assignment problem r studio

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.

Solve Linear Sum Assignment Problem

Description.

Solve the linear sum assignment problem using the Hungarian method.

If nr and nc are the numbers of rows and columns of x , solve_LSAP finds an optimal assignment of rows to columns, i.e., a one-to-one map p of the numbers from 1 to nr to the numbers from 1 to nc (a permutation of these numbers in case x is a square matrix) such that \sum_{i=1}^{nr} x[i, p[i]] is minimized or maximized.

This assignment can be found using a linear program (and package lpSolve provides a function lp.assign for doing so), but typically more efficiently and provably in polynomial time O(n^3) using primal-dual methods such as the so-called Hungarian method (see the references).

An object of class "solve_LSAP" with the optimal assignment of rows to columns.

Walter Böhm [email protected] kindly provided C code implementing the Hungarian method.

C. Papadimitriou and K. Steiglitz (1982), Combinatorial Optimization: Algorithms and Complexity . Englewood Cliffs: Prentice Hall.

• Linear programming

Solving linear transport problem with lp.transport in R, using lpSolve

• Linnart Felkl

The transportation problem is one of the classical problems teached in linear programming classes. The problem, put simply, states that a given set of customers with a specified demand must be satisfied by another set of supplier with certain capacities (“supply”). For a detailed explanation of the transportation problem you can, e.g. read this:  https://econweb.ucsd.edu/~jsobel/172aw02/notes8.pdf .

The lpSolve package available in R can be used for modelling and solving the transportation problem.

I will show how to do this. I define a problem in which 3 suppliers seek to satisfy 4 customers. The suppliers have capacities 100, 300, and 400, respectively. The customers have demand 100, 100, 200, and 400, respectively. Furthermore, the cost for supplying customer i by supplier j is defined for every possible combination and stated in a cost matrix.

Using this information we can model and solve the transportation problem (deciding which demand to fulfill by which supplier) with the lpSolve package in R.

First, I prepare the modeling-part:

Then, I solve the problem:

Let us review the “optimal” costs:

Let us review the optimal solution of the transportation problem (i.e., the optimal material flows to this problem):

The assignment problem is another classical problem, and you can see how I solved it in R here: Cost minimal production scheduling – solving the assignment problem with lpSolve, using lp.assign.

Data scientist focusing on simulation, optimization and modeling in R, SQL, VBA and Python

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hi. I am trying to “go to the next level”. I want to look deeper than this and say, “OK, truck A costs me $100 and takes 3 weeks, but flight A costs me $200 and takes just 1 week to move the good” For me, i am still trying to move things from A to B, but now i am expanding it to try and look at the total cost of holding the inventory, reduction in cash flow and the cost of delivery. I want to “optimise” my transportation so that maybe 90% of my forecast is shipped via truck, then i can “top up” with the flight if demand requires. Any suggestions how to approach this issue or where to turn for some guidance? Thanks

thanks for your comment.

You can try to formulate your problem as a linear optimization problem, from scratch. For example, the cost of holding inventory can be derived from the travel time, and the travel time depends on transportation distance (one variable) and transportation mode (binary or integer variable).

The objective of covering 90% of your forecast by truck could be formulated as a constraint, or maybe a better approach would be to define two objective functions, and to apply multi-goal linear programming.

If you provide some more info I might very well try to model it and post it, maybe in a simplified version!

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R crash course, next episode, introduction to r and rstudio.

Overview Teaching: 105 min Exercises: 30 min Questions How to find your way around RStudio? How to interact with R? How to manage your environment? How to install packages? Objectives Describe the purpose and use of each pane in the RStudio IDE Locate buttons and options in the RStudio IDE Define a variable Assign data to a variable Manage a workspace in an interactive R session Use mathematical and comparison operators Call functions Manage packages

Science is a multi-step process: once you’ve designed an experiment and collected data, the real fun begins! This lesson will teach you how to start this process using R and RStudio. We will begin with raw data, perform exploratory analyses, and learn how to plot results graphically. This example starts with a dataset from gapminder.org containing population information for many countries through time. Can you read the data into R? Can you plot the population for Senegal? Can you calculate the average income for countries on the continent of Asia? By the end of these lessons you will be able to do things like plot the populations for all of these countries in under a minute!

Before Starting The Workshop

Please ensure you have the latest version of R and RStudio installed on your machine. This is important, as some packages used in the workshop may not install correctly (or at all) if R is not up to date.

Download and install the latest version of R here

Download and install RStudio here

Introduction to RStudio

Welcome to the R portion of the Software Carpentry workshop.

Throughout this lesson, we’re going to teach you some of the fundamentals of the R language as well as some best practices for organizing code for scientific projects that will make your life easier.

We’ll be using RStudio: a free, open source R integrated development environment. It provides a built in editor, works on all platforms (including on servers) and provides many advantages such as integration with version control and project management.

Basic layout

When you first open RStudio, you will be greeted by three panels:

• The interactive R console (entire left)
• Environment/History (tabbed in upper right)
• Files/Plots/Packages/Help/Viewer (tabbed in lower right)

Once you open files, such as R scripts, an editor panel will also open in the top left.

Work flow within RStudio

There are two main ways one can work within RStudio.

• This works well when doing small tests and initially starting off.
• It quickly becomes laborious
• This is a great way to start; all your code is saved for later
• You will be able to run the file you create from within RStudio or using R’s source() function.

Tip: Running segments of your code RStudio offers you great flexibility in running code from within the editor window. There are buttons, menu choices, and keyboard shortcuts. To run the current line, you can 1. click on the Run button above the editor panel, or 2. select “Run Lines” from the “Code” menu, or 3. hit Ctrl-Enter in Windows or Linux or Command-Enter on OS X. (This shortcut can also be seen by hovering the mouse over the button). To run a block of code, select it and then Run . If you have modified a line of code within a block of code you have just run, there is no need to re-select the section and Run , you can use the next button along, Re-run the previous region . This will run the previous code block including the modifications you have made.

Introduction to R

Much of your time in R will be spent in the R interactive console. This is where you will run all of your code, and can be a useful environment to try out ideas before adding them to an R script file. This console in RStudio is the same as the one you would get if you typed in R in your command-line environment.

The first thing you will see in the R interactive session is a bunch of information, followed by a “>” and a blinking cursor. It operates on the idea of a “Read, evaluate, print loop”: you type in commands, R tries to execute them, and then returns a result.

Using R as a calculator

The simplest thing you could do with R is do arithmetic:

And R will print out the answer, with a preceding “[1]”. Don’t worry about this for now, we’ll explain that later. For now think of it as indicating output.

Like bash, if you type in an incomplete command, R will wait for you to complete it:

Any time you hit return and the R session shows a “+” instead of a “>”, it means it’s waiting for you to complete the command. If you want to cancel a command you can simply hit “Esc” and RStudio will give you back the “>” prompt.

Tip: Cancelling commands If you’re using R from the command-line instead of from within RStudio, you need to use Ctrl+C instead of Esc to cancel the command. This applies to Mac users as well! Canceling a command isn’t only useful for killing incomplete commands: you can also use it to tell R to stop running code (for example if it’s taking much longer than you expect), or to get rid of the code you’re currently writing.

When using R as a calculator, the order of operations is the same as you would have learned back in school.

From highest to lowest precedence:

• Parentheses: ( , )
• Exponents: ^ or **
• Multiply: *
• Subtract: -

Use parentheses to group operations in order to force the order of evaluation if it differs from the default, or to make clear what you intend.

This can get unwieldy when not needed, but clarifies your intentions. Remember that others may later read your code.

The text after each line of code is called a “comment”. Anything that follows after the hash (or octothorpe) symbol # is ignored by R when it executes code.

Really small or large numbers get a scientific notation:

Which is shorthand for “multiplied by 10^XX ”. So 2e-4 is shorthand for 2 * 10^(-4) .

You can write numbers in scientific notation too:

Mathematical functions

R has many built in mathematical functions. To call a function, we simply type its name, followed by open and closing parentheses. Anything we type inside the parentheses is called the function’s arguments:

Don’t worry about trying to remember every function in R. You can simply look them up on Google, or if you can remember the start of the function’s name, use the tab completion in RStudio.

This is one advantage that RStudio has over R on its own, it has auto-completion abilities that allow you to more easily look up functions, their arguments, and the values that they take.

Typing a ? before the name of a command will open the help page for that command. As well as providing a detailed description of the command and how it works, scrolling to the bottom of the help page will usually show a collection of code examples which illustrate command usage. We’ll go through an example later.

Comparing things

We can also do comparison in R:

Tip: Comparing Numbers A word of warning about comparing numbers: you should never use == to compare two numbers unless they are integers (a data type which can specifically represent only whole numbers). Computers may only represent decimal numbers with a certain degree of precision, so two numbers which look the same when printed out by R, may actually have different underlying representations and therefore be different by a small margin of error (called Machine numeric tolerance). Instead you should use the all.equal function. Further reading: http://floating-point-gui.de/

Variables and assignment

We can store values in variables using the assignment operator <- , like this:

Notice that assignment does not print a value. Instead, we stored it for later in something called a variable . x now contains the value 0.025 :

More precisely, the stored value is a decimal approximation of this fraction called a floating point number .

Look for the Environment tab in one of the panes of RStudio, and you will see that x and its value have appeared. Our variable x can be used in place of a number in any calculation that expects a number:

Notice also that variables can be reassigned:

x used to contain the value 0.025 and and now it has the value 100.

Assignment values can contain the variable being assigned to:

The right hand side of the assignment can be any valid R expression. The right hand side is fully evaluated before the assignment occurs.

Variable names can contain letters, numbers, underscores and periods. They cannot start with a number nor contain spaces at all. Different people use different conventions for long variable names, these include

• periods.between.words
• underscores_between_words
• camelCaseToSeparateWords

What you use is up to you, but be consistent .

It is also possible to use the = operator for assignment:

But this is much less common among R users. The most important thing is to be consistent with the operator you use. There are occasionally places where it is less confusing to use <- than = , and it is the most common symbol used in the community. So the recommendation is to use <- .

Vectorization

One final thing to be aware of is that R is vectorized , meaning that variables and functions can have vectors as values. For example

This is incredibly powerful; we will discuss this further in an upcoming lesson.

Managing your environment

There are a few useful commands you can use to interact with the R session.

ls will list all of the variables and functions stored in the global environment (your working R session):

Tip: hidden objects Like in the shell, ls will hide any variables or functions starting with a “.” by default. To list all objects, type ls(all.names=TRUE) instead

Note here that we didn’t give any arguments to ls , but we still needed to give the parentheses to tell R to call the function.

If we type ls by itself, R will print out the source code for that function!

You can use rm to delete objects you no longer need:

If you have lots of things in your environment and want to delete all of them, you can pass the results of ls to the rm function:

In this case we’ve combined the two. Like the order of operations, anything inside the innermost parentheses is evaluated first, and so on.

In this case we’ve specified that the results of ls should be used for the list argument in rm . When assigning values to arguments by name, you must use the = operator!!

If instead we use <- , there will be unintended side effects, or you may get an error message:

Tip: Warnings vs. Errors Pay attention when R does something unexpected! Errors, like above, are thrown when R cannot proceed with a calculation. Warnings on the other hand usually mean that the function has run, but it probably hasn’t worked as expected. In both cases, the message that R prints out usually give you clues how to fix a problem.

It is possible to add functions to R by writing a package, or by obtaining a package written by someone else. As of this writing, there are over 10,000 packages available on CRAN (the comprehensive R archive network). R and RStudio have functionality for managing packages:

• You can see what packages are installed by typing installed.packages()
• You can install packages by typing install.packages("packagename") , where packagename is the package name, in quotes.
• You can update installed packages by typing update.packages()
• You can remove a package with remove.packages("packagename")
• You can make a package available for use with library(packagename)

Challenge 1 Which of the following are valid R variable names? min_height max.height _age .mass MaxLength min-length 2widths celsius2kelvin Solution to challenge 1 The following can be used as R variables: min_height max.height MaxLength celsius2kelvin The following creates a hidden variable: .mass The following will not be able to be used to create a variable _age min-length 2widths

Challenge 2 What will be the value of each variable after each statement in the following program? mass <- 47.5 age <- 122 mass <- mass * 2.3 age <- age - 20 Solution to challenge 2 mass <- 47.5 This will give a value of 47.5 for the variable mass age <- 122 This will give a value of 122 for the variable age mass <- mass * 2.3 This will multiply the existing value of 47.5 by 2.3 to give a new value of 109.25 to the variable mass. age <- age - 20 This will subtract 20 from the existing value of 122 to give a new value of 102 to the variable age.

Challenge 3 Run the code from the previous challenge, and write a command to compare mass to age. Is mass larger than age? Solution to challenge 3 One way of answering this question in R is to use the > to set up the following: mass > age [1] TRUE This should yield a boolean value of TRUE since 109.25 is greater than 102.

Challenge 4 Clean up your working environment by deleting the mass and age variables. Solution to challenge 4 We can use the rm command to accomplish this task rm(age, mass)

Challenge 5 Install the following packages: ggplot2 , plyr , gapminder Solution to challenge 5 We can use the install.packages() command to install the required packages. install.packages("ggplot2") install.packages("plyr") install.packages("gapminder")

Key Points Use RStudio to write and run R programs. R has the usual arithmetic operators and mathematical functions. Use <- to assign values to variables. Use ls() to list the variables in a program. Use rm() to delete objects in a program. Use install.packages() to install packages (libraries).

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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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I’m Joachim Schork. On this website, I provide statistics tutorials as well as code in Python and R programming.

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Problem set 1

Due by 11:59 PM on Thursday, January 18, 2024

Task 1: Introduce yourself to R, RStudio, and the tidyverse

Go the the example page for this week, “Welcome to R, RStudio, and the tidyverse” , and work through the different primers and videos in the four parts of the page.

It seems like there’s a lot on the page, but they’re short and go fairly quickly (especially as you get the hang of the syntax). Also, I have no way of seeing what you do or what you get wrong or right, and that’s totally fine! If you get stuck and want to skip some (or if it gets too easy), go right ahead and skip them!

Task 2: Make an RStudio Project

Use either Posit.cloud ( empty project here ) or RStudio on your computer (preferably RStudio on your computer! Follow these instructions to get started! ) to create a new RStudio Project. Refer to the example page you read in Task 1 for instructions

Create a folder named “data” in the project folder you just made.

Download this CSV file and place it in that folder:

In RStudio, go to “File” > “New File…” > “Quarto Document…” and click “OK” in the dialog without changing anything.

Delete all the placeholder text in that new file and replace it with this:

Save the Quarto file with some sort of name ( without any spaces! )

Your project folder should look something like this:

Task 3: Work with R

• Remove the text that says “PUT CHUNK HERE” and insert a new R code chunk. Either type ctrl + alt + i on Windows, or ⌘ + ⌥ + i on macOS, or use the “Insert Chunk” menu:

Follow the instructions for the three chunks of code.

Render your document as a PDF or Word file. Use the “Render” menu:

Upload the rendered document to iCollege.

You’ll be doing this same process for all your future problem sets. Each problem set will involve a Quarto file. You can either create a new RStudio Project directory for all your work:

Or you can create individual projects for each assignment and project:

• Data Visualization
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• Machine Learning in R
• Data Science in R
• Packages in R
• Poisson Regression in R Programming
• Convert string from lowercase to uppercase in R programming - toupper() function
• How to find SubString in R programming?
• Adding elements in a vector in R programming - append() method
• Quosures in R language
• Replicate elements of vector in R programming - rep() Method
• Calculate arc tangent of a value in R programming - atan2(y, x) function
• Calculate arc cosine of a value in R programming - acos() function
• Performing F-Test in R programming - var.test() Method
• Performing Binomial Test in R programming - binom.test() Method
• Spline Chart using R
• Accessing variables of a data frame in R Programming - attach() and detach() function
• Comparing values of data frames in R Programming - all_equal() Function
• Check if values in a vector are True or not in R Programming - all() and any() Function
• Convert type of data object in R Programming - type.convert() Function
• Multiline Plot using Plotly in R
• Bootstrapping in R Programming
• Finding the length of string in R programming - nchar() method
• Convert String from Uppercase to Lowercase in R programming - tolower() method

R Programming Exercises, Practice Questions and Solutions

R Programming Language is an open-source language mostly used for machine learning, statistics, data visualization, etc. R was developed by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand. R is similar to S programming language which is a GNU project created by John Chambers and his team at Bell Laboratories.

It comes with a command-line interface and provides a vast list of packages for performing tasks. It is an interpreted language that supports both object-oriented and procedural programming and it is available on widely used platforms e.g. Windows , Linux and Mac. You might have seen various R tutorials explaining the concepts and the theoretical part with some examples, but that is not enough to understand this language. You need more practice to make yourself perfect as practice will make you perfect.

This R Programming Exercise article will cover all R programming practice Questions and learn R Language . You can sharpen your R programming Skills using sets of questions from basic to advance, containing a well-explained and detailed solution to each question.

Table of Content Basics – R Programming (14 exercises with solution) Lists – R Programming Data Types – R Programming Strings – R Programming Functions – R Programming Loops – R Programming If Else – R programming Variable – R programming Vector – R Programming Matrix – R Programming DataFrame – R Programming Factor – R Programming Data and Time – R Programming CSV – R Programming Excel – R Programming

List of R Exercises with Solutions :

R programming language – basic exercises with solution.

• Write an R Program for “Hello Geeks”.Write an R Program to Add Two Vectors
• Find the Sum, Mean and Product of the Vector in R Programming
• Create an R Program to Take Input From the User
• How to Generate Random Numbers from Standard Distributions in R
• R Program to Sample from a Population
• Create an R Program to Find the Minimum and Maximum
• R Program to Sort a Vector
• How to Find the Factorial of a Number
• How to create R Multiplication Table
• Write an R Program to Check Prime Number
• R Program to check Armstrong Number
• R Program to Print the Fibonacci Sequence
• R Program to Check for Leap Year
• Check if a Number is Odd or Even in R Programming

R Programming Language – List Exercises with Solution

• Count the Number of List Elements in R
• Create a list with random values in R
• How to add Key Value Pair to List in R?
• Access Index Names of List Using apply Function in R
• Convert matrix to list in R
• Convert the list to a data frame with specific column names in R
• Convert list to array in R

Also, check: More Programs on Lists

R Programming Language – Data Types Exercises with Solution

• R Data Types
• Data Type Conversion in R
• Getting different data types in R Programming – a type of the () Function .

R Programming Language – String Exercises with Solution

• Convert Character String to Variable Name in R
• Count the Number of Characters in the String in R
• Count Number of Occurrences of Certain Character in String in R
• Extract Numbers from the Character String Vector in R
• Count the Number of Words in a String using R
• How to calculate the number of occurrences of a character in each row of the R data frame?
• Write a Program to Concatenate Two Strings in R.
• R Program to Find the Length of a String
• How to Check if Characters are Present in a String in R.
• R Program to Extract n Characters From a String
• How to Replace Characters in a String in R
• Create a Program to Compare Two Strings in R.
• R Program to Convert Factors to Characters
• R Program to Trim Leading and Trailing Whitespaces

Also, check: More Programs on Strings

R Programming Language – Functions Exercises with Solution

• Types of Functions in R Programming
• Function Arguments in R Programming

R Programming Language – Looping Exercises with Solution

• for loop to print the elements of a vector?
• The sum of parts in a vector using a for loop?
• Finding the maximum value in a vector using a for loop?
• Reversing a vector using a for loop?
• Counting the number of even and odd elements in a vector using a for loop?
• while loop to print the elements of a vector?
• while loop to find the first occurrence of a specific element in a vector?
• while loop to calculate the factorial of a number?
• while loop to calculate the square of numbers?
• while loop to reverse a string?
• Looping over Objects in R Programming
• repeat to print the elements of a vector.
• Repeat loop to generate random numbers until a number greater than 0.9 is generated?
• repeat loop to generate a sequence of numbers?
• Nested for loop to print multiplication tables up to a certain number.
• Nested for loop to create a 2D matrix.
• Nested for loop to print a pattern.
• Nested for loop to calculate the transpose of a matrix.

R Programming Language – If … Else Exercises with Solution

• Check if a number is positive or negative using if-else a statement.
• if-else to find the maximum of two numbers.
• Create a programme to assign grades based on a student’s score using if-else .
• Create a programme to categorize numbers into odd or even.
• if-else to check if a number is divisible by another number.
• if-else to categorize ages into different groups.
• if-else to check if a character string contains a specific substring.
• Grade Classification Based on Multiple Conditions.
• Nested if-else for Temperature Classification.
• Quadrant Classification for Coordinates.

R Programming Language – Variable Exercises with Solution

• R Variables
• Scope of The Variables
• How to Create Categorical Variables in R?
• Accessing variables of a data frame in R Programming – attach() and detach() function
• Select variables (columns) in R using Dplyr
• Dummy Variables in R Programming

R Programming Language – Vector Exercises with Solution

• How to create an empty vector in R?
• Create empty vector and append values
• Find the Sum, Mean and Product of a Vector in R
• Find the product of vector elements in R
• Count the number of vector values in the range with R
• Count the specific value in a given vector in R
• Access the last value of a given vector in R
• Find the elements of a vector that are not in another vector in R
• Find the Nth highest value of a vector in R
• How to find Nth smallest value in vector in R?
• Extract every Nth element of a vector in R
• R Program to Concatenate a Vector of Strings
• How to Check if a Vector Contains the Given Element
• Write an R Program to Count the Number of Elements in a Vector
• R Program to Find Index of an Element in a Vector
• Write an R Program to Access Values in a Vector
• R Program to Add Leading Zeros to Vector

Also, check: More Programs on Vectors

R Programming Language – Matrix Exercises with Solution

• How to create an empty matrix in R?
• Fill an empty matrix in R
• Elementwise Matrix Multiplication in R
• Multiply Matrix by Vector in R
• Find the power of a matrix in R
• Raise a matrix to a fractional power in R
• Get the element at the specific position from the matrix in R
• Find the row and column index of maximum and minimum value in a matrix in R
• Select rows of a matrix in R that meet a condition
• Multiply a matrix by its transpose while ignoring missing values in R

Also, check: More Programs on Matrices

R Programming Language – DataFrame Exercises with Solution

• How to Convert a List to a Dataframe
• R Program to Create an Empty Dataframe
• How to Combine Two Dataframe into One
• Create an R Program to Change the Column Name of a Dataframe
• How to Extract Columns From a Dataframe
• R Program to Drop Columns in a Dataframe
• R Program to Reorder Columns in a Dataframe
• How to Split Dataframe
• R Program to Merge Multiple Dataframes
• R Program to Delete Rows From Dataframe
• R Program to Make a List of Dataframes
• How to create a data frame from given vectors in R?
• Create an empty DataFrame with only column names in R
• Insert multiple rows in R DataFrame
• How to add a column to the data frame in R?
• Extract the first N rows from the data frame in R
• How to select the row with the maximum value in each group in R Language?
• Remove rows with NA in one column of the R DataFrame
• How to remove empty rows from the R data frame?
• Find columns and rows with NA in R DataFrame
• Sort DataFrame by column name in R
• How To Merge Two DataFrames in R?
• Append one data frame to the end of another data frame in R
• How to find common rows and columns between two data frames in R?

Also, check: More Programs on DataFrame

R Programming Language – Factor Exercises with Solution

• How to count values per level in a factor in R
• Find the levels of a factor of a given vector in R
• How to change the order of levels of a factor in R?
• How to convert factor levels to list in R?
• Concatenate two given factors in a single factor in R
• Get All Factor Levels of the DataFrame Column in R

Also. check: More Programs on Factors

R Programming Language – Date and Time Exercises with Solution

• How to Add and Subtract Days to and from Date in R?
• How to subtract time in R?
• How to Extract time from the timestamp in R?
• How to calculate the number of days between two dates in R?
• How to calculate Years between Dates in R?
• How to convert a factor into a date format?

Also, check: More Programs on Date and Time

R Programming Language – File Handling Exercises with Solution

• How to check if a file already exists in R?
• R – Check if a Directory Exists and Create if It does not
• Add New Line to Text File in R
• How To Import Data from a File in R Programming
• How to export dataframe to RDATA file in R ?

Also, check: More Programs on File Handling

R Programming Language – CSV Exercises with Solution

• Reading the CSV file into Dataframes in R
• Export CSV File without Row Names in R
• How to write to CSV in R without index?
• Append row to CSV using R
• How to calculate the mean of a CSV file in R?

Also, check: More Programs on CSV

R Programming Language – Excel Exercises with Solution

• How to import an Excel File into R?
• How to export a DataFrame to Excel File in R?
• Convert an Excel column into a list of vectors in R
• How to convert an Excel column to a vector in R?
• How to convert Excel content into DataFrame in R?
• Delete rows with empty cells from Excel using R

Also, check: More Programs on Excel

R Programming Language – Data Visualization Exercises with Solution

• Adding Colors to Charts in R Programming
• How to show legend in heatmap in R?
• Display All X-Axis Labels of Barplot in R
• How to Create a Stacked Dot Plot in R?
• Change Spacing of Axis Tick Marks in Base R Plot
• Add legends without borders and with white backgrounds in R
• Plot Shaded Area between vertical lines in R
• How to add the Mean and Median to Histogram in R?
• Create a Scatter plot from CSV in R
• Customizing Colors in Plots .
• Adding Legends to Plots
• Creating Interactive Plots using Shiny
• Annotating Text and Labels in Plots
• Formatting Axis Labels and Ticks in Plots
• Working with Multiple Plots (Faceting)
• Plotting Time Series Data in R
• Visualizing Geographic Data with Maps
• Creating Animated Plots in R
• Creating 3D Plots in R
• Working with Plotly for Interactive Visualizations
• Creating Trellis (Lattice) Plots in R
• Plotting Large Datasets with ggplot2’s geom_point() and geom_bin2d()
• Visualizing Hierarchical Data with Dendrograms
• Creating Sunburst Charts for Hierarchical Data
• Working with Word Clouds in R
• Network Visualization in R using graph
• Creating Heatmaps with Hierarchical Clustering
• Plotting Multiple Data Series in a Single Plot
• Interactive Data Visualization with Plotly Express

In Conclusion, R programming exercises are a complete guide for practising R programming Exercise Questions. After theoritical reading, The best way to master anything is by practice and exercise questions. Here you have the opportunity to practice the R programming language concepts by solving the exercises starting from basic to more complex exercises. A sample solution is provided for each exercise. It is recommended to do these exercises by yourself first before checking the solution. we hope, these exercises help you to improve your R programming coding skills. At present, the following sections are accessible, and we are diligently striving to incorporate additional exercises. Keep coding with enthusiasm!

R Programming Exercises – FAQs

1. what is the r programming language used for.

R is a language for statistical computing and graphics, commonly used in data analysis and visualization.

2. Is R free to use?

Yes, R is open-source and freely available for anyone to use, modify, and distribute.

3. Can I create plots and charts in R?

Yes, R has powerful libraries like ggplot2 that allow you to create a wide variety of visualizations easily.

4. What are packages in R?

Packages are collections of R functions and data, designed to extend the capabilities of R and make specific tasks easier.

5. How can I install packages in R?

You can install packages using the install.packages(“package_name”) command in the R console.

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R Practice 2: Sampling

February 5, 2015.

CS&SS/SOC/STAT 221, University of Washington, Winter 2015

This R practice will cover some concepts and material from Chapters 8 and 10 of BPS 5e .

For the assignment, answer the problems (marked Problem ) in this document. Some guidelines that you should follow for your submission:

• Use the template here .
• You should submit you practice assignment as a Word document (.doc or .docx).
• Write text (in full sentences!) to explain what your answer is and what you did.
• Include both the code that you ran, and the output of the code.
• Format code and text output in a monotype (typewriter) font such as Courier, Consolas, or Menlo.

Saving your code

Click on File -> New -> R Script . This will open a blank document above the console. As you go along you can copy and paste your code here and save it. This is a good way to keep track of your code and be able to reuse it later. To run your code from this document you can either copy and paste it into the console, highlight the code and hit the Run button, or highlight the code and hit command+enter on a mac or control+enter on a PC.

You will also want to save this script (code document). To do so click on the disk icon. The first time you hit save, RStudio will ask for a file name; you can name it anything you like. Once you hit save you’ll see the file appear under the Files tab in the lower right panel. You can reopen this file anytime by simply clicking on it.

If you reopen RStudio you can open an existing R Script with File -> Open File . Alternatively, a convenient menu item, especially if you have forgotten the location of the file, is to find and open the file using File -> Recent Files .

Chapter 8 of BPS discussed sampling, and Chapter 9 involved random assignment. To draw a simple random sample in R, you use the sample function.

For example, to draw a random sample of size 10 from a the numbers 1 to 100, use the following:

Note that in R, 1:100 is a way to write the sequence 1, 2, 3, …, 100 without having to write out all the numbers. Try running sample a few times and you will see that the results are different each time (and different from what you see here). Note that by default this sampling is done without replacement . That means that one an element is drawn from the population, that item cannot but drawn again (e.g. there cannot be more than one 5 in the sample).

Problem 1 Exercise 8.29 in BPS 5e involves data on a 12000-acre pine forest in Louisiana. To gather data on the forest, the U.S. Forest service created a grid of 1,410 equally spaced circular plots. Randomly select 1% of these plots (round the nearest integer) for a ground crew to visit. Do this 3 times. Note that these are unlikely to be the same plots.

Simulating an Opinion Poll

You can also use the sample function to simulate an opinion poll. Suppose that you want to ask an opinion poll on whether people like eating Kale. The two responses that you allow are “Yes” or “No”. It is often convenient to represent “Yes” as 1 and “No” as 0, for reasons that will be clear in a moment. Since this is Seattle, 75% of people like eating Kale. To simulate what would happen if you randomly sampled 20 individuals and asked that question, you do the following

In this usage, the function has two more arguments replace and prob . replace = TRUE means that you are sampling with replacement, meaning that your sample can have multiple 1s or 0s. This is because you are not giving sample the full population, but only the values present in the population, and using the prob argument to tell sample what proportion of individuals in the population have those values. The prob argument assigning a probability to each value (and these must sum to 1). In this case prob = c(0.75, 0.25) means sample the first value ( 1 ) with probability 0.75 and the second value ( 0 ) with probability 0.25. When using the prob argument check that you are assigning the probabilities to the values that you intend to.

The reason for using 1 and 0 instead of “Yes” and “No” is because it makes calculating the proportion of “Yes” trivial, since the proportion of 1s in a vector of 1s and 0s is just the mean of that vector.

Try doing this a few times.

Running that code multiple times is tedious. One way to run code multiple times is to use a for loop. The following code simulates that opinion poll 5 times and calculates the proportion who like kale.

That code is equivalent to running

However, the for loop is much more concise, especially as the number of times the task needs to be repeated increases.

Note that the code inside the for loop uses the print function. print is a function that prints the output to the console. R usually does that automatically in the console, but things work a little differently inside a for loop so it is needed if you want to see the output of mean.

Problem 2 BPS 5e Exercise 10.57 discusses an opinion poll of the American Public that shows that 65% have a favorable opinion of Microsoft. I am guessing that that number is different today, but lets go ahead with it nevertheless.

• Simulate drawing an opinion poll of 25 people. Repeat this 10 times. Then simulate drawing 100 people, and repeat this 10 times. Then simulate drawing 1000 people, and repeat this 10 times.
• How does the central tendency of the survey results change as the sample size increases?
• How does the spread of the survey results change as the sample size increases?

Parts of this lab were adapted from the OpenIntro Probability , which was released under a Creative Commons Attribution-ShareAlike 3.0 Unported .

Some data and examples come from Moore, The Basic Practice of Statistics , 5th ed.