616 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>
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)
Study of Doping Phenomena in Functional Materials
No full textThe disruptive technologies that are currently emerging across the semi-conductor and electronics industries demand the need for a continual focus on decreasing the size of integrated circuits and renewable energy technology devices. To support this demand requires comprehensive research into understanding the functional properties of materials at an atomic level across a three-dimensional space. These functional properties of materials originate from atomic level properties such as structural, optical and electronic properties, all of which can be modified to optimise the functionality of a material. This is the reason why these atomic level properties were comprehensively studied and reported in this thesis. The introduction of atomic level impurities, via the phenomena of doping, has helped to modify the structural, optical and electronic properties of the materials investigated in this thesis. An insight into the effect and potential that high temperature solid-state doping can bring towards improving the functional properties of three materials namely titania-rutile (single crystal substrates), titanates (powders) and Magnesium Silicon Nitrides MgSiN2 (powders), were gained from the experiments and results reported in this thesis. NB: titanates studied include sodium and potassium hexatitanates, sodium trititanate and caesium titanate. Chapters 3-8 of this thesis were written with a specific focus on the spatial arrangement of dopant atoms (such as B, C, S and N) introduced into photocatalytic titania-rutile and the associated influence it has on bonding, diffusion behaviours as well as structural and electronic properties. The insight gained about these properties of titania-rutile are essential when working on an industrial scale to optimise the performance of renewable energy devices, or to at least match with that of fossil fuels. The choice of anionic dopant introduced into the titania-rutile can help to vary the structural or electronic properties in titania-rutile. Additionally, the unit cell structure that determines the surface and bulk structure of the titania-rutile single crystal substrate that was chosen was observed to also help modify the structural or electronic properties. While the carbon, sulphur and nitrogen anions were predominantly incorporated as surface dopants in titania-rutile, this was not the case with boron anions, which also showed results that were dependent on the orientation of the titania-rutile. Boron incorporation in (110)-titania-rutile led to the formation of a TiBO3 surface layer, approximately 120 nm thick as per XPS data. This TiBO3 layer, as per XRD data, is epitaxially arranged on the rutile (110) surface along the (108), (118) and (018) planes. While this layer was also seen on the rutile (100) surface, no XRD evidence of TiBO3 was found with the rutile (001) surface. As well as observing a shift in the XPS valence band onset, the emergence of new states and O2p orbital mixing was also observed upon anion incorporation into rutile. This study, reporting the structural and electronic effects observed as a result of doping, will be crucial when working with photocatalysts that are widely studied for the water splitting process, used to produce hydrogen, which is a ‘clean’ energy fuel. The main insight gained from chapter 9 is about making use of the structure of titanate materials (e.g. in open layered and tunnelled titanates) as a scaffold, to control the spatial distribution of any given dopant. This is particularly relevant when the material being investigated is in its powdered form, with no well-defined surface or a bulk. Chapter 9 was written with a specific focus on the effect of doping temperature on the location of the incorporated nitrogen dopant (aka. structural properties), electronic and optical properties in the open layered and tunnelled titanates. While these relationships are widely reported in the literature already, the challenge that this study addresses are about carrying out nitrogen doping at three different temperatures in the same system to ensure the same ammonia flow rate, which is a parameter that is often very challenging to reproduce. This ensures reproducibility of results and therefore reliability in the conclusions. Also, this study is also more comprehensive than that reported in literature the and discusses samples that are fully characterised. The preferential incorporation of nitrogen into the Ti-O-Ti bonds than the Na-O-Ti bonds was observed in the tunnelled titanates, Na2Ti6O13 and K2Ti6O13, and (not in the open layered titanates Na2Ti3O7 and Cs0.68Ti1.825O4), is potentially what led to the creation of Ti3+ defects as observed in their optical absorption spectrum. The resulting Ti 3d and N 1s states observed in the XPS valence band spectrum is potentially what caused the observed band gap narrowing. The modification of the electronic, optical and structural properties of the titanates using nitrogen doping can be used to optimise the functionality of titanates e.g. when it is used as photocatalysts or as battery materials. The main insight gained from chapter 10 is about exploring the alternative material that can replace the expensive aluminium nitride, which is known to be a promising substrate material with ideal thermal conductivity and minimal dissipation of heat. This was done by studying the change in structural properties including associated unit cell volume. The effect of the addition of varying amounts of aluminium as a dopant into the MgSiN2 structure, helped to find that the phase transformation from MgSiN2 to AlN-wurtzite structure is observed between 30% and 50% aluminium dopant introduction, as per XPS and XRD. While the doubling of the magnesium reactant mass led to a single phase MgSiN2, it can potentially affect particle size properties, as hinted by the XRD peak broadening observed. Increasing the aluminium content to above 50% led to unit cell volume contraction as Al3+ ions are smaller than the lattice Mg2+ ions they are substituting. These findings can help gain an understanding of the fundamental chemistry underpinning the development of cheaper, alternative materials for any applications
Eatomics: Shiny Exploration of Quantitative Proteomics Data
No full textQuantitative proteomics data are becoming increasingly more available, and as a consequence are being analyzed and interpreted by a larger group of users. However, many of these users have less programming experience. Furthermore, experimental designs and setups are getting more complicated, especially when tissue biopsies are analyzed. Luckily, the proteomics community has already established some best practices on how to conduct quality control, differential abundance analysis and enrichment analysis. However, an easy-to-use application that wraps together all steps for the exploration and flexible analysis of quantitative proteomics data is not yet available. For Eatomics, we utilize the R Shiny framework to implement carefully chosen parts of established analysis workflows to (i) make them accessible in a user-friendly way, (ii) add a multitude of interactive exploration possibilities, and (iii) develop a unique experimental design setup module, which interactively translates a given research hypothesis into a differential abundance and enrichment analysis formula. In this, we aim to fulfill the needs of a growing group of inexperienced quantitative proteomics data analysts. Eatomics may be tested with demo data directly online via https://we.analyzegenomes.com/now/eatomics/or with the user's own data by installation from the Github repository at https://github.com/Millchmaedchen/Eatomics
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
- …
