# Implement Audit Trail

> Implement audit trail functionality for R projects in regulated environments. Covers logging, provenance tracking, electronic signatures, data integrity checks, and 21 CFR Part 11 compliance. Use when an R analysis requires electronic records compliance (21 CFR Part 11), when you need to track who did what and when in an analysis, when implementing data provenance tracking, or when creating tamper-evident analysis logs for regulatory submissions.

- Skill: `pjt222/implement-audit-trail-6` (Agent Skill)
- Install (CLI): `npx skillmds@latest add pjt222/implement-audit-trail-6`
- Raw SKILL.md: https://api.skillmd.com/api/skills/pjt222/implement-audit-trail-6/raw
- Safety review: pending (external: skill-scanner PASS, skillspector CAUTION)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Security
- License: MIT
- Author: pjt222 (https://skillmd.com/u/pjt222)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/pjt222/implement-audit-trail-6

---


# Implement Audit Trail

Add audit trail capabilities to R projects for regulatory compliance.

## When Use

- R analysis needs electronic records compliance (21 CFR Part 11)
- Track who did what, when, why in analysis
- Implement data provenance tracking
- Create tamper-evident analysis logs

## Inputs

- **Required**: R project with data processing or analysis scripts
- **Required**: Regulatory requirements (which audit trail elements mandatory)
- **Optional**: Existing logging infrastructure
- **Optional**: Electronic signature requirements

## Steps

### Step 1: Set Up Structured Logging

Create `R/audit_log.R`:

```r
#' Initialize audit log for a session
#'
#' @param log_dir Directory for audit log files
#' @param analyst Name of the analyst
#' @return Path to the created log file
init_audit_log <- function(log_dir = "audit_logs", analyst = Sys.info()["user"]) {
  dir.create(log_dir, showWarnings = FALSE, recursive = TRUE)

  log_file <- file.path(log_dir, sprintf(
    "audit_%s_%s.jsonl",
    format(Sys.time(), "%Y%m%d_%H%M%S"),
    analyst
  ))

  entry <- list(
    timestamp = format(Sys.time(), "%Y-%m-%dT%H:%M:%S%z"),
    event = "SESSION_START",
    analyst = analyst,
    r_version = R.version.string,
    platform = .Platform$OS.type,
    working_directory = getwd(),
    session_id = paste0(Sys.getpid(), "-", format(Sys.time(), "%Y%m%d%H%M%S"))
  )

  write(jsonlite::toJSON(entry, auto_unbox = TRUE), log_file, append = TRUE)
  options(audit_log_file = log_file, audit_session_id = entry$session_id)

  log_file
}

#' Log an audit event
#'
#' @param event Event type (DATA_IMPORT, TRANSFORM, ANALYSIS, EXPORT, etc.)
#' @param description Human-readable description
#' @param details Named list of additional details
log_audit_event <- function(event, description, details = list()) {
  log_file <- getOption("audit_log_file")
  if (is.null(log_file)) stop("Audit log not initialized. Call init_audit_log() first.")

  entry <- list(
    timestamp = format(Sys.time(), "%Y-%m-%dT%H:%M:%S%z"),
    event = event,
    description = description,
    session_id = getOption("audit_session_id"),
    details = details
  )

  write(jsonlite::toJSON(entry, auto_unbox = TRUE), log_file, append = TRUE)
}
```

**Got:** `R/audit_log.R` created with `init_audit_log()` + `log_audit_event()` functions. Calling `init_audit_log()` creates `audit_logs/` directory + timestamped JSONL file. Each log entry = single JSON line with `timestamp`, `event`, `analyst`, `session_id` fields.

**If fail:** `jsonlite::toJSON()` fails? Ensure `jsonlite` package installed. Log directory can't be created? Check file system permissions. Timestamps lack timezone? Verify `%z` supported on platform.

### Step 2: Add Data Integrity Checks

```r
#' Compute and log data hash for integrity verification
#'
#' @param data Data frame to hash
#' @param label Descriptive label for the dataset
#' @return SHA-256 hash string
hash_data <- function(data, label = "dataset") {
  hash_value <- digest::digest(data, algo = "sha256")

  log_audit_event("DATA_HASH", sprintf("Hash computed for %s", label), list(
    hash_algorithm = "sha256",
    hash_value = hash_value,
    nrow = nrow(data),
    ncol = ncol(data),
    columns = names(data)
  ))

  hash_value
}

#' Verify data integrity against a recorded hash
#'
#' @param data Data frame to verify
#' @param expected_hash Previously recorded hash
#' @return Logical indicating whether data matches
verify_data_integrity <- function(data, expected_hash) {
  current_hash <- digest::digest(data, algo = "sha256")
  match <- identical(current_hash, expected_hash)

  log_audit_event("DATA_VERIFY",
    sprintf("Data integrity check: %s", ifelse(match, "PASS", "FAIL")),
    list(expected = expected_hash, actual = current_hash))

  if (!match) warning("Data integrity check FAILED")
  match
}
```

**Got:** `hash_data()` returns SHA-256 hash string + logs `DATA_HASH` event. `verify_data_integrity()` compares current data vs stored hash + logs `DATA_VERIFY` event with PASS or FAIL status.

**If fail:** `digest::digest()` not found? Install `digest` package. Hashes don't match for identical data? Check column order + data types consistent between hashing + verification.

### Step 3: Track Data Transformations

```r
#' Wrap a data transformation with audit logging
#'
#' @param data Input data frame
#' @param transform_fn Function to apply
#' @param description Description of the transformation
#' @return Transformed data frame
audited_transform <- function(data, transform_fn, description) {
  input_hash <- digest::digest(data, algo = "sha256")
  input_dim <- dim(data)

  result <- transform_fn(data)

  output_hash <- digest::digest(result, algo = "sha256")
  output_dim <- dim(result)

  log_audit_event("DATA_TRANSFORM", description, list(
    input_hash = input_hash,
    input_rows = input_dim[1],
    input_cols = input_dim[2],
    output_hash = output_hash,
    output_rows = output_dim[1],
    output_cols = output_dim[2]
  ))

  result
}
```

**Got:** `audited_transform()` wraps any transformation function, logs input dimensions + hash, output dimensions + hash, transformation description as `DATA_TRANSFORM` event.

**If fail:** Transform function errors? Audit event not logged. Wrap transform in `tryCatch()` to log both successes + failures. Ensure transform function accepts + returns data frame.

### Step 4: Log Session Environment

```r
#' Log complete session information for reproducibility
log_session_info <- function() {
  si <- sessionInfo()

  log_audit_event("SESSION_INFO", "Complete session environment recorded", list(
    r_version = si$R.version$version.string,
    platform = si$platform,
    locale = Sys.getlocale(),
    base_packages = si$basePkgs,
    attached_packages = sapply(si$otherPkgs, function(p) paste(p$Package, p$Version)),
    renv_lockfile_hash = if (file.exists("renv.lock")) {
      digest::digest(file = "renv.lock", algo = "sha256")
    } else NA
  ))
}
```

**Got:** `SESSION_INFO` event logged with R version, platform, locale, attached packages + versions, renv lockfile hash (if applicable).

**If fail:** `sessionInfo()` returns incomplete package info? Ensure all packages loaded via `library()` before calling `log_session_info()`. renv lockfile hash = `NA` if project doesn't use renv.

### Step 5: Implement in Analysis Scripts

```r
# 01_analysis.R
library(jsonlite)
library(digest)

# Start audit trail
log_file <- init_audit_log(analyst = "Philipp Thoss")

# Import data with audit
raw_data <- read.csv("data/raw/study_data.csv")
raw_hash <- hash_data(raw_data, "raw study data")

# Transform with audit
clean_data <- audited_transform(raw_data, function(d) {
  d |>
    dplyr::filter(!is.na(primary_endpoint)) |>
    dplyr::mutate(bmi = weight / (height/100)^2)
}, "Remove missing endpoints, calculate BMI")

# Run analysis
log_audit_event("ANALYSIS_START", "Primary efficacy analysis")
model <- lm(primary_endpoint ~ treatment + age + sex, data = clean_data)
log_audit_event("ANALYSIS_COMPLETE", "Primary efficacy analysis", list(
  model_class = class(model),
  formula = deparse(formula(model)),
  n_observations = nobs(model)
))

# Log session
log_session_info()
```

**Got:** Analysis scripts init audit log at start, log each data import, transformation, analysis step, record session info at end. JSONL log file captures complete provenance chain.

**If fail:** `init_audit_log()` missing? Ensure `R/audit_log.R` sourced or package loaded. Events missing from log? Verify `log_audit_event()` called after every significant operation.

### Step 6: Git-Based Change Control

Complement application-level audit trail with git:

```bash
# Use signed commits for non-repudiation
git config commit.gpgsign true

# Descriptive commit messages referencing change control
git commit -m "CHG-042: Add BMI calculation to data processing

Per change request CHG-042, approved by [Name] on [Date].
Validation impact assessment: Low risk - additional derived variable."
```

**Got:** Git commits signed (GPG) + use descriptive messages referencing change control IDs. Combination of application-level JSONL audit trail + git history provides complete change control record.

**If fail:** GPG signing fails? Configure signing key with `git config --global user.signingkey KEY_ID`. Key not set up? Follow `gpg --gen-key` to create one.

## Checks

- [ ] Audit log captures all required events (start, data access, transforms, analysis, export)
- [ ] Timestamps use ISO 8601 format with timezone
- [ ] Data hashes enable integrity verification
- [ ] Session information recorded
- [ ] Logs append-only (no deletion or modification)
- [ ] Analyst identity captured for each session
- [ ] Log format machine-readable (JSONL)

## Pitfalls

- **Logging too much**: Focus on regulated events. Don't log every variable assignment.
- **Mutable logs**: Audit logs must be append-only. Use JSONL (one JSON object per line).
- **Missing timestamps**: Every event needs timestamp with timezone.
- **No session context**: Each log entry should reference session for correlation.
- **Forgetting to initialize**: Scripts must call `init_audit_log()` before any analysis.

## See Also

- `setup-gxp-r-project` - project structure for validated environments
- `write-validation-documentation` - validation protocols + reports
- `validate-statistical-output` - output verification methodology
- `configure-git-repository` - version control as part of change control