Package {validateR}

Check for date-like inconsistencies in character columns

Description

Examines specified character columns of a data frame and tests whether their non-missing values can be parsed as dates under a given format. If at least 80% (but less than 100%) of a column's non-missing values parse successfully, the column is flagged, and the row positions of the non-parsable ("straggler") values are reported for inspection. Unlike check_numeric_like(), this check does not attempt to guess a date format automatically - the caller must supply the expected format for each column to be checked, since date formats are ambiguous (for example, "03/04/2023" could mean March 4th or April 3rd depending on convention) and guessing incorrectly could silently produce misleading results.

Usage

check_date_like(data, date_columns)

Arguments

Value

A named list, one element per flagged column (named by column name). Each element is itself a list containing:

expected_type

Always "date".

proportion_parsable_to_date

Percentage of non-missing values that were successfully parsed under the given format, rounded to 2 decimals.

bad_rows

Integer vector of row indices whose values could not be parsed under the given format.

Returns an empty list if no columns are flagged.

Examples

df <- data.frame(
  signup_date = c(
    "2023-01-15", "2023-02-20", "not a date",
    "2023-03-10", "2023-04-01"
  ),
  notes = c("a", "b", "c", "d", "e")
)
check_date_like(df, date_columns = c(signup_date = "%Y-%m-%d"))

Check for duplicate rows in a data frame

Description

Identifies exact duplicate rows across all columns of a data frame.

Usage

check_duplicates(data)

Arguments

Value

A list where each element is an integer vector giving the row indices of a group of duplicate rows. Returns an empty list if no duplicates are found.

Examples

df <- data.frame(
  x = c(1, 2, 1, 4, 5, 4, 4),
  y = c("a", "b", "a", "d", "e", "d", "d")
)

check_duplicates(df)

Check for missing values in a data frame

Description

Computes the count and percentage of missing (NA) values for each column in a data frame.

Usage

check_missing(data)

Arguments

Value

A data frame with one row per column of data, containing:

column_name

Name of the column.

n_missing

Number of missing values.

pct_missing

Percentage of missing values, rounded to 2 decimals.

Examples

df <- data.frame(x = c(1, NA, 3), y = c("a", "b", NA))
check_missing(df)

Check for type inconsistencies in character columns

Description

Examines each character column of a data frame and tests whether its non-missing values can be converted to numeric. If at least 80% (but less than 100%) of non-missing values are numeric-looking, the column is flagged as likely intended to be numeric, and the row positions of the non-convertible ("straggler") values are reported for inspection. Columns that are already numeric, or that fall below the 80% threshold, are not flagged.

Usage

check_numeric_like(data)

Arguments

Value

A named list, one element per flagged column (named by column name). Each element is itself a list containing:

expected_type

The type the column is likely meant to be (currently always "numeric").

proportion_convertible

Percentage of non-missing values that can be converted to numeric, rounded to 2 decimals.

bad_rows

Integer vector of row indices whose values could not be converted to numeric.

Returns an empty list if no columns are flagged.

Examples

df <- data.frame(
  income = c("50000", "62000", "N/A", "48000", "71000"),
  city = c("Thimphu", "Paro", "Punakha", "Wangdue", "Trongsa"),
  stringsAsFactors = FALSE
)
check_numeric_like(df)

Check for outlier values in a data frame

Description

Calculates the count and percentage of outlier values using IQR method (values beyond Q1 - 1.5IQR or Q3 + 1.5IQR).

Usage

check_outliers(data)

Arguments

Value

A data frame with one row per column of data, containing:

column_name

Name of the column.

n_outliers

Number of outlier values.

pct_outliers

Percentage of outlier values, rounded to 2 decimals.

Examples

df <- data.frame(
  x = c(1, 2, 3, 4, 100),
  y = c(10, 12, 11, 13, 12)
)
check_outliers(df)

Construct a validation_report object

Description

Low-level constructor that assembles already-computed check results into a single validation_report object. This function does not run any checks itself — it simply packages results produced elsewhere (typically by check_missing(), check_outliers(), check_duplicates(), check_numeric_like(), and check_date_like()) into a structured, classed object. In most cases, users should call validate_df() instead, which runs all five checks and calls this constructor automatically.

Usage

new_validation_report(
  df,
  df_name,
  missing_result,
  outliers_result,
  duplicates_result,
  numeric_like_result,
  date_like_result
)

Arguments

Value

An object of class validation_report, a list containing:

meta

A list with df_name (character), dim (integer vector of rows and columns), and timestamp (the time the report was created).

results

A list with four elements, missing, outliers, duplicates, numeric_like and date_like, containing the corresponding check results.

Examples

df <- data.frame(x = c(1, NA, 3), y = c("a", "b", "c"))
report <- new_validation_report(
  df = df,
  df_name = "df",
  missing_result = check_missing(df),
  outliers_result = check_outliers(df),
  duplicates_result = check_duplicates(df),
  numeric_like_result = check_numeric_like(df),
  date_like_result = check_date_like(df, date_columns = c(y = "%Y-%m-%d"))
)
class(report)

Plot a validation_report object

Description

Displays two side-by-side bar charts summarizing a validation_report: the count of missing values per column, and the count of outliers per numeric column.

Usage

## S3 method for class 'validation_report'
plot(x, ...)

Arguments

Value

The input x, returned invisibly. Called for its side effect of producing a plot.

Print a validation_report object

Description

Displays a concise summary of a validation_report, showing the data frame's dimensions, when it was checked, and how many columns (or groups, for duplicates) were flagged by each check.

Usage

## S3 method for class 'validation_report'
print(x, ...)

Arguments

Value

The input x, returned invisibly.

Validate a data frame for common data quality issues

Description

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

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

# 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