First you'll need to install a few packages if you don't already have them
install.packages(c("plyr", "ggplot2", "rfisheries"))
# Next load up the package
library(rfisheries)
library(plyr) # for manipulating the data
library(ggplot2) # for visualizing the dataFirst you can get a complete list of species
species_list <- of_species_codes()
head(species_list)
scientific_name taxocode a3_code isscaap
1 Petromyzon marinus 1020100101 LAU 25
2 Lampetra fluviatilis 1020100201 LAR 25
3 Lampetra tridentata 1020100202 LAO 25
4 Ichthyomyzon unicuspis 1020100401 LAY 25
5 Eudontomyzon mariae 1020100501 LAF 25
6 Geotria australis 1020100701 LAE 25
english_name
1 Sea lamprey
2 River lamprey
3 Pacific lamprey
4 Silver lamprey
5 Ukrainian brook lamprey
6 Pouched lamprey
Now you've got a list of 11562 commerically valuable species. Let's write a small function to search for species by their common names
fish_search <- function(query = NULL, species_list) {
if(!is.null(query)) {
species_list[grep(query, species_list$english_name), ]
}
}We'll create another function to fail gracefully if data are not available for a particular species or country
safe_landings <- failwith(NULL, of_landings)
Now let's search for all the lamprey species
fish_search("lamprey", species_list)
scientific_name taxocode a3_code isscaap
1 Petromyzon marinus 1020100101 LAU 25
2 Lampetra fluviatilis 1020100201 LAR 25
3 Lampetra tridentata 1020100202 LAO 25
4 Ichthyomyzon unicuspis 1020100401 LAY 25
5 Eudontomyzon mariae 1020100501 LAF 25
6 Geotria australis 1020100701 LAE 25
7 Mordacia mordax 1020100801 LAK 25
8 Caspiomyzon wagneri 1020100901 LAW 25
9 Lethenteron camtschaticum 1020101101 LSZ 25
english_name
1 Sea lamprey
2 River lamprey
3 Pacific lamprey
4 Silver lamprey
5 Ukrainian brook lamprey
6 Pouched lamprey
7 Australian lamprey
8 Caspian lamprey
9 Arctic lampreyor perhaps the cod species
cods <- fish_search(" cod$", species_list)
# the $ means that cod should be the end of the name. This avoids grabbing crocodiles for example.
> cods
scientific_name taxocode a3_code isscaap
3281 Muraenolepis microps 1480100101 MOY 32
3282 Muraenolepis marmoratus 1480100102 MVC 32
3283 Muraenolepis microcephalus 1480100103 MWS 32
3284 Muraenolepis orangiensis 1480100104 MWO 32
3296 Momonatira globosus 1480200901 MMG 32
3307 Tripterophycis gilchristi 1480201901 TPG 32
3312 Lepidion microcephalus 1480202005 LMF 32
3317 Bregmaceros mcclellandi 1480301801 UNC 32
3320 Gadus morhua 1480400202 COD 32
3321 Gadus macrocephalus 1480400211 PCO 32
3322 Gadus ogac 1480400212 GRC 32
3344 Eleginus gracilis 1480401202 SAF 32
3351 Boreogadus saida 1480401901 POC 32
3352 Arctogadus glacialis 1480402201 ATG 32
3353 Arctogadus borisovi 1480402202 ATV 32
3366 Trisopterus minutus 1480403202 POD 32
3508 Melanonus gracilis 1480700101 MLG 32
3510 Euclichthys polynemus 1481000101 EUY 32
4346 Maccullochella peelii 1700817601 MCP 13
4347 Maccullochella macquariensis 1700817602 MLQ 13
4348 Maccullochella ikei 1700817603 MLK 13
5930 Paranotothenia microlepidota 1709240003 NHJ 33
5938 Patagonotothen ramsayi 1709244002 PAT 34
6165 Parapercis colias 1720919602 NEB 33
english_name
3281 Smalleye moray cod
3282 Marbled moray cod
3283 Smallhead moray cod
3284 Patagonian moray cod
3296 Tadpole cod
3307 Grenadier cod
3312 Small-headed cod
3317 Unicorn cod
3320 Atlantic cod
3321 Pacific cod
3322 Greenland cod
3344 Saffron cod
3351 Polar cod
3352 Artic cod
3353 East Siberian cod
3366 Poor cod
3508 Pelagic cod
3510 Eucla cod
4346 Murray cod
4347 Trout cod
4348 Eastern freshwater cod
5930 Black cod
5938 Longtail Southern cod
6165 New Zealand blue codNow let's get data for these species
cod_data <- ldply(cods$a3_code, function(x) {
safe_landings(species = x)
})This will only retrieve data if avaialble for a particular species. We can plot these out:
ggplot(cod_data, aes(year, catch, color = species)) + geom_line(size = 1.1)
Now we can do the same for countries as well.
country_list <- of_country_codes()
# Grabs a list of countries and their respective country codes
head(country_list)
country iso3c
1 Afghanistan AFG
2 Albania ALB
3 Algeria DZA
4 American Samoa ASM
5 Andorra AND
6 Angola AGO
Want landings data for USA?
usa_data <- of_landings(country = "USA")
fish_plot(usa_data)
We can do some interesting comparisons of species. Let's examine the Anchovy fisheries in the Northern and Southern hemispheres (California versus Peru) and a couple of other commercially interesting fish species
anchovies <- fish_search("anchovy", species_list)
head(anchovies)
scientific_name taxocode a3_code isscaap
1078 Engraulis encrasicolus 1210600201 ANE 35
1079 Engraulis japonicus 1210600202 JAN 35
1080 Engraulis anchoita 1210600206 ANA 35
1081 Engraulis mordax 1210600207 NPA 35
1082 Engraulis ringens 1210600208 VET 35
1083 Engraulis capensis 1210600212 ANC 35
english_name
1078 European anchovy
1079 Japanese anchovy
1080 Argentine anchovy
1081 Californian anchovy
1082 Anchoveta(=Peruvian anchovy)
1083 Southern African anchovy# Let's grab the two interesting anchovy fisheries (California and Peru) along with Cod and Tuna.
who <- c("TUX", "COD", "VET", "NPA")
dat <- ldply(who, function(x) safe_landings(species = x))We can plot these data out:
ggplot(dat, aes(year, catch)) + geom_line() + facet_wrap(~species, scales = "free_y")
Why did the perivuan fishery collapse? See: http://en.wikipedia.org/wiki/Overfishing under instances Also see.