This function calculates metric scores based on a Thresholds data frame. Can generate scores for categories n=3 (e.g., 1/3/5, ScoreRegime="Cat_135") or n=4 (e.g., 0/2/4/6, ScoreRegime="Cat_0246") or continuous (e.g., 0-100, ScoreRegime="Cont_0100").
metric.scores(
DF_Metrics,
col_MetricNames,
col_IndexName,
col_IndexClass,
DF_Thresh_Metric,
DF_Thresh_Index,
col_ni_total = "ni_total",
col_IndexRegion = NULL
)Arguments
- DF_Metrics
Data frame of metric values (as columns), Index Name, and Index Region (strata).
- col_MetricNames
Names of columns of metric values.
- col_IndexName
Name of column with index (e.g., MBSS.2005.Bugs)
- col_IndexClass
Name of column with relevant bioregion or site class (e.g., COASTAL).
- DF_Thresh_Metric
Data frame of Scoring Thresholds for metrics (INDEX_NAME, INDEX_CLASS, METRIC_NAME, Direction, Thresh_Lo, Thresh_Mid, Thresh_Hi, ScoreRegime , SingleValue_Add, NormDist_Tail_Lo, NormDist_Tail_Hi, CatGrad_xvar , CatGrad_InfPt, CatGrad_Lo_m, CatGrad_Lo_b, CatGrad_Mid_m, CatGrad_Mid_b , CatGrad_Hi_m, CatGrad_Hi_b).
- DF_Thresh_Index
Data frame of Scoring Thresholds for indices (INDEX_NAME, INDEX_CLASS,METRIC_NAME, ScoreRegime, Thresh01, Thresh02 , Thresh03, Thresh04, Thresh05, Thresh06, Thresh07 , Nar01, Nar02, Nar03, Nar04, Nar05, Nar06).
- col_ni_total
Name of column with total number of individuals. Used for cases where sample was collected but no organisms collected. Default = ni_total.#'
- col_IndexRegion
Name of column with relevant bioregion or site class (e.g., COASTAL). Default = NULL. DEPRECATED
Value
vector of scores
Details
The R library dplyr is needed for this function.
For all ScoreRegime cases at the index level a "sum_Index" field is computed that is the sum of all metric scores. Valid "ScoreRegime" values are:
* SUM = all metric scores added together.
* AVERAGE = all metric scores added and divided by the number of metrics. The index is on the same scale as the individual metric scores.
* AVERAGE_100 = AVERAGE is scaled 0 to 100.
FIX, 2024-01-29, v1.0.0.9060 Rename col_IndexRegion to col_IndexClass Add col_IndexRegion as variable at end to avoid breaking existing code Later remove it as an input variable but add code in the function to accept
Examples
# Example data
library(readxl)
library(reshape2)
# Thresholds
fn_thresh <- file.path(system.file(package = "BioMonTools")
, "extdata"
, "MetricScoring.xlsx")
df_thresh_metric <- read_excel(fn_thresh, sheet = "metric.scoring")
df_thresh_index <- read_excel(fn_thresh, sheet = "index.scoring")
#~~~~~~~~~~~~~~~~~~~~~~~~
# Pacific Northwest, BCG Level 1 Indicator Taxa Index
df_samps_bugs <- read_excel(system.file("extdata/Data_Benthos.xlsx"
, package = "BioMonTools")
, guess_max = 10^6)
myIndex <- "BCG_PacNW_L1"
df_samps_bugs$Index_Name <- myIndex
df_samps_bugs$Index_Class <- "ALL"
(myMetrics.Bugs <- unique(as.data.frame(df_thresh_metric)[df_thresh_metric[
, "INDEX_NAME"] == myIndex, "METRIC_NAME"]))
#> [1] "nt_total" "nt_EPT" "nt_BCG_att1i2" "x_Shan_e"
#> [5] "nt_longlived" "nt_Ephemerellid" "nt_Hepta" "nt_Nemour"
#> [9] "nt_Perlid" "nt_Rhya" "nt_ffg_pred" "nt_noteworthy"
# Run Function
df_metric_values_bugs <- metric.values(df_samps_bugs
, "bugs"
, fun.MetricNames = myMetrics.Bugs)
#> Updated col class; TOLVAL2 to numeric
#> Joining with `by = join_by(SAMPLEID, INDEX_NAME, INDEX_CLASS)`
# index to BCG.PacNW.L1
df_metric_values_bugs$INDEX_NAME <- myIndex
df_metric_values_bugs$INDEX_CLASS <- "ALL"
# SCORE Metrics
df_metric_scores_bugs <- metric.scores(df_metric_values_bugs
, myMetrics.Bugs
, "INDEX_NAME"
, "INDEX_CLASS"
, df_thresh_metric
, df_thresh_index)
if (FALSE) { # \dontrun{
# View Results
View(df_metric_scores_bugs)
} # }
# QC, table
table(df_metric_scores_bugs$Index, df_metric_scores_bugs$Index_Nar)
#>
#> Low
#> 0 20
#> 1 27
#> 2 28
#> 3 13
#> 4 5
#> 7 1
# QC, plot
hist(df_metric_scores_bugs$Index
, main = "PacNW BCG Example Data"
, xlab = "Level 1 Indicator Taxa Index Score")
abline(v=c(21,30), col = "blue")
text(21 + c(-2, +2), 200, c("Low", "Medium"), col = "blue")
#~~~~~~~~~~~~~~~~~~~~~~~~
# Metrics, Index, Benthic Macroinvertebrates, genus
# (generate values then scores)
myIndex <- "MBSS_2005_Bugs"
# Thresholds
# imported above
# get metric names for myIndex
(myMetrics_Bugs_MBSS <- unique(df_thresh_metric[df_thresh_metric[
, "INDEX_NAME"]
== myIndex, "METRIC_NAME", TRUE]))
#> [1] "nt_total" "nt_EPT" "nt_Ephem" "pi_tv2_intol"
#> [5] "pi_Ephem" "nt_ffg_scrap" "pi_habit_climb" "pi_Chiro"
#> [9] "pi_habit_cling" "pi_Tanyt" "pi_ffg_scrap" "pi_habit_swim"
#> [13] "pi_Dipt"
# Taxa Data
myDF_Bugs_MBSS <- data_benthos_MBSS
myDF_Bugs_MBSS$NONTARGET <- FALSE
myDF_Bugs_MBSS$INDEX_CLASS <- toupper(myDF_Bugs_MBSS$INDEX_CLASS)
myDF_Bugs_MBSS$INDEX_NAME <- myIndex
myDF_Bugs_MBSS$EXCLUDE <- myDF_Bugs_MBSS$EXCLUDE=="Y"
myMetric_Values_Bugs_MBSS <- metric.values(myDF_Bugs_MBSS
, "bugs"
, myMetrics_Bugs_MBSS)
#> Warning: EXCLUDE column does not have any TRUE values.
#> Valid values are TRUE or FALSE.
#> Other values are not recognized.
#> Joining with `by = join_by(SAMPLEID, INDEX_NAME, INDEX_CLASS)`
if (FALSE) { # \dontrun{
View(myMetric_Values_Bugs_MBSS)
} # }
# SCORE
myMetric_Values_Bugs_MBSS$INDEX_CLASS <- toupper(myMetric_Values_Bugs_MBSS$INDEX_CLASS)
Metrics_Bugs_Scores_MBSS <- metric.scores(myMetric_Values_Bugs_MBSS
, myMetrics_Bugs_MBSS
, "INDEX_NAME"
, "INDEX_CLASS"
, df_thresh_metric
, df_thresh_index)
if (FALSE) { # \dontrun{
# View Results
View(Metrics_Bugs_Scores_MBSS)
} # }
# QC Index Scores and Narratives
# Set Narrative as Ordered Factor
Nar_MBSS <- c("Very Poor", "Poor", "Fair", "Good")
Metrics_Bugs_Scores_MBSS$Index_Nar <- factor(Metrics_Bugs_Scores_MBSS$Index_Nar
, levels=Nar_MBSS
, labels=Nar_MBSS
, ordered=TRUE)
table(Metrics_Bugs_Scores_MBSS$Index, Metrics_Bugs_Scores_MBSS$Index_Nar, useNA="ifany")
#>
#> Very Poor Poor Fair Good
#> 1 4 0 0 0
#> 1.28571428571429 3 0 0 0
#> 1.33333333333333 8 0 0 0
#> 1.5 6 0 0 0
#> 1.57142857142857 3 0 0 0
#> 1.66666666666667 11 0 0 0
#> 1.75 7 0 0 0
#> 1.85714285714286 6 0 0 0
#> 2 0 14 0 0
#> 2.14285714285714 0 6 0 0
#> 2.25 0 5 0 0
#> 2.33333333333333 0 9 0 0
#> 2.42857142857143 0 6 0 0
#> 2.5 0 3 0 0
#> 2.66666666666667 0 13 0 0
#> 2.71428571428571 0 5 0 0
#> 2.75 0 3 0 0
#> 3 0 0 18 0
#> 3.25 0 0 6 0
#> 3.28571428571429 0 0 2 0
#> 3.33333333333333 0 0 13 0
#> 3.5 0 0 5 0
#> 3.57142857142857 0 0 2 0
#> 3.66666666666667 0 0 12 0
#> 3.75 0 0 6 0
#> 3.85714285714286 0 0 6 0
#> 4 0 0 0 3
#> 4.14285714285714 0 0 0 10
#> 4.25 0 0 0 5
#> 4.33333333333333 0 0 0 6
#> 4.42857142857143 0 0 0 4
#> 4.5 0 0 0 1
# QC bug count (manual)
Metrics_Bugs_Scores_MBSS[Metrics_Bugs_Scores_MBSS[,"ni_total"]>120,
"QC_Count"] <- "LARGE"
Metrics_Bugs_Scores_MBSS[Metrics_Bugs_Scores_MBSS[,"ni_total"]<60,
"QC_Count"] <- "SMALL"
Metrics_Bugs_Scores_MBSS[is.na(Metrics_Bugs_Scores_MBSS[,"QC_Count"]),
"QC_Count"] <- "OK"
# table of QC_Count
table(Metrics_Bugs_Scores_MBSS$QC_Count)
#>
#> LARGE OK
#> 90 121
# QC bug count (with function)
# Import Checks
#df_checks <- read_excel(system.file("./extdata/MetricFlags.xlsx"
# , package="BioMonTools"), sheet="Flags")
# Run Function
#df_flags <- qc.checks(Metrics_Bugs_Scores_MBSS, df_checks)
# Summarize Results
# table(df_flags[,"CHECKNAME"], df_flags[,"FLAG"], useNA="ifany")