Tuna
Fajar Dwi Nugroho
4/13/2021
1. Intro
1.1 Greetings
Helo, Welcome to My Rmd In this Rmd I would explore about Tuna
1.2 Brief
I want to visualize some graphs to inform a Tuna condition
1.3 Problem Background
Atlantic Bluefin Tuna now is endangered because overexploited, I want to find out current condition. 1. Which species are the most exploited? 2. Which countries exploit the most? 3. What is the solution to the current condition?
1.4 Library
library(tidyverse)
library(readr)
library(ggthemes)
library(plotly)1.5 Dataset
Dataset is from FAO Fisheries Division
“FAO Aquaculture, Capture and Global production databases with data from 1950 to 2018”
country <- read_csv("input_fish/CL_FI_COUNTRY_GROUPS.csv")
production <- read_csv("input_fish/CL_FI_PRODUCTION_SOURCE.csv")
species <- read_csv("input_fish/CL_FI_SPECIES_GROUPS.csv")
symbol <- read_csv("input_fish/CL_FI_SYMBOL.csv")
unit <- read_csv("input_fish/CL_FI_UNIT.csv")
waterarea <- read_csv("input_fish/CL_FI_WATERAREA_GROUPS.csv")
TS <- read_csv("input_fish/TS_FI_PRODUCTION.csv")2. Data Wrangling
2.1 Filter the Species
I looking for the names of colomn
names(species) #Check colomn names## [1] "3Alpha_Code" "Taxonomic_Code" "Identifier" "Name_En"
## [5] "Name_Fr" "Name_Es" "Name_Ar" "Name_Cn"
## [9] "Name_Ru" "Scientific_Name" "Author" "Family"
## [13] "Order" "Major_Group" "Yearbook_Group" "ISSCAAP_Group"
## [17] "CPC_Class" "CPC_Group"I use three colomn ‘3Alpha_Code’, Name_En and Scientific_Name to extract the code
Nama_Tuna <- species %>%
select('3Alpha_Code', Name_En, Scientific_Name) %>%
filter(Name_En %in% c("Atlantic bluefin tuna", "Southern bluefin tuna", "Pacific bluefin tuna", "Bigeye tuna", "Albacore", "Longtail tuna", "Blackfin tuna", "Yellowfin tuna")) #Filtering speciesSpecies
Nama_TunaThe code has been extracted, next replace the code with species names
Decode - Encode
I use Time Series dataset from 1950 to 2018
TUNA <- TS %>%
filter(SPECIES %in% c("BFT",
"PBF",
"LOT",
"BLF",
"ALB",
"SBF",
"YFT",
"BET"),
YEAR %in% 1950:2018)Replacing
I using sapply function for this step
TUNA$SPECIES <- sapply(as.character(TUNA$SPECIES), switch,
"BFT" = "Atlantic Bluefin Tuna",
"PBF" = "Pacific Bluefin Tuna",
"LOT" = "Longtail Tuna",
"BLF" = "Blackfin Tuna",
"ALB" = "Albacore",
"SBF" = "Southern Bluefin Tuna",
"YFT" = "Yellowfin Tuna",
"BET" = "Bigeye Tuna")2.2 The Country
Now, country who harvesting Tuna. There are 21 top country I got from FAO and then I filtering from country dataset
topFAO <- country %>%
filter(Name_En %in% c("Thailand", "Spain", "Ecuador", "Taiwan Province of China", "China", "Indonesia", "Korea, Republic of", "Viet Nam", "Philippines", "Netherlands", "Mauritius", "Japan", "United States of America", "Italy", "France", "Germany", "United Kingdom", "Australia", "Canada", "Portugal", "Saudi Arabia")) %>%
select(UN_Code, Name_En)OK, I got the code of country
Filter Code of Country
TUNA <- TUNA %>%
filter(COUNTRY %in% c("036", "124", "156", "158", "218", "250", "276", "360", "380", "392", "410", "480", "528", "608", "620", "682", "704", "724", "764", "826", "840"))After that I do the same things like before on Species
Decode - Encode
TUNA$COUNTRY <- sapply(as.character(TUNA$COUNTRY), switch,
"036" = "Australia",
"124" = "Canada",
"156" = "China",
"158" = "Taiwan Province of China",
"218" = "Ecuador",
"250" = "France",
"276" = "Germany",
"360" = "Indonesia",
"380" = "Italy",
"392" = "Japan",
"410" = "Korea, Republic of",
"480" = "Mauritius",
"528" = "Netherlands",
"608" = "Philippines",
"620" = "Portugal",
"682" = "Saudi Arabia",
"704" = "Viet Nam",
"724" = "Spain",
"764" = "Thailand",
"826" = "United Kingdom",
"840" = "United States of America")2.3 Fishing Ground
Now, where the fish is capturing. The Fishing ground
fishing_ground <- waterarea %>%
filter(Code %in% c("77", "87", "21", "67", "61", "71", "27", "31", "51", "57", "81", "34", "37", "47", "41")) %>%
select(Code, Name_en)TUNA$AREA <- sapply(as.character(TUNA$AREA), switch,
"77" = "Pacific, Eastern Central",
"87" = "Pacific, Southeast",
"21" = "Atlantic, Northwest",
"67" = "Pacific, Northeast",
"61" = "Pacific, Northwest",
"71" = "Pacific, Western Central",
"27" = "Atlantic, Northeast",
"31" = "Atlantic, Western Central",
"51" = "Indian Ocean, Western",
"57" = "Indian Ocean, Eastern",
"81" = "Pacific, Southwest",
"34" = "Atlantic, Eastern Central",
"37" = "Mediterranean and Black Sea",
"47" = "Atlantic, Southeast",
"41" = "Atlantic, Southwest")2.4 Source
Another to hervesting the Tuna is from Marine Aquaculture
TUNA$SOURCE <- sapply(as.character(TUNA$SOURCE), switch,
"3" = "Marine Aquaculture",
"4" = "Capture Fisheries")2.5 Sub-Genus
In this step I did a little future enginering, create a new colomn ._.
TUNA <- TUNA %>%
select(-UNIT, -SYMBOL)
Thunnus <- TUNAThunnus <- Thunnus %>%
mutate(FIN = SPECIES)Next, species grouping by their sub-genus
Thunnus$FIN <- sapply(as.character(Thunnus$FIN), switch,
"Atlantic Bluefin Tuna" = "Bluefin",
"Pacific Bluefin Tuna" = "Bluefin",
"Longtail Tuna" = "Yellowfin",
"Blackfin Tuna" ="Yellowfin",
"Albacore" = "Bluefin",
"Southern Bluefin Tuna" = "Bluefin",
"Yellowfin Tuna" = "Yellowfin",
"Bigeye Tuna" = "Bluefin")Thunnus <- Thunnus %>%
rename(Country = COUNTRY,
Fishing_Ground = AREA,
Source = SOURCE,
Species = SPECIES,
Year = YEAR,
Quantity = QUANTITY,
Fin = FIN)
Thunnus$Year <- as.factor(Thunnus$Year)
Thunnus$Fin <- as.factor(Thunnus$Fin)
Thunnus$Species <- as.factor(Thunnus$Species)
Thunnus$Fishing_Ground <- as.factor(Thunnus$Fishing_Ground)3 EDA | Exploratory Data Analysis
3.1 Total Catch by Fishing Ground
Thunnus %>%
select(Country, Species, Source, Year, Quantity, Fishing_Ground) %>%
filter(Year == 2018,
Fishing_Ground == "Pacific, Southeast") %>%
group_by(Species) %>%
summarise(Quantity = sum(Quantity)) %>%
#arrange(QUANTITY) %>%
ggplot(mapping = aes(x = Quantity,
y = reorder(Species, Quantity))) +
geom_col(mapping = aes(fill = Species)) +
theme(legend.position = "none")
Pacific, Southeast is the closest ocean to Indonesia, it could be that Indonesian fishermen catch tuna at this fishing ground. The species most frequently capture is Yellowfin Tuna, which is NT IUCN status or near threatened. However, if they are almost threatened, then are they the most worthy of exploitation?
3.2 Total Yearly Catch by Sub-Genus
Thunnus %>%
select(Species, Fin, Year, Quantity) %>%
filter(Year == 2018) %>%
group_by(Fin) %>%
summarise(Quantity = sum(Quantity)) %>%
plot_ly(labels = ~Fin,
values = ~Quantity,
type = 'pie')3.3 Total Yearly Catch by Species
Thunnus %>%
select(Species, Fin, Year, Quantity) %>%
filter(Year == 2018) %>%
group_by(Species) %>%
summarise(Quantity = sum(Quantity)) %>%
plot_ly(labels = ~Species,
values = ~Quantity,
type = 'pie')3.4 Total Yearly Catch by Country
Thunnus %>%
select(Country, Species, Source, Year, Quantity) %>%
filter(Year == 2018) %>%
group_by(Country) %>%
summarise(Quantity = sum(Quantity)) %>%
#arrange(QUANTITY) %>%
ggplot(mapping = aes(x = Quantity,
y = reorder(Country, Quantity))) +
geom_col(mapping = aes(fill = Country)) +
theme(legend.position = "none")
3.5 Time Series
Blue Fin X Yellow Fin (1950 - 2018)
Thunnus %>%
filter(Source == "Capture Fisheries",
Year %in% 1950:2018) %>%
select(Species, Fin, Year, Quantity) %>%
group_by(Fin, Year) %>%
summarise(Quantity = sum(Quantity)) %>%
arrange(-Year) %>%
ggplot(mapping = aes(x = Year,
y = Quantity,
color = Fin,
group = Fin)) +
geom_line() +
geom_point() +
geom_vline(xintercept = 31, linetype="dashed",
color = "blue", size= 1) +
theme(legend.position = "none",
axis.text.x = element_text(angle = 90, size = 7, vjust =0.5, margin=margin(5,0,0,0)))
3.6 Trend
Sub-Genus Yellowfin Tuna
Thunnus %>%
filter(Source == "Capture Fisheries",
Year %in% 1988:2018,
Fin == "Yellowfin") %>%
select(Species, Fin, Year, Quantity) %>%
group_by(Fin, Year) %>%
summarise(Quantity = sum(Quantity)) %>%
arrange(-Year) %>%
ggplot(mapping = aes(x = Year,
y = Quantity,
color = Fin,
group = Fin)) +
geom_line() +
geom_point() +
geom_smooth(method = "lm") +
theme(legend.position = "none",
axis.text.x = element_text(angle = 90, size = 7, vjust =0.5, margin=margin(5,0,0,0)))
Sub-Genus Bluefin Tuna
Thunnus %>%
filter(Source == "Capture Fisheries",
Year %in% 1988:2018,
Fin == "Bluefin") %>%
select(Species, Fin, Year, Quantity) %>%
group_by(Fin, Year) %>%
summarise(Quantity = sum(Quantity)) %>%
arrange(-Year) %>%
ggplot(mapping = aes(x = Year,
y = Quantity,
color = Fin,
group = Fin)) +
geom_line() +
geom_point() +
geom_smooth(method = "lm") +
theme(legend.position = "none",
axis.text.x = element_text(angle = 90, size = 7, vjust =0.5, margin=margin(5,0,0,0)))
3.7 Capture vs Culture
Thunnus %>%
select(Source, Year, Quantity) %>%
filter(Year == 2018) %>%
group_by(Source) %>%
summarise(Quantity = sum(Quantity)) %>%
plot_ly(labels = ~Source,
values = ~Quantity,
type = 'pie',
title = "Total Catch by Source in 2018",
textinfo = "none")3.8 The Rising of Marine Aquaculture
Thunnus %>%
filter(Source == "Marine Aquaculture",
Year %in% 1980:2018) %>%
select(Species, Fin, Year, Quantity) %>%
group_by(Fin, Year) %>%
summarise(Quantity = sum(Quantity)) %>%
arrange(-Year) %>%
ggplot(mapping = aes(x = Year,
y = Quantity,
color = Fin,
group = Fin)) +
geom_line()+
geom_point() +
geom_vline(xintercept = 31, linetype="dashed",
color = "blue", size= 1) +
theme(legend.position = "none",
axis.text.x = element_text(angle = 90, size = 7, vjust =0.5, margin=margin(5,0,0,0)))