Data source: UNIVALI/EMCT/LEMA. Estatística Pesqueira de Santa Catarina. Consulta On-line. Projeto de Monitoramento da Atividade Pesqueira do Estado de Santa Catarina. Laboratório de Estudos Marinhos Aplicados (LEMA), da Escola do Mar, Ciência e Tecnologia (EMCT) da Universidade do Vale do Itajaí (UNIVALI). 2020. Disponível em: http://pmap-sc.acad.univali.br/. Acesso em: 01/07/2021.

NOTE: The data on industrial fishing in Santa Catarina for the years 2013, 2014, and 2015 are partial (do not represent the entire production of the state for any type of fishing or species, and may also contain errors)

#Read in data and update the column names to lower snake case:
sc_trawl <- read_csv(here("data", "relatorio30_17.csv")) %>% #NOTE: There was a problem reading the original CSV. I had to convert the file to CSV UTF-8 to be able to read it.
  clean_names()

Data wrangling and exploratory visualization

#Data wrangling:
sc_trawl_clean <- sc_trawl %>% 
  mutate(kg_no_periodo = str_replace(kg_no_periodo, pattern = ",.*", replacement = "")) %>% #remove ,00
  mutate(kg_no_periodo = kg_no_periodo %>% str_remove_all("\\.")) %>% #remove .
  mutate(kg_no_periodo = as.numeric(kg_no_periodo)) #convert to class numeric
  
sc_trawl_year <- sc_trawl_clean %>% 
  group_by(ano, aparelho_de_pesca) %>% 
  summarise(kg_no_periodo = sum(kg_no_periodo))

sc_trawl_landings <- sc_trawl_clean %>% 
  group_by(ano, aparelho_de_pesca) %>% 
  summarise(no_de_descargas_do_periodo = sum(no_de_descargas_do_periodo))

sc_trawl_municipio <- sc_trawl_clean %>% 
  group_by(ano, municipio) %>% 
  summarise(kg_no_periodo = sum(kg_no_periodo)) %>% 
  filter(municipio != "Florianópolis") %>% #exclude Florianópolis (only data for 2009 and 2009)
  rename(Municipality = municipio)

sc_trawl_municipio1 <- sc_trawl_clean %>% 
  group_by(ano, municipio) %>% 
  summarise(no_de_descargas_do_periodo = sum(no_de_descargas_do_periodo)) %>% 
  filter(municipio != "Florianópolis") %>% #exclude Florianópolis (only data for 2009 and 2009)
  rename(Municipality = municipio)

sc_trawl_fish <- sc_trawl_clean %>% 
  group_by(ano, pescado) %>% 
  summarise(kg_no_periodo = sum(kg_no_periodo)) %>% 
  rename(Species = pescado)
ggplot(sc_trawl_year, aes(x = ano, y = kg_no_periodo))+
  geom_col() +
  facet_wrap(~aparelho_de_pesca) +
  scale_y_continuous(labels = scales::label_number_si())+
  scale_x_continuous(breaks=c(2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019))+
  theme_minimal()+
  labs(x = "Year",
       y = "Production (kg)",
       title = "Fishing Gear: Production (kg)")+
  theme(axis.text.x = element_text(angle = 90))

ggplot(sc_trawl_landings, aes(x = ano, y = no_de_descargas_do_periodo))+
  geom_col() +
  facet_wrap(~aparelho_de_pesca) +
  scale_y_continuous(labels = scales::label_number_si())+
  scale_x_continuous(breaks=c(2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019))+
  theme_minimal()+
  labs(x = "Year",
       y = "Number of landings",
       title = "Fishing Gear: Landings")+
  theme(axis.text.x = element_text(angle = 90))

muni_plot <- ggplot(sc_trawl_municipio, aes(x= ano, y = kg_no_periodo))+
  geom_line(aes(color= Municipality)) +
  scale_y_continuous(labels = scales::label_number_si())+
  scale_x_continuous(breaks=c(2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019))+
  theme_minimal()+
  labs(x = "Year",
       y = "Production (kg)",
       title = "Municipality: Production (kg)")+
  theme(axis.text.x = element_text(angle = 90))
ggplotly(muni_plot)
muni_plot1 <- ggplot(sc_trawl_municipio1, aes(x= ano, y = no_de_descargas_do_periodo))+
  geom_line(aes(color= Municipality)) +
  scale_y_continuous(labels = scales::label_number_si())+
  scale_x_continuous(breaks=c(2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019))+
  theme_minimal()+
  labs(x = "Year",
       y = "Number of landings",
       title = "Municipality: Landings")+
  theme(axis.text.x = element_text(angle = 90))
ggplotly(muni_plot1)
fish_plot <- ggplot(sc_trawl_fish, aes(x= ano, y = kg_no_periodo))+
  geom_line(aes(color= Species)) +
  scale_y_continuous(labels = scales::label_number_si())+
  scale_x_continuous(breaks=c(2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019))+
  theme_minimal()+
  labs(x = "Year",
       y = "Production (kg)",
       title = "Fish species: Production (kg)")+
  theme(axis.text.x = element_text(angle = 90))
ggplotly(fish_plot)

NOTE: double click on a species to isolate the data.