Building a Supervised Learning Application in R

From Development to Deployment

Data science is constantly evolving, and building strong supervised learning applications is key for making informed decisions in many industries. This guide walks you through how to build, test, and deploy an application using R, RStudio, Docker, and important R packages.

Development Environment and Tools

Before starting, here’s what you’ll need:

• RStudio: An IDE for R, perfect for coding, debugging, and visualization.
• R: The programming language that shines in statistical computing.

Key Packages for Supervised Learning:

• caret: A go-to package for training and fine-tuning models across various algorithms.
• ranger: A faster version of randomForest for using the Random Forest algorithm.
• e1071: Includes SVMs and other statistical tools for classification and regression.
• torch or keras: Better choices than neuralnet for deep learning.
• xgboost: A top-performing implementation of gradient boosting for structured data.
• tidymodels: A modern machine learning workflow with packages like parsnip, recipes, tune, and yardstick.
• dplyr, tidyr: Essential for cleaning and organizing data.
• data.table: An efficient package for handling large datasets.
• readr: Speeds up data import.
• ggplot2: For creating beautiful, informative visualizations.
• plotly: Makes your visualizations and Shiny apps more interactive.

To manage your packages, use:

• renv: Keeps your package versions consistent and reproducible.

Containerization with Docker

To make sure your app works the same everywhere, use Docker:

Optimized Dockerfile:

FROM rocker/tidyverse:latest
RUN R -e "install.packages(c('caret', 'ranger', 'e1071', 'torch', 'keras', 'xgboost', 'tidymodels', 'dplyr', 'tidyr', 'data.table', 'readr', 'ggplot2', 'plotly', 'renv'), repos='http://cran.rstudio.com/')"
COPY . /app
WORKDIR /app
CMD ["R", "-e", "shiny::runApp('app.R', host='0.0.0.0', port=3838)"]

Testing Your Application

Testing helps ensure everything works smoothly:

• Unit Testing: Use testthat to check individual functions and modules.
• Integration Testing: Use shinytest2 for automated testing of the UI.
• Model Testing:
• Use caret and tidymodels for cross-validation.
• Evaluate models with yardstick and MLmetrics.
• Plumber API Testing: Test API endpoints with httr or tools like Postman.
• Benchmarking: Use microbenchmark or bench for performance testing.
• Code Validation: Run R CMD check to ensure everything is working correctly.

Packaging and Deployment

Once the app is tested and ready, it’s time to package and deploy:

Packaging:

• Use devtools::build() to create a .tar.gz package for easy distribution.

Deployment Options:

• Shiny Applications:
• Deploy to Shinyapps.io for small projects.
• Use ShinyProxy for enterprise-level deployment.
• Host on cloud services like AWS or Google Cloud Run.
• Plumber API Deployment:
• Deploy using Docker to cloud services like AWS Lambda or Google Cloud Run.
• Use platforms like DigitalOcean or Heroku for easier hosting.

Conclusion

Building a supervised learning app in R requires a solid approach, from development to deployment. With RStudio for coding, Docker for containerization, and a set of powerful R packages for building and testing models, you can create scalable, efficient applications to tackle real-world data challenges.