---
title: "The New RFishbase"
author: "Carl Boettiger"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{rfishbase 2.0}
  %\VignetteEngine{knitr::rmarkdown}
  \usepackage[utf8]{inputenc}
---





Welcome to `rfishbase 2.0`. This package is a ground-up rewrite of the original `rfishbase` package described in [Boettiger et al. (2012)](http://www.carlboettiger.info/assets/files/pubs/10.1111/j.1095-8649.2012.03464.x.pdf), 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.

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




```{r include=FALSE}
knitr::opts_chunk$set(warning=FALSE, comment=NA)
```


## Installation



```{r message=FALSE, warning=FALSE, results="hide", eval=FALSE}
remotes::install_github("ropensci/rfishbase")
```

Or with base `install.packages` function:

```{r message=FALSE, warning=FALSE, results="hide", eval=FALSE}
install.packages("rfishbase", 
                 repos = c("http://packages.ropensci.org", "http://cran.rstudio.com"), 
                 type="source")
```


```{r message=FALSE, warning=FALSE, results="hide"}
library("rfishbase")
```

## Getting started


[FishBase](http://fishbase.org) 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:

```{r}
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:

```{r}
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:


```{r}
fish <- species_list(Genus = "Labroides")
fish
```

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

```{r}
fish <- common_to_sci("trout")
fish
```

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](http://fishbase.org). `rfishbase` functions always return [tidy](http://www.jstatsoft.org/v59/i10/paper) data tables: rows are observations (e.g. a species, individual samples from a species) and columns are variables (fields).

```{r}
species(fish[1:2])
```

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:

```{r}
dat <- species(fish, fields=c("SpecCode", "PriceCateg", "Vulnerability"))
dat
```

### 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:

```{r}
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:


```{r}
resil <- stocks(fish, fields="Resilience")
merge(dat, resil)
```


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: 

```{r}
options(FISHBASE_API = "https://fishbase.ropensci.org/sealifebase")
kingcrab <- common_to_sci("king crab")
kingcrab
```

```{r eval=FALSE}
species(kingcrab)
ecology(kingcrab)
```


Set the API back to `fishbase` for finfish data:

```{r}
options(FISHBASE_API = "https://fishbase.ropensci.org")
```

Alternately, all functions can take the explicit argument `server` to indicate which database to use, like so:

```{r}
kingcrab <- common_to_sci("king crab", server = "https://fishbase.ropensci.org/sealifebase")
kingcrab
```

This supercedes the value set in `options()` and is the preferred method when using both databases in a single script.  


