voting/working1.R at master · andrewcheesman/voting · GitHub

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# Looking at count voting history
# Voting data from https://github.com/tonmcg/County_Level_Election_Results_12-16
# Population data from US Census https://www.census.gov/support/USACdataDownloads.html

library(ggplot2)
library(corrplot)

vot <- read.csv("cty_data.csv", stringsAsFactors = F)

colnames(vot) <- c("fips_code", "county",
                   "t2008", "d2008", "r2008", "o2008",
                   "t2012", "d2012", "r2012", "o2012",
                   "t2016", "d2016", "r2016", "o2016")

pop <- read.csv("cty_pop.csv", stringsAsFactors = F)

pop1 <- pop[,c("Area_name", "STCOU", "POP010130D", "POP010140D", "POP010150D", "POP010160D", "POP010170D", "POP010180D", "POP010190D", "POP010200D", "POP010210D")]
colnames(pop1) <- c("county", "fips_code", "p1930",  "p1940",  "p1950",  "p1960",  "p1970",  "p1980",  "p1990",  "p2000",  "p2010")

pop2 <- pop1[!(pop1$fips_code %in% c(0, 1000, 2000, 4000, 5000, 6000, 8000, 9000,
                                     10000, 11000, 12000, 13000, 15000, 16000, 17000, 18000, 19000,
                                     20000, 21000, 22000, 23000, 24000, 25000, 26000, 27000, 28000, 29000,
                                     30000, 31000, 32000, 33000, 34000, 35000, 36000, 37000, 38000, 39000,
                                     40000, 41000, 42000, 44000, 45000, 46000, 47000, 48000, 49000,
                                     50000, 51000, 53000, 54000, 55000, 56000, 60000, 66000, 72000, 78000)),]

vot2 <- vot[!(vot$fips_code %in% c(0, 1000, 2000, 4000, 5000, 6000, 8000, 9000,
                                   10000, 11000, 12000, 13000, 15000, 16000, 17000, 18000, 19000,
                                   20000, 21000, 22000, 23000, 24000, 25000, 26000, 27000, 28000, 29000,
                                   30000, 31000, 32000, 33000, 34000, 35000, 36000, 37000, 38000, 39000,
                                   40000, 41000, 42000, 44000, 45000, 46000, 47000, 48000, 49000,
                                   50000, 51000, 53000, 54000, 55000, 56000, 60000, 66000, 72000, 78000)),]

bas <- merge(pop2, vot2[,c(-2)], by = "fips_code")
rm(pop, pop1, pop2, vot, vot2)

# derived vars

bas$pd08 <- bas$d2008 / bas$p2010
bas$pr08 <- bas$r2008 / bas$p2010
bas$po08 <- bas$o2008 / bas$p2010
bas$pt08 <- bas$t2008 / bas$p2010

bas$pd12 <- bas$d2012 / bas$p2010
bas$pr12 <- bas$r2012 / bas$p2010
bas$po12 <- bas$o2012 / bas$p2010
bas$pt12 <- bas$t2012 / bas$p2010

bas$pd16 <- bas$d2016 / bas$p2010
bas$pr16 <- bas$r2016 / bas$p2010
bas$po16 <- bas$o2016 / bas$p2010
bas$pt16 <- bas$t2016 / bas$p2010

bas$mg08 <- bas$pd08 - bas$pr08
bas$mg12 <- bas$pd12 - bas$pr12
bas$mg16 <- bas$pd16 - bas$pr16

bas$tn12 <- bas$pt12 - bas$pt08
bas$tn16 <- bas$pt16 - bas$pt12

# wide to long, for ggplot

pl <- data.frame()
for (i in 24:35) {

  fill <- data.frame(tp = substr(colnames(bas)[i], 2, 2),
                     yr = 2000+as.numeric(substr(colnames(bas)[i], 3, 4)),
                     fc = bas$fips_code,
                     ct = bas$county,
                     val = bas[,i])
  pl <- rbind(pl, fill)

  }

# Measuring distributions of voter turnout by party and overall across years
# Omitting 'other' because it's wonky

plt <- ggplot(pl[pl$tp != "o",], aes(x = val, colour = pl[pl$tp != "o","tp"])) +
  geom_freqpoly(bins = 1000) +
  facet_wrap( ~ yr, nrow = 3) +
  xlim(0, 1) +
  theme_bw()
plt

# Creating vars that measure d, r, and t change in turnout pct from ['08 to '12] and ['12 to '16]

bas$tdd12 <- bas$pd12 - bas$pd08
bas$trd12 <- bas$pr12 - bas$pr08
bas$tod12 <- bas$po12 - bas$po08
bas$ttd12 <- bas$pt12 - bas$pt08

bas$tdd16 <- bas$pd16 - bas$pd12
bas$trd16 <- bas$pr16 - bas$pr12
bas$tod16 <- bas$po16 - bas$po12
bas$ttd16 <- bas$pt16 - bas$pt12

pl2 <- data.frame()
for (i in 41:48) {

  fill2 <- data.frame(tp = substr(colnames(bas)[i], 2, 2),
                      yr = 2000+as.numeric(substr(colnames(bas)[i], 4, 5)),
                      fc = bas$fips_code,
                      ct = bas$county,
                      val = bas[,i])
  pl2 <- rbind(pl2, fill2)

}

plt2 <- ggplot(pl2[pl2$tp != "o",], aes(x = val, colour = pl2[pl2$tp != "o","tp"])) +
  geom_freqpoly(bins = 1000) +
  facet_wrap( ~ yr, nrow = 3) +
  xlim(0, 0.25) +
  theme_bw()
plt2

# Comparing metrics - change in turnout and 2016 turnout

sbs <- data.frame("fips_code" = bas$fips_code,
                  "county" = bas$county,
                  "pd16" = bas$pd16,
                  "pr16" = bas$pr16,
                  "pt16" = bas$pt16,
                  "dd16" = bas$tdd16,
                  "dr16" = bas$trd16,
                  "dt16" = bas$ttd16)

pl3 <- data.frame()
for (i in 3:8) {

  fill3 <- data.frame(ms = substr(colnames(sbs)[i], 1, 1),
                      tp = substr(colnames(sbs)[i], 2, 2),
                      yr = 2000+as.numeric(substr(colnames(sbs)[i], 3, 4)),
                      fc = sbs$fips_code,
                      ct = sbs$county,
                      val = sbs[,i])
  pl3 <- rbind(pl3, fill3)

}

plt3 <- ggplot(pl3, aes(x = ct, y = val)) +
  geom_bar(stat = "identity", position = "identity") +
  facet_wrap( ~ yr + ms + tp, ncol = 3) +
  theme_bw()
plt3

write.csv(sbs, "sbs.csv", row.names = F)

c <- cor(sbs[,3:8])
corrplot(c)

d <- cor(bas[,24:48])
corrplot(d, type = "upper")