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Your Shiny Application

  1. Write a shiny application with associated supporting documentation. The documentation should be thought of as whatever a user will need to get started using your application.
  2. Deploy the application on Rstudio's shiny server
  3. Share the application link by pasting it into the provided text box
  4. Share your server.R and ui.R code on github

The application must include the following:

  1. Some form of input (widget: textbox, radio button, checkbox, ...)
  2. Some operation on the ui input in sever.R
  3. Some reactive output displayed as a result of server calculations
  4. You must also include enough documentation so that a novice user could use your application.
  5. The documentation should be at the Shiny website itself. Do not post to an external link.

The Shiny application in question is entirely up to you. However, if you're having trouble coming up with ideas, you could start from the simple prediction algorithm done in class and build a new algorithm on one of the R datasets packages. Please make the package simple for the end user, so that they don't need a lot of your prerequisite knowledge to evaluate your application. You should emphasize a simple project given the short time frame.

darwin22's Projects

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This repo documents some of my drafted solutions to the exercises from All of Statistics by Larry Wasserman. This repo only contains solutions to exercises that requires computer experiment. The book is a handy reference to most concepts of statistics.

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Introduction à l'analyse d'enquêtes avec R et RStudio

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This repository contains the lab files and other resources for the free Microsoft course DAT207x: Analyzing and Visualizing Data with Power BI. To learn how to connect, explore, and visualize data with Power BI, sign up for this course on edX.

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Science des Données Saison 3: Apprentissage Automatique / Statistique

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Science des Données Saison 4: Camps d'entrainement et cas d'usage pour l'apprentissage automatique / statistique de données massives

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A curated list of awesome R packages, frameworks and software.

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:mortar_board: Path to a free self-taught education in Computer Science!

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Stanford CS229 (Autumn 2017)

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Code examples for “Interactive Data Visualization for the Web”

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My Journey of Exploring different types of data and being able to make decisions based on it

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Portfolio of data science projects completed by me for academic, self learning, and hobby purposes.

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Portfolio of data science projects completed by me for academic, self learning, and hobby purposes.

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Analysis of factors related to readmission as well as other outcomes pertaining to patients with diabetes.

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Repository to house ebooks associated with learning new aspects of R

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