# 4:a
library(maps)
library(mapproj)
data("county.fips")
socr <- read.csv("socr.csv", header=TRUE)
names(socr)
# [1] "NAME"    "STATE_NAME" "STATE_FIPS" "CNTY_FIPS" "PCountyFIPS" "Long_Mean" "Lat_Mean" "Bush"    "Kerry"
# [10] "Nader"    "Total"    "Bush_pct" "Kerry_pct" "Nader_pct" "MDratio" "hosp"    "pcthisp" "pcturban"
# [19] "urbrural" "pctfemhh" "pcincome" "pctpoor" "pctlt9ed" "pcthsed" "pctcoled" "unemploy" "pctwhtcl"
# [28] "homevalu" "rent"    "popdens" "crowded" "ginirev" "SmokecurM" "SmokevrM" "SmokecurF" "SmokevrF"
# [37] "Obese"    "Noins"    "XYLENES__M" "TOLUENE" "TETRACHLOR" "STYRENE" "NICKEL_COM" "METHYLENE_" "MERCURY_CO"
# [46] "LEAD_COMPO" "BENZENE__I" "ARSENIC_CO" "POP2000" "POP00SQMIL" "MALE2000" "FEMALE2000" "MAL2FEM" "UNDER18"
# [55] "AIAN"    "ASIA"    "BLACK"    "NHPI"    "WHITE"    "AIAN_MORE" "ASIA_MORE" "BLK_MORE" "NHPI_MORE"
# [64] "WHT_MORE" "HISP_LAT" "CH19902000" "MEDAGE2000" "PEROVER65"
names(county.fips)
# [1] "fips" "polyname"
data <- merge(county.fips, socr, by.x="fips", by.y="PCountyFIPS")
dim(data) # 3083 x 69
data$Bush_pct_colorBuckets <- as.numeric( cut(data$Bush_pct, as.numeric( quantile(data$Bush_pct))) )

map("county", col=colors[data$Bush_pct_colorBuckets], fill=TRUE, lty=1, lwd=0.2, projection="polyconic")
map("state", col = "#69F0AE", fill = FALSE, add = TRUE, lty = 1, lwd = 2, projection="polyconic")
title("The percentage of votes that George W. Bush won in the 2004 election")

quantile_bush_pct <- as.numeric( quantile(data$Bush_pct) )
q1 <- quantile_bush_pct

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