assignment code 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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How to Use the assign() Function in R (3 Examples)

The assign() function in R can be used to assign values to variables.

This function uses the following basic syntax:

assign(x, value)

• x : A variable name, given as a character string.
• value : The value(s) to be assigned to x.

The following examples show how to use this function in practice.

Example 1: Assign One Value to One Variable

The following code shows how to use the assign() function to assign the value of 5 to a variable called new_variable:

When we print the variable called new_variable , we can see that a value of 5 appears.

Example 2: Assign Vector of Values to One Variable

The following code shows how to use the assign() function to assign a vector of values to a variable called new_variable:

When we print the variable called new_variable , we can see that a vector of values appears.

Example 3: Assign Values to Several Variables

The following code shows how to use the assign() function within a for loop to assign specific values to several new variables:

By using the assign() function with a for loop, we were able to create four new variables.

Additional Resources

The following tutorials explain how to use other common functions in R:

How to Use the dim() Function in R How to Use the table() Function in R How to Use sign() Function in R

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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 ->> 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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Use the max and min functions to get the maximum and minimum values of a vector or the pmax and pmin functions to return the maxima and minima between elements of vectors

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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.

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 & 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:

assign: Assign a Value to a Name

Description.

Assign a value to a name in an environment.

a variable name, given as a character string. No coercion is done, and the first element of a character vector of length greater than one will be used, with a warning.

a value to be assigned to x .

where to do the assignment. By default, assigns into the current environment. See ‘Details’ for other possibilities.

the environment to use. See ‘Details’.

should the enclosing frames of the environment be inspected?

an ignored compatibility feature.

This function is invoked for its side effect, which is assigning value to the variable x . If no envir is specified, then the assignment takes place in the currently active environment.

If inherits is TRUE , enclosing environments of the supplied environment are searched until the variable x is encountered. The value is then assigned in the environment in which the variable is encountered (provided that the binding is not locked: see lockBinding : if it is, an error is signaled). If the symbol is not encountered then assignment takes place in the user's workspace (the global environment).

If inherits is FALSE , assignment takes place in the initial frame of envir , unless an existing binding is locked or there is no existing binding and the environment is locked (when an error is signaled).

There are no restrictions on the name given as x : it can be a non-syntactic name (see make.names ).

The pos argument can specify the environment in which to assign the object in any of several ways: as -1 (the default), as a positive integer (the position in the search list); as the character string name of an element in the search list; or as an environment (including using sys.frame to access the currently active function calls). The envir argument is an alternative way to specify an environment, but is primarily for back compatibility.

assign does not dispatch assignment methods, so it cannot be used to set elements of vectors, names, attributes, etc.

Note that assignment to an attached list or data frame changes the attached copy and not the original object: see attach and with .

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

<- , get , the inverse of assign() , exists , environment .

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Learn R Programming

Welcome to the learn-r.org interactive R tutorial with Examples and Exercises.

If you want to learn R for statistics, data science or business analytics, either you are new to programming or an experienced programmer this tutorial will help you to learn the R Programming language fast and efficient. 

R is a programming language used extensively for statistics and statistical computing, data science and business analytics. There are different libraries in R which are used for statistics and graphical techniques for simple stats tests, linear and time series modeling, classification, clustering, regression analysis and many more.

Why learn R 

Learning r means more job opportunities.

The field of data science is exploding these days and R and Python are the two languages mainly used for data analytics techniques due to their syntax, ease of use and application. R has many libraries for statistical computing and data analysis. If you learn R programming you can expect a salary starting from $75k while the average salary is $120k in data science jobs in USA. Data analysts and data scientists are in demand and there are a number of opportunities if one knows the skill. For current salaries and recent openings you may google it yourself.

Open source

R is opensource which means anyone and everyone can use it without paying. It is free under GNU license. Most of R packages are also available as free and you can use them for non-commercial as well as commercial activities. Statistical and analysis softwares usually cost from a few hundred to thousands of dollar, R provides the same functionality free of cost. If you have some extra bucks you may try costly softwares though.

Cross platform

R runs equally well on all platforms windows, linux or mac. Hence you may have a linux environment at office, windows at home and mac laptop for travelling, you will have the same experience at all platforms. The software development environment is same and also the applications run seamlessly at all platforms.  

R is ranked as number 5 in most popular programming languages by IEEE. It shows the interest in R is increasing and the fields of analytics, data science, machine learning and deep learning are exploding.

R is used by renowned companies

The effectiveness and application of R programming is illustrated by the fact that many tech giants are using it. Companies like Google, Microsoft, Twitter, Ford etc are using R. This explains the concreteness and robustness of R. 

• How to Learn R

Usually it is said that the learning curve of R is steep. Well, individuals with some programming experience may learn it without any hurdle. People without any programming background can also learn it with ease with a complete learning schedule and learning it step by step. Remember Rome was not built in a day. You can not expect to learn R in one sitting or a day or a few days. Regular practice of R coding and understanding the logic and philosophy are the key to success in learning R. 

In this tutorial each R topic is divided into segments starting from a simple concept and then building on that knowledge moving towards complex ideas. The method to learn R is divide and conquer. Learn one topic at a time and get a good grasp over the concept and logic and write some R programs about the topic you are learning. Also try to solve the challenges given at the end of each tutorial. In this way you will learn this language fast and will set a solid foundation which will help you at advanced stages of data analysis. 

Start Learning R

Good enough introduction of R. There is no time to waste! lets start first concept of R programming right now. We provide R tutorial for total beginners even those who have never used any programming language before. So we start from the idea of variables.

The first idea to learn in every programming language is the concept of variables. Variables are like boxes or containers which store a value or some data. In a programming language we have to use numbers, characters, words etc but before using them we have to store them in some box or container so that we may use them later. Every variable has a name and some type, usually called as data type. In C, C++ or Java one has to tell the data type of variable, however in R you don't have to worry about it. Simply give a name to variable and thats it. Lets suppose we want to store or save age of a person in R. We give the name age to variable that will store the number in it. In R the code will be 

> age  <- 25

Here the first greater than sign       >      indicates the R prompt. We write code after that. 

<- or arrow is assignment operator in R. It assigns the value at its right to the variable at its left. Here it is assigning 25 to variable age or simply it is storing 25 number in age variable or box. 

And now if you want to print this variable use the print function like this. 

> print(age)

Wow, you have succeeded in assigning a value to a variable, storing some value in box and then later used that value in a function from that box. print function simply prints the value of any variable on R console.

This is easy, isn't it? if you follow this site, you will be able to learn R in the same simple and easy way, step by step & Fast.

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R Operator Precedence and Associativity

R Program to Add Two Vectors

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

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R Operators

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• Scope of Variable in R
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• R - Object Oriented Programming
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• S3 class in R Programming
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Error Handling

• Handling Errors in R Programming
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• Debugging in R Programming

File Handling

• File Handling in R Programming
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• Writing to Files in R Programming
• Working with Binary Files in R Programming
• Packages in R Programming
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• 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
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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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Align assign: rstudio addin to align assignment operators.

Posted on October 7, 2016 by Luke Smith in R bloggers | 0 Comments

[social4i size="small" align="align-left"] --> [This article was first published on R-Bloggers – Protocol Vital , 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.

If you ever find yourself trying to align assignment operators for a chunk of code in RStudio, then you are in luck: Align Assign makes this as easy as a mouse-click or a keyboard-shortcut.

Align Assign is an RStudio addin which does a straight, no-frills alignment of every first-occurring assignment operator within a highlighted region.

Align Assign is available in a package all its own on GitHub, and can be installed using  devtools::install_github("seasmith/AlignAssign") . Once installed, I recommend mapping the addin to a keyboard shortcut such as “Ctrl + Shift + Z” for convenient use.

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Former Tiger owed nearly $24M by Marlins in designation for assignment

• Published: Jun. 04, 2024, 1:15 p.m.

Miami Marlins' Avisail Garcia hits a solo home run against the Pittsburgh Pirates during the fourth inning of a baseball game Sunday, March 31, 2024, in Miami. (AP Photo/Rhona Wise) AP

• Matt Wenzel | [email protected]

In the third season of a disappointing tenure with the Marlins, Avisail Garcia is being designated for assignment, according to multiple reports on Tuesday.

The outfielder and former Detroit Tiger will still be owed almost $24 million by Miami, according to ESPN. The 32-year-old veteran signed a four-year deal worth $53 million as a free agent in December 2021.

Now in his 13th MLB season in a career that spans stops with five different teams, Garcia underperformed during his time with the Marlins. His .217 batting average is well below his career mark (.263) with 13 home runs and 49 RBIs in just 153 total games while battling injuries. Garcia was on a rehab assignment with Triple-A Jacksonville for a hamstring injury sustained in April.

Garcia, a Venezuelan native, made his MLB debut with the Tigers at age 21 in 2012, the same season Detroit was swept by the Giants in the World Series. He hit .269 with two home runs and 13 RBIs in 53 games spanning two seasons before being traded to the White Sox in 2013.

Garcia spent six seasons with the White Sox, including one All-Star selection, one year with the Rays and two with the Brewers. He posted one of his career-best seasons in 2021, hitting .262 with a .820 OPS, 29 home runs and 86 RBIs for Milwaukee before signing with the Marlins.

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Yankees hint at Gerrit Cole’s updated injury timetable after 1st rehab start

• Updated: Jun. 05, 2024, 5:57 p.m. |
• Published: Jun. 05, 2024, 5:20 p.m.

Gerrit Cole threw 3 1/3 scoreless innings on Tuesday night with Double-A Somerset, the first of his rehab assignment starts in the minor leagues. Andrew Mills | NJ Advance Media for NJ.com

• Max Goodman | NJ Advance Media for NJ.com

NEW YORK — Gerrit Cole looked like himself during his first rehab start with Double-A Somerset on Tuesday night, another step closer to a return from his elbow injury.

The Yankees ’ ace was throwing fastballs in the high 90s in his first in-game action of the year — holding back so he didn’t overdo it — and he retired nine of the 11 batters that he faced.

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Visual Studio Code adds multiple tabs selection

Beginning with vs code 1.90, users can select multiple tabs and apply actions to multiple editors at once..

Editor at Large, InfoWorld |

With Visual Studio Code 1.90, otherwise known as the May 2024 release of the editor, Microsoft has introduced the ability to select multiple editor tabs at once and the ability to configure a preferred profile for new windows.

Visual Studio Code 1.90 was published on June 5 . It can be downloaded for Windows, Linux, and MacOS from the Visual Studio Code website . 

With the editor tabs multi-select capability, developers now can select multiple tabs simultaneously, enabling the application of actions to multiple editors at once. This new capability lets developers move, pin, or close several tabs with a single action.

Developers now can specify which profile should be used when opening a new window by configuring the window.netWindowProfile setting. Previously, when opening a new VS Code window, the profile of the active window was used, or the default profile was used if there was no active window.

VS Code 1.90 also brings improvemens to source control and editor actions. For source control, workbench commands were added for creating keyboard shortcuts. These include capabilities to focus on the next or previous source control input field or to focus on the next or previous resource group within a repository. For editor actions, Microsoft is introducing an Always Show Editor Actions setting. When this setting is enabled, editor title actions of each editor group are shown, regardless of whether the editor is active or not. When this setting is not enabled, editor actions are shown only when the editor is active.

Notebooks in VS Code 1.90 now support a new kind of Code Action, which is defined with the notebook.format Code Action Kind prefix. These Code Actions can be triggered automatically via an explicit formatting request or a formatting on save request.

VS Code 1.90 follows last month’s VS Code 1.89 release which emphasized capabilities such as enhanced branch switching and middle-click paste support. Other new capabilities in VS Code 1.90:

• Enabling the new Always Show Editor Actions setting will show editor title actions of each editor group regardless of whether the editor is active or not.
• When the setting Debounce position changes is enabled, developers can use the Signal options delays setting to customize the debouncing time for various accessibility signals. This is an experimental capability.
• When a command lacks a keybinding assignment, developers now can configure it from within the accessibility help dialog.
• The canvas renderer, deprecated in VS Code 1.89, is now removed completely. On machines that do not support WebGL2, the terminal will use the DOM-based renderer.
• The setting terminal.integrated.rescaleOverlappingGlyphs , introduced as a preview feature in VS Code 1.88 , is now enabled by default.
• GitHub Copilot Enterprise users in VS Code now can ask questions enriched with context from web results and enterprise knowledge bases. To try out this capability, developers must install the latest release of Copilot Chat.
• Two new APIs for extension authoring, the Chat Participants API and the Language Model API , enable VS Code extensions to participate in chat and to access language models.

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Paul Krill is an editor at large at InfoWorld, whose coverage focuses on application development.

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Rep. Stefanik files misconduct complaint against Judge Juan Merchan over ‘random’ assignment to Trump’s NYC trial

R ep. Elise Stefanik (R-NY) filed a misconduct complaint Tuesday against the judge overseeing Donald Trump’s Manhattan hush money trial, alleging that his selection to handle the former president’s case — and others involving his allies — is “not random at all.” 

The House Republican Conference chairwoman’s complaint with the inspector general of the New York State Unified Court System called for an investigation into Justice Juan Merchan “to determine whether the required random selection process was in fact followed.” 

“The potential misconduct pertains to the repeated assignment of Acting Justice Juan Merchan, a Democrat Party donor, to criminal cases related to President Donald J. Trump and his allies,” Stefanik wrote.

“Acting Justice Merchan currently presides over the criminal case against President Trump brought by Manhattan District Attorney Alvin Bragg,” she said.

“Acting Justice Merchan also presided over the criminal trial against the Trump Organization and will be presiding over the criminal trial of Steve Bannon, a senior advisor in President Trump’s White House and a prominent advocate for President Trump,” Stefanik continued, noting that there were at least two dozen sitting justices eligible to oversee the cases but Merchan – an acting jurist – was selected for all three related to the presumptive 2024 GOP nominee for president and his allies. 

“If justices were indeed being randomly assigned in the Criminal Term, the probability of two specific criminal cases being assigned to the same justice is quite low, and the probability of three specific criminal cases being assigned to the same justice is infinitesimally small. And yet, we see Acting Justice Merchan on all three cases,” Stefanik argued.

The congresswoman also highlighted the judge’s political donations, for which he was cleared of misconduct last July by the New York State Commission on Judicial Conduct. 

Merchan contributed $15 earmarked for the “Biden for President” campaign on July 26, 2020, and then the following day made $10 contributions to the Progressive Turnout Project and Stop Republicans each, Federal Election Commission records show

The donations were made through ActBlue, the Democratic Party’s preferred online fundraising platform. 

The Progressive Turnout Project’s stated mission is to “rally Democrats to vote,” according to the group’s website. 

Stop Republicans is a subsidiary of the Progressive Turnout Project and describes itself as “a grassroots-funded effort dedicated to resisting the Republican Party and Donald Trump’s radical right-wing legacy.”

The judge’s daughter, Loren Merchan, is more involved in Democratic politics – through her work as head of the consulting firm Authentic Campaigns — and Stefanik argued in her missive that Loren Merchan’s “firm stands to profit greatly if Donald Trump is convicted.” 

“One cannot help but suspect that the ‘random selection’ at work in the assignment of Acting Justice Merchan, a Democrat Party donor, to these cases involving prominent Republicans, is in fact not random at all,” the New York Republican lawmaker wrote. 

Stefanik demanded an investigation into the “anomaly” and asked that anyone found to be involved in any sort of “scheme” to get Merchan on the three cases face discipline.