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# Establish the vector scalar for the wind
wind_uv_scalar <- 0.5

# The bottom figure (air)
mg_bottom <- ggplot(data = southern_africa_coast, aes(x = lon, y = lat)) +
  # The ocean temperature
    geom_raster(data = air_temp, aes(fill = value)) +
  # The land mass
    geom_polygon(aes(group = group), fill = NA, colour = "black", size = 0.5, show.legend = FALSE) +
    geom_path(data = africa_borders, aes(group = group)) +

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# False Bay inset
fb <- ggplot(data = sa_shore, aes(x = lon, y = lat)) +
  # The land mass
    geom_polygon(aes(group = PID),
                fill = "grey70", colour = NA, size = 0.5, show.legend = FALSE) +
  # The in situ sites
    geom_point(data = site_list, shape = 1,  size = 3, colour = "black") +
    geom_text(data = site_list, aes(label = order), size = 2.3, colour = "red") +
  # Text label
    geom_text(aes(x = 18.65, y = -34.25, label = "False\nBay"), size = 2.7) +

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# Establish the vector scalar for the currents
current_uv_scalar <- 2

# The top figure (sea)
mg_top <- ggplot(data = southern_africa_coast, aes(x = lon, y = lat)) +
  # The ocean temperature
    geom_raster(data = sea_temp, aes(fill = value)) +
  # The bathymetry
    stat_contour(data = sa_bathy[sa_bathy$depth < -200 & sa_bathy$depth > -2000,], 
                 aes(x = lon, y = lat, z = depth, alpha = ..level..),

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# Devide the reanalysis data
sea_temp <- filter(all_jan1_0.5, variable == "BRAN/temp")
air_temp <- filter(all_jan1_0.5, variable == "ERA/temp")
currents <- filter(all_jan1_0.5, variable == "BRAN/u" | variable == "BRAN/v") %>% 
  select(-date, -index) %>% 
  spread(key = variable, value = value) %>% 
  rename(u = "BRAN/u", v = "BRAN/v")
winds <- filter(all_jan1_0.5, variable == "ERA/u" | variable == "ERA/v") %>% 
  select(-date, -index) %>% 
  spread(key = variable, value = value) %>% 

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## Libraries
library(tidyverse)
library(viridis)
library(gridExtra)

## Data
# South Africa map data
load("../data/southern_africa_coast.Rdata") # Lowres
names(southern_africa_coast)[1] <- "lon"
load("../data/sa_shore.Rdata") # Hires

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# Top 10 goat having countries
goats_top <- goats %>% 
  arrange(desc(value)) %>% 
  filter(ccode %in% head(unique(ccode), 10))

# Bottom 10
goats_bottom <- goats %>% 
  arrange(value) %>% 
  filter(ccode %in% head(unique(ccode), 10))

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goats %>% 
  group_by(year) %>% 
  select(-ccode, -country.name) %>% 
  summarise(value = mean(value)) %>% 
  ggplot(aes(x = year, y = value)) +
  geom_boxplot(data = goats, aes(group = year)) +
  geom_smooth(method = "lm")

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# Load libraries
library(tidyverse)
library(gridExtra)

# Load data
goats <- read_csv("../data/GoatsperCapita_Compact.csv") %>% 
  filter(year >= 1900)

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# Returns
return_bar <- ggplot(data = removals, aes(x = Year, y = Returns)) +
  geom_col(aes(fill = Party), colour = "black") +
  # geom_smooth(method = "lm", colour = "black") +
  labs(x = NULL, y = "Immigrants Returned") +
  scale_fill_manual(values = c("slateblue1", "firebrick1")) +
  ggtitle("Returns")

# Removals
removal_bar <- ggplot(data = removals, aes(x = Year, y = Removals)) +

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# Calculate residuals
green_card_resids <- augment(lm(Number~Year, data = green_card))
green_card_resids <- merge(green_card_resids, party_year, by = "Year")

# Plot them
ggplot(data = green_card_resids, aes(x = Year, y = .resid)) +
  geom_col(aes(fill = Party), colour = "black") +
  geom_smooth(method = "lm", colour = "black") +
  labs(x = NULL, y = "Green Cards Granted") +
  scale_fill_manual(values = c("slateblue1", "firebrick1"))