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Getting Help with R

Helping yourself.

Before asking others for help, it’s generally a good idea for you to try to help yourself. R includes extensive facilities for accessing documentation and searching for help. There are also specialized search engines for accessing information about R on the internet, and general internet search engines can also prove useful ( see below ).

R Help: help() and ?

The help() function and ? help operator in R provide access to the documentation pages for R functions, data sets, and other objects, both for packages in the standard R distribution and for contributed packages. To access documentation for the standard lm (linear model) function, for example, enter the command help(lm) or help("lm") , or ?lm or ?"lm" (i.e., the quotes are optional).

To access help for a function in a package that’s not currently loaded, specify in addition the name of the package: For example, to obtain documentation for the rlm() (robust linear model) function in the MASS package, help(rlm, package="MASS") .

Standard names in R consist of upper- and lower-case letters, numerals ( 0-9 ), underscores ( _ ), and periods ( . ), and must begin with a letter or a period. To obtain help for an object with a non-standard name (such as the help operator ? ), the name must be quoted: for example, help('?') or ?"?" .

You may also use the help() function to access information about a package in your library — for example, help(package="MASS") — which displays an index of available help pages for the package along with some other information.

Help pages for functions usually include a section with executable examples illustrating how the functions work. You can execute these examples in the current R session via the example() command: e.g., example(lm) .

Vignettes and Code Demonstrations: browseVignettes() , vignette() and demo()

Many packages include vignettes , which are discursive documents meant to illustrate and explain facilities in the package. You can discover vignettes by accessing the help page for a package, or via the browseVignettes() function: the command browseVignettes() opens a list of vignettes from all of your installed packages in your browser, while browseVignettes(package=package-name) (e.g., browseVignettes(package="survival") ) shows the vignettes, if any, for a particular package. vignette() is employed similarly, but displays a list of vignettes in text form.

You can also use the vignette("vignette-name") command to view a vignette (possibly specifying the name of the package in which the vignette resides, if the vignette name is not unique): for example, vignette("timedep") or vignette("timedep", package="survival") (which are, in this case, equivalent).

Vignettes may also be accessed from the CRAN page for the package (e.g.  survival ), if you wish to review the vignette for a package prior to installing and/or using it.

Packages may also include extended code demonstrations (“demos”). The command demo() lists all demos for all packages in your library, while demo(package="package-name") (e.g., demo(package="stats") ) lists demos in a particular package. To run a demo, call the demo() function with the quoted name of the demo (e.g., demo("nlm") ), specifying the name of the package if the name of the demo isn’t unique (e.g., demo("nlm", package="stats") , where, in this case, the package name need not be given explicitly).

Searching for Help Within R

The help() function and ? operator are useful only if you already know the name of the function that you wish to use. There are also facilities in the standard R distribution for discovering functions and other objects. The following functions cast a progressively wider net. Use the help system to obtain complete documentation for these functions: for example, ?apropos .

The apropos() function searches for objects, including functions, directly accessible in the current R session that have names that include a specified character string. This may be a literal string or a regular expression to be used for pattern-matching (see ?"regular expression" ). By default, string matching by apropos() is case-insensitive. For example, apropos("^glm") returns the names of all accessible objects that start with the (case-insensitive) characters "glm" .

help.search() and ??

The help.search() function scans the documentation for packages installed in your library. The (first) argument to help.search() is a character string or regular expression. For example, help.search("^glm") searches for help pages, vignettes, and code demos that have help “aliases,” “concepts,” or titles that begin (case-insensitively) with the characters "glm" . The ?? operator is a synonym for help.search() : for example, ??"^glm" .

RSiteSearch()

RSiteSearch() uses an internet search engine (also see below ) to search for information in function help pages and vignettes for all CRAN packages, and in CRAN task views (described below ). Unlike the apropos() and help.search() functions, RSiteSearch() requires an active internet connection and doesn’t employ regular expressions. Braces may be used to specify multi-word terms; otherwise matches for individual words are included. For example, RSiteSearch("{generalized linear model}") returns information about R functions, vignettes, and CRAN task views related to the term "generalized linear model" without matching the individual words "generalized" , "linear" , or "model" .

findfn() and ??? in the sos package, which is not part of the standard R distribution but is available on CRAN, provide an alternative interface to RSiteSearch() .

help.start()

help.start() starts and displays a hypertext based version of R’s online documentation in your default browser that provides links to locally installed versions of the R manuals, a listing of your currently installed packages and other documentation resources.

R Help on the Internet

There are internet search sites that are specialized for R searches, including search.r-project.org (which is the site used by RSiteSearch ) and Rseek.org .

It is also possible to use a general search site like Google , by qualifying the search with “R” or the name of an R package (or both). It can be particularly helpful to paste an error message into a search engine to find out whether others have solved a problem that you encountered.

CRAN Task Views

CRAN Task Views are documents that summarize R resources on CRAN in particular areas of application, helping your to navigate the maze of thousands of CRAN packages. A list of available Task Views may be found on CRAN.

R FAQs (Frequently Asked Questions)

There are three primary FAQ listings which are periodically updated to reflect very commonly asked questions by R users. There is a Main R FAQ , a Windows specific R FAQ and a Mac OS (OS X) specific R FAQ .

Asking for Help

If you find that you can’t answer a question or solve a problem yourself, you can ask others for help, either locally (if you know someone who is knowledgeable about R) or on the internet. In order to ask a question effectively, it helps to phrase the question clearly, and, if you’re trying to solve a problem, to include a small, self-contained, reproducible example of the problem that others can execute. For information on how to ask questions, see, e.g., the R mailing list posting guide , and the document about how to create reproducible examples for R on Stack Overflow.

Stack Overflow

Stack Overflow is a well organized and formatted site for help and discussions about programming. It has excellent searchability. Topics are tagged, and “r” is a very popular tag on the site with almost 150,000 questions (as of summer 2016). To go directly to R-related topics, visit http://stackoverflow.com/questions/tagged/r . For an example both of the value of the site’s organization and information that is very useful to R users, see “How to make a great R reproducible example?” , which is also mentioned above.

R Email Lists

The R Project maintains a number of subscription-based email lists for posing and answering questions about R, including the general R-help email list, the R-devel list for R code development, and R-package-devel list for developers of CRAN packages; lists for announcements about R and R packages ; and a variety of more specialized lists. Before posing a question on one of these lists, please read the R mailing list instructions and the posting guide .

Materials for teaching

RStudio offers several resources to make it easier for you to teach R, ranging from semester-long courses to more intense (but much shorter) workshops.

Most teachers enjoy developing their own instruction materials, but the need for exercises, homework, exams, slides, and other supporting materials make this a big job. Below are some open source materials developed at RStudio and elsewhere that you can freely adapt and use for your R teaching.

Get a complete data science course in a box . Data Science in a Box contains the complete materials for teaching a semester-long introductory data science course. The “box” contains materials for an undergraduate level introductory data science course, such as slide decks, homework assignments, guided labs, sample exams, a final project assignment, as well as materials for instructors such as pedagogical tips, information on computing infrastructure, technology stack, and course logistics. The website exposes the source materials that live in a GitHub repository and use datasets from the dsbox package .

Teach with STAT 545 . STAT 545 is a course in data wrangling, analysis, and exploration in R with RStudio. Although the course was designed as a graduate-level, semester-long introduction to data science by Dr. Jennifer (Jenny) Bryan , the free online materials Jenny developed have been a valuable resource for self-directed learners and other educators. In Jenny’s own words, STAT 545 was designed to teach “everything that comes up during data analysis except for statistical modelling and inference ." The education team at RStudio has recently ported the original materials into a modern and more maintainable bookdown website .

Use R for Data Science in the classroom. Many educators use the free online book R for Data Science as a course textbook. There is also an R for Data Science Instructor’s Guide , which contains notes for people teaching R for Data Science with each chapter’s learning objectives and key points. The “unofficial” R4DS Solutions Manual , a community resource developed by and for educators, is also helpful if you want to use R4DS in the classroom.

Many of you may have taken workshops from the RStudio team, but did you know that all of RStudio’s workshop materials are available for you to use in your own workshops? Below are a few of the more popular workshop respositories proven popular with teachers:

Teach the Tidyverse. Master the Tidyverse is an award-winning two-day introduction to doing data science with the Tidyverse . This repository contains instructor materials (Keynote slides and exercises) for teaching this workshop. You can teach the workshop as is, adapt it to your needs, or divide it into two one day workshops: Welcome to the Tidyverse , which covers Exploratory Data Analysis, and Data Wrangling with the Tidyverse . See the README for teaching tips.

Teach Shiny. This workshop is designed for those who want to up their teaching Shiny game, and is particularly aimed at training partners who want to qualify as an RStudio Certified Shiny Instructor and at those who are R and Shiny advocates in their organizations.

Teach R Markdown. If you want to teach R Markdown, we have designed several workshops for teaching the basics to the more advanced topics. You can find the materials for a full two-day workshop on Advanced R Markdown ( source ), a four-hour introductory workshop on R Markdown for Medicine ( source ) aimed at clinical researchers, and a full-day workshop on Communicating with R Markdown ( source ).

Teach everything else! At the RStudio Education GitHub Organization instructors can find the materials for all workshops taught by the RStudio Education team. All of these workshop materials are openly-licensed and freely-available for reuse. Please follow the reuse guidelines outlined in the licenses in the specific repositories, and enjoy leveraging quality teaching materials designed and developed by our team!

  Take me to: tools for teaching .

Secure Your Spot in Our Data Manipulation in R Online Course Starting on July 15 (Click for More Info)

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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R programming existed as one of the oldest programming languages in the world. With the sudden boom in data science, it has gained immense popularity and students want to understand the subject thoroughly. But R is not an easy language and students need a lot of time to get command over this language. That is the reason students are always on the lookout for expert R help online.

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R is a programming language and software package setting for applied mathematics analysis, graphics illustration, and coverage. The core of R is an interpreted computer language that permits branching and iteration like programming exploitation functions. R permits integration with the procedures written within the C, C++, .Net, Python, or algebraic language. R is a well-developed, easy and effective programming language with good data handling and storage facility It also provides a collection of operators for calculations on arrays, lists, vectors, and matrices.

R is not only used in academics, but many large companies like Google, Uber, Airbnb, Facebook use an R programming language in their products. The most frequent use of R programming is data analysis. Data analysis with R is done in a step-by-step manner like programming, transforming, discovering, modeling, and communicating with the result. Apart from data analysis, R programming is also used for statistical inference and machine learning algorithms.

Being a complex programming language, students always face challenges while working with the R language. It is very tough to learn and understand complicated statistical tools and techniques used by R as a coding language. Let us go through some of the key topics in R where students find a hard time working with.

Key Topics in R

Let us understand some of the key topics of R programming language below:

• Function: A function could be a set of statements organized along to perform a particular task. R contains a sizable amount of in-built functions and also the user will produce their own functions.
• Matrices: Matrices are the R objects during which the element is organized during a two-dimensional rectangular layout. They contain parts of equivalent data types. Although we will produce a matrix containing solely characters or solely logical values, they're not of a lot of use.
• Vectors and Lists: Vectors in R are homogeneous data structures that contain elements of the same data type mostly integer, character, numeric or logical. Lists are heterogeneous data structures containing elements of mixed data types.
• Data Frames: A Data frame could be a table or a two-dimensional array-like structure during which every column contains values of 1 variable row contains one set of values from each column.
• Factors: Factors in R are data structures that signify levels and are the most appropriate for categorical variables
• R packages: Packages in R are basically libraries containing a set of library functions. For example: dplyr is an R package that contains library functions, mutate(), select(), filter(), summarise() and arrange(). In a nutshell, dplyr is the data manipulation package of R. Some other common packages of R include stats, superml, tree, MASS, ggplot2, etc.

ML Algorithms in R Programming

Below are the 6 common machine learning algorithms which are applied using R programming language-

• Linear Regression: lm() under the stats package is used for training a Linear Regression Model on Training Data in R. It models a linear relationship between features, X, and continuous target y.
• Logistic Regression: glm() under the stats package is used for training a Logistic Regression Model in R. It models a linear decision boundary for the classification of data points.
• Naive Bayes: naive_bayes() under the naive Bayes package is used for training a Naive Bayes Model in R. It is a classification algorithm that is based on Prior and Posterior Probabilities.
• SVM: SVM() under the e1071 package is for training a Support Vector Machine Model in R. Both regression and classification can be performed along with density estimation.
• Decision Tree: tree() under the tree package is for training a Decision Tree in R. Like SVM, both regression and classification can be performed by binary recursive partitioning.
• KNN: kNN() under the DMwR package is for training a k-nearest neighbor model in R. It also performs Data Normalization before training the model with Training Data.
• Clustering: kmeans() under the stats package performs k-Means Clustering on the given data matrix in R. It is an Unsupervised ML Algorithm that is used for segmentation and data-grouping on unlabelled data.

Which R Programming Concepts are Most Difficult for Students?

The following are some subtopics with which students usually face problems, and our R programming experts can help you with them:

• Complex Coding Structures: R programming involves intricate coding structures that can be intimidating for newcomers. Understanding the unique syntax and structure of R can be a significant challenge, especially for those new to programming.
• Statistical Complexity:  R is widely used for statistical analysis, which means students must grapple with complex statistical concepts. Learning to apply these concepts effectively within the R environment can be demanding.
• Data Manipulation Hurdles: While R is known for its data manipulation capabilities, students may struggle to harness this power. Handling and transforming data, especially with large datasets, can be perplexing for beginners.
• Package Puzzles: R relies on packages and libraries to extend its functionality. Selecting the right packages and understanding how to install and utilize them can be a source of confusion for students.
• Debugging Dilemmas: Just like any programming language, R can produce errors and bugs in code. Troubleshooting and deciphering error messages can pose challenges for students.
• Time-Consuming Tasks: R assignments, particularly those involving extensive data analysis or intricate statistical models, can be time-consuming. Balancing these assignments with other coursework can be demanding.
• Limited Practical Experience:  While students might grasp the theoretical aspects of R, applying it to real-world problems can be challenging. Practical experience is crucial for bridging this gap.
• Scarcity of Helpful Resources:  Finding accessible and informative documentation and support resources for R can be a struggle. Students often need reliable sources to clarify doubts and enhance their learning.
• Visualization Vexation: R excels in data visualization, but crafting meaningful and visually appealing plots and graphs can be a daunting task for students.
• Keeping Up with Updates: R is a dynamic language with frequent updates and new packages. Staying current with the latest features and best practices can be a demanding task for students.

Advantages and Features of R programming language

R is one of the most used programming languages for statistical and analysis purposes. But just like every other programming language, R has its own set of advantages. Check out some of this advantages in detail below:

• R language is very flexible and hence it is prominently used in the field of data science and statistics. Apart from this, it is also used in the field of biology and genetics to draw out predictions and analyses.
• As R language is a vector programming language, it is easy to add functions to a single vector without putting it in a loop.
• It is the best option for business as it is one of the affordable programming languages. It provides amazing visualizations and graphics.
• It enables us to perform virtual statistical computation in a short amount of time and provide error-free content.
• R programming language provides the best and most effective data handling and storage facilities.
• It allows the user to perform multiple calculations at the same time using a single command.
• R programming language can work on multiple operating systems like Mac, Windows, Linux, etc.
• It supports the data frames, arrays, and various other data types at the same time and it is also easily compatible with other programming languages.

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What Is The R Programming Language?

R is an open-source programming language. It is also a free software environment for statistical computing and graphics. Likewise, the S language is also a GNU project. Robert Gentlemen and Ross Ihaka have developed the R programming language in New Zealand. It is used for statistical computations and data analysis. R is the best programming language to perform statistical techniques such as Regression modeling, classical statistical tests, time-series analysis, data mining, classification, clustering, etc.

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These are some of the R programming libraries on which our experts provide help.

How is R programming helpful?

Being an open-source programming language, R is used for easy upgrades, and it always helps in saving money for various companies. Moreover, R has compatibility with the cross-platforms. It means you can run R on Mac OS X, Windows, and Linux. Users can import data from Microsoft Access, SQLite, Microsoft Excel, MySQL, Oracle, and more.

Besides all these facts, R programming is a scripting and powerful language. That is why it is the best for large and resource-intensive simulations. R also provides high-performance over the computer clusters. In the end, we can say that R programming supports its users in multiple ways. Our experts will assist you with all these when you avail of our R programming assignment help services

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What Is R Studio?

In order to install the R programming language, we'll need an IDE (Integrated Development Environment). Do you know what an IDE is? if you've worked with other programming languages you have heard about IDE.

An IDE is a platform in which we need to install a programming language. For example, in a python programming language, we use Jupyter notebooks as an (IDE). We use the Eclipse IDE for Java development. Similarly, R-Studio is the IDE for the R programming language. It assists you in making your programming effort more manageable. Our specialists will assist you in your R Programming Assignment help including R studio assignment help.

Advantages of R Programming Language - You Should Know

R is a popular programming language used for statistical analysis, data visualization, and machine learning. Here are eight advantages of R:

Open source

R is an open-source programming language, which means that anyone can access, use, and modify its code. This makes it easier for users to customize and enhance their analyses as needed.

Powerful statistical analysis

R is widely used for statistical analysis and modeling, with a vast library of functions and packages available. It is particularly well-suited for exploratory data analysis, linear and nonlinear modeling, and time-series analysis.

Data visualisation

R has a wide range of powerful visualization tools, including ggplot2, lattice, and base graphics. These tools make it easy to create informative and visually appealing charts, graphs, and plots.

Integration with other languages

R can be easily integrated with other programming languages, such as Python and SQL. This allows users to take advantage of the strengths of each language and use them together in their analyses.

Large and supportive community

R has a large and active community of users, which means that users can get help, share code, and collaborate with others easily.

Reproducible research

R supports reproducible research by providing tools for creating reports and documents that contain both code and results. This makes it easy to share and reproduce analyses, which is particularly important for scientific research.

Machine learning

R has a wide range of machine learning libraries and packages, including caret, randomForest, and neuralnet. These tools make it easy to build and deploy machine learning models for a variety of applications.

Cross-platform compatibility

R is available on a variety of platforms, including Windows, macOS, and Linux. This makes it easy for users to work with R on the platform of their choice.

Disadvantages Of R Programming Language

R programming has weak origin.

R is related to a much older programming language called "S." Its base package, thus, does not allow dynamic or 3D graphics. It is possible to produce vibrant, 3D, and animated visuals using ordinary R tools such as Ggplot2 and Plotly.

Basic Security

R is insecure in many ways. Most programming languages, such as Python, include the essential feature of security. As a result, R has several limitations, including the inability to be incorporated in a web application.

The language that is difficult to understand

R is a complex language to master. The learning curve is very sharp, which makes this language difficult to master. As a result, those who have never programmed before may find it challenging to learn R.

Let's Check How Our Experts Help Students In Data Visualization Using R Programming In R Assignments?

Students are assigned multiple R programming assignment tasks. And data visualization is one of those tasks that students find the most difficult. That is why our specialists always support the students in their hour of need to deliver the best R Programming Assignment help service.

Below, we have given a sample work done by our R programming experts. This is performed using the package "ggplot2." It is one of the open-source data visualization packages. And it is used for data visualization using the statistical programming R.

Adding the shape and color

Statistics layer

Adding coord_cartesian()

Is The R Programming Language Difficult To Learn?

Many years ago, R was considered a difficult programming language as it was confusing and less structured as compared to other coding languages. Hadley Wickham created various packages to make it faster, easier, and more fun. Now, graph creation is not difficult anymore. With our R Programming assignment help service, anyone can easily implement the best algorithms of machine learning.

R can easily communicate with other programming languages such as Java, Python, C++, etc. Moreover, R provides connectivity with different databases like Hadoop or Spark. Various packages such as TensorFlow and Keras enable the creation of high-quality machine learning techniques. Moreover, it provides a package to perform Xgboost, which is the most suitable algorithm for the Kaggle competition.

We understand that R programming can be challenging, and we're here to help! Our team of experts provides top-quality R programming assignment help to make sure that you thoroughly understand the concepts and techniques. We deliver error-free assignments that are created by humans, which are not just automated algorithms, as other assignment providers do.

Our R programming assignment help is designed to provide students of all levels, from beginners to advanced learners. We understand that each student has unique learning needs and styles, and we take a personalized approach to each assignment. Our expert team makes sure that you receive the best help that is as per your needs.

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We have a group of professionals who will support or guide you through any challenges you may have when beginning your R programming assignment until you finish it. Many students find it challenging, yet they have to complete it and submit it on time. In this situation, we'll provide you with the best R assignment help available on the internet. We have highly qualified professors and programmers who will work on your project to make your R assignment help online quickly and finest. We also assure you that your assignment is 100% error-free, and you will get the best grades.

R programming is the language used by statisticians. All our statistics experts are well aware of the R programming concepts. So you need not worry about the quality or complexity of your assignment. Just submit your requirement to us, and we will examine your requirement and assign the best experts to your work. Whether you need the basic R assignment help or R programming help, we provide the complete R programming assignment help solutions.

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As you might already know that, R programming is one of the oldest programming languages in the world. With the sudden boom in data science, it has gained some large amount of popularity, and students want to understand the subject thoroughly. On the other hand, R is a difficult language to learn, and students must devote significant time to mastering it. That is why students are always looking for instant R programming homework help.

If you are concerned about completing your R programming, assignment or homework, please contact Statanalytica.com. We have a team of experts who are R programming professionals with more than 5 years of experience working on any R-related problem. As a result, we have help hundreds of students, and you could be the next to benefit from our R programming homework help. So, what are you waiting for get the best R programming assignment help now at a very affordable price.

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Our priority is Students' satisfaction. That is why we never compromise on R assignments' quality and offer the best solution at pocket-friendly charges. Anyone can afford our R programming assignment help services at the best price. We guide the students in every step of their R programming to clear their doubts related to R programming assignments. We assured that the students could easily clear their doubts with our provided R assignments solutions.

We are working 24x7 to offer you the best R programming assignment writing services. Our Experts can offer you R programming help services on almost every topic, i.e., data mining, data analysis, statistical analysis, data visualization, and many more. You can contact us via email, live chat support, or call back services. Whether you need R programming help or any other statistics or programming project help, you can ask our experts for help anytime. Try us now with our R help online or R assignment help service!

The majority of students are unable to find the best R programming help for themselves. There are thousands of R programming homework help providers across the globe. But it is the most challenging job for the students to find the best R programmers or R studio experts or professionals who provide the best R help online. The majority of students are also looking for the best answer to "how to do my R homework." We offer you the best R programming homework help at pocket-friendly charges. Our R studio experts have plenty of years of experience in delivering the finest quality R Programming assignment help online.

Our homework solutions will help you to get high grades in your R homework. Our qualified and experienced R studio (programming hw help) experts are proficient in the R programming language, and thus they can solve all the queries related to the R homework. If you need R programming homework help, then we are here to help you anytime. Just submit your work to get instant and best R programming homework help online from the experts.

Can I get RStudio assignment help from R programming experts?

Yes, our experts are well-versed that RStudio is one of the IDEs (Integrated Development Environment) that is used to write the R programs for graphics and statistical computing. That is why they always deliver the best R help online and RStudio assignment help or R Studio assignment help. Our service always believes in helping the students so that their knowledge gets improved.

Just because of this, our experts are always ready to assist the students in the hour of need and without compromising with the RStudio and R assignments and solutions. That is why if you need instant and pocket-friendly assignment solutions, always contact our dedicated R help online professionals. Because of our 24/7 availability, we are accessible to the students wherever they need our R programming assignment help experts.

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RStudio is used to create open-source and free software using R programming for scientific research, data science, and technical communication. That is why tutors of the universities and colleges assign the students with different Rstudio homework problems to enhance their knowledge. But because of the less knowledge of the RStudio, students are unable to understand the problems. As a result, they are not able to write 100% accurate solutions for their R assignments.

In this kind of situation, it is always beneficial to take the best RStudio homework help online. Here, you select our service as we deliver quality homework solutions and the best R programming help. You do not need to follow too hard or strict rules to get our experts' help. Just use our live chat support option and connect with us within seconds. Because of the easy connection methods, we are considered to be the world-class R help, online providers.

We have a team of highly experienced, qualified, and trained experts to solve your different topics related to R programming. Our Experts will cover various topics involved in the R Programming assignment help service. Some of the essential topics are below-

R Programming Assignment Help Project That We Cover

Here are some of the R programming assignment help projects that we cover which are as follows:

Note: These projects are just examples; we cover almost every project/ topic in our R programming assignment help. You can contact us to know more about topics and projects.

row_names = c("row1", "row2", "row3") col_names = c("col1", "col2", "col3") M = matrix(c(1:9), nrow = 3, byrow = TRUE, dimnames = list(row_names, col_names)) print("Original Matrix:") print(M) print("Access the element at 2rd column and 3rd row:") print(M[3,2]) print("Access only the 1st row:") print(M[1,]) print("Access only the 2nd column:") print(M[,2])

"Original Matrix:" col1 col2 col3 row1 1 2 3 row2 4 5 6 row3 7 8 9 "Access the element at 2rd column and 3rd row:" 8 "Access only the 1st row:" col1 col2 col3 1 2 3 "Access only the 2nd column:" row1 row2 row3 2 5 8

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Of course! Our experts provide you the best and detailed solutions with research data for your queries. This will not only help you to improve your grades but also improve your knowledge.

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Which Students Seek R Programming Assignment Help?

Somehow counter-intuitively, R statistics help have become the most popular service that we offer. Because of that, we have a clear picture of who our clients are. We can segregate students seeking R homework help into three categories, based on the subject that they study:

• Statistics/Math students (~25%)
• Medical/Biology students (~15%)
• Social science students (~60%)

Let’s analyze these groups one-by-one:

Statistics/Math students are the ones that already have a background in programming and math. R programming should be easier for them, but many still seek help. Why is that? The main reason is poorly defined curriculum (including poor education quality in low-ranking universities) – the professors require to learn a lot and quickly while putting all the responsibility to learn on students. This is an unfeasible “the more you struggle, the better person you become” strategy. We compensate for the lack of effort your statistics professor makes. Advanced statistics proofs are what such students face daily - it is "not too hard" once you grasp it. But before that (during most of your studies...) you may need statistics homework help.

Medical/Biology students are required to know statistics for proper evaluation of diagnosis and development of treatment plans (of humans or ecology) - and statistics works best when applied to big datasets. And, of course, you need statistics software for that analysis, which most often is chosen as R. P-value evaluation, reinforced learning and other complex decision strategies are a lot to consider on top of getting used to RStudio. Interpretation of the data is not the same as writing the code to perform the statistical test. Everybody knows how tough medical student life already is. Learning to program (very rarely medical students have prior programming experience) sometimes is just too much.

Social science students (economics, political science, psychology, business, etc.) are the majority of our clients. Most of them have never considered learning advanced math and especially programming. This was a mistake that is hard to compensate for in a short amount of time. Hypotheses of social phenomena still originate brains-first, but hypothesis testing must be completed using RStudio. The more computer-literate students are, the more often R data analysis becomes the main tool for testing hypotheses. This is a major trend in social sciences – using population datasets for generating insights, theories, and plans for real action.

An example R assignment and solution of economics or political science student: plot of US monthly unemployment rate by the president:

R Versus Statistics Homework Help - What's the Difference?

The difference is one of practicality. It is one of the old philosophical discussions of data versus theory, thinking versus acting, and so on. You can complete R homework without understanding what statistics logic is behind all that, the same way most of us use computers without knowing how they work. If you look at it from a different perspective - you cannot complete many calculations only mentally, no matter how good your theory is. This is why Excel wins against accounting on paper.

However, it does not matter if it is R or statistics help you seek - the homework help we provide is almost the same in both cases. You don't have trouble using pen and paper for statistics, but you may face trouble setting up your libraries (and the correct version) in RStudio. This is a ridiculous example, of course. By that, we want to emphasize that the difference is mostly in your mind on how you really understand what you are doing. The statistics degree covers it all, but as most are not statisticians - you were likely asked just to "analyze the dataset in R for your homework".

In conclusion - we send you solutions and explanations in both cases. This discussion brings us to the next question.

Why Do Students Have a Hard Time with Statistical Data Analysis?

If we look at the dimensions other than the study subject itself, every student has personal issues from time to time. It just happens that some medical or financial problems do not leave enough time for the hands-on study of statistics and programming.

Interestingly, exchange students are very popular among our clients. The accumulation of life problems, changes in the environment, and general study issues force them to seek assistance with R programming assignments.

In general, professors assume the high computer literacy level of students by default. Most often they don’t even think to tell the difference between R programming language and RStudio interactive developing environment. The confusion builds up and even simple console error understanding becomes an issue.

Problems with R / R Studio

More specifically, R syntax is unlike normal programming language. Many programmers get confused when switching to R (most often from Python or Matlab as they are used for similar data analysis purposes). Common R console errors are caused by:

• Libraries not installed or out-of-date
• The file one tries to read is not placed in a working directory
• Confusion between R (.R), R Markdown (.Rmd) and R Notebook / HTML Notebook (also .Rmd, .nb.HTML)

We just cannot ignore this – too many students have issues with knitting the R Markdown solution. Overall, there are three ways you can write R code:

• Regular R code
• R Markdown cells
• R Notebook (almost the same as Rmd, but different!)

What is R Markdown and How Does It Differ from R?

Using R Markdown you "knit" the solution - it means you execute R code and save the results into various selected formats. Why do we need R Markdown at all? It provides extra functionality like:

• Automatically creating HTML files to open the solution in a web browser
• Creating PDF. No need to copy your code and answer from the console to a Word document!
• Creating a Word document at once
• Knit to Latex directly (this might save a lot of time…)

What is R Notebook / HTML Notebook and How Does It Differ from R?

R Notebook is very similar to R Markdown. It allows users to create nice-looking reports directly in R without having to copy R code and results to Word or other programs. Both methods allow the user to create an HTML file that includes R code and output alongside the comments of the analysis. However, R Markdown "knits" the HTML, which means that the code is executed during the "knitting" process. R Notebook, on the other hand, simply previews the output that you have already obtained by running the code in your RStudio so there is a risk of compiling an HTML file that does not show the output of all the code or is missing some part of the code that you executed but forgot to include in the notebook.

The R homework help share among regular R, Rmd, and R Notebook is as this:

• R assignment (50%)
• Rmd assignment (45%)
• R Notebook assignment (<5%)

All three homework cases are coded using RStudio! Hope this helps to reduce the confusion about this technical part of R.

Problems with Statistics Homework

Statistical concepts are usually expressed in lecture slides or books using mathematical formulas that are hard to decode; however, they are often intuitive if explained in plain language (since everybody acts on such hidden brain in-built statistics by making daily choices). An extra step from understanding math is implementing solutions in the programming code. R is somewhat helpful at times because it already has functions prepared for the most popular statistical analysis. However, you still need to understand what is being calculated to correctly interpret the results and properly check that your data meets all the assumptions. This is why most statistics assignments ask you to “explain the answer in layman's terms”, this way preparing you for real-life interactions between statisticians and business managers or customers.

When Paying for R Homework Solution Is Worth It?

The answer is context-dependent and complex: it all depends on the situation. People often advise paying attention to the long-term consequences of your actions. The cons of buying R homework solutions are that you spend less time struggling with the task, which means you remember the solution process less. On the other hand, you exchange this time for analyzing the solution itself (not the process).

There are little or no long-term benefits if, for various reasons, you cannot pass a class on your own. This is why statistics homework help is valuable. It solves the problem where it is now because the future is always uncertain.

The biggest advantage of buying R homework help is that you get rid of excessive stress. And as mentioned before, stress (in moderate amounts) facilitates your memorization and learning processes, but too much stress jeopardizes your chances. Where professors fail at personalized education – we assist you in filling in the gaps in R programming knowledge.

What R Libraries Can We Help with?

Our experts can help you work with any R libraries like:

• tidyverse (general R library including some of the libraries below)
• plotly (interactive graphs)
• stargazer (beautiful regression tables)
• Shiny app (this might be extremely challenging for R newbies!)

You may need R homework help because it is not always possible to google the answer on your own. Most of the libraries mentioned above have well-written documentation, but custom cases are not covered there. You have few choices then: 1) dig into library code 2) persistent trial and error combined with searching in forums online 3) ask for help with RStudio from an expert (most often it is your course's technical advisor).

Which Statistics and Econometrics Books Can We Help with?

We provide R Programming assignment help for solving exercises from such difficult statistics and econometrics books as (in fact, this is also our recommendation list for top quality R resources):

• Regression models from Jeffrey M. Wooldridge "Introductory Econometrics: A Modern Approach"
• Time series analysis in "Forecasting: Principles and Practice" by Rob J. Hyndman and George Athanasopoulos
• Introduction course book called "R for Data Science" by Hadley Wickham and Garrett Grolemund
• Regression analysis, hypothesis testing, Tidyverse: "Statistical Inference via Data Science" by Chester Ismay and Albert Y. Kim
• Simon J. Sheather, "A Modern Approach to Regression with R"
• Christopher R. Bilder, Thomas M. Loughin, "Analysis of Categorical Data with R"
• Brian S. Everitt, Torsten Hothorn, "A Handbook of Statistical Analyses Using R"
• Andy Field, Jeremy Miles, Zoe Field, "Discovering Statistics Using R"
• Fred Ramsey, Daniel W. Schafer, "The Statistical Sleuth: A Course in Methods of Data Analysis"
• Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, "An Introduction to Statistical Learning with Applications in R"
• Paul M. Kellstedt, Guy D. Whitten, "The Fundamentals of Political Science Research"
• R. Carter Hill, William E. Griffiths, Guay C. Lim, "Principles of Econometrics"
• James H. Stock, Mark W. Watson, "Introduction to Econometrics"

Should You Consider Looking for Programs Other than R?

So you are uncertain if it is worth investing in learning R; why your professor thinks it is worth it? R programming language is widely used for statistical computing in universities and industries. It is free yet powerful , working both on Linux, Mac, and Windows.

• Easy to create professional-looking graphs (specifically with ggplot2 library)
• Lots and lots of built-in functions for statistical tests
• Effective data storage and garbage collection
• Supports both procedural and object-oriented programming
• Has a coherent, large, and integrated collection of intermediate tools for data analysis
• RStudio is a competitive GUI, not worse than alternatives

We also have experience in Python , Matlab, and SQL for data analysis. Overall, R is our top choice. In our opinion, it is worth investing your time getting around the initial R struggles and once it is done - you will never look back. There are reasons R is in the top 10 most popular programming languages while being built for statisticians and data analysts.

Most Popular Statistics Assignments with R

• Interpretation of coefficients
• The linearity of the relationship between the dependent and independent variables
• Normality of residuals
• Homoscedasticity of residuals
• No multicollinearity
• No outliers and influential observations
• T-test (equality of means for two groups)
• ANOVA (equality of means for three or more groups) and posthoc tests
• Chi-squared test for categorical data
• Coefficient interpretation
• Calculating odds ratios
• Estimating probabilities
• means, proportions, variance, other statistics
• Autoregressive and moving average models
• Various smoothing methods
• Tests for stationarity
• Detecting and removing trend and/or seasonality
• Kaplan-Meier curves
• Estimating survival probabilities
• Decision Trees
• Random Forest
• Bayesian Classifiers
• Mixture Models
• Bootstrapping
• Probability estimates by sampling
• Gambling games analysis
• Card game simulations
• Regular computer science practice: loops, conditionals, etc.
• String manipulations, parsing, etc.
• Operation research, linear programming

A standard visualization taken from the R assignment solution looks like this:

Economics and Econometrics Assignments

Both economics and econometrics (application of the statistical methodology to economic data) theory add extra constraints on the way modeling is done. These constraints restrict your model to represent reality and economic theory better (it works the same way as conservation laws in physics!). Once such a model is completed, it can explain or predict out-of-sample data with extra precision. This is why we use theory at all.

However, beware that misusing economics or econometrics theory results in poorer performance of your model! This is why deep understanding is necessary. The data in economics and econometrics is primarily time series. It is not a simple regression-like procedure, because there are extra assumptions to be followed (if you are curious, check out the Wikipedia article on econometrics methods ).

Our goal is to provide you with econometrics assignment solutions when lecture slides or books (check out the list of econometrics textbooks mentioned above) fail to guide you through the project requirements. Just keep in mind that it takes time even for professionals - please order an econometrics assignment solution in advance.

If You Still Feel Uncertain – Consult an R Statistics Expert For Free

It is free to get your task analyzed . And we keep the pricing for the solution reasonable. We are aware that students are still in a position of caring much about the price.

By receiving our R homework help, you accelerate the studying process and get solutions from R experts. You learn from the best in the field to become a professional yourself. After graduation, lots of companies want to hire young statisticians and you get paid well. This is the aim of our services - to help students get the most out of their studies.

The solution to your R programming assignment (usually statistics-related) will be provided by making effective use of both the R language and the RStudio environment. This will be possible on account of the ability of the professionals to deal with it easily since they have hands-on experience with R. Since R language also allows the user to define new functions, R expert structures the solution optimally, choosing between inbuilt libraries and writing required custom functions. This makes the use of the language easier and the professionals provide the requisites to the students by making proper use of this feature.

Overall, our team is ready 24/7 to assist with any advanced statistics assignment, no matter if using R programming is required or if it has to be solved on paper. Submit a new order and just relax!

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It is because we don't want to lie - in such services no discount can be set in advance because we set the price knowing that there is a discount. For example, if we wanted to ask for $100, we could tell that the price is $200 and because you are special, we can do a 50% discount. It is the way all scam websites operate. We set honest prices instead, so there is no need for fake discounts.

No, it is simply not how we operate. How often do you meet a great programmer who is also a great speaker? Rarely. It is why we encourage our experts to write down explanations instead of having a live call. It is often enough to get you started - analyzing and running the solutions is a big part of learning.

Another expert will review the task, and if your claim is reasonable - we refund the payment and often block the freelancer from our platform. Because we are so harsh with our experts - the ones working with us are very trustworthy to deliver high-quality assignment solutions on time.

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Feeling overwhelmed by data and struggling to write R code?  Don't let statistical analysis challenges hold you back! Our R Programming Tutors are here to help you excel! We'll break down complex data analysis concepts into clear steps. This will build your confidence and equip you with the skills to tackle your assignments independently. Our R Programming Assignment Help goes beyond grades. Master R to analyze data across any field, from science and marketing to finance and healthcare. Make informed decisions with R's powerful tools.

R, a powerful and user-friendly programming language, R is a game-changer for data analysis. It tackles everything from data cleanup and organization to uncovering hidden patterns and creating impactful visualizations. Whether you're in research, marketing, finance, or any data-driven field, R empowers you to make informed decisions based on insights you extract from your data.

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What is R Programming?

R is an open and advanced programming language which is designed specifically for statistical computing. It’s like a special tool for researchers, marketers, and analysts to keep, analyze and comprehend their data.

R boasts a vast library of add-ons, extending its capabilities far beyond basic analysis. These "add-ons" (packages) tackle complex tasks, provide advanced statistical methods, and create even more engaging data visualizations. This makes R a truly versatile tool for in-depth data exploration. These extensions enable you to work with arising data types, conduct multiple statistical analyses, and generate compelling visualizations. Whether it is about analyzing customer data, evaluating business trends, or even analyzing scientific data, R lets you turn your data into value.

Here's a glimpse into what R Programming can do:

• Data Manipulation: R tackles messy (missing values, inconsistencies) data sets. It can clean up inconsistencies, address missing information, and transform the data into a format that's ready for analysis. This allows users to extract clear and valuable insights from your information.
• Statistical Analysis: R offers a comprehensive set of statistical tools, from basic measures like means and medians to hypothesis testing and regression analysis. It also supports advanced techniques like ANOVA, time series analysis, and survival analysis.
• Data Visualization: R is not just about computing numerical values or performing other number-based manipulations. It gives back the data in the form of better understandable visual. Analyze trends visually with bar and scatter charts, and with heat and network graphs. Users can change the colours, labels, types of charts, and presentation style to make impactful presentations.

Struggling with R Programming Challenges? Get Expert R Programming Assignment Help

While R offers immense power for data analysis, there are hurdles students often encounter when tackling assignments. Here are some of the most common challenges you might face:

• R Syntax: Learning R's syntax is like building a data analysis toolbox.  You'll use clear names for variables (like "age"), specify data types (numbers, text, etc.), use symbols (+, -, <, >) for calculations, and control code flow with commands. This toolbox empowers you to analyze data and extract insights.
• Data Wrangling and Cleaning: Real-world data is often messy (missing values, inconsistencies) and requires cleaning before analysis (importing, subsetting, handling missing values, transforming).
• Statistical Refinement: Understanding core statistical concepts behind the tools in R is crucial for interpreting results correctly (e.g., null hypothesis, p-values in hypothesis testing, coefficients and R-squared in regression analysis).
• Package Management: R offers a vast collection of packages, extending its functionalities for diverse data analysis tasks. Finding the right package and managing its dependencies can be challenging for beginners.  

What are the Core and Advanced Concepts of R Programming?

R empowers you to unlock the secrets hidden within data. Let's explore the fundamental building blocks that get you started, along with a glimpse into more advanced functionalities:

Core Concepts:

• Operators: Symbols used for calculations (+, -, <, >) and comparisons to control the flow of your code, along with logical checks (AND/OR) to determine if conditions are met.
• Vectors: One-dimensional collections of data (numbers, text).
• Matrices: Two-dimensional tables of data, like organizing product data in rows and columns.
• Data Frames: Flexible tables with named columns for different data types (numbers, text, dates).
• Lists: R's lists function as versatile data holders. They can store a variety of data types.
• Functions: R functions are like mini-programs that simplify repetitive tasks. Create functions to automate calculations (like average) or any common analysis step. This saves time, keeps your code clean, and makes it easier to manage as your projects grow.
• Control Flow Statements: These statements dictate how your code executes. If statements allow you to run code only if a certain condition is true, while loops help you repeat a block of code a specific number of times or until a condition is satisfied.
• Packages: R provides additional functionalities through specialized collections of code called packages. These packages offer tools for various data analysis tasks.

Advanced Concepts:

• Object-Oriented Programming (OOP): This approach creates reusable building blocks (objects) representing real-world things (products, customers). These objects hold data (properties) and can perform actions (methods). OOP improves code organization for complex projects.
• Data Mining Techniques: R unlocks hidden knowledge in data. It reveals patterns to optimize product placement, marketing, and customer targeting. It can also segment customers based on buying behaviors, enabling targeted promotions.
• Time Series Analysis : R's capabilities extend beyond analyzing unchanging data. It can analyze data that evolves over time, such as stock prices or website traffic. This allows you to uncover trends and make informed predictions about future values.
• Machine Learning Algorithms: R goes beyond analysis with machine learning. Predict customer behavior or identify fraud. R's algorithms can classify (e.g., spam emails) and find connections (e.g., website traffic and sales). This empowers data-driven decisions.  

What are the important Libraries used in R Programming?

A few of the libraries in R include:

• tidyverse : Tidyverse streamlines the data science workflow. Its user-friendly R packages empower you to clean, manipulate, and visualize data, all in one place. This boosts productivity and unlocks valuable insights.
• Ggplot2 : ggplot2 simplifies data visualization in R. Drag and drop variables, pick chart types, and customize visuals. Design impactful presentations that effectively communicate your data insights. 
• Dplyr : Dplyr is another sub-component of the Tidyverse and is a package that provides tools for data cleaning, filtering and transformation. dplyr provides efficiency and is further key to ensuring that data is right for analysis.
• Tidyr : It is an R package that has R functions, compiled code, and sample data. It is stored in a directory called the library in the R environment. When installing the R package, by default this package is installed. The purpose of this package is to make the process of creating Tidy data simple.
• Stringr: The Stringr package would offer a lot of functions that work with strings. The package will offer you wrappers and simplify the manipulation of using character strings in R language. 
• Plotly: It is the R graphing library that would allow you to make highly interactive graphs. These allow you to make scatter plots, area charts, bar charts, histograms, subplots, 3D charts, and so on. 
• stargazer: It is an R package that allows you to create Latex code, HTML code, and ASCII text to create a properly formatted regression table. 
• R Markdown : It is a file format that allows you to make dynamic documents using R. The R Markdown is written in the Markdown language and has huge chunks of embedded R code.
• Shiny app : This might be extremely challenging for R newbies! The shiny app allows you to develop highly interactive web apps. The best thing about this app is that it lets you extend R code to the web. 

What are the Important Topics R Programming?

R unlocks data's potential. It transforms raw data into actionable insights, empowering better decisions. Here are some important topics of R Programming to Master R's core skills to explore data, conquer your programming journey, and make a data-driven impact.

• R Syntax: This is the foundation. Learn how to write variables (names for storing data), choose data types (numbers, text), use operators for calculations (+, -, <, >), and control code flow with statements (like if statements and loops).
• Working with Data Structures: R offers various data structures: vectors (simple lists), matrices (spreadsheets with rows/columns), data frames (flexible tables with named columns), and lists (mix of data types).
• Data Import and Wrangling: Real-world data needs cleaning before analysis. R tackles this, by importing data from anywhere (spreadsheets, databases) and prepping it for analysis. Cleaning includes fixing errors, handling missing values, and restructuring. R ensures clean data, ready for reliable results.
• Functions: It is a set of program statements which are used repeatedly to accomplish a particular function in the program. Learn to use these built-in functions (e.g. mean, for finding averages) and form them for repetitive work, helping make the code shorter.
• Packages: R's extensive package collection offers specialized tools for diverse data analysis tasks. For instance, ggplot2 creates beautiful charts (histograms, scatter plots), and dplyr streamlines data manipulation.
• Control Flow Statements: Control flow statements determine how your code will run. You can also use the if statements to control code flow and while loops to run code repeatedly. Learning these statements means making your code more efficient and more flexible.
• Descriptive Statistics : Descriptive statistics summarize your data's key features. They tell you about the center (middle), spread, and presence of outliers (extreme values). Common measures include mean (average) and median (middle value).
• Hypothesis Testing : R provides statistical tools to assess your suppositions about data (hypothesis testing). These tools help you determine the validity of your claims and guide data-driven decision-making.
• Data Visualization: R effectively communicates data through visualizations. Select the appropriate chart type and customize it for clarity.  Ensure labels, titles, and legends are clear and concise. R empowers you to create impactful visualizations that effectively convey insights.
• Linear Regression: Linear regression is one of the methods available in R that is used in the cases where there are two numerical variables. Another option of R is lm() function, which can reveal this relationship and make prediction on the base of the change of one variable to the other.

What are the Important Applications of R Programming?

R's versatility extends beyond basic data analysis. Here are some important applications that showcase its transformative power:

• Data Science and Analytics: R is the benchmark when it comes to data science, providing you with an arsenal of efficient tools for manipulating data, constructing models, and finally, isolating insights that can inform smarter choices.
• Marketing and Customer Analytics: R allows marketers to dissect the behavior of their customers and make targeted campaigns towards specific groups. This will lead to better interaction, satisfaction and customer loyalty with the business or the organizations that are involved.
• Finance and Risk Management: In finance, R solves numerous problems: credit and market risk assessment to provide for financial sustainability; development and maintenance of investment portfolios; financial data and market patterns recognition.
• Social Sciences and Research: In social sciences, R provides reliable statistics and an even larger variety of frameworks and libraries. R is also employed by researchers to analyse data, hypothesis testing, and modelling of various aspects of social behaviours and occurrences.
• Healthcare and Genomics: R is of significance since R performs health analysis on medical data in order to facilitate the best results on patient care. It scans EHRs and looks for patterns to use during diagnosis and the development of treatment plans. 
• Bioinformatics and Environmental Science: R can be effectively used in the field of bioinformatics as well as in environmental science. Scientists employ R in studies involving genetics as well as the environment so as to make conclusions on diseases, and changes and come up with probable solutions.
• E-commerce and Recommendation Systems: E-commerce also incorporates personalization by presenting customers with certain products that they may be particularly inclined toward. According to the designed profiles, R provides recommendations of the client’s products.
• Official Statistics and Government Agencies: R is applied in governmental sectors to analyze demographic information, economics, public health, educational, and criminal activities data. This data is used in decision-making in various sectors and to plan and allocate available resources.
• Scientific Research: R underpins scientific research, from physics to psychology , with its statistics, data visualization, and specialized packages.

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R is an environment and programming language for statistical computing. The R programming language has many features related to statistics and graphics (linear and nonlinear models, traditional statistical tests, time-series analysis, clustering, classification, etc.). One of R's advantages is how easy it is to construct well-designed publication-quality graphs using mathematical symbols and calculations when needed. Small design choices in graphics have been carefully picked as defaults, but the user retains complete discretion.

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FAQs(Frequently Asked Questions by You)

Design perfectly fitting linear regression models & ace your R programming projects and R programming homework on the topic with aid from our R programming homework help services.

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Logistic regression helps predicts the behaviour of a dependent variable based on a given set of information. Carry out in-depth R data analysis successfully using logistic regression on any training data with our programming homework help.

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There are two kinds of assignment operators in R, the left arrow and the right arrow operator. The left arrow operator is written as x < — 3 and the right arrow operator is written as 3 à x.

The assignment operation is used to assign some value or the result of an expression to a variable.

R programming is used for statistical data analysis, statistical computing, data science & machine learning model design, graphics design, etc.

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Assignment operators in R delegate a certain value to some variable.

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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 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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Recently Asked R Programming Questions

• Q 1 : Please prepare your submission in a document (Word or PDF) and clearly label all answers and output with their corresponding question number and part. See Answer
• Q 2 : Download "llo Lab.zip" from Blackboard, rename it with your name and open (double click) the R project file. You run R script "llo Run.R" that contains all the code you need. The call to the function "Forecast Electric. Demand" in script "Project Functions.R" Calculation for R-squared measure. Plot the results (Note: To Plot type "p" in console) Run or debug "llot Run.R" to see how it works. (download needed packages if necessary) Note that the CSV data does not contain the day and the hour columns. In the function "Forecast Electric.Demand()" these fields are set to 1, thus the fit (r-square) is not good. This information can be extracted from the time stamp. See Answer
• Q 3 : Question 1. Consider a population of perennial plants that breed in the early spring and suffer high drought-related mortality late in the summer. Field monitoring experiments suggest that drought leads to a 50% decline in the population during the late summer (d = 0.5). Given this degree of mortality, use the model to calculate how many offspring each individual would, on average, have to produce during the breeding season to prevent the population from declining over time. In other words, calculate the minimum value of b that would be compatible with population growth. Scoring: Full credit for providing the correct answer and showing how the answer was obtained (i.e., show your work). Suppose that you are monitoring island endemic cricket population that has recently become threatened due to an invasive parasitoid wasp species that is attacking its members. From observations of birth and death rates, you estimate that the intrinsic growth rate of the cricket population to be r = -0.05, which has a 95% confidence interval of: 95% C.I. for r = [-0.01, -0.1] Since the entire confidence interval for your estimate of r is negative, your data imply that the population will decline over time. See Answer
• Q 4 : Question 2. The current size of the cricket population is 5,000 individuals. Assume that the current conditions of parasitism do not change and, thus, r remains constant over time. Use your point estimate for r = -0.05) and the model presented above to predict the amount of time it will take for the population size to decline below 50 individuals, which is the threshold for a "critically endangered" classification. Show how you arrived at your conclusion. See Answer
• Q 5 : Question 3. There is uncertainty around your estimate of r. Suppose that the true value of r is within the 95% confidence interval presented above. Use the model to calculate a best-case scenario and worst-case scenario for the amount of time it will take for the cricket population to become critically endangered. Show how you arrived at your conclusions. See Answer
• Q 6 : Part 1: Create an R script that computes the measures of central tendency and measures of variability and the relationships for each of the seven variables in the attitude dataset. Use the functions: var( ) sd() and cor() 3 mean, median, mode, max, min, range, quantile, IQR, Check your work by using the summary and/or describe functions. See Answer
• Q 7 : Part 2: Produce at least one scatter plot, one histogram, and one box-and-whisker plot (Box plot) for each variable. See Answer
• Q 8 : Part 3: Create a matrix of scatter plots, a matrix of histograms, and a matrix of boxplots. Complete this as a R Markdown, document what you are doing using comments, and upload. See Answer
• Q 9 : Problem 6.3.1 Use "ChickWeight" dataset and ggplot to draw box plots for weight for both the diets. See Answer
• Q 10 : Problem 6.3.2 Use "PimaIndians Diabetes2" dataset and ggplot to draw histograms for "pressure". One histogram with counts and one histogram with density. See Answer
• Q 11 : Equipment Precision Comparison Suppose you are trying to make a difficult measurement. Fortunately there is commercial equipment available for this purpose, although it is expensive. Your company has a large budget and wants to obtain the best equipment, but it also does not want to waste money needlessly. You are responsible for performing some tests to guide their decision. You have ordered two trial samples of metering equipment to test which one is better: Equipment A (which costs £60,000) and Equipment B (which costs £30,000). You take 10 measurements using each in a controlled environment. Equipment A gives the following readings: 128.00, 125.04, 125.17, 128.62, 126.06, 124.54, 128.80, 129.98, 126.49, 127.16 Equipment B gives: 122.16, 127.35, 124.73, 129.51, 123.60, 132.67, 131.07, 126.20, 132.44, 126.91 You may assume that measurement errors are normally distributed. 1. The "correct" value for the measurement is supposed to be 127. Verify that both tools are properly "calibrated" (i.e., that they provide measurements that on average are consistent with this value) with an appropriate statistical test. 2. Suppose you did not know that the true value was 127, or there was a possibility that the true value was not 127. Use a statistical test to evaluate whether the two tools produce measurements that are, on average, consistent with each other. 3. Company specifications require that the calibration accuracy (the absolute difference between the average of a very large number of measurements and the "correct" value) of the tool must be better (less) than 5. Show that both tools meet this requirement to better than 99% confidence under the assumptions above. 4. The most important consideration in your decision is precision: you want the tool that produces measurements with the least variance (lowest standard deviation). Can you tell (using an appropriate statistical test) if one tool is significantly more precise than the other? If so, which tool? Quote a p-value, and use a confidence interval to quantify how much more precise one tool is (or isn't) than the other. 5. Tool A is much more expensive, and your company might not want to spend the extra money if it cannot shown to be clearly superior. Conduct a modified version of the above hypothesis test with this information in mind, and quote a new p-value. 6. Would you recommend purchasing tool A, tool B, or would you run more tests (at a cost of £5,000 in overheads plus £500 per test)? If you run more tests, how many more tests would you run? Explain the basis for your decision in a few sentences or less. See Answer
• Q 12 : A researcher has a set of numbers whose mean is equal to 4.9. The researcher wants to know if that set of numbers likely comes from the uniform distribution on the interval of 1 to 10 using the equation method. a. Determine the theoretical expected value for the uniform distribution on the interval of 1 to 10. b. With reference to the lecture slides, create the distribution of means from 99 random simulated draws from the uniform distribution on the interval from 1 to 10. c. Plot the histogram (function hist()) of the simulated distribution of means and place a vertical line on that plot at the location of the researcher's mean (abline(v=4.9)) and another line showing the theoretically expected value. D Determine the probability that the researcher's mean comes distribution (the monte-carlo p-value). e. Explain your conclusion. See Answer
• Q 13 : A researcher has a set of numbers whose mean is equal to 13.8. The researcher wants to know if that set of numbers likely comes from the uniform distribution on the interval of 1 to 16. a. Determine the theoretical expected value for the uniform distribution on the interval of 1 to 16 using the equation method. b. With reference to the lecture slides, create the distribution of means from 99 random simulated draws from the uniform distribution on the interval from 1 to 16. C. Plot the histogram (function hist()) of the simulated distribution of means and place a vertical line on that plot at the location of the researcher's mean (abline(v=13.8)) and another line showing the theoretically expected value. d. Determine the probability that the researcher's mean comes from that distribution (the monte-carlo p-value). e. Explain your conclusion. See Answer
• Q 14 : a. With reference to the lecture slides (Lecture 4), determine the mean center and standard distance for each of the above points datasets. b. Create a plot showing the events for each dataset as well as the location of the mean center and standard distance overlaid on that plot. NOTE: see "symbols()" for plotting the standard distance and in particular the argument "inches" for that function and see "points" for plotting the centroid. See Answer
• Q 15 : With reference to the lecture slides (Lecture 4 & 5), determine the average nearest neighbor distance for each of the datasets in (3). See Answer
• Q 16 : With reference to the lecture slides (Lecture 5), determine the theoretically expected value of nearest neighbor distance for each of the datasets in (3). See Answer
• Q 17 : With reference to the lecture slides (Lecture 5), create the distribution of average nearest neighbor distances from 99 random simulated draws within each of the respective datasets' polygons from (3). a. Plot the histogram (function hist()) of the simulated distribution of means and place a vertical line on that plot at the location of the observed nearest neighbor distance from (4) as a vertical line and another line showing the theoretically expected value from (5). b. Determine the probability that the observed nearest neighbor mean comes from that distribution (the monte-carlo p-value). C. Explain your conclusion. See Answer
• Q 18 : 1. Shipments of Household Appliances: Line Graphs. The file ApplianceShipments.csv contains the series of quarterly shipments (in millions of dollars) of US household appliances between 1985 and 1989. a. Create a well-formatted time plot of the data using the ggplot2 package. Add a smoothed line to the graph. For a closer view of the patterns, zoom in to the range of 3500-5000 on the y- axis. Hint: in order to convert Quarter into a date format, use the zoo library's as.Date utility: as.Date (as. yearqtr (appship.df$Quarter,format="Q%q-%Y")). b. Does there appear to be a quarterly pattern? c. Using ggplot2 in R, create one chart with four separate lines, one line for each of Q1, Q2, Q3, and Q4. In R, this can be achieved by generating a data.frame for each quarter Q1, Q2, Q3, Q4 (use seq(1,20,4), seq (2,20,4), etc. to create indexes for different quarters), and then plotting them as separate series on the line graph. Does there appear to be a difference between quarters? Hint: For ggplot() to display the legend, the color aesthetics must be included inside the aes() specification. d. Using ggplot2, create a chart with one line of average shipments in each quarter. Hint: Use the quarter () command of the lubridate package to create a new column in the shipments data frame and use tapply to average shipments across quarters. e. Using ggplot2, create a line graph of the series at a yearly aggregated level (i.e., the total shipments in each year) and comment on what happened to shipments over years. Hint: Use the year() function of the lubridate package to extract the years the shipments data frame. See Answer
• Q 19 : 2. Sales of Riding Mowers: Scatter Plots. A company that manufactures riding mowers wants to identify the best sales prospects for an intensive sales campaign. In particular, the manufacturer is interested in classifying households as prospective owners or nonowners on the basis of Income (in $1000s) and Lot Size (in 1000 ft2). The marketing expert looked at a random sample of 24 households, given in the file Riding Mowers.csv. a. Using ggplot() in R, create a scatter plot of Lot Size vs. Income, color-coded by the outcome variable owner/nonowner. Make sure to obtain a well-formatted plot (create legible labels and a legend, etc.). 3. Laptop Sales at a London Computer Chain: Bar Charts and Boxplots. The file LaptopSales- January 2008.csv contains data for all sales of laptops at a computer chain in London in January 2008. This is a subset of the full dataset that includes data for the entire year. a. Using ggplot() in R, create a histogram and density plot of the average retail price. Overlay the histogram and density plot by a normal density plot. Does the price data look normally distributed? b. Create a Q-Q plot of the price data. Does the Q-Q plot confirm your finding (in part a.) about the normality of the data? Are there any outliers? c. Create a bar chart, showing the average retail price by store postcode (StorePostcode). Which store postcode has the highest average retail price? Which has the lowest? Hint: For better readability, feel free to rotate the x axis labels. You can do it by adding the following statement to the ggplot() statement: +theme (axis.text.x = element_text (angle = 90)). Also, in order to zoom in closer to the price limit, add the following statement to the ggplot () call: + coord_cartesian (ylim-c (480, 500)). d. Using the filter() function of the dplyr package, reduce your laptop data frame to only these two store postcodes. Using ggplot2, create a side-by-side violin plot of retail prices of the two stores. Be sure to jitter the markers for better visibility. Does there seem to be a huge difference between their prices? e. To better compare retail prices across post codes, create side-by-side boxplots of retail prices of the two postcodes and compare the price distribution in the two postcodes. Does there seem to be a difference between their price distributions? f. Suppose you are interested in what specific technical features greatly impact computer prices. Using the cut() function of the base package, create a new categorical variable in your main laptop sales data frame that contains 3 RetailPrice categories: "low", "medium", and "high." Call the variable PriceCat and make sure that its class is factor. Subsequently, create another data frame that contains this PriceCat variable and all the columns that describe laptop features (such as BatteryLife_Hrs, ScreenSize In, etc.). Finally, create a box-plot enhanced parallel coordinate plot with all the features on the horizontal axis and PriceCat on the vertical axis. Which feature(s) seem to be the most important determinants of PriceCat? See Answer
• Q 20 : Instructions You may use web searches, but not interactive methods such as asking others online or in person. In questions with code blocks, full credit will reserved for effective use of R to reach a correct solution. Questions 1. An team has 8 members. Denote them by {1,2,3,4,5,6,7,8}. Construct a reasonable, standard model for selecting a team member in such a way that any member is equally likely to be selected, recording the member selected, and repeating this process one more time using the remaining set of seven team members. Thus outcomes will be pairs of values (a, b) with a, b € {1,2,3,4,5,6,7,8} and a ‡ b. You don't have to explain the model, just provide the values requested below. What is the probability of the outcome (5,3)? (5 points) What is the probability of the event {(a, b)|a < b}? (5 points) See Answer

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The main goal in all R programming project is to import a  data set, clean and tidy the data, and perform basic exploratory data analysis; all while using R Markdown to produce an HTML report that is fully reproducible. The best way we learn anything is by practice and assignment tasks. We have started R programming assignment help service  for those (beginner to intermediate) who are familiar with R Programming and want to improve their R Programming coding skills.  

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What is R Programming?

R is a Programming language and environment for statistical computing and graphics. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity. One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control..

R Programming Assignment Help topics

Codersarts is the trusted platform for students who are looking for programming assignment help. R is a programming language and environment for statistical computing and graphics and we cover almost every topics from small assignment task to large analytics project. We provide the best academic R programming assignment help . Our  top programming expert  will assist you with the  R programming assignment help. they're available 24/7 for your help. 

But there are some important topics that you need to learn and work on R Programming assignment. As  student when you are learning machine learning with R to complete academic assignment or learning R as developer to build statistical  application there are certain topics which you must know so that you can easily get started the things.  Practice your R programming skills using R  Assignments.  Once you learn R, it is important to practice to understand R concepts. This will also help you to understand the code  and complete php assignment by yourself . 

At Codersarts,  we will  help you in your R assignment so that you can easily get solution. 

Key topics:

Statistical analysis, from descriptive to inferential, from time series to clustering.

Create statistical and machine learning models, some generic, some specific to very complex fields

Create production machine learning data products to interact with your applications.

Report statistical analysis (or whatever you want to) in professional looking reports using RMarkdown.

Statistical packages including Stata, SAS, SPSS, Mplus, G*Power and Sample_Power

R can be used for data mining, statistical computing and modelling, machine learning and even reporting upto some extent.

Computational and statistical methods for the analysis of genomic data.

Vectors, Matrices and Arrays

Factors and Tables

Data Frames

R Studio assignment help needed

Get Help In following R Programming Tools

Popular R  programming editor are RStudio , Jupyter Notebook

Caret ( classification and regression training )

The caret package stands  for  Classification And Regression Training contains functions to streamline the model training process for complex regression and classification problems. It makes the process of training, tuning and evaluating machine learning models in R consistent and easy. The caret features are data splitting, data pre-processing, feature selection, feature importance, model tuning, parallel processing, and visualization.

DataExplorer

The DataExplorer package is one of the most popular machine learning packages in R language for exploratory data analysis. This package has three main goals: Exploratory data analysis, data reporting and feature engineering. Automated data exploration process for analytic tasks and predictive modeling, so that users could focus on understanding data and extracting insights. The package scans and analyzes each variable, and visualizes them with typical graphical techniques. Common data processing methods are also available to treat and format data.

One of the core packages of the tidyverse in the R programming language, dplyr is primarily a set of functions designed to enable data frame manipulation in an intuitive, user-friendly way. Data analysts generally use dplyr in order to transform existing datasets into a format better suited for some particular type of analysis, or data visualization.

ggplot2 is one of the most popular open source data visualization packages for the statistical programming  language R. This package is a plotting package that makes it simple to create a complex plot from data in a dataframe. It provides a more programmatic interface for specifying what variables to plot, how they are displayed, and general visual properties.

kernlab is an extensible package for kernel-based machine learning methods in R. It takes advantage of R’s new S4 object model and provides a framework for creating and using kernel based algorithms. The package contains dot product primitives (kernels), implementations of support vector machines and the relevance vector machine, Gaussian processes, a ranking algorithm, kernel PCA, kernel CCA, kernel feature analysis, online kernel methods and a spectral clustering algorithm. Moreover it provides a general purpose quadratic programming solver, and an incomplete Cholesky decomposition method. 

MICE Package

The mice package implements a method to deal with missing data. The package creates multiple imputations (replacement values) for multivariate missing data. The method is based on Fully Conditional Specification, where each incomplete variable is imputed by a separate model. The MICE algorithm can impute mixes of continuous, binary, unordered categorical and ordered categorical data. In addition, MICE can impute continuous two-level data, and maintain consistency between imputations by means of passive imputation. Many diagnostic plots are implemented to inspect the quality of the imputations.

Mlr3 Package

A modern object-oriented machine learning framework in R. The R package mlr3 and its associated ecosystem of extension packages implements a powerful, object-oriented and extensible framework for machine learning (ML) in R. It provides a unified interface to many learning algorithms available on CRAN, augmenting them with model-agnostic general-purpose functionality that is needed in every ML project, for example train-test-evaluation, resampling, preprocessing, hyperparameter tuning, nested resampling, and visualization of results from ML experiments.

Plotly's R graphing library makes interactive, publication-quality graphs. Examples of how to make line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axes, and 3D (WebGL based) charts.

randomForest

randomForest is a machine learning package in  R Programming language.  It is used  for the Classification and regression based on a forest of trees using random inputs, based on the Breiman random forest algorithm.

Rpart stands for Recursive partitioning. It is a machine learning package in R programming language  for classification, regression and survival trees or rpart helps in building classification or regression models of a very general structure using a two-stage procedure and the resulting models can be represented as binary trees. The package implements many of the ideas found in the CART (Classification and Regression Trees) books.

The SuperML R package is designed to unify the model training process in R like Python.  It provides a standard interface to the users who can use both the programming languages Python and R for building machine learning models. This package basically provides the features of Scikit Learn and predicts the interface to train machine learning models in R.

Misc Functions of the Department of Statistics, Probability Theory Group. Functions for latent class analysis, short time Fourier transform, fuzzy clustering, support vector machines, shortest path computation, bagged clustering, naive Bayes classifier, generalized k-nearest neighbour etc.

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R  Programming Assignment projects

Basic statistical modelling examples.

Linear Regression 

Multiple Linear Regression

Robust Regression 

Logistic Regression 

Multinomial Logistic Regression 

Ordered Logistic Regression 

One-way ANOVA 

Two-way ANOVA 

Factor analysis

Correlation analysis

Multiple Linear Regression with interaction terms

Poisson Regression

Bayes Factors

Data Manipulation Assignment In R

Tidy evaluation is one of the major feature of the latest versions of dplyr and tidyr

Tidy eval: Programming with dplyr, tidyr, and ggplot2

Data wrangling with R and RStudio

Data Processing with dplyr & tidyr (Rpubs)

Joins: Join Functions, Joining Data in R with dplyr

data.table Package: Wrangling with data.table, Data crunching with data.table

String manipulation and stringr package: String Manipulation in R with stringr, Regular Expression in R​

Data Visualization

R graphics with ggplot2

ggplot2 package

Business Analytics Using Statistical Modeling

Methodological focus:  Analytics to explain business phenomena and inform decision making by: describing and visualizing data; creating statistical models from domain knowledge; testing our domain understanding against data; creating experiments; and guarding against fallacious use of statistics.

Statistical focus: Computational approach to statistics by using programming techniques to overcome limitations of data quality and quantity. We will learn to reshape data, simulate data and statistics, discover unseen dimensions in data, and create complex models of unobservable phenomena.

R Programming sample assignments

Interest to determine if experts perceive supermarket chocolate differently to non-experts (the amateurs).

Forecast Inventory demand using historical sales data in R

Predict Churn for a Telecom company using Logistic Regression

Cross Industry Standard Process for Data Mining (CRISP-DM)

Thera Bank - Loan Purchase Modeling

IBM used the request hyper parameter  (Differential privacy (DP) ) with Naive Bayes

Solve a multi-class classification problem of predicting new users first booking destination.

Clustering time series in R

Data analysis focusing on health problems

Call center statistics analysis.

Shiny Application for Tweets Analysis

​ Targeting calcium signaling in bone micrometastases

What types of R assignments do we help with?

We offer assistance with a wide range of R programming tasks, including:

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Working with specific R libraries and functions

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Completing R programming projects

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The best Rstudio homework help service

• R Studio Homework help

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Versions of rstudio.

• Rstudio desktop The Rstudio desktop has features such as;
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• It helps one to jump to function definitions quickly
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• Manage several working directories using projects
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Popular r-studio add-ins.

• Bookdown – This is a knitr extension that creates books
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• Datasets.load – It’s used in searching and loading datasets
• GoogleAuthR – For authentication with Google APIs

Seeking Rstudio Coursework help? Get Assistance from Experienced Academicians

• Importing data programmatically through executing this command in the console window on the Rstudio

acs<-read.csv(url("http://stat511.cwick.co.nz/homeworks/acs_or.csv")). You execute the command by pressing enter, and the dataset will be downloaded as a CSV file from the internet and assigned to variable name acs.

• By clicking on the import dataset button on the environment tab in Rstudio. After clicking the button select the file, you want to import and then open it. The imported dataset will appear on your screen with an option to import. Before importing, set up the name, preferences of a separator, and any other parameter, and then click the import button. The imported dataset will then appear on your Rstudio.

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Five things to know about Tim Walz

On Tuesday, Vice President Kamala Harris decided on Minnesota Gov. Tim Walz as her running mate in her bid for the White House.

Minnesota voters gathered outside Governor Tim Walz’s residence react as Walz was announced as the running mate of Kamala Harris in the U.S. presidential election. (AP Video by Mark Vancleave)

Vice President Kamala Harris has picked Minnesota Gov. Tim Walz to be her running mate, turning to a Midwestern governor, military veteran and union supporter who helped enact an ambitious Democratic agenda for his state.

FILE - Minnesota Gov. Tim Walz, right, laughs as he stands with Fridley, Minn., Mayor Scott Lund during a visit to the Cummins Power Generation Facility in Fridley, Minn., Monday, April 3, 2023. (AP Photo/Carolyn Kaster, File)

• Copy Link copied

FILE - Minnesota Gov. Tim Walz applauds as President Joe Biden speaks at Dutch Creek Farms in Northfield, Minn., Nov. 1, 2023. (AP Photo/Andrew Harnik, File)

FILE - Minnesota Gov. Tim Walz listens after meeting with President Joe Biden, July 3, 2024, at the White House in Washington. (AP Photo/Jacquelyn Martin, File)

Minnesota Gov. Tim Walz speaks during a news conference for the Biden-Harris campaign discussing the Project 2025 plan during the third day of the 2024 Republican National Convention near the Fiserv Forum, Wednesday, July 17, 2024, in Milwaukee. (AP Photo/Joe Lamberti)

FILE - Minnesota Governor Tim Walz greets reporters before Vice President Kamala Harris speaks at Planned Parenthood, March 14, 2024, in St. Paul, Minn. (AP Photo/Adam Bettcher, File)

FILE - Rep. Betty McCullum, D-Minn., left, and Minnesota Governor Tim Walz, listen as Vice President Kamala Harris speaks at Planned Parenthood, March 14, 2024, in St. Paul, Minn. (AP Photo/Adam Bettcher, File)

▶ Follow AP’s live coverage of the 2024 election

MINNEAPOLIS (AP) — Vice President Kamala Harris has decided on Minnesota Gov. Tim Walz as her running mate in her bid for the White House. The 60-year-old Democrat and military veteran rose to the forefront with a series of plain-spoken television appearances in the days after President Joe Biden decided not to seek a second term. He has made his state a bastion of liberal policy and, this year, one of the few states to protect fans buying tickets online for Taylor Swift concerts and other live events.

Some things to know about Walz:

Walz comes from rural America

It would be hard to find a more vivid representative of the American heartland than Walz. Born in West Point, Nebraska, a community of about 3,500 people northwest of Omaha, Walz joined the Army National Guard and became a teacher in Nebraska.

He and his wife moved to Mankato in southern Minnesota in the 1990s. That’s where he taught social studies and coached football at Mankato West High School, including for the 1999 team that won the first of the school’s four state championships. He still points to his union membership there.

Walz served 24 years in the Army National Guard, rising to command sergeant major, one of the highest enlisted ranks in the military, although he didn’t complete all the training before he retired so his rank for benefits purposes was set at master sergeant.

He has a proven ability to connect with conservative voters

In his first race for Congress, Walz upset a Republican incumbent. That was in 2006, when he won in a largely rural, southern Minnesota congressional district against six-term Rep. Gil Gutknecht. Walz capitalized on voter anger with then-President George W. Bush and the Iraq war.

During six terms in the U.S. House, Walz championed veterans’ issues.

He’s also shown a down-to-earth side, partly through social media video posts with his daughter, Hope. One last fall showed them trying a Minnesota State Fair ride, “The Slingshot,” after they bantered about fair food and her being a vegetarian.

He could help the ticket in key Midwestern states

While Walz isn’t from one of the crucial “blue wall” states of Wisconsin, Michigan and Pennsylvania, where both sides believe they need to win, he’s right next door. He also could ensure that Minnesota stays in the hands of Democrats.

That’s important because former President Donald Trump has portrayed Minnesota as being in play this year, even though the state hasn’t elected a Republican to statewide office since 2006. A GOP presidential candidate hasn’t carried the state since President Richard Nixon’s landslide in 1972, but Trump has already campaigned there .

What to know about the 2024 Election

• Democracy: American democracy has overcome big stress tests since 2020. More challenges lie ahead in 2024.
• AP’s Role: The Associated Press is the most trusted source of information on election night, with a history of accuracy dating to 1848. Learn more.
• Stay informed. Keep your pulse on the news with breaking news email alerts. Sign up here .

When Democratic Gov. Mark Dayton decided not to seek a third term in 2018, Walz campaigned and won the office on a “One Minnesota” theme.

Walz also speaks comfortably about issues that matter to voters in the Rust Belt. He’s been a champion of Democratic causes, including union organizing, workers’ rights and a $15-an-hour minimum wage.

He has experience with divided government

In his first term as governor, Walz faced a Legislature split between a Democratic-led House and a Republican-controlled Senate that resisted his proposals to use higher taxes to boost money for schools, health care and roads. But he and lawmakers brokered compromises that made the state’s divided government still seem productive.

Bipartisan cooperation became tougher during his second year as he used the governor’s emergency power during the COVID-19 pandemic to shutter businesses and close schools. Republicans pushed back and forced out some agency heads. Republicans also remain critical of Walz over what they see as his slow response to sometimes violent unrest that followed the murder of George Floyd by a Minneapolis police officer in 2020.

Things got easier for Walz in his second term, after he defeated Republican Scott Jensen , a physician known nationally as a vaccine skeptic. Democrats gained control of both legislative chambers, clearing the way for a more liberal course in state government, aided by a huge budget surplus.

Walz and lawmakers eliminated nearly all of the state abortion restrictions enacted in the past by Republicans, protected gender-affirming care for transgender youth and legalized the recreational use of marijuana.

Rejecting Republican pleas that the state budget surplus be used to cut taxes, Democrats funded free school meals for children, free tuition at public colleges for students in families earning under $80,000 a year, a paid family and medical leave program and health insurance coverage regardless of a person’s immigration status.

He has an ear for sound-bite politics

Walz called Republican nominee Donald Trump and running mate JD Vance “just weird” in an MSNBC interview last month and the Democratic Governors Association — which Walz chairs — amplified the point in a post on X . Walz later reiterated the characterization on CNN, citing Trump’s repeated mentions of the fictional serial killer Hannibal Lecter from the film “Silence of the Lambs” in stump speeches.

The word quickly morphed into a theme for Harris and other Democrats and has a chance to be a watchword of the undoubtedly weird 2024 election.

Hanna reported from Topeka, Kansas.

What has Kamala Harris accomplished as vice president? Here's a quick look.

Vice President Kamala Harris and her meteoric rise as the successor to President Joe Biden, 81, as the Democratic presidential candidate in the Nov. 5 election is the most significant seismic shift in presidential politics in recent history.

As she gears up to secure the Democratic presidential nomination in Chicago this August, we examine some of Harris’ most significant accomplishments and policy initiatives.

More: Biden drops out of 2024 presidential race: What to know as America looks to election

Immigration

In response to immigration concerns, Harris’ call to action was the public-private partnership Central America Forward (CAF). The idea behind CAF is to support the creation of local jobs and other measures in order to slow the flow of mass migration.

CAF has generated more than $5.2 billion since its launch in 2021, and its partners include more than 50 companies and organizations that have committed to supporting economic growth in the Central America region. The entities represent the financial services, textiles, apparel, agriculture, technology, telecommunications, nonprofit sectors, and others, according to the White House.

Voting rights

Harris was at the forefront of the administration’s pursuit to enshrine voting rights protection throughout the U.S. according to White House transcripts . She pushed for Congress to pass the John R Lewis Voting Rights Advancement Act , which would’ve extended the protections of the 1965 Voting Rights Act and required federal approval for some local election law changes.

In 2021, the bill did not receive the 60 votes needed to overcome a Republican filibuster, preventing the start of debate on the Senate floor where Harris would have cast the deciding vote in the evenly split chamber.

Harris visited a Planned Parenthood clinic on March 14, a historic first for any president or vice president while in office, according to previous reporting by USA TODAY.

Walking through the clinic in Minnesota, the vice president spoke with staff members and health care providers as part of her nationwide “Fight for Reproductive Freedoms” tour earlier this year.

Gun violence

In September 2023, Biden established the first-ever White House Office of Gun Violence Prevention to reduce gun violence, overseen by Vice President Harris, as announced by the White House.

The Office of Gun Violence Prevention builds upon actions taken by the Biden-Harris administration to end gun violence, which include the signing of the Bipartisan Safer Communities Act.

Heralded by the White House as the most impactful gun violence prevention measure in almost three decades, the now law bars individuals under the age of 21 from buying firearms, grants the Justice Department additional powers to prosecute gun traffickers, provides mental health services in schools to assist youth affected by gun violence trauma and grief and funds community-based violence intervention programs.

Maternal health

In her previous role as U.S. Senator for California, Harris introduced the Maternal CARE Act and the Black Maternal Health Momnibus Act , which would direct multi-agency efforts to improve maternal health, particularly among racial and ethnic minority groups, veterans, and other vulnerable populations as well as maternal health issues related to COVID-19.

The vice president’s prior work on maternal and infant health care was a key component of the Build Back Better Act , passed in 2022. The legislation expands access to maternal care and makes new investments to drive down mortality and morbidity rates.  

Broadband expansion

In 2023, Harris and U.S. Secretary of Commerce Gina Raimondo traveled to Kenosha, Wisconsin to celebrate the announcement of new electronics equipment production made possible by the Biden-Harris Administration’s “ Investing in America ” agenda and Bipartisan Infrastructure Law.

The Bipartisan Infrastructure Law requires the use of American-made materials and products for federally funded infrastructure projects, with the goal of bringing hundreds of new jobs to the U.S. The law also notably includes a historic $65 billion investment to expand affordable and reliable high-speed Internet access in communities across the U.S.

“Our investments in broadband infrastructure are creating jobs in Wisconsin and across the nation and increasing access to reliable, high-speed internet so everyone in America has the tools they need to thrive in the 21st century,” said Harris.

In 2021, President Biden declared Juneteenth a federal holiday. Often referred to as the “Second Independence Day,” it commemorates June 19, 1865, the day when 2,000 Union troops reached Galveston, Texas, to announce that enslaved African Americans were freed by executive order two years after the signing of the Emancipation Proclamation, according to the National Museum of African American History and Culture .

“As a United States Senator, I was proud to co-sponsor a bill to make Juneteenth a federal holiday,” said Harris during the Juneteenth concert at the White House. “This [day], we will hold a national day of action on voting.  And I call on all the leaders here to please join us in helping more Americans register to vote.”

Reuters contributed to the reporting of this story.