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We guarantee:

  • We will solve your assignment on time
  • We analyze task description with extra scrutiny
  • R code is always well commented
  • Statistics proofs are well explained (unlike your professor's!)
  • We do tutoring – the solution is explained if you wish to learn more
  • Our R expert gets you a high grade (we offer a money-back guarantee)

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 ggplot2 graph seasonality adjusted US unemployment rate by 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:

R assignment solution survival plot

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!

Frequently Asked Questions

Yes. No hidden fees. You pay for the solution only, and all the explanations about how to run it are included in the price. It takes up to 24 hours to get a quote from an expert. In some cases, we can help you faster if an expert is available, but you should always order in advance to avoid the risks. You can place a new order here .

The cost depends on many factors: how far away the deadline is, how hard/big the task is, if it is code only or a report, etc. We try to give rough estimates here, but it is just for orientation (in USD):

Credit card or PayPal. You don't need to create/have a Payal account in order to pay by a credit card. Paypal offers you "buyer's protection" in case of any issues.

We have no way to request money after we send you the solution. PayPal works as a middleman, which protects you in case of any disputes, so you should feel safe paying using PayPal.

No, unless it is a data analysis essay or report. This is because essays are very personal and it is easy to see when they are written by another person. This is not the case with math and programming.

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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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 .

Obtaining R

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

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

r program

Entering Commands

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

r console

The Workspace

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

Graphic User Interfaces

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

Operators in R

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

Arithmetic Operators include:

Logical Operators include:

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

Creating New Variables

Use the assignment operator <- to create new variables.

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

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

Importing Data

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

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

Descriptive Statistics

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

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

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

Plotting in R

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

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

plot in r

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

Getting Help

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

Going Further

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

Learn R Programming

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

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

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

Why learn R 

Learning r means more job opportunities.

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

Open source

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

Cross platform

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

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

R is used by renowned companies

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

  • How to Learn R

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

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

Start Learning R

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

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

> age  <- 25

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

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

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

> print(age)

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

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

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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.

R programming language is compatible with all kinds of operating systems, i.e., UNIX, Linux, Windows, and Mac. R also offers you to interact with many data sources and statistical packages, i.e., SAS, SPSS, etc. Our R assignments Experts are well versed with all the topics related to the language covered in Your R programming assignment help, such as:-

  • Logistic regression
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  • Bayesian probability

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

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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.

R programming sample 1

Adding the shape and color

Adding the shape and color

Statistics layer

Statistics layer

Adding coord_cartesian()

Statistics layer

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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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.

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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?

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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.

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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-

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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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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.

If you are worried about completing your R assignment or homework, you can connect with us at FavTutor. We have a team of experts who are professionals in R programming and have years of experience in working on any problem related to R. Since our inception, we have been helping hundreds of students so you could be the next one to get our R programming homework help.

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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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 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.

R Programming Exercises, Practice Questions and Solutions

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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Are you struggling with your R programming assignments? You are not alone. Data analysis using R programming can be challenging, but you don’t have to fear. Our professional R Programming Tutors are here to help and assist you with your learning journey. They'll break down concepts clearly and provide tailored support, transforming your confusion into understanding and frustrations into accomplishments. With our assistance, you'll conquer data analysis and elevate your skills. Don't wait, empower your learning and get R Programming Assignment Help now!

R is a powerful data analysis tool that empowers you to explore and process data efficiently. From handling massive datasets to performing advanced statistical analysis, R equips you with a comprehensive suite of functions. The advantage automatic area can bring is it processes work tasks that might be frequently performed by a human. This leaves you free to concentrate on the important parts of involving numbers in business regarding transactions with greater potential profit per unit.

In addition, R has a large, thriving community and many dedicated packages. These packages address specific needs, from financial modeling and time series analysis to machine learning and text mining.  With R, you have the flexibility to delve into complex data analysis tasks while benefiting from the expertise of experienced developers who have created these valuable tools.

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What is R Programming?

R Programming gives researchers and data analysts a great set of tools to get to various secrets that the data holds. It guides you on a data analysis journey, offering capabilities for:

  • Data Exploration: Immerse yourself into data sets, look for patterns and trends, and grasp the fundamentals of what this information communicates.
  • Data Cleaning: It becomes important to clean the data before analysis to avoid confusion and mess of the data. It's a pro at handling missing values, fixing errors, and dealing with outliers. With R, you can transform raw data into a structured neat & clean format, making it ready for analysis.
  • Statistical Analysis: Conduct statistically sound analyses with confidence. R offers a comprehensive library of functions for hypothesis testing, allowing you to test assumptions and draw meaningful conclusions. If you need to compare group means with a t-test or understand relationships between variables with a linear regression model, R has the tools you need.
  • Data Visualization: With the help of ggplot2, an exceptional R package, you can craft data-driven presentations. Visualize numbers with stunning graphics and charts. Present your insights in a compelling manner, allowing your message to resonate with your audience.
  • Domain-Specific Analysis: Delve deeper into complex data analysis tasks tailored to your specific field. R offers a wealth of specialized packages for finance, biology, social sciences, and more. These packages provide functions and tools designed for your unique research or analytical needs.

Why is R Programming challenging?

R Programming's way of writing instructions, with its special characters and mathematical symbols, can feel less familiar than other languages. However, with practice, you'll become more comfortable with R's logic and how it works. Here are some key areas that students might find challenging:

  • Unique Syntax: Unlike some programming languages, R has its own syntax for writing code. This can be unfamiliar for those new to programming altogether. Using symbols and operators in computer programming can be initially confusing. Regular practice is needed to become familiar with the language's logic and syntax.
  • Data Wrangling: Cleaning and transforming data (data wrangling) can be challenging. R offers tools, but mastering them takes time. Students might struggle with handling missing values, merging datasets, and organizing data for analysis.
  • Statistical implication: To get the most out of R's statistical capabilities, you need to know what statistics are all about. This will help you pick the right statistical tests for your data, understand the results, and make sound decisions based on what you find.
  • Package Ecosystem: R boasts a vast library of specialized packages, extending its functionalities to diverse fields. This variety can be overwhelming for beginners. Knowing which package to use requires exploration and guidance from the R community's resources.
  • Visualization Complexity: Effective data visualization in R requires balancing technical skills with design principles. Consider your visualization's purpose and audience to choose the right chart type and level of detail.  

What are the important libraries and Syntax used in R Programming?

R offers a rich ecosystem of libraries that extend its capabilities beyond core data analysis. These libraries provide specialized functions and tools for various tasks, making R a versatile language for diverse fields. Here are some key libraries and foundational syntax elements to explore:

Essential Libraries:

  • Base R: R's core component, Base R, offers fundamental capabilities for - Handling data, including importing from various sources; Performing statistical operations and calculations; and Generating basic graphical representations of data.
  • dplyr: dplyr, a renowned data manipulation library, makes it a breeze to work with data. It provides a range of intuitive verbs for filtering, summarizing, and transforming data. With dplyr, you can effortlessly filter out rows based on your criteria or compute summary statistics for any groups within your data.
  • ggplot2: ggplot2, an R library, excels in creating visually appealing data representations. It employs an incremental approach, allowing users to construct intricate plots by sequentially incorporating elements such as data points and annotations. This user-controlled method ensures precise data presentation and maximizes clarity.
  • tidyr: Complements dplyr by providing functions for reshaping data into different formats. It allows you to melt data from a wide format (many variables in columns) to a long format (one variable per column) and vice versa. This reshaping is often crucial for specific data analysis tasks.
  • tidyverse: This "meta-package" conveniently bundles dplyr, tidyr, ggplot2, and other related packages under one section. This simplifies installation and ensures compatibility between these frequently used tools.

Syntax Essentials:

  • Objects: In R, everything is an object, including data, functions, and results. Understanding how to create and manipulate these objects is fundamental.
  • Operators: R provides familiar operators (+, -, *, /) for calculations and comparisons (==, !=, <, >) on your data. You can also use assignment operators (=, <-) to store values. 
  • Control Flow: Control flow statements (if/else & loops) are game-changers. Write R programs that make decisions & repeat tasks automatically.  Unlock R's true potential. Create flexible, efficient code.  
  • Functions: R provides a wealth of built-in functions, like time-saving tools, for common data analysis tasks.  You can also create your own custom functions to streamline your coding and solve complex problems step-by-step.
  • Packages: Packages extend R's functionality by providing additional functions and datasets. You can install and load specific packages to access the tools you need for your analysis.

What are the Important Topics of R Programming?

R programming offers a vast landscape of possibilities for data exploration and analysis. To navigate this landscape effectively, it's beneficial to explore key topics that will equip you with the essential skills. Here are some fundamental areas to focus on:

  • Data Structures: Mastering R's data structures is crucial for efficient data handling.  These structures, like vectors (ordered lists), matrices (grids of numbers), and data frames (flexible tables), each have strengths for organizing your information. Choosing the right one ensures smooth data manipulation and analysis in R.
  • Data Import and Export: R provides you with the opportunity to quickly load data in different formats like CSV, Excel, and text files. In addition, uploading also brings simplicity to sharing or analyzing of the results by exporting of them in various formats. This characteristic of R to bring in and take out data makes your data analysis process smooth and efficient.
  • Data Cleaning and Manipulation: The world of real data most likely will require some sort of pre-processing before starting an analysis. R package gives you the opportunity to deal with missing attributes, fix the errors, and make some adjustments to your data that will be suitable for analyzing. This prevents outcome distortion because data is provided with clean and exact information.
  • Descriptive Statistics: Descriptive statistics describe and quantify patterns by summarizing key characteristics of your data. The course program includes the important calculations that are used like central tendency (the mean, the median) and dispersion (variance, standard deviation), etc.
  • Hypothesis Testing: The testing of hypotheses is the key principle in statistics. The R keeps you covered by having different functions for the necessary statistical tests, so you can do the validity check for the claims about your data.
  • Regression Analysis: Regression analysis is a tool used to establish connections between variables. Thereby you would be capable to construct diverse models of regression in R as to understand the affect one variable has on another.
  • Data Visualization: An efficient way in which data gets put visually ensures that information gets understood. ggplot2 library of R makes it easy to create captivating graphs that have in-depth features for the interpretation of numerical data into comprehensive stories.
  • Debugging: Even the best programmers encounter errors. Learning how to debug your R code is essential for identifying and fixing mistakes to ensure your code functions as intended.
  • Packages and Community: R's ecosystem can be considered as a huge library that contains tools for many different areas. By doing this, you will know the way to search and install packages specifically designed to run the functions that you need. There would be an array of resources, tutorials, and forums within the R community that you could utilize during your learning process.

Some of the popular topics in R Programming on which our programming assignment experts work on a daily basis are listed below:

What are the Important Applications of R Programming?

R programming's versatility extends beyond core data analysis , making it a valuable tool across a wide range of fields.  Here are some prominent applications where R's capabilities shine:

  • Social Sciences: R empowers researchers to analyze surveys, study demographics, and model social phenomena. Imagine analyzing public opinion or voting behavior – R provides tools to extract insights from social data.
  • Business and Finance: Financial analysts leverage R for risk assessment, portfolio analysis, and building financial models. R allows them to assess investment risks, track market trends, and create forecasting models for informed decisions.
  • Healthcare and Biology: R plays a vital role, in facilitating the analysis of clinical trial data, exploring genetic data, and modeling disease spread. Researchers use R to analyze drug trials, identify genetic markers for diseases, and model the spread of viruses.
  • Marketing: R helps marketing professionals analyze customer data, understand market trends, and build models to predict purchasing behavior – leading to targeted campaigns.
  • Environmental Science: R provides environmental scientists with the opportunity to work with the environmental data as well as analyzing the data (air, water, soil) to determine the environmental quality, the degree to which humans have an impact (pollution, deforestation), and modeling climate change scenarios.
  • Data Journalism: R gives a journalist the opportunity of revealing insights in public data sources that otherwise might go unnoticed (e.g., budget allocation, check posts, social media analysis), making the data-driven stories more interesting through interactive visualizations.
  • Sports Analytics: R revolutionizes sports analytics for strategy optimization and talent identification, giving teams a competitive edge.
  • Public Policy: R helps policymakers make data-driven decisions on social and economic issues. By analyzing data, R allows them to identify areas needing intervention, evaluate existing programs, and forecast policy impacts.
  • Education and Psychology: R helps in making the data analysis of students and their learning outcomes as well as in the studying of human behavior easier for education researchers and psychologists, respectively.
  • Text Mining and Natural Language Processing (NLP) : R however, is capable of carrying out NLP tasks that involve text mining or of analyzing social moods, with the help of modules such as tidytext and quanteda.  

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Align assign: rstudio addin to align assignment operators.

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

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

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

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

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

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

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