Voter Overview

---
title: "Davidson County Voter Analytics | Masheke Analytics"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    theme: flatly
    social: menu
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(dplyr)
library(tidyr)
library(sf)
library(tigris)
library(leaflet)
library(plotly)
library(DT)
library(htmltools)
library(scales)

options(tigris_use_cache = TRUE)
```

```{r data-processing, include=FALSE}
# --- DATA PROCESSING ---

dcvoter <- read.table(
  "C:/Users/richa/OneDrive/Desktop/ResearchData/PoliticalData/dcvoter.txt",
  header = TRUE,
  sep = "\t",
  stringsAsFactors = FALSE
)

status_2 <- dcvoter %>% 
  filter(voter_status_desc == "ACTIVE") %>% 
  select(voter_reg_num, ncid, zip_code, party_cd)

voter_counts <- status_2 %>%
  # 1. Count by zip and party
  count(zip_code, party_cd) %>%
  
  # 2. Pivot to wide format
  pivot_wider(names_from = party_cd, values_from = n, values_fill = 0) %>%
  
  # 3. Clean up the zip code column first
  filter(!is.na(zip_code) & zip_code != "NA") %>%
  mutate(zip_code = as.character(zip_code)) %>%
  
  # 4. Safely check and add core party columns if they are missing from the raw slice
  mutate(
    DEM = if ("DEM" %in% names(.)) DEM else 0,
    REP = if ("REP" %in% names(.)) REP else 0,
    UNA = if ("UNA" %in% names(.)) UNA else 0
  ) %>%
  
  # 5. FIXED: Explicitly sum only the columns you want
  mutate(
    total_active_voters = DEM + REP + UNA
  )

# Spatial Data
nc_zctas <- zctas(
  state = "NC",
  year = 2000,
  cb = TRUE,
  class = "sf"
)

davidson_boundary <- counties(
  state = "NC",
  cb = TRUE,
  class = "sf"
) %>%
  filter(NAME == "Davidson")

davidson_map_data <- nc_zctas %>%
  st_filter(davidson_boundary)

final_map_data <- davidson_map_data %>%
  inner_join(voter_counts, by = c("ZCTA" = "zip_code"))

total_v <- sum(final_map_data$total_active_voters, na.rm = TRUE)

p_totals <- colSums(
  st_drop_geometry(final_map_data)[, c("DEM", "REP", "UNA")],
  na.rm = TRUE
)

p_df <- data.frame(
  Party = names(p_totals),
  Count = as.numeric(p_totals)
)
```

Voter Overview
=====================================

Column {data-width=250 .sidebar}
-------------------------------------

### Executive Summary

Based on current active voter registration data for Davidson County:

**Party Dominance:** Republican registration leads across much of the county.

**Key Hubs:** ZIP codes 27295 and 27360 represent major concentrations of active voters.

**Unaffiliated Growth:** Unaffiliated voters are a critical segment and may influence local election outcomes.

<br>

**Produced by Masheke Analytics**

Column {data-width=750}
-------------------------------------

### Interactive Voter Density Map

```{r voter-map}
pal <- leaflet::colorNumeric(
  palette = "YlOrRd",
  domain = final_map_data$total_active_voters
)

leaflet(data = final_map_data) %>%
  addProviderTiles(providers$CartoDB.Positron) %>%
  addPolygons(
    fillColor = ~pal(total_active_voters),
    weight = 1,
    color = "white",
    fillOpacity = 0.7,
    highlightOptions = highlightOptions(
      weight = 3,
      color = "#666",
      bringToFront = TRUE
    ),
    label = lapply(1:nrow(final_map_data), function(i) {
      HTML(paste0(
        "<b>ZIP Code: </b>", final_map_data$ZCTA[i], "<br>",
        "<b>Total Active Voters: </b>", format(final_map_data$total_active_voters[i], big.mark = ","), "<br>",
        "<b>DEM: </b>", format(final_map_data$DEM[i], big.mark = ","), "<br>",
        "<b>REP: </b>", format(final_map_data$REP[i], big.mark = ","), "<br>",
        "<b>UNA: </b>", format(final_map_data$UNA[i], big.mark = ",")
      ))
    }),
    labelOptions = labelOptions(
      style = list("font-weight" = "normal", padding = "3px 8px"),
      textsize = "13px",
      direction = "auto"
    )
  ) %>%
  addLegend(
    pal = pal,
    values = ~total_active_voters,
    title = "Active Voters",
    position = "bottomright"
  )
```

Column {data-width=350}
-------------------------------------

### Total Active Voters

```{r total-active-voters}
valueBox(
  format(total_v, big.mark = ","),
  icon = "fa-users",
  color = "info"
)
```

### County-Wide Party Distribution

```{r party-distribution}
plot_ly(
  p_df,
  x = ~Party,
  y = ~Count,
  type = "bar",
  marker = list(color = c("#1F77B4", "#D62728", "#A9A9A9"))
) %>%
  layout(
    xaxis = list(title = ""),
    yaxis = list(title = "Total Count")
  )
```

### Searchable Data by ZIP

```{r zip-table}
voter_counts %>%
  select(zip_code, total_active_voters, DEM, REP, UNA) %>%
  arrange(desc(total_active_voters)) %>%
  datatable(
    options = list(pageLength = 6, dom = "tp"),
    rownames = FALSE
  )
```