Let’s take a look at the early voting data for Harris County

Since I already have a bunch of data for Harris county precincts and zipcodes, why not make some use of it?

path <- "/home/ajackson/Dropbox/Rprojects/Voting/"
BBM <- read_csv(paste0(path, "Cumulative_BBM_1120.csv"), 
                col_types = "ccccccccccccccccccccccccccccccccccccccccc")
BBM <- BBM %>% 
  mutate(ActivityDate=mdy_hms(ActivityDate)) %>% 
  mutate(ActivityDate=force_tz(ActivityDate, tzone = "US/Central")) %>% 
  select(ElectionCode:ActivityDate) %>% 
  mutate(Ballot_Type="Mail")

EV  <- list.files(path=path, pattern="Cumulative_EV_1120_1*", full.names=TRUE) %>% 
  map_df(~read_csv(., col_types = "ccccccccccccccccccccccccccccccccccccccccc"))


EV <- EV %>% 
  mutate(ActivityDate=mdy_hms(ActivityDate)) %>% 
  mutate(ActivityDate=force_tz(ActivityDate, tzone = "US/Central")) %>% 
  select(ElectionCode:ActivityDate) %>% 
  mutate(Ballot_Type="Early")

Votes <- rbind(BBM, EV)

VotesByZipDate <- Votes %>% 
  mutate(Date=floor_date(ActivityDate, unit="day")) %>% 
  group_by(Date, Ballot_Type, VoterZIP) %>% 
    summarise(Votes=n()) %>% 
  ungroup() %>% 
  rename(Zip=VoterZIP) %>% 
  drop_na()

###########   registered voters
path <- paste0(path, "HarrisRegisteredVoters/")

files <- dir(path=path, pattern = "*.csv", full.names=TRUE)

Registered <- files %>% 
  map_dfr(read_csv, col_types=cols(.default = "c"))

Registered %>% 
  filter(Status=="Active") %>% 
  mutate(`Effective Date`=mdy(`Effective Date`)) %>% 
  filter(!is.na(`Effective Date`)) %>% 
  ggplot(aes(`Effective Date`)) +
  geom_histogram()

Registered <- Registered %>% 
  filter(Status=="Active") %>% 
  mutate(`Effective Date`=mdy(`Effective Date`)) %>% 
  filter(!is.na(`Effective Date`)) %>% 
  mutate(NewVoter=if_else(`Effective Date`>=ymd("2020-01-01"), "NewVoter", "OldVoter")) %>% 
  group_by(NewVoter, Zip) %>% 
    summarise(Registered=n()) %>% 
  ungroup()

Registered <- Registered %>% 
  pivot_wider(id_cols=Zip, names_from=NewVoter, values_from=Registered)

###########   ancillary data
path <- "/home/ajackson/Dropbox/Rprojects/Datasets/"

#   SF file of zipcode outlines and areas

Zip_outlines <- readRDS(paste0(path, "ZipCodes_sf.rds"))

#   Census data for 2016

Zip_census16 <- readRDS(paste0(path, "TexasZipcode_16.rds"))
Zip_census16 <- Zip_census16 %>% 
  mutate(ZCTA=as.character(ZCTA))

Zip_census16 <- Zip_census16 %>% 
  mutate(Race=case_when(
    White/(Pop)>0.5    ~ "White",
    Black/(Pop)>0.5    ~ "Black",
    Hispanic/(Pop)>0.5 ~ "Hispanic",
    TRUE               ~ "Mixed"
    )
  )


#   Median family income and number of families
Income <- readRDS("/home/ajackson/Dropbox/Rprojects/Datasets/IncomeByZip.rds")

#   Many vs 2 generational households
House <- readRDS("/home/ajackson/Dropbox/Rprojects/Datasets/HouseholdByZip.rds")

#   Blueness

Blueness <- readRDS(paste0(path,"HarrisBlueness.rds")) 

knitr::opts_chunk$set(warning=FALSE, message=FALSE)

Initial look

First off, let’s explore the data for issues, and for ideas about what might be interesting.

VotesByZip <- VotesByZipDate %>% 
  group_by(Zip) %>% 
    summarise(Votes=sum(Votes)) %>% 
  ungroup()
  
ByZip <- Zip_census16 %>% 
  rename(Zip=ZCTA) %>% 
  left_join(VotesByZip,.)

ByZip <- Registered %>% 
  left_join(ByZip,.)

ByZip <- Income %>% 
  rename(Zip=ZCTA) %>% 
  left_join(ByZip,.)

ByZip <- Blueness %>% 
  rename(Zip=ZCTA) %>% 
  left_join(ByZip,.)

ByZip %>% 
  mutate(VotesPct=100*Votes/(NewVoter+OldVoter)) %>% 
  filter(Votes>100) %>% 
  ggplot(aes(x=VotesPct))+
  geom_histogram()+
  theme(legend.position = "none")+
  labs(x="% Registered Voters", y="Number of Zipcodes",
       title=paste("Percent of Registered Voters Voted by Zipcode",
       today()))

okay let’s have fun

#   Distributions of new and old voters

ByZip %>% 
  mutate(Total=NewVoter+OldVoter) %>% 
  mutate(NewVoter=100*NewVoter/(Total)) %>% 
  mutate(OldVoter=100*OldVoter/(Total)) %>% 
  select(Zip, NewVoter, OldVoter) %>% 
  pivot_longer(!Zip, names_to="Voter", values_to="Number" ) %>% 
  ggplot(aes(x=Number)) +
  geom_density(aes(color=Voter))+
  geom_histogram(aes(color=Voter))+
  labs(x="% of Registered Voters", y="Number of Zip codes",
       title="Proportion of voters new since January 2020")

#   New vs blueness

ByZip %>% 
  mutate(Total=NewVoter+OldVoter) %>% 
  mutate(NewVoter=100*NewVoter/(Total)) %>% 
  mutate(OldVoter=100*OldVoter/(Total)) %>% 
  select(Zip, NewVoter, OldVoter, blueness) %>% 
  ggplot(aes(x=blueness, y=NewVoter)) +
  geom_point()+
  labs(x="% Voted for Hillary Clinton", y="% Registered Voters that are New",
       title="Blueness of Zipcode vs. % New Registered Voters")

ByZip %>% 
  select(Zip, NewVoter, OldVoter, blueness) %>% 
  ggplot(aes(x=blueness, y=NewVoter)) +
  geom_point()+
  labs(x="% Voted for Hillary Clinton", y="Number Registered Voters that are New",
       title="Blueness of Zipcode vs. Number of New Registered Voters")

group.colors <- c(Rep = "red", Dem = "blue")
ByZip %>% 
  select(Zip, NewVoter, OldVoter, blueness) %>% 
  mutate(Dem=NewVoter*blueness,
         Rep=NewVoter*(1-blueness)) %>% 
  select(Zip, Rep, Dem) %>% 
  pivot_longer(!Zip, names_to="Party", values_to="Number" ) %>% 
  ggplot(aes(x=Number)) +
  geom_histogram(aes(fill=Party))+
  scale_fill_manual(values=group.colors)+
  labs(x="Number of New Registered Voters", y="Number of Zip codes",
       title="Proportion of voters new since January 2020")

#   % voted vs blueness

ByZip %>% 
  select(Zip, Votes, NewVoter, OldVoter, blueness) %>% 
  mutate(Pct_Voted=100*Votes/(NewVoter+OldVoter)) %>% 
  mutate(blueness=round(blueness*20, 0)/20) %>% 
  group_by(blueness) %>% 
    summarise(Pct_Voted=100*sum(Votes)/(sum(NewVoter)+sum(OldVoter))) %>% 
  ungroup() %>% 
  ggplot(aes(x=blueness, y=Pct_Voted)) +
  geom_histogram(stat="identity")+
  labs(x="Percent zipcode voted for Clinton", y="Percent of Registered Voters",
       title="Percent Registered Voters who have Voted vs. Blueness")

#   % voted vs median age

ByZip %>% 
  select(Zip, Votes, NewVoter, OldVoter, MedianAge) %>% 
  mutate(Pct_Voted=100*Votes/(NewVoter+OldVoter)) %>% 
  mutate(MedianAge=as.numeric((MedianAge))) %>% 
  ggplot(aes(x=MedianAge, y=Pct_Voted)) +
  geom_point() +
  geom_smooth(method="lm") +
  labs(x="Median Age", y="Percent of Registered Voters",
       title="Percent Reg Voters who have Voted vs. Median Age in Zipcode")

#   % voted vs median income

ByZip %>% 
  select(Zip, Votes, NewVoter, OldVoter, Med_Income) %>% 
  mutate(Pct_Voted=100*Votes/(NewVoter+OldVoter)) %>% 
  ggplot(aes(x=Med_Income, y=Pct_Voted)) +
  geom_point() +
  geom_smooth(method="lm") +
  labs(x="Median Income", y="Percent of Registered Voters",
       title="Percent Reg Voters who have Voted vs. Median Income in Zipcode")

#   % voted vs % black

ByZip %>% 
  select(Zip, Votes, NewVoter, OldVoter, Black, Pop) %>% 
  mutate(Pct_Voted=100*Votes/(NewVoter+OldVoter),
         Pct_Black=100*Black/Pop) %>% 
  ggplot(aes(x=Pct_Black, y=Pct_Voted)) +
  geom_point() +
  geom_smooth(method="lm") +
  labs(x="Percent Black", y="Percent of Registered Voters",
       title="Percent Reg Voters who have Voted vs. Percent Black in Zipcode")

#   % voted vs % hispanic

ByZip %>% 
  select(Zip, Votes, NewVoter, OldVoter, Hispanic, Pop) %>% 
  mutate(Pct_Voted=100*Votes/(NewVoter+OldVoter),
         Pct_Hispanic=100*Hispanic/Pop) %>% 
  ggplot(aes(x=Pct_Hispanic, y=Pct_Voted)) +
  geom_point() +
  geom_smooth(method="lm") +
  labs(x="Percent Hispanic", y="Percent of Registered Voters",
       title="Percent Reg Voters who have Voted vs. Percent Hispanic in Zipcode")

#   % voted vs Age 20-34 %

ByZip %>% 
  select(Zip, Votes, NewVoter, OldVoter, Age25to34, Pop) %>% 
  mutate(Pct_Voted=100*Votes/(NewVoter+OldVoter),
         Pct_Age25to34=100*Age25to34/Pop) %>% 
  ggplot(aes(x=Pct_Age25to34, y=Pct_Voted)) +
  geom_point() +
  geom_smooth(method="lm") +
  labs(x="Percent Age25 to 34", y="Percent of Registered Voters",
       title="Pct Reg Voters who have Voted vs. Pct Age25 to 34 in Zipcode")

#   # by party per time

VotesByZipDate %>%
  left_join(., Blueness, by=c("Zip"="ZCTA")) %>% 
  left_join(., Registered) %>% 
  select(Date, Zip, Votes, blueness, Ballot_Type, NewVoter, OldVoter) %>% 
  group_by(Ballot_Type, Date) %>% 
    summarise(Pct_Voted=100*sum(Votes, na.rm=TRUE)/
                (sum(NewVoter, na.rm = TRUE)+sum(OldVoter, na.rm = TRUE))) %>% 
  ungroup() %>% 
  group_by(Ballot_Type) %>% 
    mutate(Cum_Pct_Voted=cumsum(Pct_Voted)) %>% 
  ungroup() %>% 
  ggplot(aes(x=Date, y=Cum_Pct_Voted, color=Ballot_Type)) +
  geom_line() +
  geom_point() +
  labs(x="Date", y="Cumulative Percent of Registered Voters",
       title="Percent Registered Voters who have Voted vs. Date")

Map

MapData <- ByZip %>% 
  mutate(Pct_Voted=100*Votes/(NewVoter+OldVoter)) %>% 
  select(Zip, Pct_Voted)
  
MapData <- left_join(MapData, Zip_outlines, by=c("Zip"="Zip_Code"))

MapData <- sf::st_as_sf(MapData)

#   percent voted by zipcode
    
    kmeans_loc <- c(1+which(diff(kmeans(sort(MapData$Pct_Voted), 5)[["cluster"]])!=0))
    kmeans_bins <- signif(c(0,
                     sort(MapData$Pct_Voted)[kmeans_loc],
                     max(MapData$Pct_Voted, na.rm=TRUE)),3)
    
    pal <- leaflet::colorBin(palette = heat.colors(5), 
                    bins = 4, 
                    pretty = TRUE,
                    na.color = "transparent",
                    domain = MapData$Pct_Voted,
                    alpha = FALSE, 
                    right = FALSE)

      
leaflet::leaflet(MapData) %>% 
        leaflet::setView(lng = -95.3103, lat = 29.7752, zoom = 8 ) %>%   
        leaflet::addTiles() %>%
        leaflet::addPolygons(data = MapData,
                    weight = 1,
                    stroke=TRUE,
                    smoothFactor = 0.2,
                    fillOpacity = 0.7,
                    fillColor =  ~pal(MapData$Pct_Voted)) %>%
  leaflet::addLegend("bottomleft", pal = pal, 
                     values = MapData$Pct_Voted,
            labels= as.character(seq(Range[1], Range[2], length.out = 5)),
            labFormat = function(type, cuts, p) {
            n = length(cuts)
          paste0(signif(cuts[-n],2), " &ndash; ", signif(cuts[-1],2))
                  },
    title = "Percent Voted per zipcode",
    opacity = 1
  )