Author: Erik.Leppo@tetratech.com
Date: 2025-10-06

# Vignette, BioMonTools

#### Erik.Leppo@tetratech.com

#### 2025-10-06

# Purpose

The `BioMonTools` package was created to enable users to have a common set of tools for bioassessment and biomonitoring data.

# Installation

The package is hosted on GitHub (https://github.com/leppott/BioMonTools) and can be installed using the following lines of code. It is necessary to install the `devtools` package.

    # Installing the BioMonTools library (with the vignette) from GitHub
    library(remotes)
    install_github("leppott/BioMonTools", force=TRUE, build_vignettes=TRUE)

# Help

After the `BioMonTools` package has been installed running the following line of code will open the help file with links to all functions.

    help(package="BioMonTools")

# Package Functions

The suite of functions in the `BioMonTools` package are presented below.

- metric.values
- markExcluded
- rarify

## metric.values

Calculate metric values from a data frame with taxa by sample with fully qualified master taxa information. All percent metric results are 0-1.

### Data Preparation

There are a number of required fields for the input file. If any fields are missing the user will be prompted as to which are missing and the user can decide whether to continue or quit. If the user continues, the missing fields will be added but will be filled with zero or NA (as appropriate). Any metrics based on the missing fields will not be valid.

Required Fields:

- SAMPLEID

  - Unique sample identifier

  - Valid values: character or number, must be unique

- TAXAID

  - Unique taxa identifier

  - Valid values: character or number, must be unique

- N_TAXA

  - Number of individuals

  - Valid values: number

- EXCLUDE

  - Non-unique/non-distinct taxa are excluded from richness metric calculations but are counted in the other metrics. Appendix B of the ‘BCGcalc_README_20180919’ Word document describes the Exclude Taxa Decision Criteria that was used during BCG model calibration.

  - Valid values: TRUE or FALSE

  - Non-unique/non-distinct taxa should be entered as “TRUE”

- NONTARGET

  - Non-target taxa are not part of the intended capture list; e.g., fish, herps, water column taxa. They are excluded from all metric calculations.

  - Valid values: TRUE or FALSE.

  - NonTarget taxa should be entered as “TRUE”

- INDEX_NAME

  - Name of the BCG rules worksheet in the ‘Rules’ file (in the ‘extdata’ folder) that the R code is referencing.

  - Valid values for the Puget Lowlands/Willamette Valley model: BCG_PacNW_v1_500ct or BCG_PacNW_v1_300ct.

- SITE_TYPE

  - Select which BCG model to apply: Low (lo) gradient (<1% NHD+ v2 flowline slope) or high (hi) gradient (≥ 1% NHD+ v2 flowline slope).

  - Valid values: “hi” or “lo”.

- PHYLUM, SUBPHYLUM, CLASS, ORDER, FAMILY, SUBFAMILY, TRIBE, GENUS

  - Phylogeny

  - Other phylogenetic rankings (e.g., SubOrder or SuperFamily) can be included but are not used in the current metric calculations.

  - Valid values: text

- BCG_Attr

  - BCG attribute assignments (for example, Stamp and Gerritsen 2018).

  - Valid values: 1i, 1m, 2, 3, 4, 5, 6; if not available, leave blank or enter ‘NA’.

- FFG, HABIT, LIFE_CYCLE, TOLVAL, THERMAL_INDICATOR, UFC, ELEVATION_ATTR, GRADIENT_ATTR, WSAREA_ATTR, REPRODUCTION, CONNECTIVITY

  - FFG

    - Valid values: CG, CF, PR, SC, SH

    - Function feeding group entries.

  - HABIT

    - Valid values: BU, CB, CN, SP, SW

    - Habit designations.

  - LIFE_CYLCE

    - Valid values: UNI, SEMI, MULTI

    - Life cycle designations.

  - THERMAL_INDICATOR

    - Valid values: STENOC, COLD, COOL, WARM, STENOW , EURYTHERMAL, INCONCLUSIVE, NA

  - LONGLIVED

    - Valid values: TRUE, FALSE

  - NOTEWORTHY

    - Valid values: TRUE, FALSE

  - HABITAT

    - Valid values: BRAC, DEPO, GENE, HEAD, RHEO, RIVE, SPEC, UNKN

  - UFC

    - Valid values: numeric; 1, 2, 3, 4, 5, 6

    - Taxonomic Uncertainty Frequency Class

  - ELEVATION_ATTR

    - Valid values: LOW, HIGH

  - GRADIENT_ATTR

    - Valid values: LOW, MOD, HIGH

  - WSAREA_ATTR

    - Valid values: SMALL, MEDIUM, LARGE, XLARGE

  - REPRODUCTION

    - Valid values: BROADCASTER, SIMPLE NEST, COMPLEX NEST, BEARER, MIGRATORY

  - CONNECTIVITY

    - Valid values: TRUE, FALSE

Fields that are optional can be in the output (see examples below).

    # Packages
    # library(readxl)
    # library(knitr)
    # library(BioMonTools)
    # library(dplyr)

    # Load Data
    df_data <- readxl::read_excel(system.file("./extdata/Data_Benthos.xlsx"
                                           , package = "BioMonTools")
                          , guess_max = 10^6)
    # Columns to keep
    myCols <- c("Area_mi2", "SurfaceArea", "Density_m2", "Density_ft2")

    # Run Function
    df_metval <- BioMonTools::metric.values(df_data, "bugs", fun.cols2keep = myCols)
    #> Updated col class; TOLVAL2 to numeric
    #> Joining with `by = join_by(SAMPLEID, INDEX_NAME, INDEX_CLASS)`
    # Metrics of Interest
    ## thermal indicator (_ti_)
    #names(df.metval)[grepl("_ti_", names(df.metval))]
    col_met2keep <- c("ni_total"
                      , "nt_total"
                      , "nt_ti_stenocold" # renamed from corecold
                      , "nt_ti_cold"
                      , "nt_ti_cool"
                      , "pi_ti_stenocold" # renamed from corecold
                      , "pi_ti_cold"
                      , "pi_ti_cool")
    col_ID <- c("SAMPLEID"
                , toupper(myCols)
                , "INDEX_NAME"
                , "INDEX_CLASS")
    # Ouput
    df_metval_ci <- df_metval[, c(col_ID, col_met2keep)]
    # RMD table
    knitr::kable(head(df_metval_ci),
                 caption = "Metric Calculation, select metrics")

Metric Calculation, select metrics
SAMPLEID | AREA_MI2 | SURFACEAREA | DENSITY_M2 | DENSITY_FT2 | INDEX_NAME | INDEX_CLASS | ni_total | nt_total | nt_ti_stenocold | nt_ti_cold | nt_ti_cool | pi_ti_stenocold | pi_ti_cold | pi_ti_cool
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
02087REF_Bug_2002-08-22_0 | 1.791475 | NA | NA | NA | BCG_PacNW_v1_500ct | Hi | 6 | 4 | 0 | 2 | 2 | 0 | 66.666667 | 33.33333
03054CSR_Bug_2003-08-18_0 | 71.249741 | 8 | NA | NA | BCG_PacNW_v1_500ct | Lo | 3 | 1 | 0 | 1 | 0 | 0 | 100.000000 | 0.00000
06012CSR_Bug_2006-09-05_0 | 82.338353 | 8 | NA | NA | BCG_PacNW_v1_500ct | Hi | 25 | 4 | 0 | 3 | 3 | 0 | 80.000000 | 76.00000
06027CSR_Bug_2006-07-17_0 | 33.632627 | 8 | NA | NA | BCG_PacNW_v1_500ct | Lo | 1 | 1 | 0 | 0 | 1 | 0 | 0.000000 | 100.00000
06029CSR_Bug_2006-09-27_0 | 48.091837 | 8 | NA | NA | BCG_PacNW_v1_500ct | Lo | 3 | 1 | 0 | 0 | 1 | 0 | 0.000000 | 100.00000
07036OPR_Bug_2007-09-27_0 | 31.817986 | 8 | NA | NA | BCG_PacNW_v1_500ct | Lo | 22 | 2 | 0 | 1 | 1 | 0 | 9.090909 | 90.90909

## markExcluded

Takes as an input data frame with Sample ID, Taxa ID, and phlogenetic name fields and returns a similar dataframe with a column for “exclude” taxa (TRUE or FALSE).

Exclude taxa are refered to by multiple names; ambiguous, non-distinct, and non-unique. The “exclude” name was chosen so as to be consistent with “non-target” taxa. That is, taxa marked as “TRUE” are treated as undesirables. Exclude taxa are those that are present in a sample when taxa of the same group are present in the same sample are identified at a finer level. That is, the parent is marked as exclude when child taxa are present in the same sample.

The exclude taxa are referenced in the metric values function. These taxa are removed from the taxa richness metrics. This is because these are coarser level taxa.

Exceptions is a 2 column data frame of synonyms or other exceptions. Column 1 is the name used in the TaxaID column the input data frame (df_samptax). Column 2 is the name used in the TaxaLevels columns of the input data frame (df_samptax). The phylogenetic columns (TaxaLevels) will be modified from Column 2 of the Exceptions data frame to match Column 1 of the Exceptions data frame. This ensures that the algorithm for markExcluded works properly. The changes will not be stored and the original names provided in the input data frame (df_samptax) will be returned in the final result. The function example below includes a practical case.

Taxa Levels are phylogenetic names that are to be checked. They should be listed in order from course (kingdom) to fine (species). Names not appearing in the data will be skipped.

The spelling of names must be consistent (including case) for this function to produce the intended output.

Returns a data frame of df_samptax with an additional column, Exclude.

    # Packages
    #library(readxl)
    #library(dplyr)
    #library(lazyeval)
    #library(knitr)

    # Define pipe
    `%>%` <- dplyr::`%>%`

    # Data
    df_samps_bugs <- readxl::read_excel(system.file("./extdata/Data_Benthos.xlsx"
                                            , package = "BioMonTools")
                                , guess_max = 10^6)

    # Variables
    SampID     <- "SampleID"
    TaxaID     <- "TaxaID"
    TaxaCount  <- "N_Taxa"
    Exclude    <- "Exclude_New"
    TaxaLevels <- c("Kingdom"
                    , "Phylum"
                    , "SubPhylum"
                    , "Class"
                    , "SubClass"
                    , "Order"
                    , "SubOrder"
                    , "SuperFamily"
                    , "Family"
                    , "SubFamily"
                    , "Tribe"
                    , "Genus"
                    , "SubGenus"
                    , "Species"
                    , "Variety")
    # Taxa that should be treated as equivalent
    Exceptions <- data.frame("TaxaID" = "Sphaeriidae", "PhyloID" = "Pisidiidae")

    # Filter Data
    # df_samptax <- filter(df_samps_bugs, !!as.name(SampID) ==
    # "08BEA3478__2013-08-21_0")
    # df_tst_small <- markExcluded(df_samptax, SampID, TaxaID, TaxaCount, TaxaLevels
    #, Exceptions, Exclude)

    # EXAMPLE 1
    df_tst <- BioMonTools::markExcluded(df_samps_bugs
                                        , SampID = "SampleID"
                                        , TaxaID = "TaxaID"
                                        , TaxaCount = "N_Taxa"
                                        , Exclude = "Exclude_New"
                                        , TaxaLevels = TaxaLevels
                                        , Exceptions = Exceptions)
    #> *WARNING*, 3 taxa levels missing from input data frame;
    #>  KINGDOM, SUBORDER, VARIETY

    # Compare
    df_compare <- dplyr::summarise(dplyr::group_by(df_tst, SampleID)
                                   , Exclude_Import = sum(Exclude)
                                   , Exclude_R = sum(Exclude_New))
    df_compare$Diff <- df_compare$Exclude_Import - df_compare$Exclude_R
    #
    tbl_diff <- table(df_compare$Diff)
    #kable(tbl_diff)
    # sort
    df_compare <- df_compare %>% dplyr::arrange(desc(Diff))

    # Number with issues
    #sum(abs(df_compare$Diff))
    # total samples
    #nrow(df_compare)

    # confusion matrix
    tbl_results <- table(df_tst$Exclude, df_tst$Exclude_New, useNA = "ifany")
    #
    # Show differences
    knitr::kable(tbl_results, caption = "Confusion Matrix")

Confusion Matrix
| FALSE | TRUE
--- | --- | ---
FALSE | 144 | 0
TRUE | 0 | 1

    # samples with differences
    samp_diff <- as.data.frame(df_compare[df_compare[,"Diff"] != 0, "SampleID"])
    # results for only those with differences
    df_tst_diff <- df_tst[df_tst[,"SampleID"] %in% samp_diff$SampleID, ]
    # add diff field
    df_tst_diff$Exclude_Diff <- df_tst_diff$Exclude - df_tst_diff$Exclude_New

    # Classification Performance Metrics
    class_TP <- tbl_results[2,2] # True Positive
    class_FN <- tbl_results[2,1] # False Negative
    class_FP <- tbl_results[1,2] # False Positive
    class_TN <- tbl_results[1,1] # True Negative
    class_n <- sum(tbl_results)  # total
    #
    # sensitivity (recall); TP / (TP+FN); measure model to ID true positives
    class_sens <- class_TP / (class_TP + class_FN)
    # precision; TP / (TP+FP); accuracy of model positives
    class_prec <- class_TP / (class_TP + class_FP)
    # specifity; TN / (TN + FP); measure model to ID true negatives
    class_spec <- class_TN  / (class_TN + class_FP)
    # overall accuracy; (TP + TN) / all cases; accuracy of all classifications
    class_acc <- (class_TP + class_TN) / class_n
    # F1; 2 * (class_prec*class_sens) / (class_prec+class_sens)
    ## balance of precision and recall
    class_F1 <- 2 * (class_prec * class_sens) / (class_prec + class_sens)
    #
    results_names <- c("Sensitivity (Recall)"
                       , "Precision", "Specificity"
                       , "OVerall Accuracy"
                       , "F1")
    results_values <- c(class_sens
                        , class_prec
                        , class_spec
                        , class_acc
                        , class_F1)
    #
    tbl_class <- data.frame(results_names, results_values)
    names(tbl_class) <- c("Performance Metrics", "Percent")
    tbl_class$Percent <- round(tbl_class$Percent * 100, 2)
    knitr::kable(tbl_class, caption = "Classification Performance Metrics")

Classification Performance Metrics
Performance Metrics | Percent
--- | ---
Sensitivity (Recall) | 100
Precision | 100
Specificity | 100
OVerall Accuracy | 100
F1 | 100

The same data with no “exceptions” leaves 8 records misclassified.

    # Packages
    #library(readxl)
    #library(dplyr)
    #library(lazyeval)
    #library(knitr)

    # Define pipe
    `%>%` <- dplyr::`%>%`

    # Data
    df_samps_bugs <- readxl::read_excel(system.file("./extdata/Data_Benthos.xlsx"
                                                  , package = "BioMonTools")
                                      , guess_max = 10^6)

    # Variables
    SampID     <- "SampleID"
    TaxaID     <- "TaxaID"
    TaxaCount  <- "N_Taxa"
    Exclude    <- "Exclude_New"
    TaxaLevels <- c("Kingdom"
                    , "Phylum"
                    , "SubPhylum"
                    , "Class"
                    , "SubClass"
                    , "Order"
                    , "SubOrder"
                    , "SuperFamily"
                    , "Family"
                    , "SubFamily"
                    , "Tribe"
                    , "Genus"
                    , "SubGenus"
                    , "Species"
                    , "Variety")
    # Taxa that should be treated as equivalent
    Exceptions <- NA

    # EXAMPLE 2
    ## No Exceptions

    df_tst2 <- BioMonTools::markExcluded(df_samps_bugs
                                         , SampID = "SampleID"
                                         , TaxaID = "TaxaID"
                                         , TaxaCount = "N_Taxa"
                                         , Exclude = "Exclude_New"
                                         , TaxaLevels = TaxaLevels
                                         , Exceptions = NA)
    #> *WARNING*, 3 taxa levels missing from input data frame;
    #>  KINGDOM, SUBORDER, VARIETY

    # Compare
    df_compare2 <- dplyr::summarise(dplyr::group_by(df_tst2, SampleID)
                                   , Exclude_Import = sum(Exclude)
                                   , Exclude_R = sum(Exclude_New))
    df_compare2$Diff <- df_compare2$Exclude_Import - df_compare2$Exclude_R
    #
    tbl_diff2 <- table(df_compare2$Diff)
    #kable(tbl_diff2)
    # sort
    df_compare2 <- df_compare2 %>% dplyr::arrange(desc(Diff))

    # Number with issues
    #sum(abs(df_compare2$Diff))
    # total samples
    #nrow(df_compare2)

    # confusion matrix
    tbl_results2 <- table(df_tst2$Exclude, df_tst2$Exclude_New, useNA = "ifany")
    #
    # Show differences
    knitr::kable(tbl_results2, caption = "Confusion Matrix")

Confusion Matrix
| FALSE | TRUE
--- | --- | ---
FALSE | 144 | 0
TRUE | 0 | 1

    knitr::kable(df_compare2[1:10, ])

SampleID | Exclude_Import | Exclude_R | Diff
--- | --- | --- | ---
02087REF_Bug_2002-08-22_0 | 0 | 0 | 0
03054CSR_Bug_2003-08-18_0 | 0 | 0 | 0
06012CSR_Bug_2006-09-05_0 | 0 | 0 | 0
06027CSR_Bug_2006-07-17_0 | 0 | 0 | 0
06029CSR_Bug_2006-09-27_0 | 0 | 0 | 0
07036OPR_Bug_2007-09-27_0 | 0 | 0 | 0
08CED2518__2015-08-13_0 | 0 | 0 | 0
08ISS4294__2015-08-26_0 | 1 | 1 | 0
08LAK3627__2015-08-10_0 | 0 | 0 | 0
09JEN1318__2015-08-24_0 | 0 | 0 | 0

    knitr::kable(tail(df_compare2))

SampleID | Exclude_Import | Exclude_R | Diff
--- | --- | --- | ---
WAM06600-011609_Bug_2014-07-07_0 | 0 | 0 | 0
WAM06600-122423__2015-09-17_0 | 0 | 0 | 0
WCFA__2010-08-19_0 | 0 | 0 | 0
lpil66__2010-08-13_0 | 0 | 0 | 0
olneyfh__2013-09-13_0 | 0 | 0 | 0
purdy__2013-09-10_0 | 0 | 0 | 0

    # samples with differences
    (samp_diff2 <- as.data.frame(df_compare2[df_compare2[,"Diff"] != 0, "SampleID"]))
    #> [1] SampleID
    #> <0 rows> (or 0-length row.names)
    # results for only those with differences
    df_tst_diff2 <- dplyr::filter(df_tst2, SampleID %in% samp_diff2$SampleID)
    # add diff field
    df_tst_diff2$Exclude_Diff <- df_tst_diff2$Exclude - df_tst_diff2$Exclude_New

    # Classification Performance Metrics
    class_TP2 <- tbl_results2[2,2] # True Positive
    class_FN2 <- tbl_results2[2,1] # False Negative
    class_FP2 <- tbl_results2[1,2] # False Positive
    class_TN2 <- tbl_results2[1,1] # True Negative
    class_n2 <- sum(tbl_results2)  # total
    #
    # sensitivity (recall); TP / (TP+FN); measure model to ID true positives
    class_sens2 <- class_TP2 / (class_TP2 + class_FN2)
    # precision; TP / (TP+FP); accuracy of model positives
    class_prec2 <- class_TP2 / (class_TP2 + class_FP2)
    # specifity; TN / (TN + FP); measure model to ID true negatives
    class_spec2 <- class_TN2 / (class_TN2 + class_FP2)
    # overall accuracy; (TP + TN) / all cases; accuracy of all classifications
    class_acc2 <- (class_TP2 + class_TN2) / class_n2
    # F1; 2 * (class_prec*class_sens) / (class_prec+class_sens)
    ## balance of precision and recall
    class_F12 <- 2 * (class_prec2 * class_sens2) / (class_prec2 + class_sens2)
    #
    results_names2 <- c("Sensitivity (Recall)"
                        , "Precision"
                        , "Specificity"
                        , "OVerall Accuracy"
                        , "F1")
    results_values2 <- c(class_sens2
                         , class_prec2
                         , class_spec2
                         , class_acc2
                         , class_F12)
    #
    tbl_class2 <- data.frame(results_names2, results_values2)
    names(tbl_class2) <- c("Performance Metrics", "Percent")
    tbl_class2$Percent <- round(tbl_class2$Percent * 100, 2)
    knitr::kable(tbl_class2, caption = "Classification Performance Metrics")

Classification Performance Metrics
Performance Metrics | Percent
--- | ---
Sensitivity (Recall) | 100
Precision | 100
Specificity | 100
OVerall Accuracy | 100
F1 | 100

## rarify

The rarify function subsamples count data to a fixed count per sample. It takes as an input a 3 column data frame (SampleID, TaxonID, Count) and returns a similar dataframe with revised Counts. The names of the columns does not matter as they are specified in the code. Any non-count taxa (e.g., fish in a bug sample) should be removed prior to using the `rarify` function. The function code is from USEPA Corvallis John Van Sickle’s R code for RIVPACS (v1.0, 2005-06-10) and was tweaked for the addition of a user provided seed so repeatable results can be obtained.

The other function inputs are subsample size (target number of organisms in each sample) and seed. The seed is given so the results can be reproduced from the same input file. If no seed is given a random seed is used. An example seed is the date of admission to the Union for each state where the data is collected (e.g., Washington is 18891111). These values can be found on Wikipedia on the right sidebar for each State.

If you are running the 500-count BCG model and any of your samples have more than 600 organisms (the upper limit for the model), you should randomly subsample your data to 600 (600 is +20% of the 500-count target). This is done to make richness metrics comparable across the 500-count samples. You can do this with the Rarify routine.

    # Subsample to 500 organisms (from over 500 organisms) for 12 samples.

    # Packages
    #library(BioMonTools)
    #library(knitr)

    # load bio data
    df_biodata <- BioMonTools::data_bio2rarify
    #dim(df_biodata)
    #kable(head(df_biodata))

    # subsample
    mySize <- 500
    Seed_OR <- 18590214
    Seed_WA <- 18891111
    Seed_US <- 17760704
    bugs_mysize <- BioMonTools::rarify(inbug = df_biodata
                                       , sample.ID = "SampleID"
                                       , abund = "N_Taxa"
                                       , subsiz = mySize
                                       , mySeed = Seed_US)

    # view results
    #dim(bugs_mysize)
    #kable(head(bugs_mysize))

    # Compare pre- and post- subsample counts
    df_compare <- merge(df_biodata
                        , bugs_mysize
                        , by = c("SampleID", "TaxaID")
                        , suffixes = c("_Orig","_500"))
    df_compare <- df_compare[,c("SampleID", "TaxaID", "N_Taxa_Orig", "N_Taxa_500")]
    knitr::kable(head(df_compare), caption = "Comparison, by Sample")

Comparison, by Sample
SampleID | TaxaID | N_Taxa_Orig | N_Taxa_500
--- | --- | --- | ---
01103CSR_Bug_2001-08-27_0 | Ceratopogoninae | 237 | 142
01103CSR_Bug_2001-08-27_0 | Cheumatopsyche | 2 | 0
01103CSR_Bug_2001-08-27_0 | Chironomini | 8 | 4
01103CSR_Bug_2001-08-27_0 | Cladocera | 2 | 1
01103CSR_Bug_2001-08-27_0 | Coenagrionidae | 2 | 1
01103CSR_Bug_2001-08-27_0 | Corbicula | 2 | 1

    # compare totals
    tbl_totals <- aggregate(cbind(N_Taxa_Orig, N_Taxa_500) ~ SampleID
                            , df_compare
                            , sum)
    knitr::kable(head(tbl_totals), caption = "Comparison, sample totals")

Comparison, sample totals
SampleID | N_Taxa_Orig | N_Taxa_500
--- | --- | ---
01103CSR_Bug_2001-08-27_0 | 816 | 500
02087REF_Bug_2002-08-22_0 | 542 | 500
03013CSR_Bug_2003-07-01_0 | 508 | 500
03053CSR_Bug_2003-08-14_0 | 573 | 500
03054CSR_Bug_2003-08-18_0 | 558 | 500
06012CSR_Bug_2006-09-05_0 | 545 | 500

### Flags

Results should be interpreted with caution if they are flagged for any of the criteria listed below. It is up to the user’s discretion as to how to handled flagged samples.

    # Packages
    #library(readxl)
    #library(reshape2)
    #library(knitr)
    #library(BioMonTools)

    # Import
    df.samps.bugs <- readxl::read_excel(system.file("extdata/Data_Benthos.xlsx"
                                                    , package = "BioMonTools")
                               , guess_max = 10^6)

    # Calculate Metrics
    # Extra columns to keep in results
    keep.cols <- c("Area_mi2", "SurfaceArea", "Density_m2", "Density_ft2")
    # Run Function
    df.metrics <- BioMonTools::metric.values(df.samps.bugs
                                             , "bugs"
                                             , fun.cols2keep = keep.cols)
    #> Updated col class; TOLVAL2 to numeric
    #> Joining with `by = join_by(SAMPLEID, INDEX_NAME, INDEX_CLASS)`

    # Flags
    # Import QC Checks
    df.checks <- readxl::read_excel(system.file("extdata/MetricFlags.xlsx"
                                              , package = "BioMonTools")
                                    , sheet = "Flags")
    # Run Function
    df.flags <- BioMonTools::qc.checks(df.metrics, df.checks)

    # Change terminology; PASS/FAIL to NA/flag
    df.flags[, "FLAG"][df.flags[, "FLAG"] == "FAIL"] <- "flag"
    df.flags[, "FLAG"][df.flags[, "FLAG"] == "PASS"] <- NA
    # long to wide format
    df.flags.wide <- reshape2::dcast(df.flags
                                     , SAMPLEID ~ CHECKNAME
                                     , value.var = "FLAG")
    # Calc number of "flag"s by row.
    df.flags.wide$NumFlags <- rowSums(df.flags.wide == "flag", na.rm = TRUE)
    # Rearrange columns
    NumCols <- ncol(df.flags.wide)
    df.flags.wide <- df.flags.wide[, c(1, NumCols, 2:(NumCols - 1))]
    # View(df.flags.wide)

    # Summarize Results
    knitr::kable(table(df.flags[,"CHECKNAME"], df.flags[,"FLAG"], useNA = "ifany"))

| flag | NA
--- | --- | ---
Low density (ft2) | 0 | 47
Low density (m2) | 0 | 47
Ramellogammarus | 0 | 47
brackish organisms present | 0 | 47
catchment, Large | 0 | 47
catchment, small | 10 | 37
individuals, Large | 0 | 47
individuals, dominant 02, Large | 46 | 1
individuals, small | 47 | 0
surface area, small | 7 | 40