West Michigan R User Group

MEETING ANNOUNCEMENT: November 2022

Mon, 31 Oct 2022 00:00:00 +0000

Meeting Details
Date	November 08, 2022
Time	18:00
Location	Grand Valley State University University Club DEV 107C 401 Fulton St W, Grand Rapids, MI 49504
Parking	DeVos ramp

Presentations

Join us as we explore new and interesting R packages that you might not be aware of. Students and faculty will demonstrate some of their favorite R packages for getting things done. If time allows after the planned presentations, attendees will be welcome to demonstrate other R packages.

Slides: baseballr ggarrange

MEETING ANNOUNCEMENT: September 2022

Wed, 07 Sep 2022 00:00:00 +0000

Meeting Details
Date	September 13, 2022
Time	18:00
Location	Calvin University North Hall Room 276
Parking	Lots 4 & 5 West of North Hall

Presentations

We will be viewing/discussing one of the keynotes from rstudio::conf.

rstudio::conf Keynotes

MEETING ANNOUNCEMENT: August 2022

Sun, 31 Jul 2022 00:00:00 +0000

Meeting Details
Date	August 9, 2022
Time	18:00
Location	DeVos 107D Grand Valley State University: Pew Campus 401 Fulton St W, Grand Rapids, MI 49504
Parking	DeVos lot and ramp

Business meeting

This is our last summer 2022 ‘back from pandemic’ series! We will start the meeting with a discussion of how WMRUG will go forward into the fall. Discussion to include:

Frequency - monthly, alternate months, other?
Format - speakers as we’ve done in the past or more hands on R?
Location - GVSU or Calvin as we’ve been doing, or find another venue?
Suggestions - other suggestions?

Presentations

We hopefully have two presentations, the first is by Andrew Borgman and maybe a second talk by Paul Egeler (to be announced).

Beyond Basic Shiny: a Survey of R’s Not Dull Web Ecosystem

Do you love R? What about Shiny? Or just the web in general? If so, you may love this not dull discussion of R’s web ecosystem beyond basic Shiny applications. We’ll review exciting extensions to the Shiny ecosystem and check out other non-Shiny web frameworks that have been built with different design philosophies. Whether you are looking for better application styling, improved end-user experience, or are hoping to develop something other than a single-page application within R, this talk may be for you.

Slides

Presenter: Andrew Borgman,

Andrew Borgman leads a “full stack” Shiny development team at a large life sciences company where he helps create innovative data products that speed up the drug development process. He is a long-time R user and has been building Shiny applications for almost a decade.

MEETING ANNOUNCEMENT: July 2022 w links to materials

Mon, 04 Jul 2022 00:00:00 +0000

Meeting Details
Date	July 12, 2022
Time	18:00
Location	DeVos 107D Grand Valley State University: Pew Campus 401 Fulton St W, Grand Rapids, MI 49504
Parking	DeVos lot and ramp

Presentation

We continue our ‘back from pandemic’ series with a talk by co-organizer Aaron Clark! Join us at 6pm in 107D DeVos on the GVSU downtown (Pew) campus.

Tracking shiny app usage tools

You’ve been working on a new shiny app for weeks, months, or maybe even years. You’re ready to release it to the public or maybe train a group of new users soon. After release, maybe you can’t gather feedback from your end users easily. How do you know if people are actually using it? In particular, are they using the app how you intended them to use it? User adoption is SO important in software development and tracking user adoption metrics through the tools presented in this talk can help inform a developer of many things: is my app being used? Is a specific person using my app (like the CEO)? If so, which features or workflows are being used most/least? Does that align with my expectations? Do I need to enhance the user friendliness of certain widgets to increase the adoption of certain features? Do I need to spend extra time marketing my application, or perhaps certain features?

Shiny developers not tracking usage statistics are simply working in the dark. As such, this talk with cover various tools that exist to help answer these questions (above), making you a well-informed developer. Specifically this talk will cover adding google analytics (for public apps) or matomo analytics (for private apps), exploring built in RS Connect “instrumentation” tracking, the {shinylogs} package, the {shiny.stats} package, and more.

Presentation YouTube link: Github repository of slides:

Presenter: Aaron Clark, Biogen

Aaron is a Principal Data Scientist at Biogen developing R packages and Shiny apps to advance the use of R in clinical trials.

LinkedIn:

MEETING ANNOUNCEMENT: June 2022

Sat, 28 May 2022 00:00:00 +0000

Meeting Details
Date	June 14, 2022
Time	18:00
Location	DeVos 107D Grand Valley State University: Pew Campus 401 Fulton St W, Grand Rapids, MI 49504
Parking	DeVos lot and ramp

GVSU’s Statistics department is providing Jimmy Johns.

Thanks Paul! Go to this link by June 8 to RSVP/specify your sandwich order for the meeting. It appears you’ll need to be logged into a google account before clicking on the link, at least it did for me. If you have trouble, email me (zeitlerd@mail.gvsu.edu) or Bradford (dykesb@gvsu.edu) and we’ll get you signed up.

Presentation

We are excited to return to in person meetings the second Tuesday of June on GVSU’s DeVos campus downtown.

Visualizations for Non-trivial Linear Models

Classroom examples and problems for most users of statistics seldom go beyond a simple two factor fixed effects linear model while real research is more often a mixture of fixed and random effects as well as continuous covariates. Statisticians readily work with these more advanced models, but researchers, particularly graduate students outside of statistics generally need help understanding what these statistical models are telling them. Visualizations greatly aid in understanding interactions, fixed versus random effects and the interplay between continuous and categorical factors. We will look at a couple of examples of models from the GVSU Biomechanics lab and how visualizations in R have helped graduate students in the Physical Therapy program to understand their data.

Presenter: David Zeitler, PhD Foundation Stochastics LLC

Presenter Bio:

Dr. Zeitler retired in 2020 from Grand Valley State University where he taught statistics for 17 years after having worked in West Michigan industry for 23 years. Today he continues to advise Physical Therapy research groups working in the GVSU Biomechanics lab and works part time through his own company Foundation Stochastics, LLC. He is co-organizer of the West Michigan R Users Group and a member of the advisory board as well as program co-chair for the Big Data Ignite conferences in Grand Rapids, Michigan.

Migrating CRAN Packages after R Upgrades

Wed, 12 Aug 2020 00:00:00 +0000

Background

This blog post is a supplement to the West Michigan R User Group March 2020 Meeting.

Introduction

You may have noticed that sometimes after upgrading to a new version of R, you need to install all of your packages again. This happens because the new install of R can’t find the location of the old packages. Even if it could, it would still be a good idea to rebuild all of your packages because packages are built for specific versions of R. So sometimes a package built for one version will need to be rebuilt for another. I’m leaving out a lot of details here, but that is the gist of it.

When I upgrade my version of R, many scripts will start breaking since R can’t find required packages on my system. Then I have to go track down which packages are missing in my new environment and install them manually. This can take a long time, especially on systems where you must compile from source. I would like to have all the same packages avialable to me that I had in my previous version so that everything just works out of the box. What is the best way to do that? I’m not sure, but I have whipped up a solution that has worked for me, although it has its flaws.

My Current-State Workflow

Since the majority of the packages that I use are from CRAN, I have written an R script that goes through a library location to identify and install CRAN packages while telling me which packages I will need to install from elsewhere (e.g., Bioconductor, Omegahat, github, internal and personal packages).

I still install non-CRAN pacakges manually, but it reduces the number of packages that I have to process considerably. Installing non-CRAN packages could be automated to a degree, but since packages are installed from so many different places, I have not seen the benefit.

Here are the simple steps that I use:

Finding the Library Path

Prior to installing a new version of R, I find out where my existing version of R is searching to find its packages. These are the locations that I will be coming back to in order to get a list of packages to build under my new version of R. To do that, I use the .libPaths() function.

In particular, the first element returned is my personal library (this may also be defined by the R_LIBS_USER environment variable). Unless you are a site administrator, that is likely where the packages you want will be. I take note of these locations, as I will need them in the next step.

After Upgrade

Now that I have the location to the user library of my prior installation, I’m ready to upgrade. Once I’ve have upgraded, I start an R session and source() this file:

The function install_packages_from_library() takes a vector of library paths to check and the dots (...) are passed to install.packages() so that I can pass custom parameters such as a CRAN mirror or specify building from source.

An example usage would be:

install_packages_from_library('/home/user1/R/x86_64-pc-linux-gnu-library/3.5')

I get one or two prompts and then package installation commences.

Alternative Approaches

The internet is full of articles, blog posts, Stack Overflow answers, and forum posts on the subject.

A very popular answer seems to be to copy all packages from the old library directory to the new one and then run update.packages(ask = FALSE, checkBuilt = TRUE) (note the checkBuilt parameter, which will cause the function to upgrade packages built under a different version). This seems like a very rational and convenient solution that would work for most cases. However, a quick glance at the function documentation and source code indicates to me that it does not solve the problem of packages from non-standard sources. As a result, the update.packages() approach risks moving packages into your new R library that were built for the wrong version of R. Hence my reason for creating the script above.

I still think there must to be a better way. How can I automate installation of all packages in a library, not just CRAN packages? Also, is there a better way to standardize my environment across workstations? For example, what do I do when I move to a new machine which does not have access to those old package libraries?

Metapackage, or One Package to Install Them All

That got me thinking about the power of the packages themselves. Specifically, metapackages, or packages that install/invoke other packages. A popular metapackage that you probably already know is tidyverse. So why not a metapackage for personal use? Or even metapackages for specific use-cases?

With the Remotes field in a package DESCRIPTION file, even packages from non-standard sources can be installed automatically with devtools.

So with that, I have created my first metapackage.

Conclusion

There does not seem to be a clear consensus on exactly the right way to handle package migration between versions of R. My previous workflow has served me well, but I am looking forward to using my new metapackage in the future. We will soon see how fastidous I am about updating packages that I begin using more or removing those that fall by the wayside.

Open Forum

What is your strategy?

MEETING ANNOUNCEMENT: May 2020

Thu, 28 May 2020 00:00:00 +0000

Meeting Details
Date	May 28, 2020
Time	19:00
Location	https://youtu.be/fCWGevB366g
Parking	None

Presentation

We are excited to co sponsor another joint meeting with our partners at Data Science and Analytics West Michigan and Machine Learning GR.

RSVP and details at Meetup.

JOIN on YouTube Live

Tune in to the presentation on YouTube Live.

Join in the discussion via Slack.

Improved Machine Learning with Kedro & MLflow

Hosted by Elliott J. R. Church

Presenter: Michael Bloem, PhD, will explain and demonstrate the Kedro & MLflow machine-learning frameworks.

Kedro and MLflow are relatively new open source software packages that can make the development & deployment of machine learning models (and the data pipelines that support them) more robust, reproducible, & collaborative. In this talk, I will introduce these software packages and describe how they can provide complementary functionality. I will then demonstrate their use from soup to nuts (e.g., from a CSV or database to an API model deployment). Time permitting, I will discuss how pipelines in Python’s scikit-learn package can provide additional complementary functionality that facilitates model deployment.

Presenter Bio:

Michael Bloem is a principal data scientist with Mosaic Data Science. In this role, he leads and executes the design, development, and deployment of data science-enabled solutions with organizations in a variety of verticals. Prior to joining Mosaic Data Science, he led the development of analytics that enabled new Internet-of-Things-based products and services at Steelcase, Inc. and researched air traffic management in the Systems Modeling and Optimization branch of the Aviation Systems Division at NASA Ames Research Center. He received his B.S.E. degree with majors in electrical and computer engineering and economics from Calvin College in 2004 and his M.S. degree in electrical and computer engineering from the University of Illinois at Urbana-Champaign in 2007. In 2015, Michael received his PhD in operations research from the Department of Management Science and Engineering at Stanford University.

Virtual Meetings

Sun, 15 Mar 2020 00:00:00 +0000

Due to the emerging public health concern regarding COVID-19, we will be practicing social distancing. This means that all meetings will occur virtually until further notice. See meeting announcement posts for details. Please post any questions, comments, or suggestions on the forum.

MEETING ANNOUNCEMENT: March 2020

Mon, 02 Mar 2020 00:00:00 +0000

Meeting Details
Date	March 17, 2020
Time	18:00
Location	WebEx
Parking	None

Presentation

We are excited to host another joint meeting with our partners at Data Science and Analytics West Michigan.

This meeting will be held on WebEx. This is our first time using this technology to host our meeting, so please bear with us :-).

Join by clicking on the link below. If you’re not familiar with WebEx, it would be good to try joining early to get things setup. Note there is an audio only phone line if all you want to do is listen in. Within the WebEx environment we will be able to share video, computer screen, post comments and chat.

JOIN WEBEX MEETING

https://dwz-83.my.webex.com/dwz-83.my/j.php?MTID=m6c986929101edb19bc2ad861fdf82256

Meeting number (access code): 794 328 892

JOIN BY PHONE

+1-408-418-9388 United States Toll

Tap here to call (mobile phones only, hosts not supported).

Big Data Hubris: Limitations in Aggregating Uber and Google Data

By Thomas Weinandy, Ph.D. Candidate in Applied Economics at Western Michigan University

Big data hubris is the belief that big data sets are a substitute for, rather than a supplement to, traditional data collection and analysis. Thomas will share how his understanding of big data has evolved during his economics research and why 273 million Uber trips is not considered “big” enough. He will also discuss lessons learned from the rise and fall of Google Flu Trends.

Lightning Talk: Migrating CRAN Packages After R Upgrades

By Paul Egeler, M.S., GStat, Spectrum Health Office of Research and Education

This will be an interactive dialogue on how to best migrate packages from previous versions of R into the new R environment after a system upgrade. Paul will discuss common strategies found online as well as his own approach.

MEETING ANNOUNCEMENT: February 2020

Tue, 21 Jan 2020 00:00:00 +0000

Meeting Details
Date	February 18, 2020
Time	18:00
Location	DeVos 209E Grand Valley State University: Pew Campus 401 Fulton St W, Grand Rapids, MI 49504
Parking	Parking in DeVos ramp - Tell attendant you’re with WMRUG

Presentation

Data Science in Manufacturing Applications

Manufacturing has a rich history of using statistics in monitoring processes, quality control, and improve the manufacturing efficiencies. This gave a lead to manufacturing industries to use this data and leverage different levels of analytics to expand the scope to other applications. In this talk we will discuss how we turned our organization to a data-driven organization, by defining the five levels of analytics and how we used it to design our data science journey (or road map), setting up a team for a data science project, setting up data science infrastructure (R, Docker, Rancher), and a brief overview of various data science applications in manufacturing such as predictive maintenance, process quality prediction, quality control through vision AI, safety improvement using NLP, and NLP on historical maintenance records to define new projects. For each of the applications, we will discuss R packages used, the best method to automating R scripts, and the workflow between R and other applications. At the end of this talk, we will also discuss FAQ such as business decisions, deployment, auditing, and many as such.

About the Author

Dr. Nagdev Amruthnath currently works as Data Scientist III at DENSO North America. He has earned his Ph.D. and Master’s in Industrial Engineering Department, Western Michigan University (WMU), and a Bachelor’s degree in Information Science and Engineering from Visvesvaraya Institute of Technology, Karnataka, India. He has four years of experience working in the manufacturing industry, specializing in the implementation of lean manufacturing, JIT technologies, and production system, three years of full-stack data science experience in manufacturing and undergraduate and graduate teaching experience. He has also authored in journal and conference proceeding publications in production flow analysis, ergonomics, machine learning, and wireless sensor networks. Currently, his research focus is on developing new machine learning models and AI technologies for manufacturing applications. Nagdev continues to serve as a reviewer of scholarly journals, which includes the Journal of Electrical Engineering, IEEE Transactions on Reliability, and Machine Learning and Applications: An International Journal. He has also open-sourced all his projects, including various R-packages on GitHub.

MEETING CANCELLED: January 2020

Tue, 14 Jan 2020 00:00:00 +0000

Updated 2020-01-21

We apologize for the late notice. However, due to an error in coordinating, the January meeting scheduled for Tuesday, 2020-01-21 has been rescheduled for Tuesday, 2020-02-18. We are sorry for the confusion.

The room is still available to our group. So if people want to get together to discuss R or work on some data together, we can still have an informal meeting. If you are interested, please reply on the Forum so that others are sure to attend.

Otherwise, we look forward to seeing you all in February!

MEETING ANNOUNCEMENT: April 2019

Tue, 16 Apr 2019 00:00:00 +0000

Meeting Details
Date	April 16, 2019
Time	18:00
Location	DeVos 225E Grand Valley State University: Pew Campus 401 Fulton St W, Grand Rapids, MI 49504
Parking	Parking in DeVos ramp - Tell attendant you’re with WMRUG

Presentations

Graph and workflow plotting with R

By Derek Nedveck

Over at my place of employment, we’ve been working on defining our process / workflow. Part of this work is creating a visual representation of our workflow. Currently the visualization is done in Visio (or some other gross, non-scriptable program), but I figured this would be better suited to automated means, especially when I found out that our dept was already using something called a RACI table. So this is my excuse to take a naive dive into workflow plotting.

I’m likely going to be getting into the grid graphics engine, and DiagrammeR … or not, the only thing that’s for sure is that I’ll have gifs.

MEETING ANNOUNCEMENT: March 2019

Tue, 19 Mar 2019 00:00:00 +0000

Meeting Details
Date	March 19, 2019
Time	18:00
Location	DeVos 225E Grand Valley State University: Pew Campus 401 Fulton St W, Grand Rapids, MI 49504
Parking	Parking in DeVos ramp - Tell attendant you’re with WMRUG

Presentations

Tidy Modeling with modelr and broom

By Dr. David Zeitler

We will work our way through the modeling chapters of ‘R for Data Science’, looking at how to construct and work with statistical models in a tidy fashion.

MEETING ANNOUNCEMENT: February 2019

Tue, 19 Feb 2019 00:00:00 +0000

Meeting Details
Date	February 19, 2019
Time	18:00
Location	DeVos 225E Grand Valley State University: Pew Campus 401 Fulton St W, Grand Rapids, MI 49504
Parking	[Parking in DeVos ramp - Tell attendant your with WMRUG

Presentations

Open meeting

The planned talk fell through, so we will have an open discussion. If there’s enough interest we might dive into a chapter of Hadley’s R for Data Science.

MEETING ANNOUNCEMENT: December 2018

Sun, 18 Nov 2018 00:00:00 +0000

Meeting Details
Date	December 18, 2018
Time	–Canceled–
Location	–Canceled–

December meeting is canceled.

Watch for a January meeting on 1/15/2019.

MEETING ANNOUNCEMENT: November 2018

Wed, 14 Nov 2018 00:00:00 +0000

Meeting Details
Date	November 20, 2018
Time	18:00
Location	DeVos 121E Grand Valley State University: Pew Campus 401 Fulton St W, Grand Rapids, MI 49504
Parking	Parking in DeVos lot/ramp

Presentations

Iteration: Will it go round in circles?

by Dr. David Zeitler

Long time R users know that we never use for loops except as a last resort. Most of us have learned to use and create vectorized code and (rarely) use the powerful built in apply iterator functions. These are equivalents of the container class iterator functions that have made C++, Java, Python and other languages much more robust. They are elegant and obtuse, making their use difficult at best. (I got a song that ain’t got no melody…) The introduction of the purrr package from the team at RStudio makes those apply functions clearer and easier to use. (Apologies to Billy Preston!)