Overview

This project is a relatively simple map demonstration using Oregon election precincts and results from the 2020 general election. The map uses tmap’s Leaflet implementation and let’s users drill into a display that shows address-level two party election result with color coding and vote detail. This example displays percentages of the two party presidential election results.

The challenge is consolidating precinct data that isn’t consolidated at the state level by the Oregon secretary of state office. Each county reports its own data at the precinct level, and consolidated map files are hard to find.

Libraries

This list and sequence should be cleaned up. I’m using the easypackages function libraries.

Addresses for input

This chunk can produce an address marker using the geocode_OSM function from tmap. An example is shown here. In the next version this will use a text input for Shiny.

Shapefiles and sf objects

Easiest to find old fashioned shapefiles with all the headaches described by Lovelace. I read them as sf objects using st_read function. In this case, I combine an Oregon shapefile for counties with Clark County in Washington, included because Clark County is part of the Portland Metro area.

County Shapefiles

I added labels from the USMAP fips lookup. Is there a better lookup table provided by the Census bureau from tidycensus?

Notice that I transform the sf objects into a coordinate reference system of NAD83, although they appear to have been read with that CRS. The bind_rows function doesn’t appear to work otherwise.

I need to add a state boundary, unless Oregon decides to annex Clark County.

[Need to add a section with sources for all data and shapefiles]

Oregon boundaries from https://opendata.imspdx.org/dataset/november-2018-election-oregon-results-by-precinct

oregon_counties_sf <- st_read("shapefiles/oregon_counties/counties.shp", quiet = TRUE) %>% 
    st_transform(or_counties, crs = "NAD83") %>% 
    select(!COUNTY)

washington_state_counties_sf <-
    st_read("shapefiles/washington_counties/WA_County_Boundaries.shp", quiet = TRUE) %>%
    st_transform(crs = "NAD83") %>%
    mutate(STFID = as.character(JURISDIC_5)) 

washington_state <- st_union(washington_state_counties_sf)
  
clark_county_sf <- washington_state_counties_sf %>% 
    filter(JURISDIC_2 == "Clark") 

oregon_counties_and_clark_sf <- bind_rows(oregon_counties_sf, clark_county_sf) %>% 
    st_transform(crs = "NAD83")

oregon_fips <- fips_info(oregon_counties_and_clark_sf$STFID) %>% 
    mutate(county = gsub(" County", "", county))

oregon_counties_and_clark_sf <- left_join(oregon_counties_and_clark_sf, oregon_fips, by= c("STFID" = "fips"))

oregon_state <- st_union(oregon_counties_sf)

oregon_bb <- st_bbox(oregon_counties_and_clark_sf)

tmap_mode("plot")
## tmap mode set to plotting
tm_shape(oregon_counties_and_clark_sf, bbox = oregon_bb) + 
    tm_text("county", size = 0.5) + 
    tm_polygons(alpha = 0, id = "county") +
tm_shape(oregon_state) +
    tm_borders(col = "orange", lwd = 2.5)

Precinct Shapefiles

Metro Portland the rest of the state are processed separately.

metro_portland_precinct_sf <-  st_read("shapefiles/metro_portland_precinct/precinct.shp", quiet = TRUE) %>% 
    select(PRECINCTID, COUNTY, geometry) %>%
    mutate(COUNTY = case_when(
        COUNTY == "W" ~ "Washington",
        COUNTY == "M" ~ "Multnomah",
        COUNTY == "C" ~ "Clackamas",
        TRUE ~ "Other"
        ) 
    ) %>% 
    st_transform(crs = "NAD83")


clark_wa_precincts_sf  <- st_read("shapefiles/clark_precinct_shapefiles/Precinct.shp", quiet = TRUE) %>%
    select(PRECINCTID = PRECINCT, geometry) %>%
    mutate(PRECINCTID = str_c("K", as.character(PRECINCTID)), COUNTY = "Clark") %>% 
    st_transform(crs = "NAD83")

metro_portland_precinct_sf <- bind_rows(metro_portland_precinct_sf,
                                        clark_wa_precincts_sf)

tm_shape(metro_portland_precinct_sf) + tm_polygons()

or_precinct_sf <- st_read("shapefiles/oregon_precincts/OregonPrecinctsNov2018.shp", quiet = TRUE) %>% 
    filter(!County %in% c("Multnomah", "Washington", "Clackamas")) %>% 
    st_transform("NAD83")

tm_shape(or_precinct_sf) + tm_polygons()

Vote data for presidential election (two major parties only)

This section processes the various files with precinct level election results.

Oregon, excluding Metro Portland

Lane County vote totals, hand tabulated from county pdf

lane_votes <- read_csv("data/votes/lane.csv", col_types = cols(.default = "d", precinct = "c")) %>% 
  mutate(county = "Lane") %>% 
  rename(DEM = Biden, REP = Trump)

Marion, Polk, and Yamhill County voter data. Note these use the OpenElections standard format. Portland Metro counties generated further down

Future: use purrr::map_dfr to produce a single data table

open_election_col_types = cols(.default = "c", votes = "d")

open_election_single_county_votes <- function(csv, coltypes = open_election_col_types) {
    
    county_df <- read_csv(csv, col_types = coltypes) %>% 
    filter(office == "President") %>% 
    mutate(party = case_when (
        str_detect(candidate,"Biden") ~ "DEM",
        str_detect(candidate,"Trump") ~ "REP",
        TRUE ~ party
        )
    ) %>% 
    filter(party %in% c("DEM", "REP")) %>% 
    select(!candidate) %>% 
    pivot_wider(names_from = party, values_from = votes) %>% 
    select(county, precinct, REP, DEM)
    return(county_df)
}
marion_votes <- open_election_single_county_votes("data/votes/20201103__or__general__marion__precinct.csv")

polk_votes <- open_election_single_county_votes("data/votes/20201103__or__general__polk__precinct.csv")

yamhill_votes <- open_election_single_county_votes("data/votes/20201103__or__general__yamhill__precinct.csv")

Tillamook County, hand tabulated from the published county pdf

tillamook_votes <- read_csv("data/votes/tillamook_votes.csv", col_types = cols(.default = "c", DEM = "d", REP = "d")) %>% 
  rename(precinct = precinct_votefile) %>% 
  select(county, precinct, DEM, REP)

Consolidate the five additional counties with or_precincts_votes

or_precincts_votes <- bind_rows(or_precincts_votes, 
                                tillamook_votes, 
                                lane_votes, 
                                polk_votes, 
                                marion_votes, 
                                yamhill_votes
                                )

read in a lookup table that matches vote file precinct names and shape file precinct names

lookup <- read_csv("data/votes_shapefile_lookup.csv", 
                   col_types = cols(.default = "c")) %>% 
    mutate(county = str_to_title(tolower(county)))

combine lookup table and shapefile

Two columns have white space in the text and needs to be trimmed. Could replace regular expressions with simpler tidy version.

or_precinct_sf$precinct_votefile <- gsub('\\s+', '', or_precinct_sf$precinct_votefile)
or_precincts_votes$precinct <- gsub('\\s+', '', or_precincts_votes$precinct)
or_precinct_sf <- left_join(or_precinct_sf, or_precincts_votes, 
                            by = c("County" = "county",
                                   "precinct_votefile" = "precinct")) %>%
    select(county = County, precinct = precinct_votefile, REP, DEM, geometry) %>%
    st_transform(crs = "NAD83")

Metro Portland

Multnomah, Clackamas, and Washington, plus Clark County WA

Complicated because the Clackamas County reports combine several precincts. This chunk consolidates the affected precincts

The vote data was generated by hand before it became available in the Open Elections extract.

Voting tables and sf objects joined here

metro_portland_precinct_sf <- inner_join(metro_portland_precinct_sf, 
                                         metro_portland_votes, 
                                         by=("PRECINCTID")) %>% 
    select(county = COUNTY, precinct, DEM = Biden, REP = Trump)

or_precinct_sf <- bind_rows(or_precinct_sf, 
                            metro_portland_precinct_sf
                            )

##The Leaflet Map

Some additional formatting variables added here.

or_precinct_sf <- or_precinct_sf %>% 
    mutate(vpct = DEM/(DEM + REP)) %>%
    mutate(pwinner = ifelse(vpct >=.5, 
                            "DEM", 
                            "REP"), 
           DPR = DEM + REP) %>% 
    mutate(pct_lbl = ifelse(pwinner == "DEM", 
                            str_c(pwinner, " ", format(vpct*100, digits = 3)), 
                            str_c(pwinner, " ", format(100 - vpct*100, digits = 2)))) %>%
    mutate(hover = str_c(county," ",precinct, ": ", pct_lbl," ",DPR)) %>%
    dplyr::select(county, precinct, vpct, DEM, REP, hover)

The final or_precinct_votes table has this structure:

or_precinct_df <- st_drop_geometry(or_precinct_sf)
or_precinct_df %>%
  filter(county=="Multnomah" & precinct == "3605") %>%
  kbl() %>%
  kable_styling()
county precinct vpct DEM REP hover
Multnomah 3605 0.875155 2117 302 Multnomah 3605: DEM 87.52 2419

Draw the map

tmap_mode("view")
## tmap mode set to interactive viewing
rwb <- colorRampPalette(c("red", "white", "blue"))(256)
pctiles <- c(0,.1,.3,.45,.5,.55,.7,.9,1)
bb_or <- bb(or_precinct_sf)

tmap_options(basemaps = c(Canvas = "OpenStreetMap"))

or_precincts_tmap <-
    tm_shape(or_precinct_sf, bbox = bb_or) +
        tm_polygons(col = "vpct", 
                    n=length(5), 
                    id = "hover", 
                    style = "fixed", 
                    breaks = pctiles, 
                    alpha = 0.7, 
                    palette = rwb) +
        tm_shape(oregon_counties_and_clark_sf) + 
            tm_borders(lwd = 1, col = "green") +
        tm_shape(address_sf) + 
            tm_dots()

or_precincts_tmap