# The New rfishbase

#### Carl Boettiger

#### 2019-06-27

Welcome to `rfishbase 3.0`. This package is the third rewrite of the original `rfishbase` package described in Boettiger et al. (2012).

`rfishbase` 3.0 queries pre-compressed tables from a static server and employs local caching (through memoization) to provide much greater performance and stability, particularly for dealing with large queries involving 10s of thousands of species. The user is never expected to deal with pagination or curl headers and timeouts.

We welcome any feedback, issues or questions that users may encounter through our issues tracker on GitHub: https://github.com/ropensci/rfishbase/issues

## Installation

    remotes::install_github("ropensci/rfishbase")

    library("rfishbase")
    library("dplyr") # convenient but not required

## Getting started

FishBase makes it relatively easy to look up a lot of information on most known species of fish. However, looking up a single bit of data, such as the estimated trophic level, for many different species becomes tedious very soon. This is a common reason for using `rfishbase`. As such, our first step is to assemble a good list of species we are interested in.

### Building a species list

Almost all functions in `rfishbase` take a list (character vector) of species scientific names, for example:

    fish <- c("Oreochromis niloticus", "Salmo trutta")

You can also read in a list of names from any existing data you are working with. When providing your own species list, you should always begin by validating the names. Taxonomy is a moving target, and this well help align the scientific names you are using with the names used by FishBase, and alert you to any potential issues:

    fish <- validate_names(c("Oreochromis niloticus", "Salmo trutta"))

Another typical use case is in wanting to collect information about all species in a particular taxonomic group, such as a Genus, Family or Order. The function `species_list` recognizes six taxonomic levels, and can help you generate a list of names of all species in a given group:

    fish <- species_list(Genus = "Labroides")
    fish

    [1] "Labroides dimidiatus"    "Labroides bicolor"
    [3] "Labroides pectoralis"    "Labroides phthirophagus"
    [5] "Labroides rubrolabiatus"

`rfishbase` also recognizes common names. When a common name refers to multiple species, all matching species are returned:

    trout <- common_to_sci("trout")
    trout

    # A tibble: 306 x 4
       Species                   ComName              Language SpecCode
       <chr>                     <chr>                <chr>       <dbl>
     1 Salmo obtusirostris       Adriatic trout       English      6210
     2 Schizothorax richardsonii Alawan snowtrout     English      8705
     3 Schizopyge niger          Alghad snowtrout     English     24454
     4 Salvelinus fontinalis     American brook trout English       246
     5 Salmo trutta              Amu-Darya trout      English       238
     6 Salmo kottelati           Antalya trout        English     67602
     7 Oncorhynchus apache       Apache Trout         English      2687
     8 Oncorhynchus apache       Apache trout         English      2687
     9 Plectropomus areolatus    Apricot trout        English      6082
    10 Salmo trutta              Aral Sea Trout       English       238
    # … with 296 more rows

Note that there is no need to validate names coming from `common_to_sci` or `species_list`, as these will always return valid names.

### Getting data

With a species list in place, we are ready to query fishbase for data. Note that if you have a very long list of species, it is always a good idea to try out your intended functions with a subset of that list first to make sure everything is working.

The `species()` function returns a table containing much (but not all) of the information found on the summary or homepage for a species on fishbase.org. `rfishbase` functions always return tidy data tables: rows are observations (e.g. a species, individual samples from a species) and columns are variables (fields).

    species(trout$Species)

    # A tibble: 306 x 100
       SpecCode Species SpeciesRefNo Author FBname PicPreferredName
          <dbl> <chr>          <dbl> <chr>  <chr>  <chr>
     1     6210 Salmo …        59043 (Heck… Adria… Saobt_u0.jpg
     2     8705 Schizo…         4832 (Gray… Snowt… Scric_u1.jpg
     3    24454 Schizo…         4832 (Heck… Algha… <NA>
     4      246 Salvel…        86798 (Mitc… Brook… Safon_u4.jpg
     5      238 Salmo …         4779 Linna… Sea t… Satru_u2.jpg
     6    67602 Salmo …        99540 Turan… Antal… Sakot_m0.jpg
     7     2687 Oncorh…         5723 (Mill… Apach… Onapa_u0.jpg
     8     2687 Oncorh…         5723 (Mill… Apach… Onapa_u0.jpg
     9     6082 Plectr…         5222 (R<fc… Squar… Plare_u4.jpg
    10      238 Salmo …         4779 Linna… Sea t… Satru_u2.jpg
    # … with 296 more rows, and 94 more variables: PicPreferredNameM <chr>,
    #   PicPreferredNameF <chr>, PicPreferredNameJ <chr>, FamCode <dbl>,
    #   Subfamily <chr>, GenCode <dbl>, SubGenCode <dbl>, BodyShapeI <chr>,
    #   Source <chr>, AuthorRef <lgl>, Remark <chr>, TaxIssue <dbl>,
    #   Fresh <dbl>, Brack <dbl>, Saltwater <dbl>, DemersPelag <chr>,
    #   Amphibious <lgl>, AmphibiousRef <lgl>, AnaCat <chr>, MigratRef <dbl>,
    #   DepthRangeShallow <dbl>, DepthRangeDeep <dbl>, DepthRangeRef <dbl>,
    #   DepthRangeComShallow <dbl>, DepthRangeComDeep <dbl>,
    #   DepthComRef <dbl>, LongevityWild <dbl>, LongevityWildRef <dbl>,
    #   LongevityCaptive <dbl>, LongevityCapRef <dbl>, Vulnerability <dbl>,
    #   Length <dbl>, LTypeMaxM <chr>, LengthFemale <dbl>, LTypeMaxF <chr>,
    #   MaxLengthRef <dbl>, CommonLength <dbl>, LTypeComM <chr>,
    #   CommonLengthF <dbl>, LTypeComF <chr>, CommonLengthRef <dbl>,
    #   Weight <dbl>, WeightFemale <dbl>, MaxWeightRef <dbl>, Pic <chr>,
    #   PictureFemale <chr>, LarvaPic <chr>, EggPic <chr>,
    #   ImportanceRef <dbl>, Importance <chr>, PriceCateg <chr>,
    #   PriceReliability <chr>, Remarks7 <chr>, LandingStatistics <chr>,
    #   Landings <chr>, MainCatchingMethod <chr>, II <chr>, MSeines <dbl>,
    #   MGillnets <dbl>, MCastnets <dbl>, MTraps <dbl>, MSpears <dbl>,
    #   MTrawls <dbl>, MDredges <dbl>, MLiftnets <dbl>, MHooksLines <dbl>,
    #   MOther <dbl>, UsedforAquaculture <chr>, LifeCycle <chr>,
    #   AquacultureRef <dbl>, UsedasBait <chr>, BaitRef <dbl>, Aquarium <chr>,
    #   AquariumFishII <chr>, AquariumRef <dbl>, GameFish <dbl>,
    #   GameRef <dbl>, Dangerous <chr>, DangerousRef <dbl>,
    #   Electrogenic <chr>, ElectroRef <dbl>, Complete <lgl>,
    #   GoogleImage <dbl>, Comments <chr>, Profile <chr>, PD50 <dbl>,
    #   Emblematic <dbl>, Entered <dbl>, DateEntered <dttm>, Modified <dbl>,
    #   DateModified <dttm>, Expert <dbl>, DateChecked <dttm>, TS <lgl>

Most tables contain many fields. To avoid overly cluttering the screen, `rfishbase` displays tables as “tibbles” from the `dplyr` package. These act just like the familiar `data.frames` of base R except that they print to the screen in a more tidy fashion. Note that columns that cannot fit easily in the display are summarized below the table. This gives us an easy way to see what fields are available in a given table.

Most `rfishbase` functions will let the user subset these fields by listing them in the `fields` argument, for instance:

    dat <- species(trout$Species, fields=c("Species", "PriceCateg", "Vulnerability"))
    dat

    # A tibble: 306 x 3
       Species                   PriceCateg Vulnerability
       <chr>                     <chr>              <dbl>
     1 Salmo obtusirostris       very high           47.0
     2 Schizothorax richardsonii unknown             34.8
     3 Schizopyge niger          unknown             46.8
     4 Salvelinus fontinalis     very high           43.4
     5 Salmo trutta              very high           60.0
     6 Salmo kottelati           <NA>                33.7
     7 Oncorhynchus apache       very high           53.8
     8 Oncorhynchus apache       very high           53.8
     9 Plectropomus areolatus    very high           30.3
    10 Salmo trutta              very high           60.0
    # … with 296 more rows

Alternatively, just subset the table using the standard column selection in base R (`[[`) or `dplyr::select`.

### FishBase Docs: Discovering data

Unfortunately identifying what fields come from which tables is often a challenge. Each summary page on fishbase.org includes a list of additional tables with more information about species ecology, diet, occurrences, and many other things. `rfishbase` provides functions that correspond to most of these tables.

Because `rfishbase` accesses the back end database, it does not always line up with the web display. Frequently `rfishbase` functions will return more information than is available on the web versions of the these tables. Some information found on the summary homepage for a species is not available from the `species` summary function, but must be extracted from a different table. For instance, the species `Resilience` information is not one of the fields in the `species` summary table, despite appearing on the species homepage of fishbase.org. To discover which table this information is in, we can use the special `rfishbase` function `list_fields`, which will list all tables with a field matching the query string:

    list_fields("Resilience")

    # A tibble: 1 x 1
      table
      <chr>
    1 stocks

This shows us that this information appears on the `stocks` table. We can then request this data from the stocks table:

    stocks(trout$Species, fields=c("Species", "Resilience", "StockDefs"))

    # A tibble: 408 x 3
       Species           Resilience StockDefs
       <chr>             <chr>      <chr>
     1 Salmo obtusirost… Medium     Europe:  Adriatic basin in Krka, Jardo, Vr…
     2 Schizothorax ric… Medium     Asia:  Himalayan region of India, Sikkim a…
     3 Schizopyge niger  Medium     Asia:  Kashmir Valley in India and Azad Ka…
     4 Salvelinus fonti… Medium     North America:  native to most of eastern …
     5 Salmo trutta      Medium     Europe and Asia:  Atlantic, North, White a…
     6 Salmo trutta      <NA>       <i>Salmo trutta aralensis</i>:  Asia:  end…
     7 Salmo trutta      Medium     <i>Salmo trutta fario</i>:  Northeast  Atl…
     8 Salmo trutta      Low        "<i>Salmo trutta lacustris</i>\t:  Europe:…
     9 Salmo trutta      <NA>       "<i>Salmo trutta oxianus</i>\t:  Asia:  Am…
    10 Salmo trutta      <NA>       <i>Salmo trutta aralensis</i>:  Asia:  Ara…
    # … with 398 more rows

## Version stability

`rfishbase` relies on periodic cache releases. By default `rfishbase` will access the latest available release. Note that releases are roughly semi-annual and may not match the live version at Fishbase.org. You can view available releases with:

    available_releases()

    [1] "19.04" "18.1"  "18.07" "17.07"

and you can set the version of FishBase you wish to access by setting the environmental variable:

    options(FISHBASE_VERSION="19.04")

Note that the same version number applies to both the `fishbase` and `sealifebase` data.

## SeaLifeBase

SeaLifeBase.org is maintained by the same organization and largely parallels the database structure of Fishbase. As such, almost all `rfishbase` functions can instead be instructed to address the

We can begin by getting the taxa table for sealifebase:

    sealife <- load_taxa(server="sealifebase")

(Note: running `load_taxa()` at the beginning of any session, for either fishbase or sealifebase is a good way to “warm up” rfishbase by loading in taxonomic data it will need. This information is cached throughout your session and will make all subsequent commands run faster. But no worries if you skip this step, `rfishbase` will peform it for you on the first time it is needed, and will cache these results thereafter.)

Let’s look at some Gastropods:

    sealife %>% filter(Class == "Gastropoda")

    # A tibble: 19,479 x 9
       SpecCode Species    Genus  Subfamily Family  Order  Class Phylum Kingdom
          <dbl> <chr>      <chr>  <chr>     <chr>   <chr>  <chr> <chr>  <chr>
     1       57 Salinator… Salin… <NA>      Amphib… Pulmo… Gast… Mollu… Animal…
     2       58 Tasmaphen… Tasma… <NA>      Rhytid… Pulmo… Gast… Mollu… Animal…
     3       59 Tasmaphen… Tasma… <NA>      Rhytid… Pulmo… Gast… Mollu… Animal…
     4       60 Torresiro… Torre… <NA>      Rhytid… Pulmo… Gast… Mollu… Animal…
     5       61 Victaphan… Victa… <NA>      Rhytid… Pulmo… Gast… Mollu… Animal…
     6       62 Victaphan… Victa… <NA>      Rhytid… Pulmo… Gast… Mollu… Animal…
     7       63 Victaphan… Victa… <NA>      Rhytid… Pulmo… Gast… Mollu… Animal…
     8       64 Victaphan… Victa… <NA>      Rhytid… Pulmo… Gast… Mollu… Animal…
     9       65 Anoglypta… Anogl… <NA>      Caryod… Pulmo… Gast… Mollu… Animal…
    10       66 Brazieres… Brazi… <NA>      Caryod… Pulmo… Gast… Mollu… Animal…
    # … with 19,469 more rows

All other tables can also take an argument to `server`:

    species(server="sealifebase")

    # A tibble: 121,348 x 108
       SpecCode Species Author SpeciesRefNo FBname FamCode Subfamily GenCode
          <dbl> <chr>   <chr>         <dbl> <chr>    <dbl> <chr>       <dbl>
     1        1 Phoron… Wrigh…            1 <NA>      4793 <NA>        12365
     2        2 Phoron… Wrigh…          997 <NA>      4793 <NA>        12365
     3        3 Phoron… Oka, …            1 horse…    4793 <NA>        12365
     4        4 Phoron… Haswe…            1 Phoro…    4793 <NA>        12365
     5        5 Phoron… Selys…          997 <NA>      4793 <NA>        12365
     6        6 Phoron… Cori,…            1 phoro…    4793 <NA>        12365
     7        7 Phoron… (Schn…            1 <NA>      4793 <NA>        12365
     8        8 Phoron… Gilch…            1 <NA>      4793 <NA>        12366
     9        9 Phoron… Pixel…            1 large…    4793 <NA>        12366
    10       10 Phoron… Hilto…            1 Calif…    4793 <NA>        12366
    # … with 121,338 more rows, and 100 more variables: TaxIssue <dbl>,
    #   Remark <chr>, PicPreferredName <chr>, PicPreferredNameM <chr>,
    #   PicPreferredNameF <chr>, PicPreferredNameJ <chr>, Source <chr>,
    #   AuthorRef <dbl>, SubGenCode <lgl>, Fresh <dbl>, Brack <dbl>,
    #   Saltwater <dbl>, Land <dbl>, BodyShapeI <lgl>, DemersPelag <chr>,
    #   AnaCat <chr>, MigratRef <dbl>, DepthRangeShallow <dbl>,
    #   DepthRangeDeep <dbl>, DepthRangeRef <dbl>, DepthRangeComShallow <dbl>,
    #   DepthRangeComDeep <dbl>, DepthComRef <dbl>, LongevityWild <dbl>,
    #   LongevityWildRef <dbl>, LongevityCaptive <dbl>, LongevityCapRef <dbl>,
    #   Vulnerability <dbl>, Length <dbl>, LTypeMaxM <chr>,
    #   LengthFemale <dbl>, LTypeMaxF <chr>, MaxLengthRef <dbl>,
    #   CommonLength <dbl>, LTypeComM <chr>, CommonLengthF <dbl>,
    #   LTypeComF <chr>, CommonLengthRef <dbl>, Weight <dbl>,
    #   WeightFemale <dbl>, MaxWeightRef <dbl>, Pic <lgl>,
    #   PictureFemale <lgl>, LarvaPic <lgl>, EggPic <lgl>,
    #   ImportanceRef <dbl>, Importance <chr>, Remarks7 <chr>,
    #   PriceCateg <chr>, PriceReliability <chr>, LandingStatistics <chr>,
    #   Landings <chr>, MainCatchingMethod <chr>, II <chr>, MSeines <dbl>,
    #   MGillnets <dbl>, MCastnets <dbl>, MTraps <dbl>, MSpears <dbl>,
    #   MTrawls <dbl>, MDredges <dbl>, MLiftnets <dbl>, MHooksLines <dbl>,
    #   MOther <dbl>, UsedforAquaculture <chr>, LifeCycle <chr>,
    #   AquacultureRef <dbl>, UsedasBait <chr>, BaitRef <dbl>, Aquarium <chr>,
    #   AquariumFishII <chr>, AquariumRef <dbl>, GameFish <dbl>,
    #   GameRef <lgl>, Dangerous <chr>, DangerousRef <dbl>,
    #   Electrogenic <chr>, ElectroRef <dbl>, Complete <lgl>, ASFA <lgl>,
    #   GoogleImage <dbl>, Emblematic <dbl>, Entered <dbl>,
    #   DateEntered <dttm>, Modified <dbl>, DateModified <dttm>, Expert <dbl>,
    #   DateChecked <dttm>, Synopsis <lgl>, DateSynopsis <lgl>, Flag <lgl>,
    #   Comments <chr>, VancouverAquarium <dbl>, Profile <lgl>,
    #   Sp2000_NameCode <chr>, Sp2000_HierarchyCode <chr>,
    #   Sp2000_AuthorRefNumber <chr>, E_Append <dbl>, E_DateAppend <date>,
    #   TS <dttm>

CAUTION: if switching between `fishbase` and `sealifebase` in a single R session, we strongly advise you always set `server` explicitly in your function calls. Otherwise you may confuse the caching system.

## Backwards compatibility

`rfishbase` 3.0 tries to maintain as much backwards compatibility as possible with rfishbase 2.0. However, there are cases in which the rfishbase 2.0 behavior was not desirable – such as throwing errors when a introducing simple `NA`s for missing data would be more appropriate, or returning vectors where `data.frame`s were needed to include all the context.

- Argument names have been retained where possible to maximize backwards compatibility. Using previous arguments that are no longer relevant (such as `limit` for the maximum number of records) will not now introduce errors, but nor will they have any effect (they are simply consumed by the `...`). There are no longer any limits in return sizes.

- You can still specify server using the rfishbase `2.x` format of providing a URL argument for server, e.g. `"http://fishbase.ropensci.org/sealifebase"` or `Sys.setenv(FISHBASE_API = "http://fishbase.ropensci.org/sealifebase")`, or simply `Sys.setenv("FISHBASE_API" = "sealifebase")` if you prefer. Also recall that environmental variables can always be set in an `.Renviron` file.