Vaccine Reluctance Let’s look at Texas counties and test various factors for correlations to the vaccination rate. We’ll primarily look at the rate of the first vaccination, since there are a variety of reasons why someone might not get the second dose. Let’s start with the raw rates of vaccination by county. Vaccine %>% mutate(Pct_one_dose=People_one_dose/Pop_total) %>% ggplot(aes(x=Date, y=Pct_one_dose, color=County)) + geom_line(show.legend = FALSE) + labs(x="Date", y="First Dose Percentage", title="Texas counties Vaccine Progress") Hmmm… let’s do a little cleanup.

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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? Setup 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 = "*.

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