dfe-ees-screener

Proof of concept for dockerising an R Plumber API for the DfE's data screener.

Running locally in VS Code

1. Download R binary (https://www.stats.bris.ac.uk/R/)
2. Download the R Extension for VS Code, you may be prompted to download the languageservice to use R code locally. Alternatively you can use an R-specific IDE such as RStudio
3. Open server.R click the Run button at the top of the file. Alternatively, open an R Terminal and use the command source("server.R")
4. Open up Postman/PowerShell/curl etc. to hit the endpoints:

GET localhost:8000/screen
POST localhost:8000/screen

The GET endpoint is just to confirm the API is running.

For the POST endpoint, dataFile and metaFile arguments should be submitted with a form-data request body. Example files can be found in the "example-data" folder

Docker

The API can also be run in a Docker container. Open up a terminal in the root of the project, and create an image using

docker build -t eesyscreener .

then run it using

docker run --rm --name eesyscreener -p 8000:8000 eesyscreener

Dependencies

You may need to enter pak::lockfile_install to install the dependencies locally before running the API.

If any additional dependencies are added, run the following command to update the lockfile before commiting changes.

pak::lockfile_create(pkg = c("plumber", "github::dfe-analytical-services/eesyscreener", "readr", "<additional dependency 1>", "<additional dependency 2>"))

Testing

If the data and meta files supplied to the POST endpoint generate an error from eesyscreener, and you only want to generate a successful response for testing, replace the function call in screen_Controller.R:

result <- screen_files(req$body$dataFile$filename, req$body$metaFile$filename, req$body$dataFile, req$body$metaFile)

with

write.csv(example_data, "example_data.csv", row.names = FALSE)
write.csv(example_data.meta, "example_data.meta.csv", row.names = FALSE)
result <- screen_files("example_data.csv", "example_data.meta.csv", example_data, example_data.meta)