Check if a dataframe/table is compliant to peekbank json before database import

ds.validate_table(
  df_table,
  table_type,
  cdi_expected,
  dir_csv,
  is_null_field_required = TRUE
)

Arguments

df_table: the dataframe to be saved
table_type: the type of dataframe, for the most updated table types specified by schema, please use functionds.list_ds_tables()
cdi_expected: specifies whether cdi_data is to be expected to be present in the imported data; only relevant for subjects table
dir_csv: the folder directory containing all the csv files, used for stimulus image path validation
is_null_field_required: by default is set to TRUE which means that all the columns in the json file are required; when user specifically sets this to FALSE, then the fields that are allowed null values are not required.

Value

A list with two elements:

errors: Character vector of validation errors (blocking), or NULL if none.
warnings: Named list of validation warnings (suppressable). Names are warning IDs (e.g. "cdi_collision").

Examples

if (FALSE) { # \dontrun{
result <- ds.validate_table(df_table = df_table, table_type = "xy_data", cdi_expected = F, dir_csv = "../processed_data")
result$errors    # blocking issues
result$warnings  # warnings that can be opted out of on a case by case basis
} # }