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Use from R

The simplest R integration is to call the CLI with --json and parse the result with jsonlite.

  • no Perl-to-R bridge is required
  • the validator keeps one stable machine-readable output format
  • the same command works in scripts, notebooks, and pipelines

Minimal example​

library(jsonlite)

cmd <- c(
  "--ddl", "ddl/OMOPCDM_postgresql_5.4_ddl.sql",
  "--input", "example/DRUG_EXPOSURE.csv",
  "--json"
)

raw <- system2(
  "bin/omop-csv-validator",
  args = cmd,
  stdout = TRUE,
  stderr = FALSE
)

result <- fromJSON(paste(raw, collapse = "\n"))
result

What you get back​

The JSON result contains:

  • input_file
  • schema_name
  • ok
  • error_count
  • row_errors

If validation cannot even start, such as when no schema can be inferred, the result also contains fatal_error.

These fields are intended to be the stable automation interface for R clients.

Typical R branching​

if (isTRUE(result$ok)) {
  message("CSV validated successfully")
} else if (!is.null(result$fatal_error)) {
  stop(result$fatal_error)
} else {
  print(result$row_errors)
}

Batch validation in R​

For multiple OMOP CSV files, loop over files and run the validator once per file.

library(jsonlite)

csv_files <- list.files("exports", pattern = "\\.csv$", full.names = TRUE)

results <- lapply(csv_files, function(csv_file) {
  raw <- system2(
    "bin/omop-csv-validator",
    args = c(
      "--ddl", "ddl/OMOPCDM_postgresql_5.4_ddl.sql",
      "--input", csv_file,
      "--json"
    ),
    stdout = TRUE,
    stderr = FALSE
  )

  fromJSON(paste(raw, collapse = "\n"))
})

That pattern keeps the validator simple while giving R users a straightforward batch workflow.

Separator override is usually unnecessary

The CLI normally infers the separator. Add --sep in R only if you know the file needs an explicit override.

Row numbering​

Row numbers in row_errors are data rows, not physical line numbers including the header. The first row after the header is row 1.