---
title: "ECOTOX database schema"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{ECOTOX database schema}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

Writing your own queries for the local ECOTOX database is often much more effective
than applying the one-size-fits-all function `search_ecotox()`. But writing custom
queries requires your to understand the structure of the ECOTOX database.

## The database's relational structure

The diagram below shows which tables are available in the ECOTOX database and how they
are related. The arrows are labelled with the key field that connects the two tables.
The structure may seem a bit intimidating but once you realise which tables are most
relevant, you can screen the diagram with more focus. First of all, tables containing
actual data are coloured light yellow. All other tables (coloured light blue) contain
lookup information. In general most users will be interested in the 'tests' table
(describing test setup) and the 'results' table (describing the test results).

<button onclick="diagzoom('0.00 0.00 3012.00 2028.00')" class="bt">Overview</button>
<button onclick="diagzoom('0.00 0.00 1506.00 1014.00')" class="bt">Magnified</button>

```{r, echo=FALSE, results='asis'}
htmltools::tags$div(
  htmltools::includeHTML("../man/figures/ecotox-schema.svg"),
  onmousemove = "mousemove_schema();",
  style = "position: relative; width: 100%; height: 400px; overflow: scroll;"
)
```

```{js, echo=FALSE}
function findAncestor (el, cls) {
  if (el.classList.contains(cls)) return el;
  while ((el = el.parentElement) && !el.classList.contains(cls));
  return el;
}

function mousemove_schema() {
  var e = window.event;
  var posX = e.clientX;
  var posY = e.clientY;
  var el = document.elementFromPoint(posX, posY);
  var ancest = findAncestor(el, "schema_any");
  if (ancest) {
    var cl = ancest.classList;
    for (i = 0; i < cl.length; i ++) {
      if (cl[i].startsWith("schema_") && cl[i] != "schema_any") {
        var pols_all = document.getElementById("ecotoxschema").getElementsByClassName("node");
        if (pols_all) {
          for (j = 0; j < pols_all.length; j++) {
            if (pols_all[j].classList.contains(cl[i])) {
              pols_all[j].getElementsByTagName("polygon")[0].setAttribute("fill", "pink");
            } else {
              if (pols_all[j].classList.contains("eco_Data")) {
                pols_all[j].getElementsByTagName("polygon")[0].setAttribute("fill", "lightyellow");
              } else {
                pols_all[j].getElementsByTagName("polygon")[0].setAttribute("fill", "lightblue");
              }
            }
          }
        }
        var edge_all = document.getElementById("ecotoxschema").getElementsByClassName("edge");
        if (edge_all) {
          for (j = 0; j < edge_all.length; j++) {
            if (edge_all[j].classList.contains(cl[i])) {
              edge_all[j].getElementsByTagName("path")[0].setAttribute("stroke", "red");
              edge_all[j].getElementsByTagName("polygon")[0].setAttribute("fill", "red");
            } else {
              edge_all[j].getElementsByTagName("path")[0].setAttribute("stroke", "#DDDDDD");
              edge_all[j].getElementsByTagName("polygon")[0].setAttribute("fill", "#DDDDDD");
            }
          }
        }
      }
    }
  }
}

const svgImage=document.getElementById("ecotoxschema");
function diagzoom(val){
  svgImage.setAttribute("viewBox",val);
}
```

You can also list the tables in the database using `DBI::dbListTables()`. To list table fields,
you can use `list_ecotox_fields("all", FALSE)`.

When constructing a query, you should ask yourself what information do I wish to retrieve,
and how do I restrict/filter the data to the scope of your research? For that purpose,
first focus on the yellow coloured tables, then join information from the lookup tables
if required for your purpose.

## Setting up queries: some examples

For setting up custom queries, I recommend to use [tidyverse](https://www.tidyverse.org/)
packages like [dplyr](https://dplyr.tidyverse.org/). This is why in the following examples
the `dplyr` package is used to construct queries. The examples intend to show how the
diagram above can be used to collect information from the database.

### All test results for tests carried out with Insecta

In this case you want to restrict your data to species that are in the class of Insecta.

```{r, eval = FALSE}
library(dplyr)

if (check_ecotox_availability()) {
  con <- dbConnectEcotox()
  
  insects <-
    tbl(con, "species") |>
    filter(class == "Insecta") |>
    ## Specify which fields you want from the table.
    ## Make sure to include the key that links with
    ## other tables ('species_number')
    select(species_number, common_name, latin_name)
  
}
```

As you can see in the diagram of the database schema, the table 'species'
is not directly linked to results. Instead, it is linked to the table
'tests' which in turn is linked to 'results'. This means you have to join
the data to these tables in this order, using the appropriate keys.

```{r, eval = FALSE}
if (check_ecotox_availability()) {
  results <-
    insects |>
    
    ## First join the 'tests' table
    
    left_join(
      tbl(con, "tests") |> select(test_id, species_number),
      by = "species_number"
    ) |>
    
    ## Then join the 'results' table

    left_join(
      tbl(con, "results"),
      by = "test_id"
    ) |>
    
    ## dplyr performs queries lazily, call
    ## 'collect' to actually get the results
    
    collect()

  close(con)
}
```

### A random substance and random species

Although not a very realistic case, this example shows how you can
pick a chemical and a species and see if there are any test results
available for their combination. Note that the chance that any
random chemical is tested with any random species is very small.
Therefore, in most cases the example will yield zero test results.

The point of this example is that you can elegantly use the different
join types (like left, right, inner and anti; see `?dplyr::join`) to
efficiently join tables from the database. Note that `search_ecotox()`
creates a query based on the provided search terms and output fields,
and 'blindly' uses many left joins to get the information from the
database. For complex queries, this is not a very efficient method to
obtain data from the database.

```{r eval = FALSE}
if (check_ecotox_availability()) {
  con <- dbConnectEcotox()
  
  ## pick a random species
  species <-
    tbl(con, "species") |>
    mutate(random = runif(n())) |>
    slice_min(n = 1, order_by = random) |>
    select(!random)
  
  ## pick a random chemical
  chem <-
    tbl(con, "chemicals") |>
    rename(test_cas = "cas_number") |>
    mutate(random = runif(n())) |>
    slice_min(n = 1, order_by = random) |>
    select(!random)

  ## join the randomly picked species and test chemical
  ## with the 'tests' and 'results' table  
  results <-
    tbl(con, "tests") |>
    select(test_id, test_cas, species_number) |>
    right_join(species,
               by = "species_number") |>
    right_join(chem,
               by = "test_cas") |>
    inner_join(tbl(con, "results") |>
                 select(1:10),
               by = "test_id") |>
    collect()
  
  close(con)
}
```

### Dose information

Not all information has one-to-one or many-to-one relationship. In the case of dose,
there is a many-to-many relationship with tests and therewith test results. This means
if the dose information from one test is joined with test results, like in the example
below.

```{r eval = FALSE}
if (check_ecotox_availability()) {
  con <- dbConnectEcotox()
  
  dose_info <-
    tbl(con, "doses") |>
    ## Let's select the dose information for the first test in the database
    filter(test_id == 1) |>
    left_join(tbl(con, "dose_response_details"), by = "dose_id") |>
    left_join(tbl(con, "dose_responses"), by = c("dose_resp_id", "test_id")) |>
    left_join(tbl(con, "dose_response_links"), by = "dose_resp_id") |>
    left_join(tbl(con, "results"), by = c("result_id", "test_id")) |>
    collect()

  close(con)
}
```

Indeed, the query below yields multiple doses for a single test result. Depending
on how you wish to further process the data, you may want to: aggregate; nest; or pivot
the dose information to single test results. When using `search_ecotox()`, the dose
information is automatically nested:

```{r, eval = FALSE}
## In this example dose information is automatically nested
if (check_ecotox_availability()) {
  doses <-
    search_ecotox(
      list(
        result_id = list(
          terms = 1182449,
          method = "exact")),
      output_fields = c("results.result_id", "doses.dose_number"))
}
```

## Automatically generating queries

With a call to `search_query_ecotox()` you can retrieve the SQL query
underpinning a search using `search_ecotox()`. As pointed out earlier,
this method is very generic and may not provide the most elegant query.
But it can be used for inspiration and understanding the database.

## Overview of tables and fields

The table below lists all tables in the database and its fields. Primary and foreign
keys are in **bold** face. Use the [diagram](#ecotoxschema) to see how the tables are linked with these
keys. The table below only lists the available fields in each table. Use the EPA ECOTOX
documentation provided with the downloaded data for detailed information on the fields.

```{r tab-dif, echo=FALSE, message=FALSE, warning=FALSE}
library(kableExtra)
library(dplyr)
ECOTOXr:::.db_specs |>
  mutate(
    table = sprintf("<div id='ec_%s'>%s</div>", .data$table, .data$table),
    field_name = ifelse(.data$primary_key != "" | .data$foreign_key != "",
                        sprintf("**%s**", .data$field_name), .data$field_name)
  ) |>
  select(table, fields = "field_name") |>
  kbl(escape = FALSE) |>
  collapse_rows(valign = "top")
```
