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06-Category4.Rmd
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# Category 4: Sex and Employment
This category shows the population of the country by sex and employment. The first variable `pct_male_employment` is the percentage of the male population 16 years old or older in the U.S. that is employed. The second variable `pct_female_employment` is the percentage of the female population 16 years old or older in the U.S. that is employed.
## Category 4 Variable 1: Male Employment
[Map]
```{r, message=FALSE, warning=FALSE, include=TRUE}
popupLabels_employment_male <- 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 Male Employment: ", format(round(countyGIS_map$pct_employment_male, 2),big.mark=",", trim=TRUE),
"%</font>"
) %>%
lapply(htmltools::HTML)
```
```{r, message=FALSE, warning=FALSE, include=TRUE}
pal <- colorNumeric(
palette = colorRampPalette(c('blue', 'white'))(length(countyGIS_map$pct_employment_male)),
domain = countyGIS_map$pct_employment_male, reverse=TRUE)
leaflet(countyGIS_map, options = leafletOptions(crsClass = "L.CRS.EPSG3857"), width="100%") %>%
addPolygons(weight = 0.5, color = "gray", opacity = 0.7,
fillColor = ~pal(pct_employment_male), fillOpacity = 1, smoothFactor = 0.5,
label = popupLabels_employment_male,
labelOptions = labelOptions(direction = "auto")) %>%
addPolygons(data = stateGIS,fill = FALSE,color="black",weight = 1) %>%
addLegend(pal = pal,values = ~countyGIS_map$pct_employment_male, opacity = 0.7, title = "% Male Employment",position = "bottomright")
```
[Scatter plot]
```{r, message=FALSE, warning=FALSE, include=TRUE}
ggplot(countyGIS_stat, aes(pct_employment_male, pctGOP)) +
geom_point(aes(alpha = pct_white, shape = ".")) +
geom_smooth(method = "lm", se = FALSE)
```
[Regression]
```{r}
# Estimate regression model
pct_employment_male_reg <- lm(pctGOP ~ pct_employment_male, data=countyGIS_stat)
# Display model results
pander(summary(pct_employment_male_reg))
```
## Category 4 Variable 2: Female Employment
[Map]
```{r, message=FALSE, warning=FALSE, include=TRUE}
popupLabels_employment_female <- 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 Female Employment: ", format(round(countyGIS_map$pct_employment_female, 2),big.mark=",", trim=TRUE),
"%</font>"
) %>%
lapply(htmltools::HTML)
```
```{r, message=FALSE, warning=FALSE, include=TRUE}
pal2 <- colorNumeric(
palette = colorRampPalette(c('red', 'white'))(length(countyGIS_map$pct_employment_female)),
domain = countyGIS_map$pct_employment_female, reverse=TRUE)
leaflet(countyGIS_map, options = leafletOptions(crsClass = "L.CRS.EPSG3857"), width="100%") %>%
addPolygons(weight = 0.5, color = "gray", opacity = 0.7,
fillColor = ~pal2(pct_employment_female), fillOpacity = 1, smoothFactor = 0.5,
label = popupLabels_employment_female,
labelOptions = labelOptions(direction = "auto")) %>%
addPolygons(data = stateGIS,fill = FALSE,color="black",weight = 1) %>%
addLegend(pal = pal2,values = ~countyGIS_map$pct_employment_female, opacity = 0.7, title = "% Female Employment",position = "bottomright")
```
[Scatter plot]
```{r, message=FALSE, warning=FALSE, include=TRUE}
ggplot(countyGIS_stat, aes(pct_employment_female, pctGOP)) + geom_point(aes(alpha = pct_white, shape = ".")) + geom_smooth(method = "lm", se = FALSE)
```
[Regression]
```{r}
# Estimate regression model
pct_employment_female_reg <- lm(pctGOP ~ pct_employment_female, data=countyGIS_stat)
# Display model results
pander(summary(pct_employment_female_reg))
```