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ECOTOXr

Harness information from the US EPA ECOTOXicology Knowledgebase

Overview

ECOTOXr can be used to explore and analyse data from the US EPA ECOTOX database. More specifically you can:

  • Build a local SQLite copy of the US EPA ECOTOX database
  • Search and extract data from the local database
  • Use experimental features to search the on-line dashboards: ECOTOX and CompTox

Why use ECOTOXr?

The ECOTOXr package allows you to search and extract data from the ECOTOXicological Knowledgebase and import it directly into R. This will allow you to formalize and document the search- and extract-procedures in R code. This makes it easier to share and reproduce such procedures and its results. Moreover, you can directly apply any statistical analysis offered in R.

Installation

Get CRAN version

install.packages("ECOTOXr")

Get development version from r-universe

install.packages("ECOTOXr", repos = c("https://pepijn-devries.r-universe.dev", "https://cloud.r-project.org"))

Usage

Preparing the database

Although ECOTOXr has experimental features to search the on-line database. The package will reach its full potential when you build a copy of the database on your local machine.

Download and build a local copy of the latest ASCII export of the US EPA ECOTOX database

Searching the local database for species and substances

Obviously, searching the local database is only possible after the download and build is ready (see previous section).

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 anything 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 = F
)
#> 'dose_responses.response_site' was renamed 'dose_link_response_site'
#> 'chemicals.cas_number' was renamed 'test_cas'
#> 'chemicals.chemical_name' was renamed 'test_chemical'
#> 'dose_responses.dose_resp_id' was renamed 'dose_link_dose_resp_id'
#> # A tibble: 1 × 98
#>   test_cas test_grade test_grade_comments test_purity_mean_op test_purity_mean
#> *    <int> <chr>      <chr>               <chr>               <chr>           
#> 1    71432 NR         ""                  ""                  NR              
#> # ℹ 93 more variables: test_purity_min_op <chr>, test_purity_min <chr>,
#> #   test_purity_max_op <chr>, test_purity_max <chr>,
#> #   test_purity_comments <chr>, organism_lifestage <chr>,
#> #   organism_age_mean_op <chr>, organism_age_mean <chr>,
#> #   organism_age_min_op <chr>, organism_age_min <chr>,
#> #   organism_age_max_op <chr>, organism_age_max <chr>,
#> #   exposure_duration_mean_op <chr>, exposure_duration_mean <chr>, …

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.

Using dplyr verbs

con <- dbConnectEcotox()
dplyr::tbl(con, "results") |>
  dplyr::filter(result_id == "401386") |>
  dplyr::collect()
#> # A tibble: 1 × 137
#>   result_id test_id sample_size_mean_op sample_size_mean sample_size_min_op
#>       <int>   <int> <chr>               <chr>            <chr>             
#> 1    401386 1020021 ""                  NC               ""                
#> # ℹ 132 more variables: sample_size_min <chr>, sample_size_max_op <chr>,
#> #   sample_size_max <chr>, sample_size_unit <chr>, sample_size_comments <chr>,
#> #   obs_duration_mean_op <chr>, obs_duration_mean <chr>,
#> #   obs_duration_min_op <chr>, obs_duration_min <chr>,
#> #   obs_duration_max_op <chr>, obs_duration_max <chr>, obs_duration_unit <chr>,
#> #   obs_duration_comments <chr>, endpoint <chr>, endpoint_comments <chr>,
#> #   trend <chr>, effect <chr>, effect_comments <chr>, measurement <chr>, …

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.

Using SQL syntax

dbGetQuery(con, "SELECT * FROM results WHERE result_id='401386'") |>
  dplyr::as_tibble()
#> # A tibble: 1 × 137
#>   result_id test_id sample_size_mean_op sample_size_mean sample_size_min_op
#>       <int>   <int> <chr>               <chr>            <chr>             
#> 1    401386 1020021 ""                  NC               ""                
#> # ℹ 132 more variables: sample_size_min <chr>, sample_size_max_op <chr>,
#> #   sample_size_max <chr>, sample_size_unit <chr>, sample_size_comments <chr>,
#> #   obs_duration_mean_op <chr>, obs_duration_mean <chr>,
#> #   obs_duration_min_op <chr>, obs_duration_min <chr>,
#> #   obs_duration_max_op <chr>, obs_duration_max <chr>, obs_duration_unit <chr>,
#> #   obs_duration_comments <chr>, endpoint <chr>, endpoint_comments <chr>,
#> #   trend <chr>, effect <chr>, effect_comments <chr>, measurement <chr>, …

Disclaimers

It is the end-users own responsibility to check the quality of collected data, using the original referenced source in order to evaluate its fitness for use, see also: https://cfpub.epa.gov/ecotox/help.cfm#info-limitations.

Note that the package maintainer is not affiliated with the US EPA, this package is therefore not official US EPA software.

Resources