rfishbase 2.0. This package is a ground-up rewrite of the original
rfishbase package described in Boettiger et al. (2012), and is not backwards compatible with the original. The first version of
rfishbase relied on the XML summary pages provided by FishBase, which contained relatively incomplete data and have since been deprecated. The package later added functions that relied on HTML scraping of fishbase.org, which was always slow, subject to server instabilities, and carried a greater risk of errors. To address all of these issues, we have now created a stand-alone FishBase API with the blessing of the FishBase.org team, who have kindly provided copies of the backend SQL database to our team for this purpose. At this time the API does not cover all tables provided by the SQL backend, but does access the largest and most commonly used. A list of all tables available from the API (and from rfishbase) can be seen using the
rfishbase package queries this API directly rather than the FishBase.org website. This reduces load on the FishBase web servers and increases both the performance and the breadth of data avaialble.
rfishbase functions are primarily aimed at facilitating queries for specific data across a given list of many species. This is a task that is common to much scientific research and tedious to perform on the FishBase.org website, which requires a user to visit a separate page for each species. Aimed at scientific use, the
rfishbase package returns all data as
data.frames, usually organized in "tidy data" style with individual species as rows and observations of species traits as columns (also referred to as fields). Users will frequently have to subset the resulting data frames, or join them with other data frames provided by the package, to obtain the data they need. We recommend the
dplyr package to facilitate these tasks, which
rfishbase also uses internally.
In having access to much more data, the new
rfishbase can be difficult to navigate. We have provided several helper functions for users to discover which tables they need, as illustrated below. Unfortunately, FishBase.org lacks detailed documentation of all of the tables and fields contained in it's database. For the most part, table and column names are self-documenting, but details are often missing which can create a puzzle for researchers trying to figure out precisely what data is provided in a given column. To address this challenge, we have created a crowd-sourced collection of documentation that can be queried from the API to provide more detailed descriptions.
We welcome any feedback, issues or questions that users may encounter through our issues tracker on GitHub: [https://github.com/ropensci/rfishbase/issues].
install.packages("rfishbase", repos = c("http://packages.ropensci.org", "http://cran.rstudio.com"), type="source")
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.
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
 "Labroides bicolor" "Labroides dimidiatus"  "Labroides pectoralis" "Labroides phthirophagus"  "Labroides rubrolabiatus"
rfishbase also recognizes common names. When a common name refers to multiple species, all matching species are returned:
fish <- common_to_sci("trout") fish
 "Salmo trutta" "Oncorhynchus mykiss"  "Salvelinus fontinalis" "Salvelinus alpinus alpinus"  "Lethrinus miniatus" "Salvelinus malma"  "Plectropomus leopardus" "Schizothorax richardsonii"  "Arripis truttacea"
Note that there is no need to validate names coming from
species_list, as these will always return valid names.
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.
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).
sciname Genus Species SpeciesRefNo Author 1 Salmo trutta Salmo trutta 4779 Linnaeus, 1758 2 Oncorhynchus mykiss Oncorhynchus mykiss 4706 (Walbaum, 1792) FBname PicPreferredName PicPreferredNameM PicPreferredNameF 1 Sea trout Satru_u2.jpg NA NA 2 Rainbow trout Onmyk_f0.jpg NA NA PicPreferredNameJ FamCode Subfamily GenCode SubGenCode 1 Satru_uc.jpg 76 Salmoninae 6009 NA 2 <NA> 76 Salmoninae 2445 NA BodyShapeI Source AuthorRef Remark TaxIssue Fresh Brack Saltwater 1 fusiform / normal R NA NA 0 -1 -1 -1 2 fusiform / normal R NA NA 0 -1 -1 -1 DemersPelag AnaCat MigratRef DepthRangeShallow DepthRangeDeep 1 pelagic-neritic anadromous 51243 0 28 2 benthopelagic anadromous 51243 0 200 DepthRangeRef DepthRangeComShallow DepthRangeComDeep DepthComRef 1 101587 1 2 101587 2 50550 NA NA NA LongevityWild LongevityWildRef LongevityCaptive LongevityCapRef 1 38 32682 10.3 274 2 11 12193 4.0 273 Vulnerability Length LTypeMaxM LengthFemale LTypeMaxF MaxLengthRef 1 59.96 140 SL NA NA 682 2 36.29 122 TL NA NA 96339 CommonLength LTypeComM CommonLengthF LTypeComF CommonLengthRef Weight 1 72 TL NA NA 3397 50000 2 60 TL NA NA 5504 25400 WeightFemale MaxWeightRef Pic PictureFemale LarvaPic EggPic 1 NA 682 SATRU_U2 NA NA NA 2 NA 7251 ONMYK_U2 NA NA NA ImportanceRef Importance PriceCateg 1 4931 commercial very high 2 4931 highly commercial low PriceReliability Remarks7 1 Reliable: based on ex-vessel price for this species <NA> 2 Reliable: based on ex-vessel price for this species Important game fish. LandingStatistics Landings MainCatchingMethod II MSeines 1 from 1,000 to 10,000 Finland in area 27 seines 0 2 from 100,000 to 500,000 <NA> hooks and lines 0 MGillnets MCastnets MTraps MSpears MTrawls MDredges MLiftnets 1 -1 0 -1 0 0 0 0 2 -1 0 0 0 0 0 0 MHooksLines MOther UsedforAquaculture 1 -1 0 commercial 2 -1 0 commercial LifeCycle AquacultureRef UsedasBait 1 12228 never/rarely 2 life cycle closed in commercial culture 12108 never/rarely BaitRef Aquarium AquariumFishII AquariumRef GameFish 1 NA never/rarely based mainly on breeding 274 -1 2 NA never/rarely based mainly on capture 9183 -1 GameRef Dangerous DangerousRef Electrogenic ElectroRef 1 4699 potential pest NA no special ability NA 2 4699 potential pest NA no special ability NA Complete GoogleImage 1 NA -1 2 NA -1 Comments 1 Found in streams, ponds, rivers and lakes (Ref. 5951). Individuals spend 1 to 5 years in fresh water and 6 months to 5 years in salt water (Ref. 51442). Juveniles mature in 3-4 years (Ref. 6885). Lacustrine populations undertake migration to tributaries and lake outlets to spawn, rarely spawning on stone, wave-washed lake shores. Spawns in rivers and streams with swift current, usually characterized by downward movement of water intro gravel (Ref. 59043). Spawning takes place normally more than one time (Ref. 51442). They prefer cold, well-oxygenated upland waters although their tolerance limits are lower than those of rainbow trout and favors large streams in the mountainous areas with adequate cover in the form of submerged rocks, undercut banks, and overhanging vegetation (Ref. 6465). Life history and spawning behavior is similar to the salmon <i>Salmo salar</i> (Ref. 51442). Each female produces about 10.000 eggs (Ref. 35388, Ref. 51442). Mainly diurnal (Ref. 682). Sea and lake trouts forage in pelagic and littoral habitats, while sea trouts mainly close to coast, not very far from estuary of natal river (Ref. 59043). Juveniles feed mainly on aquatic and terrestrial insects; adults on mollusks, crustaceans and small fish (Ref. 26523, Ref. 51442). Marketed fresh and smoked; eaten fried, broiled, boiled, cooked in microwave, and baked (Ref. 9988). 2 Adults inhabit cold headwaters, creeks, small to large rivers, and lakes. Anadromous in coastal streams (Ref. 5723). Stocked in almost all water bodies as lakes, rivers and streams, usually not stocked in water reaching summer temperatures above 25°C or ponds with very low oxygen concentrations. Feed on a variety of aquatic and terrestrial invertebrates and small fishes. At the sea, they prey on fish and cephalopods. Mature individuals undertake short spawning migrations. Anadromous and lake forms may migrate long distances to spawning streams (Ref. 59043). Utilized fresh, smoked, canned, and frozen; eaten steamed, fried, broiled, boiled, microwaved and baked (Ref. 9988). Cultured in many countries and is often hatched and stocked into rivers and lakes especially to attract recreational fishers (Ref. 9988). Profile PD50 Emblematic Entered DateEntered Modified 1 NA 0.5 0 2 1990-10-17T00:00:00.000Z 10 2 NA 0.5 0 2 1990-10-17T00:00:00.000Z 2291 DateModified Expert DateChecked TS SpecCode 1 2015-05-12T00:00:00.000Z 97 2003-01-03T00:00:00.000Z NA 238 2 2014-12-16T00:00:00.000Z 97 2003-01-15T00:00:00.000Z NA 239
Most tables contain many fields. To avoid overly cluttering the screen,
rfishbase displays tables as
data_frame objects 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. For instance, from this table we may only be interested in the
PriceCateg (Price category) and the
Vulnerability of the species. We can repeat the query for our full species list, asking for only these fields to be returned:
dat <- species(fish, fields=c("SpecCode", "PriceCateg", "Vulnerability")) dat
sciname Vulnerability PriceCateg SpecCode 1 Salmo trutta 59.96 very high 238 2 Oncorhynchus mykiss 36.29 low 239 3 Salvelinus fontinalis 43.37 very high 246 4 Salvelinus alpinus alpinus 74.33 very high 247 5 Lethrinus miniatus 52.78 very high 1858 6 Salvelinus malma 69.97 very high 2691 7 Plectropomus leopardus 51.04 very high 4826 8 Schizothorax richardsonii 34.78 unknown 8705 9 Arripis truttacea 47.96 unknown 14606
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.
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
list_fields, which will list all tables with a field matching the query string:
# A tibble: 2 × 2 table_name column_name <chr> <chr> 1 stocks Resilience 2 stocks ResilienceRemark
This shows us that this information appears on the
stocks table. Working in R, it is easy to query this additional table and combine the results with the data we have collected so far:
resil <- stocks(fish, fields="Resilience") merge(dat, resil)
sciname SpecCode Vulnerability PriceCateg Resilience 1 Arripis truttacea 14606 47.96 unknown Medium 2 Lethrinus miniatus 1858 52.78 very high Medium 3 Oncorhynchus mykiss 239 36.29 low Medium 4 Plectropomus leopardus 4826 51.04 very high Medium 5 Salmo trutta 238 59.96 very high High 6 Salmo trutta 238 59.96 very high <NA> 7 Salmo trutta 238 59.96 very high Medium 8 Salmo trutta 238 59.96 very high Low 9 Salmo trutta 238 59.96 very high <NA> 10 Salmo trutta 238 59.96 very high <NA> 11 Salmo trutta 238 59.96 very high <NA> 12 Salvelinus alpinus alpinus 247 74.33 very high Low 13 Salvelinus fontinalis 246 43.37 very high Medium 14 Salvelinus malma 2691 69.97 very high Low 15 Salvelinus malma 2691 69.97 very high <NA> 16 Schizothorax richardsonii 8705 34.78 unknown Medium
Sometimes it is more useful to search for a broad description of the tables.
The FishBase team has also created the SeaLifeBase project, which seeks to provide much the same data and layout as fishbase.org and the fishbase schema, but covering all sea life apart from the finfish covered in FishBase. The rOpenSci team has created a pilot API for SeaLifeBase as well. Most of the functions in
rfishbase can be used directly to query SeaLifeBase data by explicitly specifying the
server argument to use the SeaLifeBase API at
http://fishbase.ropensci.org/sealifebase, like so:
options(FISHBASE_API = "https://fishbase.ropensci.org/sealifebase") kingcrab <- common_to_sci("king crab") kingcrab
 "Glyptocephalus cynoglossus"
Set the API back to
fishbase for finfish data:
options(FISHBASE_API = "https://fishbase.ropensci.org")
Alternately, all functions can take the explicit argument
server to indicate which database to use, like so:
kingcrab <- common_to_sci("king crab", server = "https://fishbase.ropensci.org/sealifebase")
This supercedes the value set in
options() and is the preferred method when using both databases in a single script.
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.