Welcome
This document has been created following the generic assessment guidance.
This is the Rapid Assessment Technique (RAT) for results from bankside invertebrates analysis to check consistency with Water Body Status.
Description
Basic details about the assessment.
| question | response |
|---|---|
| name_short | Bankside Consistency |
| name_long | Rapid Assessment of Bankside Consistency with Water Body Status |
| parameter | River Family Inverts |
| status | testing |
| type | metric |
Input
A list of questions required to run the assessment.
| question | response |
|---|---|
| Taxon abundance | 12 |
| Live abundance | 21 |
Assessment
Function code used to run the metric.
Code
assessment_function <- function(data, ...) {
# Some calculated a statistic...
# Note, any non-standard base R library must be call using require().
require(dplyr)
require(tidyr)
require(magrittr)
require(tibble)
require(whpt)
require(macroinvertebrateMetrics)
data$date_taken <- as.character(format.Date(data$date_taken, "%Y/%m/%d"))
catalogue <- hera::catalogue
metric_function <- catalogue[catalogue$assessment ==
"Macroinvertebrate Metrics", 3][[1]]
output <- metric_function[[1]](data)
output <- filter(output, question %in% c("WHPT_ASPT", "WHPT_NTAXA"))
predictors <- utils::read.csv(system.file("extdat",
"predictors.csv",
package = "whpt"
), check.names = FALSE)
# Downgrade Typical class for testing worst-case scenario
# predictors$`Typical ASPT Class`[predictors$`Typical ASPT Class` == "Poor"] <- "Bad"
# predictors$`Typical ASPT Class`[predictors$`Typical ASPT Class` == "Moderate"] <- "Poor"
# predictors$`Typical ASPT Class`[predictors$`Typical ASPT Class` == "Good"] <- "Moderate"
# predictors$`Typical ASPT Class`[predictors$`Typical ASPT Class` == "Good"] <- "Moderate"
# predictors$`Typical ASPT Class`[predictors$`Typical ASPT Class` == "High"] <- "Good"
#
#
# predictors$`Typical NTAXA Class`[predictors$`Typical NTAXA Class` == "Poor"] <- "Bad"
# predictors$`Typical NTAXA Class`[predictors$`Typical NTAXA Class` == "Moderate"] <- "Poor"
# predictors$`Typical NTAXA Class`[predictors$`Typical NTAXA Class` == "Good"] <- "Moderate"
# predictors$`Typical NTAXA Class`[predictors$`Typical NTAXA Class` == "Good"] <- "Moderate"
# predictors$`Typical NTAXA Class`[predictors$`Typical NTAXA Class` == "High"] <- "Good"
predictors$location_id <- as.character(predictors$location_id)
predict_data <- filter(predictors, location_id %in% unique(data$location_id))
output_location <- inner_join(output,
data[, c(
"location_id",
"sample_id",
"date_taken"
)],
by = "sample_id",
relationship = "many-to-many"
)
output_location$location_id <-
as.character(output_location$location_id)
whpt_input <- inner_join(output_location,
predict_data,
by = "location_id"
)
whpt_input$question[whpt_input$question == "WHPT_ASPT"] <-
"WHPT ASPT Abund"
whpt_input$question[whpt_input$question == "WHPT_NTAXA"] <-
"WHPT NTAXA Abund"
if (nrow(whpt_input) < 1) {
return(NULL)
} else {
whpt_input <- unique(whpt_input)
whpt_input$response <- as.numeric(whpt_input$response)
consistency_check <- whpt::whpts(whpt_input)
consistency_check$response <-
as.character(consistency_check$response)
}
report <- tidyr::pivot_wider(consistency_check, names_from = question, values_from = response)
vars <- c("location_id", "location_description", "sample_id", "date_taken")
location_ids <- dplyr::select(data, any_of(vars)) %>% unique()
location_ids$season <- hera:::season(location_ids$date_taken, output = "shortname")
report <- inner_join(report, location_ids, by = join_by(sample_id))
new_predictors <- read.csv(
system.file("extdat", "predictors.csv", package = "whpt"),
check.names = FALSE)
report <- dplyr::inner_join(report, new_predictors, by = join_by(location_id))
whpt_wide <- tidyr::pivot_wider(output, names_from = question, values_from = response)
report <- dplyr::inner_join(report, whpt_wide, by = join_by(sample_id))
vars <- c(
"water body sampled",
"sample_id",
"date_taken",
"location_id",
"location_description",
"season",
"Reference NTAXA",
"Reference ASPT",
"assessment",
"driver",
"WHPT_NTAXA",
"WHPT_ASPT",
"Typical ASPT Class",
"Typical NTAXA Class",
"Reported WHPT Class Year"
)
report <- dplyr::select(report, any_of(vars))
vars <- c(
"season",
"Reference NTAXA",
"Reference ASPT",
"assessment",
"driver",
"WHPT_NTAXA",
"WHPT_ASPT",
"Typical ASPT Class",
"Typical NTAXA Class",
"Reported WHPT Class Year",
"water body sampled"
)
report$`water body sampled` <- as.character(report$`water body sampled`)
report$`Reported WHPT Class Year` <- as.character(report$`Reported WHPT Class Year`)
consistency_check <- pivot_longer(report, cols = all_of(vars), names_to = "question", values_to = "response")
consistency_check$date_taken <- as.Date(consistency_check$date_taken)
consistency_check$parameter <- "Bankside Consistency"
return(consistency_check)
}Outcome
The outcome of your assessment.
| question | response |
|---|---|
| season | AUT |
| Reference NTAXA | 21.14 |
| Reference ASPT | 7.32 |
| assessment | Likely problem detected |
| driver | ntaxa |
| WHPT_NTAXA | 1 |
| WHPT_ASPT | 5.9 |
| Typical ASPT Class | Poor |
| Typical NTAXA Class | Poor |
| Reported WHPT Class Year | 2013 |
| water body sampled | 23020 |
Check
Run checks on the assessment.
#> Test passed 🥇
#> Test passed 🎉| check | value |
|---|---|
| standard_names | TRUE |
| standard_required | TRUE |
| standard_required_values | TRUE |
Update
Update the catalogue of assessments to make them available.
After updating the catalogue, rebuild the package, click on Build > Install and Restart menu or ‘Install and Restart’ button in the Build pane.