At our initial meeting, we'll see how many people in the area use R and where your interests and backgrounds lie. Once we've made contact, our meetings may grow to include presentations or tutorials. Come and tell us what you'd like from your local R user group!

We are the local R user group for Portland, Oregon. R is a free software environment for statistical computing and graphics (http://www.r-project.org/ ). Our goal is to support and share R experience and knowledge among its users in the Portland community. We would like to hear how you use and enjoy the R language and statistical programming environment.

At our initial meeting, we'll see how many people in the area use R and where your interests and backgrounds lie. Once we've made contact, our meetings may grow to include presentations or tutorials. Come and tell us what you'd like from your local R user group!

Presented by Chad Scherrer.

Sampling from a large discrete distribution is a common problem in statistics. In this talk, we'll consider a real-world situation where the properties of the distribution cause common approaches to break down, and we'll arrive at a Haskell-based solution that fixes the problem.

Portland Pearl Rotary Club Program: Risky Business - Decision Making Under Uncertain Outcomes Speaker: Steven A. Bleiler, PhD Mathematics & Statics, PSU

Eco Trust Building, 721 NW 9th Ave. Meeting begins around 7:15, $10 includes program and buffet breakfast Meeting adjourns at 8:30 a.m.

Attend this free seminar to find out how you can use MATLAB to build mathematical models for forecasting and optimizing the behavior of complex systems.

In this seminar, MathWorks engineers demonstrate how you can: * Develop models using data fitting and first-principles modeling techniques * Simulate models and create custom post-processing routines * Generate reports that document models and simulation results

After this session, you’ll understand the range of modeling approaches possible with MATLAB, when to use each, and why.

Previous MATLAB software experience is not required to attend but registration is required to reserve your seat.

Josh Reich will be presenting on the data.table package!

Additionally, Simple's Director of Research Allen Goodman will be giving a talk on time series analysis:

"From a statistical perspective, the analysis of time series hasn't dramatically diverted from the program established by George Box and Gwilym Jenkins in 'Time series analysis: Forecasting and Control'. However, from a computational perspective, statisticians haven't adopted best practices for model identification, parameter estimation, model diagnostics, or forecasting. I'll model a time series using the Box-Jenkins program in R, outlining contemporary attitudes within the R community on the subject"

We're sprinting to create a draft of the Data Wrangling Handbook, which is a supporting text for the School of Data. The School of Data will be a place where anyone anywhere can learn how to find, clean, analyze, and communicate with data.

If you plan to attend please sign up on the etherpad.

The School of Data is a partnership between the Open Knowledge Foundation and Peer 2 Peer University, with the goal of making open data accessible to everyone.

"No talks. You may opt to take up to 60 seconds to complain about Big Data. One paper per month, no obligation to read it."

We'll start by letting anyone who wants to take up to a minute to tell us what they've been doing with data lately.

This month's paper is A time-efficient, linear-space local similarity algorithm by Huang and Miller. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

"No talks. You may opt to take up to 60 seconds to complain about Big Data. One paper per month, no obligation to read it."

We'll start by letting anyone who wants to take up to a minute to tell us what they've been doing with data lately.

This month's paper is Disk Aware Discord Discovery: Finding Unusual Time Series in Terabyte Sized Datasets by Yankov, Keogh and Rebbapragada. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

"No talks. You may opt to take up to 60 seconds to complain about Big Data. One paper per month, no obligation to read it."

We'll start by letting anyone who wants to take up to a minute to tell us what they've been doing with data lately.

This month's paper is Large-Scale Matrix Factorization with Distributed Stochastic Gradient Descent by Gemulla, Haas, Nijkamp and Sismanis. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

"No talks. You may opt to take up to 60 seconds to complain about Big Data. One paper per month, no obligation to read it."

We'll start by letting anyone who wants to take up to a minute to tell us what they've been doing with data lately.

This month's paper is Computational Methods for Dynamic Graphs by Cortes, Pregibon, and Volinsky. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

"No talks. You may opt to take up to 60 seconds to complain about Big Data. One paper per month, no obligation to read it."

We'll start by letting anyone who wants to take up to a minute to tell us what they've been doing with data lately.

This month's paper is Ad Click Prediction: a View from the Trenches by McMahan et al. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

"No talks. One paper per month, no obligation to read it."

This month's paper is Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget by Korattikara, Chen, and Welling. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

CANCELED! Forecast is calling for 3-7 inches of snow tonight, the sinus plague is going around, and it's just damn cold. See you in March!

"No talks. One paper per month, no obligation to read it."

This month's paper is Image Mining of Historical Manuscripts to Establish Provenance by Hu et al. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

"No talks. One paper per month, no obligation to read it."

This month's paper is Image Mining of Historical Manuscripts to Establish Provenance by Hu et al. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

"No talks. One paper per month, no obligation to read it."

This month's paper is Palette Power: Enabling Visual Search through Colors by Bhardwaj et al. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

"No talks. One paper per month, no obligation to read it."

This month's paper is Profiler: Integrated Statistical Analysis and Visualization for Data Quality Assessment by Kandel et al. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

"No talks. One paper per month, no obligation to read it."

This month's paper is High-Dimensional Visual Analytics: Interactive Exploration Guided by Pairwise Views of Point Distributions by Wilkinson et al. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

"No talks. One paper per month, no obligation to read it."

This month's paper is Mondrian Forests: Efficient Online Random Forests by Lakshminarayanan et al. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

"No talks. You may opt to take up to 60 seconds to complain about Big Data. One paper per month, no obligation to read it."

We'll start by letting anyone who wants to take up to a minute to tell us what they've been doing with data lately.

This month's paper is Dynamo: Amazon’s Highly Available Key-value Store by DeCandia etal. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

"No talks. You may opt to take up to 60 seconds to complain about Big Data. One paper per month, no obligation to read it."

We'll start by letting anyone who wants to take up to a minute to tell us what they've been doing with data lately.

This month's paper is A Crowd of Your Own: Crowdsourcing for On-Demand Personalization by Organisciak etal. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

"No talks. You may opt to take up to 60 seconds to complain about Big Data. One paper per month, no obligation to read it."

We'll start by letting anyone who wants to take up to a minute to tell us what they've been doing with data lately.

This month's paper is Beyond Clicks: Dwell Time for Personalization by Yi etal. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

There will be pizza.

"No talks. You may opt to take up to 60 seconds to complain about Big Data. One paper per month, no obligation to read it."

We'll start by letting anyone who wants to take up to a minute to tell us what they've been doing with data lately.

This month's paper is Materialization Strategies in a Column-Oriented DBMS by Abadi etal. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

There will be pizza.

"No talks. You may opt to take up to 60 seconds to complain about Big Data. One paper per month, no obligation to read it."

We'll start by letting anyone who wants to take up to a minute to tell us what they've been doing with data lately.

This month's paper is Large-Scale High-Precision Topic Modeling on Twitter by Yang etal. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

There will be pizza.

"No talks. You may opt to take up to 60 seconds to complain about Big Data. One paper per month, no obligation to read it."

We'll start by letting anyone who wants to take up to a minute to tell us what they've been doing with data lately.

This month's paper is Visual Analysis of Large Heterogeneous Social Networks by Semantic and Structural Abstraction by Shen etal. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

There will be pizza.

"No talks. You may opt to take up to 60 seconds to complain about Big Data. One paper per month, no obligation to read it."

We'll start by letting anyone who wants to take up to a minute to tell us what they've been doing with data lately.

This month's paper is Geotagging One Hundred Million Twitter Accounts with Total Variation Minimization by Compton etal. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

There will be food.

"No talks. You may opt to take up to 60 seconds to complain about Big Data. One paper per month, no obligation to read it."

We'll start by letting anyone who wants to take up to a minute to tell us what they've been doing with data lately.

This month's paper is Feature Selection For High-Dimensional Clustering by Wasserman, Azizyan, and Singh. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

There will be food.

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

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.

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.

Cascadia R Conference is an R conference serving the Cascadia-ish region (Oregon/Washington/BC).

Check out https://cascadiarconf.com for details - that should be coming out soon.

Cascadia R Conference is an R conference serving the Cascadia-ish region (Oregon/Washington/BC).

This is the first time this event has happened!

Talk submissions are now closed but tickets are on sale: https://cascadiarconf.com/tickets/

See the website for more information.

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. 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.

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. 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

Brad will lead a discussion on managing uncertainty in ML. Topics may include:

• Tests of statistical significance
• Confidence intervals
• Credible intervals
• Central limit theorem
• Monte Carlo simulation
• Density estimation
• Model selection
• Uncertainty in deep learning
• Probabilistic calibration
• and others ...

Check back for a rescheduled time.

Sunil Rao will be presenting his past research on SNOTEL data using the time series prediction.

Drought is a serious problem in much of the U.S., with the worst conditions across the southern and western parts of the nation. Much of irrigation and recreation facilities depend on proper forecasting of streamflow. The water supply for irrigation largely comes from rivers and creeks, whose streamflow originates from the springtime melting of winter snow. A water supply forecast is a prediction of streamflow volume that will flow past a point on a stream during a specified season, typically in the spring and summer. One of the primary sources for the data is through NRCS SNOTEL( Snow Telemetry) data (available to public as part of tax dollars at work). In this demo, we showcase one such tool (Timeseries ARIMAX model) to forecast Streamflow volume for Deschutes River Basin, OR and later compare with actual data to see how it performed.

If you would like to join the discussion check us out on Zoom https://zoom.us/j/7891236789.

Do you want to learn and share your passion in a supportive community? Knowledge Mavens is an ethos of sharing, creativity, and inspiration.

Our Meetup provides an opportunity to "Show and Tell" followed by feedback and Q&A. You'll have the opportunity to share with our channels such as Meetup, GitHub, YouTube, and Facebook to connect with more passionate people.

The second half of our session we'll collaborate on new topics. The winner wins an award for the most interesting topic and the opportunity to share in an upcoming session.

As you know this is more of group discussion meetup where we get together to discuss what we are learning, interested in, stumped by, curious about, etc...

This month I propose we talk about Bayesian Deep Learning and study the materials from Yarin Gal's presentation at MLSS Moscow 2019 http://bdl101.ml

There wont be a presentation. Instead I encourage you to read over the materials, try out some experiments and come share your findings and experiences. I have lots of questions about when/where it is useful and what kind of future it may have. I'm looking forward to hearing your questions and thoughts.

All levels of experience and backgrounds are welcome so don't be shy. We all learn from each other.

Also, if you have ideas for topics you'd like to see in future meetings please let me know. Thanks.