GA_RO.Rmd

---
title: "Georgia Early Voting Statistics - 2021 Senate Run-Off Election"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
library(knitr)
library(kableExtra)
library(scales)
library(DT)
library(highcharter)

GA_stats <- read_csv("D:/DropBox/Dropbox/Mail_Ballots_2020/markdown/2020RO_Early_Vote_GA.csv")

# Setup

party_shell <- data.frame(Party=character(),
                                  Count=integer(),
                                  Percent=double(),
                                  stringsAsFactors=FALSE)
party_shell[1,1] <- "Democrats"
party_shell[2,1] <- "Republicans"
party_shell[3,1] <- "Minor"
party_shell[4,1] <- "No Party Affiliation"
party_shell[5,1] <- "TOTAL"

race_shell <- data.frame(Race=character(),
                                  Count=integer(),
                                  Percent=double(),
                                  stringsAsFactors=FALSE)
race_shell[1,1] <- "Non-Hispanic White"
race_shell[2,1] <- "Non-Hispanic Black"
race_shell[3,1] <- "Hispanic"
race_shell[4,1] <- "Non-Hispanic Asian American"
race_shell[5,1] <- "Non-Hispanic Native American"
race_shell[6,1] <- "Other/Multiple/Unknown"
race_shell[7,1] <- "TOTAL"

race_shell_returned <- data.frame(Race=character(),
                                  Count=integer(),
                                  Frequency=double(),
                                  Count2=integer(),
                                  Rate=integer(),
                                  stringsAsFactors=FALSE)
race_shell_returned[1,1] <- "Non-Hispanic White"
race_shell_returned[2,1] <- "Non-Hispanic Black"
race_shell_returned[3,1] <- "Hispanic"
race_shell_returned[4,1] <- "Non-Hispanic Asian American"
race_shell_returned[5,1] <- "Non-Hispanic Native American"
race_shell_returned[6,1] <- "Other/Multiple/Unknown"
race_shell_returned[7,1] <- "TOTAL"

GA_age_shell <- data.frame(Age=character(),
                                  Count=integer(),
                                  Percent=double(),
                                  stringsAsFactors=FALSE)
GA_age_shell[1,1] <- "18 to 24"
GA_age_shell[2,1] <- "25 to 34"
GA_age_shell[3,1] <- "35 to 44"
GA_age_shell[4,1] <- "45 and 55"
GA_age_shell[5,1] <- "56 and 65"
GA_age_shell[6,1] <- "66 and up"
GA_age_shell[7,1] <- "Age Unknown"
GA_age_shell[8,1] <- "TOTAL"

GA_age_shell_returned <- data.frame(Race=character(),
                                  Count=integer(),
                                  Frequency=double(),
                                  Count2=integer(),
                                  Rate=integer(),
                                  stringsAsFactors=FALSE)
GA_age_shell_returned[1,1] <- "18 to 24"
GA_age_shell_returned[2,1] <- "25 to 34"
GA_age_shell_returned[3,1] <- "35 to 44"
GA_age_shell_returned[4,1] <- "45 and 55"
GA_age_shell_returned[5,1] <- "56 and 65"
GA_age_shell_returned[6,1] <- "66 and up"
GA_age_shell_returned[7,1] <- "Age Unknown"
GA_age_shell_returned[8,1] <- "TOTAL"

gender_shell <- data.frame(Gender=character(),
                                  Count=integer(),
                                  Percent=double(),
                                  stringsAsFactors=FALSE)
gender_shell[1,1] <- "Female"
gender_shell[2,1] <- "Male"
gender_shell[3,1] <- "Unknown"
gender_shell[4,1] <- "TOTAL"


gender_shell_full <- data.frame(Gender=character(),
                                  Count=integer(),
                                  Frequency=double(),
                                  Count2=integer(),
                                  Rate=integer(),
                                  stringsAsFactors=FALSE)
gender_shell_full[1,1] <- "Female"
gender_shell_full[2,1] <- "Male"
gender_shell_full[3,1] <- "Unknown"
gender_shell_full[4,1] <- "TOTAL"

age_shell <- data.frame(Age=character(),
                                  Count=integer(),
                                  Percent=double(),
                                  stringsAsFactors=FALSE)
age_shell[1,1] <- "18 to 24"
age_shell[2,1] <- "25 to 34"
age_shell[3,1] <- "35 to 44"
age_shell[4,1] <- "45 to 54"
age_shell[5,1] <- "55 to 64"
age_shell[6,1] <- "65 and up"
age_shell[7,1] <- "TOTAL"


voted_shell <- data.frame(Voted=character(),
                                  Count=integer(),
                                  Percent=double(),
                                  stringsAsFactors=FALSE)
voted_shell[1,1] <- "Voted in 2020 General"
voted_shell[2,1] <- "Did Not Vote in 2020 General"
voted_shell[3,1] <- "TOTAL"

voted_shell_full <- data.frame(Voted=character(),
                                  Count=integer(),
                                  Frequency=double(),
                                  Count2=integer(),
                                  Rate=integer(),
                                  stringsAsFactors=FALSE)
voted_shell_full[1,1] <- "Voted in 2020 General"
voted_shell_full[2,1] <- "Did Not Vote in 2020 General"
voted_shell_full[3,1] <- "TOTAL"

# Georgia

GA_req_race <- race_shell

GA_req_race[1,2] <- sum(GA_stats$Mail.Req.nhwhite.Tot)
GA_req_race[2,2] <- sum(GA_stats$Mail.Req.nhblack.Tot)
GA_req_race[3,2] <- sum(GA_stats$Mail.Req.hisp.Tot) 
GA_req_race[4,2] <- sum(GA_stats$Mail.Req.nhasian.Tot) 
GA_req_race[5,2] <- sum(GA_stats$Mail.Req.nhna.Tot) 
GA_req_race[6,2] <- sum(GA_stats$Mail.Req.oth.Tot) 
GA_req_race[7,2] <- sum(GA_stats$Mail.Req.Tot) 

GA_req_race$Percent <- 100*GA_req_race$Count/GA_req_race[7,2]


GA_req_age <- GA_age_shell

GA_req_age[1,2] <- sum(GA_stats$Mail.Req.age1824.Tot)
GA_req_age[2,2] <- sum(GA_stats$Mail.Req.age2534.Tot)
GA_req_age[3,2] <- sum(GA_stats$Mail.Req.age3544.Tot) 
GA_req_age[4,2] <- sum(GA_stats$Mail.Req.age4554.Tot) 
GA_req_age[5,2] <- sum(GA_stats$Mail.Req.age5564.Tot) 
GA_req_age[6,2] <- sum(GA_stats$Mail.Req.age65up.Tot) 
GA_req_age[7,2] <- sum(GA_stats$Mail.Req.ageunk.Tot) 
GA_req_age[8,2] <- sum(GA_stats$Mail.Req.Tot) 

GA_req_age$Percent <- 100*GA_req_age$Count/GA_req_age[8,2]

GA_req_voted <- voted_shell

GA_req_voted[1,2] <- sum(GA_stats$Mail.Req.Vote.Tot)
GA_req_voted[2,2] <- sum(GA_stats$Mail.Req.Novote.Tot) 
GA_req_voted[3,2] <- sum(GA_stats$Mail.Req.Tot) 

GA_req_voted$Percent <- 100*GA_req_voted$Count/GA_req_voted[3,2]


GA_mail_accept_race <- race_shell_returned

GA_mail_accept_race[1,2] <- sum(GA_stats$Mail.Accept.nhwhite.Tot)
GA_mail_accept_race[2,2] <- sum(GA_stats$Mail.Accept.nhblack.Tot)
GA_mail_accept_race[3,2] <- sum(GA_stats$Mail.Accept.hisp.Tot) 
GA_mail_accept_race[4,2] <- sum(GA_stats$Mail.Accept.nhasian.Tot) 
GA_mail_accept_race[5,2] <- sum(GA_stats$Mail.Accept.nhna.Tot) 
GA_mail_accept_race[6,2] <- sum(GA_stats$Mail.Accept.oth.Tot) 
GA_mail_accept_race[7,2] <- sum(GA_stats$Mail.Accept.Tot) 

GA_mail_accept_race[1,4] <- sum(GA_stats$Mail.Req.nhwhite.Tot)
GA_mail_accept_race[2,4] <- sum(GA_stats$Mail.Req.nhblack.Tot)
GA_mail_accept_race[3,4] <- sum(GA_stats$Mail.Req.hisp.Tot) 
GA_mail_accept_race[4,4] <- sum(GA_stats$Mail.Req.nhasian.Tot) 
GA_mail_accept_race[5,4] <- sum(GA_stats$Mail.Req.nhna.Tot) 
GA_mail_accept_race[6,4] <- sum(GA_stats$Mail.Req.oth.Tot) 
GA_mail_accept_race[7,4] <- sum(GA_stats$Mail.Req.Tot) 

GA_mail_accept_race$Frequency <- 100 * GA_mail_accept_race$Count/GA_mail_accept_race[7,2]
GA_mail_accept_race$Rate <- 100*GA_mail_accept_race$Count/GA_mail_accept_race$Count2

colnames(GA_mail_accept_race) <- c("Race/Ethnicity", "Returned Ballots", "Freq. Distribution", "Requested Ballots", "Return Rate")

GA_mail_accept_age <- GA_age_shell_returned

GA_mail_accept_age[1,2] <- sum(GA_stats$Mail.Accept.age1824.Tot)
GA_mail_accept_age[2,2] <- sum(GA_stats$Mail.Accept.age2534.Tot)
GA_mail_accept_age[3,2] <- sum(GA_stats$Mail.Accept.age3544.Tot) 
GA_mail_accept_age[4,2] <- sum(GA_stats$Mail.Accept.age4554.Tot) 
GA_mail_accept_age[5,2] <- sum(GA_stats$Mail.Accept.age5564.Tot) 
GA_mail_accept_age[6,2] <- sum(GA_stats$Mail.Accept.age65up.Tot) 
GA_mail_accept_age[7,2] <- sum(GA_stats$Mail.Accept.ageunk.Tot) 
GA_mail_accept_age[8,2] <- sum(GA_stats$Mail.Accept.Tot) 

GA_mail_accept_age[1,4] <- sum(GA_stats$Mail.Req.age1824.Tot)
GA_mail_accept_age[2,4] <- sum(GA_stats$Mail.Req.age2534.Tot)
GA_mail_accept_age[3,4] <- sum(GA_stats$Mail.Req.age3544.Tot) 
GA_mail_accept_age[4,4] <- sum(GA_stats$Mail.Req.age4554.Tot) 
GA_mail_accept_age[5,4] <- sum(GA_stats$Mail.Req.age5564.Tot) 
GA_mail_accept_age[6,4] <- sum(GA_stats$Mail.Req.age65up.Tot) 
GA_mail_accept_age[7,4] <- sum(GA_stats$Mail.Req.ageunk.Tot) 
GA_mail_accept_age[8,4] <- sum(GA_stats$Mail.Req.Tot) 

GA_mail_accept_age$Frequency <- 100 * GA_mail_accept_age$Count/GA_mail_accept_age[8,2]
GA_mail_accept_age$Rate <- 100*GA_mail_accept_age$Count/GA_mail_accept_age$Count2

colnames(GA_mail_accept_age) <- c("Age", "Returned Ballots", "Freq. Distribution", "Requested Ballots", "Return Rate")

GA_mail_accept_voted <- voted_shell_full

GA_mail_accept_voted[1,2] <- sum(GA_stats$Mail.Accept.Vote.Tot)
GA_mail_accept_voted[2,2] <- sum(GA_stats$Mail.Accept.Novote.Tot) 
GA_mail_accept_voted[3,2] <- sum(GA_stats$Mail.Accept.Tot) 

GA_mail_accept_voted[1,4] <- sum(GA_stats$Mail.Req.Vote.Tot)
GA_mail_accept_voted[2,4] <- sum(GA_stats$Mail.Req.Novote.Tot) 
GA_mail_accept_voted[3,4] <- sum(GA_stats$Mail.Req.Tot) 

GA_mail_accept_voted$Frequency <- 100 * GA_mail_accept_voted$Count/GA_mail_accept_voted[3,2]
GA_mail_accept_voted$Rate <- 100*GA_mail_accept_voted$Count/GA_mail_accept_voted$Count2

GA_inperson_accept_race <- race_shell_returned

GA_inperson_accept_race[1,2] <- sum(GA_stats$Inperson.Accept.nhwhite.Tot)
GA_inperson_accept_race[2,2] <- sum(GA_stats$Inperson.Accept.nhblack.Tot)
GA_inperson_accept_race[3,2] <- sum(GA_stats$Inperson.Accept.hisp.Tot) 
GA_inperson_accept_race[4,2] <- sum(GA_stats$Inperson.Accept.nhasian.Tot) 
GA_inperson_accept_race[5,2] <- sum(GA_stats$Inperson.Accept.nhna.Tot) 
GA_inperson_accept_race[6,2] <- sum(GA_stats$Inperson.Accept.oth.Tot) 
GA_inperson_accept_race[7,2] <- sum(GA_stats$Inperson.Accept.Tot) 

GA_inperson_accept_race[1,4] <- sum(GA_stats$Reg.Voters.nhwhite)
GA_inperson_accept_race[2,4] <- sum(GA_stats$Reg.Voters.nhblack)
GA_inperson_accept_race[3,4] <- sum(GA_stats$Reg.Voters.hisp) 
GA_inperson_accept_race[4,4] <- sum(GA_stats$Reg.Voters.nhasian) 
GA_inperson_accept_race[5,4] <- sum(GA_stats$Reg.Voters.nhna) 
GA_inperson_accept_race[6,4] <- sum(GA_stats$Reg.Voters.oth) 
GA_inperson_accept_race[7,4] <- sum(GA_stats$Reg.Voters) 

GA_inperson_accept_race$Frequency <- 100 * GA_inperson_accept_race$Count/GA_inperson_accept_race[7,2]
GA_inperson_accept_race$Rate <- 100*GA_inperson_accept_race$Count/GA_inperson_accept_race$Count2

colnames(GA_inperson_accept_race) <- c("Race/Ethnicity", "In-Person Votes", "Freq. Distribution", "Registered Voters", "In-Person Rate")

GA_inperson_accept_age <- GA_age_shell_returned

GA_inperson_accept_age[1,2] <- sum(GA_stats$Inperson.Accept.age1824.Tot)
GA_inperson_accept_age[2,2] <- sum(GA_stats$Inperson.Accept.age2534.Tot)
GA_inperson_accept_age[3,2] <- sum(GA_stats$Inperson.Accept.age3544.Tot) 
GA_inperson_accept_age[4,2] <- sum(GA_stats$Inperson.Accept.age4554.Tot) 
GA_inperson_accept_age[5,2] <- sum(GA_stats$Inperson.Accept.age5564.Tot) 
GA_inperson_accept_age[6,2] <- sum(GA_stats$Inperson.Accept.age65up.Tot) 
GA_inperson_accept_age[7,2] <- sum(GA_stats$Inperson.Accept.ageunk.Tot) 
GA_inperson_accept_age[8,2] <- sum(GA_stats$Inperson.Accept.Tot) 

GA_inperson_accept_age[1,4] <- sum(GA_stats$Reg.Voters.age1824)
GA_inperson_accept_age[2,4] <- sum(GA_stats$Reg.Voters.age2534)
GA_inperson_accept_age[3,4] <- sum(GA_stats$Reg.Voters.age3544) 
GA_inperson_accept_age[4,4] <- sum(GA_stats$Reg.Voters.age4554) 
GA_inperson_accept_age[5,4] <- sum(GA_stats$Reg.Voters.age5564) 
GA_inperson_accept_age[6,4] <- sum(GA_stats$Reg.Voters.age65up) 
GA_inperson_accept_age[7,4] <- sum(GA_stats$Reg.Voters.ageunk) 
GA_inperson_accept_age[8,4] <- sum(GA_stats$Reg.Voters) 

GA_inperson_accept_age$Frequency <- 100 * GA_inperson_accept_age$Count/GA_inperson_accept_age[8,2]
GA_inperson_accept_age$Rate <- 100*GA_inperson_accept_age$Count/GA_inperson_accept_age$Count2

colnames(GA_inperson_accept_age) <- c("Age", "In-Person Votes", "Freq. Distribution", "Registered Voters", "In-Person Rate")

GA_inperson_accept_voted <- voted_shell_full

GA_inperson_accept_voted[1,2] <- sum(GA_stats$Inperson.Accept.Vote.Tot)
GA_inperson_accept_voted[2,2] <- sum(GA_stats$Inperson.Accept.Novote.Tot) 
GA_inperson_accept_voted[3,2] <- sum(GA_stats$Inperson.Accept.Tot) 

GA_inperson_accept_voted[1,4] <- sum(GA_stats$Reg.Voters.Vote)
GA_inperson_accept_voted[2,4] <- sum(GA_stats$Reg.Voters.Novote) 
GA_inperson_accept_voted[3,4] <- sum(GA_stats$Reg.Voters) 

GA_inperson_accept_voted$Frequency <- 100 * GA_inperson_accept_voted$Count/GA_inperson_accept_voted[3,2]
GA_inperson_accept_voted$Rate <- 100*GA_inperson_accept_voted$Count/GA_inperson_accept_voted$Count2

colnames(GA_inperson_accept_voted) <- c("2020 General Voted", "In-Person Votes", "Freq. Distribution", "Registered Voters", "In-Person Rate")

GA_voted_race <- race_shell_returned

GA_voted_race[1,2] <- sum(GA_stats$Inperson.Accept.nhwhite.Tot) + sum(GA_stats$Mail.Accept.nhwhite.Tot)
GA_voted_race[2,2] <- sum(GA_stats$Inperson.Accept.nhblack.Tot) + sum(GA_stats$Mail.Accept.nhblack.Tot)
GA_voted_race[3,2] <- sum(GA_stats$Inperson.Accept.hisp.Tot) + sum(GA_stats$Mail.Accept.hisp.Tot) 
GA_voted_race[4,2] <- sum(GA_stats$Inperson.Accept.nhasian.Tot) + sum(GA_stats$Mail.Accept.nhasian.Tot)
GA_voted_race[5,2] <- sum(GA_stats$Inperson.Accept.nhna.Tot) + sum(GA_stats$Mail.Accept.nhna.Tot) 
GA_voted_race[6,2] <- sum(GA_stats$Inperson.Accept.oth.Tot) + sum(GA_stats$Mail.Accept.oth.Tot)
GA_voted_race[7,2] <- sum(GA_stats$Inperson.Accept.Tot) + sum(GA_stats$Mail.Accept.Tot) 

GA_voted_race[1,4] <- sum(GA_stats$Reg.Voters.nhwhite)
GA_voted_race[2,4] <- sum(GA_stats$Reg.Voters.nhblack)
GA_voted_race[3,4] <- sum(GA_stats$Reg.Voters.hisp) 
GA_voted_race[4,4] <- sum(GA_stats$Reg.Voters.nhasian) 
GA_voted_race[5,4] <- sum(GA_stats$Reg.Voters.nhna) 
GA_voted_race[6,4] <- sum(GA_stats$Reg.Voters.oth) 
GA_voted_race[7,4] <- sum(GA_stats$Reg.Voters) 

GA_voted_race$Frequency <- 100 * GA_voted_race$Count/GA_voted_race[7,2]
GA_voted_race$Rate <- 100*GA_voted_race$Count/GA_voted_race$Count2

colnames(GA_voted_race) <- c("Race/Ethnicity", "Total Early Votes", "Freq. Distribution", "Registered Voters", "Turnout Rate")

GA_voted_age <- GA_age_shell_returned

GA_voted_age[1,2] <- sum(GA_stats$Inperson.Accept.age1824.Tot) + sum(GA_stats$Mail.Accept.age1824.Tot)
GA_voted_age[2,2] <- sum(GA_stats$Inperson.Accept.age2534.Tot) + sum(GA_stats$Mail.Accept.age2534.Tot)
GA_voted_age[3,2] <- sum(GA_stats$Inperson.Accept.age3544.Tot) + sum(GA_stats$Mail.Accept.age3544.Tot) 
GA_voted_age[4,2] <- sum(GA_stats$Inperson.Accept.age4554.Tot) + sum(GA_stats$Mail.Accept.age4554.Tot) 
GA_voted_age[5,2] <- sum(GA_stats$Inperson.Accept.age5564.Tot) + sum(GA_stats$Mail.Accept.age5564.Tot) 
GA_voted_age[6,2] <- sum(GA_stats$Inperson.Accept.age65up.Tot) + sum(GA_stats$Mail.Accept.age65up.Tot) 
GA_voted_age[7,2] <- sum(GA_stats$Inperson.Accept.ageunk.Tot) + sum(GA_stats$Mail.Accept.ageunk.Tot) 
GA_voted_age[8,2] <- sum(GA_stats$Inperson.Accept.Tot) + sum(GA_stats$Mail.Accept.Tot) 

GA_voted_age[1,4] <- sum(GA_stats$Reg.Voters.age1824)
GA_voted_age[2,4] <- sum(GA_stats$Reg.Voters.age2534)
GA_voted_age[3,4] <- sum(GA_stats$Reg.Voters.age3544) 
GA_voted_age[4,4] <- sum(GA_stats$Reg.Voters.age4554) 
GA_voted_age[5,4] <- sum(GA_stats$Reg.Voters.age5564) 
GA_voted_age[6,4] <- sum(GA_stats$Reg.Voters.age65up) 
GA_voted_age[7,4] <- sum(GA_stats$Reg.Voters.ageunk) 
GA_voted_age[8,4] <- sum(GA_stats$Reg.Voters) 

GA_voted_age$Frequency <- 100 * GA_voted_age$Count/GA_voted_age[8,2]
GA_voted_age$Rate <- 100*GA_voted_age$Count/GA_voted_age$Count2

GA_voted_age[7,5] <- NA

colnames(GA_voted_age) <- c("Age", "Total Early Votes", "Freq. Distribution", "Registered Voters", "Turnout Rate")

GA_voted_voted <- voted_shell_full

GA_voted_voted[1,2] <- sum(GA_stats$Inperson.Accept.Vote.Tot) + sum(GA_stats$Mail.Accept.Vote.Tot)
GA_voted_voted[2,2] <- sum(GA_stats$Inperson.Accept.Novote.Tot) + sum(GA_stats$Mail.Accept.Novote.Tot) 
GA_voted_voted[3,2] <- sum(GA_stats$Inperson.Accept.Tot) + sum(GA_stats$Mail.Accept.Tot)

GA_voted_voted[1,4] <- sum(GA_stats$Reg.Voters.Vote)
GA_voted_voted[2,4] <- sum(GA_stats$Reg.Voters.Novote) 
GA_voted_voted[3,4] <- sum(GA_stats$Reg.Voters) 

GA_voted_voted$Frequency <- 100 * GA_voted_voted$Count/GA_voted_voted[3,2]
GA_voted_voted$Rate <- 100*GA_voted_voted$Count/GA_voted_voted$Count2

colnames(GA_voted_voted) <- c("2020 General Voted", "Total Early Votes", "Freq. Distribution", "Registered Voters", "Turnout Rate")

GA_stats <- mutate(GA_stats, Mail.Req.All = Mail.Req.Tot + Mail.App.Reject.Tot)
GA_stats <- mutate(GA_stats, Mail.Return.All = Mail.Accept.Tot + Mail.Reject.Tot)
GA_stats <- mutate(GA_stats, Voted.Tot = (Mail.Accept.Tot + Inperson.Accept.Tot))

GA_stats_req <- select(GA_stats, County, Reg.Voters, Mail.Req.Tot, Pct.Req)
GA_stats_reject_app <- select(GA_stats, County, Mail.Req.All, Mail.App.Reject.Tot, Pct.App.Reject)
GA_stats_accept <- select(GA_stats, County, Mail.Accept.Tot, Mail.Req.Tot, Pct.Mail.Accept)
GA_stats_inperson <- select(GA_stats, County, Inperson.Accept.Tot, Reg.Voters, Pct.Inperson)
GA_stats_voted <- select(GA_stats, County, Voted.Tot, Reg.Voters, Pct.Voted)
GA_stats_reject_mail <- select(GA_stats, County, Mail.Return.All, Mail.Reject.Tot, Pct.Mail.Reject)
```
## {.tabset}
Last Report: 1/6/2021  
Source: [https://elections.sos.ga.gov/Elections/voterabsenteefile.do](https://elections.sos.ga.gov/Elections/voterabsenteefile.do)

Statistics from the 2020 general election are available [here](https://electproject.github.io/Early-Vote-2020G/GA.html)

### Total Early Votes

Total Early Votes: **`r format(as.numeric(sum(GA_stats$Inperson.Accept.Tot) + sum(GA_stats$Mail.Accept.Tot)), big.mark =",")`**  
Percent Turnout (of Registered Voters): **`r paste(round(100*(sum(GA_stats$Inperson.Accept.Tot) + sum(GA_stats$Mail.Accept.Tot)) /sum(GA_stats$Reg.Voters), digits = 1),"%", sep = "")`**  

``` {r echo = FALSE}
GA_map_data <- GA_stats

GA_map_data <- mutate(GA_map_data, percent = round(100*(Pct.Voted), digits = 1))

# GA_map_data <- rename(GA_map_data, fips = FIPS_CODE)
GA_map_data <- mutate(GA_map_data, fips = as.character(FIPS_CODE))

mapfile <- download_map_data("countries/us/us-ga-all.js")
mapdata <- get_data_from_map(mapfile)

mapdata$row <- as.integer(rownames(mapdata))

GA_map_data <- left_join(GA_map_data, mapdata, by = "fips")

GA_map_data <- arrange(GA_map_data, row)

hcmap(map = "countries/us/us-ga-all", data = GA_map_data,
      value = "percent", name = "Percent Accepted", joinBy = "fips") %>%
  hc_title(text ="Total Votes as Percent of Registered Voters") %>%
  hc_subtitle(text = "County plots may not be shaded using the same scale")
```

``` {r echo = FALSE}
datatable(GA_stats_voted, colnames = c("County", "Total Votes", "Reg Voters", "Turnout Rate"), rownames = F) %>%
  formatPercentage('Pct.Voted', 1) %>%
  formatRound(c('Voted.Tot', 'Reg.Voters'), 0, mark = ",")
```

#### Total Early Votes by Race and Ethnicity

``` {r echo = FALSE}
kable(GA_voted_race, format.args = list(big.mark = ",", 
  scientific = FALSE), digits = 1) %>%
  kable_styling(bootstrap_options = "striped", full_width = F, position = "left")
```

#### Total Early Votes by Age

``` {r echo = FALSE}
kable(GA_voted_age, format.args = list(big.mark = ",", 
  scientific = FALSE), digits = 1) %>%
  kable_styling(bootstrap_options = "striped", full_width = F, position = "left")
```

Note: The "Unknown" Age category is primarily voters who registered to vote after I purchased my most recent statewide voter file in October.

#### Total Early Votes by Vote in 2020 General Election

``` {r echo = FALSE}
kable(GA_voted_voted, format.args = list(big.mark = ",", 
  scientific = FALSE), digits = 1) %>%
  kable_styling(bootstrap_options = "striped", full_width = F, position = "left")
```

### In-Person Early Votes

Total In-Person Early Votes: **`r format(as.numeric(sum(GA_stats$Inperson.Accept.Tot)), big.mark =",")`**  
Percent In-Person Early Votes (of Registered Voters): **`r paste(round(100*sum(GA_stats$Inperson.Accept.Tot)/sum(GA_stats$Reg.Voters), digits = 1),"%", sep = "")`**  

``` {r echo = FALSE}
GA_map_data <- GA_stats

GA_map_data <- mutate(GA_map_data, percent = round(100*(Pct.Inperson), digits = 1))

# GA_map_data <- rename(GA_map_data, fips = FIPS_CODE)
GA_map_data <- mutate(GA_map_data, fips = as.character(FIPS_CODE))

mapfile <- download_map_data("countries/us/us-ga-all.js")
mapdata <- get_data_from_map(mapfile)

mapdata$row <- as.integer(rownames(mapdata))

GA_map_data <- left_join(GA_map_data, mapdata, by = "fips")

GA_map_data <- arrange(GA_map_data, row)

hcmap(map = "countries/us/us-ga-all", data = GA_map_data,
      value = "percent", name = "Percent Accepted", joinBy = "fips") %>%
  hc_title(text ="In-Person Votes as Percent of Registered Voters") %>%
  hc_subtitle(text = "County plots may not be shaded using the same scale")
```

``` {r echo = FALSE}
datatable(GA_stats_inperson, colnames = c("County", "In-Person Votes", "Reg Voters", "Percent Voted"), rownames = F) %>%
  formatPercentage('Pct.Inperson', 1) %>%
  formatRound(c('Inperson.Accept.Tot', 'Reg.Voters'), 0, mark = ",")
```

#### In-Person Votes by Race and Ethnicity

``` {r echo = FALSE}
kable(GA_inperson_accept_race, format.args = list(big.mark = ",", 
  scientific = FALSE), digits = 1) %>%
  kable_styling(bootstrap_options = "striped", full_width = F, position = "left")
```

#### In-Person Votes by Age

``` {r echo = FALSE}
kable(GA_inperson_accept_age, format.args = list(big.mark = ",", 
  scientific = FALSE), digits = 1) %>%
  kable_styling(bootstrap_options = "striped", full_width = F, position = "left")
```

#### In-Person Votes by Vote in 2020 General Election

``` {r echo = FALSE}
kable(GA_inperson_accept_voted, format.args = list(big.mark = ",", 
  scientific = FALSE), digits = 1) %>%
  kable_styling(bootstrap_options = "striped", full_width = F, position = "left")
```


### Mail Ballots Accepted

Total Accepted Ballots: **`r format(as.numeric(sum(GA_stats$Mail.Accept.Tot)), big.mark =",")`**  
Return Rate (of Requested Ballots): **`r paste(round(100*sum(GA_stats$Mail.Accept.Tot)/sum(GA_stats$Mail.Req.Tot), digits = 1),"%", sep = "")`**  

``` {r echo = FALSE}
GA_map_data <- GA_stats

GA_map_data <- mutate(GA_map_data, percent = round(100*(Pct.Mail.Accept), digits = 1))

# GA_map_data <- rename(GA_map_data, fips = FIPS_CODE)
GA_map_data <- mutate(GA_map_data, fips = as.character(FIPS_CODE))

mapfile <- download_map_data("countries/us/us-ga-all.js")
mapdata <- get_data_from_map(mapfile)

mapdata$row <- as.integer(rownames(mapdata))

GA_map_data <- left_join(GA_map_data, mapdata, by = "fips")

GA_map_data <- arrange(GA_map_data, row)

hcmap(map = "countries/us/us-ga-all", data = GA_map_data,
      value = "percent", name = "Percent Accepted", joinBy = "fips") %>%
  hc_title(text ="Mail Ballot Acceptance Rate of Requested Ballots") %>%
  hc_subtitle(text = "County plots may not be shaded using the same scale")
```

``` {r echo = FALSE}
datatable(GA_stats_accept, colnames = c("County", "Accepted Mail Ballots", "Mail Ballot Requests", "Percent Accepted"), rownames = F) %>%
  formatPercentage('Pct.Mail.Accept', 1) %>%
  formatRound(c('Mail.Accept.Tot', 'Mail.Req.Tot'), 0, mark = ",")
```


#### Mail Ballots Accepted by Race and Ethnicity

``` {r echo = FALSE}
kable(GA_mail_accept_race, format.args = list(big.mark = ",", 
  scientific = FALSE), digits = 1) %>%
  kable_styling(bootstrap_options = "striped", full_width = F, position = "left")
```

#### Mail Ballots Accepted by Age

``` {r echo = FALSE}
kable(GA_mail_accept_age, format.args = list(big.mark = ",", 
  scientific = FALSE), digits = 1) %>%
  kable_styling(bootstrap_options = "striped", full_width = F, position = "left")
```

#### Mail Ballots Accepted by Vote in 2020 General Election

``` {r echo = FALSE}
kable(GA_mail_accept_voted, format.args = list(big.mark = ",", 
  scientific = FALSE), digits = 1) %>%
  kable_styling(bootstrap_options = "striped", full_width = F, position = "left")
```


### Mail Ballots Rejected

Total Rejected Mail Ballots: **`r format(as.numeric(sum(GA_stats$Mail.Reject.Tot)), big.mark =",")`**  
Rejection Rate (of All Returned Mail Ballots): **`r paste(round(100*sum(GA_stats$Mail.Reject.Tot)/sum(GA_stats$Mail.Return.All), digits = 1),"%", sep = "")`**  

Note: "All Returned Mail Ballots" includes Accepted Mail Ballots and Rejected Mail Ballots. I count only once voters with multiple rejected mail ballots and I exclude any voters who had a rejected mail ballot but subsequently successfully voted.

``` {r echo = FALSE}
GA_map_data <- GA_stats

GA_map_data <- mutate(GA_map_data, percent = round(100*(Pct.Mail.Reject), digits = 1))

# GA_map_data <- rename(GA_map_data, fips = FIPS_CODE)
GA_map_data <- mutate(GA_map_data, fips = as.character(FIPS_CODE))

mapfile <- download_map_data("countries/us/us-ga-all.js")
mapdata <- get_data_from_map(mapfile)

mapdata$row <- as.integer(rownames(mapdata))

GA_map_data <- left_join(GA_map_data, mapdata, by = "fips")

GA_map_data <- arrange(GA_map_data, row)

hcmap(map = "countries/us/us-ga-all", data = GA_map_data,
      value = "percent", name = "Percent Rejected", joinBy = "fips") %>%
  hc_title(text ="Mail Ballot Rejection Rate of All Returned Mail Ballots") %>%
  hc_subtitle(text = "County plots may not be shaded using the same scale")
```

``` {r echo = FALSE}
datatable(GA_stats_reject_mail, colnames = c("County", "All Mail Ballots Returned", "Rejected Mail Ballots", "Percent Rejected"), rownames = F) %>%
  formatPercentage('Pct.Mail.Reject', 1) %>%
  formatRound(c('Mail.Reject.Tot', 'Mail.Return.All'), 0, mark = ",")
```

### Mail Ballot Requests 

Total Requests: **`r format(as.numeric(sum(GA_stats$Mail.Req.Tot)), big.mark =",")`**  
Request Rate (of Registered voters): **`r paste(round(100*sum(GA_stats$Mail.Req.Tot)/sum(GA_stats$Reg.Voters), digits = 1),"%", sep = "")`**  

``` {r echo = FALSE}
GA_map_data <- GA_stats

GA_map_data <- mutate(GA_map_data, percent = round(100*(Pct.Req), digits = 1))

# GA_map_data <- rename(GA_map_data, fips = FIPS_CODE)
GA_map_data <- mutate(GA_map_data, fips = as.character(FIPS_CODE))

mapfile <- download_map_data("countries/us/us-ga-all.js")
mapdata <- get_data_from_map(mapfile)

mapdata$row <- as.integer(rownames(mapdata))

GA_map_data <- left_join(GA_map_data, mapdata, by = "fips")

GA_map_data <- arrange(GA_map_data, row)

hcmap(map = "countries/us/us-ga-all", data = GA_map_data,
      value = "percent", name = "Percent Requested", joinBy = "fips") %>%
  hc_title(text ="Mail Ballot Request Rate of Registered Voters") %>%
  hc_subtitle(text = "County plots may not be shaded using the same scale")
```

``` {r echo = FALSE}
datatable(GA_stats_req, colnames = c("County", "Registered Voters", "Mail Ballot Requests", "Percent Requested"), rownames = F) %>%
  formatPercentage('Pct.Req', 1) %>%
  formatRound(c('Mail.Req.Tot', 'Reg.Voters'), 0, mark = ",")
```


#### Mail Ballots Requested by Race and Ethnicity

``` {r echo = FALSE}
kable(GA_req_race, format.args = list(big.mark = ",", 
  scientific = FALSE), digits = 1) %>%
  kable_styling(bootstrap_options = "striped", full_width = F, position = "left")
```

#### Mail Ballots Requested by Age

``` {r echo = FALSE}
kable(GA_req_age, format.args = list(big.mark = ",", 
  scientific = FALSE), digits = 1) %>%
  kable_styling(bootstrap_options = "striped", full_width = F, position = "left")
```

#### Mail Ballots Requested by Vote in 2020 General Election

``` {r echo = FALSE}
kable(GA_req_voted, format.args = list(big.mark = ",", 
  scientific = FALSE), digits = 1) %>%
  kable_styling(bootstrap_options = "striped", full_width = F, position = "left")
```

### Mail Ballot Rejected Applications  

Total Rejected Applications: **`r format(as.numeric(sum(GA_stats$Mail.App.Reject.Tot)), big.mark =",")`**  
Rejection Rate (of All Applications): **`r paste(round(100* sum(GA_stats$Mail.App.Reject.Tot)/(sum(GA_stats$Mail.App.Reject.Tot)+sum(GA_stats$Mail.Req.Tot)), digits = 1),"%", sep = "")`**  

Notes: The mail ballot application rejection rate is the number of rejected applications divided by the sum of the number of accepted applications plus the number of rejected applications. I count only once voters with multiple rejected applications and I exclude any voters who had a rejected application but subsequently successfully corrected their application issue. 

``` {r echo = FALSE}
GA_map_data <- GA_stats

GA_map_data <- mutate(GA_map_data, percent = round(100*(Pct.App.Reject), digits = 1))

# GA_map_data <- rename(GA_map_data, fips = FIPS_CODE)
GA_map_data <- mutate(GA_map_data, fips = as.character(FIPS_CODE))

mapfile <- download_map_data("countries/us/us-ga-all.js")
mapdata <- get_data_from_map(mapfile)

mapdata$row <- as.integer(rownames(mapdata))

GA_map_data <- left_join(GA_map_data, mapdata, by = "fips")

GA_map_data <- arrange(GA_map_data, row)

hcmap(map = "countries/us/us-ga-all", data = GA_map_data,
      value = "percent", name = "Percent App Rejected", joinBy = "fips") %>%
  hc_title(text ="Mail Ballot Application Rejection Rate of All Applications") %>%
  hc_subtitle(text = "County plots may not be shaded using the same scale")
```

``` {r echo = FALSE}
datatable(GA_stats_reject_app, colnames = c("County", "All Mail Ballot Applications", "Rejected Applications", "Percent Rejected"), rownames = F) %>%
  formatPercentage('Pct.App.Reject', 1) %>%
  formatRound(c('Mail.Req.All', 'Mail.App.Reject.Tot'), 0, mark = ",")
```