MassWateR data input and checks

Four types of data are used with the MassWateR package.

1. Water quality results organized by sample location and date.
2. Summary of data quality objectives that describe quality control accuracy, frequency, and completeness measures for data in the results file.
3. A site metadata file, including location names, latitude, longitude, and additional grouping factors for sites.
4. A wqx metadata file required for generating output to facilitate data upload to WQX.

Templates with instructions for each of the types of input data are available for download in the Resources tab. Additionally, example files, described below, are provided with the package to demonstrate and test the functions.

Load the package in an R session after installation:

library(MassWateR)

The following shows how to specify a path and import each required data file. These are hypothetical files and the path will need to be changed to where your data are located on your computer.

# import results data
respth <- "C:/Documents/MassWateR/MyResults.xlsx"
resdat <- readMWRresults(respth)

# import dqo accuracy data
accpth <- "C:/Documents/MassWateR/MyDQOAccuracy.xlsx"
accdat <- readMWRacc(accpth)

# import dqo frequency and completeness data
frecompth <- "C:/Documents/MassWateR/MyDQOFreCom.xlsx"
frecomdat <- readMWRfrecom(frecompth)

# import site data
sitpth <- "C:/Documents/MassWateR/MySites.xlsx"
sitdat <- readMWRsites(sitpth)

# import WQX meta data
wqxpth <- "C:/Documents/MassWateR/MyWQXMeta.xlsx"
wqxdat <- readMWRwqx(wqxpth)

Example files included with the package are imported for demonstration in this vignette. The paths to these files are identified using the system.file() function and used in the examples below. In practice, alternative data files that follow the same format as the examples will be used with the functions and imported using code similar to above.

Make sure to close any data files on your desktop before importing them in MassWateR. Some operating systems may require the file to be closed for successful import.

Data import and checks

The MassWateR package is developed for quality control and exploratory analysis of surface water quality data. Before these analyses can occur, the data must be formatted correctly. There are several checks included in the data import functions to ensure the files are formatted correctly for downstream use. If any of the checks fail, an error message will be returned that prompts the necessary changes that must be made to the Excel file before the data can be used.

For many of the checks, parameter names and units need to match the following columns in the paramsMWR file included with the package. Specifically parameter names (either Characteristic Name in the results file, or Parameter in the data quality objectives files) can be the simple or WQX format as below. The units can be any that apply for a given parameter, although only one is allowed per parameter. All entries are case-sensitive. This file is also available in the Resources tab.

Simple Parameter	WQX Parameter	Units of measure
Air Temp	Temperature, air	deg C, deg F
Ammonia	Ammonia	mg/l, ug/l, umol/l, ppm
Ammonium	Ammonium	mg/l, ug/l, umol/l, ppm
Chl a	Chlorophyll a	mg/l, ug/l, umol/l, ppm
Chl a (probe)	Chlorophyll a (probe)	mg/l, ug/l, umol/l, ppm
Chloride	Chloride	mg/l, ug/l, umol/l, ppm
Conductivity	Conductivity	uS/cm, mS/cm, S/m
Cyanobacteria (lab)	Algae, blue-green (phylum cyanophyta) density	mg/l, ug/l, umol/l, ppm
Cyanobacteria (probe)	Chlorophyll a (probe) concentration, Cyanobacteria (bluegreen)	mg/l, ug/l, umol/l, ppm
Depth	Depth	m, cm, ft
DO	Dissolved oxygen (DO)	mg/l, ug/l
DO saturation	Dissolved oxygen saturation	%
E.coli	Escherichia coli	cfu/100ml, MPN/100ml, #/100ml
Enterococcus	Enterococcus	cfu/100ml, MPN/100ml, #/100ml
Fecal Coliform	Fecal Coliform	cfu/100ml, MPN/100ml, #/100ml
Flow	Flow	cfs, cfm, mgd, l/sec, l/min,
Gage	Height, gage	m, cm, ft
Metals	Metals	mg/l, ug/l, umol/l, ppm
Microcystins	Microcystins	mg/l, ug/l, umol/l, ppm
Nitrate	Nitrate	mg/l, ug/l, umol/l, ppm
Nitrate + Nitrite	Nitrate + Nitrite	mg/l, ug/l, umol/l, ppm
Nitrite	Nitrite	mg/l, ug/l, umol/l, ppm
Ortho P	Orthophosphate	mg/l, ug/l, umol/l, ppm
pH	pH	blank, s.u., None
Pheophytin	Pheophytin a	mg/l, ug/l, umol/l, ppm
POC	Particulate organic carbon	mg/l, ug/l, umol/l, ppm
PON	Total Nitrogen, mixed forms	mg/l, ug/l, umol/l, ppm
POP	Phosphorus, Particulate Organic	mg/l, ug/l, umol/l, ppm
Salinity	Salinity	ppth, PSU, PSS, g/kg, ppt
Secchi Depth	Depth, Secchi disk depth	m, cm, ft
Silicate	Silicate	mg/l, ug/l, umol/l, ppm
Sp Conductance	Specific conductance	uS/cm, mS/cm, S/m
Sulfate	Sulfate	mg/l, ug/l, umol/l, ppm
Surfactants	Surfactants	mg/l, ug/l, umol/l, ppm
TDN	Total Nitrogen, mixed forms	mg/l, ug/l, umol/l, ppm
TDP	Total Phosphorus, mixed forms	mg/l, ug/l, umol/l, ppm
TDS	Total dissolved solids	mg/l, ug/l, umol/l, ppm
TKN	Total Kjeldahl nitrogen	mg/l, ug/l, umol/l, ppm
TN	Total Nitrogen, mixed forms	mg/l, ug/l, umol/l, ppm
TP	Total Phosphorus, mixed forms	mg/l, ug/l, umol/l, ppm
TSS	Total suspended solids	mg/l, ug/l, umol/l, ppm
Turbidity	Turbidity	FTU, FNU, JTU, NTU, AU, BU, FAU, FBU, FNMU, FNRU, NTMU, NTRU
Water Temp	Temperature, water	deg C, deg F

The readMWRresultsview() function can be used to help troubleshoot issues that are encountered importing the water quality results file (next section). This function can be used to create a .csv spreadsheet that shows the unique values within columns of the results file. This information can be used to verify if the values in each conform to the requirements for the data import checks, including acceptable values for the table above. By default, a .csv is created for all columns. The columns argument can be used to select columns of interest. Below shows how to view the unique entries for parameters ("Characteristic Name") and units ("Result Unit"). The .csv is created in the directory specified by output_dir. Visually evaluating the results for conformance to the package requirements and manually editing the input file can help with the import checks, described below.

# find path to the file included with the package, replace with a path to your file as needed
respth <- system.file("extdata/ExampleResults.xlsx", package = "MassWateR")

# create a .csv file for the two columns that show unique values in each
readMWRresultsview(respth, columns = c("Characteristic Name", "Result Unit"), output_dir = tempdir())

Surface water quality results

First, the surface water quality results can be imported with the readMWRresults() function. This is designed to import an Excel file external to R, run checks on the data, and provide some minor formatting for downstream quality control or exploratory analysis. In this example, the system file ExampleResults.xlsx is imported. In practice, the pth argument will point to an external file in the WQX format. See the Resources tab for the Excel file template and detailed instructions (in the template’s instructions worksheet). Note that runchk = TRUE is set to run the checks on data import. This is the default setting and it is not necessary to explicitly set this argument on import.

respth <- system.file("extdata/ExampleResults.xlsx", package = "MassWateR")
resdat <- readMWRresults(respth, runchk = TRUE)
#> 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!
head(resdat)
#> # A tibble: 6 × 18
#>   `Monitoring Location ID` `Activity Type` `Activity Start Date`
#>   <chr>                    <chr>           <dttm>               
#> 1 ABT-026                  Field Msr/Obs   2022-05-15 00:00:00  
#> 2 ABT-077                  Field Msr/Obs   2022-05-15 00:00:00  
#> 3 ABT-301                  Field Msr/Obs   2022-05-15 00:00:00  
#> 4 ABT-312                  Field Msr/Obs   2022-05-15 00:00:00  
#> 5 DAN-013                  Field Msr/Obs   2022-05-15 00:00:00  
#> 6 ELZ-004                  Field Msr/Obs   2022-05-15 00:00:00  
#> # ℹ 15 more variables: `Activity Start Time` <chr>,
#> #   `Activity Depth/Height Measure` <chr>, `Activity Depth/Height Unit` <chr>,
#> #   `Activity Relative Depth Name` <chr>, `Characteristic Name` <chr>,
#> #   `Result Value` <chr>, `Result Unit` <chr>, `Quantitation Limit` <chr>,
#> #   `QC Reference Value` <chr>, `Result Measure Qualifier` <chr>,
#> #   `Result Attribute` <chr>, `Sample Collection Method ID` <chr>,
#> #   `Project ID` <chr>, `Local Record ID` <chr>, `Result Comment` <chr>

Several checks are run automatically when the data are imported. These file checks are as follows (also viewed from the help file for checkMWRresults()):

• Column name spelling: Should be the following: Monitoring Location ID, Activity Type, Activity Start Date, Activity Start Time, Activity Depth/Height Measure, Activity Depth/Height Unit, Activity Relative Depth Name, Characteristic Name, Result Value, Result Unit, Quantitation Limit, QC Reference Value, Result Measure Qualifier, Result Attribute, Sample Collection Method ID, Project ID, Local Record ID, Result Comment
• Columns present: All columns from the previous should be present
• Activity Type: Should be one of Field Msr/Obs, Sample-Routine, Quality Control Sample-Field Blank, Quality Control Sample-Lab Blank, Quality Control Sample-Lab Duplicate, Quality Control Sample-Lab Spike, Quality Control-Calibration Check, Quality Control-Meter Lab Duplicate, Quality Control-Meter Lab Blank
• Date formats: Should be mm/dd/yyyy and parsed correctly on import
• Depth data present: Depth data should be included in Activity Depth/Height Measure or Activity Relative Depth Name for all rows where Activity Type is Field Msr/Obs or Sample-Routine
• Non-numeric Activity Depth/Height Measure: All depth values should be numbers, excluding missing values
• Activity Depth/Height Unit: All entries should be ft, m, or blank
• Activity Relative Depth Name: Should be either Surface, Bottom, Midwater, Near Bottom, or blank (warning only)
• Activity Depth/Height Measure out of range: All depth values should be less than or equal to 1 meter / 3.3 feet or entered as Surface in the Activity Relative Depth Name column (warning only)
• Characteristic Name: Should match parameter names in the Simple Parameter or WQX Parameter column of the paramsMWR data (warning only)
• Result Value: Should be a numeric value or a text value as AQL or BDL
• Non-numeric Quantitation Limit: All values should be numbers, excluding missing values
• QC Reference Value: Any entered values should be numeric or a text value as AQL or BDL
• Result Unit: No missing entries in Result Unit, except pH which can be blank
• Single Result Unit: Each unique parameter in Characteristic Name should have only one entry in Result Unit (excludes entries for lab spikes reported as % or % recovery)
• Correct Result Unit: Each unique parameter in Characteristic Name should have an entry in Result Unit that matches one of the acceptable values in the Units of measure column of the paramsMWR data (excludes entries for lab spikes reported as % or % recovery), see the table above.

An informative error is returned if the input data fail any of the checks. The input data should be corrected by hand in the Excel file by altering the appropriate rows or column names indicated in the error. Checks with warnings can be fixed at the discretion of the user before proceeding.

Here is an example of an error that might be returned for an incorrect data entry (using the checkMWRresults() function, which is used inside of readMWRresults()). To remedy the issue, change the entries in row 4 and 135 in the Activity Type column to Sample-Routine and Field Msr/Obs, respectively. This must be done in the original Excel file. Import the data again in R to verify the data are corrected.

chk <- resdat
chk[4, 2] <- "Sample"
chk[135, 2] <- "Field"
checkMWRresults(chk)
#> Running checks on results data...
#>  Checking column names... OK
#>  Checking all required columns are present... OK
#> Error:   Checking valid Activity Types...
#>  Incorrect Activity Type found: Sample, Field in row(s) 4, 135

Data imported with readMWRresults() are also formatted to address a few minor issues for downstream analysis. This formatting includes:

• Fix date and time inputs: Activity Start Date is converted to YYYY-MM-DD as a date object, Activity Start Time is converted to HH:MM as a character to fix artifacts from Excel import.
• Minor formatting for Result Unit: For conformance to WQX, e.g., ppt is changed to ppth, s.u. is changed to NA.
• Convert characteristic names: All parameters in Characteristic Name are converted to Simple Parameter in paramsMWR as needed.

A few other points about the Activity Type column are worth mentioning. As noted in the list above, the package uses specific activity types for data organization. Sample-Routine should be used for collected water samples. Field Msr/Obs should be used for in-situ measurements. The rest of the activity types are all for QC data. The input file should only contain the activity types in the Input Activity Type column below. The WQX output that is generated by the package will create extra rows with the activity types in the second column below. The new WQX output rows will take the value from the QC Reference Value column as their Result Value. Please view the Water Quality Exchange output vignette for a complete description of creating output for WQX upload with MassWateR.

Input Activity Type	WQX output new row Activity Type
Sample-Routine	Quality Control Sample-Field Replicate
Quality Control Sample-Field Blank	NA
Quality Control Sample-Lab Duplicate	Quality Control Sample-Lab Duplicate 2
Quality Control Sample-Lab Blank	NA
Quality Control Sample-Lab Spike	Quality Control Sample-Lab Spike Target
Field Msr/Obs	Quality Control Field Replicate Msr/Obs
Quality Control-Meter Lab Duplicate	Quality Control-Meter Lab Duplicate 2
Quality Control-Meter Lab Blank	NA
Quality Control-Calibration Check	Quality Control-Calibration Check Buffer

Data quality objectives

To use the quality control functions in MassWateR, Excel files that describe the data quality objectives for accuracy, frequency, and completeness must be provided. The system files included with the package, described above, demonstrate the required information and format for these files. They can be imported into R using the readMWRacc() and readMWRfrecom() functions for the accuracy, frequency, and completeness files. The pth argument will point to the location of the external files on your computer. See the Resources tab for the Excel file template and detailed instructions (in the template’s instructions worksheet). As above, the system files included with the package are used for the examples.

# import data quality objectives for accuracy
accpth <- system.file("extdata/ExampleDQOAccuracy.xlsx", package = "MassWateR")
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!
head(accdat)
#> # A tibble: 6 × 10
#>   Parameter    uom     MDL   UQL `Value Range` `Field Duplicate` `Lab Duplicate`
#>   <chr>        <chr> <dbl> <dbl> <chr>         <chr>             <chr>          
#> 1 Water Temp   deg C    NA    NA all           <= 1.0            <= 1.0         
#> 2 pH           NA       NA    NA all           <= 0.5            <= 0.5         
#> 3 DO           mg/l     NA    NA < 4           < 20%             NA             
#> 4 DO           mg/l     NA    NA >= 4          < 10%             NA             
#> 5 Sp Conducta… uS/cm    NA    NA < 250         < 30%             < 30%          
#> 6 Sp Conducta… uS/cm    NA 10000 >= 250        < 20%             < 20%          
#> # ℹ 3 more variables: `Field Blank` <chr>, `Lab Blank` <chr>,
#> #   `Spike/Check Accuracy` <chr>

# import data quality objectives for frequency and completeness
frecompth <- system.file("extdata/ExampleDQOFrequencyCompleteness.xlsx", package = "MassWateR")
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!
head(frecomdat)
#> # A tibble: 6 × 7
#>   Parameter      `Field Duplicate` `Lab Duplicate` `Field Blank` `Lab Blank`
#>   <chr>                      <dbl>           <dbl>         <dbl>       <dbl>
#> 1 Water Temp                    10              10            NA          NA
#> 2 pH                            10              10            NA          NA
#> 3 DO                            10              NA            NA          NA
#> 4 Sp Conductance                10              10            NA          10
#> 5 TP                            10               5            10           5
#> 6 Nitrate                       10               5            10           5
#> # ℹ 2 more variables: `Spike/Check Accuracy` <dbl>, `% Completeness` <dbl>

Both the readMWRacc() and readMWRfrecom() functions will run a series of checks to ensure the imported data are formatted correctly. The checkMWRacc() and checkMWRfrecom() functions run these checks when the readMWRacc() and readMWRfrecom() functions are executed, respectively. The checks for each are as follows.

File checks for accuracy:

• Column name spelling: Should be the following: Parameter, uom, MDL, UQL, Value Range, Field Duplicate, Lab Duplicate, Field Blank, Lab Blank, Spike/Check Accuracy
• Columns present: All columns from the previous check should be present
• Column types: All columns should be characters/text, except for MDL and UQL
• Value Range column na check: The character string "na" should not be in the Value Range column, "all" should be used if the entire range applies
• Unrecognized characters: Fields describing accuracy checks should not include symbols or text other than \(<=\), \(\leq\), \(<\), \(>=\), \(\geq\), \(>\), \(\pm\) "\%", "BDL", "AQL", "log", or "all"
• Overlap in Value Range column: Entries in Value Range should not overlap for a parameter (excludes ascending ranges)
• Gap in Value Range column: Entries in Value Range should not include a gap for a parameter, warning only
• Parameter: Should match parameter names in the Simple Parameter or WQX Parameter columns of the paramsMWR data
• Units: No missing entries in units (uom), except pH which can be blank
• Single unit: Each unique Parameter should have only one type for the units (uom)
• Correct units: Each unique Parameter should have an entry in the units (uom) that matches one of the acceptable values in the Units of measure column of the paramsMWR data, see the table above.
• Empty columns: Columns with all missing or NA values will return a warning

File checks for frequency and completeness:

• Column name spelling: Should be the following: Parameter, Field Duplicate, Lab Duplicate, Field Blank, Lab Blank, Spike/Check Accuracy, % Completeness
• Columns present: All columns from the previous check should be present
• Non-numeric values: Values entered in columns other than the first should be numeric
• Values outside of 0 - 100: Values entered in columns other than the first should not be outside of 0 and 100
• Parameter: Should match parameter names in the Simple Parameter or WQX Parameter columns of the paramsMWR data
• Empty columns: Columns with all missing or NA values will return a warning

Minor formatting of the input files is also done to address a few minor issues for downstream analysis. This formatting includes:

• Minor formatting for units: For conformance to WQX, e.g., ppt is changed to ppth, s.u. is changed to NA for the uom column (readMWRacc() only).
• Convert parameter names: All parameters in Parameter are converted to Simple Parameter in paramsMWR as needed.
• Remove unicode: Remove or replace unicode characters with those that can be used in logical expressions in qcMWRacc(), e.g., replace \(\geq\) with \(>=\).
• Convert limits to numeric: Convert MDL and UQL columns in the accuracy file to numeric

Site metadata

An Excel file for site metadata that describes spatial location and any other grouping factors for the sites in the results file can be imported using readMWRsites(). The system file included with the package, described above, demonstrates the required information and format for the file. The pth argument will point to the location of the external file on your computer. See the Resources tab for the Excel file template. As above, the system file included with the package is used for the example.

# import site metadata
sitpth <- system.file("extdata/ExampleSites.xlsx", package = "MassWateR")
sitdat <- readMWRsites(sitpth)
#> Running checks on site metadata...
#>  Checking column names... OK
#>  Checking all required columns are present... OK
#>  Checking for missing latitude or longitude values... OK
#>  Checking for non-numeric values in latitude... OK
#>  Checking for non-numeric values in longitude... OK
#>  Checking for positive values in longitude... OK
#>  Checking for missing entries for Monitoring Location ID... OK
#> 
#> All checks passed!
head(sitdat)
#> # A tibble: 6 × 5
#>   `Monitoring Location ID` `Monitoring Location Name` Monitoring Location Lati…¹
#>   <chr>                    <chr>                                           <dbl>
#> 1 ABT-026                  Rte 2, Concord                                   42.5
#> 2 ABT-062                  Rte 62, Acton                                    42.4
#> 3 ABT-077                  Rte 27/USGS, Maynard                             42.4
#> 4 ABT-144                  Rte 62, Stow                                     42.4
#> 5 ABT-162                  Cox Street bridge                                42.4
#> 6 ABT-237                  Robin Hill Rd, Marlboro                          42.3
#> # ℹ abbreviated name: ¹​`Monitoring Location Latitude`
#> # ℹ 2 more variables: `Monitoring Location Longitude` <dbl>,
#> #   `Location Group` <chr>

The readMWRsites() function runs several checks on the file using the checkMWRsites() function. Most of the checks are to ensure the latitude and longitude data are present and properly formatted. It is assumed that latitude and longitude data are entered in decimal degrees. The projection can be entered in other functions used in exploratory analysis. Details on the file checks are as follows:

• Column name spelling: Should be the following: Monitoring Location ID, Monitoring Location Name, Monitoring Location Latitude, Monitoring Location Longitude, Location Group
• Columns present: All columns from the previous check should be present
• Missing longitude or latitude: No missing entries in Monitoring Location Latitude or Monitoring Location Longitude
• Non-numeric latitude values: Values entered in Monitoring Location Latitude must be numeric
• Non-numeric longitude values: Values entered in Monitoring Location Longitude must be numeric
• Positive longitude values: Values in Monitoring Location Longitude must be negative
• Missing Location ID: No missing entries for Monitoring Location ID

WQX metadata

An Excel file for wqx metadata that is required to generate output for upload to WQX can be imported using readMWRwqx(). The system file included with the package, described above, demonstrates the required information and format for the file. The pth argument will point to the location of the external file on your computer. See the Resources tab for the Excel file template and detailed instructions (in the template’s instructions worksheet). As above, the system file included with the package is used for the example.

# import wqx metadata
wqxpth <- system.file("extdata/ExampleWQX.xlsx", package = "MassWateR")
wqxdat <- readMWRwqx(wqxpth)
#> Running checks on WQX metadata...
#>  Checking column names... OK
#>  Checking all required columns are present... OK
#>  Checking unique parameters... OK
#>  Checking Parameter formats... OK
#> 
#> All checks passed!
head(wqxdat)
#> # A tibble: 6 × 6
#>   Parameter    Sampling Method Cont…¹ `Method Speciation` Result Sample Fracti…²
#>   <chr>        <chr>                  <chr>               <chr>                 
#> 1 Water Temp   NA                     NA                  NA                    
#> 2 pH           NA                     NA                  NA                    
#> 3 DO           NA                     NA                  NA                    
#> 4 Sp Conducta… NA                     NA                  NA                    
#> 5 TP           MassWateR              as P                Unfiltered            
#> 6 Nitrate      MassWateR              as N                Unfiltered            
#> # ℹ abbreviated names: ¹​`Sampling Method Context`, ²​`Result Sample Fraction`
#> # ℹ 2 more variables: `Analytical Method` <chr>,
#> #   `Analytical Method Context` <chr>

The readMWRwqx() function runs a few checks on the file using the checkMWRwqx() function. Details on the file checks are as follows:

• Column name spelling: Should be the following: Parameter, Sampling Method Context, Method Speciation, Result Sample Fraction, Analytical Method, Analytical Method Context.
• Columns present: All columns from the previous should be present
• Unique parameters: Values in Parameter should be unique (no duplicates)
• Parameter: Should match parameter names in the Simple Parameter or WQX Parameter column of the paramsMWR data (warning only)

An informative error is returned if the input data fail any of the checks. The input data should be corrected by hand in the Excel file by altering the appropriate rows or column names indicated in the error. Checks with warnings can be fixed at the discretion of the user before proceeding.

Using the fset argument

All of the quality control, outlier, analysis, and wqx functions in MassWateR require the inputs described above, depending on the function. These are generally passed to each function using the appropriate arguments, e.g., res for the surface water quality results, acc and frecom for the data quality objective files for accuracy, frequency, and completeness, sit for the site metadata, and wqx for the wqx metadata. The values passed to these functions can be file paths that specify the location of the file or as data frames returned by the relevant import functions (i.e., readMWRresults(), readMWRacc(), readMWRfrecom(), readMWRsites(), readMWRwqx()).

Because it can be tedious to specify each of the input files in the arguments for the MassWateR functions, the fset (file set) argument can be used as an alternative method. The fset argument for each function accepts a named list with the relevant input file locations or data frames. This can be created at the top of a script and recycled as necessary for an analysis workflow. The examples below demonstrate creating the list as file paths or as exported data frames from the import functions. Examples in the vignettes show how this list can be used as alternative input using the fset argument.

# a list of input file paths
fsetls <- list(
  res = respth, 
  acc = accpth,
  frecom = frecompth,
  sit = sitpth, 
  wqx = wqxpth
)

# a list of input data frames
fsetls <- list(
  res = resdat, 
  acc = accdat,
  frecom = frecomdat,
  sit = sitdat, 
  wqx = wqxdat
)