https://github.com/onlyphantom/darkershiny
A Shiny web app template using a dark theme with support for custom CSS
https://github.com/onlyphantom/darkershiny
shiny shiny-apps shinyapps workshop-materials
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A Shiny web app template using a dark theme with support for custom CSS
- Host: GitHub
- URL: https://github.com/onlyphantom/darkershiny
- Owner: onlyphantom
- License: gpl-3.0
- Created: 2019-02-04T14:34:33.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2019-02-24T08:28:23.000Z (about 6 years ago)
- Last Synced: 2024-11-15T16:23:22.384Z (5 months ago)
- Topics: shiny, shiny-apps, shinyapps, workshop-materials
- Language: R
- Size: 1.97 MB
- Stars: 13
- Watchers: 4
- Forks: 23
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# DarkerShiny
## Description
An out-of-the-box Shiny app template that we'll use to walk through the hypothetical example of a cybersecurity company. The components are:
- Shiny web app framework
- `ggplot2` visualization toolkit
- `maps` package for geo-visualization tool
- `dplyr` and other members of the `tidyverse` family
- [Bootstrap 4](https://getbootstrap.com/)
- HTML and CSS
This serves as some boilerplate code we will use for the code-along session. It's a starting point that you can take and flesh it out into a minimal [Intrusion Detection System](https://en.wikipedia.org/wiki/Intrusion_detection_system). Throughout the session we'll be adding some extra web framework components, visualization components and walk through the best practice of integration HTML, CSS, and JavaScript into a Shiny project (for R developers) or Flask project (Python developers).
## Installation and Usage
#### Method 1: Fork to your own repo (Recommended)
1. Forking it and then using `git clone`.
- Example: `git clone https://github.com/onlyphantom/darkershiny.git`
2. Run `app.R`
#### Method 2: Download and Run
1. Download the repo from [this repo](https://github.com/onlyphantom/darkershiny).
2. Run `app.R`
The dependencies are automatically installed if R doesn't detect them. After installation, it is recommended that you stay connected to the internet while running `app.R` for the first time. R packages are installed on your computer so you will not need to have an active internet connection after this.
## Workshop Notes
The code and its asset can be used to follow along the code-along session of Algoritma's Kickstart Series: Gentle introduction to Developing Web Apps in R & Python.
1. The `supreme` folder contains the Flask template that we created together in the final 30 mins of the class. Make sure you've installed Flask in the virtual environment (`conda env` or `pip env`) as per the in-classroom instructions
2. `plan_bandung.md` is the lecture plan and notes used for the Bandung session on 16/2/19
3. `plan_jakarta.md` is the lecture plan and notes used for the Jakarta session on 23/2/19
#### Bandung Session
- Date: Saturday, **February 16th** 2019
- Time: 1300 - 1600
- Venue: Innovation Factory, Bandung
- Lead Instructor: [Samuel Chan](https://id.linkedin.com/in/chansamuel)
#### Jakarta Session
- Date: Saturday, **February 23rd** 2019
- Time: 1300 - 1600
- Venue: Innovation Factory, Jakarta
- Lead Instructor: [Samuel Chan](https://id.linkedin.com/in/chansamuel)
### Syllabus
The workshop aims to provide a beginner-friendly introduction to packaging your data science work into a user-friendly, modernly-themed web analytics application.
Corporate consultant and course producer Samuel Chan has worked for a number of public-listed company prior to his role at Algoritma. His involvement has always heavily centered around product development, from building chatbots for companies to developing cloud-based products. In this workshop, he will walk us through his favorite tools, workflow, and productivity tips:
- Recommendations on productionizing R code
- The Shiny framework for data scientists
- Using Python's web frameworks for application development
- TensorFlow as a web service
- Cloud deployment options for R- or Python-based applications
- Production concerns: security, uptime, and continuous integration
- Productivity tools for real-life data science: Git, IDE extensions, Virtual Environments vs Docker
---
## Credits
The dataset is sampled from AlienVault's [IP Reputation database](http://reputation.alienvault.com/ reputation.data) for educational purpose. AlienVault, Inc. is a developer of commercial and open source solutions to manage cyber attacks, including the Open Threat Exchange, the world's largest crowd-sourced computer-security platform.
The assets (badge, images and other intellectual property) is taken from [Algoritma Data Science Education Center](https://algorit.ma) with permission.
The work is sponsored by:
- BLOCK71's Innovation Factory
- Algoritma, a data science education center on a mission to democratize world-class education, helping professionals to gain proficiency in machine learning and data visualization by building with real-world projects.
## Screenshot
