vignettes/vignette_BioMonTools.Rmd
vignette_BioMonTools.RmdThe BioMonTools package was created to enable users to
have a common set of tools for bioassessment and biomonitoring data.
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.
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")The suite of functions in the BioMonTools package are
presented below.
Calculate metric values from a data frame with taxa by sample with fully qualified master taxa information. All percent metric results are 0-1.
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
LONGLIVED
NOTEWORTHY
HABITAT
UFC
Valid values: numeric; 1, 2, 3, 4, 5, 6
Taxonomic Uncertainty Frequency Class
ELEVATION_ATTR
GRADIENT_ATTR
WSAREA_ATTR
REPRODUCTION
CONNECTIVITY
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)`
#> Warning: The `.dots` argument of `group_by()` is deprecated as of dplyr 1.0.0.
#> ℹ The deprecated feature was likely used in the dplyr package.
#> Please report the issue at <https://github.com/tidyverse/dplyr/issues>.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
# 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")| 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 01103CSR_Bug_2001-08-27_0 | 44.084184 | NA | NA | NA | BCG_PacNW_v1_500ct | Lo | 589 | 27 | 0 | 1 | 10 | 0 | 40.237691 | 60.95076 |
| 02087REF_Bug_2002-08-22_0 | 1.791475 | NA | NA | NA | BCG_PacNW_v1_500ct | Hi | 542 | 38 | 0 | 23 | 28 | 0 | 18.265683 | 20.66421 |
| 03013CSR_Bug_2003-07-01_0 | 9.613734 | 8 | NA | NA | BCG_PacNW_v1_500ct | Hi | 507 | 16 | 0 | 3 | 5 | 0 | 2.958580 | 13.21499 |
| 03053CSR_Bug_2003-08-14_0 | 3.061723 | 8 | NA | NA | BCG_PacNW_v1_500ct | Hi | 573 | 36 | 0 | 19 | 24 | 0 | 46.073298 | 58.46422 |
| 03054CSR_Bug_2003-08-18_0 | 71.249741 | 8 | NA | NA | BCG_PacNW_v1_500ct | Lo | 558 | 13 | 0 | 4 | 4 | 0 | 6.989247 | 11.82796 |
| 06012CSR_Bug_2006-09-05_0 | 82.338353 | 8 | NA | NA | BCG_PacNW_v1_500ct | Hi | 545 | 40 | 0 | 18 | 28 | 0 | 33.944954 | 75.96330 |
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
#> [1] "Working on item (1/12); PHYLUM"
#> [1] "Working on item (2/12); SUBPHYLUM"
#> [1] "Working on item (3/12); CLASS"
#> [1] "Working on item (4/12); SUBCLASS"
#> [1] "Working on item (5/12); ORDER"
#> [1] "Working on item (6/12); SUPERFAMILY"
#> [1] "Working on item (7/12); FAMILY"
#> [1] "Working on item (8/12); SUBFAMILY"
#> [1] "Working on item (9/12); TRIBE"
#> [1] "Working on item (10/12); GENUS"
#> [1] "Working on item (11/12); SUBGENUS"
#> [1] "Working on item (12/12); SPECIES"
# 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$Exclude_New, useNA = "ifany")
#
# Show differences
knitr::kable(tbl_results, caption = "Confusion Matrix")| FALSE | TRUE | |
|---|---|---|
| FALSE | 24653 | 0 |
| TRUE | 0 | 2350 |
# samples with differences
samp_diff <- as.data.frame(df_compare[df_compare[,"Diff"] != 0, "SampleID"])
# results for only those with differences
df_tst_diff <- df_tst[df_tst[,"SampleID"] %in% samp_diff$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$Percent * 100, 2)
knitr::kable(tbl_class, caption = "Classification Performance Metrics")| Performance Metrics | Percent |
|---|---|
| Sensitivity (Recall) | 100 |
| Precision | 100 |
| Specificity | 100 |
| OVerall Accuracy | 100 |
| F1 | 100 |
The same data with no “exceptions” leaves 8 records misclassified.
# Packages
#library(readxl)
#library(dplyr)
#library(lazyeval)
#library(knitr)
# Define pipe
`%>%` <- dplyr::`%>%`
# Data
df_samps_bugs <- readxl::read_excel(system.file("./extdata/Data_Benthos.xlsx"
, package = "BioMonTools")
, guess_max = 10^6)
# Variables
SampID <- "SampleID"
TaxaID <- "TaxaID"
TaxaCount <- "N_Taxa"
Exclude <- "Exclude_New"
TaxaLevels <- c("Kingdom"
, "Phylum"
, "SubPhylum"
, "Class"
, "SubClass"
, "Order"
, "SubOrder"
, "SuperFamily"
, "Family"
, "SubFamily"
, "Tribe"
, "Genus"
, "SubGenus"
, "Species"
, "Variety")
# Taxa that should be treated as equivalent
Exceptions <- NA
# EXAMPLE 2
## No Exceptions
df_tst2 <- BioMonTools::markExcluded(df_samps_bugs
, SampID = "SampleID"
, TaxaID = "TaxaID"
, TaxaCount = "N_Taxa"
, Exclude = "Exclude_New"
, TaxaLevels = TaxaLevels
, Exceptions = NA)
#> *WARNING*, 3 taxa levels missing from input data frame;
#> KINGDOM, SUBORDER, VARIETY
#> [1] "Working on item (1/12); PHYLUM"
#> [1] "Working on item (2/12); SUBPHYLUM"
#> [1] "Working on item (3/12); CLASS"
#> [1] "Working on item (4/12); SUBCLASS"
#> [1] "Working on item (5/12); ORDER"
#> [1] "Working on item (6/12); SUPERFAMILY"
#> [1] "Working on item (7/12); FAMILY"
#> [1] "Working on item (8/12); SUBFAMILY"
#> [1] "Working on item (9/12); TRIBE"
#> [1] "Working on item (10/12); GENUS"
#> [1] "Working on item (11/12); SUBGENUS"
#> [1] "Working on item (12/12); SPECIES"
# Compare
df_compare2 <- dplyr::summarise(dplyr::group_by(df_tst2, SampleID)
, Exclude_Import = sum(Exclude)
, Exclude_R = sum(Exclude_New))
df_compare2$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")| FALSE | TRUE | |
|---|---|---|
| FALSE | 24653 | 0 |
| TRUE | 8 | 2342 |
knitr::kable(df_compare2[1:10, ])| SampleID | Exclude_Import | Exclude_R | Diff |
|---|---|---|---|
| 08BEA3478__2013-08-21_0 | 3 | 2 | 1 |
| 08BEA3571__2014-09-03_0 | 5 | 4 | 1 |
| 08LIT2692__2012-09-10_0 | 8 | 7 | 1 |
| 08SAM0000__2012-07-11_0 | 4 | 3 | 1 |
| 08SAM0000__2013-07-25_0 | 4 | 3 | 1 |
| 09BLA0716__2014-08-14_0 | 3 | 2 | 1 |
| 95C__2012-10-02_0 | 1 | 0 | 1 |
| 95T__2012-10-03_0 | 1 | 0 | 1 |
| 01103CSR_Bug_2001-08-27_0 | 1 | 1 | 0 |
| 02087REF_Bug_2002-08-22_0 | 0 | 0 | 0 |
| SampleID | Exclude_Import | Exclude_R | Diff |
|---|---|---|---|
| silvr192__2012-08-24_0 | 3 | 3 | 0 |
| skytrmann__2010-08-12_0 | 6 | 6 | 0 |
| skytrmann__2013-08-28_0 | 8 | 8 | 0 |
| swampcart__2012-08-20_0 | 6 | 6 | 0 |
| woodshand__2010-08-06_0 | 12 | 12 | 0 |
| woodshand__2013-09-03_0 | 4 | 4 | 0 |
# samples with differences
(samp_diff2 <- as.data.frame(df_compare2[df_compare2[,"Diff"] != 0, "SampleID"]))
#> SampleID
#> 1 08BEA3478__2013-08-21_0
#> 2 08BEA3571__2014-09-03_0
#> 3 08LIT2692__2012-09-10_0
#> 4 08SAM0000__2012-07-11_0
#> 5 08SAM0000__2013-07-25_0
#> 6 09BLA0716__2014-08-14_0
#> 7 95C__2012-10-02_0
#> 8 95T__2012-10-03_0
# 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")| Performance Metrics | Percent |
|---|---|
| Sensitivity (Recall) | 99.66 |
| Precision | 100.00 |
| Specificity | 100.00 |
| OVerall Accuracy | 99.97 |
| F1 | 99.83 |
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)
#> Rarify of samples complete.
#> Number of samples = 12
#> Execution time (sec) = 0.00900000000000034
# 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")| 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")| 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 |
## Not run:
# save the data
#write.table(bugs_mysize, paste("bugs",mySize,"txt",sep="."),sep="\t")
## End(Not run)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 | |
|---|---|---|
| brackish organisms present | 7 | 671 |
| catchment, Large | 0 | 678 |
| catchment, small | 158 | 520 |
| individuals, dominant 02, Large | 204 | 474 |
| individuals, Large | 0 | 678 |
| individuals, small | 80 | 598 |
| Low density (ft2) | 0 | 678 |
| Low density (m2) | 0 | 678 |
| Ramellogammarus | 8 | 670 |
| surface area, small | 112 | 566 |