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data_gender <- years_of_education[,-5] %>% 
  left_join(., gender_equality_education[,-5], by = c("ccode", "country.name", "year")) %>% 
  rename(., education_years = value.x, gender_equality_education = value.y)
data_gender <- data_gender[complete.cases(data_gender$gender_equality_education),]

lm_data_gender1 <- glance(lm(gender_equality_education ~ education_years, data = data_gender))
lm_data_gender2 <- glance(lm(gender_equality_education ~ education_years, 
                             data = filter(data_gender, education_years >= 5)))

# And now for a scatterplot

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# Load libraries
library(tidyverse)
library(lubridate)
library(reshape2)
library(MBA)
library(mgcv)

# Load and screen data
  # For ease I am only using monthly means
  # and depth values rounded to 0.1 metres

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# The date column must then be converted to numeric values
ctd$date <- decimal_date(ctd$date)

# Now we may interpolate the data
ctd_mba <- mba.surf(ctd, no.X = 300, no.Y = 300, extend = T)
dimnames(ctd_mba$xyz.est$z) <- list(ctd_mba$xyz.est$x, ctd_mba$xyz.est$y)
ctd_mba <- melt(ctd_mba$xyz.est$z, varnames = c('date', 'depth'), value.name = 'temp') %>% 
  filter(depth < 0) %>% 
  mutate(temp = round(temp, 1))

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# Create a bounding box
  # We want to slightly extend the edges so as to use all of our data
left <- ctd[ctd$date == min(ctd$date),] %>% 
  select(-temp) %>% 
  ungroup() %>% 
  mutate(date = date-0.01)
bottom <- ctd %>%
  group_by(date) %>% 
  summarise(depth = min(depth)) %>% 
  mutate(depth = depth-0.01)

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# Libraries
library(tidyverse)
library(lubridate)
library(broom)
library(gridExtra)

# President data
data(presidential)
presidential$start <- year(presidential$start)
presidential$end <- year(presidential$end)-1

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ggplot(data = green_card, aes(x = Year, y = Number)) +
  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"))

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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"))

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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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# Load libraries
library(tidyverse)
library(gridExtra)

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

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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")