Apply "QC checks" on calculated metrics and station/sample attributes to "flag" samples for the user. Examples include watershed size or total number of individuals. Can have checks for both high and low values. Checks are stored in separate file. For structure see df.checks in example.
qc.checks(df.metrics, df.checks, input.shape = "wide")Wide data frame with metric values to be evaluated.
Data frame of metric thresholds to check.
Shape of df.metrics; wide or long. Default is wide.
Returns a data frame of SampleID checks and results; Pass and Fail.
used reshape2 package
library(readxl)
# Calculate Metrics
df.samps.bugs <- 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
myDF <- df.samps.bugs
df.metric.values.bugs <- metric.values(myDF, "bugs", fun.cols2keep=myCols)
#> Updated col class; TOLVAL2 to numeric
#> Joining with `by = join_by(SAMPLEID, INDEX_NAME, INDEX_CLASS)`
# Import Checks
df.checks <- read_excel(system.file("./extdata/MetricFlags.xlsx"
, package="BioMonTools")
, sheet="Flags")
if (FALSE) {
# View Checks
View(df.checks)
}
# Run Function
df.flags <- qc.checks(df.metric.values.bugs, df.checks)
# Summarize Results
table(df.flags[,"CHECKNAME"], df.flags[,"FLAG"], useNA="ifany")
#>
#> FAIL PASS <NA>
#> Low density (ft2) 0 0 678
#> Low density (m2) 0 0 678
#> Ramellogammarus 8 670 0
#> brackish organisms present 7 671 0
#> catchment, Large 0 678 0
#> catchment, small 158 520 0
#> individuals, Large 0 678 0
#> individuals, dominant 02, Large 204 474 0
#> individuals, small 80 598 0
#> surface area, small 112 556 10