r assignment help

Obtaining R

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

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

Entering Commands

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

The Workspace

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

Graphic User Interfaces

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

Operators in R

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

Arithmetic Operators include:

Logical Operators include:

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

Creating New Variables

Use the assignment operator <- to create new variables.

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

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

Importing Data

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

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

Descriptive Statistics

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

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

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

Plotting in R

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

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

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

Getting Help

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

Going Further

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

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

Which Students Seek R Programming Assignment Help?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Problems with R / R Studio

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

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

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

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

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

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

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

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

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

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

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

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

Problems with Statistics Homework

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

When Paying for R Homework Solution Is Worth It?

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

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

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

What R Libraries Can We Help with?

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

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

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

Which Statistics and Econometrics Books Can We Help with?

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

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

Should You Consider Looking for Programs Other than R?

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

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

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

Most Popular Statistics Assignments with R

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

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

Economics and Econometrics Assignments

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

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

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

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

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

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

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

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

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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.

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

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

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Getting Help with R

Helping yourself.

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

R Help: help() and ?

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

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

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

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

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

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

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

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

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

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

Searching for Help Within R

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

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

help.search() and ??

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

RSiteSearch()

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

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

help.start()

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

R Help on the Internet

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

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

CRAN Task Views

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

R FAQs (Frequently Asked Questions)

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

Asking for Help

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

Stack Overflow

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

R Email Lists

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

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This course is part of multiple programs. Learn more

This course is part of multiple programs

Taught in English

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Instructors: Roger D. Peng, PhD +2 more

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Recommended experience

Intermediate level

Familiarity with regression is recommended.

What you'll learn

Understand critical programming language concepts

Configure statistical programming software

Make use of R loop functions and debugging tools

Collect detailed information using R profiler

Skills you'll gain

• Data Analysis

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There are 4 modules in this course

In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.

Week 1: Background, Getting Started, and Nuts & Bolts

This week covers the basics to get you started up with R. The Background Materials lesson contains information about course mechanics and some videos on installing R. The Week 1 videos cover the history of R and S, go over the basic data types in R, and describe the functions for reading and writing data. I recommend that you watch the videos in the listed order, but watching the videos out of order isn't going to ruin the story.

What's included

28 videos 9 readings 1 quiz 7 programming assignments

28 videos • Total 129 minutes

• Installing R on a Mac • 1 minute • Preview module
• Installing R on Windows • 3 minutes
• Installing R Studio (Mac) • 1 minute
• Writing Code / Setting Your Working Directory (Windows) • 7 minutes
• Writing Code / Setting Your Working Directory (Mac) • 7 minutes
• Introduction • 1 minute
• Overview and History of R • 16 minutes
• Getting Help • 13 minutes
• R Console Input and Evaluation • 4 minutes
• Data Types - R Objects and Attributes • 4 minutes
• Data Types - Vectors and Lists • 6 minutes
• Data Types - Matrices • 3 minutes
• Data Types - Factors • 4 minutes
• Data Types - Missing Values • 2 minutes
• Data Types - Data Frames • 2 minutes
• Data Types - Names Attribute • 1 minute
• Data Types - Summary • 0 minutes
• Reading Tabular Data • 5 minutes
• Reading Large Tables • 7 minutes
• Textual Data Formats • 4 minutes
• Connections: Interfaces to the Outside World • 4 minutes
• Subsetting - Basics • 4 minutes
• Subsetting - Lists • 4 minutes
• Subsetting - Matrices • 2 minutes
• Subsetting - Partial Matching • 1 minute
• Subsetting - Removing Missing Values • 3 minutes
• Vectorized Operations • 3 minutes
• Introduction to swirl • 1 minute

9 readings • Total 90 minutes

• Welcome to R Programming • 10 minutes
• About the Instructor • 10 minutes
• Pre-Course Survey • 10 minutes
• Syllabus • 10 minutes
• Course Textbook • 10 minutes
• Course Supplement: The Art of Data Science • 10 minutes
• Data Science Podcast: Not So Standard Deviations • 10 minutes
• Getting Started and R Nuts and Bolts • 10 minutes
• Practical R Exercises in swirl Part 1 • 10 minutes

1 quiz • Total 30 minutes

• Week 1 Quiz • 30 minutes

7 programming assignments • Total 1,260 minutes

• swirl Lesson 1: Basic Building Blocks • 180 minutes
• swirl Lesson 2: Workspace and Files • 180 minutes
• swirl Lesson 3: Sequences of Numbers • 180 minutes
• swirl Lesson 4: Vectors • 180 minutes
• swirl Lesson 5: Missing Values • 180 minutes
• swirl Lesson 6: Subsetting Vectors • 180 minutes
• swirl Lesson 7: Matrices and Data Frames • 180 minutes

Week 2: Programming with R

Welcome to Week 2 of R Programming. This week, we take the gloves off, and the lectures cover key topics like control structures and functions. We also introduce the first programming assignment for the course, which is due at the end of the week.

13 videos 3 readings 2 quizzes 3 programming assignments

13 videos • Total 90 minutes

• Control Structures - Introduction • 0 minutes • Preview module
• Control Structures - If-else • 1 minute
• Control Structures - For loops • 4 minutes
• Control Structures - While loops • 3 minutes
• Control Structures - Repeat, Next, Break • 4 minutes
• Your First R Function • 10 minutes
• Functions (part 1) • 9 minutes
• Functions (part 2) • 7 minutes
• Scoping Rules - Symbol Binding • 10 minutes
• Scoping Rules - R Scoping Rules • 8 minutes
• Scoping Rules - Optimization Example (OPTIONAL) • 9 minutes
• Coding Standards • 8 minutes
• Dates and Times • 10 minutes

3 readings • Total 30 minutes

• Week 2: Programming with R • 10 minutes
• Practical R Exercises in swirl Part 2 • 10 minutes
• Programming Assignment 1 INSTRUCTIONS: Air Pollution • 10 minutes

2 quizzes • Total 60 minutes

• Week 2 Quiz • 30 minutes
• Programming Assignment 1: Quiz • 30 minutes

3 programming assignments • Total 540 minutes

• swirl Lesson 1: Logic • 180 minutes
• swirl Lesson 2: Functions • 180 minutes
• swirl Lesson 3: Dates and Times • 180 minutes

Week 3: Loop Functions and Debugging

We have now entered the third week of R Programming, which also marks the halfway point. The lectures this week cover loop functions and the debugging tools in R. These aspects of R make R useful for both interactive work and writing longer code, and so they are commonly used in practice.

8 videos 2 readings 1 quiz 2 programming assignments 1 peer review

8 videos • Total 61 minutes

• Loop Functions - lapply • 9 minutes • Preview module
• Loop Functions - apply • 7 minutes
• Loop Functions - mapply • 4 minutes
• Loop Functions - tapply • 3 minutes
• Loop Functions - split • 9 minutes
• Debugging Tools - Diagnosing the Problem • 12 minutes
• Debugging Tools - Basic Tools • 6 minutes
• Debugging Tools - Using the Tools • 8 minutes

2 readings • Total 20 minutes

• Week 3: Loop Functions and Debugging • 10 minutes
• Practical R Exercises in swirl Part 3 • 10 minutes
• Week 3 Quiz • 30 minutes

2 programming assignments • Total 360 minutes

• swirl Lesson 1: lapply and sapply • 180 minutes
• swirl Lesson 2: vapply and tapply • 180 minutes

1 peer review • Total 60 minutes

• Programming Assignment 2: Lexical Scoping • 60 minutes

Week 4: Simulation & Profiling

This week covers how to simulate data in R, which serves as the basis for doing simulation studies. We also cover the profiler in R which lets you collect detailed information on how your R functions are running and to identify bottlenecks that can be addressed. The profiler is a key tool in helping you optimize your programs. Finally, we cover the str function, which I personally believe is the most useful function in R.

6 videos 4 readings 2 quizzes 3 programming assignments

6 videos • Total 42 minutes

• The str Function • 6 minutes • Preview module
• Simulation - Generating Random Numbers • 7 minutes
• Simulation - Simulating a Linear Model • 4 minutes
• Simulation - Random Sampling • 2 minutes
• R Profiler (part 1) • 10 minutes
• R Profiler (part 2) • 10 minutes

4 readings • Total 40 minutes

• Week 4: Simulation & Profiling • 10 minutes
• Practical R Exercises in swirl Part 4 • 10 minutes
• Programming Assignment 3 INSTRUCTIONS: Hospital Quality • 10 minutes
• Post-Course Survey • 10 minutes
• Week 4 Quiz • 30 minutes
• Programming Assignment 3: Quiz • 30 minutes
• swirl Lesson 1: Looking at Data • 180 minutes
• swrl Lesson 2: Simulation • 180 minutes
• swirl Lesson 3: Base Graphics • 180 minutes

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Learner reviews

Showing 3 of 22199

22,199 reviews

Reviewed on Feb 2, 2016

The content is superbly designer for a beginner. The Swirl assignments need to make compulsory. Infact they contributed more to the learning process. More Swirl contents will make the course richer.

Reviewed on Aug 11, 2019

Very challenging, but good course. I've been programming in R for over a year, but there were still some things for me to pick up in this class. Assignments were a challenge, but satisfying to tackle.

Reviewed on Feb 20, 2017

I am pleasantly surprised with the quality of this course. For a beginner, the Swirl exercises are incredibly helpful and I was able to build confidence in working with R because of them. Thank you!

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