0.1 Working with FAO data for Caribbean region

data retrieved by Dr. Einar of Iceland from FAO website. He is having full code of data retrieve from the website.

0.2 Call the R libraries

library(tidyverse)

Warning: package 'ggplot2' was built under R version 4.3.3

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(plotly)


Attaching package: 'plotly'

The following object is masked from 'package:ggplot2':

    last_plot

The following object is masked from 'package:stats':

    filter

The following object is masked from 'package:graphics':

    layout

library("ggplot2")
library(gridExtra)


Attaching package: 'gridExtra'

The following object is masked from 'package:dplyr':

    combine

library(patchwork)

0.3 Read the data from the Iceland website

fao <- read_csv("https://heima.hafro.is/~einarhj/data/fao-capture-statistics_area31.csv")

Rows: 66673 Columns: 9
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (6): country, species, measure, sid, latin, country_iso3
dbl (3): area, year, catch

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

x=fao|>distinct(country)

x

# A tibble: 61 × 1
   country                 
   <chr>                   
 1 Anguilla                
 2 Antigua and Barbuda     
 3 Aruba                   
 4 Bahamas                 
 5 Barbados                
 6 Belize                  
 7 Bermuda                 
 8 Bonaire/S.Eustatius/Saba
 9 Brazil                  
10 British Virgin Islands  
# ℹ 51 more rows

0.4 Country wise production trend in CRFM member countries

y= fao|> 
  filter(country %in% c("Barbados","Belize","Anguilla", "Antigua and Barbuda", "Bahamas", "Dominica", "Grenada", "Guyana", "Haiti", "Jamaica", "Montserrat", "Saint Kitts and Nevis", "Saint Lucia", "Saint Vincent/Grenadines", "Suriname", "Trinidad and Tobago", "Turks and Caicos Is."))|>
  group_by(country, year)|>
  summarise(catch=sum(catch))|>
  ggplot(aes(year, catch))+geom_col(fill="steelblue")+
  facet_wrap(~ country, scales="free_y", ncol = 3)

`summarise()` has grouped output by 'country'. You can override using the
`.groups` argument.

ggplotly(y)

The 17 member countries are defined and the data was extracted from the FAO data set stored in Iceland website

1 Flying fish catch trend

p1 = fao|> 
  filter(country %in% c("Barbados","Belize","Anguilla", "Antigua and Barbuda", "Bahamas", "Dominica", "Grenada", "Guyana", "Haiti", "Jamaica", "Montserrat", "Saint Kitts and Nevis", "Saint Lucia", "Saint Vincent/Grenadines", "Suriname", "Trinidad and Tobago", "Turks and Caicos Is.")) |>
  group_by(year)|>
  filter(str_starts(species, "Fly")) |> 
  select(year, species, catch)|> 
  summarise(catch=sum(catch))|> 
  ggplot(aes(year, catch))+ 
  geom_line(colour= "blue")+ 
  geom_smooth(colour="black")+
  ggtitle("Flying fish") + 
  theme_classic() + 
  labs(y = " Catch in tons", x = "Year") + 
  theme(axis.text = element_text(face="bold"))

ggplotly(p1)

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

1.1 Lobster catch trend

p2 = fao|> 
  filter(country %in% c("Barbados","Belize","Anguilla", "Antigua and Barbuda", "Bahamas", "Dominica", "Grenada", "Guyana", "Haiti", "Jamaica", "Montserrat", "Saint Kitts and Nevis", "Saint Lucia", "Saint Vincent/Grenadines", "Suriname", "Trinidad and Tobago", "Turks and Caicos Is." ))|>
  group_by(year)|>
  filter(grepl("lobster", species)) |> 
  select(year, species, catch)|> 
  summarise(catch=sum(catch))|> 
  ggplot(aes(year, catch))+ 
  geom_line(colour= "blue")+ 
  geom_smooth(colour="black")+
  ggtitle("Lobster") + 
  theme_classic()+ 
  labs(y = " Catch in tons", x = "Year") + 
  theme(axis.text = element_text(face="bold"))

ggplotly(p2)

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

1.2 Conch catch trend

p3=fao|> 
  filter(country %in% c("Barbados","Belize","Anguilla", "Antigua and Barbuda", "Bahamas", "Dominica", "Grenada", "Guyana", "Haiti", "Jamaica", "Montserrat", "Saint Kitts and Nevis", "Saint Lucia", "Saint Vincent/Grenadines", "Suriname", "Trinidad and Tobago", "Turks and Caicos Is." ))|>
  group_by(year)|>
  filter(grepl("conch", species)) |> 
  select(year, species, catch)|> 
  summarise(catch=sum(catch))|> 
  ggplot(aes(year, catch))+ 
  geom_line(colour= "blue")+ 
  geom_smooth(colour="black")+
  ggtitle("Conch") + 
  theme_classic()+ 
  labs(y = " Catch in tons", x = "Year") + 
  theme(axis.text = element_text(face="bold"))

ggplotly(p3)

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

1.3 Dolphinfish catch trend

p4=fao|> 
  filter(country %in% c("Barbados","Belize","Anguilla", "Antigua and Barbuda", "Bahamas", "Dominica", "Grenada", "Guyana", "Haiti", "Jamaica", "Montserrat", "Saint Kitts and Nevis", "Saint Lucia", "Saint Vincent/Grenadines", "Suriname", "Trinidad and Tobago", "Turks and Caicos Is." ))|>
  group_by(year)|>
  filter(grepl("dolphinfish", species)) |> 
  select(year, species, catch)|> 
  summarise(catch=sum(catch))|> 
  ggplot(aes(year, catch))+ 
  geom_line(colour= "blue")+ 
  geom_smooth(colour="black")+
  ggtitle("Dolphin fish") + 
  theme_classic()+ 
  labs(y = " Catch in tons", x = "Year") + 
  theme(axis.text = element_text(face="bold"))

ggplotly(p4)

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

2 Yellowfin tuna catch trend

p5 = fao|> 
  filter(country %in% c("Barbados","Belize","Anguilla", "Antigua and Barbuda", "Bahamas", "Dominica", "Grenada", "Guyana", "Haiti", "Jamaica", "Montserrat", "Saint Kitts and Nevis", "Saint Lucia", "Saint Vincent/Grenadines", "Suriname", "Trinidad and Tobago", "Turks and Caicos Is." ))|>
  group_by(year)|>
  filter(grepl("Yellowfin tuna", species)) |> 
  select(year, species, catch)|> 
  summarise(catch=sum(catch))|> 
  ggplot(aes(year, catch))+ 
  geom_line(colour= "blue")+ 
  geom_smooth(colour="black")+
  ggtitle("Yellow-fin tuna") + 
  theme_classic()+ 
  labs(y = " Catch in tons", x = "Year") + 
  theme(axis.text = element_text(face="bold"))

ggplotly(p5)

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

2.1 Attaching multiple graphs

p5/(p4+p3)/(p1+p2)+ 
  plot_annotation(title="Fish catch trend in CRFM regional scale", tag_levels = 'A', tag_prefix = 'Fig.') & theme(plot.title = element_text(hjust=0.5))

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

2.2 another way of presentation the result

p5|((p4+p3)/(p1+p2))+plot_annotation(title="Fish catch trend in CRFM regional scale", tag_levels = 'A', tag_prefix = 'Fig.') & theme(plot.title = element_text(hjust=0.5))

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

2.2.1 Save the plot

To save the plot just save the code as a name and use the following code

ggsave("combined1.png", dpi = 300, height = 10, width = 15, unit = 'in')