Arizona_expected_votes_2020.R

percentage <- c(82, 82, 83, 75, 89, 82, 99, 85, 97, 97, 89, 78)
votes_trump <- c(69830, 38710, 24445, 6715, 4542, 84386, 29561, 231845, 323863, 254561, 31183, 97439)
votes_biden <- c(22980,21684, 21374, 13979, 2050, 47164, 17843, 489165, 431656, 216512, 14282, 102322)
counties <- c("Mohave",
              "Coconino",
              "Navajo",
              "Apache",
              "La Paz",
              "Yavapai",
              "Gila",
              "Graham",
              "Greenlee",
              "Yuma",
              "Maricopa",
              "Pinal",
              "Pima",
              "Santa Cruz",
              "Cochise")
expected_biden <- votes_biden / percentage * 100
expected_trump <- votes_trump / percentage * 100
sumTrump <- cumsum(expected_trump)
sumBiden <- cumsum(expected_biden)

data <- data.frame(
  counties,
  votes_trump,
  votes_biden,
  expected_trump,
  expected_biden,
  sumTrump,
  sumBiden,
  percentage)

TotalvotesTrump <- data[12, 6]
TotalvotesBiden <- data[12, 7]
Difference <- TotalvotesTrump - TotalvotesBiden
data <- data.frame(
  counties,
  votes_trump,
  votes_biden,
  expected_trump,
  expected_biden,
  sumTrump,
  sumBiden,
  percentage,
  TotalvotesBiden,
  TotalvotesTrump,
  Difference)
df1 <- data.frame(counties, expected_biden, votes_biden, expected_trump, votes_trump)
df2 <- melt(df1, id.vars ='counties')

ggplot(df2, aes(x=counties, y=value, fill=variable)) + geom_bar(stat = 'identity', position = 'dodge') + scale_fill_manual("Legend", values = c("expected_trump" = "red", "expected_biden" = "blue", "votes_biden" = "cyan", "votes_trump" = "pink"))