Teaching Econometrics Dynamically with R-Shiny

Background: With the development of continuous glucose monitoring systems (CGMS), detailed glycemic data are now available for analysis. Yet analysis of this data-rich information can be formidable. The power of CGMS-derived data lies in its characterization of glycemic variability. In contrast, many standard glycemic measures like hemoglobin A1c (HbA1c) and self-monitored blood glucose inadequately describe glycemic variability and run the risk of bias toward overreporting hyperglycemia. Methods that adjust for this bias are often overlooked in clinical research due to difficulty of computation and lack of accessible analysis tools. Methods: In response, we have developed a new R package rGV, which calculates a suite of 16 glycemic variability metrics when provided a single individual’s CGM data. rGV is versatile and robust; it is capable of handling data of many formats from many sensor types. We also created a companion R Shiny web app that provides these glycemic variability analysis tools without prior knowledge of R coding. We analyzed the statistical reliability of all the glycemic variability metrics included in rGV and illustrate the clinical utility of rGV by analyzing CGM data from three studies. Results: In subjects without diabetes, greater glycemic variability was associated with higher HbA1c values. In patients with type 2 diabetes mellitus (T2DM), we found that high glucose is the primary driver of glycemic variability. In patients with type 1 diabetes (T1DM), we found that naltrexone use may potentially reduce glycemic variability. Conclusions: We present a new R package and accompanying web app to facilitate quick and easy computation of a suite of glycemic variability metrics.

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Unified exact design with early stopping rules for single arm clinical trials with multiple endpoints

Statistical Methods in Medical Research ◽

10.1177/09622802211013062 ◽

2021 ◽

pp. 096228022110130

Author(s):

Wei Wei ◽

Denise Esserman ◽

Michael Kane ◽

Daniel Zelterman

Keyword(s):

Clinical Trials ◽

Early Phase ◽

Decision Rules ◽

Stopping Rules ◽

Multiple Endpoints ◽

Shiny App ◽

R Shiny ◽

Early Phase Clinical Trials ◽

User Friendly ◽

Exact Design

Adaptive designs are gaining popularity in early phase clinical trials because they enable investigators to change the course of a study in response to accumulating data. We propose a novel design to simultaneously monitor several endpoints. These include efficacy, futility, toxicity and other outcomes in early phase, single-arm studies. We construct a recursive relationship to compute the exact probabilities of stopping for any combination of endpoints without the need for simulation, given pre-specified decision rules. The proposed design is flexible in the number and timing of interim analyses. A R Shiny app with user-friendly web interface has been created to facilitate the implementation of the proposed design.

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Matching Pragmatic Lithic Analysis and Proper Data Architecture

Advances in Archaeological Practice ◽

10.1017/aap.2021.11 ◽

2021 ◽

pp. 1-13

Author(s):

Daniel A. Contreras ◽

Zachary Batist ◽

Ciara Zogheib ◽

Tristan Carter

Keyword(s):

Data Structure ◽

Lithic Analysis ◽

Database Construction ◽

Data Architecture ◽

R Shiny ◽

Database Interface ◽

Archaeological Project ◽

Existing Data

Abstract The documentation and analysis of archaeological lithics must navigate a basic tension between examining and recording data on individual artifacts or on aggregates of artifacts. This poses a challenge both for artifact processing and for database construction. We present here an R Shiny solution that enables lithic analysts to enter data for both individual artifacts and aggregates of artifacts while maintaining a robust yet flexible data structure. This takes the form of a browser-based database interface that uses R to query existing data and transform new data as necessary so that users entering data of varying resolutions still produce data structured around individual artifacts. We demonstrate the function and efficacy of this tool (termed the Queryable Artifact Recording Interface [QuARI]) using the example of the Stelida Naxos Archaeological Project (SNAP), which, focused on a Paleolithic and Mesolithic chert quarry, has necessarily confronted challenges of processing and analyzing large quantities of lithic material.

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Tests multiples corrélés et contrôle du « Family-Wise Error Rate » (FWER) dans une application R Shiny interactive

Revue d Épidémiologie et de Santé Publique ◽

10.1016/j.respe.2018.03.296 ◽

2018 ◽

Vol 66 ◽

pp. S175

Author(s):

A. Aurousseau ◽

F. Gillaizeau ◽

C. Maudet ◽

P. Vaissié ◽

S. Leuillet

Keyword(s):

Error Rate ◽

Family Wise Error Rate ◽

R Shiny

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MALDIViz: A Comprehensive Informatics Tool for MALDI-MS Data Visualization and Analysis

SLAS DISCOVERY Advancing Life Sciences ◽

10.1177/2472555217727517 ◽

2017 ◽

Vol 22 (10) ◽

pp. 1246-1252 ◽

Cited By ~ 1

Author(s):

Kishore Kumar Jagadeesan ◽

Simon Ekström

Keyword(s):

Mass Spectrometry ◽

High Throughput ◽

High Throughput Screening ◽

Web Application ◽

Low Cost ◽

Maldi Ms ◽

Ionization Mass ◽

Label Free ◽

Label Free Detection ◽

R Shiny

Recently, mass spectrometry (MS) has emerged as an important tool for high-throughput screening (HTS) providing a direct and label-free detection method, complementing traditional fluorescent and colorimetric methodologies. Among the various MS techniques used for HTS, matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) provides many of the characteristics required for high-throughput analyses, such as low cost, speed, and automation. However, visualization and analysis of the large datasets generated by HTS MALDI-MS can pose significant challenges, especially for multiparametric experiments. The datasets can be generated fast, and the complexity of the experimental data (e.g., screening many different sorbent phases, the sorbent mass, and the load, wash, and elution conditions) makes manual data analysis difficult. To address these challenges, a comprehensive informatics tool called MALDIViz was developed. This tool is an R-Shiny-based web application, accessible independently of the operating system and without the need to install any program locally. It has been designed to facilitate easy analysis and visualization of MALDI-MS datasets, comparison of multiplex experiments, and export of the analysis results to high-quality images.

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Quickomics: exploring omics data in an intuitive, interactive and informative manner

10.1101/2021.01.19.427296 ◽

2021 ◽

Author(s):

Benbo Gao ◽

Jing Zhu ◽

Soumya Negi ◽

Xinmin Zhang ◽

Stefka Gyoneva ◽

...

Keyword(s):

Modular Design ◽

Functional Module ◽

Supplementary Information ◽

Data Sets ◽

Omics Data ◽

Proteomics Data ◽

Primary Analysis ◽

Link Type ◽

R Shiny ◽

Advanced Analysis

AbstractSummaryWe developed Quickomics, a feature-rich R Shiny-powered tool to enable biologists to fully explore complex omics data and perform advanced analysis in an easy-to-use interactive interface. It covers a broad range of secondary and tertiary analytical tasks after primary analysis of omics data is completed. Each functional module is equipped with customized configurations and generates both interactive and publication-ready high-resolution plots to uncover biological insights from data. The modular design makes the tool extensible with ease.AvailabilityResearchers can experience the functionalities with their own data or demo RNA-Seq and proteomics data sets by using the app hosted at http://quickomics.bxgenomics.com and following the tutorial, https://bit.ly/3rXIyhL. The source code under GPLv3 license is provided at https://github.com/interactivereport/[email protected], [email protected] informationSupplementary materials are available at https://bit.ly/37HP17g.

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A R-SHINY BASED PHENOLOGY ANALYSIS SYSTEM AND CASE STUDY USING DIGITAL CAMERA DATASET

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-2571-2018 ◽

2018 ◽

Vol XLII-3 ◽

pp. 2571-2574

Author(s):

Y. K. Zhou

Keyword(s):

Data Processing ◽

Web Application ◽

Vegetation Index ◽

Digital Camera ◽

Parameter Extraction ◽

Growth Trajectory ◽

Phenological Data ◽

Combination Methods ◽

Parameters Extraction ◽

R Shiny

Accurate extracting of the vegetation phenology information play an important role in exploring the effects of climate changes on vegetation. Repeated photos from digital camera is a useful and huge data source in phonological analysis. Data processing and mining on phenological data is still a big challenge. There is no single tool or a universal solution for big data processing and visualization in the field of phenology extraction. In this paper, we proposed a R-shiny based web application for vegetation phenological parameters extraction and analysis. Its main functions include phenological site distribution visualization, ROI (Region of Interest) selection, vegetation index calculation and visualization, data filtering, growth trajectory fitting, phenology parameters extraction, etc. the long-term observation photography data from Freemanwood site in 2013 is processed by this system as an example. The results show that: (1) this system is capable of analyzing large data using a distributed framework; (2) The combination of multiple parameter extraction and growth curve fitting methods could effectively extract the key phenology parameters. Moreover, there are discrepancies between different combination methods in unique study areas. Vegetation with single-growth peak is suitable for using the double logistic module to fit the growth trajectory, while vegetation with multi-growth peaks should better use spline method.

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IMPLEMENTASI R-SHINY UNTUK ANALISIS BIPLOT KOMPONEN UTAMA (Studi Kasus: Penggunaan Alat Kontrasepsi pada Peserta Aktif KB di Provinsi Jawa Tengah Tahun 2019)

Jurnal Gaussian ◽

10.14710/j.gauss.v10i4.33097 ◽

2022 ◽

Vol 10 (4) ◽

pp. 499-507

Author(s):

Andreanto Andreanto ◽

Hasbi Yasin ◽

Agus Rusgiyono

Keyword(s):

Open Source Software ◽

Birth Rate ◽

Principal Component ◽

Contraceptive Device ◽

Biplot Analysis ◽

Contraceptive Devices ◽

The Family ◽

R Shiny ◽

Main Components ◽

The Relationship

The population problem is a fairly complex and complicated problem. Therefore, Indonesia seeks to control the birth rate with the Family Planning program. The implementation of this program can be evaluated through statistical data. The statistical analysis used is biplot principal component analysis to see the relationship between districts/cities in choosing the contraceptive device/method used, the variance of each contraceptive device/method, the correlation between contraceptive devices/methods, and the superiority value of the contraceptive device/method in the population. each district/city. The problem with performing the analysis is the limitations of easy-to-use open source software. As with R, users must understand writing code to perform data analysis. Therefore, to perform a biplot analysis of the principal components, an RShiny application has been created using RStudio. The R-Shiny that has been made has many advantages, including complete results which include data display, data transformation, SVD matrix, to graphs along with plot graph interpretation. The results of the principal component biplot analysis using R-Shiny with α =1 have the advantage of a good principal component biplot, which is 95.63%. This shows that the biplot interpretation of the main components produced can be explained well the relationship between the district/city and the contraceptive methods/devices used.

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SOCRATES: An online tool leveraging a social contact data sharing initiative to assess mitigation strategies for COVID-19

10.1101/2020.03.03.20030627 ◽

2020 ◽

Cited By ~ 5

Author(s):

Lander Willem ◽

Thang Van Hoang ◽

Sebastian Funk ◽

Pietro Coletti ◽

Philippe Beutels ◽

...

Keyword(s):

Data Sharing ◽

Social Contact ◽

School Closure ◽

Mitigation Strategies ◽

Online Application ◽

Online Tool ◽

R Shiny ◽

Contact Data ◽

Considerable Impact ◽

Disease Specific

AbstractObjectiveEstablishing a social contact data sharing initiative and an interactive tool to assess mitigation strategies for COVID-19.ResultsWe organized data sharing of published social contact surveys via online repositories and formatting guidelines. We analyzed this social contact data in terms of weighted social contact matrices, next generation matrices, relative incidence and R0. We incorporated location-specific isolation measures (e.g. school closure or telework) and capture their effect on transmission dynamics. All methods have been implemented in an online application based on R Shiny and applied to COVID-19 with age-specific susceptibility and infectiousness. Using our online tool with the available social contact data, we illustrate that social distancing could have a considerable impact on reducing transmission for COVID-19. The effect itself depends on assumptions made about disease-specific characteristics and the choice of intervention(s).

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