Run quality control frequency checks for water quality monitoring results

Usage

qcMWRfre(
  res = NULL,
  acc = NULL,
  frecom = NULL,
  fset = NULL,
  runchk = TRUE,
  warn = TRUE
)

Arguments

res: character string of path to the results file or data.frame for results returned by readMWRresults
acc: character string of path to the data quality objectives file for accuracy or data.frame returned by readMWRacc
frecom: character string of path to the data quality objectives file for frequency and completeness or data.frame returned by readMWRfrecom
fset: optional list of inputs with elements named res, acc, frecom, sit, or wqx overrides the other arguments
runchk: logical to run data checks with checkMWRresults and checkMWRfrecom, applies only if res or frecom are file paths
warn: logical to return warnings to the console (default)

Value

The output shows the frequency checks from the input files. Each row applies to a frequency check for a parameter. The Parameter column shows the parameter, the obs column shows the total records that apply to regular activity types, the check column shows the relevant activity type for each frequency check, the count column shows the number of records that apply to a check, the standard column shows the relevant percentage required for the quality control check from the quality control objectives file, and the met column shows if the standard was met by comparing if percent is greater than or equal to standard.

Details

The function can be used with inputs as paths to the relevant files or as data frames returned by readMWRresults, readMWRacc, and readMWRfrecom. For the former, the full suite of data checks can be evaluated with runkchk = T (default) or suppressed with runchk = F. In the latter case, downstream analyses may not work if data are formatted incorrectly. For convenience, a named list with the input arguments as paths or data frames can be passed to the fset argument instead. See the help file for utilMWRinput.

Note that frequency is only evaluated on parameters in the Parameter column in the data quality objectives frequency and completeness file. A warning is returned if there are parameters in Parameter in the frequency and completeness file that are not in Characteristic Name in the results file.

Similarly, parameters in the results file in the Characteristic Name column that are not found in the data quality objectives frequency and completeness file are not evaluated. A warning is returned if there are parameters in Characteristic Name in the results file that are not in Parameter in the frequency and completeness file.

Examples

##
# using file paths

# results path
respth <- system.file('extdata/ExampleResults.xlsx', package = 'MassWateR')

# dqo accuracy data path
accpth <- system.file('extdata/ExampleDQOAccuracy.xlsx', package = 'MassWateR')

# frequency and completeness path
frecompth <- system.file('extdata/ExampleDQOFrequencyCompleteness.xlsx', 
     package = 'MassWateR')

qcMWRfre(res = respth, acc = accpth, frecom = frecompth)
#> Running checks on results data...
#> 	Checking column names... OK
#> 	Checking all required columns are present... OK
#> 	Checking valid Activity Types... OK
#> 	Checking Activity Start Date formats... OK
#> 	Checking depth data present... OK
#> 	Checking for non-numeric values in Activity Depth/Height Measure... OK
#> 	Checking Activity Depth/Height Unit... OK
#> 	Checking Activity Relative Depth Name formats... OK
#> 	Checking values in Activity Depth/Height Measure > 1 m / 3.3 ft... OK
#> 	Checking Characteristic Name formats... OK
#> 	Checking Result Values... OK
#> 	Checking for non-numeric values in Quantitation Limit... OK
#> 	Checking QC Reference Values... OK
#> 	Checking for missing entries for Result Unit... OK
#> 	Checking if more than one unit per Characteristic Name... OK
#> 	Checking acceptable units for each entry in Characteristic Name... OK
#> 
#> All checks passed!
#> Running checks on data quality objectives for accuracy...
#> 	Checking column names... OK
#> 	Checking all required columns are present... OK
#> 	Checking column types... OK
#> 	Checking no "na" in Value Range... OK
#> 	Checking for text other than <=, ≤, <, >=, ≥, >, ±, %, AQL, BQL, log, or all... OK
#> 	Checking overlaps in Value Range... OK
#> 	Checking gaps in Value Range... OK
#> 	Checking Parameter formats... OK
#> 	Checking for missing entries for unit (uom)... OK
#> 	Checking if more than one unit (uom) per Parameter... OK
#> 	Checking acceptable units (uom) for each entry in Parameter... OK
#> 	Checking empty columns... OK
#> 
#> All checks passed!
#> Running checks on data quality objectives for frequency and completeness...
#> 	Checking column names... OK
#> 	Checking all required columns are present... OK
#> 	Checking for non-numeric values... OK
#> 	Checking for values outside of 0 and 100... OK
#> 	Checking Parameter formats... OK
#> 	Checking empty columns... OK
#> 
#> All checks passed!
#> # A tibble: 40 × 7
#>    Parameter   obs check                count standard percent met  
#>    <chr>     <int> <chr>                <int>    <dbl>   <dbl> <lgl>
#>  1 Ammonia      43 Field Duplicate          4       10    9.30 FALSE
#>  2 Ammonia      43 Lab Duplicate           10        5   23.3  TRUE 
#>  3 Ammonia      43 Field Blank              7       10   16.3  TRUE 
#>  4 Ammonia      43 Lab Blank                7        5   16.3  TRUE 
#>  5 Ammonia      43 Spike/Check Accuracy     9        5   20.9  TRUE 
#>  6 DO           49 Field Duplicate         11       10   22.4  TRUE 
#>  7 DO           49 Lab Duplicate            0       NA   NA    NA   
#>  8 DO           49 Field Blank              0       NA   NA    NA   
#>  9 DO           49 Lab Blank                0       NA   NA    NA   
#> 10 DO           49 Spike/Check Accuracy     0       NA   NA    NA   
#> # ℹ 30 more rows

##
# using data frames

# results data
resdat <- readMWRresults(respth)
#> Running checks on results data...
#> 	Checking column names... OK
#> 	Checking all required columns are present... OK
#> 	Checking valid Activity Types... OK
#> 	Checking Activity Start Date formats... OK
#> 	Checking depth data present... OK
#> 	Checking for non-numeric values in Activity Depth/Height Measure... OK
#> 	Checking Activity Depth/Height Unit... OK
#> 	Checking Activity Relative Depth Name formats... OK
#> 	Checking values in Activity Depth/Height Measure > 1 m / 3.3 ft... OK
#> 	Checking Characteristic Name formats... OK
#> 	Checking Result Values... OK
#> 	Checking for non-numeric values in Quantitation Limit... OK
#> 	Checking QC Reference Values... OK
#> 	Checking for missing entries for Result Unit... OK
#> 	Checking if more than one unit per Characteristic Name... OK
#> 	Checking acceptable units for each entry in Characteristic Name... OK
#> 
#> All checks passed!

# accuracy data
accdat <- readMWRacc(accpth)
#> Running checks on data quality objectives for accuracy...
#> 	Checking column names... OK
#> 	Checking all required columns are present... OK
#> 	Checking column types... OK
#> 	Checking no "na" in Value Range... OK
#> 	Checking for text other than <=, ≤, <, >=, ≥, >, ±, %, AQL, BQL, log, or all... OK
#> 	Checking overlaps in Value Range... OK
#> 	Checking gaps in Value Range... OK
#> 	Checking Parameter formats... OK
#> 	Checking for missing entries for unit (uom)... OK
#> 	Checking if more than one unit (uom) per Parameter... OK
#> 	Checking acceptable units (uom) for each entry in Parameter... OK
#> 	Checking empty columns... OK
#> 
#> All checks passed!

# frequency and completeness data
frecomdat <- readMWRfrecom(frecompth)
#> Running checks on data quality objectives for frequency and completeness...
#> 	Checking column names... OK
#> 	Checking all required columns are present... OK
#> 	Checking for non-numeric values... OK
#> 	Checking for values outside of 0 and 100... OK
#> 	Checking Parameter formats... OK
#> 	Checking empty columns... OK
#> 
#> All checks passed!

qcMWRfre(res = resdat, acc = accdat, frecom = frecomdat)
#> # A tibble: 40 × 7
#>    Parameter   obs check                count standard percent met  
#>    <chr>     <int> <chr>                <int>    <dbl>   <dbl> <lgl>
#>  1 Ammonia      43 Field Duplicate          4       10    9.30 FALSE
#>  2 Ammonia      43 Lab Duplicate           10        5   23.3  TRUE 
#>  3 Ammonia      43 Field Blank              7       10   16.3  TRUE 
#>  4 Ammonia      43 Lab Blank                7        5   16.3  TRUE 
#>  5 Ammonia      43 Spike/Check Accuracy     9        5   20.9  TRUE 
#>  6 DO           49 Field Duplicate         11       10   22.4  TRUE 
#>  7 DO           49 Lab Duplicate            0       NA   NA    NA   
#>  8 DO           49 Field Blank              0       NA   NA    NA   
#>  9 DO           49 Lab Blank                0       NA   NA    NA   
#> 10 DO           49 Spike/Check Accuracy     0       NA   NA    NA   
#> # ℹ 30 more rows