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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Guide to Using the assign() Function in R – With Examples

The R programming language is among the single most popular options for statistical computing and data science. One of the reasons for this popularity stems from the fact that R is designed around data processing. All programming languages work with data processing to some extent. But with R, that functionality is at the heart of every design decision. However, this can also make R a little unconventional when compared to other popular programming languages. And this is especially apparent when we look at the proper use and functionality of R’s assign() function. But you’ll soon see why assign is important, how it allows for functionality not found in other languages, and how to get the most out of it.

The Basics of R’s Assign

Assign is among the more unassuming functions in R’s toolkit. At first glance, it seems like one of those cruft functions that often persist in a language long after its usefulness is over. This is because by default the assign function is just a slightly more verbose way of enacting a standard variable assignment. Assign lives up to its name by quite literally just taking a value and assigning it to a string. But assign’s true potential comes from the fact that it leverages functionality typically limited by standard assignment operators. This gives us the power to essentially override standard variable assignments in a number of creative ways.

A Simple Example of Apply in Action

We can begin by looking at the most basic usage scenarios for assign. Try running the following code.

assign(“ourVar”, 999) ourVar2 <- 999

print(ourVar) print(ourVar2) print(typeof(ourVar)) print(typeof(ourVar2))

This code demonstrates how similar assign is to a standard variable assignment. We begin by calling assign with two arguments. These are a string consisting of “ourVar” and the number 999. Next, we use the standard R syntax to assign 999 to a second variable called ourVar2. We proceed to print out some information about each variable so that we can compare them against each other. You’ll see that both variables are seen by R as a double whose value is 999.

In other words, the assign function and a standard variable assignment have produced identical results. The main difference seems to be that assign requires slightly more work to set up than a standard assignment operation. You’d be justified in wondering what the function’s point is. But, as mentioned earlier, we can use extend to leverage a lot of additional power from R’s underlying functionality that’s normally closed off from end users.

More Advanced Usage Scenarios for Assign

We can most easily understand apply’s real power by seeing it in action within a more complex context. Take a look at the following example.

ourData <- data.frame( first = c (“alpha”,”beta”,”epsilon”,”eta”), second = c(“A”,”B”,”E”,”H”) )

nu <- “N”

for(x in 1:ncol(ourDataFrame)) { print(ourData[x,1]) print(ourData[x,2]) assign(paste0(ourData[x,1]), ourData[x,2]) }

print(typeof(alpha)) print(alpha)

print(typeof(nu)) print(nu)

We begin by defining a new data frame as ourData. This contains two separate rows with values corresponding to elements of the greek alphabet. The data frame definition is followed by a single definition of a variable, nu, as “N”. We move onward to create a for loop that iterates over the columns in ourData. With each loop the code prints out the current value of the row and column in ourData. We also call the assign function using those same values.

When the loop is finished we print out the type and contents of two variables. These consist of an alpha variable and nu variable. The nu variable is exactly as we’d expect given the explicit declaration at the start of the code. But alpha will probably come as more of a surprise. Because there is no alpha variable declaration in our code. And yet the R interpreter doesn’t throw an error message when we attempt to work with it. We even find a legitimate value stored within the alpha variable. How did that happen? The answer comes down to the assign function we used within the loop.

Remember how we printed out the corresponding items in each of ourData’s columns? Those were also passed to assign. We gave the names in the first row to assign as its initial argument. And the value in the second row was passed as the second argument. For the first pass in the loop we essentially called assign as seen in the line below.

assign(“alpha”, “A”)

And this assignment remains consistent for every other item within ourData. And at this point, you’ve seen the true power of R’s assign function. We normally can’t run an assignment operation with entirely programmatically defined elements. But assign essentially lets us bypass that limitation to directly access assignment logic. And because we’re able to manipulate it any way we want we can integrate it into loops or any other logic structure.

Another interesting component of the assign function stems from the fact that it only needs an input string to create variable names. Likewise, any standard value can be passed as the second argument. In the initial example, we simply provided a string and value of 999. But in this example, we’re technically not working with a string for assign’s initial argument. We’re instead working with a data frame. And it’s that data frame that contains a string. This all comes down to R’s inherent flexibility.

R’s focus on data manipulation means that it’s generally trivial to convert different data types. And we see this with the way we call assign in the second example. We’re not just passing the contents of the data frame as assign’s first argument. We use print0 to ensure that anything passed to assign is in string format. So if we had a numeric value instead of the alpha string then assign would still be able to accept it as a valid variable name. Of course in a larger project, we would generally want to avoid using numbers as a variable name. But this can be quite useful for smaller projects where we simply need to import and manipulate a collection of data.

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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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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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 .

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:

Obtaining R

R is available for Linux, MacOS, and Windows. Software can be downloaded from The Comprehensive R Archive Network (CRAN) .

After R is downloaded and installed, simply find and launch R from your Applications folder.

Entering Commands

R is a command line driven program. The user enters commands at the prompt ( > by default) and each command is executed one at a time.

The Workspace

The workspace is your current R working environment and includes any user-defined objects (vectors, matrices, data frames, lists, functions). At the end of an R session, the user can save an image of the current workspace that is automatically reloaded the next time R is started.

Graphic User Interfaces

Aside from the built in R console, RStudio is the most popular R code editor, and it interfaces with R for Windows, MacOS, and Linux platforms.

Operators in R

R's binary and logical operators will look very familiar to programmers. Note that binary operators work on vectors and matrices as well as scalars.

Arithmetic Operators include:

Logical Operators include:

R has a wide variety of data types including scalars, vectors (numerical, character, logical), matrices, data frames, and lists.

Creating New Variables

Use the assignment operator <- to create new variables.

Almost everything in R is done through functions. A function is a piece of code written to carry out a specified task; it may accept arguments or parameters (or not) and it may return one or more values (or not!). In R, a function is defined with the construct:

The code in between the curly braces is the body of the function. Note that by using built-in functions, the only thing you need to worry about is how to effectively communicate the correct input arguments (arglist) and manage the return value/s (if any).

Importing Data

Importing data into R is fairly simple. R offers options to import many file types, from CSVs to databases.

For example, this is how to import a CSV into R.

Descriptive Statistics

R provides a wide range of functions for obtaining summary statistics. One way to get descriptive statistics is to use the sapply( ) function with a specified summary statistic.

Below is how to get the mean with the sapply( ) function:

Possible functions used in sapply include mean, sd, var, min, max, median, range , and quantile .

Plotting in R

In R, graphs are typically created interactively. Here is an example:

The plot( ) function opens a graph window and plots weight vs. miles per gallon. The next line of code adds a regression line to this graph. The final line adds a title.

Packages are collections of R functions, data, and compiled code in a well-defined format. The directory where packages are stored is called the library. R comes with a standard set of packages. Others are available for download and installation. Once installed, they have to be loaded into the session to be used.

Getting Help

Once R is installed, there is a comprehensive built-in help system. At the program's command prompt you can use any of the following:

Going Further

If you prefer an online interactive environment to learn R, this free R tutorial by DataCamp is a great way to get started.

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.

R for Statistics, Data Science

• R Hello World!
• Set working directory
• if else statement
• Repeat Loop
• R break & next
• R Read CSV file
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• ifelse() Function
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All The Dots Are Connected / It's Not Rocket Science

Transportation and assignment problems with r.

In the previous post “ Linear Programming with R ” we examined the approach to solve general linear programming problems with “Rglpk” and “lpSolve” packages. Today, let’s explore “lpSolve” package in depth with two specific problems of linear programming: transportation and assignment.

1. Transportation problem

Code & Output:

The solution is shown as lptrans$solution and the total cost is 20500 as lptrans$objval.

2. Assignment problem

Similarly, the solution and the total cost are shown as lpassign$solution and lpassign$objval respectively.

This article was really helpful, but I am facing issue while solving unbalanced transportation problem when there is excess demand. Could you please guide me on what has to be done in this case.

Hello sir, this article was really helpful. But, I am facing issue while solving unbalanced transportation problem when there is excess demand, it gives solution as no feasible solution. works perfectly fine for balanced and excess supply problems. Could you please guide me on why this issue is occurring and a possible solution for the same. Thank you.

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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.

To leave a comment for the author, please follow the link and comment on their blog: R-Bloggers – Protocol Vital . 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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Honkai: Star Rail Hope Is The Thing With Feathers (Robin Secret Quest)

Quick links, the feather he dropped achievement in honkai: star rail, how to use lost property in honkai: star rail penacony.

In the aftermath of the Trailblaze Mission in Honkai: Star Rail , there's a Robin hidden quest where she talks about how cruel fate is that she and Sunday have to be apart. She subtly blames herself for leaving Sunday alone while she spreads words of Harmony throughout the universe. Meanwhile, the ex-head of the Oak Family is left alone in Penacony, experiencing things that falter his beliefs in Xipe, and nurtures the faith towards Ena, the Aeon that once existed, but gets absorbed by Xipe due to their similar values.

This hidden quest with Honkai: Star Rail Robin also gives other stuff as a reward, so here's how players can trigger it.

All Active Honkai: Star Rail Redeem Codes (May 2024)

Hope is the Thing With Feathers record in Honkai: Star Rail is obtainable through a hidden mission with Robin at the Penacony Grand Theater. Obtaining this disc will also give The Feather He Dropped achievement. Robin's assigment requires players to give the Lost Property to the singer.

To trigger Robin's hidden mission, switch the map to Penacony Grand Theater , then teleport to Ascension Hallway Space Anchor. Next, walk forward until Robin sends you a message . Open the text, and she'll say something about how she's worried about her brother, Sunday.

Ever since the entire event occurred and his plan was foiled, Sunday is nowhere to be found, and Robin wishes to find some clues about her brother at the Grand Theater. Agree to complete Robin's assignment, and players can head deeper into Penacony Grand Theater to find the remnants Sunday left behind. Robin also says, "Look for me near the ticketing booth , I'll be disguised as an Intellitron dressed in blue formal attire."

To find some clues about Robin's brother at the Grand Theater, go to the grand stage where players fight the boss , or simply teleport to the Echo of War. Nearby, there's a floating book with bubbles that players can investigate. Press the button to read a journal that Sunday wrote called Lost Property .

After obtaining the Lost Property in Honkai: Star Rail , return to the Ascension Hallway Space Anchor, and walk forward a little bit while looking to the left . Near the curtain that goes to another room, presumably the ticketing booth, players will find a Honkai: Star Rail Intellitron . Approach the Intellitron dressed in blue formal attire, who turns out to be Robin in disguise. Give her Sunday's Lost Property, and she'll understand that this is as much of a clue that Trailblazers can get.

Robin leaves after the short conversation, and players will get The Feather He Dropped achievement, Program List Dreamscape Pass sticker in Honkai: Star Rail , and, of course, Hope is the Thing With Feathers disc, which players can play in the Parlor Car of the Astral Express. Other than that, Trailblazers will also earn 2 Lost Crystals , and 5000 Credit .

Honkai: Star Rail

Platform(s) PS5, PC, iOS, Android

Released April 26, 2023

Developer(s) HoYoverse (Formerly miHoYo)

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Northern lights maps predict where you may see them in Michigan on Saturday, Sunday

If clouds cooperate, there's a chance the northern lights could be visible again Saturday and Sunday nights in metro Detroit. The geomagnetic storm's colors dazzled Michigan skies , including in the southern part of the state, Friday night.

While Michiganders are used to the northern lights, which are also called the aurora borealis, occasionally appearing in northern Michigan, the metro Detroit sighting was a treat. The National Weather Service called Friday's storm severe .

And there could be a repeat this weekend.

Northern lights forecast map

NWS maps predicting the intensity and location of the northern lights Saturday and Sunday show the aurora will be visible in mid to northern Michigan and the Upper Peninsula.

The green oval on the map indicates where the lights will be visible and the oval turns red where they are forecasted to be more intense. Most of the included Michigan areas are in the green oval, but some parts of the Upper Peninsula are in the red.

More: Michigan dark sky parks, sanctuaries are best spots to see northern lights, stars

The NWS also said the aurora doesn't have to be directly above an area for it to be visible and can be observed more than 600 miles away when the aurora is bright and weather conditions are right. So, even though the aurora won't be directly over metro Detroit either night, it's definitely possible it will be visible in the region.

Weather, sky cover forecast for northern lights viewing in Michigan

Both Saturday and Sunday are predicted to be mostly cloudy with showers and isolated thunderstorms. From 8 p.m. Saturday to 2 a.m. Sunday, the NWS is predicting sky cover ranging from 48-58% in metro Detroit, which could affect northern lights viewing. It drops to 34% at 5 a.m. The west side of both peninsulas will have a lower percentage of cover during the same time periods.

The sky cover percentage in metro Detroit looks better for Sunday night with a range of 31-44% coverage between 8 p.m. Sunday to 2 a.m. Monday.

Best time to view northern lights

The best time to view the northern lights is between 10 p.m. and 2 a.m. , according to the National Oceanic and Atmospheric Administration.

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Python Project for Data Engineering

This course is part of multiple programs. Learn more

This course is part of multiple programs

Taught in English

Some content may not be translated

Instructors: Ramesh Sannareddy +2 more

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Intermediate level

Basic Python Programming and using Jupyter Notebooks

What you'll learn

Demonstrate your skills in Python for working with and manipulating data

Implement webscraping and use APIs to extract data with Python

Play the role of a Data Engineer working on a real project to extract, transform, and load data

Use Jupyter notebooks and IDEs to complete your project

Skills you'll gain

• Python Programming
• Information Engineering
• Extract Transform and Load (ETL)
• Data Engineer
• Web Scraping

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There are 3 modules in this course

Showcase your Python skills in this Data Engineering Project! This short course is designed to apply your basic Python skills through the implementation of various techniques for gathering and manipulating data.

You will take on the role of a Data Engineer by extracting data from multiple sources, and converting the data into specific formats and making it ready for loading into a database for analysis. You will also demonstrate your knowledge of web scraping and utilizing APIs to extract data. By the end of this hands-on project, you will have shown your proficiency with important skills to Extract Transform and Load (ETL) data using an IDE, and of course, Python Programming. Upon completion of this course, you will also have a great new addition to your portfolio! PRE-REQUISITE: **Python for Data Science, AI and Development** course from IBM is a pre-requisite for this project course. Please ensure that before taking this course you have either completed the Python for Data Science, AI and Development course from IBM or have equivalent proficiency in working with Python and data. NOTE: This course is not intended to teach you Python and does not have too much new instructional content. It is intended for you to mostly apply prior Python knowledge.

Extract, Transform, Load (ETL)

Module 1 introduces you to Extract, Transform, and Load operations basics. You will learn to extract required information from web pages using web scraping techniques and APIs. You will also access databases using Python and save the processed information as a table in a database.

What's included

5 videos 3 readings 2 quizzes 4 app items 1 plugin

5 videos • Total 25 minutes

• Course Introduction • 3 minutes • Preview module
• Demo: Working with an IDE • 5 minutes
• Extract, Transform, Load (ETL) • 6 minutes
• (Optional) Web Scraping • 4 minutes
• [Optional] REST APIs & HTTP Requests • 4 minutes

3 readings • Total 13 minutes

• Helpful Tips for Course Completion • 2 minutes
• Pre-requisites and Course Syllabus • 10 minutes
• Module Summary: Extract, Transform, Load (ETL) • 1 minute

2 quizzes • Total 25 minutes

• Practice Quiz: Extract, Transform, Load (ETL) • 10 minutes
• Graded Quiz: Extract, Transform, Load (ETL) • 15 minutes

4 app items • Total 120 minutes

• Hands-on Lab: Getting Started with IDE  • 15 minutes
• Hands-on Lab: Extract, Transform, Load (ETL) • 30 minutes
• Hands-on Lab: Web scraping and Extracting Data using APIs • 45 minutes
• Hands-on Lab: Accessing Databases using Python script • 30 minutes

1 plugin • Total 5 minutes

• Reading: Querying SQLite3 database • 5 minutes

Final Project

In this lesson, you will complete two projects, one for practice and one for assessment to apply what you’ve learned. These projects have you implement your skills learned in the previous course and the last module regarding the Extract, Transform, and Load process using web scraping and accessing databases using REST APIs and Python.

2 readings 1 assignment 1 peer review 2 app items 2 plugins

2 readings • Total 15 minutes

• Congratulations and Next Steps • 10 minutes
• Thanks from the Course Team • 5 minutes

1 assignment • Total 15 minutes

• Graded Quiz: Final Project • 15 minutes

1 peer review • Total 60 minutes

• Submit your work and grade your peers • 60 minutes

2 app items • Total 120 minutes

• Practice Project: Extract, Transform and Load GDP data • 60 minutes
• Final Project: Acquiring and processing information on world's largest banks • 60 minutes

2 plugins • Total 10 minutes

• Practice Project Overview • 5 minutes
• Final Project Overview • 5 minutes

[Optional] Python Coding Practices and Packaging Concepts

In this bonus module, you will become familiar with the best practices for coding as documented in the Python Enhancement Proposal (PEP8) style guide. You will learn about static code analysis, ensuring that your code adheres to the coding rules. Next, you will learn how to create and run unit tests. Finally, you will learn how to create, verify, and run Python packages.

3 videos 2 readings 1 quiz 3 app items 2 plugins

3 videos • Total 19 minutes

• Python Style Guide and Coding Practices • 6 minutes • Preview module
• Unit Testing • 6 minutes
• Packaging • 6 minutes

2 readings • Total 7 minutes

• Module Introduction • 2 minutes
• Module Summary: Python Coding Practices and Packaging Concepts • 5 minutes

1 quiz • Total 10 minutes

• Practice Quiz: Python Coding Practices and Packaging Concepts • 10 minutes

3 app items • Total 90 minutes

• Hands-on Lab: Static Code Analysis • 30 minutes
• Hands-on Lab: Practice Session and Assignment on Unit Testing • 30 minutes
• Hands-on Lab: Practice Session and Assignment on Python Packaging • 30 minutes

2 plugins • Total 7 minutes

• Reading: Static Code Analysis Resources • 2 minutes
• Cheatsheet: Python Coding Practices and Packaging Concepts • 5 minutes

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Recommended if you're interested in Data Management

Introduction to Relational Databases (RDBMS)

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588 reviews

Reviewed on Apr 26, 2024

Extremely helpful course with multiple hands-on projects and one graded project. Loved the structure of the course !

Reviewed on Oct 22, 2021

This may be irrelevant to this course but I need more exercises, to let me sharpen their new skill.

Reviewed on Aug 12, 2021

The course is enthralling and informative. You can put the knowledge in practice lessons at once. thank you for the great course!

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