social4i size="small" align="align-left"] --> [This article was first published on Why? » R , and kindly contributed to R-bloggers ]. (You can report issue about the content on this page here ) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

In R, you can use  both ‘=’ and ‘<-’ as assignment operators. So what’s the difference between them and which one should you use?

What’s the difference?

The main difference between the two assignment operators is scope. It’s easiest to see the difference with an example: ##Delete x (if it exists) > rm(x) > mean(x=1:10) #[1] 5.5 > x #Error: object 'x' not found Here x is declared within the function’s scope of the function, so it doesn’t exist in the user workspace. Now, let’s run the same piece of code with using the > mean(x <- 1:10)# [1] 5.5 > x # [1] 1 2 3 4 5 6 7 8 9 10 This time the x variable is declared within the user workspace.

Which one should I use

Well there’s quite a strong following for the “<-” operator:

• The Google R style guide prohibits the use of “=” for assignment.
• Hadley Wickham’s style guide recommends “<-”
• If you want your code to be compatible with S-plus you should use “<-”
• I believe that the General R community recommend using “<-”, but I can’t find anything on the mailing list.

However, I tend always use the “=” operator for the following reasons:

• The other languages I program in (python, C and occasionally JavaScript) use the “=” operator.
• It’s quicker to type “=” and “<-”.
• Typically, when I type declare a variable – I only want it to exist in the current workspace.
• Since I have the pleasure of teaching undergraduates their first course in programming, using “=” avoids misleading expressions like if (x[1]<-2)

Also Introducing Monte Carlo Methods with R , by Robert and Casella recommends using “=”.

If I’m missing something or you disagree, please leave a comment – I would be very interested.

• Stackoverflow question
• The R manual’s description of assignment operators

To leave a comment for the author, please follow the link and comment on their blog: Why? » R . R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job . Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

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