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Updating AGENTS.md #105

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94ea0dc
Updating per meeting discussion (23 July 2026)
DmitryMK Jul 31, 2026
132efdd
Fixed description
DmitryMK Jul 31, 2026
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6 changes: 5 additions & 1 deletion AGENTS.md
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Expand Up @@ -8,6 +8,7 @@ This repository builds ADaM analysis datasets from SDTM Dataset-JSON inputs usin
### Project layout
- `code/adam/`: one dataset-building script per ADaM domain (e.g., `adsl.r`, `adae.r`, `advs.r`).
- `code/utils/`: shared utilities (currently `save_dataset_json.r`).
- `code/tables/`: one script per table (e.g., `t_14_6_01.r`).
- `data/sdtm/`: SDTM Dataset-JSON inputs (`*.json`, tracked with DVC).
- `data/adam/`: generated ADaM Dataset-JSON outputs (`*.json`, tracked with DVC).
- `data/adam_reference/`: specs and define metadata inputs.
Expand Down Expand Up @@ -38,10 +39,12 @@ This repository builds ADaM analysis datasets from SDTM Dataset-JSON inputs usin

### TLF output conventions (docorator)
- For table/listing/figure outputs, use `gt` + `docorator` for PDF rendering.
- Use `gtsummary` for calculating table summaries (descriptive statistics, frequencies, etc.).
- TLF program naming convention:
- `t_<name>.r` for tables,
- `l_<name>.r` for listings,
- `f_<name>.r` for figures.
- Use zero-padding in the last numeric segment of the name (e.g., `t_14_6_02.r`, not `t_14_6_2.r`).
- Output artifact naming should mirror program naming (for example, `t_<name>.rds` and `t_<name>.pdf`).
- Keep TLF programs under `code/tables/` and produce both:
- ARD-style RDS output (for reproducible intermediate data),
Expand Down Expand Up @@ -69,7 +72,8 @@ This repository builds ADaM analysis datasets from SDTM Dataset-JSON inputs usin
- Use `<-` for assignment.
- Prefer `|>` pipelines for multi-step transformations.
- Prefer `snake_case` for local object names and helper functions.
- Keep CDISC variable names in canonical uppercase where required by standards.
- Use lowercase for variable names in programming scripts (local variables, variables from source datasets).
- Keep CDISC variable names in canonical uppercase where required by standards (variables in the final SDTM/ADaM datasets).
- Use `dplyr::mutate()`, `dplyr::select()`, `dplyr::left_join()`, etc. in clear staged steps (avoid large monolithic mutations).
- Prefer `case_when()` over deeply nested `ifelse()` logic.
- Keep one logical derivation block per object to preserve traceability.
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