Alexander Landa – R-posts.com

15+ Resources to Get Started with R

R is the second most sought after language in data science behind Python, so gaining mastery of R is a prerequisite to a thriving career in the field. Whether you’re an experienced developer or a newbie considering a career move, here are some excellent resources so you can get started with R.

What is R?

R is a programming language and environment designed for statistical analysis. It’s used mainly by data miners and statisticians. It’s a free resource and runs on a wide variety of platforms, including UNIX, Windows, and Mac OS. It has thousands of well-documented extensions and is cross-platform compatible. It’s not quite as popular outside of the field of data science, but it’s one of the best options for exploring datasets in a deep dive manner or for going after data insights for a single time. Head over to the R sight and download a copy of R, so you’re ready to get started.

Free R Resources for Beginners

Let’s take a look at how a beginner might break into R. It’s not quite as friendly as Python, but it’s definitely accessible with good resources and practice.

Platforms and Documentation

r-bloggers.com: R-bloggers is a collection of blogs designed by R experts that covers a wide range of R topics. No matter what you’re curious about or have an issue with, R-bloggers has it covered.

Books

R for Data Science: This classic handbook provides resources and documentation. It’s available for free on the website, or you can purchase a physical copy from Amazon. Hands-on Programming with R: Garrett Grolemund’s classic is a practical, hands-on approach to R programming. It gives you the instruction you need plus practical programming skills to begin with R right from the very beginning.

Courses

Codecademy: Codecademy’s mission is to bring development knowledge even to beginners, and its R offers are no different. While many of the lessons will require a membership, it does offer a basic set of courses to get you started. edX.org: EdX offers a range of free R courses to get you started, but we recommend starting with Microsoft’s Introduction to R for Data Science for a comprehensive overview. The courses cost nothing and are offered asynchronously. Some do come with official certification for a fee.

Free R Resources for Developers

If you’ve already got some development experience under your belt, these resources could be a great way to get started with R by utilizing your current experience. Even better, they’re free.

Platforms and Documentation

storybench.com: Storybench is an experiential learning platform designed to provide exercises in digital storytelling. They offer projects in R, most notably “How to Explore Correlations in R.” Once you’ve gotten the basics, the next logical step is to take on projects for hands-on learning.

Books

R Programming for Data Science: This option is available for free (though you can choose to donate in support of the project). It offers full resources for learning R and understanding key data science principles. If you upgrade the package, the online book comes with a full video suite. Text Mining with R: Another book available for free on the website, this option offers a targeted approach to text mining with a full Github repository as support. R in Action: Another entirely free resource for business developers. If you’ve already got an established career in development in the business world, this could be an excellent resource for getting started with R in the business world.

Courses

Coursera: Johns Hopkins’s popular partnership with Coursera, “Data Science, Foundations Using R” is a great way for developers to build skills to break into the field of Data Science. edX + Harvard: X Series Program in Data Analysis for Life Sciences is a course series designed to implement both learning R and real-life projects for a full learning experience. You can upgrade to an official learning certificate for a fee or take the individual courses for free.

Paid Resources for Beginners and Beyond

Sometimes, you’ve got to invest a little in your learning experience. Here are a couple of things that can really jumpstart your R-programming skills if you’ve got some wiggle room in your budget. Getting Started with R: A primer on using R for the biological sciences. It contains valuable information for getting started on statistical analysis using the R programming language. flowingdata.com: Flowingdata is a membership site designed to elevate your visualizations. It’s another excellent way to get experiential learning with R projects. Rstudio: It’s not cheap, but if you’re serious about making a career in R, you’ll want to get it. Save up and invest. They do, however, have a series of free webinars you can peruse.

Bonus! More Blogs and Newsletters

https://blog.revolutionanalytics.com/r/ : Blog designed to highlight milestones in Data Science and includes a range of topics including both R and Python for you multilingual developers out there. https://journal.r-project.org/: Open access, refereed journal detailing the latest in R-programming news and projects. Papers have a focus on accessibility, and the articles are tended to reach a wide audience. https://morningcupofcoding.com/: Offers a wide range of curated coding articles, including R programming. Check their back issues for articles of interest. opendatascience.com: ODSC’s general weekly newsletter provides members with trending topics in the fields of modeling, tools & platforms, and more.

Getting Started with R Programming

While both Python and R are invaluable resources for getting started in Data Science, the statistical capabilities of R are in wide demand. Whether you’re new to the world of coding or an experienced developer, R can open doors into the world of Data Science.

ODSC West 2019 Talks and Workshops to Expand and Apply R Skills (20% discount)

Go HERE to learn more about the ODSC West 2019 conference with a 20% discount! (or use the code: ODSCRBloggers)

At this point, most of us know the basics of using and deploying R—maybe you took a class on it, maybe you participated in a hackathon. That’s all important (and we have tracks for getting started with Python if you’re not there yet), but once you have those baseline skills, where do you go? You move from just knowing how to do things, to expanding and applying your skills to the real world.

For example, in the talk “Introduction to RMarkdown in Shiny” you’ll go beyond the basic tools and techniques and be able to focus in on a framework that’s often overlooked. You’ll be taken through the course by Jared Lander, the chief data scientist at Lander Analytics, and the author of R for Everyone. If you wanted to delve into the use of a new tool, this talk will give you a great jumping-off point.

Or, you could learn to tackle one of data science’s most common issues: the black box problem. Presented by Rajiv Shah, Data Scientist at DataRobot, the workshop “Deciphering the Black Box: Latest Tools and Techniques for Interpretability” will guide you through use cases and actionable techniques to improve your models, such as feature importance and partial dependence.

If you want more use cases, you can be shown a whole spread of them and learn to understand the most important part of a data science practice: adaptability. The talk, “Adapting Machine Learning Algorithms to Novel Use Cases” by Kirk Borne, the Principal Data Scientist and Executive Advisor at Booz Allen Hamilton, will explain some of the most interesting use cases out there today, and help you develop your own adaptability.

More and more often, businesses are looking for specific solutions to problems they’re facing—they don’t want to waste money on a project that doesn’t pan out. So maybe instead, you want to learn how to use R for common business problems. In the session “Building Recommendation Engines and Deep Learning Models using Python, R, and SAS,” Ari Zitin, an analytical training consultant at SAS, will take you through the steps to apply neural networks and convolutional neural networks to business issues, such as image classification, data analysis, and relationship modeling.

You can even move beyond the problems of your company and help solve a deep societal need, in the talk “Tackling Climate Change with Machine Learning.” Led by NSF Postdoctoral Fellow at the University of Pennsylvania, David Rolnick, you’ll see how ML can be a powerful tool in helping society adapt and manage climate change.

And if you’re keeping the focus on real-world applications, you’ll want to make sure you’re up-to-date on the ones that are the most useful. The workshop “Building Web Applications in R Using Shiny” by Dean Attali, Founder and Lead Consultant at AttaliTech, will show you a way to build a tangible, accessible web app that (by the end of the session) can be deployed for use online, all using R Shiny. It’ll give you a skill to offer employers, and provide you with a way to better leverage your own work.

Another buzz-worthy class that will keep you in the loop is the “Tutorial on Deep Reinforcement Learning” by Pieter Abbeel, a professor at UC Berkeley, the founder/president/chief scientist at covariant.ai, and the founder of Gradescope. He’ll cover the basics of deepRL, as well as some deeper insights on what’s currently successful and where the technology is heading. It’ll give you information on one of the most up-and-coming topics in data science.

After all that, you’ll want to make sure your data looks and feels good for presentation. Data visualization can make or break your funding proposal or your boss’s good nature, so it’s always an important skill to brush up on. Mark Schindler, co-founder and Managing Director of GroupVisual.io, will help you get there in his talk “Synthesizing Data Visualization and User Experience.” Because you can’t make a change in your company, in your personal work, or in the world, without being able to communicate your ideas.

Ready to apply all of your R skills to the above situations? Learn more techniques, applications, and use cases at ODSC West 2019 in San Francisco this October 29 to November 1! Register here.

Save 10% off the public ticket price when you use the code RBLOG10 today. Register Here

More on ODSC:

ODSC West 2019 is one of the largest applied data science conferences in the world. Speakers include some of the core contributors to many open source tools, libraries, and languages. Attend ODSC West in San Francisco this October 29 to November 1 and learn the latest AI & data science topics, tools, and languages from some of the best and brightest minds in the field.

R-Related Talks Coming to ODSC West 2019 (and a 30% discount)

Press HERE to register to the ODSC West 2019 conference with a 30% discount! (or use the code: ODSCRBloggers)

R is one of the most commonly-used languages within data science, and its applications are always expanding. From the traditional use of data or predictive analysis, all the way to machine or deep learning, the uses of R will continue to grow and we’ll have to do everything we can to keep up.

To help those beginning their journey with R, we have made strides to bring some of the best possible talks and workshops ODSC West 2019 to make sure you know how to work with R.

Learn the Basics of R for Machine Learning

At the talk “Machine Learning in R” by Jared Lander of Columbia Business School and author of R for Everyone, you will go through the basic steps to begin using R for machine learning. He’ll start out with the theory behind machine learning and the analyzation of model quality, before working up to the technical side of implementation. Walk out of this talk with a solid, actionable groundwork of machine learning.

Dive Deeper with R

After you have the groundwork of machine learning with R, you’ll have a chance to dive even deeper, with the talk “Causal Inference for Data Science” by Data Scientist at Coursera, Vinod Bakthavachalam. First he’ll present an overview of some causal inference techniques that could help any data scientist, and then will dive into the theory and how to perform these with R. You’ll also get an insight into the recent advancements and future of machine learning with R and causal inference. This is perfect if you have the basics down and want to be pushed a little harder. Read ahead on this topic with Bakthavachalam’s speaker blog here.

Learn About Popular Applications

Here, you’ll get to implement all you’ve learned by learning some of the most popular applications of R. Joy Payton, the Supervisor of Data Education at Children’s Hospital of Philadelphia will give a talk on “Mapping Geographic Data in R.” It’s a hands-on workshop where you’ll leave having gone through the steps of using open-source data, and applying techniques in R into real data visualization. She’ll leave you will the skills to do your own mapping projects and a publication-quality product.

Learn to tell a story

Throughout ODSC West, you’ll have learned the foundations, the deeper understanding, and visualization in popular applications, but the last step is to learn how to tell a story with all this data. Luckily, Paul Kowalczyk, the Senior Data Scientist at Solvay, will be giving a talk on just that. He knows that the most important part of doing data science is making sure others are able to implement and use your work, so he’s going to take you step-by-step through literate computing, with particular focus on reporting your data. The workshop shows you three ways to report your data: HTML pages, slides, and a pdf document (like you would submit to a journal). Everything will be done in R and Python, and the code will be made available. We’ve tried our best to make these talks and workshops useful to you, taking you from entry-level to publication-ready materials in R. To learn all of this and even more—and be in the same room as hundreds of the leading data scientists today—sign up for ODSC West. It’s hosted in San Francisco (one of the best tech cities in the world) from October 29th through November 1st.

Press HERE to register to the ODSC West 2019 conference with a 30% discount! (or use the code: ODSCRBloggers)