CRUD-Capable Mobile Apps with R and shinyMobile: a Case Study in Rapid Prototyping

• URL: http://arxiv.org/abs/2409.00582v1
• Date: Sun, 1 Sep 2024 02:27:36 GMT
• Title: CRUD-Capable Mobile Apps with R and shinyMobile: a Case Study in Rapid Prototyping
• Authors: Nathan Henry,
• Abstract summary: "Harden" is a Progressive Web Application (PWA) for Ecological Momentary Assessment (EMA) developed mostly in R. It leverages the shinyMobile package for creating a reactive mobile user interface (UI) This paper outlines the methodology used to create the Harden application, and discusses the advantages and limitations of the shinyMobile approach to app development.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: "Harden" is a Progressive Web Application (PWA) for Ecological Momentary Assessment (EMA) developed mostly in R, which runs on all platforms with an internet connection, including iOS and Android. It leverages the shinyMobile package for creating a reactive mobile user interface (UI), PostgreSQL for the database backend, and Google Cloud Run for scalable hosting in the cloud, with serverless execution. Using this technology stack, it was possible to rapidly prototype a fully CRUD-capable (Create, Read, Update, Delete) mobile app, with persistent user data across sessions, interactive graphs, and real-time statistical calculation. This framework is compared with current alternative frameworks for creating data science apps; it is argued that the shinyMobile package provides one of the most efficient methods for rapid prototyping and creation of statistical mobile apps that require advanced graphing capabilities. This paper outlines the methodology used to create the Harden application, and discusses the advantages and limitations of the shinyMobile approach to app development. It is hoped that this information will encourage other programmers versed in R to consider developing mobile apps with this framework.

Related papers

• A3: Android Agent Arena for Mobile GUI Agents [46.73085454978007]
  Mobile GUI agents are designed to autonomously perform tasks on mobile devices. Android Agent Arena (A3) is a novel evaluation platform for assessing performance on real-world, in-the-wild tasks. A3 includes 21 widely used general third-party apps and 201 tasks representative of common user scenarios.
  arXiv  Detail & Related papers  (2025-01-02T09:03:56Z)
• Large Action Models: From Inception to Implementation [51.81485642442344]
  Large Action Models (LAMs) are designed for action generation and execution within dynamic environments. LAMs hold the potential to transform AI from passive language understanding to active task completion. We present a comprehensive framework for developing LAMs, offering a systematic approach to their creation, from inception to deployment.
  arXiv  Detail & Related papers  (2024-12-13T11:19:56Z)
• Intelligent Spark Agents: A Modular LangGraph Framework for Scalable, Visualized, and Enhanced Big Data Machine Learning Workflows [1.4582633500696451]
  LangGraph framework is designed to enhance machine learning through scalability, visualization, and intelligent process optimization. At its core, the framework introduces Agent AI, a pivotal innovation that leverages Spark's distributed computing capabilities. The framework also incorporates large language models through the LangChain ecosystem, enhancing interaction with unstructured data.
  arXiv  Detail & Related papers  (2024-12-02T13:41:38Z)
• Foundations and Recent Trends in Multimodal Mobile Agents: A Survey [57.677161006710065]
  Mobile agents are essential for automating tasks in complex and dynamic mobile environments. Recent advancements enhance real-time adaptability and multimodal interaction. We categorize these advancements into two main approaches: prompt-based methods and training-based methods.
  arXiv  Detail & Related papers  (2024-11-04T11:50:58Z)
• MobileAgentBench: An Efficient and User-Friendly Benchmark for Mobile LLM Agents [7.4568642040547894]
  Large language model (LLM)-based mobile agents are increasingly popular due to their capability to interact directly with mobile phone Graphic User Interfaces (GUIs) Despite their promising prospects in both academic and industrial sectors, little research has focused on benchmarking the performance of existing mobile agents. We propose an efficient and user-friendly benchmark, MobileAgentBench, designed to alleviate the burden of extensive manual testing.
  arXiv  Detail & Related papers  (2024-06-12T13:14:50Z)
• Skeet: Towards a Lightweight Serverless Framework Supporting Modern AI-Driven App Development [0.0]
  Skeet was recently released to general use, alongside an initial evaluation. Skeet provides an app structure that reflects current trends in architecture, and tool suites that allow developers with minimal knowledge of AI internals to easily incorporate such technologies into their apps and deploy them.
  arXiv  Detail & Related papers  (2024-05-10T01:00:20Z)
• CLAID: Closing the Loop on AI & Data Collection -- A Cross-Platform Transparent Computing Middleware Framework for Smart Edge-Cloud and Digital Biomarker Applications [2.953239144917]
  We present CLAID, an open-source framework based on transparent computing compatible with Android, iOS, WearOS, Linux, and Windows. We provide modules for data collection from various sensors as well as for the deployment of machine-learning models. We propose a novel methodology, "ML-Model in the Loop," for verifying deployed machine learning models.
  arXiv  Detail & Related papers  (2023-10-09T11:56:51Z)
• In Situ Framework for Coupling Simulation and Machine Learning with Application to CFD [51.04126395480625]
  Recent years have seen many successful applications of machine learning (ML) to facilitate fluid dynamic computations. As simulations grow, generating new training datasets for traditional offline learning creates I/O and storage bottlenecks. This work offers a solution by simplifying this coupling and enabling in situ training and inference on heterogeneous clusters.
  arXiv  Detail & Related papers  (2023-06-22T14:07:54Z)
• DUET: A Tuning-Free Device-Cloud Collaborative Parameters Generation Framework for Efficient Device Model Generalization [66.27399823422665]
  Device Model Generalization (DMG) is a practical yet under-investigated research topic for on-device machine learning applications. We propose an efficient Device-cloUd collaborative parametErs generaTion framework DUET.
  arXiv  Detail & Related papers  (2022-09-12T13:26:26Z)
• Mobile Sensing for Multipurpose Applications in Transportation [0.0]
  The State Departments of Transportation struggles to collect consistent data for analyzing and resolving transportation problems in a timely manner. Recent advancements in the sensors integrated into smartphones have resulted in a more affordable method of data collection. The developed app was evaluated by collecting data on the i70W highway connecting Columbia, Missouri, and Kansas City, Missouri.
  arXiv  Detail & Related papers  (2021-06-20T17:56:12Z)
• Emerging App Issue Identification via Online Joint Sentiment-Topic Tracing [66.57888248681303]
  We propose a novel emerging issue detection approach named MERIT. Based on the AOBST model, we infer the topics negatively reflected in user reviews for one app version. Experiments on popular apps from Google Play and Apple's App Store demonstrate the effectiveness of MERIT.
  arXiv  Detail & Related papers  (2020-08-23T06:34:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.