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This is where the source code, standalone versions and documentation of the shiny app (an:a)-lyzer are stored.

Background Individual patient data meta-analyses (IPD-MA) are regarded as the gold standard for systematic reviews, which also applies to systematic reviews of diagnostic test accuracy (DTA) studies. An increasing number of DTA systematic reviews with IPD-MA have been published in recent years, but there is much variation in how these IPD-MA were performed. A number of existing methods were found, but there is no consensus as to which methods are preferred as the standard methods for statistical analysis in DTA IPD-MA. Objectives To create a web-based tool which integrates recommended statistical analyses for DTA IPD-MA, and allows researchers to analyse the data and visualize the results with interactive plots. Methods A systematic methodological review was performed to identify statistical analyses and data visualization methods used in DTA IPD-MA. Methods were evaluated by the authors and recommended analyses were integrated into the IPDmada tool which is freely available online with the user interface developed with R Shiny package. Results IPDmada allows users to upload their own data, perform the meta-analysis with both continuous and dichotomized tests, and incorporate individual level covariate-adjusted analysis. All tables and figures can be exported as .csv or .pdf files. A hypothetical dataset was used to illustrate the application of IPDmada. Conclusions IPDmada will be very helpful to researchers doing DTA IPD-MA, since it not only facilitates the statistical analysis but also provides a standard framework. The introduction of IPDmada will harmonize the methods used in DTA IPD-MA and ensure the quality of such analyses. Highlights IPDmada is a newly developed web-based tool for performing statistical analysis of individual patient data meta-analysis of diagnostic accuracy and visualizing the results. The tool is freely available to all the researchers, and requiring no installation of statistical software/packages. The tool has an user-friendly interface, and allows meta-analysis on both dichotomized and continuous test results. Researchers can easily use this tool to investigate the threshold effect and covariate effect on the summary accuracy. The introduction and implementation of IPDmada will serve as a useful tool for DTA IPD-MA and increase the quality of such studies.

BackgroundRNA sequencing (RNA-seq) is an ever increasingly popular tool for transcriptome profiling. A key point to make the best use of the available data is to provide software tools that are easy to use but still provide flexibility and transparency in the adopted methods. Despite the availability of many packages focused on detecting differential expression, a method to streamline this type of bioinformatics analysis in a comprehensive, accessible, and reproducible way is lacking.ResultsWe developed the ideal software package, which serves as a web application for interactive and reproducible RNA-seq analysis, while producing a wealth of visualizations to facilitate data interpretation. ideal is implemented in R using the Shiny framework, and is fully integrated with the existing core structures of the Bioconductor project. Users can perform the essential steps of the differential expression analysis work-flow in an assisted way, and generate a broad spectrum of publication-ready outputs, including diagnostic and summary visualizations in each module, all the way down to functional analysis. ideal also offers the possibility to seamlessly generate a full HTML report for storing and sharing results together with code for reproducibility.Conclusionideal is distributed as an R package in the Bioconductor project (http://bioconductor.org/packages/ideal/), and provides a solution for performing interactive and reproducible analyses of summarized RNA-seq expression data, empowering researchers with many different profiles (life scientists, clinicians, but also experienced bioinformaticians) to make the ideal use of the data at hand.

BackgroundThe interpretation of results from transcriptome profiling experiments via RNA sequencing (RNA-seq) can be a complex task, where the essential information is distributed among different tabular and list formats - normalized expression values, results from differential expression analysis, and results from functional enrichment analyses. A number of tools and databases are widely used for the purpose of identification of relevant functional patterns, yet often their contextualization within the data and results at hand is not straightforward, especially if these analytic components are not combined together efficiently.ResultsWe developed the GeneTonic software package, which serves as a comprehensive toolkit for streamlining the interpretation of functional enrichment analyses, by fully leveraging the information of expression values in a differential expression context. GeneTonic is implemented in R and Shiny, leveraging packages that enable HTML-based interactive visualizations for executing drilldown tasks seamlessly, viewing the data at a level of increased detail. GeneTonic is integrated with the core classes of existing Bioconductor workflows, and can accept the output of many widely used tools for pathway analysis, making this approach applicable to a wide range of use cases. Users can effectively navigate interlinked components (otherwise available as flat text or spreadsheet tables), bookmark features of interest during the exploration sessions, and obtain at the end a tailored HTML report, thus combining the benefits of both interactivity and reproducibility.ConclusionGeneTonic is distributed as an R package in the Bioconductor project (https://bioconductor.org/packages/GeneTonic/) under the MIT license. Offering both bird’s-eye views of the components of transcriptome data analysis and the detailed inspection of single genes, individual signatures, and their relationships, GeneTonic aims at simplifying the process of interpretation of complex and compelling RNA-seq datasets for many researchers with different expertise profiles.

It is often challenging to present the available evidence in a timely and comprehensible manner. We aimed to visualize the evolution of evidence about antidepressants for depression by conducting cumulative network meta-analyses (NMAs) and to examine whether it could have helped the selection of optimal drugs. We built a Shiny web application that performs and presents cumulative NMAs based on R netmeta. We used a comprehensive dataset of double-blind randomized controlled trials of 21 antidepressants in the acute treatment of major depression. The primary outcomes were efficacy (treatment response) and acceptability (all-cause discontinuation), and treatment effects were summarized via odds ratios. We evaluated the confidence in evidence using the CINeMA (Confidence in Network Meta-Analysis) framework for a series of consecutive NMAs. Users can change several conditions for the analysis, such as the period of synthesis, among the others. We present the league tables and two-dimensional plots that combine efficacy, acceptability and level of confidence in the evidence together, for NMAs conducted in 1990, 1995, 2000, 2005, 2010, and 2016. They reveal that through the past four decades, newly approved drugs often showed initially exaggerated results, which tended to diminish and stabilize after approximately a decade. Over the years, the drugs with relative superiority changed dramatically; but as the evidence network grew larger and better connected, the overall confidence improved. The Shiny app visualizes how evidence evolved over years, emphasizing the need for a careful interpretation of relative effects between drugs, especially for the potentially amplified performance of newly approved drugs. HIGHLIGHTS: Network meta-analysis is considered to be a proper way of demonstrating the available evidence, since it allows comparisons between multiple interventions, and has been proved to be statistically powerful. It is challenging to present the voluminous results of NMA in an efficient and comprehendible manner. Evidence evolution based on the relatively new method NMA has not been investigated yet. The results of NMA should not only include the effects but also the confidence in the evidence, which can help interpret the findings appropriately. Effective use of rapidly developing statistical analysis and presentation tools such as Shiny package in R, may facilitate and simplify the visualization of NMA output. We should stay conservative towards new drugs, as their performance was often shown to be exaggerated initially, and it took time to become stable.

Športna statistika in analitika v zadnjih desetletjih pridobivata na veljavi. Vzporedno s športno analitiko se razvija tudi vizualizacija podatkov. Z razvojem interneta so se pojavile spletne strani, ki ponujajo vsebino s športno statistiko. Športni navdušenci, navijači, športni novinarji, uporabniki športnih stav in igralci fantazijskih lig so ciljno občinstvo takih spletnih strani. V diplomski nalogi je predstavljen razvoj spletne aplikacije za vizualizacijo podatkov prve slovenske nogometne lige, razvite v programskem jeziku R s paketom Shiny. Spletna aplikacija je zgrajena v obliki preglednih plošč, kjer vsaka plošča predstavlja zaključeno celoto. S to aplikacijo želim nadgraditi obstoječe rešitve, ki ponujajo podatke prve slovenske nogometne lige. Aplikacija vsebuje vizualizacijo rezultatov tekem in zadetkov, končnih uvrstitev, izbrane statistike po sezonah, omogoča primerjavo podatkov dveh poljubnih klubov in pa podatke o lestvicah in rezultatih poljubne sezone. Aplikacija je zgrajena dinamično, kar pomeni, da se na podlagi uporabnikovih vhodnih podatkov posodabljajo izhodni elementi. Ugotovljeno je bilo, da je programski jezik R v povezavi s paketom Shiny močno orodje za vizualizacijo podatkov in izdelavo preglednih plošč. Sports statistics and analytics have been gaining in importance in recent decades. In parallel with sports analytics, data visualization is also being developed. With the development of the internet, websites offering sports statistics content have emerged. Sports enthusiasts, fans, sports journalists, sports bettors and fantasy league players are the target audience of such websites. This diploma thesis presents the development of a web application for data visualization of the first Slovenian football league, developed in the R programming language with the Shiny package. The web application is built as dashboards, where each represents its entirety. With this application, I want to upgrade the existing solutions that offer data from the first Slovenian football league. The application contains a visualization of the results of matches and goals, final rankings, selected statistics by seasons, allowing users to compare data from two arbitrary clubs and data on the rankings and results of arbitrary season. The application is built dynamically, which means that the output elements are updated based on the user's input data. The R programming language in conjunction with the Shiny package has been found to be a powerful tool for data visualization and the development of dashboards.

Motivation: Somatic mutations can have critical prognostic and therapeutic implications for cancer patients. Although targeted methods are often used to assay specific cancer driver mutations, high throughput sequencing is frequently applied to discover novel driver mutations and to determine the status of less-frequent driver mutations. The task of recovering somatic mutations from these data is nontrivial as somatic mutations must be distinguished from germline variants, sequencing errors, and other artefacts. Consequently, bioinformatics pipelines for recovery of somatic mutations from high throughput sequencing typically involve a large number of analytical choices in the form of quality filters. Results: We present vcfView, an interactive tool designed to support the evaluation of somatic mutation calls from cancer sequencing data. The tool takes as input a single variant call format (VCF) file and enables researchers to explore the impacts of analytical choices on the mutant allele frequency spectrum, on mutational signatures and on annotated somatic variants in genes of interest. It allows variants that have failed variant caller filters to be re-examined to improve sensitivity or guide the design of future experiments. It is extensible, allowing other algorithms to be incorporated easily. Availability: The shiny application can be downloaded from GitHub ( https://github.com/BrianOSullivanGit/vcfView ). All data processing is performed within R to ensure platform independence. The app has been tested on RStudio, version 1.1.456, with base R 3.6.2 and Shiny 1.4.0. A vignette based on a publicly available data set is also available on GitHub.

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence Conseguir passar um ponto de vista claro através da visualização de dados é um dos principais objetivos das organizações dos dias de hoje. O principal objetivo deste projeto foi perceber qual a melhor maneira de utilizar interatividade em diferentes tipos de indivíduos, especialistas e novatos, e descobrir as principais diferenças entre os dois grupos. Isto foi feito através da criação de um protótipo com diversas visualizações interativas, onde em cada uma delas foram utilizadas diferentes técnicas de visualização e interatividade. Após a sua criação, seguiu-se a validação de cada uma delas, de modo a chegar a conclusões sobre os melhores métodos a utilizar para melhorar a análise e comunicação da informação, para os diferentes grupos de indivíduos. O desenvolvimento do protótipo foi realizado com o software R, mais especificamente o pacote Shiny. O estudo contribuiu com uma metodologia para avaliar as diferenças entre grupos de especialistas e de novatos, relativamente ao protótipo de visualização que foi validado recorrendo a 6 medidas quantitativas e qualitativas. Utilizando um teste ANOVA de fator único foi possível concluir que em relação às medidas quantitativas não foram encontradas diferenças com significância estatística e em relação às medidas qualitativas a única medida que mostrou diferenças com significância estatística entre ambos os grupos foi o nível de interação (engagement). Isto significa que esta é a única métrica possível de melhorar para diminuir as diferenças entre ambos os grupos. Em relação às visualizações ambos os grupos, concordaram que as melhores foram o mapa de calor (heatmap) e o gráfico de barras e as piores visualizações foram o mapa coropleto e o gráfico de barras empilhadas. Houve, no entanto, diferenças entre a forma como os diferentes grupos interagiram com os componentes. Por exemplo, a select box foi uma melhor opção para o grupo de novatos, enquanto que a radio box foi a melhor para o grupo de especialistas. Os tooltips e o slider foram adequados para os dois tipos de indivíduos. Também foi comprovado que o pacote Shiny é uma ferramenta capaz de criar visualizações interativas eficazes para diferentes tipos de indivíduos uma vez que, em média, os participantes obtiveram ótimos resultados utilizando medidas qualitativas ou quantitativas. Os resultados deste estudo, permitirão às organizações a adaptação eficiente das suas visualizações a diferentes tipos de audiência. Getting a clear point of view through data visualization is one of the main goals of todays’ organizations. The main objective of this project was to understand the most efficient way to use interactivity in different groups of individuals, experts and novices, and to discover the main differences between these two groups. This was achieved through the creation of a prototype with several interactive visualizations, where in each of them different visualizations and interaction techniques were used. After the creation of the prototype, the next step was the validation of each one of them to reach conclusions on what are the most effective means to improve visual analysis and communication, in different groups of individuals. The development of the prototype was done using the R software, and most specifically the Shiny package. This study contributed with a methodology to evaluate the differences between experts and novices, using the visualization prototype that was validated with 6 quantitative and qualitative metrics. Using an ANOVA single factor test it was possible to conclude that regarding the quantitative measures no statistically significant differences were found. However, regarding the qualitative measures the only measure that had statistically significant differences between both groups was the engagement measure. This means that this is the only metric where results can be improved in order to close the gap between the group of experts and novices. Regarding the visualizations, both groups agreed that the best visualizations were the heatmap and the bar chart and the worst visualizations were the choropleth map and the stacked bar chart. Nevertheless, there were differences between how the different groups interacted with the components. For example, the select box was a better option for the novice’s group, while the radio box was the best for the expert’s group. The tooltips and the slider are adequate for both types of individuals. It was also proved that the Shiny package is a tool that is capable of creating effective interactive visualizations for different types of individuals, since that on average the participants obtained great results using qualitative or quantitative measures. The results of this study will allow organizations to efficiently adapt their visualizations to different types of audiences.

Viana, M. D. M., & Cabral, P. (2020). How to effectively use interactivity to improve visual analysis in groups of novices or experts. In CAPSI 2020. 20ª Conferência da Associação Portuguesa de Sistemas de Informação, "Artificialização, Humanização: Os desafios dos Sistemas de Informação na transformação da sociedade". [20th Portuguese Association of Information Systems Conference] (pp. 1-18). [33] Associação Portuguesa de Sistemas de Informação. Link: https://aisel.aisnet.org/capsi2020/33 This study contributes with a methodology to evaluate the differences between different groups of individuals, using a prototype with different interactive visualizations, created with the Shiny package, using 6 quantitative and qualitative metrics for the validation. Using an ANOVA single factor test only 1 of the 6 variables showed statistically significant differences between both groups: the engagement. This means that this is the only metric where results can be improved in order to close the gap between the group of experts and novices. The heatmap and the bar chart were considered the best visualizations for both groups, and the worst were the choropleth map and the stacked bar chart. Regarding the interactive component, the select box was a better option for the group of novices and the radio box for the group of experts. Using this study, organizations will be able to create visualizations that are suitable for different audiences. publishersversion published

This work presents a use case of building a data visualization interface for open access repositories. The case of analysis is the Brazilian Digital Library of Thesis and Dissertations (BDTD). R is a statistical tool very used among developers and programmers. One of its packages is called Shiny, that makes it easy to build interactive web apps straight from R. Through the app, the user can visualize data in a fast and customizable way. It could help them to keep track of metadata and usage statistics over the institutional repositories and can also be applied to discovering scientific information, such as bibliographic references and lists of specialists in certain research domain. These data visualization tools can stimulate others to create open repositories and join either national, regional or international repositories networks.

Background The development of next generation sequencing (NGS) methods led to a rapid rise in the generation of large genomic datasets, but the development of user-friendly tools to analyze and visualize these datasets has not developed at the same pace. This presents a two-fold challenge to biologists; the expertise to select an appropriate data analysis pipeline, and the need for bioinformatics or programming skills to apply this pipeline. The development of graphical user interface (GUI) applications hosted on web-based servers such as Shiny can make complex workflows accessible across operating systems and internet browsers to those without programming knowledge. Results We have developed GENAVi (Gene Expression Normalization Analysis and Visualization) to provide a user-friendly interface for normalization and differential expression analysis (DEA) of human or mouse feature count level RNA-Seq data. GENAVi is a GUI based tool that combines Bioconductor packages in a format for scientists without bioinformatics expertise. We provide a panel of 20 cell lines commonly used for the study of breast and ovarian cancer within GENAVi as a foundation for users to bring their own data to the application. Users can visualize expression across samples, cluster samples based on gene expression or correlation, calculate and plot the results of principal components analysis, perform DEA and gene set enrichment and produce plots for each of these analyses. To allow scalability for large datasets we have provided local install via three methods. We improve on available tools by offering a range of normalization methods and a simple to use interface that provides clear and complete session reporting and for reproducible analysis. Conclusion The development of tools using a GUI makes them practical and accessible to scientists without bioinformatics expertise, or access to a data analyst with relevant skills. While several GUI based tools are currently available for RNA-Seq analysis we improve on these existing tools. This user-friendly application provides a convenient platform for the normalization, analysis and visualization of gene expression data for scientists without bioinformatics expertise.

Environmental DNA (eDNA) metabarcoding is becoming a core tool in ecology and conservation biology, and is being used in a growing number of education, biodiversity monitoring, and public outreach programs in which professional research scientists engage community partners in primary research. Results from eDNA analyses can engage and educate natural resource managers, students, community scientists, and naturalists, but without significant training in bioinformatics, it can be difficult for this diverse audience to interact with eDNA results. Here we present the R package ranacapa, at the core of which is a Shiny web app that helps perform exploratory biodiversity analyses and visualizations of eDNA results. The app requires a taxonomy-by-sample matrix and a simple metadata file with descriptive information about each sample. The app enables users to explore the data with interactive figures and presents results from simple community ecology analyses. We demonstrate the value of ranacapa to two groups of community partners engaging with eDNA metabarcoding results.

Objective The presentation describes the design and the main functionalities of two user-friendly applications developed using R-shiny to support the statistical analysis of morbidity and mortality data from the French syndromic surveillance system SurSaUD. Introduction The French syndromic surveillance system SursaUD® has been set up by Sante publique France, the national public health agency (formerly French institute for public health - InVS) in 2004. In 2016, the system is based on three main data sources: the attendances in about 650 emergency departments (ED), the consultations to 62 emergency general practitioners’ (GPs) associations SOS Medecins and the mortality data from 3,000 civil status offices [1]. Daily, about 60,000 attendances in ED (88% of the national attendances), 8,000 visits in SOS Medecins associations (95% of the national visits) and 1,200 deaths (80% of the national mortality) are recorded all over the territory and transmitted to Sante publique France. About 100 syndromic groupings of interest are constructed from the reported diagnostic codes, and monitored daily or weekly, for different age groups and geographical scales, to characterize trends, detect expected or unexpected events (outbreaks) and assess potential impact of both environmental and infectious events. All-causes mortality is also monitored in similar objectives. Two user-friendly interactive web applications have been developed using the R shiny package [2] to provide a homogeneous framework for all the epidemiologists involved in the syndromic surveillance at the national and the regional levels. Methods The first application, named MASS-SurSaUD, is dedicated to the analysis of the two morbidity data sources in Sursaud, along with data provided by a network of Sentinel GPs [3]. Based on pre-aggregated data availaible daily at 10:30 am, R programs create daily, weekly and monthly time series of the proportion of each syndromic grouping among all visits/attendances with a valid code at the national and regional levels. Twelve syndromic groupings (mainly infectious and respiratory groups, like ILI, gastroenteritis, bronchiolitis, pulmonary diseases) and 13 age groups have been chosen for this application. For ILI, 3 statistical methods (periodic regression, robust periodic regression and Hidden Markov model) have been implemented to identify outbreaks. The results of the 3 methods applied to the 3 data sources are combined with a voting algorithm to compile the influenza alarm level for each region each week: non-epidemic, pre/post epidemic or epidemic. The second application, named MASS-Euromomo, allows consulting results provided by the model developed by the European project EuroMomo for the common analysis of mortality in the European countries (www.euromomo.eu). The Euromomo model, initially developed using Stata software, has been transcripted in R. The model has been adapted to run in France both at a national, regional and other geographical administrative levels, and for 7 age groups. Results The two applications, accessible on a web-portal, are similarly designed, with: - a dropdown menu and radio buttons on the left hand side to select the data to display (e.g. filter by data source, age group, geographical levels, syndromic grouping and/or time period), - several tab panels allowing to consult data and statistical results through tables, static and dynamic charts, statistical alarm matrix, geographical maps,... (Figure 1), - a “help” tab panel, including documentations and guidelines, links, contact details. The MASS-SurSaUD application has been deployed in December 2015 and used during the 2015-2016 influenza season. MASS- Euromomo application has been deployed in July 2016 for the heat- wave surveillance period. Positive feedbacks from several users have been reported. Conclusions Business Intelligence tools are generally focused on data visualisation and are not generally tailored for providing advanced statistical analysis. Web applications built with the R-shiny package combining user-friendly visualisations and advanced statistics can be rapidly built to support timely epidemiological analyses and outbreak detection. Figure 1: screen-shots of a page of the two applications

The study of human perception has helped psychologists effectively communicate data rich stories by converting numbers into graphical illustrations and data visualization remains a powerful means for psychology to discover, understand, and present results to others. However, despite an exponential rise in computing power, the World Wide Web, and ever more complex data sets, psychologists often limit themselves to static visualizations. While these are often adequate, their application across professional psychology remains limited. This is surprising as it is now possible to build dynamic representations based around simple or complex psychological data sets. Previously, knowledge of HTML, CSS, or Java was essential, but here we develop several interactive visualizations using a simple web application framework that runs under the R statistical platform: Shiny. Shiny can help researchers quickly produce interactive data visualizations that will supplement and support current and future publications. This has clear benefits for researchers, the wider academic community, students, practitioners, and interested members of the public.

International audience; Using the re-emergence of the /h/ onset from Early Modern to Present-Day English as a case study, we illustrate the making and the functions of a purpose-built web application named (an:a) lyzer for the interactive visualization of the raw n-gram data provided by Google Books Ngrams (GBN). The database has been compiled from the full text of over 4.5 million books in English, totalling over 468 billion words and covering roughly five centuries. We focus on bigrams consisting of words beginning with graphic preceded by the indefinite article allomorphs a and an, which serve as a diagnostic of the consonantal strength of the initial /h/. The sheer size of this database affords us the possibility to attain a maximal diachronic resolution, to distinguish highly specific groups of -initial lexical items, and even to trace the diffusion of the observed changes across individual lexical units. The functions programmed into the app enable us to explore the data interactively by filtering, selecting and viewing them according to various parameters that were manually annotated into the data frame. We also discuss limitations of the database, of the app and of the explorative data analysis. The app is publicly accessible online at https://osf.io/ht8se/.

Using the re-emergence of the /h/ onset from Early Modern to Present-Day English as a case study, we illustrate the making and the functions of a purpose-built web application named (an:a) lyzer for the interactive visualization of the raw n-gram data provided by Google Books Ngrams (GBN). The database has been compiled from the full text of over 4.5 million books in English, totalling over 468 billion words and covering roughly five centuries. We focus on bigrams consisting of words beginning with graphic preceded by the indefinite article allomorphs a and an, which serve as a diagnostic of the consonantal strength of the initial /h/. The sheer size of this database affords us the possibility to attain a maximal diachronic resolution, to distinguish highly specific groups of -initial lexical items, and even to trace the diffusion of the observed changes across individual lexical units. The functions programmed into the app enable us to explore the data interactively by filtering, selecting and viewing them according to various parameters that were manually annotated into the data frame. We also discuss limitations of the database, of the app and of the explorative data analysis. The app is publicly accessible online at https://osf.io/ht8se/.