annual.landings = rfisheries::of_landings()

Country’s Total Landings

Sometimes global landings may not suit the questions you want to answer, for instance you may be interested to compare the annual totat catches for multiple countries. To obtain the catches for a country of interest you must specify the argument country. Be aware that only the iso3c is accepted. the iso3c codes are three-letter that identify and represent represent countries, dependent territories, and special areas of geographical interest. If you cant figure out the country code, you can simply run `of_country_codes()’ to obtain the list of countries with their respective codes.

coundry.codes = of_country_codes()

A list of ten sampled International Organization for Standardization (ISO) codes are shown in table 2

Table 2: International Organization for Standardization (ISO) country code

Country	code
Albania	ALB
St. Pierre and Miquelon	SPM
Nigeria	NGA
Bahamas	BHS
Luxembourg	LUX
South Africa	ZAF
Lao People’s Dem. Rep.	LAO
Falkland Is.(Malvinas)	FLK
Côte d’Ivoire	CIV
Cape Verde	CPV

Once we know the codes we can fetch the annual landing catches for the country of interest, for instance, we are interested to obtain the annual catches landed over the available period. the chunk below highlight the code that was used to access the total catches in Tanzania shown in figure 2.

annual.landings.tza = rfisheries::of_landings(country = "TZA")

Landings from multiple countries.

Our interest is to compare the total landings from the WIO region, unfortunate, the of_landings() function does not allow us to query more than one at a time. To overcome this package limitation, we have to expand the process with other function available in R. And because the landing search is based on the country code, we first create a tibble that has a country code and their corresponding names (Müller and Wickham, 2018)

wio.countries = tibble(code = c("KEN", "TZA", "EAZ", "SYC", "ZAF", "MOZ", 
                                "SOM", "MUS", "MYT", "MDG"),
                       name = c("Kenya", "Tanzania", "Zanzibar", "Seychelles",
                                "South Africa",   "Mozambique", "Somalia", 
                                "Mauritius", "Mayotte", "Madagascar"))

We then iterate the process that search and download the landing of each country as the data. To chain the process, we make a landings.wio.list as a container that will store the download files. This is important because each iteration feeds into this list file. If you are unfamiliar with looping in R, please consult the relevant resources that guides on how you can use for loop to iterate processes.

landings.wio.list = list()

for (catches in 1:nrow(wio.countries)){
  landings.wio.list[[catches]] = rfisheries::of_landings(country = wio.countries$code[catches]) %>% 
    rename(code = country) %>% 
    mutate(name = wio.countries$name[catches])
}

The loop output a list files with multiple data frames of total catches for each country selected in the region. To expand our analysis and visualize the catch, we need to make a single data frame that contains all countries’ total landing from a list file. dplyr package has a nifty bind_row() function that does the work (Wickham et al., 2018) with a single line of code highlighted below;

landings.wio.tb = landings.wio.list %>% bind_rows()

Once we have the data in the right format, we can visualize. The first I wanted to understand is how the catches varied among these countries in 2015 as seen. With few lines of code I was able to understand that Somalia reported the least catch and South Africa nailed the list with the highest catch in 2015 (Table 3). Here is the code that generated table 3.

Table 3: Country Total Annual Landings

Year	Catch	Code	Country
1969	800	MYT	Mayotte
1976	5477	MYT	Mayotte
1960	9366	EAZ	Zanzibar
1968	16578	TZA	Tanzania
1984	17251	MYT	Mayotte
1955	47183	SYC	Seychelles
1960	62832	SYC	Seychelles
2002	68134	MDG	Madagascar
2003	326993	SYC	Seychelles
1992	3878960	ZAF	South Africa

Comparing catches of all countries over the period , We notice that the higher catch landings from South Africa masks catches from other countries in the region (Figure 3).

 ggplot() +
  geom_line(data = landings.wio.tb, 
            aes(x = year, y = catch/1000, col = name))+
  labs(x = "", y = "Catches (T)")+
  # theme(panel.grid.minor = element_blank(), legend.key = element_blank())+
  # scale_y_continuous(limits = c(0,100), breaks = seq(10,100,20))+
  scale_x_continuous(breaks = seq(1955,2015,20))+
  # scale_color_discrete(name = "Countries")+
  see::scale_color_material_d(name = "Countries")+
  see::theme_blackboard()

To make the plot visible and standout for each country, I decided to switch to multiple plots and make the landing scale free for each country. This make it easier to see the trends of fisheries landings separately for each country (Figure 4)

I notice that the landing pattern differs from each country. While all other countries shows a dwindling trends, of interest is the increasing positive trend of landings in Mayotte (Figure 4). South Africa, Madagascar and Mauritius have the highest landings as shown in figure 5

Species landed

The rfisheries package has a of_species_code() function that enable retrieving catches of particular species. Although the species name are given in either scientific or english name, you can only query the database using the species codes. To obtain the species code simply run a chunk below;

species = rfisheries::of_species_codes()

This gives a total of 11562 species. Going through this data frame looking for a instance species of tuna is tedious. But the filter() from dplyr package (Wickham et al., 2018) only work if you can use the full variable content. But in my cases though I need to filter based on partial matches. In this case, we need a function that will evaluate regular expressions on strings and return boolean values. Whenever the statement is TRUE the row will be filtered. Fortunate, the stringr (Wickham, 2019) package has a str_detect() function that can do a partial match. However, it is important to remember that R is case sensitive. I used str_detect() below to pick tuna species name and drop other species as the code below illustrates;

tuna = species %>% 
  filter(str_detect(english_name %>% tolower(), pattern = "tuna"))

And obtain tuna and tuna–like species shown in table 4

Table 4: Scientific, English Name and code of Tuna and Tuna like Species

Scientific Name	Taxonomical Code	Species Code	English name
Gymnosarda unicolor	1750101202	DOT	Dogtooth tuna
Auxis thazard	1750102301	FRI	Frigate tuna
Auxis rochei	1750102303	BLT	Bullet tuna
Auxis thazard, A. rochei	17501023XX018	FRZ	Frigate and bullet tunas
Katsuwonus pelamis	1750102501	SKJ	Skipjack tuna
Thunnus thynnus	1750102601	BFT	Atlantic bluefin tuna
Thunnus orientalis	1750102602	PBF	Pacific bluefin tuna
Thunnus tonggol	1750102603	LOT	Longtail tuna
Thunnus atlanticus	1750102604	BLF	Blackfin tuna
Thunnus maccoyii	1750102608	SBF	Southern bluefin tuna
Thunnus albacares	1750102610	YFT	Yellowfin tuna
Thunnus obesus	1750102612	BET	Bigeye tuna
Thunnus spp	17501026XX	TUS	True tunas nei
Allothunnus fallai	1750102701	SLT	Slender tuna
Thunnini	17501XXXXX043	TUN	Tunas nei
Scombroidei	175XXXXXXX	TUX	Tuna-like fishes nei

Once the code for species are known, we can now retrieve total annual catches for that particular species. However, notice that of_landings() only accept species code an neither the scientific name nor the english name is acceptable. The chunk below was used to extract annual catches of Yellowfin tuna shown in figure 6).

yellow.fin.tuna = rfisheries::of_landings(species = "YFT")

If we are interested to compare catches of multiple tuna around the world, then we must download the long–term catches data for each tuna species. With a total of 16 species that is simple and we can easily do it manually. However, that task is hard and may introduce error if you have many species to deal with. That is where looping becomes handy in programming. In this case, we use a for() loop to extract tuna and tuna like catches for all sixteen species shown in table 4. First we create a tuna_like_tuna list file as a preoccupied contain to store the data frame that has catch for each species of tuna. We also notice that Thunnus spp is vaque and the API can not recognize. Hence we need to clean the Tuna species dataset by removing missing species

## clean the dataset by removing species missing in the FAO database
tuna.clean = tuna %>% 
  filter(scientific_name != "Thunnus spp")%>% 
  filter(scientific_name != "Thunnini")

## create a dummy list file
tuna_like_tuna = list()

## loop through the species
for (j in 1:nrow(tuna.clean)){
  tuna_like_tuna[[j]] = rfisheries::of_landings(species = tuna.clean$a3_code[j]) %>%
    mutate(scientific_name = tuna$scientific_name[j], 
           english_name = tuna.clean$english_name[j])
}

## ubind the data frame from the list
tuna_like_tuna_df = tuna_like_tuna %>% bind_rows()

Once the tuna and tuna like species catches are downloaded and in the tidy format, we can use the power of ggplot2 package to visualize catch trend of these species as shown in figure 7.

Final thought

Though the rfisheries package does well to obtain the catch landing both by country and species, it only offer an opportunity to query either by species or country alone. It never allow you to query multiple variables i.e you can not fetch data like catches of certain tuna species. I hope this drawback of this package will be addressed in the future releases.