FinalProject

Crime Across South America

#make SF object for south america and left join crime data by country name
sa_map <- ne_countries(continent = 'south america', returnclass = 'sf')

sa_map <- left_join(sa_map, southamerica_crime, by = "sovereignt") |> 
  select(name, pop_est, Country, sovereignt, Year, Series, Value, geometry)

#  see if you can wrap by series, and only plot the value for each individual series
p1 <- ggplot(data = southamerica_crime, mapping = aes(x = Year, y = Value)) +
  geom_smooth() + 
  scale_y_log10(labels = comma) +
  facet_wrap(~sovereignt) +
  labs(Title = 'Total Crime Rates Per South American Nation',
       caption = "Data sourced from: https://data.un.org/")

p1 + enter_fade() + exit_recolor()

#join in cartel data

drug_clean <- drug_data |>
  count(Countryofseizure)

drug_clean <- mutate(drug_clean, 
                     Countryofseizure = case_when(
                       Countryofseizure == 'Bolivia (Plurinational State of)' ~ 'Bolivia',
                        Countryofseizure == 'Venezuela (Bolivarian Republic of)' ~ 'Venezuela',
                         Countryofseizure == 'Argentina' ~ 'Argentina',
                         Countryofseizure == 'Colombia' ~ 'Colombia',
                         Countryofseizure == 'Peru' ~ 'Peru',
                         Countryofseizure == 'Paraguay' ~ 'Paraguay',
                         Countryofseizure == 'Chile' ~ 'Chile',
                         Countryofseizure == 'Brazil' ~ 'Brazil',
                         Countryofseizure == 'French Guiana' ~ 'French Guiana',
                         Countryofseizure == 'Uruguay' ~ 'Uruguay',
                         Countryofseizure == 'Ecuador' ~ 'Ecuador',
                         Countryofseizure == 'Guyana' ~ 'Guyana',
                         Countryofseizure == 'Suriname' ~ 'Suriname',
                         Countryofseizure == 'Belize' ~ 'Belize',
                         Countryofseizure == 'Falkland Islands' ~ 'Falkland Islands'))

colnames(drug_clean)[1] = "sovereignt"


final_map <- left_join(sa_map, drug_clean, by = 'sovereignt')

#make leaflet with all the data used so far layered neatly 

pal <- colorNumeric(palette = c('grey', 'pink', 'red'),
                    domain = final_map$n)

s <- leaflet(data = final_map) |>
  addProviderTiles("Thunderforest.OpenCycleMap") |>
  addPolygons(stroke = FALSE,
              smoothFactor = 0.2,
              color = ~pal(n),
              opacity = 0.35,
              fillOpacity = 0.25) |>
  addLegend( colors = "White",
    labels = "Drug busts are concentrated in Colombia and Bolivia",
    title = "Drugs and Violence in South America",
    opacity = 0.5,
    position = "bottomleft")
s