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Journal articles on the topic 'R shiny'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Asmarianti, Wahidah Alwi, and Adnan Sauddin. "PENGEMBANGAN MEDIA PEMBELAJARAN ANALISIS KOMPONEN UTAMA BERBASIS WEB MENGGUNAKAN SHINY R." Jurnal MSA ( Matematika dan Statistika serta Aplikasinya) 10, no. 2 (2022): 63–72. http://dx.doi.org/10.24252/msa.v10i2.32158.

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Penelitian ini membahas mengenai pengembangan media pembelajaran analisis komponen utama berbasis web menggunakan shiny R. Media pembelajaran yang dapat dimanfaatkan untuk meningkatkan efisiensi belajar yaitu media yang berbasis web, salah satunya Shiny R. Shiny R adalah sebuah package bahasa pemrograman R yang dapat membangun aplikasi berbasis website menggunakan paket Shiny Web Framework yang merupakan pengembangan dari R studio Server. Salah satu teknik atau metode yang digunakan adalah Principal Component Analysis (PCA). Data yang digunakan dalam penelitian menggunakan data mtcars bawaan p

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Bernardo, Nicholas D., and Gretchen A. Macht. "Learning ‘Learning Curves’ with R Shiny." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 65, no. 1 (2021): 1567–71. http://dx.doi.org/10.1177/1071181321651119.

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Learning curves are fundamental in understanding individual task performance, with ubiquitous implementation in task assignments, worker scheduling, team formulation, etc., in domains bridging from manufacturing to healthcare. With a broad range of applicability, it is critical that students conceptualize, visualize, and build learning curves to activate that knowledge for effective decision-making. This paper describes a hands-on experiential approach for teaching learning curves that utilizes building LEGO® sets with mathematical formulation and data visualization in an open-source R Shiny a

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Metzger, Shawna K. "Teaching Econometrics Dynamically with R-Shiny." PS: Political Science & Politics 55, no. 1 (2021): 225–29. http://dx.doi.org/10.1017/s1049096521001141.

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Sauddin, Adnan, Irwan Irwan, and Astuti Astuti. "TIME SERIES MOVING AVERAGE BERBASIS WEB DENGAN SHINY R." Jurnal Edukasi dan Sains Matematika (JES-MAT) 9, no. 1 (2023): 53–68. http://dx.doi.org/10.25134/jes-mat.v9i1.7145.

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Penelitian ini membahas mengenai pembuatan shiny R untuk analisis time series moving average berbasis web. Adapun tujuan penelitian ini yaitu untuk mengetahui bagaimana cara membuat shiny R berbasis web untuk Moving Average. Hasil penelitian ini menghasilkan sebuah aplikasi shiny R yang berbasis web yang dapat digunakan untuk mengolah data time series. Nama aplikasi ini yaitu LSDA. Aplikasi ini dapat dibuat dengan cara membuat script ui dan server, kemudian mendeploy dan mengunggah aplikasi ke web. dari semua tahap ini akan menghasilkan sebuah link. Link ini dapat dibagikan kepada orang lain m

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Jan, Baumgart. "Implementation of a test environment in R." Studies and Materials in Applied Computer Science (ISSN 1689-6300) 11, no. 1 (2020): 19–24. https://doi.org/10.5281/zenodo.4321164.

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The work presents libraries and their application for implementing the swarm algorithms test environment. The modules used allow you to prepare a fully functional tool quickly. The test environment presented in this article has been prepared to support research on swarm algorithms and can be used as a network resource as well as a locally available script. The methods used in this article can be used to build test environments for many other scenery not related to swarm algorithms.

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Lee, YongJu, and Young Min Kim. "Development of Financial Early Warning System with R/Shiny." Journal of Korean Institute of Information Technology 22, no. 5 (2024): 173–83. http://dx.doi.org/10.14801/jkiit.2024.22.5.173.

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Kim, Jinhwan, Sora Yoon, and Dougu Nam. "netGO: R-Shiny package for network-integrated pathway enrichment analysis." Bioinformatics 36, no. 10 (2020): 3283–85. http://dx.doi.org/10.1093/bioinformatics/btaa077.

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Abstract Summary We present an R-Shiny package, netGO, for novel network-integrated pathway enrichment analysis. The conventional Fisher’s exact test (FET) considers the extent of overlap between target genes and pathway gene-sets, while recent network-based analysis tools consider only network interactions between the two. netGO implements an intuitive framework to integrate both the overlap and networks into a single score, and adaptively resamples genes based on network degrees to assess the pathway enrichment. In benchmark tests for gene expression and genome-wide association study (GWAS)

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Toledo San Martín, Álvaro, and Inés Vicencio Pardo. "Aplicaciones interactivas basadas en el paquete SHIMY/R para explicar conceptos estadísticos." HUMAN REVIEW. International Humanities Review / Revista Internacional de Humanidades 12, no. 4 (2023): 1–15. http://dx.doi.org/10.37467/revhuman.v12.4740.

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Shiny es una aplicación para el software R que permite la creación de interfaces para usuarios sin conocimiento de programación. En este trabajo utilizamos em método de mapeo sistemático para la recopilación, análisis y extracción de información en publicaciones que indican el uso de Shiny para explicar conceptos estadísticos. Dentro de las conclusiones se tiene que Shiny es utilizado como herramienta para la realización de experiencias académicas, además como medio para la solución de problemas en las áreas de educación y ciencias naturales y de la vida abordando tópicos de estadística relaci

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Toledo San Martín, Álvaro, and Inés Vicencio Pardo. "Aplicaciones interactivas basadas en el paquete SHINY/R para explicar conceptos estadísticos." HUMAN REVIEW. International Humanities Review / Revista Internacional De Humanidades 17, no. 4 (2023): 1–15. https://doi.org/10.37819/revhuman.v17i4.1587.

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Shiny is an application for R software that allows the creation of interfaces for users without programming knowledge. In this work we use a systematic mapping method for the collection, analysis, and extraction of information in publications that indicate the use of Shiny to explain statistical concepts. Among the conclusions, it is found that Shiny is used as a tool for carrying out academic experiences, as well as a means for solving problems in the areas of education and natural and life sciences, addressing statistical topics related to pre-inferential statistics and inferential statistic

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Konrath, Andréa Cristina, Silvio Aparecido da Silva, Elisa Henning, Luciane Mulazani dos Santos, Rodrigo Gabriel de Miranda, and Robert Wayne Samohyl. "Desenvolvimento de Aplicativos Web Com R e Shiny: inovações no ensino de Estatística." Abakós 6, no. 2 (2018): 55–71. http://dx.doi.org/10.5752/p.2316-9451.2018v6n2p55-71.

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A utilização de objetos de aprendizagem com interfaces digitais, interativas e dinâmicas é uma alternativa que pode ser inovadora no ensino de conceitos de Estatística e na realização de suas análises, uma vez que possibilita tipos de visualização e interatividade que, em grande parte das vezes, não são alcançados no ambiente lápis e papel. Neste artigo, apresenta-se uma discussão a respeito das características e das potencialidades do pacote Shiny do software R para desenvolver aplicativos web voltados ao ensino e à pesquisa, no âmbito da Estatística estudada no Ensino Superior. O Shiny é um

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Morota, Gota. "79 Statistical Graphics and Interactive Visualization in Animal Science." Journal of Animal Science 99, Supplement_3 (2021): 45. http://dx.doi.org/10.1093/jas/skab235.079.

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Abstract Statistical graphics has advanced significantly in recent years with the development of statistical computing tools that allow us to create reports dynamically, facilitate reproducible research, and explore data interactively. In particular, data visualization is a fundamental aspect of big data analysis in animal science. However, the static nature of standard visualization limits the information that can be displayed and extracted. The objectives of this hands-on workshop are to learn how to utilize interactive visualization and investigate both global and local structures of graphs

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Morota, Gota. "312 Statistical graphics and interactive visualization in animal science." Journal of Animal Science 98, Supplement_4 (2020): 45–46. http://dx.doi.org/10.1093/jas/skaa278.083.

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Abstract Statistical graphics has advanced significantly in recent years with the development of statistical computing tools that allow us to create reports dynamically, facilitate reproducible research, and explore data interactively. In particular, data visualization is a fundamental aspect of big data analysis in animal science. However, the static nature of standard visualization limits the information that can be displayed and extracted. The objectives of this hands-on workshop are to learn how to utilize interactive visualization and investigate both global and local structures of graphs

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Uttiramerur, Arvind. "Managing Adverse Events in Clinical Trials with R and Shiny." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (2024): 1–15. http://dx.doi.org/10.55041/ijsrem36769.

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Adverse events (AEs) are critical occurrences that can significantly impact patient safety and the overall success of clinical trials. Effective management of AEs is essential for ensuring regulatory compliance and safeguarding participants' well-being. This paper explores the integration of R and Shiny as innovative tools for the real-time monitoring, visualization, and analysis of AEs in clinical trials. By leveraging R’s robust statistical capabilities and Shiny’s interactive interface, researchers can enhance data management processes, facilitate informed decision- making, and foster colla

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Irwan, Irwan, Adnan Sauddin, and Reni Astuti. "Pembelajaran Statistik Deskriptif dan Statistik Inferensial Berbasis Web dengan Shiny R." SAINTIFIK 9, no. 2 (2023): 211–19. http://dx.doi.org/10.31605/saintifik.v9i2.413.

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Sesuai dengan pentingnya statistik, mempelajari statistik memerlukan pemahaman yang kuat tentang ide-ide mendasar dalam statistik dan merupakan langkah paling penting dalam proses pembelajaran. Saat mempelajari statistik, beberapa orang mengalami ketakutan dan kecemasan karena menurut mereka statistik merupakan hal yang menarik, sementara yang lain menganggapnya statistika adalah hal yang tidak menarik. Oleh karena itu, menggunakan model simulasi dan visualisasi untuk mempelajari statistika sangat penting bagi instruktur dan pembelajar. Mencari tahu bagaimana cara membuat aplikasi R untuk memp

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Gu, Zuguang, and Daniel Hübschmann. "Make Interactive Complex Heatmaps in R." Bioinformatics 38, no. 5 (2021): 1460–62. http://dx.doi.org/10.1093/bioinformatics/btab806.

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Abstract Summary Heatmap is a powerful visualization method on two-dimensional data to reveal patterns shared by subsets of rows and columns. In this work, we introduce a new R package InteractiveComplexHeatmap that brings interactivity to the widely used ComplexHeatmap package. InteractiveComplexHeatmap is designed with an easy-to-use interface where static complex heatmaps can be directly exported to an interactive Shiny web application only with one additional line of code. InteractiveComplexHeatmap also provides flexible functionalities for integrating interactive heatmap widgets to build

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Smith, Robert, and Paul Schneider. "Making health economic models Shiny: A tutorial." Wellcome Open Research 5 (April 14, 2020): 69. http://dx.doi.org/10.12688/wellcomeopenres.15807.1.

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Health economic evaluation models have traditionally been built in Microsoft Excel, but more sophisticated tools are increasingly being used as model complexity and computational requirements increase. Of all the programming languages, R is most popular amongst health economists because it has a plethora of user created packages and is highly flexible. However, even with an integrated development environment such as R Studio, R lacks a simple point and click user interface and therefore requires some programming ability. This might make the switch from Microsoft Excel to R seem daunting, and i

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Smith, Robert, and Paul Schneider. "Making health economic models Shiny: A tutorial." Wellcome Open Research 5 (July 31, 2020): 69. http://dx.doi.org/10.12688/wellcomeopenres.15807.2.

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Health economic evaluation models have traditionally been built in Microsoft Excel, but more sophisticated tools are increasingly being used as model complexity and computational requirements increase. Of all the programming languages, R is most popular amongst health economists because it has a plethora of user created packages and is highly flexible. However, even with an integrated development environment such as R Studio, R lacks a simple point and click user interface and therefore requires some programming ability. This might make the switch from Microsoft Excel to R seem daunting, and i

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Uttiramerur, Arvind. "Streamlining Clinical Trial Data from Raw to Regulatory Submission with R Shiny and Pharmaverse." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (2024): 1–15. http://dx.doi.org/10.55041/ijsrem36907.

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In recent years, the pharmaceutical and biotech industries have increasingly adopted open-source tools to enhance the efficiency, transparency, and reproducibility of clinical trial data analysis. R Shiny, a web application framework for R, has become a key component in creating interactive dashboards and visualizations that support real-time data exploration and decision-making. As part of the Pharmaverse ecosystem, R Shiny plays a vital role in regulatory submissions, data analysis, and reporting, offering dynamic, user-friendly interfaces for clinical trial monitoring, patient safety report

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Oberti, Mauricio, and Iosif Vaisman. "shiny-pred: a server for the prediction of protein disordered regions." F1000Research 8 (February 28, 2019): 230. http://dx.doi.org/10.12688/f1000research.17669.1.

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Intrinsically disordered proteins or intrinsically disordered regions (IDR) are segments within a protein chain lacking a stable three-dimensional structure under normal physiological conditions. Accurate prediction of IDRs is challenging due to their genome wide occurrence and low ratio of disordered residues, making them a difficult target for traditional classification techniques. Existing computational methods mostly rely on sequence profiles to improve accuracy, which is time consuming and computationally expensive. The shiny-pred application is an ab initio sequence-only disorder predict

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Kemperman, Lauren, and Matthew N. McCall. "miRcomp-Shiny: Interactive assessment of qPCR-based microRNA quantification and quality control algorithms." F1000Research 6 (November 23, 2017): 2046. http://dx.doi.org/10.12688/f1000research.13098.1.

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The miRcomp-Shiny web application allows interactive performance assessments and comparisons of qPCR-based microRNA expression and quality estimation methods using a benchmark data set. This work is motivated by two distinct use cases: (1) selection of methodology and quality thresholds for use analyzing one's own data, and (2) comparison of novel expression estimation algorithms with currently-available methodology. The miRcomp-Shiny application is implemented in the R/Shiny language and can be installed on any operating system on which R can be installed. It is made freely available as part

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Li, Jianfeng, Bowen Cui, Yuting Dai, Ling Bai, and Jinyan Huang. "BioInstaller: a comprehensive R package to construct interactive and reproducible biological data analysis applications based on the R platform." PeerJ 6 (October 31, 2018): e5853. http://dx.doi.org/10.7717/peerj.5853.

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The increase in bioinformatics resources such as tools/scripts and databases poses a great challenge for users seeking to construct interactive and reproducible biological data analysis applications. Here, we propose an open-source, comprehensive, flexible R package named BioInstaller that consists of the R functions, Shiny application, the HTTP representational state transfer application programming interfaces, and a docker image. BioInstaller can be used to collect, manage and share various types of bioinformatics resources and perform interactive and reproducible data analyses based on the

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Wang, Shizhen S., and John Ihrie. "On the Estimation of POD and LOD of Qualitative Microbiological Assays from a Multi-Laboratory Validation Study." Journal of AOAC INTERNATIONAL 105, no. 2 (2021): 641–47. http://dx.doi.org/10.1093/jaoacint/qsab130.

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Abstract Background Jarvis et al. in 2019 (J. AOAC Int. 102: 1617–1623) estimated the mean laboratory effect (µ), standard deviation of laboratory effects (σ), probability of detection (POD), and level of detection (LOD) from a multi-laboratory validation study of qualitative microbiological assays using a random intercept complementary log–log model. Their approach estimated σ based on a Laplace approximation to the likelihood function of the model, but estimated µ from a fixed effectmodel due to a limitation in the MS Excel spreadsheet which was used by the authors to develop a calculation t

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Zezheng, Geng, Fang Xiaonan, Bi Chuanmei, and Dong Ling. "Development of a network visualization and analysis system for malignant tumors based on transcriptome data." E3S Web of Conferences 271 (2021): 03077. http://dx.doi.org/10.1051/e3sconf/202127103077.

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Shiny technology has developed rapidly in recent years, as an R package for developing interactive app, through which we can package the written R code into a web app, which can not only save user time, but also accelerate the development of the speed of user-end communication, analyze the transcriptome data of related malignant tumors, and construct a ceRNA network diagram of desired malignant tumors. The code utilizing shiny technology package can facilitate users to map the ceRNA network associated with malignant tumors only through screen operation, significantly improving the efficiency a

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Chou, Fu-Sheng. "An Unexpected Use of the Shiny Package for R." Neonatology Today 15, no. 11 (2020): 26–28. http://dx.doi.org/10.51362/neonatology.today/20201115112628.

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Caleb, Christopher. "Dashboard Using R Shiny For Arima and Prophet Comparison." Jurnal Indonesia Sosial Teknologi 5, no. 01 (2024): 309–17. https://doi.org/10.59141/jist.v5i01.806.

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As technology progresses, more and more business processes are digitized. This research was made to find out how the document industry is affected and make an educated guess on revenue forecasts based on previous data. Data will be taken daily for five years, from January 2017 to December 2022. Revenue data will be forecasted with two methods: ARIMA and Prophet. Data will be plotted on a graph in a user-authenticated dashboard made with R Shiny. The results of each forecast will be error-tested with MAE and RMSE. The results show that Prophet consistently produces a smaller number in both test

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Wojciechowski, J., AM Hopkins, and RN Upton. "Interactive Pharmacometric Applications Using R and the Shiny Package." CPT: Pharmacometrics & Systems Pharmacology 4, no. 3 (2015): 146–59. http://dx.doi.org/10.1002/psp4.21.

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De La Hoz-M, Javier, Susana Mendes, and María José Fernandez-Gómez. "GeoWeightedModel : An R-Shiny package for Geographically Weighted Models." SoftwareX 20 (December 2022): 101250. http://dx.doi.org/10.1016/j.softx.2022.101250.

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Oktavia, Cintika, Budi Warsito, and Vincensius Gunawan Slamet Kadarrisman. "Development of Customer Loyalty Measurement Application using R Shiny." E3S Web of Conferences 448 (2023): 02038. http://dx.doi.org/10.1051/e3sconf/202344802038.

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Shopee is a successful e-commerce platform in Southeast Asia today, becoming the leading choice for customers who want to shop online and has built loyal relationships with customers. Apart from Shopee, many other e-commerce companies also emphasize the importance of building customer loyalty as their primary goal. This research will explore the main factors that play a role in creating Shopee customer loyalty. By measuring customer loyalty, which refers to the variables of customer satisfaction, promotions, data security, and customer service. Through the Structural Equation Modeling Partial

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Gueta, Tomer, Vijay Barve, Thiloshon Nagarajah, Povilas Gibas, and Yohay Carmel. "Introducing 'The bdverse': a family of R packages for biodiversity data." Biodiversity Information Science and Standards 3 (July 2, 2019): e37643. https://doi.org/10.3897/biss.3.37643.

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The bdverse is a collection of packages that form a general framework for facilitating biodiversity science in R. We build it to serve as a sustainable and agile infrastructure that enhances the value of biodiversity data by allowing users to conveniently employ R, for data exploration, quality assessment, data cleaning, and standardization. The <em>bdverse</em> supports users with and without programming capabilities. It includes six unique packages in a hierarchal structure — representing different functionality levels (Fig. 1). Major features of three core packages will be highlighted and d

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Gueta, Tomer, Rahul Chauhan, Thiloshon Nagarajah, et al. "bddashboard: An infrastructure for biodiversity dashboards in R." Biodiversity Information Science and Standards 5 (September 27, 2021): e75684. https://doi.org/10.3897/biss.5.75684.

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<u>The bdverse</u> is a collection of packages that form a general framework for facilitating biodiversity science in R (programming language). Exploratory and diagnostic visualization can unveil hidden patterns and anomalies in data and allow quick and efficient exploration of massive datasets. The development of an interactive yet flexible dashboard that can be easily deployed locally or remotely is a highly valuable biodiversity informatics tool. To this end, we have developed 'bddashboard', which serves as an agile framework for biodiversity dashboard development. This project is built in

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Kaya, Efdal. "Shiny Kütüphanesi ile Açık Kaynak Kodlu WEB Tabanlı CBS Uygulaması Geliştirme." Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 7, no. 5 (2024): 2010–25. https://doi.org/10.47495/okufbed.1399033.

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Coğrafi bilgi sistemleri (CBS) kullanılarak yapılan analiz sonuçları, farklı görselleştirme teknikleri ile birleştirildiğinde daha anlaşılır olmaktadır. Python, R ve Julia gibi açık kaynak kodlu programlama dilleri ile mekânsal veriler interaktif şekilde görselleştirilebilmektedir. Bu çalışmada, R Programlama dili içerisindeki shiny kütüphanesi kullanılarak WEB tabanlı CBS uygulaması geliştirilmiştir. Türkiye’de son 53 yılda meydana gelen deprem verileri Amerikan Jeolojik Araştırmalar Merkezi (United States Geological Survey-USGS; AJAM)’nin WEB adresinden indirilmiştir. İndirilen veriler R pro

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Shi, Yushu, Liangliang Zhang, Kim-Anh Do, Christine B. Peterson, and Robert R. Jenq. "aPCoA: covariate adjusted principal coordinates analysis." Bioinformatics 36, no. 13 (2020): 4099–101. http://dx.doi.org/10.1093/bioinformatics/btaa276.

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Abstract Summary In fields, such as ecology, microbiology and genomics, non-Euclidean distances are widely applied to describe pairwise dissimilarity between samples. Given these pairwise distances, principal coordinates analysis is commonly used to construct a visualization of the data. However, confounding covariates can make patterns related to the scientific question of interest difficult to observe. We provide adjusted principal coordinates analysis as an easy-to-use tool, available as both an R package and a Shiny app, to improve data visualization in this context, enabling enhanced pres

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Luna, Augustin, Omar Shah, Chris Sander, and Paul Shannon. "cyjShiny: A cytoscape.js R Shiny Widget for network visualization and analysis." PLOS ONE 18, no. 8 (2023): e0285339. http://dx.doi.org/10.1371/journal.pone.0285339.

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cyjShiny is an open-source R package that allows users to embed network visualization into Shiny apps and R Markdown documents. cyjShiny (https://github.com/cytoscape/cyjShiny) builds on the cytoscape.js Javascript graph library. Additionally, the package provides helper functions to convert common R data representations (e.g., data.frame) into forms compatible with cytoscape.js.

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Briz-Redón, Álvaro. "SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks." Research Ideas and Outcomes 5 (February 1, 2019): e33521. https://doi.org/10.3897/rio.5.e33521.

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Spatial statistics is an important field of data science with many applications in very different areas of study such as epidemiology, criminology, seismology, astronomy and econometrics, among others. In particular, spatial statistics has frequently been used to analyze traffic accidents datasets with explanatory and preventive objectives. Traditionally, these studies have employed spatial statistics techniques at some level of areal aggregation, usually related to administrative units. However, last decade has brought an increasing number of works on the spatial incidence and distribution of

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Titz, Johannes, and Markus Burkhardt. "cofad: An R package and shiny app for contrast analysis." Journal of Open Source Software 6, no. 67 (2021): 3822. http://dx.doi.org/10.21105/joss.03822.

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Kaufman, Aaron R. "Implementing novel, flexible, and powerful survey designs in R Shiny." PLOS ONE 15, no. 4 (2020): e0232424. http://dx.doi.org/10.1371/journal.pone.0232424.

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Kochanova, Natalia Y., Maria Alba Abad, Petra Vizjak, A. Arockia Jeyaprakash, William C. Earnshaw, and Georg Kustatscher. "ChromatoShiny: an interactive R/Shiny App for plotting chromatography profiles." Wellcome Open Research 8 (August 8, 2023): 332. http://dx.doi.org/10.12688/wellcomeopenres.19708.1.

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Background: UnicornTM software on Äkta liquid chromatography instruments outputs chromatography profiles of purified biological macromolecules. While the plots generated by the instrument software are very helpful to inspect basic chromatogram properties, they lack a range of useful annotation, customization and export options. Methods: We use the R Shiny framework to build an interactive app that facilitates the interpretation of chromatograms and the generation of figures for publications. Results: The app allows users to fit a baseline, to highlight selected fractions and elution volumes in

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Kochanova, Natalia Y., Maria Alba Abad, Petra Vizjak, A. Arockia Jeyaprakash, William C. Earnshaw, and Georg Kustatscher. "ChromatoShiny: an interactive R/Shiny App for plotting chromatography profiles." Wellcome Open Research 8 (January 22, 2024): 332. http://dx.doi.org/10.12688/wellcomeopenres.19708.2.

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Background Unicorn™ software on Äkta liquid chromatography instruments outputs chromatography profiles of purified biological macromolecules. While the plots generated by the instrument software are very helpful to inspect basic chromatogram properties, they lack a range of useful annotation, customization and export options. Methods We use the R Shiny framework to build an interactive app that facilitates the interpretation of chromatograms and the generation of figures for publications. Results The app allows users to fit a baseline, to highlight selected fractions and elution volumes inside

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Setiawan, Yayan, Tarno Tarno, and Puspita Kartikasari. "PREDIKSI HARGA JUAL KAKAO DENGAN METODE LONG SHORT-TERM MEMORY MENGGUNAKAN METODE OPTIMASI ROOT MEAN SQUARE PROPAGATION DAN ADAPTIVE MOMENT ESTIMATION DILENGKAPI GUI RSHINY." Jurnal Gaussian 11, no. 1 (2022): 99–107. http://dx.doi.org/10.14710/j.gauss.v11i1.33994.

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Cocoa is a leading commodity from Indonesia. Cocoa prices from time to time fluctuate. Accurate Cocoa price predictions are very important to ensure future prices and help decision making. Cocoa price data is non-stationary and nonlinear, so to make accurate predictions, an Artificial Neural Network (ANN) model is applied. One type of ANN is Long Short-Term Memory (LSTM). LSTM has superior performance for time series based prediction. Optimization methods used are Root Mean Square Propagation, and Adaptive Moment Estimation. The best model was selected based on the Means Square Error (MSE) and

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Perold, S. M., and O. H. Volk. "Studies in the genus Riccia (Marchantiales) from southern Africa. 9. R. nigrella and the status of R. capensis." Bothalia 18, no. 1 (1988): 43–49. http://dx.doi.org/10.4102/abc.v18i1.980.

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R. nigrella DC. (Lamarck De Candolle 1815), with which R. capensis auct. non Steph.: S. Amell (1952, 1963), is conspecific, is now also recognized from southern Africa. The thallus is brown marginally and dorsally and has shiny black scales with smooth margins at which coloured and hyaline cells alternate irregularly. Arnell's (1961) records of R. capensis Steph. from the Canary Islands are to be regarded as erroneous. R. capensis Steph. (Stephani in Brunnthaler 1913) is now placed in synonymy under R. limbata Bisch.

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ÖZENOĞLU, HATİCE, and MESUT KIRMACI. "Riccia anatolica sp. nov. a new liverwort (Ricciaceae) species from Turkey." Phytotaxa 532, no. 1 (2022): 78–84. http://dx.doi.org/10.11646/phytotaxa.532.1.6.

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Riccia anatolica sp. nov. is described and illustrated as a new species from Tarsus-Mersin in the southern part of Turkey. The new species is morphologically similar to some common Turkish (R. nigrella and R. trabutiana) and Middle Eastern (R. okahandjana and R. congoana) Riccia species. It can be distinguished from two Riccia species with shiny black scales that occur in Turkey—R. nigrella and R. trabutiana- by the two layered epidermis, plant size, spores and thallus colour.

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Aswi, Aswi, Muhammad Arif Tiro, Sudarmin Sudarmin, et al. "MAKING BAYESIAN DISEASE MAPPING EASY AND INTERACTIVE: AN R SHINY APPLICATION." MEDIA STATISTIKA 16, no. 2 (2023): 148–59. http://dx.doi.org/10.14710/medstat.16.2.148-159.

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Spatial analysis of count data is important in epidemiology and other domains to identify spatial patterns. While Bayesian spatial models are a popular approach, they do require detailed knowledge of the process for model fitting, checking, and visualising results. Although a number of R packages are available to simplify running the model, there are still complexities when checking the model. This paper aims to provide a user-friendly and interactive R Shiny web application for the analysis of spatial data using Bayesian spatial Conditional Autoregressive Leroux models. The web application is

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Zhang, Xiao-Fei, Le Ou-Yang, Shuo Yang, Xing-Ming Zhao, Xiaohua Hu, and Hong Yan. "EnImpute: imputing dropout events in single-cell RNA-sequencing data via ensemble learning." Bioinformatics 35, no. 22 (2019): 4827–29. http://dx.doi.org/10.1093/bioinformatics/btz435.

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Abstract Summary Imputation of dropout events that may mislead downstream analyses is a key step in analyzing single-cell RNA-sequencing (scRNA-seq) data. We develop EnImpute, an R package that introduces an ensemble learning method for imputing dropout events in scRNA-seq data. EnImpute combines the results obtained from multiple imputation methods to generate a more accurate result. A Shiny application is developed to provide easier implementation and visualization. Experiment results show that EnImpute outperforms the individual state-of-the-art methods in almost all situations. EnImpute is

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ARSLAN, Ahmet Kadir, Şeyma YAŞAR, Cemil ÇOLAK, and Saim YOLOĞLU. "WSSPAS: An Interactive Web Application for Sample Size and Power Analysis with R Using Shiny." Turkiye Klinikleri Journal of Biostatistics 10, no. 3 (2018): 224–46. http://dx.doi.org/10.5336/biostatic.2018-62787.

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Obermayer, Alyssa, Li Dong, Qianqian Hu, et al. "DRPPM-EASY: A Web-Based Framework for Integrative Analysis of Multi-Omics Cancer Datasets." Biology 11, no. 2 (2022): 260. http://dx.doi.org/10.3390/biology11020260.

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High-throughput transcriptomic and proteomic analyses are now routinely applied to study cancer biology. However, complex omics integration remains challenging and often time-consuming. Here, we developed DRPPM-EASY, an R Shiny framework for integrative multi-omics analysis. We applied our application to analyze RNA-seq data generated from a USP7 knockdown in T-cell acute lymphoblastic leukemia (T-ALL) cell line, which identified upregulated expression of a TAL1-associated proliferative signature in T-cell acute lymphoblastic leukemia cell lines. Next, we performed proteomic profiling of the U

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Fajar, Fajar Hari Prasetyo, Budi Warsito, and Aris Sugiharto. "Pengembangan Aplikasi Analisis PLS-SEM berbasis R Shiny dan Penerapan UTAUT2 untuk Evaluasi Penerimaan Sistem Informasi." JST (Jurnal Sains dan Teknologi) 13, no. 1 (2024): 147–58. http://dx.doi.org/10.23887/jstundiksha.v13i1.68568.

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Pengembangan aplikasi analisis Partial Least Square Structural Equation Modeling berbasis R Shiny (SEMRS) diperlukan sebagai alternatif selain aplikasi konvensional seperti SmartPLS. R Shiny dapat dikembangkan secara gratis dan efisien. Metode Prototyping digunakan untuk pengembangan aplikasi dengan Analisa kebutuhan, Desain UI, Fungsi Server, Pengujian dan Implementasi. Aplikasi SEMRS dapat digunakan untuk berbagai kebutuhan analisis seperti Evaluasi Penerimaan Sistem Informasi Akademik di Universitas PGRI Semarang (SIA UPGRIS). Evaluasi penerimaan diperlukan karena evaluasi sebelumnya hanya

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Buonomo, Pasquale. "fishRman: A Shiny R Dashboard improving Global Fishing Watch data availability." Journal of Open Source Software 6, no. 66 (2021): 3467. http://dx.doi.org/10.21105/joss.03467.

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Andersen, Nico, and Fabian Zehner. "shinyReCoR: A Shiny Application for Automatically Coding Text Responses Using R." Psych 3, no. 3 (2021): 422–46. http://dx.doi.org/10.3390/psych3030030.

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In this paper, we introduce shinyReCoR: a new app that utilizes a cluster-based method for automatically coding open-ended text responses. Reliable coding of text responses from educational or psychological assessments requires substantial organizational and human effort. The coding of natural language in responses to tests depends on the texts’ complexity, corresponding coding guides, and the guides’ quality. Manual coding is thus not only expensive but also error-prone. With shinyReCoR, we provide a more efficient alternative. The use of natural language processing makes texts utilizable for

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Henkin, Rafael, and Michael Barnes. "visxhclust: An R Shiny package for visual exploration of hierarchical clustering." Journal of Open Source Software 7, no. 70 (2022): 4074. http://dx.doi.org/10.21105/joss.04074.

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Siregar, Erika, and Aris Prawisudatama. "LiteRate: A Shiny R app for improving data literacy in Indonesia1." Statistical Journal of the IAOS 36, no. 4 (2020): 905–13. http://dx.doi.org/10.3233/sji-200755.

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The longer a decision-maker has to wait for the statistics, the less useful they are likely to be. This statement is not only related to how fast the data is available, but also how fast the data can be understood. As the leading portal in presenting trusted data in Indonesia, the Badan Pusat Statistik (BPS) website provides complete data that covers various areas, subjects, and domains. However, the visualization of these data seems to be lagging. These data are presented in the form of tables and static graphs that are monotonous and the lack of interactivity discourages users from exploring

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