R Notebook

library(tidyverse)

── Attaching packages ────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
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✓ tidyr   1.0.0     ✓ stringr 1.4.0
✓ readr   1.3.1     ✓ forcats 0.4.0
── Conflicts ───────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()

library(leaflet)
library(sf)

Linking to GEOS 3.5.1, GDAL 2.2.2, PROJ 4.9.2

library(readxl)
library(DT)
library(plotly)

Attaching package: ‘plotly’

The following object is masked from ‘package:ggplot2’:

    last_plot

The following object is masked from ‘package:stats’:

    filter

The following object is masked from ‘package:graphics’:

    layout

library(broom)
library(tidycensus)

census_api_key("a7c59af9ec8e85e3b63423792c176d986478bd94") 

To install your API key for use in future sessions, run this function with `install = TRUE`.

house_counties <- read_xlsx("Statewide Results.xlsx", sheet = 2)

New names:
* `` -> ...2
* `` -> ...3
* `` -> ...4
* `` -> ...5

house_counties <- read_xlsx("Statewide Results.xlsx", sheet = 2, range = "B7:E63")

1. First,I created a table that shows the counties’ senate results categorized by Democrat, Republican, and Libertarian votes. The Republican-Democrat split is shown in column “Repub_advantage”. A higher number here indicates a Republican advantage, and a lower number (like the negatives) depicts a Democrat advantage.

glimpse(house_counties)

Observations: 56
Variables: 4
$ County                          <chr> "Beaverhead", "Big Horn", "Blaine", "Broadwater", "Carbon", "Carter", "Cascade", "Chouteau…
$ `GREG GIANFORTE\r\nRepublican`  <dbl> 3113, 1705, 1173, 2249, 3450, 636, 17219, 1515, 3085, 684, 2811, 1379, 1025, 3915, 28450, …
$ `KATHLEEN WILLIAMS\r\nDemocrat` <dbl> 1681, 2865, 1742, 897, 2442, 100, 15826, 1071, 1638, 240, 1128, 2693, 226, 1752, 18051, 31…
$ `ELINOR SWANSON\r\nLibertarian` <dbl> 122, 106, 87, 105, 184, 12, 925, 66, 168, 20, 125, 155, 24, 139, 1277, 1523, 20, 111, 22, …

house_counties <- house_counties %>% 
  rename(Republican = "GREG GIANFORTE\r\nRepublican") %>% 
  rename(Democrat = "KATHLEEN WILLIAMS\r\nDemocrat") %>% 
  rename(Libertarian = "ELINOR SWANSON\r\nLibertarian")

glimpse(house_counties)

Observations: 56
Variables: 4
$ County      <chr> "Beaverhead", "Big Horn", "Blaine", "Broadwater", "Carbon", "Carter", "Cascade", "Chouteau", "Custer", "Daniel…
$ Republican  <dbl> 3113, 1705, 1173, 2249, 3450, 636, 17219, 1515, 3085, 684, 2811, 1379, 1025, 3915, 28450, 23157, 606, 1307, 32…
$ Democrat    <dbl> 1681, 2865, 1742, 897, 2442, 100, 15826, 1071, 1638, 240, 1128, 2693, 226, 1752, 18051, 31297, 55, 3564, 108, …
$ Libertarian <dbl> 122, 106, 87, 105, 184, 12, 925, 66, 168, 20, 125, 155, 24, 139, 1277, 1523, 20, 111, 22, 62, 231, 200, 34, 35…

house_counties <- house_counties %>% 
  mutate(total_votes = Republican + Democrat + Libertarian) %>% 
  mutate(Repub_advantage = Republican/total_votes - Democrat/total_votes) %>% 
  mutate(Repub_advantage = round(Repub_advantage*100, 1))

house_counties %>% 
  arrange(-Repub_advantage)

NA

2. Here is a datatable of all the Senate votings of Montana counties.

mt_counties <- get_acs(geography = "county",
                       variables = "B01003_001",
                       state = "MT",
                       geometry = TRUE) 

Getting data from the 2014-2018 5-year ACS
Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
Using FIPS code '30' for state 'MT'

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3. The following choreopleth is of the senate voting in Montana counties, distinguished by color according to Republican or Democrat advantage. You can see that places such as Bozeman, Missoula, and the Crow Reservation have a very low Republican advantage.

house_counties[25, "County"] <- "Lewis and Clark"              # Changes  "&" "and"

mt_counties <- mt_counties %>% 
  mutate(County = gsub(" County, Montana", "", NAME)) %>%      # Removes unnecessary words
  rename(Population = estimate)                                # Renames the 'estimate' to 'Population'

4. Next, I created a table of a linear regression of the Republican advantage due to population. There is a positive correlation between having a higher population, and having a higher Democratic vote advantage.

house_election <- mt_counties %>% 
  full_join(house_counties)

Joining, by = "County"

house_election %>%
  as_tibble() %>% 
  select(County, Population, Democrat, Republican, Libertarian, total_votes, Repub_advantage) %>% 
  datatable()

NA

5. The following graph graph shows the population by county, and the Republican/Democrat advantage is shown by the line. The linear regression illustrates a pattern between the population of a county and it’s political advantage. Eastern counties leaned more towards Rosendale, while western counties tended to leab towards Tester.

vote_colors <- colorNumeric(palette = "viridis", domain = house_election$Repub_advantage)

house_election %>%
  leaflet() %>% 
  addTiles() %>%
  addPolygons(weight = 1,
              fillColor = ~vote_colors(Repub_advantage), 
              label = ~paste0(County, ", Republican advantage = ", Repub_advantage),
              highlight = highlightOptions(weight = 2)) %>% 
  setView(-110, 47, zoom = 6) %>% 
  addLegend(pal = vote_colors, values = ~Repub_advantage)

sf layer has inconsistent datum (+proj=longlat +datum=NAD83 +no_defs).
Need '+proj=longlat +datum=WGS84'

house_election %>%
  plot_ly(x = ~Population, y = ~Repub_advantage) %>% 
  add_markers()

6. The following graph shows the “center” of each county, geographically.

house_election %>%
  plot_ly(x = ~Population, 
          y = ~Repub_advantage,
          hoverinfo = "text", 
          text = ~paste("County:", 
                        County, "<br>", 
                        "Population: ", Population, "<br>", 
                        "Republican advantage: ", Repub_advantage)) %>% 
  add_markers(marker = list(opacity = 0.7)) %>%
  layout(title = "Predicting Republican Vote Advantage from Population, by County",
         xaxis = list(title = "County population"),
         yaxis = list(title = "Republican vote advantage"))     

NA

7. This table shows a linear regression of population by geography.

pop_model <- lm(Repub_advantage ~ Population, data = house_election)

summary(pop_model)

Call:
lm(formula = Repub_advantage ~ Population, data = house_election)

Residuals:
    Min      1Q  Median      3Q     Max 
-73.973 -10.320   3.959  15.305  47.232 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept) 34.1220288  4.0462304   8.433 1.99e-11 ***
Population  -0.0003977  0.0001090  -3.648 0.000597 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 26.2 on 54 degrees of freedom
Multiple R-squared:  0.1977,    Adjusted R-squared:  0.1828 
F-statistic: 13.31 on 1 and 54 DF,  p-value: 0.0005968

8. This graph depicts the population density of Montana by geography, with the linear regression indicating a positive correlation between eastern counties voting more Republican in the senate election.

tidy(pop_model)

glance(pop_model)

NA
NA

9. Next, we have a multiple regression predicting the geographic area (east or west) and population.

tidy(pop_model)

glance(pop_model) %>%
  add_lines(y = ~fitted(pop_model)) %>%
  add_markers(showlengend = F)

Unknown or uninitialised column: 'x'.Unknown or uninitialised column: 'x'.Error: Must supply `x` and `y` attributes

house_election <- house_election %>% 
  mutate(Longitude = as_tibble(st_coordinates(st_centroid(house_election$geometry)))$X) %>% 
  mutate(Latitude = as_tibble(st_coordinates(st_centroid(house_election$geometry)))$Y)

st_centroid does not give correct centroids for longitude/latitude datast_centroid does not give correct centroids for longitude/latitude data

10. Finally, here is a 3D graph showing the relationship between longitude, population, and voting behavior in Montana. The farther east you get, the more votes for Rosendale. The farther west, the more votes for Tester.

house_election %>%
  leaflet() %>% 
  addTiles() %>%
  addPolygons(weight = 1) %>% 
  setView(-110, 47, zoom = 6) %>% 
addCircleMarkers(~Longitude, ~Latitude)

sf layer has inconsistent datum (+proj=longlat +datum=NAD83 +no_defs).
Need '+proj=longlat +datum=WGS84'