Accepted Ballots

The map below shows voters (number of accepted ballots) by precinct in Washington in the 2024 general election. Hovering over the precinct shows the county and precinct name. Click on a precinct to see chart showing accepted ballots broken down by imputed race. The full dataset, “Voters by Race, Precinct, Year, and Election”, contains years 2020-2024, and is available for download in the Datasets tab, under Section 2.1.

Please allow up to 60 seconds for the interface to load

shp <- read_sf('P:/Projects/UWED/Master Team Folder/Data/Inputs/SoS/Precinct_Shapefiles/2024/Statewide_Precincts_2024General.shp')
cvap <- fread('P:/Projects/UWED/Master Team Folder/Data/Outputs/CVAP_by_Precinct_Race_2010-2024_2025-07-16.csv')
registrants <- fread('P:/Projects/UWED/Master Team Folder/Data/Outputs/Registrant_Population_by_Precinct_Race_Year2025-07-25.csv')
voters <- fread('P:/Projects/UWED/Master Team Folder/Data/Outputs/Voter_Population_by_Precinct_Race_Year2025-07-25.csv')

shp_no_geo <- st_drop_geometry(shp) 

  

registrants <- registrants %>%
    mutate(PrecName = tolower(PrecName)) %>%
    rename(precinctname = PrecName,
           countycode = County)

voters <- voters %>%
    mutate(PrecName = tolower(PrecName)) %>%
    rename(precinctname = PrecName,
           countycode = County)

make_voter_pl <- function(i, overwrite = FALSE, verbose = FALSE){
    message(i)
    x <- shp_no_geo[i,]
    # year-create image dirs
    # id, etc.
    stcode <- x$St_Code
    # county, precinct
    mycounty <- x$CountyName
    ctycode <- x$County
    myprecinct <- x$PrecinctNa
    myprecinctnum <- x$PrecinctNu
    mytitle <- str_c(mycounty, str_to_title(myprecinct), myprecinctnum, sep = ", " )

    vot_sub <- voters %>% 
        filter(St_Code == stcode,
               Election_date == "2024-11-05")

    all <- vot_sub %>%
      pivot_longer(cols = c('Total_Pop', "Black_Pop", "White_Pop", "Hispanic_Pop", "Asian_Pop", "OtherRace_Pop")) %>%
        rename(Race = name,
               Population = value) %>%
        filter(Race != "Total_Pop") %>%
        mutate(
            Race = str_remove_all(string = Race, pattern = "_Pop") %>%
                factor(levels = c("White", "Black", "Asian", "Hispanic", "OtherRace"), 
                       labels = c("White", "Black", "Asian", "Hispanic", "Other"))
        )
    
    
      
    g1 <- ggplot(data = all, aes(x = Race, y = Population)) +
         geom_col(aes(), show.legend = F) +
         theme_minimal() +
         geom_text(aes(label=ceiling(Population)), vjust = -1, color = "gray20") +
         ggtitle(paste0("Voting Population by Imputed Race in ", mycounty, " County, \n", str_to_title(myprecinct), " Precinct, ", " 2024 General Election")) +
         theme(axis.text.x = element_text(angle = 45, hjust=1)) +
         ylim(c(0, max(all$Population)+ 0.05*max(all$Population))) 


 return(g1)
}


# a function to create a pie chart
make_cvap_pl <- function(i, overwrite = FALSE, verbose = FALSE){
    message(i)
    x <- shp_no_geo[i,]
    # year-create image dirs
    # id, etc.
    stcode <- x$St_Code
    # county, precinct
    mycounty <- x$CountyName
    ctycode <- x$County
    myprecinct <- x$PrecinctNa
    myprecinctnum <- x$PrecinctNu
    mytitle <- str_c(mycounty, str_to_title(myprecinct), myprecinctnum, sep = ", " )

    cvap_sub <- cvap %>% 
        filter(St_Code == stcode,
               Year == 2024)

    all <- cvap_sub %>%
       mutate(Other = AIAN_White_CVAP + Asian_White_CVAP + Black_White_CVAP + TwoOrMore_CVAP + AIAN_Black_CVAP) %>%
      select(-Total_CVAP,-NotHispanic_CVAP, -AIAN_White_CVAP, -Asian_White_CVAP, -Black_White_CVAP, -TwoOrMore_CVAP, -AIAN_Black_CVAP) %>%
      rename(AIAN = AIANAlone_CVAP,
             Asian = AsianAlone_CVAP,
             Black = BlackAlone_CVAP,
             NHOPI = NHOPIAlone_CVAP,
             White = WhiteAlone_CVAP,
             Hispanic = Hispanic_CVAP) %>%
      pivot_longer(cols = c("White", "Black", "Hispanic", "AIAN", "Asian", "NHOPI", "Other")) %>%
        rename(Race = name,
               Population = value) %>%
      mutate(
            Race = 
                factor(Race, levels = c("White", "Black", "Hispanic","AIAN", "Asian","NHOPI", "Other"), 
                       labels = c("White", "Black", "Hispanic","AIAN", "Asian","NHOPI", "Other"))
        )

    
      
    g1 <- ggplot(data = all, aes(x = Race, y = Population)) +
         geom_col( show.legend = F) +
         theme_minimal() +
         geom_text(aes(label=ceiling(Population)), vjust = -1, color = "gray20") +
         ggtitle(paste0("Citizen Voting Age Population by Race in ", mycounty, " County, \n", str_to_title(myprecinct), " Precinct, ", " 2024")) +
         theme(axis.text.x = element_text(angle = 45, hjust=1)) +
         ylim(c(0, max(all$Population)+ 0.05*max(all$Population))) +
         labs(caption = "*AIAN: American Indian/Alaska Native\nNHOPI: Native Hawaiin/Pacific Islander ")

 return(g1)
}

make_reg_pl <- function(i, overwrite = FALSE, verbose = FALSE){
    message(i)
    x <- shp_no_geo[i,]
    # year-create image dirs
    # id, etc.
    stcode <- x$St_Code
    # county, precinct
    mycounty <- x$CountyName
    ctycode <- x$County
    myprecinct <- x$PrecinctNa
    myprecinctnum <- x$PrecinctNu
    mytitle <- str_c(mycounty, str_to_title(myprecinct), myprecinctnum, sep = ", " )

    reg_sub <- registrants %>% 
        filter(St_Code == stcode,
               Year == "2024")

    all <- reg_sub %>%
      pivot_longer(cols = c('Total_Pop', "Black_Pop", "White_Pop", "Hispanic_Pop", "Asian_Pop", "OtherRace_Pop")) %>%
        rename(Race = name,
               Population = value) %>%
        filter(Race != "Total_Pop") %>%
        mutate(
            Race = str_remove_all(string = Race, pattern = "_Pop") %>%
                factor(levels = c("White", "Black", "Asian", "Hispanic", "OtherRace"), 
                       labels = c("White", "Black", "Asian", "Hispanic", "Other"))
        )
    
    
      
    g1 <- ggplot(data = all, aes(x = Race, y = Population)) +
         geom_col(aes(), show.legend = F) +
         theme_minimal() +
         geom_text(aes(label=ceiling(Population)), vjust = -1, color = "gray20") +
         ggtitle(paste0("Voter Registrant Population by Imputed Race in ", mycounty, " County, \n", str_to_title(myprecinct), " Precinct, ", " 2024 General Election")) +
         theme(axis.text.x = element_text(angle = 45, hjust=1)) +
         ylim(c(0, max(all$Population)+ 0.05*max(all$Population))) 


 return(g1)
}

#cvap_pls <- lapply(c(1:nrow(shp_no_geo)), make_cvap_pl)
vote_pls <- lapply(c(1:nrow(shp_no_geo)), make_voter_pl)
#reg_pls <- lapply(c(1:nrow(shp_no_geo)), make_reg_pl)



# labels for hover
labels <- shp_no_geo %>%
    mutate(labs = paste0("County: ", CountyName, "<br  />Precinct: ", str_to_title(PrecinctNa)))%>%
    pull(labs) %>% 
    lapply(htmltools::HTML)

shp_simple <- shp
#shp_simple <- ms_simplify(shp, keep_shapes = TRUE)

# cvap <- leaflet(data = shp_simple, width = "100%") %>%
#     addProviderTiles(providers$CartoDB.Positron) %>%
#     addPolygons(weight = 2, label = ~labels, group = "shp_simple", color = 'gray',
#                 fillColor = 'gray', opacity = 0.5,
#                 highlight = highlightOptions(
#                       weight = 3,
#                       fillOpacity = 0.6,
#                       color = "gray",
#                       opacity = 1.0,
#                       bringToFront = TRUE,
#                       sendToBack = TRUE) ) %>% 
#     leafpop::addPopupGraphs(graph = cvap_pls, group = "shp_simple", height = 450, width = 300)

# registrants <- leaflet(data = shp_simple, width = "100%") %>%
#     addProviderTiles(providers$CartoDB.Positron) %>%
#     addPolygons(weight = 2, label = ~labels, group = "shp_simple", color = 'gray',
#                 fillColor = 'gray', opacity = 0.5,
#                 highlight = highlightOptions(
#                       weight = 3,
#                       fillOpacity = 0.6,
#                       color = "gray",
#                       opacity = 1.0,
#                       bringToFront = TRUE,
#                       sendToBack = TRUE) ) %>%
#     leafpop::addPopupGraphs(graph = reg_pls, group = "shp_simple", height = 450, width = 300)


voters <- leaflet(data = shp_simple, width = "100%") %>%
    addProviderTiles(providers$CartoDB.Positron) %>%
    addPolygons(weight = 2, label = ~labels, group = "shp_simple", color = 'gray',
                fillColor = 'gray', opacity = 0.5,
                highlight = highlightOptions(
                      weight = 3,
                      fillOpacity = 0.6,
                      color = "gray",
                      opacity = 1.0,
                      bringToFront = TRUE,
                      sendToBack = TRUE) ) %>%
    leafpop::addPopupGraphs(graph = vote_pls, group = "shp_simple", height = 450, width = 300)

#cvap
#registrants
voters