Aggregate meetup No. 8! Everyone is welcome!

Agenda:

• brief intro/R project or problem: We'll go around the room, each person talk about an R project they've been working on, or a data/data-science problem, or something they want to learn, etc.

• After going around the room, anyone can share some code they've been working on and want help with / want feedback on. Make sure to have this ready before the meeting. Discuss here https://github.com/pdxrlang/aggregate/issues/8

• Before arriving, use the pdxrlang/aggregate GitHub repository to discuss ideas for things to work on at aggregate -https://github.com/pdxrlang/aggregate/issues

• Do bring your computer (if you have one) in case you want to work on something.

Let's have a Shiny night! If you're not familiar with Shiny, check it out at http://shiny.rstudio.com - Shiny is a web application framework for R. Many people use Shiny to convey business/research/etc. findings to collaborators/bosses/etc. without needing the person to be able to code. Checkout https://shiny.rstudio.com/gallery to see some really cool examples.

We're going to have a brief tutorial for those that haven't used Shiny, as well as some Shiny use cases to see what you can do with Shiny in the real world.

Speakers:

• Winston Saunders - intro to Shiny

• Jessica Minnier - speaking on https://kcvi.shinyapps.io/START/

• Kinga Farkas - CUSUM Anomaly Detection App Using Shiny

• John Smith - Rise and shine. Shiny as an everyday tool.

Speaker: Chester Ismay (https://github.com/ismayc) - Instructional Technologist and Consultant for Data Science, Statistics, and R at Reed College

Abstract: In this talk, I will discuss the motivation behind creating a data package using the data from the stories produced by FiveThirtyEight. I’ll also walk through the process of creating a data package in R and some of the vignettes for the package that have been created by my students and others from throughout the world. Lastly, I’ll discuss some ideas (that I’d love to work with others on) for other data packages in R that can better serve the R community by helping novice and intermediate R users work with and tidy “messy” data.

Speaker: Matt Laidler

Abstract: R can be a powerful tool for the process of systematically tracking health-related outcomes (i.e. surveillance) identified from administrative data sources. Tracking health outcomes in populations often relies on coded administrative data to standardize metrics and simplify processes. This may have the effect of overlooking useful information described in free text data, but due to data volume/size, may not be practical to process outside of text mining methods. This presentation will describe a use case of text mining and association rule mining in R for tracking health outcomes.

Women Who Code Portland and Portland R User Group are coming together to host a night full of talks on R and Machine Learning and an opportunity to network with like-minded individuals.

Program

6.00 - 6:30: Doors Open

6.30 - 7:30: Talks

7:30 - 8:00: Networking and wrap up.

Talk #1 - Predictive Analytics and Machine Learning in R - Myffy Hopkins

All data has a story to tell. You can force a story upon it, or you can breath life into it to let it speak for itself. Myfanwy "Myffy" Hopkins has been using advanced statistical methods, R, and machine learning for over 10 years to transform data into information for decision making. The tools provided by R packages are unparalleled at quickly transforming data into a working predictive model. Myffy brings the statistical and data mining expertise needed to make R a highly productive space for generating predictive models using machine learning methods. In her talk, Myffy will show how R makes the difficult statistical concepts of Predictive Analytics manageable to execute. Seeing the actual results of a predictive model will help get through the more grueling parts of the learning curve to becoming a Data Scientist.

Talk #2 - Automated Feature Selection of Predictors in Electronic Medical Records Data - Jessica Minnier

Jessica Minnier is an assistant professor of biostatistics at Oregon Health & Sciences University in the OHSU-PSU School of Public Health. Her research interests include statistical methods for risk prediction and classification, the analysis of 'omics data, and the analysis of large data sets such as electronic health records data. She is interested in reproducible research as well as statistical computing and data visualization with R and shiny.

In her talk, Jessica will talk about "emrselect" - https://github.com/jminnier/emrselect. "emrselect" is an R package that automates the feature selection method for phenotype prediction with Electronic Medical Record (EMR) data to reduce the number of candidate predictors and in turn improve model performance by relying entirely on unlabeled observations. The proposed method generates a comprehensive surrogate for the underlying phenotype with an unsupervised clustering of disease status based on several highly predictive features such as diagnosis codes and mentions of the disease in text fields available in the entire set of EMR data. A sparse regression model is then built with the estimated outcomes and remaining covariates to identify those features most informative of the phenotype of interest. Empirical results suggest that this procedure reduces the number of gold-standard labels necessary for phenotyping, thereby harnessing the automated power of EMR data and improving efficiency.

Who Should Attend?

Anyone who is interested in exploring the world of R and Machine Learning or just curious to learn what they are. By attending our event, you are agreeing to support our mission and follow our Code of Conduct.

Aggregate meetup No. 3! Everyone is welcome!

Agenda:

• brief intro/R project or problem: We'll go around the room, each person talk about an R project they've been working on, or a data/data-science problem, or something they want to learn, etc.

• After going around the room, anyone can share some code they've been working on and want help with / want feedback on. Make sure to have this ready before the meeting. Discuss here https://github.com/pdxrlang/aggregate/issues/8

• Before arriving, use the pdxrlang/aggregate GitHub repository to discuss ideas for things to work on at aggregate -https://github.com/pdxrlang/aggregate/issues - One idea was proposed

• Do bring your computer (if you have one) in case you want to work on something.

Time: 630pm - 8pm

Doors open after 6 - DON'T SHOW UP BEFORE 6 - we'll start between 615 and 630.

Enter on the 8th Ave. entrance where there's gates - The gates may be locked, but a security person will let you in. If you have any trouble message me in the meetup app, or on Twitter at @pdxrlang or @sckottie

We have monthly meetups with someone(s) giving a talk(s) on some usually technical topic involving R. We also have a great set of meetings run by Audrey Julian going over Coursera R course material.

We want to have another set of meetings where pdxrlang meetup members can meet each other, chat, hack on R projects, learn from one another, etc. Basically anything goes.

This is our first meetup of this type. Everyone is welcome!

Time: 615pm - 8pm

Doors open at 6 - DON'T SHOW UP BEFORE 6 - we'll start at 615.

Speaker: Ted Laderas (http://laderast.github.io/) - Instructor, Medical Informatics and Clinical Epidemiology, OHSU

Abstract: Many graduate students in the basic sciences are afraid of data exploration and cleaning, which can greatly impact their downstream analysis results. By using a synthetic dataset, some simple dplyr commands, and a shiny dashboard, we teach graduate students how to explore their data and how to handle issues that can arise (missing values, differences in units). For this talk, we'll run through a simple EDA example (combining two weight loss datasets) with a general data explorer in shiny that can be easily customized to teach specific EDA concepts.

Aggregate meetup No. 17! Everyone is welcome!

shiny (https://shiny.rstudio.com/) is a popular web application framework for making interactive visualizations and dashboards, allowing R users to share their analyses in an interactive format with minimal web programming expertise. In this workshop, we will produce a simple data exploration dashboard with tooltips using the shiny, ggplot2 and flexDashboard packages. I will introduce the basic concepts behind programming a shiny application, as well as some useful design patterns (reactives, tooltips, observe/update/isolate). Basic familiarity with R programming concepts and ggplot2 are suggested

We will not have pizza this time, but we will have some drinks, and perhaps some light snacks.

Agenda:

• This aggregate will be a bit different from the others. pdxrlang member Ted Laderas (http://laderast.github.io/ - https://github.com/laderast) will give a workshop on using Shiny. More details to come!

• Bring your computer!

• Time: 630pm - 8pm

Following our December meetup with an intro to Shiny and some use cases, we'll dive a bit deeper into Shiny with more use cases and Shiny-how-to. We'll have two speakers:

• Jasmine Dumas - Extending Shiny by enhancing user experience with shinyLP - Slides
• Lina Gao - Treatment Assignment in Adaptive Bayesian Sequential Design using Shiny

We'll be borrowing a projector so we won't be dealing with tiny screen this time!

Doors open after 615 pm. DO NOT SHOW UP BEFORE 615 PM. Talks start about 630 pm.

Repeat: DO NOT SHOW UP BEFORE 615 PM.

Go to 720 NW Davis, and someone will be there to let you in through the locked door.

We'll visit a local watering hole afterwards

Propose a talk! Or suggest a talk you want to hear or attend. At this site https://sckott.typeform.com/to/ShM55K , or message Scott on meetup.com or in the Slack room below or on twitter.

Hashtag for PDX R meetups: #pdxrlang & the Twitter account to follow/tweet at is @pdxrlang - We also use http://pdxdata.slack.com/for a back-channel during MeetUps and in between. Invite yourself here: http://pdxdata.org/slack/

Aggregate meetup No. 16! Everyone is welcome!

We will not have pizza this time, but we will have some drinks.

Agenda:

• brief intro/R project or problem: We'll go around the room, each person talk about an R project they've been working on, or a data/data-science problem, or something they want to learn, etc.

• After going around the room, anyone can share some code they've been working on and want help with / want feedback on. Make sure to have this ready before the meeting. Discuss here https://github.com/pdxrlang/aggregate/issues/8

• Before arriving, use the pdxrlang/aggregate GitHub repository to discuss ideas for things to work on at aggregate - https://github.com/pdxrlang/aggregate/issues

• Do bring your computer (if you have one) in case you want to demo something.

• Time: 630pm - 8pm

Aggregate meetup No. 13! Everyone is welcome!

We'll have pizza and drinks.

Agenda:

• brief intro/R project or problem: We'll go around the room, each person talk about an R project they've been working on, or a data/data-science problem, or something they want to learn, etc.

• After going around the room, anyone can share some code they've been working on and want help with / want feedback on. Make sure to have this ready before the meeting. Discuss here https://github.com/pdxrlang/aggregate/issues/8

• Before arriving, use the pdxrlang/aggregate GitHub repository to discuss ideas for things to work on at aggregate -https://github.com/pdxrlang/aggregate/issues

• Topics for the night so far:

•  coming soon ...

• Do bring your computer (if you have one) in case you want to work on something.

pdxrlang Aggregate meetup No. 4! Everyone is welcome!

Agenda:

• brief intro/R project or problem: We'll go around the room, each person talk about an R project they've been working on, or a data/data-science problem, or something they want to learn, etc.

• After going around the room, anyone can share some code they've been working on and want help with / want feedback on. Make sure to have this ready before the meeting. Discuss here https://github.com/pdxrlang/aggregate/issues/8

• Before arriving, use the pdxrlang/aggregate GitHub repository to discuss ideas for things to work on at aggregate -https://github.com/pdxrlang/aggregate/issues

• Do bring your computer (if you have one) in case you want to work on something.

To do data science you need five sets of verbs: import, tidy, transform, visualise, and model. Importantly, you also need a way to connect these tools together so that your analysis flows from one step to another, without you beating your head against the wall. In this talk, I discuss the idea of the pipe as it is implemented in R with the magrittr package. You’ll learn why the pipe makes your code easier to read, and see how it provides a unifying interface throughout your complete workflow.

Come along to learn about why I think pipelines are awesome and see how pipelines + tidyr, dplyr, ggplot2, and purrr can make your data analyses fast, fluent and fun. I’m a passionate believer that code should be an artefact of clear communication, so even if you’ve never used R before, you’ll be able to follow this talk.

Sign up at: https://members.wework.com/events/david-robinson-broom-tidy-model-outputs-19084 - you must sign up at the link to be able to attend.

Speaker: David Robinson (http://varianceexplained.org/) - Data Scientist at StackOverflow

David will talk about broom (https://github.com/dgrtwo/broom), his R package for tidying model outputs.

Andrew Bray will discuss Collaborative coding with GitHub and RStudio server. He's in the faculty at Reed College here in Portland.

Doors open ~6 pm, talk starts at 6:30 pm

We'll visit a local watering hole afterwards.

Speaker: Tim Kaye

Title: Fun with data streams in R

Abstract: Often in practice, not all of your data will have been generated or collected yet, so you need to be able to process that data and update your models on-the-fly. This talk will provide a brief exploration of streaming algorithms and how they can be used in R. The primary applications will be data mining for data streams and online inference using non-parametric Bayesian models.

We may visit a local watering hole afterwards, or I may bring beverages.

We'll have two talks from Microsoft/Revolution Analytics:

• Joseph Rickert on R at Microsoft
• John Lam on A Lap Around R Tools for Visual Studio

Doors open ~6 pm, talks start at 6:30 pm

Speaker: Chester Ismay (https://github.com/ismayc) - Instructional Technologist/Statistical Consultant at Reed College

Abstract: One of the great recent additions from RStudio is the ability to create templates in R Markdown that allows R users to customize output to a variety of document formats while only needing to write in Markdown. You can create templates for outputting Word documents, HTML documents, and PDF documents all including R code and its output. I’ll discuss how to go about creating templates, demonstrate an R Markdown senior thesis template I created for Reed College seniors that interfaces with the traditional LaTeX thesis template, and hopefully provide you with an opportunity to write a template of your own during this meeting.

We'll have two talks this meetup:

• Jim Walter: Adapting A/B Testing To Software Performance And Efficiency

• Scott Chamberlain: request - an R library to make http requests a snap & http request caching

Doors open after 6 pm. DO NOT SHOW UP BEFORE 6 PM. Talks start at 6:30 pm. Repeat: DO NOT SHOW UP BEFORE 6 PM.

Doors are open at bottom, take elevator to 3rd floor, door should be open for suite 320

We'll visit a local watering hole afterwards.

Speaker: De'Mel Mojica

Abstract: This talk will be on a general approach to automatically join large-scale, geospatial data across distinct data sets, using a mix between Levenshtein Distance thresholds and Haversine Distance thresholds. This approach permits joining multiple data sets without the need to provide ad hoc normalization conventions for each data resource. In addition, this approach can be generalized beyond a geospatial field and applied any domain which requires joining across two or more non-identical dimensions.

We'll visit a local watering hole afterwards.

Speaker: Winston Saunders

Abstract: Word vectors, derived by deep learning algorithms applied to billions of words of text, provide powerful semantic models of language. Code in R, demonstrating [queen] + [man] - [woman] ~ [King] to about 90% accuracy will be reviewed. Building first on exploratory "bag of words" analysis of Presidential debate texts, we'll explore, using pre-computed GloVe vectors (Pennington et al http://nlp.stanford.edu/projects/glove/), relationships like [sanders] + [trump] - [clinton] ~ [cruz] and how candidate positions align to rhetorical sentiment like [government] + [people] - [tax]. This analysis is work in progress. We'll also test empirical limits (aka failed experiments). Active feedback is both sought and welcome.

Details:

Doors open ~6 pm, talk starts at 6:30 pm

Doors are open at bottom, take elevator to 3rd floor, door should be open for suite 320

We'll visit a local watering hole afterwards.

Topic: Series of short lightning talks. Each one will be 5-10 minutes (including questions).

Speakers: None yet. Get in touch in the comments for this meetup, contact me via email ([email protected]), use the group mailing list, or contact me via Twitter @pdxrlang I'll fill in names and titles as we get them.

We will have pizza and beers. Doors open at 6:00 pm - talk starts 6:30 pm

Hashtag for R meetups: #pdxrlang

Satish Doguparthy will talk about the application of analytics using R for optimizing wireless plans for IoT devices.

Doors open at 6:00 pm - talk starts 6:30 pm - We need to be out of the venue before 8 pm

NOTE 1: We have a new venue. We now meet at Mozilla's offices at 1120 NW Couch St #320 - still downtown, not far from our previous location.

We'll go to a local watering hole (to be decided) after the talk if there's enough interest.

Hashtag for PDX R meetups: #pdxrlang & the Twitter account to follow/tweet at is @pdxrlang