| Workshop | Instructor(s) | More info |
|---|---|---|
| Introduction to Data Science in the Tidyverse | Amelia McNamara, Hadley Wickham | > |
| Building Tidy Tools | Charlotte Wickham, Hadley Wickham | > |
| What They Forgot to Teach You About R | Jenny Bryan, Jim Hester | > |
| Intro to Shiny and R Markdown | Danny Kaplan | > |
| Advanced R Markdown | Alison Hill, Yihui Xie | > |
| Intermediate Shiny | Aimee Gott, Winston Chang | > |
| Using Shiny in Production | Kelly O'Briant, Sean Lopp | > |
| Applied Machine Learning | Max Kuhn, Davis Vaughn, Alex Hayes | > |
| Introduction to Deep Learning | Sigrid Keydana, Kevin Kuo, Rick Scavetta | > |
| Deep Learning: Beyond the Basics | Sigrid Keydana, Kevin Kuo, Rick Scavetta | > |
| Big Data with R | Edgar Ruiz, James Blair | > |
| Train-the-Trainer Certification Workshop | Greg Wilson | > |
| Shiny Train-the-Trainer Certification Workshop | Mine Çetinkaya-Rundel | > |
| Tidyverse Train-the-Trainer Certification Workshop | Garrett Grolemund | > |
| RStudio Professional Administrator Certification Workshop | Andrie de Vries | > |
This is a two-day hands on workshop based on the book “R for Data Science”. You will learn how to visualize, transform, and model data in R and work with date-times, character strings, and untidy data formats. Along the way, you will learn and use many packages from the tidyverse including ggplot2, dplyr, tidyr, readr, purrr, tibble, stringr, lubridate, and forcats.
You should take this course if you are newer to R and want to establish good habits or have some experience and simply want to learn the tidiest ways to use R.
This course is led by friend of RStudio, Amelia McNamara, from the Department of Computer & Information Sciences at the University of St, Thomas, and Hadley Wickham, RStudio Chief Scientist and co-author of the book R for Data Science.
More info and materials can be found here.
This is a two-day hands on workshop for those who have embraced the tidyverse and now want to expand it to meet their own needs. We'll discuss API design, functional programming tools, the basics of object design in S3, and the tidy eval system for NSE.
Learn efficient workflows for developing high-quality R functions, using the set of conventions codified by a package. You'll also learn workflows for unit testing, which helps ensure that your functions do exactly what you think they do. Master the art of writing functions that do one thing well and can be fluently combined together to solve more complex problems.We'll cover common function writing pitfalls and how to avoid them.
Learn how to write collections of functions that work well together, and adhere to existing conventions so they're easy to pick up for newcomers.
You should take this workshop if you have experience programming in R and want to learn how to tackle larger scale problems. You'll get the most from it if you're already familiar with functions and are comfortable with R’s basic data structures (vectors, matrices, arrays, lists, and data frames). Note: There is ~30% overlap in the material with Hadley’s previous "R Masterclass". However, the material has been substantially reorganized, so if you've taken the R Masterclass in the past, you'll still learn a lot in this class.
This course is led by friend of RStudio, Charlotte Wickham, a professor and award winning teacher and data analyst at Oregon State University, and co-taught by her brother Hadley, who works at RStudio.
More info and materials can be found here.
This is a two-day hands on workshop designed for experienced R and RStudio users who want to (re)design their R lifestyle. You’ll learn holistic workflows that address the most common sources of friction in data analysis. We’ll work on project-oriented workflows, version control for data science (Git/GitHub!), and how to plan for collaboration, communication, and iteration (incl. RMarkdown). In terms of your R skills, expect to come away with new knowledge of your R installation, how to maintain it, robust strategies for working with the file system, and ways to use the purrr package for repetitive tasks.
You should take this workshop if you’ve been using R for a while and you feel like writing R code is not what’s holding you back the most. You’ve realized that you have more pressing “meta” problems that no one seems to talk about: how to divide your work into projects and scripts, how to expose your work to others, and how to get more connected to the R development scene. The tidyverse is not an explicit focus of the course (other than the purrr segment) and you can certainly work through the content without it. But you should expect a great deal of tidyverse exposure.
This course is taught by Jenny Bryan and Jim Hester.
Jenny is a Software Engineer and Data Scientist at RStudio and Adjunct Professor of Statistics at the University of British Columbia. Jenny is widely hailed for making Github a catalyst rather than an impediment to R happiness.
Jim is a software engineer on the tidyverse team at RStudio, with a background in Bioinformatics and Genomics. He is the author and maintainer of a number of R packages including covr, devtools, glue, readr, and more.
More info and materials can be found here.
This is a two-day, hands-on workshop for people who know their way around the RStudio IDE and R and are now looking for the most effective way to become proficient in the basics of Shiny application development and using R Markdown to communicate insights from data analysis to others.
You should take this course if you have used R and RStudio but don’t know as much as you’d like about Shiny and R Markdown. This workshop will reveal the amazing universe of what’s possible with Shiny and R Markdown and let you practice what you’ve learned.
This workshop is led by Danny Kaplan, a highly regarded educator at Macalester College, long-time friend of RStudio, and author of several textbooks on data science, statistical modeling and computing, and even chaos theory.
More info and materials can be found here.
This is a two-day hands on workshop based on the book R Markdown: The Definitive Guide. This workshop is designed for those who want to take their R Markdown skills to the next level. We'll talk about many low-level details in the rmarkdown package and the whole R Markdown ecosystem. The two goals of this workshop are: 1) learn how to fully customize R Markdown output (HTML, LaTeX/PDF, Word, and PowerPoint); and 2) learn more about existing R Markdown extensions in the ecosystem, such as flexdashboard, bookdown, blogdown, pkgdown, xaringan, rticles, and learnr. We will also talk about how to use or develop new language engines (languages that are not R), how to develop HTML widgets, and integrate Shiny with R Markdown.
You should take this workshop if you have experience programming in R and want to learn how to take advantage of the amazing breadth and depth of R Markdown. You'll get the most from it if you enjoy learning how R Markdown works under the hood (which will involve reading some source code), and are seriously interested in hacking (playing) with HTML, JavaScript, CSS, LaTeX, and command-line tools. We will give minimal tutorials on these languages and tools in the workshop, but it may be easier for you to keep pace with the instructor if you already know them before.
This workshop is led by Yihui Xie, Software Engineer and Data Scientist at RStudio. Yihui is the main author of the open-source knitr package for reproducible research and dynamic report generation. He has also created and co-authored several other R packages, including rmarkdown, bookdown, blogdown, xaringan, tinytex, DT, shiny, and leaflet. He has authored and co-authored four books: Dynamic Documents with R and knitr, bookdown: Authoring Books and Technical Documents with R Markdown, blogdown: Creating Websites with R Markdown, and R Markdown: The Definitive Guide.
More info and materials can be found here.
This two-day workshop is designed by Shiny author Joe Cheng for the experienced Shiny developer. By taking this workshop, you’ll improve your understanding of shiny’s foundations and learn how to make the most of reactive programming, techniques for extending and improving UI, techniques for debugging and tools for modularizing applications. By the end of the two days, you’ll be able to push the envelope of what you and your organizations can do with Shiny.
You should take this workshop if you are already familiar with the basics of shiny and you have built your own simple applications.
This course is led by friend of RStudio and Education Practice Lead at RStudio Certified Partner Mango Solutions, Aimee Gott. Winston Chang, RStudio Data Scientist, Developer, and author of the R Graphics Cookbook will join Aimee to provide hands on advice and answers to the Shiny application development questions that stump you.
More info and materials can be found here.
This two-day workshop will teach you the best practices that make production-quality Shiny application performance possible. Does 30,000 simultaneous users for a Shiny application sound challenging? Shiny is used all over the world to deliver interactive, visual data products from data science teams to internal and external audiences at scale. The course will not spend time on writing applications, but will focus on the open source and professional ecosystem around shiny that includes performance optimization, testing, and production deployment. This workshop relies on RStudio professional products for deployment.
You should take this workshop if you are comfortable writing Shiny apps but are afraid for what will happen when people start looking at them.
The workshop is taught by Sean Lopp, RStudio Solutions Engineer. Sean works with RStudio product teams and helps enterprise customers to realize the value of R and Shiny.
More info and materials can be found here.
This two-day workshop will provide an overview of using R for supervised learning. The session will step through the process of building, visualizing, testing and comparing models that are focused on prediction. The goal of the course is to provide a thorough workflow in R that can be used with many different regression or classification techniques. Case studies are used to illustrate functionality.
You should take this workshop if you are interested in making accurate predictions and would like to learn about the entire process of creating predictive models.
This course is taught by Max Kuhn, Software Engineer and Data Scientist at RStudio and co-author of Applied Predictive Modeling. Basic familiarity with R and modeling is required.
More info and materials can be found here.
This one-day workshop introduces the essential concepts of building deep learning models with TensorFlow and Keras. During this course, you will build and train neural networks using the Keras package. The day includes:
-
Introduction
- Understand the basic layers, loss functions and optimizers, and how to combine these to
- Build and train your first model using the keras package
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Systematically building better models
- Understand capacity, overfitting and remedies to overfitting
- Additional layers: convolution, dropout, maximum pooling
- Using the tfruns package for systematic modeling
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Introduction to image processing
- Tasks in image processing (overview)
- Convolutional Neural Networks
- Architectures and pretrained models
- Understanding and interpreting the layers
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Introduction to text processing
- Understanding how to represent text for input into a neural network
- Using embeddings
- Basic recurrent layers and LSTM (Long short term memory)
You should take this workshop if you’ve used R but are new to the field of deep learning and neural networks. The workshop will give you the essential understanding to attend the (optional) second day of training: “Deep Learning: Beyond the Basics”.
This workshop is co-taught by Rick Scavetta author of Deep Learning with R in Motion, Sigrid Keydana, RStudio Tensorflow Developer Advocate, and Kevin Kuo, RStudio Software Engineer.
More info and materials can be found here.
This one-day workshop introduces some intermediate material in deep learning, including object detection, structured data, and time series with recurrent networks. The day also introduces the important topic of interpretation and uncertainty in neural networks. You will build models using the keras package.
The day includes:
- Overview of object detection
- Understand how to detect where an object is located inside an image
- The essentials of YOLO (you only look once) and SSD (single shot detection)
- Modeling structured data
- Deep Learning for structured data
- Understand the functional API of Keras, to allow the incorporation of heterogeneous inputs and outputs simultaneously
- Embedding categorical variables
- Time series and recurrent networks
- Using RNN for time series forecasting
- Introduction to sequence to sequence learning
- Interpretation and uncertainty
- Approaches to explainability in Deep Learning (gradient-based methods, LIME)
- Representation learning and Variational Autoencoders
- Measuring uncertainty in Deep Learning: A Bayesian approach
- Practical approaches for outputting prediction intervals
You should take this workshop if are already familiar with the basic concepts of neural networks (as covered by the optional course “Introduction to deep learning”), and want to get familiar with the intermediate aspects of deep networks.
This workshop is led by Kevin Kuo, RStudio Software Engineer and Sigrid Keydana, RStudio Tensorflow Developer Advocate, and supported by Andrie de Vries, RStudio Solutions Engineer and co-author of R for Dummies.
More info and materials can be found here.
A two-day workshop. We will cover how to connect to and analyze data that exists outside of R. For databases, we will focus on the dplyr, DBI and odbc packages. For Big Data clusters, we will also learn how to use the sparklyr package to run models inside Spark and return the results to R. These packages enable us to use the same dplyr verbs inside R but are translated to SQL queries. We also will review recommendations for connection settings, security best practices and deployment options. Throughout the workshop, we will take advantage of RStudio’s professional tools such as RStudio Server Pro, the new professional data connectors, and RStudio Connect.
You should take this workshop if you want to learn how to use R with databases, such as SQL Server, Oracle, or PostgreSQL, and/or other scalable external data sources, such as Hadoop clusters with Hive and Spark.
This workshop is taught by Edgar Ruiz. Edgar Ruiz is a solutions engineer at RStudio with a background in deploying enterprise reporting and business intelligence solutions. He is the author of multiple articles and blog posts sharing analytics insights and server infrastructure for data science. Edgar is the author and administrator of the https://db.rstudio.com web site, and current administrator of the sparklyr web site: https://spark.rstudio.com. Co-author of the dbplyr package, and creator of the dbplot, tidypredict and modeldb package.
More info and materials can be found here.
This is the first day of a two-day workshop that will equip you to teach R effectively. We will draw on RStudio's experience teaching R to recommend tips for designing, teaching, and supporting short R courses.
On Day 1 of the course, you will learn practical activities that you can use immediately to improve your presentation style, learning outcomes, and student engagement. You will leave the class with a cognitive model of learning that you can use to develop your own effective workshops or courses within your organization. The course will also cover how to use RStudio Cloud and its curriculum of tutorials to jump-start your own lessons.
More info and materials can be found here.
This is the second day of a two-day workshop that will equip you to teach R effectively. We will draw on RStudio's experience teaching R to recommend tips for designing, teaching, and supporting short R courses.
On Day 2 of the course, participants will have the option to choose one of two tracks: Teaching the Tidyverse or Teaching Shiny. - Teaching Shiny: Classroom examples will focus on teaching Shiny at the beginner and intermediate levels. The course materials will build on RStudio's Mastering Shiny workshop as well as the upcoming book from the author of the Shiny package, Joe Cheng, and they will cover the entire lifecycle of a Shiny app: build > improving > share. Participants will receive the course materials for teaching Mastering Shiny. You should take this workshop if you work as a training partner and want to qualify as an RStudio Certified Shiny Instructor or if you are an advocate for R in your organization. You should be proficient in Shiny already and be prepared to submit examples of your work. Prior teaching experience is helpful, but not required. Please bring a laptop and a device that has video recording capabilities (such as a laptop or cell phone).
More info and materials can be found here.
This is the second day of a two-day workshop that will equip you to teach R effectively. We will draw on RStudio's experience teaching R to recommend tips for designing, teaching, and supporting short R courses.
On Day 2 of the course, participants will have the option to choose one of two tracks: Teaching the Tidyverse or Teaching Shiny. - Teaching the Tidyverse: Classroom examples will focus on how to teach students to do data analysis with the Tidyverse. We will use Master the Tidyverse, which is an award-winning two-day workshop developed by RStudio, as an example. Participants will receive the course materials for teaching Master the Tidyverse. You should take this workshop if you work for a training partner and want to qualify as an RStudio Certified Tidyverse Instructor or if you are an advocate for R in your organization. You should be proficient in the Tidyverse already and be prepared to submit examples of your work. Prior teaching experience is helpful, but not required. Please bring a laptop and a device that has video recording capabilities (such as a laptop or cell phone).
More info and materials can be found here.
This is a two-day workshop for system administrators responsible for RStudio Server Pro, RStudio Connect, and RStudio Package Manager (in beta August 2018). It is designed to cover everything you need to know to setup and administer RStudio professional products as an integrated tool chain for your R users. Topics will include installation and configuration, user, content, and process management, and advanced topics like offline deployment, high availability, DevOps integration, and security best practices. Learn how to support R users and the people who need access to their work. Students who complete all of the exercises successfully will receive an RStudio Professional Administrator certificate of completion.
You should take this workshop if you work in an IT organization responsible for supporting data scientists or are the person on a data science team responsible for infrastructure and tools. Consulting partner employees who want to qualify as a Certified RStudio Professional Administrator are also welcome. You should be comfortable in the Linux terminal. Experience with R is helpful but not required.
The workshop will be taught by Solutions Engineers Andrie deVries co-author of "R for Dummies", Kristopher Overholt, and Cole Arendt. It is based on best practices harvested from working with hundreds of RStudio customers and the popular RStudio Connect Administrator workshop from last year’s conference.
More info and materials can be found here.