useR!2017

Keywords: psychometrics, educational test, item response theory, shiny, R
Webpages: https://CRAN.R-project.org/package=ShinyItemAnalysis, https://shiny.cs.cas.cz/ShinyItemAnalysis/
This work introduces ShinyItemAnalysis (Martinková, Drabinová, Leder, & Houdek, 2017) R package and an online shiny application for psychometric analysis of educational tests and their items.
ShinyItemAnalysis covers broad range of methods and offers data examples, model equations, parameter estimates, interpretation of results, together with selected R code, and is thus suitable for teaching psychometric concepts with R. It is based on examples developed for course of Item Response Theory models for graduate students at University of Washington.
Besides, the application aspires to be a simple tool for analysis of educational tests by allowing the users to upload and analyze their own data and to automatically generate analysis report in PDF or HTML. It has been used at workshops for educators developing admission tests and in development of instruments for classroom testing such as concept inventories, see McFarland et al. (2017).
We argue that psychometric analysis should be a routine part of test development in order to gather proofs of reliability and validity of the measurement. With example of admission test to medical school we demonstrate how ShinyItemAnalysis may provide a simple and free tool to routinely analyze tests and to explain advanced psychometric models to students and those who develop educational tests.
References Martinková, P., Drabinová, A., Leder, O., & Houdek, J. (2017). ShinyItemAnalysis: Test and item analysis via shiny. Retrieved from shiny.cs.cas.cz/ShinyItemAnalysis/; https://CRAN.R-project.org/package=ShinyItemAnalysis

McFarland, J. L., Price, R. M., Wenderoth, M. P., Martinková, P., Cliff, W., Michael, J., & Modell, H. (2017). Development and validation of the Homeostasis Concept Inventory. CBE-Life Sciences Education.

Keywords: Shiny, JavaScript, Data Collection, User Experience
Webpages: https://github.com/nstrayer/shinysense, https://nickstrayer.shinyapps.io/shinysense_earr_demo/, https://nickstrayer.shinyapps.io/shinysense_swipr_demo/
shinysense is a package containing shiny modules all geared towards helping users make mobile-first apps for collecting data, or helping Shiny “sense” the outside world. Currently the package contains modules for gathering data on swiping (shinyswipr), audio (shinyearr), and from accelerometers (shinymovr). The goal of these functions is to take Shiny from a tool for demonstrating finished models or workflows into being a tool for data collection, enabling its use for training/testing models or building richer user experiences.
Several demo apps are contained in the package, including training and testing a speech recognition system using shinyearr and detecting spell casts performed by swinging your phone like a wand with shinymovr. In addition, the package is already being used in real-world products. Notable examples being the app papr which allows users to rapidly read and react to abstracts by swiping cards containing their content using shinyswipr, validating algorithm output in GenomeBot Tweet Generator, and contributr: an app that allows users to review github issues on various R packages.
A major goal of the construction of shinysense was mobile-friendly behavior. The massive proliferation of smartphones laden with sensors is a potential goldmine of data and use cases for statisticians and data scientists. This package attempts to help users harness this new flood of opportunities. A side effect of mobile oriented design is increased usability in non-static environments (Dou and Sundar 2016, Wigdor, Fletcher, and Morrison (2009)). For instance: an app running on a smartphone allowing physicians to input parameters into clinical models and instantly see the results (shinyswipr), to the generation and testing of fitness tracking algorithms by carrying a phone in a pocket (shinymovr).
Much in keeping with the primary goal of Shiny, by bringing powerful software tools such as inputs typically reserved for JavaScript/ native apps to a tool used by scientists such as R we hope to lower the costs (monetary and otherwise) of bringing innovative applications to fruition.
References Dou, Xue, and S Shyam Sundar. 2016. “Power of the Swipe: Why Mobile Websites Should Add Horizontal Swiping to Tapping, Clicking, and Scrolling Interaction Techniques.” International Journal of Human-Computer Interaction 32 (4). Taylor & Francis: 352–62.

Wigdor, Daniel, Joe Fletcher, and Gerald Morrison. 2009. “Designing User Interfaces for Multi-Touch and Gesture Devices.” In CHI’09 Extended Abstracts on Human Factors in Computing Systems, 2755–8. ACM.

papr is an R Shiny web application and social network for evaluating bioRxiv pre-prints. The app serves multiple purposes, allowing the user to quickly swipe through pertinent abstracts as well as find a community of researchers with similar interests. It also serves as a portal for accessible “open science”, getting abstracts into the hands of users of all skill levels. Additionally, the data could help build a general understanding of what research the community finds exciting.

We allow the user to log in via Google to track multiple sessions and have implemented a recommender engine, allowing us to tailor which abstracts are shown based on each user’s previous abstract rankings. While using the app, users view an abstract pulled from bioRxiv and rate it as “exciting and correct”, “exciting and questionable”, “boring and correct”, or “boring and questionable” by swiping the abstract in a given direction. The app includes optional social network features, connecting users who provide their twitter handle to users who enjoy similar papers.

This presentation will demonstrate how to incorporate tactile interfaces, such as swiping, into a Shiny application using a package we created for this functionality shinysense, store real-time user data on Dropbox using drop2, login in capabilities using googleAuthR and googleID, how to implement a recommender engine using principle component analysis, and how we have handled issues of data safety/security through proactive planning and risk mitigation. Finally, we will report the app activity, summarizing both the user traffic and what research users are finding exciting.

Keywords: Shiny, data applications, UX, live collaboration, data persistence
Webpages: https://appsilon.github.io/shiny.collections/
What users expect from web applications today differs dramatically from what was available 5 years ago. They are used to interactivity, data persistence, and what’s more, the ability to share live collaboration experiences, like in Google Docs. If one user changes the data, other users want to see the changes immediately on their screens. They don’t care whether it is a data-exploration app from a data scientist or a solution built by a team of software engineers.
Shiny is perfect for building interactive data-driven applications suited for the modern user. In this presentation, we show how to create real-time collaboration experience in Shiny apps.
From the presentation, you will learn the concepts of reactive databases, how to use them in Shiny, and how to adapt existing components to provide live collaboration.
We will present a package we developed for that. shiny.collections adds persistent reactive collections that can be effortlessly integrated with components like Shiny inputs, DT data table or rhandsontable. The package makes it easy to build collaborative Shiny applications with persistent data.
The presentation will be very actionable. Our goal is for everyone in the audience to be able to add persistence and collaboration to their apps in less than 10 minutes.
References “RethinkDB.” https://rethinkdb.com/.