# Prep data
xx <- d1 %>% filter(STATE != 0) %>% arrange(desc(POPESTIMATE2022)) %>%
  mutate(NAME = factor(NAME, levels = .$NAME))
# Plot
mp <- ggplot(xx, aes(y = POPESTIMATE2022 / 1000000, x = NAME)) +
  geom_col(fill = "darkgreen", alpha = 0.7) +
  theme_agData(axis.text.x = element_text(angle = 45, hjust = 1)) +
  labs(title = "Population By State", y = "Million People", x = NULL)
ggsave("usa_population_1_01.png", mp, width = 8, height = 4)

# Prep data
xx <- d1 %>% rename(fips=STATE) %>% mutate(POPESTIMATE2022 = POPESTIMATE2022 / 1000000)
# Plot
mp <- plot_usmap(data = xx, values = "POPESTIMATE2022", exclude = c("AK","HI")) + 
  scale_fill_gradient(name = "x 1,000,000", 
                       high = "darkgreen", low = "white") + 
  theme(legend.position = "right") +
  labs(title = "Population By State")
ggsave("usa_population_1_02.png", mp, width = 6, height = 4, bg = "white")

# colnames(d1)
#
x1 <- d1 %>% select(Name=NAME, BasePop=POPESTIMATE2010)
xx <- d1 %>% 
  select(Name=NAME, fips=STATE,
         POPESTIMATE2010, POPESTIMATE2011, POPESTIMATE2012, POPESTIMATE2013,
         POPESTIMATE2014, POPESTIMATE2015, POPESTIMATE2016, POPESTIMATE2017,
         POPESTIMATE2018, POPESTIMATE2019, POPESTIMATE2020, POPESTIMATE2021,
         POPESTIMATE2022) %>%
  gather(Year, Population, 3:ncol(.)) %>% 
  left_join(x1, by = "Name") %>% 
  mutate(Year = as.integer(gsub("POPESTIMATE", "", Year)),
         Change = 100 * (Population - BasePop) / BasePop)
# Plot
mp <- plot_usmap(data = xx, values = "Change", exclude = c("AK","HI")) + 
  scale_fill_gradient2(name = "%", 
                       high = "darkgreen", mid = "white", low = "darkred") + 
  theme(legend.position = "right",
        plot.background = ) + 
  labs(title = paste("Year =", "{frame_time}"), subtitle = "Percent Change Since 2010") +
  transition_time(Year)
anim_save("usa_population_state_gif_01.gif", mp,
          nframes = 300, fps = 10, end_pause = 100, 
          width = 600, height = 400, res = 100)

# Prep data
myStates <- c("California", "New York", "Texas", "Florida")
myColors <- c("darkgreen", "steelblue", "darkred", "darkorange")
xx <- d1 %>% 
  filter(NAME %in% myStates) %>%
  select(NAME, NPOPCHG_2013, NPOPCHG_2014, NPOPCHG_2015, NPOPCHG_2016, 
         NPOPCHG_2017, NPOPCHG_2018, NPOPCHG_2019, NPOPCHG_2020, NPOPCHG_2021,
         NPOPCHG_2022) %>%
  gather(Year, Value, 2:ncol(.)) %>%
  mutate(Year = as.factor(substr(Year, 9, 12)),
         NAME = factor(NAME, levels = myStates))
# Plot
mp <- ggplot(xx, aes(x = Year, Value / 100000, fill = NAME)) + 
  geom_bar(stat = "identity", color = "black", alpha = 0.7) +
  facet_grid(. ~ NAME) +
  scale_fill_manual(values = myColors) +
  theme_agData(legend.position = "none",
               axis.text.x = element_text(angle = 45, hjust = 1)) +
  labs(title = "Population Change", x = NULL, 
       y = "Hundred Thousand People", caption = myCaption)
ggsave("usa_population_1_03.png", mp, width = 10, height = 6)

# Create plotting function
ggStatePop <- function(myTrait, myTitle, myFacet, myLims = 525) {
  xx <- d1 %>% 
    filter(SUMLEV == 40) %>%
    arrange(get(myTrait)) %>%
    mutate(NAME = factor(NAME, levels = .$NAME))
  # Plot
  mp <- ggplot(xx, aes(x = NAME, y = get(myTrait) / 1000)) +
    geom_bar(stat = "identity", color = "black", lwd = 0.4,
             fill = "steelblue", alpha = 0.7) +
    facet_grid(. ~ paste(myFacet)) +
    ylim(c(-myLims, myLims)) +
    coord_flip() +
    theme_agData(legend.position = "none") +
    labs(title = myTitle, x = NULL, 
         y = "Thousand People", caption = myCaption)
}

# Plot
mp <- ggStatePop("NPOPCHG_2022", "Population Change", "2023")
ggsave("usa_population_2_02.png", mp, width = 4, height = 6)

# Prep data
xx <- d2 %>% 
  filter(Year == 2021, Age != "Median age", Sex != "Both sexes") 
yy <- xx %>% spread(Sex, Population) %>% 
  mutate(Population = Females - Males,
         Sex = ifelse(Population < 0, "Males", "Females"))
xx <- xx %>% 
  mutate(Population = ifelse(Sex == "Males", -Population, Population))
# Plot
mp <- ggplot(xx, aes(y = Population / 1000000, x = Age, fill = Sex)) + 
  geom_col(color = "black", alpha = 0.7) +
  geom_col(data = yy, color = "black", alpha = 0.7) +
  scale_fill_manual(name = NULL, values = myColorsMF) +
  facet_grid(. ~ Year) + 
  theme_agData(legend.position = "bottom") + 
  labs(title = "Population In The United States", x = NULL, 
       y = "Million People", caption = myCaption) +
  coord_cartesian(ylim = c(-max(xx$Population), max(xx$Population))) +
  coord_flip()
ggsave("usa_population_3_01.png", mp, width = 6, height = 4)

# Prep data
xx <- d2 %>% 
  filter(Year %in% c(2007, 2021), 
         Age != "Median age", Sex != "Both sexes") 
yy <- xx %>% spread(Sex, Population) %>% 
  mutate(Population = Females - Males,
         Sex = ifelse(Population < 0, "Males", "Females"))
xx <- xx %>% 
  mutate(Population = ifelse(Sex == "Males", -Population, Population))
# Plot
mp <- ggplot(xx, aes(y = Population / 1000000, x = Age, fill = Sex)) + 
  geom_col(color = "black", alpha = 0.7) +
  geom_col(data = yy, color = "black", alpha = 0.7) +
  scale_fill_manual(name = NULL, values = myColorsMF) +
  facet_grid(. ~ Year) + 
  theme_agData(legend.position = "bottom") + 
  labs(title = "Population Change in The United States", x = NULL, 
       y = "Million People", caption = myCaption) +
  coord_cartesian(ylim = c(-max(xx$Population), max(xx$Population))) +
  coord_flip()
ggsave("usa_population_3_02.png", mp, width = 8, height = 4)

# Prep data
xx <- d2 %>% filter(Year %in% c(2007,2021), Sex == "Both sexes") 
yy <- xx %>% spread(Year, Population) %>%
  mutate(Population = `2021` - `2007`) %>%
  mutate(Year = ifelse(Population < 0, 2007, 2021),
         Year = factor(Year))
xx <- xx %>% 
  mutate(Population = ifelse(Year == 2007, -Population, Population),
         Year = factor(Year))
# Plot
mp <- ggplot(xx, aes(y = Population / 1000000, x = Age, fill = Year)) + 
  geom_col(color = "black", alpha = 0.7) +
  geom_col(data = yy, color = "black", alpha = 0.7) +
  scale_fill_manual(name = NULL, values = c("darkgreen","purple4")) +
  theme_agData(legend.position = "bottom") + 
  labs(title = "Population Change in The United States", x = NULL, 
       y = "Million People", caption = myCaption) +
  coord_cartesian(ylim = c(-max(xx$Population), max(xx$Population))) +
  coord_flip()
ggsave("usa_population_3_03.png", mp, width = 6, height = 4)

# Prep data
myPartyColors <- c("darkblue","darkred","darkgreen")
xx <- read.csv("data_us_elections.csv") %>% 
  mutate(Party = factor(Party, levels = c("Dem","Rep","Other")),
         Year = factor(Year))
# Plot
mp <- ggplot(xx, aes(x = Year, y = Votes / 1000000, fill = Party)) +
  geom_col(position = "dodge", color = "black", lwd = 0.2, alpha = 0.7) + 
  scale_fill_manual(values = myPartyColors) +
  theme_agData(legend.position = "bottom") +
  labs(title = "Votes in US Elections", x = NULL, y = "Million Votes")
ggsave("usa_population_4_01.png", mp, width = 6, height = 4)

# Plot
mp <- ggplot(xx, aes(x = Year, y = Votes / 1000000, fill = Party)) +
  geom_col(color = "black", lwd = 0.2, alpha = 0.7) + 
  scale_fill_manual(values = myPartyColors) +
  theme_agData(legend.position = "bottom") +
  labs(title = "Votes in US Elections", x = NULL, y = "Million Votes")
ggsave("usa_population_4_02.png", mp, width = 6, height = 4)

mp <- ggplot(xx, aes(x = Party, y = Votes / 1000000, fill = Party)) +
  geom_col(color = "black", alpha = 0.7) + 
  geom_text(aes(label = Candidate), angle = 90, y = 20) +
  facet_grid(. ~ Year) +
  scale_fill_manual(values = myPartyColors) +
  theme_agData(legend.position = "bottom",
               axis.text.x = element_blank(),
               axis.ticks.x = element_blank()) +
  labs(title = "US Election Results", x = NULL, y = "Million Votes")
ggsave("usa_population_4_03.png", mp, width = 6, height = 4)