Join us on Slack
All activities will take place in one of three rooms:
- (Rm. 1/2) 3A001 & 3A002 learning studios (two rooms joined into one large room)
- (Rm. 1) 3A001 learning studio
- (Rm. 2) 3A002 learning studio
- (Rm. 3) 3A003A used for workshops only (please be quiet when crossing skywalk, due to event in atrium, thanks!)
Alternative to workshops: if you're not attending workshops you're welcome to do the hack room
(times subject to change)
- 8:00 - 9:00: (Rm. 1/2) Registration + coffee/tea/pastries + cRaggy submissions open (link)
- 9:00 - 9:10: (Rm. 1/2) Introduction
- 9:10 - 9:45: (Rm. 1/2) Keynote: Alison Hill (link)
- 9:45 cRaggy submissions due
- 9:45 - 10:30: (Rm. 1/2) Coffee break + Panel Discussion
- 10:30 - 12:00: (Rm. 2/3) Workshops (link)
- (Rm. 2) A gRadual intRoduction to Data Wrangling
- (Rm. 3) Intermediate Machine Learning 1
- 12:00 - 1:15: (Rm. 1) Lunch (catered) + (Rm. 2) cRaggy Browsing/Voting
- 1:15 - 1:50: (Rm. 1/2) Keynote: Kara Woo (link)
- 1:50 - 2:15: (Rm. 1) Break/Socializing
- 2:15 cRaggy votes due
- 2:15 - 3:45: (Rm. 2/3) Workshops (link)
- (Rm. 2) A gRadual intRoduction to Data Visualization
- (Rm. 3) Using R with Databases
- 3:45 - 4:00: (Rm. 1/2) Break (snacks/beverages)
- 4:00 - 6:00: (Rm. 1/2) Lightning talks (link) and Wrap Up
- cRaggy Winner Presentations
- Lightning Talk Session 1 ~ 50 minutes
- 15 minute break
- Lightning Talk Session 2 ~ 45 minutes
- 6:00: (Rm. 1/2) Closing remarks
- 6:00: (Rm. 1/2) Social
- (Portland) Bird of Feather Dinners (link)
Title: Big Magic with R: Creative Learning Beyond Fear
Bio: Alison is an Associate Professor of Pediatrics at Oregon Health Science University (OHSU) and the Assistant Director of OHSU's Center for Spoken Language Understanding, home to the Computer Science graduate education program. Her current research aims to evaluate whether Natural Language Processing methods can be translated into meaningful outcome measures for individuals with neurodevelopmental disorders like Autism, Down Syndrome, and Fragile X Syndrome. Her work has been published in numerous peer-reviewed journals and book chapters, and has been funded by the Oregon Clinical and Translational Research Institute, the National Institutes of Health Office of Research on Women's Health, and the National Institute on Deafness & Other Communication Disorders. Alison began using and teaching R seven years ago. She teaches four graduate-level courses using R, and is the author of "Working with Data in the Tidyverse" to be offered by DataCamp.com. She is also a co-author of the book "blogdown: Creating Websites with R Markdown" with Yihui Xie and Amber Thomas.
Abstract: Inspired by the book "Big Magic: Creative Living Beyond Fear" by Elizabeth Gilbert, Alison will talk about the five essential ingredients needed to creatively learn R and why these elements are also essential for advanced users to take their R skills to the next level. You will hear practical advice for when, where, and how to start a project in R, and how your learning can add value- both to your own knowledge and to contribute to the larger community of R learners. Along the way, she will share recommended resources and evidence-based strategies for project-based learning. Alison's background working with both new and advanced R users gives her a unique perspective on this topic.
Title: Anyone Can Play Git/R: Tips for First-Time Contributions to R Packages
Bio: Kara Woo is a research scientist in data curation at Sage Bionetworks. She has a master's in library and information science from the University of Washington and is interested in data management, data visualization, and open and reproducible research. Kara is also a co-maintainer of accidental aRt, a blog of data visualizations gone beautifully wrong.
Abstract: Contributing to R packages and projects can be a rewarding way to give back to the tools you use and to improve your own programming skills in the process. In this talk, Kara will discuss some of the varied ways to contribute to existing projects. Anyone, whether a seasoned programmer or someone brand new to R, can make useful contributions to R packages. Kara will draw on her experience working on ggplot2 to offer strategies for finding your way in an unfamiliar codebase, and to give insights into the relationship between maintainers and contributors.
A gRadual intRoduction to Data Wrangling (Beginner)
Data comes in a variety of formats and even working with spreadsheet data can pose tricky problems. In this workshop, you'll use the `tidyverse` to transform messy data into nice summary tables and get a handle on dates, times, and strings as well. You'll find that using the `tidyverse`, it's easier to make and share reproducible data wrangling workflows that will increase your overall productivity. You might even have some fun doing it!
A gRadual intRoduction to Data Visualization (Beginner)
An exploration into the `ggplot2` package. Focus will be on creating and tweaking basic plots while reviewing the underlying Grammar of Graphics throughout. You'll also see an easy way to turn many `ggplot2` graphics into dynamic plots with a simple function call.
Using R with Databases (Intermediate)
Would you like to learn how R is used to advance valuable information from data stored in relational databases? With the proliferation of data and increased reliance on data-driven knowledge, many companies need skilled individuals who can extract meaningful information form data in order to drive informed decision making. This intermediate workshop will introduce you to various methods of using R with databases to derive valuable information from data. The workshop will give you hands-on experience in the following areas:
- how to connect to a database from R
- how to create and manage database objects.
- how populate tables in a database, and issue SQL queries to retrieve and transform your data using R.
Introduction to Deep Learning using TensorFlow with R (Intermediate)
TensorFlow is an open-source software library for numerical computing (similar to Python’s numpy) that has made waves recently for its impact in deep learning. The R interface to TensorFlow allows you to work productively using the high-level Keras and Estimator APIs – and, when you need more control, provides full access to the core TensorFlow API. Once the models have been created, you can visualize them both in RStudio and using TensorBoard.
- What is deep learning?
- What are TensorFlow and Keras?
- How do you build a neural network?
- How do you visualize what you have just created?
|Paige Bailey||Microsoft||Illuminating TensorBoard outputs – how to structure your neural network model topologies to take advantage of TensorFlow’s graph visualizations|
|cRaggy speaker 1||TBD|
|cRaggy speaker 2||TBD|
|Caitlin Hudon||R-Ladies; Shelfbucks||NULL: When Missing Data is Valuable|
|Jay Lee||Reed College||Ain't No Party Like a Political Party|
|Jonathan Nolis||Using deep learning in R to generate offensive license plates|
|Zachary Foster||Oregon State University||Taxa: An R package for parsing and manipulation of taxonomic data|
|Nicole Michel||National Audubon Society||Put a biRd on it: using R and Big Data to inform avian conservation|
|Daniel Anderson||University of Oregon||Contribute to Open Source with Pretty Slides|
|Frank Farach||Slalom Consulting||Continuous Data Quality Improvement with R|
|Jose Hernandez||University of Washington, eScience Institute||A Perfect Match: Using RODBC to connect R + SQL server|
|Sondra Stegenga||University of Oregon||Data Ethics: Considerations for Special Populations|
|David Severski||Starbucks||Enterprise Technology Risk Management - With R!|
|Eric Leung||OHSU DMICE||Underutilized functions for data exploration: tips from exploring hundreds of variables|
|Dror Berel||Fred Hutch||A Systematic approach to retrieve data from S4 object-oriented system using a tidy solution|
|Norma Padron||American Hospital Association||Exciting applications and uses of R in health care|
|Alex Hayes||Rice University||Intuitive modelling in R through statistical type safety|
cRaggy graphics show-and-tell
Analyze BIKETOWN data, join it with any other data that makes sense to you, and make a summary graphic to tell a story. Best submissions will get to give a lightning talk to describe their work.
The first annual cRaggy graphics show-and-tell will be an informal event held during CascadiaRConf. Everyone can participate by submitting an entry (as a group or individually), by upvoting entries that you like during the day, and by listening to the 3 lightning talks about the most popular entries at the end of the day.
Everybody gets a look at one dataset (details below), announced on Friday May 18th (about 2 weeks before the conference) on the conference website and in an email sent to everyone who’s pre-registered. The dataset is not mtcars or one of the usual suspects! It is public and easy-to-get-to, but relatively obscure and fun to play with.
On June 2, conference attendees post an entry by 9 am (look for the signs for location in the main meeting room), which consists of:
- One graph printed on 8 1/2” * 11” paper that explores the cRaggy dataset (which you can subset or augment with other data if you’d like)
- A URL pointing to the code that produced your graph (preferably on GitHub)
- Share your code in a print-out (or project it during a possible lighting talk)
- Contact info so you can be notified during the conference that you are invited to do a lighting talk
Entries will be posted so everyone can see, comment, and vote with a green cRaggy dot that you get at registration. Remember (lifted from TidyTuesday guidelines):
- This is for fun
- It is NOT about criticizing the original article or graph. Real people made the graphs, collected or acquired the data! Focus on the provided dataset, learning, and improving your techniques in R.
- It is NOT about criticizing or tearing down your fellow #rstats practitioners! Be supportive and kind to each other! Like other’s entries if you want to hear more from them!
Votes will be counted after the cutoff at 2 pm, 3 speakers will be notified immediately, and lightning talks will be around 4:30 pm. Each lighting talk will be about 5 minutes, plus time to connect your computer to the projection screen.
The BIKETOWN dataset is described here: https://www.biketownpdx.com/system-data
Here is a minimal example: https://github.com/smithjd/cRaggy-example/blob/master/minimal-cRaggy.R
This is a social data project in R - along the lines of Tidy Tuesday exercises
A good portion of the day will be taken up with workshops. You do not have to attend a workshop. If you don't one of the two big rooms will be set aside for a socializing, discussing, coding and learning together.
There's no format per se for the hack room. Feel free to get discussions going on a topic you're interested in, or learn some new package together.