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.

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

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.

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. 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.

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.

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: 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.