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Validate a data frame for common data quality issues

Source: R/validate_df.R
validate_df.Rd

Runs a full suite of data quality checks on a data frame — missing values, outliers, duplicate rows, and type inconsistencies — and returns a single, structured report summarizing the results. This is the main entry point for the package; most users should start here rather than calling the individual check_*() functions directly.

Usage

validate_df(data, date_columns = NULL)

Arguments

data

A data frame to validate.

date_columns

Optional. A named character vector mapping column names in data to their expected date format (using the format codes accepted by base::as.Date()). If supplied, check_date_like() is run on those columns as a fifth check. If NULL (the default), date checking is skipped entirely, and the report notes it was not checked.

Value

An object of class validation_report. See new_validation_report() for details on its structure. Printing the result shows a short summary; the full results for each check are available under report$results$missing, report$results$outliers, report$results$duplicates, report$results$numeric_like and report$results$date_like.

Examples

df <- data.frame(
  age = c(25, 30, NA, 45, 200),
  income = c("50000", "62000", "N/A", "48000", "71000"),
  stringsAsFactors = FALSE
)
report <- validate_df(df)
report
#> < validation_report for 'df' >
#> 5 rows, 2 columns
#> Checked: 2026-09-12 04:07:46 
#> 
#> Missing values:          1  column(s) affected
#> Outliers:                1  column(s) affected
#> Duplicates:              0  group(s) affected
#> Numeric-like issues:     1  column(s) affected
#> Date-like issues:        NOT checked 
#> 
#> For details, inspect report$results$missing, $outliers, $duplicates, $numeric_like, $date_like 

# With an optional date check
df2 <- data.frame(
  signup_date = c(
    "2023-01-15", "2023-02-20", "not a date",
    "2023-03-10", "2023-04-01"
  ),
  stringsAsFactors = FALSE
)
report2 <- validate_df(df2, date_columns = c(signup_date = "%Y-%m-%d"))
report2
#> < validation_report for 'df2' >
#> 5 rows, 1 columns
#> Checked: 2026-09-12 04:07:46 
#> 
#> Missing values:          0  column(s) affected
#> Outliers:                0  column(s) affected
#> Duplicates:              0  group(s) affected
#> Numeric-like issues:     0  column(s) affected
#> Date-like issues:        1  column(s) affected 
#> 
#> For details, inspect report$results$missing, $outliers, $duplicates, $numeric_like, $date_like