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Identify records with zero-valued measurement data

Source: R/check_logical.R
check_zero_value.Rd

This function scans a dataset for cases where the measurement column (value) contains zero (0) values, which may indicate missing, censored, or erroneous data. It returns either a DT::datatable for easy inspection or a plain data.frame of the affected rows. This function is useful for quality control and validation prior to data aggregation, reporting, or database submission.

Usage

check_zero_value(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 a data.frame of zero-value records, or NULL (invisibly) if no zero values are found.

Examples

# Example dataset
df <- data.frame(
  station_name = c("A", "B", "C", "D"),
  sample_date = as.Date(c("2023-06-01", "2023-06-02", "2023-06-03", "2023-06-04")),
  value = c(3.2, 0, 1.5, 0)
)

# Return a plain data.frame of zero-value records
check_zero_value(df, return_df = TRUE)
#> ERROR: Value contain zeroes (0). Please check zero values!
#>   station_name sample_date value
#> 1            B  2023-06-02     0
#> 2            D  2023-06-04     0