A post-processing, interactive visualization, and analysis tool to synthesize multi-scenario, multi-watershed outputs from process-based geospatial models WEPP and SWAT
-
Updated
Mar 2, 2022 - R
A post-processing, interactive visualization, and analysis tool to synthesize multi-scenario, multi-watershed outputs from process-based geospatial models WEPP and SWAT
Synthesize multi-scenario, multi-watershed outputs from process-based geospatial model WEPP (WEPPcloud) using this post-processing, interactive visualization, and analysis tool. A Shiny Web app implementation to assist in targeted management using WEPPcloud simulated outputs.
Sequence2Branches: Creating a species-level phylogenetic tree from paired FASTQ reads
An R Shiny App that assesses sampling effects (e.g. sample size and BEAST parameters) on GMYC output for species delimitation.
Shiny apps for and load analysis - statistical evaluation of fatigue data and rainflow cycles
R Shiny App to determine the factors that are most influential in patients’ survival of CHD. I created a Logistic Regression model in R using RStudio to predict the survival of CHD patients. Retrieved the data from the PHIS database using SQL & built tableau dashboards. The model predicted the survival of CHD with an AUC of over .90 and indicate…
R Shiny app about Florence Nightingale data viz: https://e11i3n0r.shinyapps.io/631-EllieByler-FinalProject/
Global Monkeypox Tracker using R Shiny
Visualise avaocado sale across US by R Shiny
R Shiny visualisation of coral bleaching data
This ShinyApp automates an essential, and previously manual, business process
A web application for the conversion of vegetation data into a common exchange format defined by the VegX standard. (under development)
Codes for R shiny app on the spatial distribution of mortality due to infectious diseases in Amsterdam
A narrative visualisation of crime trends in NSW using R Shiny
Data science projects worked on during an intensive R boot camp with the Sewanee DataLab over Summer 2022
Open source dataset projects using various R studio And Jupyter notebook Environment
Add a description, image, and links to the r-shiny-apps topic page so that developers can more easily learn about it.
To associate your repository with the r-shiny-apps topic, visit your repo's landing page and select "manage topics."