📊 Shiny Plot Studio

Shiny Plot Studio is a productivity tool designed to bridge the gap between technical and non-technical users. It allows anyone to create publication-ready ggplot2 visualizations via a GUI, while simultaneously generating the underlying R code for reproducibility and learning.

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🔥 Key Capabilities

Zero-Code Visualization: Upload datasets and generate complex plots using intuitive dropdown menus—no R knowledge required.
Meta-Programming (Code Export): Automatically generates the exact ggplot2 R code used to create the plot. Perfect for "copy-paste" reproducibility in reports.
Custom Branding: Integrated engine to add organizational logos or background watermarks to plots.
Dynamic Annotations: Point-and-click interface to add real-time text annotations directly on the canvas.
Flexible Layouts: Instantly toggle between different plot types and professional themes.

🛠️ Technical Highlights

Reactive Data Handling: Uses renderUI to adapt the interface to the specific columns and data types of any uploaded dataset.
Stringified Logic: Implements reactive logic to capture user inputs and translate them into clean, human-readable R scripts.
Coordinate Mapping: Uses Shiny's plot_click coordinates to programmatically place annotations within the ggplot2 coordinate system.

🚀 Getting Started

To run this application locally, ensure you have R installed and run the following in your console:

# Install core dependencies
install.packages(c("shiny", "ggplot2", "shinyjs", "grid"))

# Run the app directly from GitHub
shiny::runGitHub("shiny_plots", "CodingTigerTang")

🧠 Motivation

In many organizations, the ability to visualize data is restricted by technical barriers. This project was born from a simple goal: Automate the boring stuff. By providing a GUI that outputs code, this app serves as a "bridge" for new R adopters to learn syntax while still being immediately productive.

For Non-Technical Users: High-quality plots without writing a single line of code.
For R Beginners: A "bridge" to learn ggplot2 syntax by observing generated code.
For Pros: Turns a manual 30-minute plotting task into a 30-second configuration.

"Data visualization is a good way to understand datasets... the need to utilize this has expanded beyond just technical users." — Read the full breakdown on my blog

Example Uses:

1. Create a scatter plot:

2. Add multiple annotation with different color with mouse click:

3. Add a logo to the plot: