In ecodata1/hera: Building Environmental Models Together

knitr::opts_chunk$set(echo = TRUE)
library(hera)
library(dplyr)
library(purrr)
library(tidyr)

Quick Start

To add a new assessment, install hera package.

install.packages("devtools")
devtools::install_github("aquaMetrics/hera", dependencies = TRUE)
library(hera)

1. Open a new File > New File > R Markdown
2. Select 'From Template'
3. Search for 'Assessment Template' and open
4. Change the title: "Trophic Diatom Index" title to match your assessment name
5. Change the vignette entry %\VignetteIndexEntry{Trophic Diatom Index} to match the name of your assessment.
6. Save the document using name-of-your-assessment format to the package vignette folder.

Congratulations! You now have a new assessment template ready to populate.

Complete the following sections.

Description

The first piece of information to add is details on what your assessment is called and its status. This table shows the options to to choose from and which fields are required.

description <- tibble(
  Columns = c("name_short", "name_long", "parameter", "status"),
  Required = c(TRUE, TRUE, TRUE, FALSE),
  Details = c(
    "Short name for assessment < 26 characters for example an abbrivation like TDI",
    "Long name for assessment",
    "Parameter or observation method being assessed for example Freshwater Diatoms, River Barriers or Estaurine Fish.",
    "Required - either: \'prototype\', \'development\',\'on-hold\', \'consultation\','deprecated\' or 'deployed\'"
  )
)
knitr::kable(description)

Example

The code below creates a tibble/dataframe called 'standard' containing metadata about the LEAFPACS parameter. This tibble will be later saved into the 'hera' package for future reference.

Code:

description <- tribble(
  ~question, ~response,
  "name_short", "LEAFPACS",
  "name_long", "UKTAG River Assessment Method Macrophytes and Phytobenthos",
  "parameter", "River Macrophytes",
  "status", "testing"
)

description

Input

Create a demo input data.

input_table <- tibble(
  Columns = c("sample_id", "question", "response", "label", "..."),
  Required = c(TRUE, TRUE, TRUE, FALSE, FALSE),
  Details = c(
    "Sample ID for example 1234523",
    "Question you have recorded a repsonse too",
    "Example response",
    "Some question also have an associated 'label' for instance Taxon abundance will have a label for the Taxon name - is not required enter NA",
    "..."
  )
)
knitr::kable(input_table)

For example:

input <- tibble(
  sample_id = c("12345", "12345"),
  question = c("Taxon abundance", "Alkalinity"),
  response = c("12", "45"),
  label = c("Gomphonema olivaceum", NA),
  parameter = c("River Diatoms", "Chemistry"),
  type = c("number", "number"),
  max = c(NA, NA),
  min = c(NA, NA),
  source = c("sepa_ecology_results", "location_attributes")
)
input

Assessment

This is where the magic happens...

Initially, it may be easier to write a placeholder for what you would expect the function to return. An example of this is given in the assessment template.

Your script must return a dataframe with three columns: sample_id, question and response.

For instance, questions could be EQR, Complaint, Inspection Outcome. And responses could be 0.6, TRUE, FAIL. sample_id will much your input data.

Using your input, write a function to assess your data. You may call on a web service, import data for elsewhere, or run script from another programming language (python, matlab etc).

Outcome

This section display your outcome from the assessemnt function to show a example outcome from your assessment.

Checklist

These final sections cover various check and test which don't need to be edited. However, they may indicate problems in your description, input, assessment or outcomes.

The data is formatted using the hera_format function to neatly present within the documentation for this assessment.The hera_format function will also check the data for errors or omissions.

standard_format <- hera:::hera_format(description = description) # format table
standard_format %>%
  knitr::kable()

Update

Access pre-existing metadata from within hera package. All metadata is saved in the catalogue nested dataframe.

You may need to unnest the list columns to see all the information.

catalogue %>%
  filter(assessment == "Example") %>%
  unnest(data)

Launch

TODO

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