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Check continuous monitoring data

Source: R/checkASRcont.R
checkASRcont.Rd

Check continuous monitoring data

Usage

checkASRcont(contdat)

Arguments

contdat

input data frame for results

Value

contdat is returned as is if no errors are found. An informative error is raised for structural problems (unrecognised column names, missing required columns, unparseable date/time values, or non-numeric parameter values). Missing values in parameter columns produce a warning instead of an error.

Details

This function is used internally within readASRcont to run several checks on the input data to verify correct formatting before downstream analysis.

The input data can use either of two formats:

  • Separate columns: Date, Time, and at least one parameter column

  • Combined column: DateTime, and at least one parameter column

The following checks are made:

  • Column names: Should include only Date, Time, DateTime, and at least one parameter column that matches the Parameter column in paramsASR

  • Required columns are present: Either Date + Time or DateTime are required for downstream analysis and upload to WQX

  • At least one parameter column is present: At least one parameter column that matches the Parameter column in paramsASR is required for downstream analysis and upload to WQX

  • Date format (separate columns only): Should be parseable by lubridate::parse_date_time() using year-first ("2024-06-01"), month-first ("06/01/2024"), or day-first ("01/06/2024") formats

  • Time format (separate columns only): Should be parseable by lubridate::parse_date_time() using 24-hour ("16:30:33"), 12-hour AM/PM ("4:30:33 PM"), or Excel-prefixed ("1899-12-31 16:30:33") formats

  • DateTime format (combined column only): Should be parseable by lubridate::parse_date_time() using year-first, month-first, or day-first date order combined with 24-hour or 12-hour AM/PM time (e.g. "2024-06-01 16:30:33", "06/01/2024 16:30:33", or "2024-06-01 4:30:33 PM")

  • Missing values: Missing values in parameter columns produce a warning rather than an error, since cleaned data files may legitimately contain NA values. Missing values in DateTime, Date, or Time columns still cause an error.

  • Parameter columns should be numeric: All parameter columns should be numeric values

Examples

contpth <- system.file('extdata/ExampleCont1.xlsx', package = 'AquaSensR')

contdat <- utilASRimportcont(contpth)

checkASRcont(contdat)
#> Running checks on continuous data...
#> 	Checking column names... OK
#> 	Checking Date, Time are present... OK
#> 	Checking at least one parameter column is present... OK
#> 	Checking date format... OK
#> 	Checking time format... OK
#> 	Checking for missing values... OK
#> 	Checking parameter columns for non-numeric values... OK
#> 
#> All checks passed!
#> # A tibble: 927 × 9
#>    Date       Time    Water_Temp_C DO_pctsat DO_mg_l Conductivity_uS_cm TDS_mg_l
#>    <chr>      <chr>          <dbl>     <dbl>   <dbl>              <dbl>    <dbl>
#>  1 2024-08-14 13:56:…         24.2      76.9    6.44               410.      266
#>  2 2024-08-14 13:56:…         24.2      76.7    6.43               410.      266
#>  3 2024-08-14 13:56:…         24.2      76.6    6.42               410.      266
#>  4 2024-08-14 13:57:…         24.2      76.5    6.41               410.      266
#>  5 2024-08-14 13:57:…         24.2      76.3    6.4                409       266
#>  6 2024-08-14 13:57:…         24.2      76.3    6.39               409.      266
#>  7 2024-08-14 13:57:…         24.2      76.2    6.39               409.      266
#>  8 2024-08-14 13:57:…         24.2      76.1    6.38               409.      266
#>  9 2024-08-14 13:57:…         24.2      76.5    6.41               404.      262
#> 10 2024-08-14 13:58:…         24.2      77.6    6.5                399.      259
#> # ℹ 917 more rows
#> # ℹ 2 more variables: Salinity_ppt <dbl>, pH_SU <dbl>