This repository provides a Flask API that allows users to execute R scripts with Docker. The API supports file inputs via URLs, executes R code, and returns the result as a downloadable file or image.
- File Input via URL: Users can submit file URLs (e.g., CSV files) for processing.
- R Script Execution: Users can send R code to be executed on the provided file.
- Output as Downloadable File: Generated images or other output files are returned as downloadable links.
- Docker Integration: The R script is executed inside a Docker container with all required dependencies pre-installed.
Ensure you have the following installed:
- Docker
- Python 3
- Flask
-
Clone the repository:
git clone https://github.com/your-repo-url.git cd your-repo-url -
Create a Dockerfile and
run_r_script.shas described in this guide (they are also provided in this repository). -
Build the Docker image:
docker build -t r-script-runner . -
Install the Python dependencies:
pip install flask requests
Run the following command to start the Flask API server:
python app.pyThe API will be accessible at http://127.0.0.1:5000.
You can send a POST request to http://127.0.0.1:5000/run-r with the following parameters:
file_url: URL to the file (e.g., a CSV file) to be processed by the R code.code: R code that processes the file. The R code can generate images, process data, and so on.
Here is an example using curl:
curl -X POST http://127.0.0.1:5000/run-r \
-F 'file_url=http://example.com/data.csv' \
-F 'code=library(readr); data <- read_csv("static/input_<unique_id>.csv"); print(data)'-
If the R code generates an image, the API will return a JSON response containing a download link:
{ "download_link": "http://127.0.0.1:5000/static/output_<unique_id>.png" } -
If the R code generates text output, the response will contain the printed output of the R script:
{ "output": "Printed output from the R script" }
.
├── app.py # Flask API logic
├── Dockerfile # Dockerfile to create the R environment
├── run_r_script.sh # Shell script to run R code inside Docker
└── static/ # Folder where output files will be stored
app.py: Flask API logic that handles file downloads, R script execution, and returns the results.Dockerfile: Defines the R environment with necessary dependencies installed (e.g.,readr,ggplot2).run_r_script.sh: Script that runs inside the Docker container to execute the provided R code.static/: Directory where generated files (e.g., images) are stored for download.
- File Input: The user submits a URL that points to a file (e.g., a CSV file) and provides R code to process it.
- File Download: The Flask server downloads the file from the provided URL.
- Docker Execution: The R code and the downloaded file are passed to a Docker container, where the code is executed.
- Return Output: If the R code generates an image or file, it is saved to the
static/directory, and a download link is provided in the API response.
You can use this API to automate data analysis, generate reports, or visualize data using R. For example, a user could submit a CSV file link and R code that generates a plot. The API will return the plot as a downloadable image.
- Security Considerations: Ensure that the API is properly secured when deployed in a production environment. R code execution can potentially be dangerous, so validation or sandboxing of code execution is recommended.
- File Size: Depending on the size of the file submitted, ensure that the server has enough storage space to handle large data files.
This project is licensed under the MIT License.