Identify non-numeric or non-logical values in measurement data

This function checks whether entries in the value column of a dataset are valid numeric or logical values. It is particularly useful for identifying common data entry errors such as inequality symbols (<, >) or unintended text strings (e.g., "NA", "below detection"). The function reports any invalid entries in an interactive DT::datatable for easy inspection.

Usage

check_value_logical(data, return_df = FALSE)

Arguments

data: A data frame. Must contain a column named value.
return_df: Logical. If TRUE, return a plain data.frame of problematic rows instead of a DT datatable. Default = FALSE.

Value

A DT::datatable or data frame listing unique invalid entries, or NULL (invisibly) if all values are correctly formatted as numeric or logical.

Examples

# Example dataset with mixed valid and invalid values
df <- data.frame(
  station_name = c("A", "B", "C", "D", "E"),
  value = c("3.4", "<0.2", "TRUE", "NA", "5e-3")
)

# Check for invalid (non-numeric / non-logical) entries
check_value_logical(df, return_df = TRUE)
#> ERROR: Expected numerical/logical value but found invalid characters.
#> Common problems are e.g. '<', '>' signs, text labels, or malformed numbers.
#>   value
#> 1  <0.2
#> 2    NA

# Example with all valid numeric and logical values
df_valid <- data.frame(value = c(1.2, 0, TRUE, FALSE, 3.5))
check_value_logical(df_valid)
#> All values are correctly formatted as numeric or logical.