rstudio homework help

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 .

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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 .

• [email protected]

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Need R programming homework help?

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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lesson home

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

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

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

Before Starting The Workshop

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

Download and install the latest version of R here

Download and install RStudio here

Introduction to RStudio

Welcome to the R portion of the Software Carpentry workshop.

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

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

Basic layout

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

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

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

Work flow within RStudio

There are two main ways one can work within RStudio.

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

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

Introduction to R

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

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

Using R as a calculator

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

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

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

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

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

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

From highest to lowest precedence:

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

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

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

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

Really small or large numbers get a scientific notation:

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

You can write numbers in scientific notation too:

Mathematical functions

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

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

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

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

Comparing things

We can also do comparison in R:

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

Variables and assignment

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

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

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

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

Notice also that variables can be reassigned:

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

Assignment values can contain the variable being assigned to:

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

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

• periods.between.words
• underscores_between_words
• camelCaseToSeparateWords

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

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

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

Vectorization

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

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

Managing your environment

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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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!

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):

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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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The learnr package makes it easy to turn any R Markdown document into an interactive tutorial. Tutorials consist of content along with interactive components for checking and reinforcing understanding. Tutorials can include any or all of the following:

Narrative, figures, illustrations, and equations.

Code exercises (R code chunks that users can edit and execute directly).

Quiz questions.

Videos (supported services include YouTube and Vimeo).

Interactive Shiny components.

Tutorials automatically preserve work done within them, so if a user works on a few exercises or questions and returns to the tutorial later they can pick up right where they left off.

Here are some examples of tutorials created with the learnr package.

Setting Up R

A tutorial featuring videos and interactive questions to guide a new R user through the installation and set up of everything they’ll need to get started with R.

Filtering Observations

An example tutorial teaching a common data transformation: filtering rows of a data frame with dplyr::filter() .

Summarizing Data

An example tutorial where learners are introduced to dplyr::summarise() . Along the way, learners also gain practice with the pipe operator, %>% , and dplyr::group_by() .

Installation

Install the latest official learnr release from CRAN:

Or you can install the most recent version in-development from GitHub with the remotes package :

learnr works best with a recent version of RStudio (v1.0.136 or later) which includes tools for easily running and previewing tutorials.

Hello, Tutorial!

To create a tutorial, set runtime: shiny_prerendered in the YAML frontmatter of your .Rmd file to turn your R Markdown document into an interactive app .

Then, call library(learnr) within your Rmd file to activate tutorial mode, and use the exercise = TRUE chunk option to turn code chunks into exercises. Users can edit and execute the R code and see the results right within their browser.

For example, here’s a very simple tutorial:

This is what the running tutorial document looks like after the user has entered their answer:

• Cheatsheets
• R Markdown Guide
• Project Sharing

Loading Data

• Contingency Tables
• Calculating Statistics
• Calculating Statistics for Groups
• 5 Number Summary
• Correlation
• ggplot2 Basics
• Scatterplot
• Mosaic Plot
• Single Mean
• Single Proportion
• Two Proportions
• Linear Regression Basics
• ANOVA Basics
• Goodness of Fit
• Test for Association
• Simulations
• Permutations and Combinations
• Normal Distributions
• t Distributions

R Studio Help

The software used in this course is called R Studio. R Studio is free and exists as a downloadable application as well as a web based application. R Studio is installed on the computers in the MCS department and is available on the Gustavus Virtual Lab . You can learn more about, and download, R Studio here. Gustavus hosts its own web based version of R Studio which can be found at rstudio.gac.edu .

There are many websites designed to provide help on how to use R, R Studio, and R Markdown. Here are some of the more popular websites:

• Markdown Basics
• Advanced Markdown
• R Video Tutorials

R Studio on the Web

After going to rstudio.gac.edu you should see a screen that looks like the one below. You can log in with your Gustavus username and password.

Once you login

Once you log in to R Studio you should see a screen that looks like the following.

R Studio uses R and Markdown to generate complicated documents with relatively little overhead. We will be using it to generate statistics, plots, and charts.

Creating a Markdown Document

To create an R markdown file click on File , New File , R Markdown . Give your document a title and click OK . You will be given a document that looks like the one below.

The Knit HTML button, (in green), is the button used to generate your document once you are ready.

The information in yellow is the information used to generate the HTML document. You will most likely want to change the author option to your name and give your document a better title than "untitled".

The text in the blue circle is just text. You can type summaries and comments in the document without having to do anything special. If you look at the provided text you will see that there is a URL included in the text. Markdown provides formatting short-cuts to include links, images, and text formats. For a quick list of these formatting options you can look at the Markdown Basics website.

The portion circled in orange is a code chunk. Code chunks produce statistical output. The output is placed right in the document so there is no need to copy and paste. The circled code chunk produces summary statistics for the cars dataset.

Create a new R Markdown document by clicking on File , New File , R Markdown . You will be prompted to name your document. You can name it whatever you wish. Press the OK button to proceed. Take a look at the R Markdown document that is displayed. The document contains both text and code. Click on the knit HTML button to generate an html document. Notice that the generated document contains text and a scatter plot. Find the code chunk in the markdown document that creates the plot.

There are many ways to load data into R Studio. You can read data directly from a file or you can load data from a URL.

Loading Data from a Package

Fortunately, the authors of "Statistics, Unlocking the Power of Data" also created a package that contains all the datasets mentioned in the book. This package is installed on the Gustavus RStudio server. You need to load the package into your document before you can use it.

Once the package is loaded you should have access to all of the datasets from the package. So, for instance, if the book refers to the AllCountries dataset, you should be able to look up all of the variables in the dataset by typing library(Lock5Data) and ?AllCountries into the console to get a more detailed description of the cases and variables contained in the dataset.

Loading Data from a URL

Two statements are needed to read in data from a web URL.

The first gives the web page where the data is stored.

The second reads in the data and gives a name to the dataset.

To read in and view the data from the example dataset copy the following code and paste it into either the console and press Enter , or into an R Markdown code chunk in a Markdown document and press the knit HTML button.

Loading Data from a File

Loading data can be done by clicking on the Environment tab in the upper right window of R Studio. Then click on Import Dataset and choose From Text File .

If you are loading data from a file make sure the file is saved as a .csv file, (.csv stands for comma separated variable). If the file is not a .csv file you can make one by opening the file in Excel or Numbers by using the Save As option in the File menu.

After you have selected the file, you will be presented with the following screen. Be sure to give the data a meaningful name. Besides naming the dataset you should not have to change any of the options on this screen. Once you load the data it will be displayed in the upper left window.

Once you have loaded the data, either from a URL or from a file, you should see a line of code in the console, bottom left window, that looks similar to the code below. This code was generated when the file was loaded.

Either read the data from http://homepages.gac.edu/~anienow2/MCS_142/R/example.csv or save the file and read it in. You do not need to do both. The data from the file should look like the following.

Saving a Document

Once you have your document ready to turn in you need to save it. By default your R Markdown document and your output document are saved on the RStudio server. If you would like a copy saved to your computer you need to click on the File tab, circled in green, at the top of the output document. Next you need to select the file you wish to save. Do this by checking the box next to the file you want, circled in blue. Finally, click on the More button, circled in yellow. There you should see the option to Export your selection. Select Export and download the document to your computer.

Once you have exported the document to your computer you may want to open the document to make sure that is saved correctly.

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2.7: Letting RStudio Help You with Your Commands

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• Danielle Navarro
• University of New South Wales

Time for a bit of a digression. At this stage you know how to type in basic commands, including how to use R functions. And it’s probably beginning to dawn on you that there are a lot of R functions, all of which have their own arguments. You’re probably also worried that you’re going to have to remember all of them! Thankfully, it’s not that bad. In fact, very few data analysts bother to try to remember all the commands. What they really do is use tricks to make their lives easier. The first (and arguably most important one) is to use the internet. If you don’t know how a particular R function works, Google it. Second, you can look up the R help documentation. I’ll talk more about these two tricks in Section 4.12. But right now I want to call your attention to a couple of simple tricks that RStudio makes available to you.

Autocomplete using “tab”

The first thing I want to call your attention to is the autocomplete ability in RStudio. 32

Let’s stick to our example above and assume that what you want to do is to round a number. This time around, start typing the name of the function that you want, and then hit the “tab” key. RStudio will then display a little window like the one shown in Figure 3.2. In this figure, I’ve typed the letters ro at the command line, and then hit tab. The window has two panels. On the left, there’s a list of variables and functions that start with the letters that I’ve typed shown in black text, and some grey text that tells you where that variable/function is stored. Ignore the grey text for now: it won’t make much sense to you until we’ve talked about packages in Section 4.2. In Figure 3.2 you can see that there’s quite a few things that start with the letters ro : there’s something called rock , something called round , something called round.Date and so on. The one we want is round , but if you’re typing this yourself you’ll notice that when you hit the tab key the window pops up with the top entry (i.e., rock ) highlighted. You can use the up and down arrow keys to select the one that you want. Or, if none of the options look right to you, you can hit the escape key (“esc”) or the left arrow key to make the window go away.

In our case, the thing we want is the round option, so we’ll select that. When you do this, you’ll see that the panel on the right changes. Previously, it had been telling us something about the rock data set (i.e., “Measurements on 48 rock samples…”) that is distributed as part of R. But when we select round , it displays information about the round() function, exactly as it is shown in Figure 3.2. This display is really handy. The very first thing it says is round(x, digits = 0) : what this is telling you is that the round() function has two arguments. The first argument is called x , and it doesn’t have a default value. The second argument is digits , and it has a default value of 0. In a lot of situations, that’s all the information you need. But RStudio goes a bit further, and provides some additional information about the function underneath. Sometimes that additional information is very helpful, sometimes it’s not: RStudio pulls that text from the R help documentation, and my experience is that the helpfulness of that documentation varies wildly. Anyway, if you’ve decided that round() is the function that you want to use, you can hit the right arrow or the enter key, and RStudio will finish typing the rest of the function name for you.

Start typing the name of a function or a variable, and hit the “tab” key. RStudio brings up a little dialog box like this one that lets you select the one you want, and even prints out a little information about it.

The RStudio autocomplete tool works slightly differently if you’ve already got the name of the function typed and you’re now trying to type the arguments. For instance, suppose I’ve typed round( into the console, and then I hit tab. RStudio is smart enough to recognise that I already know the name of the function that I want, because I’ve already typed it! Instead, it figures that what I’m interested in is the arguments to that function. So that’s what pops up in the little window. You can see this in Figure ?? . Again, the window has two panels, and you can interact with this window in exactly the same way that you did with the window shown in 3.2. On the left hand panel, you can see a list of the argument names. On the right hand side, it displays some information about what the selected argument does.

If you’ve typed the name of a function already along with the left parenthesis and then hit the “tab” key, RStudio brings up a different window to the one shown in Figure 3.2 . This one lists all the arguments to the function on the left, and information about each argument on the right.

Browsing your command history

One thing that R does automatically is keep track of your “command history”. That is, it remembers all the commands that you’ve previously typed. You can access this history in a few different ways. The simplest way is to use the up and down arrow keys. If you hit the up key, the R console will show you the most recent command that you’ve typed. Hit it again, and it will show you the command before that. If you want the text on the screen to go away, hit escape 33 Using the up and down keys can be really handy if you’ve typed a long command that had one typo in it. Rather than having to type it all again from scratch, you can use the up key to bring up the command and fix it.

The second way to get access to your command history is to look at the history panel in RStudio. On the upper right hand side of the RStudio window you’ll see a tab labelled “History”. Click on that, and you’ll see a list of all your recent commands displayed in that panel: it should look something like Figure ?? . If you double click on one of the commands, it will be copied to the R console. (You can achieve the same result by selecting the command you want with the mouse and then clicking the “To Console” button). 34

• Top 10 Resources to do your R Programming Homework | StatisticsHomeworkHelper.com

Top 10 Resources to do your R Programming Homework

For data analysis, statistical modeling, and visualization, R programming is a potent language that is frequently used. You might come across difficult homework as a student studying R programming that calls for in-depth knowledge and practical abilities. We have put together a list of the top 10 resources to help you successfully complete your statistics homework so that you can excel in your R programming homework . These resources, which range from online classes and documentation to forums and coding environments, will give you the skills and encouragement you need to succeed in your R programming endeavors.

Documentation in R

Every R programmer should have access to the R Documentation. It offers thorough documentation for all R language functions and packages. The documentation is readily available online and contains thorough justifications, examples, and usage guidelines. Use the R Documentation to understand the syntax, parameters, and capabilities of different functions as you work on your homework. It is a helpful resource that will enable you to create accurate and effective code.

It is simple to navigate and find the information you need in the R Documentation because it uses a consistent format. It offers thorough explanations of each function, detailing its objective, required inputs, and expected outcomes. Additionally, the documentation frequently offers concrete examples that show how to apply the function in various contexts. You can develop a deeper understanding of how to use the function to address particular issues by studying these examples.

The R Documentation also discusses the various R packages that are available. It describes each package's use and purpose as well as the features it offers. When you need to use particular packages for your homework, this is especially useful. You can successfully install, load, and use the packages with the help of the documentation.

Explore the "See Also" section of the documentation; it frequently suggests additional functions and packages that can improve your understanding of R programming and your ability to solve problems.

Stack Exchange

There are many R programmers in the well-known online developer community Stack Overflow, where you can post questions and look for answers to programming-related issues. When you run into problems with your R programming homework, check Stack Overflow for related questions or create your own post. The community is very active and provides knowledgeable commentary. To receive targeted assistance, keep in mind to provide specific information about your issue and any error messages you encounter. Understanding the best practices in R programming and troubleshooting problems can both be accomplished with the help of Stack Overflow.

It's crucial to look for related questions on Stack Overflow before posting your own. There's a good chance that someone has already run into this issue and solved it. Spend some time reading the responses and comprehending the suggested fixes. This will not only assist you in finishing your current homework assignment but also improve your ability to solve problems in the future.

Be sure to abide by the rules and etiquette as you engage with the Stack Overflow community. Ask concise, clear questions, whenever possible provide a minimal reproducible example (reprex), and express gratitude to those who assist you. To get the most out of this tool, remember that the Stack Overflow community is based on the concepts of cooperation and knowledge sharing.

An integrated development environment (IDE) created especially for R programming is called RStudio. It offers an intuitive user interface, robust features, and top-notch tools for developing, testing, and debugging R code. Students can take advantage of the free version of RStudio. It has a workspace, console, code editor, and a number of practical tools like package management, code completion, and syntax highlighting. For your R programming homework, use RStudio to improve productivity and streamline your coding processes.

The intuitive interface of RStudio makes it simple to arrange your code and files for various homework. It offers project management tools and a file explorer to help you keep your work accessible and organized. With separate projects for each homework assignment, you can concentrate on particular tasks without clogging up your workspace.

Numerous features in RStudio's code editor make it easier to write and edit R code. It makes it simpler to write accurate and error-free code by offering syntax highlighting, automatic indentation, and code completion. You can quickly navigate through your code, find and replace text, and manage multiple files at once using the code editor.

The interactive environment of RStudio's console allows you to run R code and view the outcomes in real time. When working on homework that call for data analysis or visualization, this is especially helpful. You can play around with various functions and parameter values, see the result, and directly debug your code in the console.

The integration of RStudio with version control programs like Git is another useful feature. You can collaborate with others, keep track of changes made to your code, and go back to earlier versions if necessary with version control. You can keep track of your homework, work with classmates, and guarantee the quality of your work by utilizing version control in RStudio.

A well-known blog aggregator and online community for R programmers is called R-bloggers. It includes a sizable collection of articles, guides, and illustrations provided by seasoned R programmers. Data analysis, data visualization, machine learning, and other topics are all covered in these resources. Regularly visiting R-bloggers will help you learn new approaches, gain insight into practical applications, and find inspiration for your R programming homework.

The R-bloggers community is made up of professionals and enthusiasts who blog about their experiences and knowledge. These blog posts frequently offer detailed explanations, snippets of code, and real-world examples that you can use with your own homework projects. You can pick up new skills, find useful R packages, and comprehend the best R programming practices by reading these articles.

A wide range of subjects related to R programming are covered by R-bloggers. You can find articles that explore these topics, whether you're working on data manipulation, statistical modeling, or making data visualizations. The authors frequently offer code snippets and visual results to help you comprehend the ideas and apply them successfully to your homework.

R-bloggers offers tutorials and guides that are tailored to different skill levels in addition to the blog posts. You can find introductory tutorials that cover the fundamentals of R programming and offer practical exercises if you're a beginner. You can look up tutorials on machine learning, deep learning, or specialized statistical methods for topics that are more complex. You can find resources that match your level of expertise and assignment requirements thanks to the variety of content available.

Numerous online courses on data science and R programming are available through Coursera. Reputable professors from prestigious universities and institutions instruct these courses. Enrolling in a relevant R programming course on Coursera can give you structured instruction, practical exercises, and homework that match your needs. You will gain a deeper understanding of R programming concepts and be better able to apply them in your homework thanks to the interactive course materials and feedback from peers and instructors.

R programming courses on Coursera range from introductory courses that introduce the language's basics to specialized courses on data analysis, data visualization, and statistical modeling. These courses frequently have programming homework, quizzes, and video lectures. The homework are made to test your comprehension of the ideas and give you a chance to put what you've learned into practice.

The interactive learning environment that Coursera courses provide is one of their main benefits. You can interact with peers and instructors, take part in forums and discussions, and get feedback on your work. This interactive component improves your learning process by enabling you to get answers to questions, learn from others' perspectives, and hone your understanding of R programming.

The structured curriculum offered by Coursera courses enables you to move systematically through the subject matter. When working on homework that demand a step-by-step methodology or build upon prior concepts, this can be especially beneficial. You will gain a thorough understanding of how to apply R programming in various contexts thanks to the course materials' frequent use of examples, case studies, and real-world applications that fill the gap between theory and practice.

The platform for data science and machine learning competitions is known as Kaggle. It offers a collaborative setting for data scientists and hosts a sizable collection of datasets. Find publicly accessible datasets for your R programming homework by using Kaggle's resources. In addition, Kaggle offers a notebook interface where you can type R code directly into the browser and run it. To gain knowledge and develop your coding abilities, explore and learn from the notebooks shared by the Kaggle community.

When you need real-world data for your R programming homework, Kaggle's dataset repository is a great tool. It provides a huge selection of datasets from many different industries, including finance, healthcare, marketing, and more. You can look for datasets related to the subject of your homework, explore the data, and download it in an R-compatible format. Your data analysis, visualization, and modeling tasks can be built on top of these datasets.

Kaggle offers a notebook interface called Kaggle Kernels in addition to datasets. Kernels make it simple to experiment with code and run analyses by enabling you to write and execute R code directly in your browser. You can develop your own kernels and distribute them to others, or you can look through the notebooks that the Kaggle community has shared. You can learn useful tips on how to approach various data science problems using R from the notebooks, which frequently offer in-depth explanations, code snippets, and visualizations.

Additionally, Kaggle hosts contests where you can use your R programming expertise to address particular data science challenges. Competing can be a great way to hone your abilities, learn from others, and compare your performance to others. Even if you don't actively compete, you can still examine the winning solutions and pick up cutting-edge skills from top competitors. The community forums and discussions on Kaggle help data scientists collaborate and share knowledge.

A popular platform for version control and team development is GitHub. It hosts countless R programming projects and packages among its millions of repositories. Find open-source R projects related to the subject of your homework using GitHub. You can pick up new skills, deepen your understanding, and even find pre-made functions or modules that will speed up your development by looking at the code and contributions of other developers.

You can use the search function on GitHub to look for repositories that contain R code based on particular keywords or topics. You can look for projects that meet the criteria for your homework and look through the codebase to see how various functionalities are implemented. Reading the code and documentation can give you useful knowledge about coding standards, best practices, and effective problem-solving techniques.

The ability to contribute to open-source projects is another benefit of GitHub. You can submit a pull request with your changes if you discover a repository that needs enhancements or bug fixes in relation to your homework. This not only enhances your programming abilities but also enables you to contribute significantly to the R programming community.

Additionally, GitHub provides a platform for working together and exchanging knowledge. You can follow developers or repositories whose work interests you. You can then keep up with their most recent initiatives and contributions. You can also take part in discussions, address problems, and ask the community for help. Utilizing the collaborative tools on GitHub can help you build relationships, accelerate your learning, and get support as you progress with R programming.

RDocumentation.org

An extensive online tool that indexes R packages and their documentation is called RDocumentation.org. You can use it to search for particular functions, packages, or topics, and it will provide you with links to the pertinent documentation. Additionally, the website offers curated lists of well-liked and in-demand packages, making it simpler for you to locate helpful resources for your R programming homework. Utilize RDocumentation.org to discover new packages, become familiar with their features, and use their abilities in your homework.

You can use the keyword search feature on RDocumentation.org's user-friendly interface to look up functions or packages. A list of pertinent functions is shown in the search results, along with a description and links to the corresponding documentation. You can access comprehensive details about a function's usage, parameters, and effective use cases by clicking on it.

RDocumentation.org's emphasis on offering the most recent documentation for R packages is one of its benefits. The documentation on the website is updated frequently to reflect additions and changes to package functionalities. This guarantees that when working on your R programming homework, you have access to accurate and trustworthy information.

RDocumentation.org offers curated lists of well-liked and trending packages in addition to its search function. These lists can be useful when you want to research new software or look for different ways to complete your homework. The curated lists frequently offer a succinct summary of each package, highlighting its key attributes and potential applications.

You can take advantage of the extensive ecosystem of R packages and keep up with the most recent developments in the R programming community by using RDocumentation.org. With the help of this resource, you can learn new techniques, broaden your skill set, and resolve your homework problems quickly.

Tutorials on YouTube

There is a sizable collection of video tutorials on R programming available on YouTube. Through interesting video content, many seasoned programmers and educators impart their knowledge and experience. Find tutorials on YouTube for topics like data manipulation, data visualization, or statistical modeling that are relevant to your homework. Video tutorials can give you step-by-step instructions, visual examples, and real-world examples, which will make it simpler for you to understand difficult ideas and incorporate them into your own code.

R programming tutorials on YouTube are available for users of all skill levels, from absolute beginners to experts. You can find tutorials that suit your needs whether you're just getting started with R or tackling more complex ideas. These tutorials frequently explain fundamental ideas before walking you through practical examples that illustrate how to apply the ideas in R.

When learning R visualization techniques, video tutorials can be especially helpful. Many YouTubers produce tutorials that take you step-by-step through the process of making various visualizations while outlining the underlying concepts and outlining the code. You can improve your knowledge of data visualization in R and produce eye-catching visuals for your homework by following along with these tutorials.

Consider the authority and knowledge of the content creators when looking for YouTube tutorials. To determine the caliber of the tutorials, look for channels that specialize in R programming or data science and review the comments and ratings. You can stay informed about new tutorials and the most recent R community content by subscribing to channels specifically devoted to R programming.

R programming communities online

Joining online R programming communities can be very helpful for your learning process. Websites like r-programming and RStudio Community offer venues for communicating with other R programmers, exchanging information, and getting advice. By participating in these communities, you can learn from seasoned experts, discuss difficult ideas, and receive code-related feedback. You can build relationships, encourage collaborations, and improve your R programming abilities by actively participating in discussions.

Programmers, students, and enthusiasts congregate in online R programming communities to discuss a range of topics related to the language. These groups frequently have forums or message boards where you can post queries, ask for counsel, or offer your opinions. Posting specific questions on these forums when you are having trouble with your homework can result in insightful responses from educators and other students.

Engaging with the community enables you to explore various viewpoints, learn alternate strategies, and uncover novel R programming techniques in addition to asking for help. You can keep up with the newest trends, new packages, and industry best practices by following discussions and reading through threads.

Keep in mind the etiquette and community rules when interacting in online R programming communities. For discussions to be fruitful, respectful communication, precise questions, and context-setting are necessary. Furthermore, actively participating in the community can improve your understanding of R programming and encourage collaborative learning.

Successfully completing R programming homework requires having access to the appropriate resources and taking a proactive approach to learning. You will have a complete toolkit at your disposal if you use the top 10 resources listed in this blog, which include R Documentation, Stack Overflow, RStudio, R-bloggers, Coursera, Kaggle, GitHub, RDocumentation.org, YouTube tutorials, and online R programming communities. With the help of these resources, you can overcome obstacles, gain a deeper understanding of the subject, and succeed in your R programming endeavors. To become a skilled R programmer, make use of these resources, maintain your curiosity, and encourage continuous learning.

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Tag your question as homework and say that you are working on an assignment

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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:-

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How is R programming helpful?

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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?

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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.

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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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Efficient Ways To Get Help On An R Package In RStudio

Navigating RStudio to find help on R packages can be a challenge for developers. This article guides you through practical methods to access documentation, understand vignettes, and utilize community forums, ensuring efficient problem-solving in R programming.

💡 KEY INSIGHTS

• Familiarize yourself with RStudio’s help system , which is essential for efficient programming.
• Strategies for accessing detailed documentation for specific R packages directly within RStudio.
• Leverage RStudio’s advanced search features for streamlining the process of finding relevant help information.
• Interpreting and utilizing package vignettes and manuals , an often-overlooked aspect of R programming.

Navigating the intricacies of R packages in RStudio can often pose a challenge, even for seasoned programmers. This article provides clear, step-by-step guidance to efficiently seek and utilize the help resources available within RStudio. You'll learn practical strategies to quickly resolve issues and enhance your coding workflow in R.

Understanding The Help System In RStudio

Accessing documentation for specific r packages, using rstudio's help search feature, interpreting r package vignettes and manuals, utilizing community forums and online resources, troubleshooting common issues with r packages, frequently asked questions.

RStudio's help system is an invaluable resource for programmers, providing detailed insights into various R packages and functions. This system allows for effective exploration and application of R's extensive functionalities.

Accessing Help For Functions And Packages

Using help search, navigating help pages.

To query the documentation for a specific function or package, utilize the ? or help() function. This opens up a detailed help page with useful insights.

To start a basic search , use the ?? command followed by your query for searching across all documentation. It scans through all help files for the specified term.

Understanding the structure of help pages is crucial. They typically include sections like Description, Usage, Arguments, and Examples, each offering specific information.

• Description: Gives an overall view of the function or package.
• Usage: Explains how to use the function.
• Arguments: Describes each argument of the function.
• Examples: Shows real-world application examples.

Executing examples from the help pages in RStudio can enhance your understanding. They provide a practical perspective on how functions work.

In conclusion, mastering the help system in RStudio is a key skill for any R programmer. It empowers you to independently explore and understand various aspects of R programming, significantly enhancing your proficiency and problem-solving capabilities in R.

Gaining insights into R packages within RStudio is essential for effective programming. RStudio provides several ways to access this information, ensuring you can fully leverage the capabilities of each package.

Direct Access To Package Documentation

Viewing function details within a package, browsing package content.

To immediately open the main documentation page of a specific package, use the help() or ? command. This command brings up a comprehensive overview, including a list of functions and a general description of the package.

Often, you need details about a specific function in a package. The ? command followed by package_name::function_name provides this.

For a broader view of the contents of a package, library(help="package_name") gives a complete list of functions and datasets available in the package.

These methods of accessing documentation for specific R packages in RStudio ensure that you have the information needed to fully utilize these tools in your programming tasks. The ease of access to detailed documentation is one of the strengths of working with R in RStudio, empowering you to quickly find and apply the right solutions for your data analysis and programming challenges.

RStudio's help search feature is a powerful tool for finding information across a wide range of documentation. This functionality is crucial for programmers seeking quick answers or specific details.

Quick Keyword Search

Advanced search with filters, exploring help topics, utilizing rstudio's gui.

For a quick overview of a topic, use the ?? command followed by your search term. This provides a broad search across all help files related to the term.

For a more targeted search , combine your query with specific keywords or package names. This narrows down the search results, focusing on the most relevant entries.

RStudio also allows you to browse various help topics . This is useful when you want to explore related functions or concepts within a package.

If you are looking for specific topics or functions, using the help.search() function can be more precise. This function allows you to include more detailed search parameters.

Additionally, RStudio's graphical interface provides a user-friendly way to search through documentation. The 'Help' tab in RStudio allows you to enter your search terms and view results in a well-organized format.

RStudio's help search feature enhances your ability to quickly find and utilize the wealth of information available in R documentation. Whether you're searching for general concepts or specific functions, this tool streamlines the process, allowing for more efficient and effective programming in R.

Vignettes and manuals are crucial resources in R, providing comprehensive guides and in-depth explanations about R packages. These documents are invaluable for understanding the practical use of packages and their functions.

Accessing Vignettes

Reading a specific vignette, understanding package manuals, browsing manuals online.

To view vignettes for a specific package, use the vignette() function. This command lists all available vignettes for the package, giving you a selection of detailed guides.

For reading a specific vignette , specify the name of the vignette in the vignette() function. This opens the vignette directly, allowing for a focused study.

Package manuals contain exhaustive information about every function in a package. Access these manuals using the help() function with the package name.

You can also access package manuals online . Most R packages have a dedicated website or a section on CRAN where their manuals are available.

By leveraging vignettes and manuals, you gain a deeper understanding of R packages, from general overviews to detailed function references. These resources are indispensable for effective and informed use of R packages in your programming projects.

Engaging with community forums and online resources is a key strategy for R programmers to expand their knowledge and solve complex problems. These platforms offer a wealth of information, from user-generated solutions to expert advice.

Stack Overflow: A Prime Resource

R-help mailing list, github: exploring code and issues, r-bloggers: learning through blogs.

Stack Overflow is one of the most widely used forums for programming questions. To find R-related solutions, use specific tags in your search.

The R-Help mailing list is another valuable resource, hosting discussions and solutions from R users worldwide. Subscribe to this list to stay updated with the latest discussions and solutions.

GitHub is not only for code sharing but also a platform to explore issues and solutions in R package development. Browse through repositories of popular R packages to understand code implementations and reported issues.

R-Bloggers aggregates blog posts from various R bloggers, providing insights, tutorials, and updates on R programming.

By leveraging these community forums and online resources, you gain access to a broader range of perspectives and solutions, enhancing your R programming skills and knowledge. These platforms are vital for continuous learning and staying connected with the global R community.

Working with R packages can sometimes lead to challenges. Knowing how to troubleshoot common issues is essential for maintaining a smooth workflow.

Resolving Installation Errors

Dealing with deprecated functions, solving loading conflicts, addressing performance issues.

One common issue is installation errors . This can occur due to version incompatibilities or missing dependencies. Use install.packages() with the dependencies argument set to TRUE.

Another issue is the use of deprecated functions in older scripts or packages. Using help() can provide alternatives.

Loading conflicts between packages can occur when different packages have functions with the same name. Use the conflict() function to identify these conflicts.

When dealing with performance issues , such as slow execution of package functions, consider profiling your code using Rprof() .

By effectively troubleshooting these common issues, you can ensure that your use of R packages is as efficient and error-free as possible. Understanding how to tackle these problems enhances your problem-solving skills and contributes to a smoother R programming experience.

How can I find the version history of an R package in RStudio?

To find the version history of an R package, you can check the package's NEWS file, usually accessible through RStudio's help pane or on the package's CRAN or GitHub page. This file typically contains a log of changes, updates, and fixes for each version.

How do I know if the documentation I am reading is up-to-date with the latest version of the package?

To ensure the documentation is up-to-date, check the package version at the top of the help page in RStudio and compare it with the latest version on CRAN. Always update your packages regularly using update.packages() for the most recent documentation.

Can I access the source code of a function directly from its help page in RStudio?

Yes, you can usually access the source code of a function from its help page. Look for a link at the bottom of the help page that says 'View source.' Clicking on this link will show you the function's source code.

Are there any shortcuts in RStudio to quickly access help documentation for a specific term or function?

Yes, you can quickly access documentation by typing ?function_name or ??search_term in the console. Additionally, using the 'Help' tab in RStudio, you can type in a search term and get a list of relevant documentation pages.

How can I differentiate between user-contributed vignettes and official package vignettes in RStudio?

Official package vignettes are typically included with the package and can be accessed using vignette() . User-contributed vignettes or tutorials might be found on external sites like R-bloggers or personal blogs and are usually not included in the package's official documentation.

Let’s test your knowledge!

How can you view the detailed documentation of a specific R package in RStudio?

Continue learning with these rstudio guides.

• Loading R Packages In RStudio: A Step-By-Step Approach
• Importing Data In RStudio: A Step-By-Step Approach
• Wrangling And Analyzing Data In RStudio
• Writing R Scripts In RStudio: A Step-By-Step Approach
• The Essentials Of Using RStudio

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Versions of rstudio.

• Rstudio desktop The Rstudio desktop has features such as;
• Ability to execute R code directly from the source editor
• Access to Rstudio locally
• It helps one to jump to function definitions quickly
• View changes in the content through the visual markdown editor
• Manage several working directories using projects
• An interactive debugger to diagnose and fix errors
• Rstudio server Features of Rstudio server include;
• Easy access through a web browser
• Scale compute and RAM centrally
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• Advanced resource management
• Monitoring and metrics

Our Rstudio Homework Help Service Includes an In-depth Of all The Requirements of Your Task

Parts of rstudio, how much will you charge me to do my r assignment before the deadline, get the best help with rstudio homework by hiring our professional writers, popular r-studio add-ins.

• Bookdown – This is a knitr extension that creates books
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• 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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