rfishbase tutorial
for v2.1.2
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 heartbeat() function.
The new 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.
Installation
install.packages("rfishbase",
repos = c("http://packages.ropensci.org", "http://cran.rstudio.com"),
type="source")
Or development version from GitHub
install.packages("devtools")
devtools::install_github("ropensci/rfishbase")
library("rfishbase")
Usage
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 bicolor" "Labroides dimidiatus"
#> [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:
fish <- common_to_sci("trout")
fish
#> [1] "Salmo trutta" "Oncorhynchus mykiss"
#> [3] "Salvelinus fontinalis" "Salvelinus alpinus alpinus"
#> [5] "Lethrinus miniatus" "Salvelinus malma"
#> [7] "Plectropomus leopardus" "Schizothorax richardsonii"
#> [9] "Arripis truttacea"
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(fish[1:2])[, 1:6]
#> sciname Genus Species SpeciesRefNo Author
#> 1 Salmo trutta Salmo trutta 4779 Linnaeus, 1758
#> 2 Oncorhynchus mykiss Oncorhynchus mykiss 4706 (Walbaum, 1792)
#> FBname
#> 1 Sea trout
#> 2 Rainbow trout
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
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")
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.
SeaLifeBase
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
species(kingcrab)
ecology(kingcrab)
Citing
To cite rfishbase in publications use:
C. Boettiger, D. T. Lang and P. C. Wainwright. “rfishbase: exploring, manipulating and visualizing FishBase data from R”. In: Journal of Fish Biology 81.6 (Nov. 2012), pp. 2030-2039. DOI: 10.1111/j.1095-8649.2012.03464.x
Carl Boettiger, Scott Chamberlain, Duncan Temple Lang, Peter Wainwright (2017). R Interface to ‘FishBase’. R package version 2.1.2. https://CRAN.R-project.org/package=rfishbase
License and bugs
- License: CC0
- Report bugs at our Github repo for rfishbase
