Check for type inconsistencies in character columns — check_numeric

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

data: A data frame.

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)
#> $income
#> $income$expected_type
#> [1] "numeric"
#> 
#> $income$proportion_convertible
#> [1] 80
#> 
#> $income$bad_rows
#> [1] 3
#> 
#>