scemetrics.qmd

# Sharing R code without sharing R code

Chairs: James Black

## 2024

### Objectively track migrations/language use in studies?

- Most companies are using Github or Gitlab for study code - if we know where all the repos are, can we scan them via the API to track things like which studies are using R, Python or still on SAS?
- **Key Questions:**
  - What code should be scanned? Look for files present? `.sas`, `.R`, `.py`, `.sql`, etc.? Is it more accurate than inbuilt language detection present in API endpoints?
  - Should the focus be on studying all code written or only focus on study code (e.g. limit scope to org that holds studies)?
  - Is there an API endpoint that can indicate which repository is being used?

### Can we understand what packages are being used in studies?

- **Idea:** 
  - The Posit Package Manager API doesn't reliably say what validated R packages are used in each study, but as `renv` is used more, 
    can we scan the `renv.lock` files to see what packages are being used? 
- **Purpose:** 
  - This helps validation teams, our teams working on R packages, as well as help flag if we need to identify who used 
    a specific version of a specific package in a study.
  - Where packages are 'pre-baked' into containers to speed up 'time to pulling data', want to keep that as lean as possible using metrics like this.

### Understanding container use

- **Challenges:**
  - How do we understand uptake of managed images, and who is using old images? 
  - Need for a strategy to manage container upgrades effectively.
  - Actively understand patterns of image use across your SCE
  - Look at patterns that should be dealt with - e.g. large numbers of idle interactive containers clogging worker nodes
- **Idea:** 
  - Pull k8's logs to get list of all active containers by person over time  

### Keeping Connect lean

- **Idea:** 
  - Roche had an example where >500 broken items, and >500 items not touched in more than a year were on the server
  - Use Connect data (the postgres database as the API is very slow) to remove content
- **Purpose:** 
  - Remove potentially GBs of un-used data on the Connect server, and also enforce retention rules

### General notes

  - Shift continues to data being outside of the SCE
  - Some companies moved to 'iterate anywhere - final batch runs matter' stance. Some still consider every activity requiring validation.
  - Discussion highlighted a split on whether internet access is blocked - in some companies there is no air gap, in ohers there is, particularly on 'validated batch runs'
  - singularity compresses containers better
  - big celebrations that now we do not need to rebuild containers each time workbench is updated!!
  - Provenance is an audit/validation requirement
  - Explore the potential use of common tools to data engineering that are absent in clinical reporting (e.g. dbt, airflow, prefect) 

### Action Items and Questions
- **Action Item:** 
  - White paper with Mark Bynens on the SCE.
  - In white paper, clarify what the SCE is and how to handle program environments and data integration more effectively.

### References

- James' posit::conf talk: [epijim.uk/talk/giving-your-scientific-computing-environment-sce-a-voice](https://epijim.uk/talk/giving-your-scientific-computing-environment-sce-a-voice/)
- Alanah Jonas from GSK may have a relevant talk later in the year at PHUSE EU 2024