4.1 Category 2 Variable 1: White Adults

[map]

popupLabels_WhiteAge <- paste0("<b>",countyGIS_map$name," (",countyGIS_map$FIPS,")</b>",
                    "<br><font color='",countyGIS_map$FontColorWinner,"'>",countyGIS_map$winner, 
                    ": ",
                    format(countyGIS_map$pctWinner*100,digits=4, trim=TRUE),
                    "%</font>",
                    "<br>Total votes: ", format(countyGIS_map$totalVotes,big.mark=",", trim=TRUE),
                    "<br>Percent White Age: ", format(round(countyGIS_map$pct_whiteage, 2),big.mark=",", trim=TRUE),
                    "%</font>"
                    ) %>% 
              lapply(htmltools::HTML)

pal <- colorNumeric(
  palette = colorRampPalette(c('Pink', 'white'))(length(countyGIS_map$pct_whiteage)), 
  domain = countyGIS_map$pct_whiteage, reverse=TRUE)

leaflet(countyGIS_map, options = leafletOptions(crsClass = "L.CRS.EPSG3857"), width="100%") %>%
  addPolygons(weight = 0.5, color = "gray", opacity = 0.7,
    fillColor = ~pal(pct_whiteage), fillOpacity = 1, smoothFactor = 0.5,
    label = popupLabels_WhiteAge,
    labelOptions = labelOptions(direction = "auto")) %>%
    addPolygons(data = stateGIS,fill = FALSE,color="black",weight = 1) %>%
    addLegend(pal = pal,values = ~countyGIS_map$pct_whiteage, opacity = 0.7, title = "% White Age",position = "bottomright")

[Scatter plot]

pct_whiteage_vs_pctGOP <- ggplot(countyGIS_stat, aes(pct_whiteage, pctGOP)) + 
  geom_point(aes(alpha = pct_whiteage, shape = ".")) + 
  geom_smooth(method = "lm", se = FALSE) 
  
pct_whiteage_vs_pctGOP

[Regression]

# Estimate regression model
pct_whiteage_reg <- lm(pctGOP ~ pct_whiteage, data=countyGIS_stat)

# Display model results
pander(summary(pct_whiteage_reg))

	Estimate	Std. Error	t value	Pr(>|t|)
(Intercept)	0.2514	0.01277	19.68	2.475e-81
pct_whiteage	0.006331	0.0001924	32.91	8.709e-204

Fitting linear model: pctGOP ~ pct_whiteage

Observations	Residual Std. Error	\(R^2\)	Adjusted \(R^2\)
3083	0.1387	0.2601	0.2598