summary_state<- election_data %>% group_by(state) %>% 
  summarise(mean_v=round(mean(candidatevotes),2),
            median_v=round(median(candidatevotes),2),
            sd_v=round(sd(candidatevotes),2),
            max_v=round(max(candidatevotes),2),
            min_v=round(min(candidatevotes),2),.groups = 'drop') %>%
  as.data.frame()

summary_state_table <- flextable::qflextable(summary_state)
summary_state_table

aggregate_table <- aggregate(candidatevotes ~ year + candidate, data = election_data, FUN = sum)

# Order the aggregate_table by year and candidatevotes
aggregate_table_ordered <- arrange(aggregate_table, year, desc(candidatevotes))


# Spread the aggregate_table to have each unique year as a separate row
spread_table <- spread(aggregate_table_ordered, key = year, value = candidatevotes)

spread_table_filled <- spread_table %>%
  mutate(across(-candidate, ~ replace(., is.na(.), "-")))

# Printing the spread_table
flextable_spread_table <- flextable::qflextable(spread_table_filled)
flextable_spread_table

# Filteing the election_data for the years 2000 to 2020
year_data <- election_data[election_data$year >= 2000 & election_data$year <= 2020, ]

# Group by year and candidate, and calculate the total votes for each candidate in each year
candidate_votes <- year_data %>%
  group_by(year, candidate) %>%
  summarize(total_votes = sum(candidatevotes))

# Calculating the total votes for each year
total_votes_per_year <- year_data %>%
  group_by(year) %>%
  summarize(total_votes = sum(candidatevotes))

# Merging the two data frames to get the total votes as percentages
candidate_votes_percentage <- merge(candidate_votes, total_votes_per_year, by = "year") %>%
  mutate(percentage_votes = (total_votes.x / total_votes.y) * 100) %>%
  select(-total_votes.x, -total_votes.y) %>%
  spread(year, percentage_votes, fill = 0)

# Converting percentage values to numeric, round to two decimals, and add "%" symbol
candidate_votes_percentage[, -1] <- lapply(candidate_votes_percentage[, -1], function(x) paste0(format(round(as.numeric(x), 2), nsmall = 2), "%"))

# Printing the candidate_votes_percentage data frame
candidate_votes_percentage_table <- flextable::qflextable(candidate_votes_percentage)
candidate_votes_percentage_table

# Filtering the election_data for the years 2000 to 2020
year_data <- election_data[election_data$year >= 2000 & election_data$year <= 2020, ]

# Obtaining the unique candidates for each year
candidate_occurrence <- with(year_data, tapply(candidate, year, function(x) unique(x)))

# Creating a data frame to display the occurrence of candidates
occurrence_data_frame <- as.data.frame(candidate_occurrence)

# Replacing empty cells with zero
occurrence_data_frame[is.na(occurrence_data_frame)] <- 0

# Printing the candidate occurrence data frame
occurrence_data_frame

# Creating a lookup table for electoral college votes allocation
default_electoral_college_seats_state_wise <- data.frame(
  state = c("Alabama", "Kentucky", "North Dakota", "Alaska", "Louisiana", "Ohio", "Arizona", "Maine", "Oklahoma", "Arkansas",
            "Maryland", "Oregon", "California", "Massachusetts", "Pennsylvania", "Colorado", "Michigan", "Rhode Island", "Connecticut",
            "Minnesota", "South Carolina", "Delaware", "Mississippi", "South Dakota", "District of Columbia", "Missouri", "Tennessee",
            "Florida", "Montana", "Texas", "Georgia", "Nebraska", "Utah", "Hawaii", "Nevada", "Vermont", "Idaho", "New Hampshire",
            "Virginia", "Illinois", "New Jersey", "Washington", "Indiana", "New Mexico", "West Virginia", "Iowa", "New York", "Wisconsin",
            "Kansas", "North Carolina", "Wyoming"),
  electoral_seats = c(9, 8, 3, 3, 8, 17, 11, 4, 7, 6, 10, 8, 54, 11, 19, 10, 15, 4, 7, 10, 9, 3, 6, 3, 3, 10, 11, 30, 4, 40, 16, 5, 6,
                      4, 4, 3, 4, 4, 13, 19, 14, 12, 11, 5, 4, 6, 28, 10, 6, 16, 3)
)

# Merging election_data with the electoral_seats lookup table based on state
merged_election_data <- merge(election_data, default_electoral_college_seats_state_wise, by = "state", all.x = TRUE)

# Calculating total votes for each party in each state for each year
merged_election_data <- merged_election_data %>%
  group_by(year, state, party) %>%
  summarise(total_votes_state_party = sum(candidatevotes))

# Determining the winner party in each state for each year based on total votes majority
state_winners <- merged_election_data %>%
  group_by(year, state) %>%
  filter(total_votes_state_party == max(total_votes_state_party)) %>%
  ungroup()

# Merging the state_winners with the electoral_seats table to assign electoral votes to each party
state_winners_with_votes <- state_winners %>%
  left_join(default_electoral_college_seats_state_wise, by = c("state" = "state"))

# Calculating the total electoral votes won by each party for each year
electoral_college_seats <- state_winners_with_votes %>%
  group_by(year, party) %>%
  summarise(total_electoral_seats = sum(electoral_seats, na.rm = TRUE))

electoral_college_seats

# Determining the winning party based on total electoral votes for each year
winning_party <- electoral_college_seats %>%
  group_by(year) %>%
  filter(total_electoral_seats == max(total_electoral_seats)) %>%
  ungroup()

# Select the final columns to display and rename the columns
winning_party_table_each_term <- winning_party %>%
  select(year, party, total_electoral_seats) %>%
  rename("Total Electoral College Seats Won" = total_electoral_seats, "Winning Party" = party)

winning_party_table_each_term

# plot 1: Line chart showing in which year most votings took place

# Calculating total votes per year
total_votes_per_year <- election_data %>%
  group_by(year) %>%
  summarize(total_votes = sum(candidatevotes))

# Creating the line graph
line_graph <- ggplot(total_votes_per_year, aes(x = year, y = total_votes)) +
  geom_line(size=1,color="Red") +
  scale_y_continuous(breaks = scales::pretty_breaks(n = 5),
                     labels = scales::comma) +
  labs(title = "Total Number of Polling in the United States from 2000-2020",
       x = "Year",
       y = "Number of Votes (in numbers)",
       caption = "Data source: https://electionlab.mit.edu/data") +
  theme(plot.title = element_text(hjust = 0.5))+
  theme_get()

line_graph

# Calculate the percentage of votes for each party in each year
votes_of_party_in_percentage <- election_data %>%
  group_by(year, party) %>%
  summarize(percentage_votes = sum(candidatevotes) / sum(totalvotes) * 100) %>%
  ungroup()

# Reorder the levels of the party variable based on average percentage of votes across all years
votes_of_party_in_percentage <- votes_of_party_in_percentage %>%
  mutate(party = fct_reorder(party, percentage_votes, .desc = TRUE))

# Creating the stacked bar chart with facet_wrap by year
ggplot(votes_of_party_in_percentage, aes(fill = party, y = percentage_votes, x = party)) +
  geom_bar(position = position_dodge(width = 0.7), stat = "identity") +
  scale_fill_viridis(discrete = TRUE, option = "E") +
  facet_wrap(~ year, ncol = 3, scales = "free_x") +
  theme_minimal() +
  xlab("Party") +
  labs(title = "Percentage of Votes Received by Each Party Each Year",
       x = "Party",
       y = "Percentage of Votes",
       caption = "Data source: https://electionlab.mit.edu/data") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  theme(legend.position = "bottom") +
  scale_y_continuous(breaks = seq(0, 100, by = 10))

# Function to load data from gtrends, if the gtrends fetching fails, will load the local RDS file
load_gtrends_data <- function() {
  tryCatch({
    # Attempt to fetch data from gtrends
    data <- gtrends(c("US Election Results 2020", "Election Fraud"), 
                    time = "2020-08-02 2020-12-03", geo = "US")
    write_rds(data, file = "importedDataGtrends.rds")  # Save data to RDS file
    return(data)  # Return the data
  }, error = function(err) {
    # If gtrends call fails, attempt to read data from RDS file
    if (file.exists("importedDataGtrends.rds")) {
      data <- read_rds("importedDataGtrends.rds")
      return(data)  # Return the data from RDS file
    } else {
      message("Failed to fetch data from gtrends and no RDS file found.")
      return(NULL)  # Return NULL or any other default value if both attempts fail
    }
  })
}
# Load data using the function
gTrendsElectionSearchData <- load_gtrends_data()

if (!is.null(data)) {
  plot_trend <- function(keyword_string, data) {
    time_trend <- data$interest_over_time %>%
      mutate(hits = ifelse(hits == "<1", 0.5, as.numeric(hits)),
             date = as.Date(date),
             keyword = factor(keyword, levels = keyword_string))
    
    plot <- plot_ly(data = time_trend, x = ~date, y = ~hits, color = ~keyword,
                    type = "scatter", mode = "lines", fill = "tozeroy") %>%
      layout(title = "Exploring the Search Interest for 'Election Fraud' and 'US Election Results 2020' during the 2020 US Election Season",
             xaxis = list(title = "Months in year 2020", standoff = 20),
             yaxis = list(title = "Hits (Relative to Peak from 0 - 100)", standoff = 20),
             legend = list(orientation = "v"),
             showlegend = TRUE,
             margin = list(l = 100, r = 100, b = 100, t = 100),  # Adjust margins
             padding = list(r = 10, b = 50)  # Adjust padding
      )
    return(plot)
  }
  election_fraud_trend_plot <- plot_trend(keyword_string = c("US Election Results 2020", "Election Fraud"),gTrendsElectionSearchData)
  election_fraud_trend_plot
}

# Loading the us_states shapefile from the maps package
us_states <- map_data("state")

# Renamed the "region" column to "state" in us_states
us_states <- us_states %>% 
  rename(state = region)

us_states <- us_states %>%
  mutate(state = str_to_title(state))

# Filtering the state_winners_with_votes dataset for the year 2020
state_winners_2020 <- state_winners_with_votes %>%
  filter(year == 2020)

state_winners_2016 <- state_winners_with_votes %>%
  filter(year == 2016)

# Left join the us_states dataset with state_winners_2020 based on the "state" column
map_data_combined_2020 <- left_join(us_states, state_winners_2020, by = "state", copy = TRUE)
map_data_combined_2016 <- left_join(us_states, state_winners_2016, by = "state", copy = TRUE)

# Displaying the map view which show the winners of different states by the parties

election_outcome_map_2016 <- ggplot(map_data_combined_2016, aes(x = long, y = lat, group = group, fill = party)) +
  geom_polygon(color = "white", size = 0.25) +  # Add thick white borders between states
  coord_quickmap() +
  scale_fill_manual(values = c("Democrat" = "blue", "Republican" = "red", "Other" = "gray")) +
  labs(title = "2016 Election outcome by state",
       caption = "Data source:  https://electionlab.mit.edu/data") +
  theme_void()

election_outcome_map_2016

election_outcome_map_2020 <- ggplot(map_data_combined_2020, aes(x = long, y = lat, group = group, fill = party)) +
  geom_polygon(color = "white", size = 0.25) +  # Add thick white borders between states
  coord_quickmap() +
  scale_fill_manual(values = c("Democrat" = "blue", "Republican" = "red", "Other" = "gray")) +
  labs(title = "2020 Election outcome by state",
       caption = "Data source:  https://electionlab.mit.edu/data") +
  theme_void()

election_outcome_map_2020

# Merge shape and census data
ggMapData <- county_shape %>% 
  full_join(census_data, by = c("GEOID" = "county_fips")) %>% 
  dplyr::filter(year == 2021)

# Fixing issue with Alaska and Hawaii
ggMapDataFix <- ggMapData %>% 
  tigris::shift_geometry()

# Plotting by thresholding max poverty level at 50%
povertyGG <- ggMapDataFix %>% rowwise() %>% 
  mutate(pov_plot = min(median_income, 156821)) %>% 
  ggplot(aes(fill = pov_plot)) +
  geom_sf(color = "#C0C0C0",
          size = 0.1) +
  scale_fill_gradient(low = "white", high = "dodgerblue",
                      limits = c(0, 180000)) +
  labs( title = "County-level median income, 2021",
        caption = "Data source: https://electionlab.mit.edu/data",
        fill = "Median Income") +
  theme_void()

povertyGG