# devtools::install_github("derekmichaelwright/agData")
library(agData)
library(readxl)

# Prep data
myCaption <- "www.dblogr.com/ or derekmichaelwright.github.io/dblogr/ | Data: USCB"
myColorsMF <- c("steelblue", "palevioletred3")
myAges <- c("0 to 4 years", "5 to 9 years", "10 to 14 years", 
            "15 to 19 years", "20 to 24 years", "25 to 29 years",
            "30 to 34 years", "35 to 39 years", "40 to 44 years",
            "45 to 49 years", "50 to 54 years", "55 to 59 years",
            "60 to 64 years", "65 to 69 years", "70 to 74 years",
            "75 to 79 years", "80 to 84 years", "85 years and over")
#
fixSheet <- function(xx, myYear) { 
  colnames(xx) <- c("Age", "Both sexes", "Both sexes - Percent",
                    "Males", "Males - Percent",
                    "Females", "Females - Percent")
  xx <- xx %>% select(Age, `Both sexes`, Males, Females) %>%
    mutate(Age = gsub("\\.", "", Age),
           Year = myYear) %>%
    select(Year, everything()) %>%
    gather(Sex, Population, 3:ncol(.)) %>%
    mutate(Population = 1000 * Population)
  xx
}
xx <- read_xlsx("data_usa_population.xlsx", "2007", range = "A7:G25")
p1 <- bind_rows(
    read_xlsx("data_usa_population.xlsx", "2007", range = "A7:G25") %>% fixSheet(myYear = 2007),
    read_xlsx("data_usa_population.xlsx", "2008", range = "A7:G25") %>% fixSheet(myYear = 2008),
    read_xlsx("data_usa_population.xlsx", "2009", range = "A7:G25") %>% fixSheet(myYear = 2009),
    read_xlsx("data_usa_population.xlsx", "2010", range = "A7:G25") %>% fixSheet(myYear = 2010),
    read_xlsx("data_usa_population.xlsx", "2011", range = "A7:G25") %>% fixSheet(myYear = 2011),
    read_xlsx("data_usa_population.xlsx", "2012", range = "A7:G25") %>% fixSheet(myYear = 2012),
    read_xlsx("data_usa_population.xlsx", "2013", range = "A7:G25") %>% fixSheet(myYear = 2013),
    read_xlsx("data_usa_population.xlsx", "2014", range = "A7:G25") %>% fixSheet(myYear = 2014),
    read_xlsx("data_usa_population.xlsx", "2015", range = "A7:G25") %>% fixSheet(myYear = 2015),
    read_xlsx("data_usa_population.xlsx", "2016", range = "A7:G25") %>% fixSheet(myYear = 2016),
    read_xlsx("data_usa_population.xlsx", "2017", range = "A7:G25") %>% fixSheet(myYear = 2017),
    read_xlsx("data_usa_population.xlsx", "2018", range = "A7:G25") %>% fixSheet(myYear = 2018),
    read_xlsx("data_usa_population.xlsx", "2019", range = "A7:G25") %>% fixSheet(myYear = 2019),
    read_xlsx("data_usa_population.xlsx", "2020", range = "A7:G25") %>% fixSheet(myYear = 2020),
    read_xlsx("data_usa_population.xlsx", "2021", range = "A7:G25") %>% fixSheet(myYear = 2021) ) %>%
  mutate(Age = factor(Age, levels = myAges),
         Sex = factor(Sex, levels  = c("Both sexes", "Males", "Females")))
#
p2 <- bind_rows(read.csv("nation-populationpyramid-2000-2010-2020.csv"),
                read.csv("state-populationpyramid-2000-2010-2020.csv") )

# Prep data
xx <- p1 %>% 
  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_01.png", mp, width = 6, height = 4)

# Prep data
xx <- p1 %>% 
  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_02.png", mp, width = 8, height = 4)

# Prep data
xx <- p1 %>% 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_03.png", width = 6, height = 4)

myRaces1 <- c("White alone, not Hispanic or Latino",
             "Asian alone, not Hispanic or Latino", 
             "Black or African American alone, not Hispanic or Latino",
             "American Indian and Alaska Native alone, not Hispanic or Latino",
             "Hispanic or Latino")
myRaces2 <- c("White", "Asian", "Black", "Indigenous", "Hispanic")
myColors <- c("darkgreen", "darkblue", "black", "darkred", "darkorange")
#
xx <- p2 %>% filter(Group %in% myRaces1, Nation == "United States") %>%
  mutate(Group = plyr::mapvalues(Group, myRaces1, myRaces2),
         Group = factor(Group, levels = myRaces2))
#
mp <- ggplot(xx, aes(y = Median.age, x = Year, color = Group)) + 
  geom_line() + geom_point() +
  scale_color_manual(name = NULL, values = myColors) +
  guides(color = guide_legend(ncol = 1)) +
  theme_agData() + 
  labs(title = "Population Change in The United States", 
       y = "Median Age", x = NULL, caption = myCaption)
ggsave("usa_population_04.png", width = 6, height = 4)

# Prep data
xx <- read.csv("rural_urban.csv") %>%
  gather(Type, Value, Rural, Urban) %>%
  mutate(Value = 100 * Value)
# Plot
mp <- ggplot(xx, aes(x = Year, y = Value, color = Type)) +
  geom_line(size = 2) + 
  geom_label(aes(label = Value), size = 2.5, label.padding = unit(0.1, "lines")) +
  scale_color_manual(name = NULL, values = c("darkred", "darkblue")) +
  scale_x_continuous(breaks = unique(xx$Year), minor_breaks = unique(xx$Year)) +
  theme_agData(legend.position = "bottom") +
  labs(x = "Year (Election)", 
       y = "Percent of Votes Going to\nRepublicans Candidate (County-Level)",
       caption = "www.dblogr.com/ or derekmichaelwright.github.io/dblogr/ | Data: Brown & Mettler (2023)")
ggsave("usa_population_05.png", width = 7, height = 4)