timeliness_check: Perform timeliness Check for Data Frame Columns

In DQA: Data Quality Assessment Tools

Perform timeliness Check for Data Frame Columns

Description

This function evaluates a source dataframe ('S_data') against a set of rules defined in a metadata dataframe ('M_data'). it Checks timeliness rules (Temporal and availability conditions) on columns of a data frame, based on metadata specifications. Supports flexible rule definition,date literal handling, and customizable output.

Usage

timeliness_check(
  S_data,
  M_data,
  Result = FALSE,
  show_column = NULL,
  date_parser_fun = smart_to_gregorian_vec,
  var_select = "all",
  verbose = FALSE
)

Arguments

S_data	data.frame. The source data in which rules will be evaluated. Each column may be referenced by the rules.
M_data	data.frame. Metadata describing variables and their timeliness rules. Must include columns VARIABLE, Timeliness_Rule and TYPE. Optionally includes Timeliness_Error_Type.
Result	logical (default: FALSE). If TRUE, returns row-by-row evaluation results for each rule. If FALSE, returns a summary table for each rule.
show_column	character vector (default: NULL). Names of columns from S_data to include in the result when Result = TRUE. Ignored otherwise.
date_parser_fun	function (default: smart_to_gregorian_vec). Converting Persian dates to English,Function to convert date values or date literals to Date class. Must accept character vectors and return Date objects.
var_select	character, numeric, or "all" (default: "all"). Subset of variables (rules) to check. Can be a character vector of variable names, numeric vector of row indices in M_data, or "all" to run all rules.
verbose	logical (default: FALSE). If TRUE, prints diagnostic messages during rule processing and evaluation.

Details

The metadata data.frame (M_data) **must** contain the following columns:

• VARIABLE: Name of the variable in S_data to which the rule applies.

• Timeliness_Rule: A logical rule provided as a string that defines temporal (date/time) conditions to be evaluated.

• TYPE: Specifies the type of the variable (e.g., "numeric", "date", "character").

• Timeliness_Error_Type: The error type for each rule will be reported in the summary output.Based on the importance and severity of the rule, it can include two options: "Warning" or "Error".

For each variable described in M_data, the function:

• Preprocesses the rule: replaces 'val' with the variable name, parses date literals and substitutes them with placeholders.

• Converts referenced data columns to appropriate types (numeric, date) based on metadata.

• Evaluates the rule for each row, either vectorized or row-wise if needed.

If Result = FALSE, returns a summary table with counts and percentages of rows meeting/not meeting each condition. If Result = TRUE, returns a data.frame with boolean results for each rule, optionally including selected columns from the source data.

Value

If Result = FALSE: a data.frame summary with columns:

• VARIABLE: Name of the variable/rule.

• Condition_Met: Number of rows where the rule is TRUE.

• Condition_Not_Met: Number of rows where the rule is FALSE.

• NA_Count: Number of rows with missing/indeterminate result.

• Total_Applicable: Number of non-NA rows.

• Total_Rows: Number of total rows.

• Percent_Met: Percentage of applicable rows meeting the condition.

• Percent_Not_Met: Percentage of applicable rows not meeting the condition.

• Timeliness_Error_Type: Error type from metadata (if available).

If Result = TRUE: a data.frame with one column per rule (variable), each containing logical values for every row, plus optional columns from the source data.

Examples

# Source data
S_data <- data.frame(
  VisitDate = c("2025-09-23", "2021-01-10", "2021-01-03","1404-06-28","1404-07-28",NA),
  Test_date = c("1404-07-01", "2021-01-09", "2021-01-14","1404-06-29","2025-09-19",NA)
)

# META DATA
M_data <- data.frame(
  VARIABLE = c("VisitDate","Test_date"),
  Timeliness_Rule = c(
    "",
    "VisitDate<=Test_date",
    " Test_date-VisitDate <10 ",
    ""),
  TYPE=c("date","date"),
  Timeliness_Error_Type = c("Error","warning"),
  stringsAsFactors = FALSE
)

result <- timeliness_check(
  S_data = S_data,
  M_data = M_data,
  Result = TRUE,
  show_column = FALSE
)

print(result)

result <- timeliness_check(
  S_data = S_data,
  M_data = M_data,
  Result = FALSE,
  var_select = c("VisitDate","Test_date")
)

print(result)

DQA documentation built on April 20, 2026, 9:06 a.m.