# Searching ECOTOX

    library(ECOTOXr)

## Searching the local database

Obviously, searching the local database is only possible after the download and build is ready (see `?download_ecotox_data` or `?build_ecotox_sqlite`). Once built, it can be queried with `?search_ecotox`.

> Search the local database for tests of water flea Daphnia magna exposed to benzene

    search_ecotox(
      list(
        latin_name    = list(terms = "Daphnia magna", method = "exact"),
        chemical_name = list(terms = "benzene",       method = "exact")
      )
    )

### Three ways of querying the local database

Let’s have a look at 3 different approaches for retrieving a specific record from the local database, using the unique identifier `result_id`. The first option is to use the build in `search_ecotox` function. It uses simple `R` syntax and allows you to search and collect any field from any table in the database. Furthermore, all requested output fields are automatically joined to the result without the end-user needing to know much about the database structure.

> Using the prefab function `search_ecotox` packaged by `ECOTOXr`

    search_ecotox(
      list(
        result_id = list(terms = "401386", method = "exact")
      ),
      as_data_frame = FALSE
    )

The approach shown above is provided for the user’s convenience. However, it is a relatively blunt instrument. It’s a hammer, which is fine when you have nails, but less effective when you have screws. In most cases it is more efficient to create a custom query for you specific needs. In those cases you can use two different strategies, involving either `tidyverse` verbs or the simple query language (SQL).

If you like to use `dplyr` verbs, you are in luck. SQLite database can be approached using `dplyr` verbs. This approach will only return information from the `results` table. The end-user will have to join other information (like test species and test substance) manually. This does require knowledge of the database structure (see `vignette("ecotox-schema")`).

> Using `dplyr` verbs

    con <- dbConnectEcotox()
    dplyr::tbl(con, "results") |>
      dplyr::filter(result_id == "401386") |>
      dplyr::collect()

If you prefer working using `SQL` directly, that is fine too. The `RSQLite` package allows you to get queries using `SQL` statements. The result is identical to that of the previous approach. Here too the end-user needs knowledge of the database structure in order to join additional data. For more details see `vignette("ecotox-schema")`, it shows the database structure and provides clues on how to construct custom queries with `dplyr`.

> Using `SQL` syntax

    dbGetQuery(con, "SELECT * FROM results WHERE result_id='401386'") |>
      dplyr::as_tibble()

All three approaches shown above generate identical results. Although, the first has additional information joined automatically.

### The curious case of chemical CAS numbers

This example shows a pitfall when using the generic `search_ecotox()` function, and how to deal with it. In the database, chemicals are stored with the CAS Registry number as unique identifier. It is stored in a table named `"chemicals"` in a field named `"cas_number"`. When you are searching the database for a particular chemical, based on its CAS registry number, one may be tempted to search for a matching `"cas_number"` field. That would be a mistake, and this is why.

There are multiple tables associated with the CAS registry number. Both the table `"chemical_carriers"` and `"tests"` rely on data from the `"chemicals"` table, and both use the CAS registry number to refer to it. You can see this in the schema of the database (`vignette("ecotox-schema")`). So when you are searching for a matching `"cas_number"` field, the `search_ecotox()` routine doesn’t know if you are looking for a test chemical, or a chemical carrier.

The solution? Most frequently, people search for test chemicals. So, search explicitly for a matching `"test_cas"` field in the `"tests"` table. This is shown in the code snippet below.

    search_ecotox(
      list(
        test_cas   = list(terms = "71432",         method = "exact"),
        latin_name = list(terms = "Daphnia magna", method = "exact")
      )
    )

## Searching the online database

You can also use the package to search using the online webform:

    tryCatch({
      search_fields <-
        list_ecotox_web_fields(
          txAdvancedSpecEntries     = "daphnia magna",
          RBSPECSEARCHTYPE          = "EXACT",
          txAdvancedChemicalEntries = "benzene",
          RBCHEMSEARCHTYPE          = "EXACT")

      search_results <- websearch_ecotox(search_fields, verify_ssl = FALSE)

      search_results$`Aquatic-Export`
    })

    #> # A tibble: 56 × 87
    #>    `CAS Number` `Chemical Name` `Chemical Grade` `Chemical Analysis`
    #>           <dbl> <chr>           <chr>            <chr>
    #>  1        71432 Benzene         NA               NA
    #>  2        71432 Benzene         NA               NA
    #>  3        71432 Benzene         NA               Unmeasured
    #>  4        71432 Benzene         NA               Unmeasured
    #>  5        71432 Benzene         NA               Unmeasured
    #>  6        71432 Benzene         NA               Unmeasured
    #>  7        71432 Benzene         NA               Unmeasured
    #>  8        71432 Benzene         NA               Measured
    #>  9        71432 Benzene         NA               Unmeasured
    #> 10        71432 Benzene         NA               Unmeasured
    #> # ℹ 46 more rows
    #> # ℹ 83 more variables: `Chemical Purity Mean Op` <chr>,
    #> #   `Chemical Purity Mean(%)` <dbl>, `Chemical Purity Min Op` <lgl>,
    #> #   `Chemical Purity Min(%)` <lgl>, `Chemical Purity Max Op` <lgl>,
    #> #   `Chemical Purity Max(%)` <lgl>, `Species Scientific Name` <chr>,
    #> #   `Species Common Name` <chr>, `Species Group` <chr>,
    #> #   `Organism Lifestage` <chr>, `Organism Age Mean Op` <chr>, …

When aiming for reproducibility it is better to query the local database, as the online tool may not be able to search and export all fields; data may change and is beyond your control; and it is processed on the server which forms a black box. See also `vignette("reproducibility")`.