# 1. Basic mapping with `ggmap`
library(ggplot2)
library(readr)
library(dplyr)
library(tidyr)
library(stringr)

spd_raw <- read_csv("https://clanfear.github.io/CSSS508/Seattle_Police_Department_911_Incident_Response.csv")
glimpse(spd_raw)

ggplot(spd_raw, 
       aes(Longitude, Latitude)) + 
  geom_point() + 
  coord_fixed() + # evenly spaces x and y
  ggtitle("Seattle Police Incidents",
          subtitle="March 25, 2016") +
  theme_classic()

# install.packages("ggmap")
# if(!requireNamespace("remotes")){install.packages("remotes")}
# remotes::install_github("dkahle/ggmap", ref = "tidyup")

library(ggmap)

qmplot(data = spd_raw,
       x = Longitude, 
       y = Latitude)

qmplot(data = spd_raw,
       x = Longitude, 
       y = Latitude,
       color=I("navy")
)

qmplot(data = spd_raw,
       x = Longitude, 
       y = Latitude,
       color=I("navy"),
       alpha=I(0.5))

qmplot(data = spd_raw,
       geom = "blank", #<<
       x = Longitude, 
       y = Latitude,
       darken = 0.5)+
  stat_density_2d(
    aes(fill = stat(level)), #<<
    geom = "polygon", 
    alpha = .2, color = NA
  )+
  scale_fill_gradient2(
    "Incident\nConcentration", 
    low = "white", 
    mid = "yellow", 
    high = "red")+
  theme(legend.position = "bottom")

qmplot(data = spd_raw,
       geom = "blank", #<<
       x = Longitude, 
       y = Latitude)

qmplot(data = spd_raw,
       geom = "blank",
       x = Longitude, 
       y = Latitude)+
  stat_density_2d( #<<
    aes(fill = stat(level)), #<<
    geom = "polygon") #<<

qmplot(data = spd_raw,
       geom = "blank",
       x = Longitude, 
       y = Latitude)+
  stat_density_2d(
    aes(fill = stat(level)),
    geom = "polygon", 
  )+
  scale_fill_gradient2( #<<
    low = "white", #<<
    mid = "yellow", #<<
    high = "red") #<<

qmplot(data = spd_raw,
       geom = "blank",
       x = Longitude, 
       y = Latitude,
       darken = 0.5)+ #<<
  stat_density_2d(
    aes(fill = stat(level)), 
    geom = "polygon", 
    alpha = .2, #<<
  )+
  scale_fill_gradient2(
    "Incident\nConcentration", #<<
    low = "white", 
    mid = "yellow", 
    high = "red")+
  theme(legend.position = "bottom") #<<

downtown <- spd_raw %>%
  filter(Latitude > 47.58, Latitude < 47.64,
         Longitude > -122.36, Longitude < -122.31)

assaults <- downtown %>% 
  filter(`Event Clearance Group` %in%
           c("ASSAULTS", "ROBBERY")) %>%
  mutate(assault_label = `Event Clearance Description`)

qmplot(data = downtown,
       x = Longitude,
       y = Latitude,
       maptype = "toner-lite",
       color = I("firebrick"),
       alpha = I(0.5)) + 
  geom_label(data = assaults,
             aes(label = assault_label),
             size=2)

library(ggrepel)
qmplot(data = 
         downtown,
       x = Longitude,
       y = Latitude,
       maptype = "toner-lite", 
       color = I("firebrick"), 
       alpha = I(0.5)) + 
  geom_label_repel(
    data = assaults,
    aes(label = assault_label), 
    size=2)


# 2. Advanced mapping

library(sf)

# Data Download Link: https://github.com/clanfear/CSSS508/raw/master/Lectures/Week9/data/district.zip
# After, ensure you have the correct working directory set below:
precinct_shape <- st_read("./data/district/votdst.shp",
                          stringsAsFactors = F) %>% 
  select(Precinct=NAME, geometry)

precincts_votes_sf <- 
  read_csv("./data/king_county_elections_2016.txt") %>%
  filter(Race=="US President & Vice President",
         str_detect(Precinct, "SEA ")) %>% 
  select(Precinct, CounterType, SumOfCount) %>%
  group_by(Precinct) %>%
  filter(CounterType %in% 
           c("Donald J. Trump & Michael R. Pence",
             "Hillary Clinton & Tim Kaine",
             "Registered Voters",
             "Times Counted")) %>%
  mutate(CounterType =
           recode(CounterType, 
                  `Donald J. Trump & Michael R. Pence` = "Trump",
                  `Hillary Clinton & Tim Kaine` = "Clinton",
                  `Registered Voters`="RegisteredVoters",
                  `Times Counted` = "TotalVotes")) %>%
  spread(CounterType, SumOfCount) %>%
  mutate(P_Dem = Clinton / TotalVotes, 
         P_Rep = Trump / TotalVotes, 
         Turnout = TotalVotes / RegisteredVoters) %>%
  select(Precinct, P_Dem, P_Rep, Turnout) %>% 
  filter(!is.na(P_Dem)) %>%
  left_join(precinct_shape) %>%
  st_as_sf() # Makes sure resulting object is an sf dataframe

glimpse(precincts_votes_sf)
ggplot(precincts_votes_sf, 
       aes(fill=P_Dem)) + #<<
  geom_sf(size=NA) +
  theme_void() +
  theme(legend.position = 
          "right")


library(tidycensus)
# census_api_key("PUT YOUR KEY HERE", install=TRUE)
acs_2015_vars <- load_variables(2015, "acs5")
acs_2015_vars[10:18,] %>% print() 


king_county <- get_acs(geography="tract", state="WA", 
                       county="King", geometry = TRUE,
                       variables=c("B02001_001E", 
                                   "B02009_001E"), 
                       output="wide")

glimpse(king_county)

king_county <-  king_county %>%
  select(-ends_with("M")) %>%
  rename(`Total Population`=B02001_001E,
         `Any Black`=B02009_001E) %>%
  mutate(`Any Black` = `Any Black` / `Total Population`)
glimpse(king_county)

king_county %>% 
  ggplot(aes(fill = `Any Black`)) + 
  geom_sf(size = NA) + 
  coord_sf(crs = "+proj=longlat +datum=WGS84", datum = NA) + 
  scale_fill_continuous(name = "Any Black\n", 
                        low  = "#d4d5f9",
                        high = "#00025b") + 
  theme_minimal() + ggtitle("Proportion Any Black")

st_erase <- function(x, y) {
  st_difference(x, st_make_valid(st_union(st_combine(y))))
}
kc_water <- tigris::area_water("WA", "King", class = "sf") 

sf::sf_use_s2(FALSE)

kc_nowater <- king_county %>% 
  st_erase(kc_water)

ggplot(kc_nowater, 
       aes(fill=`Any Black`)) + 
  geom_sf(size = NA) + 
  coord_sf(crs = "+proj=longlat +datum=WGS84", datum=NA) + 
  scale_fill_continuous(name="Any Black\n", 
                        low="#d4d5f9",
                        high="#00025b") + 
  theme_minimal() + ggtitle("Proportion Any Black")