In ecodata1/hera: Building Environmental Models Together

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  echo = FALSE
)
library(hera)
library(dplyr)
library(purrr)
library(tidyr)
library(tibble)
library(magrittr)
library(testthat)
library(visNetwork)

Welcome

This document has been created following the generic assessment guidance.

Description

Basic details about the assessment. Update the 'response' values as required.

description <- tribble(
  ~question, ~response,
  "name_short", "Macroinvertebrate Metrics",
  "name_long", "Freshwater Macroinvertebrate Metrics",
  "parameter", "River Invertebrates",
  "status", "prototype",
  "type", "metric"
)

knitr::kable(description)

Input

A list of questions required to run the assessment.

input <- tribble(
  ~sample_id,
  ~question,
  ~response,
  ~label,
  ~parameter,
  ~type,
  ~max,
  ~min,
  ~source,
  "12345",
  "Taxon abundance",
  "12",
  "Baetidae",
  "River Family Inverts",
  "number",
  NA,
  NA,
  "sepa_ecology_results",
  "12345",
  "Live abundance",
  "12",
  "Baetidae",
  "BANKSIDE_INVERTS",
  "number",
  NA,
  NA,
  "sepa_ecology_results"
)

data <- input
data <- select(data, question, response)
data <- data[!duplicated(data[, c("question")]), ]

knitr::kable(data)

Assessment

If applicable, write a function to assess your input data and return an outcome. For example, a metric, statistic, prediction etc.

assessment_function <- function(data, ...) {
  # Calculated invert metrics...
  # Note, any non-standard base R library must be call using require().
  require(dplyr)
  require(whpt)
  require(macroinvertebrateMetrics)
  input <- data
  input$label <- trimws(input$label)
  input <- dplyr::filter(
    input,
    parameter %in% c(
      "River Family Inverts",
      "BANKSIDE_INVERTS"
    )
  )
  input <- dplyr::filter(input, question %in% c(
    "Taxon abundance",
    "Live abundance"
  ))
   if(nrow(input) < 1) {
     return(NULL)
   }
  input <- ungroup(input)
  input <- dplyr::select(
    input, "sample_id", "question", "response", "label", "parameter"
  )

  output <- macroinvertebrateMetrics::calc_metric(input)

  # riverfly <- macroinvertebrateMetrics::calc_riverfly(input)

  # output <- dplyr::bind_rows(whpt, riverfly)

  output <- dplyr::select(
    output, "sample_id", "question", "response","parameter"

  )
  names(output) <- c("sample_id", "question", "response","parameter")
  return(output)
}

Outcome

The outcome of your assessment.

# data <- get_data(location_id = 8175)
# outcome <- assessment_function(data)
outcome <- assessment_function(input)
outcome_table <- select(outcome, question, response)
outcome_table <- outcome_table[!duplicated(outcome_table[, c("question")]), ]
knitr::kable(outcome_table)

Check

Run checks on the assessment.

# No need to edit this code
# Format description
standard_format <- hera:::hera_format(description = description)
# Check description
check_list <- hera:::hera_test(description = description)
knitr::kable(check_list$standard_check)

Update

Update the catalogue of assessments to make them available.

# No need to edit this code
hera:::update_catalogue(
  description = description,
  input = input,
  assessment_function = assessment_function,
  output = outcome
)

After updating the catalogue, rebuild the package, click on Build > Install and Restart menu or 'Install and Restart' button in the Build pane.

Test

This section tests if this assessment is usable using assessment function.

# No need to edit this code
# assess(
#   data = hera::demo_data,
#   name = description$response[description$question == "name_short"]
# )

Launch app

Below is an interactive application displaying the results of your assessment.

# No need to edit this code
# launch_app(new_catalogue = catalogue, data = data)

ecodata1/hera documentation built on April 5, 2025, 1:48 a.m.