591 research outputs found
thevaachandereng/SSNI-shiny: Minor edits on files
No full text<ul>
<li>Removed unwanted files</li>
<li>Added badge and author in the GUI </li>
</ul>
brunomontezano/randomizacao-shiny: Clinical Trial Randomization Tool
No full text<p>I'm excited to introduce the "Ferramenta para randomização em ensaios clínicos" (Clinical Trial Randomization Tool) repository, a robust solution meticulously crafted for the generation of randomization tables in clinical trials. Developed using R and Shiny, this project offers a user-friendly interface accessible via shinyapps.io. The tool is currently deployed in Brazilian Portuguese.</p>
<p>Overview:
This web application generates two essential tables for clinical trials: a randomization table and a table to unveil initially blinded treatment arms. By leveraging R's and <code>set.seed</code> functions, the tool ensures both randomness and replicability in the randomization process, facilitating robust research outcomes.</p>
<p>Key Features:</p>
<ul>
<li>Randomization table generation: Seamlessly generate randomization tables to allocate participants to treatment arms in clinical trials.</li>
<li>Reproducibility: Utilize R's <code>set.seed</code> function to ensure replicability, enabling researchers to reproduce results consistently.</li>
<li>Data export: Easily download generated tables in CSV format for local analysis and record-keeping purposes.</li>
</ul>
<p>Acknowledgments:
This project draws inspiration from a similar initiative by the author <a href="https://github.com/aurora-mareviv">aurora-mareviv</a>, underscoring the collaborative spirit of the scientific community.</p>
<p>License:
The repository is licensed under GPL-3, fostering openness and accessibility. Please refer to the <code>LICENSE</code> file for the full license details.</p>
<p>For International Users:
Designed with simplicity and efficiency in mind, this tool caters to researchers worldwide, facilitating randomized allocation in clinical trials with ease and reliability. Unfortunately, the tool is currently avaiable only in Brazilian Portuguese.</p>
<p>We invite you to explore the "Ferramenta para randomização em ensaios clínicos" repository, contribute to its development, and leverage its capabilities to enhance the rigor and efficiency of clinical research endeavors.</p>
<p>Thank you for your interest and support.</p>
e-CRM and Multimedia Application Processor
No full textM.E. (CSED) ThesisThesis deals with the study of CRM, why it has become as essential tool for todays organizations. It also includes the jpeg compression process and design of a jpeg parser for reading compressed jpeg files
Performance analysis of interest point detection/matching on shiny and non-textured surfaces
No full text3D modeling techniques can be used to automate processes such as damage assessment in aircraft engines. Aircraft engines often have shiny and non-textured surfaces, where these modeling techniques often have poor performance. This paper gives more insight into the performance of interest detection/matching algorithms on shiny and non-textured surfaces as found in aircraft engine borescope inspection videos. These algorithms are often used in 3D modeling techniques. Three interest point detection/matching algorithms are executed on different test videos, and various metrics are calculated for each algorithm. This paper is the first paper that compares both recent and traditional computer vision interest point detection/matching algorithms in these specific settings, and contributes to a better understanding of the usability of feature-based 3D reconstruction techniques. The results show that more recent neural network-based approaches outperform traditional approaches.CSE3000 Research ProjectComputer Science and Engineerin
Learn ggplot2 using Shiny App
No full textThis book and app is for practitioners, professionals, researchers, and students who want to learn how to make a plot within the R environment using ggplot2, step-by-step without coding. In widespread use in the statistical communities, R is a free software language and environment for statistical programming and graphics. Many users find R to have a steep learning curve but to be extremely useful once overcome. ggplot2 is an extremely popular package tailored for producing graphics within R but which requires coding and has a steep learning curve itself, and Shiny is an open source R package that provides a web framework for building web applications using R without requiring HTML, CSS, or JavaScript. This manual—"integrating" R, ggplot2, and Shiny—introduces a new Shiny app, Learn ggplot2, that allows users to make plots easily without coding. With the Learn ggplot2 Shiny app, users can make plots using ggplot2 without having to code each step, reducing typos and error messages and allowing users to become familiar with ggplot2 code. The app makes it easy to apply themes, make multiplots (combining several plots into one plot), and download plots as PNG, PDF, or PowerPoint files with editable vector graphics. Users can also make plots on any computer or smart phone. Learn ggplot2 Using Shiny App allows users to Make publication-ready plots in minutes without coding Download plots with desired width, height, and resolution Plot and download plots in png, pdf, and PowerPoint formats, with or without R code and with editable vector graphics Keon-Woong Moon, M.D., Ph.D., is Professor of Cardiology at the Catholic University of Korea and serves as the Director of Cardiology at St. Vincent’s hospital. In 2014, he completed the Data Science Specialization course authorized by Johns Hopkins University offered through Coursera. Recently he developed four R packages (mycor, moonBook, ztable, and ggiraphExtra) for distribution on CRAN. He has taught residents, fellows, and junior staff about R and ggplot2 for many years, and he is the author of two books in Korean: R Statistics and Graphs for Medical Papers (2015, Hannarae) and Web-Based Analysis without R in Your Computer (2015, Hannarae)
Learn ggplot2 Using Shiny App [electronic resource] /
No full textThis book and app is for practitioners, professionals, researchers, and students who want to learn how to make a plot within the R environment using ggplot2, step-by-step without coding. In widespread use in the statistical communities, R is a free software language and environment for statistical programming and graphics. Many users find R to have a steep learning curve but to be extremely useful once overcome. ggplot2 is an extremely popular package tailored for producing graphics within R but which requires coding and has a steep learning curve itself, and Shiny is an open source R package that provides a web framework for building web applications using R without requiring HTML, CSS, or JavaScript. This manual—"integrating" R, ggplot2, and Shiny—introduces a new Shiny app, Learn ggplot2, that allows users to make plots easily without coding. With the Learn ggplot2 Shiny app, users can make plots using ggplot2 without having to code each step, reducing typos and error messages and allowing users to become familiar with ggplot2 code. The app makes it easy to apply themes, make multiplots (combining several plots into one plot), and download plots as PNG, PDF, or PowerPoint files with editable vector graphics. Users can also make plots on any computer or smart phone. Learn ggplot2 Using Shiny App allows users to Make publication-ready plots in minutes without coding Download plots with desired width, height, and resolution Plot and download plots in png, pdf, and PowerPoint formats, with or without R code and with editable vector graphics Keon-Woong Moon, M.D., Ph.D., is Professor of Cardiology at the Catholic University of Korea and serves as the Director of Cardiology at St. Vincent’s hospital. In 2014, he completed the Data Science Specialization course authorized by Johns Hopkins University offered through Coursera. Recently he developed four R packages (mycor, moonBook, ztable, and ggiraphExtra) for distribution on CRAN. He has taught residents, fellows, and junior staff about R and ggplot2 for many years, and he is the author of two books in Korean: R Statistics and Graphs for Medical Papers (2015, Hannarae) and Web-Based Analysis without R in Your Computer (2015, Hannarae).1 Make a plot by click -- 2 Make a plot by ggplot2 -- 3 Show Data Distribution -- 4 Scatter Plots(I) -- 5 Scatter Plot(II) -- 6 Logistic regression -- 7 Labeling points in a scatter plot -- 8 Making a 2D density plot -- 9 Draw 2-dimensional contours -- 10 Ballloon Plot -- 11 Cleveland Dot Plot -- 12 Wilkinson dot plot -- 13 Bar plot(I) -- 14 Bar plot(II) -- 15 Labelling a bar plot(I) -- 16 Labelling a bar plot(II) -- 17 Line Graph -- 18 Multiplot with error bars -- 19 Boxplot -- 20 Violin plot -- 21 Area plot -- 22 Polar Plot -- 23 Annotations -- 24 Add a Table Annotation -- 25 Adding the Regression Results in Scatter Plot -- 26 Heatmap -- Horizontal Boxplot -- 29 Drawing a Map -- Interactive Plot.This book and app is for practitioners, professionals, researchers, and students who want to learn how to make a plot within the R environment using ggplot2, step-by-step without coding. In widespread use in the statistical communities, R is a free software language and environment for statistical programming and graphics. Many users find R to have a steep learning curve but to be extremely useful once overcome. ggplot2 is an extremely popular package tailored for producing graphics within R but which requires coding and has a steep learning curve itself, and Shiny is an open source R package that provides a web framework for building web applications using R without requiring HTML, CSS, or JavaScript. This manual—"integrating" R, ggplot2, and Shiny—introduces a new Shiny app, Learn ggplot2, that allows users to make plots easily without coding. With the Learn ggplot2 Shiny app, users can make plots using ggplot2 without having to code each step, reducing typos and error messages and allowing users to become familiar with ggplot2 code. The app makes it easy to apply themes, make multiplots (combining several plots into one plot), and download plots as PNG, PDF, or PowerPoint files with editable vector graphics. Users can also make plots on any computer or smart phone. Learn ggplot2 Using Shiny App allows users to Make publication-ready plots in minutes without coding Download plots with desired width, height, and resolution Plot and download plots in png, pdf, and PowerPoint formats, with or without R code and with editable vector graphics Keon-Woong Moon, M.D., Ph.D., is Professor of Cardiology at the Catholic University of Korea and serves as the Director of Cardiology at St. Vincent’s hospital. In 2014, he completed the Data Science Specialization course authorized by Johns Hopkins University offered through Coursera. Recently he developed four R packages (mycor, moonBook, ztable, and ggiraphExtra) for distribution on CRAN. He has taught residents, fellows, and junior staff about R and ggplot2 for many years, and he is the author of two books in Korean: R Statistics and Graphs for Medical Papers (2015, Hannarae) and Web-Based Analysis without R in Your Computer (2015, Hannarae)
Survapp: A Shiny Application for Survival Data Analysis
No full textThere is a substantial demand for user-friendly graphical interfaces that empower professionals with limited programming knowledge to perform statistical analysis. Although R software is widely used for statistical analysis, it lacks an adequately intuitive graphical interface for individuals without statistical and programming skills. This paper aims to address this gap by introducing an application called Survapp, enabling users, regardless of their computational knowledge, to conduct survival analysis. The development leveraged R software, RStudio, and the Shiny package to create an interactive web app. Survapp incorporates diverse methodologies for analyzing survival data, including Kaplan-Meier, log-rank tests, Cox regression models, parametric accelerated failure time models, decision trees, random forests, and competitive risk analysis (a specific case of multi-state models). Survapp enables users to analyze survival data, offering example databases for various methodologies within the application. However, the primary objective is to allow users to import their own data and conduct their respective analyses in a user-friendly environment. A distinguishing aspect of Survapp is its interface, bridging the gap between complex statistical methods and users with limited statistical and programming expertise. Overall, Survapp proves to be a highly valuable tool for survival data analysis, catering to users needs and providing a user-friendly interface with a wide range of survival analysis methods. The Shiny app is available at the Shiny Apps repository: https://emanuel-vieira.shinyapps.io/survapp. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025
Evaluating Structure-from-Motion on shiny and non-textured surfaces in borescope videos
No full textTo aid in damage assessment, creating 3D reconstruction from borescope videos of jet engines could be very beneficial. However, jet engines often have shiny and non-textured surfaces, and the performance of 3D reconstruction methods is unknown in this case. This paper aims to qualitatively and quantitatively evaluate Structure from Motion (SfM) on these borescope videos. SfM is a technique for 3D reconstruction that uses collections of images to create 3D models. An evaluation was done on borescope videos with differing characteristics using SIFT, SuperGlue, and ground truth for feature detection. Even though small experiments with the global SfM approach produced insufficient results, more extensive experiments using incremental SfM show promising performance on borescope videos and potential for accurate damage assessment, especially when combined with multi-view stereo.CSE3000 Research ProjectComputer Science and Engineerin
Using interactive <em>Shiny</em> applications to facilitate research-informed learning and teaching
No full textIn this article we discuss our attempt to incorporate research-informed learning and teaching activities into a final year undergraduate Statistics course. We make use of the Shiny web-based application framework for R to develop “Shiny apps” designed to help facilitate student interaction with methods from recently published papers in the author\u27s primary research field (extreme value theory and applications). We also replace some lectures with dedicated “reading group tutorials”. Here, students work in small groups to discuss and critique carefully selected papers from the field. They are also encouraged to use our Shiny apps to implement some of the methods discussed in the papers with their own data, for use in project work. We attempt to evaluate our innovation by comparing students, responses in open-ended data analysis work, and work requiring the interpretation of methods in a recently published paper, to those of students who took the same course two years earlier when our Shiny apps were not available and when research tutorials were not used. This comparison, along with results from a student questionnaire, gives us some confidence that our methods have benefitted students, not only in terms of their ability to understand and implement advanced techniques from the recent literature but also in terms of their confidence and overall satisfaction with the course
Author Correction: Age-related mitochondrial alterations in brain and skeletal muscle of the YAC128 model of Huntington disease
No full textAuthor Correction to "Age-related mitochondrial alterations in brain and skeletal muscle of the YAC128 model of Huntington disease
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