GA.Rmd

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

state_stats <- read_csv("D:/DropBox/Dropbox/Mail_Ballots_2020/markdown/2020G_Early_Vote.csv")
GA_stats <- read_csv("D:/DropBox/Dropbox/Mail_Ballots_2020/markdown/2020G_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 2016 General"
voted_shell[2,1] <- "Did Not Vote in 2016 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 2016 General"
voted_shell_full[2,1] <- "Did Not Vote in 2016 General"
voted_shell_full[3,1] <- "TOTAL"

# Georgia

GA_inperson_race <- race_shell

GA_inperson_race[1,2] <- sum(state_stats[11,81])
GA_inperson_race[2,2] <- sum(state_stats[11,82]) 
GA_inperson_race[3,2] <- sum(state_stats[11,85]) 
GA_inperson_race[4,2] <- sum(state_stats[11,83]) 
GA_inperson_race[5,2] <- sum(state_stats[11,84]) 
GA_inperson_race[6,2] <- sum(state_stats[11,86]) 
GA_inperson_race[7,2] <- sum(state_stats[11,7]) 

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


GA_voted_race <- race_shell

GA_voted_race[1,2] <- sum(state_stats[11,81]) + sum(state_stats[11,33])
GA_voted_race[2,2] <- sum(state_stats[11,82]) + sum(state_stats[11,34])
GA_voted_race[3,2] <- sum(state_stats[11,85]) + sum(state_stats[11,37]) 
GA_voted_race[4,2] <- sum(state_stats[11,83]) + sum(state_stats[11,35]) 
GA_voted_race[5,2] <- sum(state_stats[11,84]) + sum(state_stats[11,36]) 
GA_voted_race[6,2] <- sum(state_stats[11,86]) + sum(state_stats[11,38]) 
GA_voted_race[7,2] <- sum(state_stats[11,7]) + sum(state_stats[11,6])

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


GA_inperson_age <- GA_age_shell

GA_inperson_age[1,2] <- sum(state_stats[11,90])
GA_inperson_age[2,2] <- sum(state_stats[11,91]) 
GA_inperson_age[3,2] <- sum(state_stats[11,92]) 
GA_inperson_age[4,2] <- sum(state_stats[11,93]) 
GA_inperson_age[5,2] <- sum(state_stats[11,94])
GA_inperson_age[6,2] <- sum(state_stats[11,95])
GA_inperson_age[7,2] <- sum(state_stats[11,96])
GA_inperson_age[8,2] <- sum(state_stats[11,7])

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

GA_voted_age <- GA_age_shell

GA_voted_age[1,2] <- sum(state_stats[11,90]) + sum(state_stats[11,42])
GA_voted_age[2,2] <- sum(state_stats[11,91]) + sum(state_stats[11,43]) 
GA_voted_age[3,2] <- sum(state_stats[11,92]) + sum(state_stats[11,44]) 
GA_voted_age[4,2] <- sum(state_stats[11,93]) + sum(state_stats[11,45]) 
GA_voted_age[5,2] <- sum(state_stats[11,94]) + sum(state_stats[11,46])
GA_voted_age[6,2] <- sum(state_stats[11,95]) + sum(state_stats[11,47])
GA_voted_age[7,2] <- sum(state_stats[11,96]) + sum(state_stats[11,75])
GA_voted_age[8,2] <- sum(state_stats[11,7]) + sum(state_stats[11,6])

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


GA_inperson_gender <- gender_shell

GA_inperson_gender[1,2] <- sum(state_stats[11,87])
GA_inperson_gender[2,2] <- sum(state_stats[11,88]) 
GA_inperson_gender[3,2] <- sum(state_stats[11,89]) 
GA_inperson_gender[4,2] <- sum(state_stats[11,7]) 

GA_inperson_gender$Percent <- 100*GA_inperson_gender$Count/GA_inperson_gender[4,2]


GA_voted_gender <- gender_shell

GA_voted_gender[1,2] <- sum(state_stats[11,87]) + sum(state_stats[11,39])
GA_voted_gender[2,2] <- sum(state_stats[11,88]) + sum(state_stats[11,40]) 
GA_voted_gender[3,2] <- sum(state_stats[11,89]) + sum(state_stats[11,41])
GA_voted_gender[4,2] <- sum(state_stats[11,7]) + sum(state_stats[11,6])

GA_voted_gender$Percent <- 100*GA_voted_gender$Count/GA_voted_gender[4,2]


GA_request_race <- race_shell

GA_request_race[1,2] <- sum(state_stats[11,14])
GA_request_race[2,2] <- sum(state_stats[11,15]) 
GA_request_race[3,2] <- sum(state_stats[11,16]) 
GA_request_race[4,2] <- sum(state_stats[11,17]) 
GA_request_race[5,2] <- sum(state_stats[11,18]) 
GA_request_race[6,2] <- sum(state_stats[11,19]) 
GA_request_race[7,2] <- sum(state_stats[11,5]) 

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

colnames(GA_request_race) <- c("Race/Ethnicity", "Returned Ballots", "Freq. Distribution")

GA_request_voted <- voted_shell

GA_request_voted[1,2] <- sum(state_stats[11,72])
GA_request_voted[2,2] <- sum(state_stats[11,73]) 
GA_request_voted[3,2] <- sum(state_stats[11,5]) 

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

colnames(GA_request_voted) <- c("Voted", "Returned Ballots", "Freq. Distribution")

GA_request_age <- GA_age_shell

GA_request_age[1,2] <- sum(state_stats[11,23])
GA_request_age[2,2] <- sum(state_stats[11,24]) 
GA_request_age[3,2] <- sum(state_stats[11,25]) 
GA_request_age[4,2] <- sum(state_stats[11,26]) 
GA_request_age[5,2] <- sum(state_stats[11,27])
GA_request_age[6,2] <- sum(state_stats[11,28])
GA_request_age[7,2] <- sum(state_stats[11,74])
GA_request_age[8,2] <- sum(state_stats[11,5])

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

colnames(GA_request_age) <- c("Age", "Returned Ballots", "Freq. Distribution")

GA_request_gender <- gender_shell

GA_request_gender[1,2] <- sum(state_stats[11,21])
GA_request_gender[2,2] <- sum(state_stats[11,20]) 
GA_request_gender[3,2] <- sum(state_stats[11,22]) 
GA_request_gender[4,2] <- sum(state_stats[11,5]) 

GA_request_gender$Percent <- 100*GA_request_gender$Count/GA_request_gender[4,2]

colnames(GA_request_gender) <- c("Gender", "Returned Ballots", "Freq. Distribution")

GA_accept_race <- race_shell_returned

GA_accept_race[1,2] <- sum(state_stats[11,33])
GA_accept_race[2,2] <- sum(state_stats[11,34]) 
GA_accept_race[3,2] <- sum(state_stats[11,37]) 
GA_accept_race[4,2] <- sum(state_stats[11,35]) 
GA_accept_race[5,2] <- sum(state_stats[11,36]) 
GA_accept_race[6,2] <- sum(state_stats[11,38]) 
GA_accept_race[7,2] <- sum(state_stats[11,6]) 

GA_accept_race[1,4] <- sum(state_stats[11,14])
GA_accept_race[2,4] <- sum(state_stats[11,15]) 
GA_accept_race[3,4] <- sum(state_stats[11,16]) 
GA_accept_race[4,4] <- sum(state_stats[11,17]) 
GA_accept_race[5,4] <- sum(state_stats[11,18]) 
GA_accept_race[6,4] <- sum(state_stats[11,19]) 
GA_accept_race[7,4] <- sum(state_stats[11,5]) 

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

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

GA_accept_age <- GA_age_shell_returned

GA_accept_age[1,2] <- sum(state_stats[11,42])
GA_accept_age[2,2] <- sum(state_stats[11,43]) 
GA_accept_age[3,2] <- sum(state_stats[11,44]) 
GA_accept_age[4,2] <- sum(state_stats[11,45]) 
GA_accept_age[5,2] <- sum(state_stats[11,46])
GA_accept_age[6,2] <- sum(state_stats[11,47])
GA_accept_age[7,2] <- sum(state_stats[11,75])
GA_accept_age[8,2] <- sum(state_stats[11,6])

GA_accept_age[1,4] <- sum(state_stats[11,23])
GA_accept_age[2,4] <- sum(state_stats[11,24]) 
GA_accept_age[3,4] <- sum(state_stats[11,25]) 
GA_accept_age[4,4] <- sum(state_stats[11,26]) 
GA_accept_age[5,4] <- sum(state_stats[11,27])
GA_accept_age[6,4] <- sum(state_stats[11,28])
GA_accept_age[7,4] <- sum(state_stats[11,74])
GA_accept_age[8,4] <- sum(state_stats[11,5])

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

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

GA_accept_voted <- voted_shell_full

GA_accept_voted[1,2] <- sum(state_stats[11,68])
GA_accept_voted[2,2] <- sum(state_stats[11,69]) 
GA_accept_voted[3,2] <- sum(state_stats[11,6]) 

GA_accept_voted[1,4] <- sum(state_stats[11,72])
GA_accept_voted[2,4] <- sum(state_stats[11,73]) 
GA_accept_voted[3,4] <- sum(state_stats[11,5]) 

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

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

GA_accept_gender <- gender_shell_full

GA_accept_gender[1,2] <- sum(state_stats[11,39])
GA_accept_gender[2,2] <- sum(state_stats[11,40]) 
GA_accept_gender[3,2] <- sum(state_stats[11,41]) 
GA_accept_gender[4,2] <- sum(state_stats[11,6]) 

GA_accept_gender[1,4] <- sum(state_stats[11,21])
GA_accept_gender[2,4] <- sum(state_stats[11,20]) 
GA_accept_gender[3,4] <- sum(state_stats[11,22]) 
GA_accept_gender[4,4] <- sum(state_stats[11,5]) 

GA_accept_gender$Frequency <- 100 * GA_accept_gender$Count/GA_accept_gender[4,2]
GA_accept_gender$Rate <- 100*GA_accept_gender$Count/GA_accept_gender$Count2

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

GA_reject_race <- race_shell_returned

GA_reject_race[1,2] <- sum(state_stats[11,53])
GA_reject_race[2,2] <- sum(state_stats[11,54]) 
GA_reject_race[3,2] <- sum(state_stats[11,57]) 
GA_reject_race[4,2] <- sum(state_stats[11,55]) 
GA_reject_race[5,2] <- sum(state_stats[11,56]) 
GA_reject_race[6,2] <- sum(state_stats[11,58]) 
GA_reject_race[7,2] <- sum(state_stats[11,48]) 

GA_reject_race[1,4] <- sum(state_stats[11,33]) + sum(state_stats[11,53])
GA_reject_race[2,4] <- sum(state_stats[11,34]) + sum(state_stats[11,54])
GA_reject_race[3,4] <- sum(state_stats[11,37]) + sum(state_stats[11,57])
GA_reject_race[4,4] <- sum(state_stats[11,35]) + sum(state_stats[11,55])
GA_reject_race[5,4] <- sum(state_stats[11,36]) + sum(state_stats[11,56])
GA_reject_race[6,4] <- sum(state_stats[11,38]) + sum(state_stats[11,58])
GA_reject_race[7,4] <- sum(state_stats[11,6]) + sum(state_stats[11,48])

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

colnames(GA_reject_race) <- c("Race/Ethnicity", "Rejected Ballots", "Freq. Distribution", "Returned Ballots (All)", "Rejection Rate")

GA_reject_voted <- voted_shell_full

GA_reject_voted[1,2] <- sum(state_stats[11,70])
GA_reject_voted[2,2] <- sum(state_stats[11,71]) 
GA_reject_voted[3,2] <- sum(state_stats[11,48]) 

GA_reject_voted[1,4] <- sum(state_stats[11,70]) + sum(state_stats[11,68])
GA_reject_voted[2,4] <- sum(state_stats[11,71]) + sum(state_stats[11,69])
GA_reject_voted[3,4] <- sum(state_stats[11,48]) + sum(state_stats[11,6])

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

colnames(GA_reject_voted) <- c("Voted", "Rejected Ballots", "Freq. Distribution", "Returned Ballots (All)", "Rejection Rate")

GA_reject_age <- GA_age_shell_returned

GA_reject_age[1,2] <- sum(state_stats[11,62])
GA_reject_age[2,2] <- sum(state_stats[11,63]) 
GA_reject_age[3,2] <- sum(state_stats[11,64]) 
GA_reject_age[4,2] <- sum(state_stats[11,65]) 
GA_reject_age[5,2] <- sum(state_stats[11,66])
GA_reject_age[6,2] <- sum(state_stats[11,67])
GA_reject_age[7,2] <- sum(state_stats[11,76])
GA_reject_age[8,2] <- sum(state_stats[11,48])

GA_reject_age[1,4] <- sum(state_stats[11,42]) + sum(state_stats[11,62])
GA_reject_age[2,4] <- sum(state_stats[11,43]) + sum(state_stats[11,63])
GA_reject_age[3,4] <- sum(state_stats[11,44]) + sum(state_stats[11,64])
GA_reject_age[4,4] <- sum(state_stats[11,45]) + sum(state_stats[11,65])
GA_reject_age[5,4] <- sum(state_stats[11,46]) + sum(state_stats[11,66])
GA_reject_age[6,4] <- sum(state_stats[11,47]) + sum(state_stats[11,67])
GA_reject_age[7,4] <- sum(state_stats[11,75]) + sum(state_stats[11,68])
GA_reject_age[8,4] <- sum(state_stats[11,6]) + sum(state_stats[11,48])

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

colnames(GA_reject_age) <- c("Age", "Rejected Ballots", "Freq. Distribution", "Returned Ballots (All)", "Rejection Rate")

GA_reject_gender <- gender_shell_full

GA_reject_gender[1,2] <- sum(state_stats[11,59])
GA_reject_gender[2,2] <- sum(state_stats[11,60]) 
GA_reject_gender[3,2] <- sum(state_stats[11,61]) 
GA_reject_gender[4,2] <- sum(state_stats[11,48]) 

GA_reject_gender[1,4] <- sum(state_stats[11,59]) + sum(state_stats[11,39]) 
GA_reject_gender[2,4] <- sum(state_stats[11,60]) + sum(state_stats[11,40])
GA_reject_gender[3,4] <- sum(state_stats[11,61]) + sum(state_stats[11,41])
GA_reject_gender[4,4] <- sum(state_stats[11,48]) + sum(state_stats[11,6])

GA_reject_gender$Frequency <- 100 * GA_reject_gender$Count/GA_reject_gender[4,2]
GA_reject_gender$Rate <- 100*GA_reject_gender$Count/GA_reject_gender$Count2

colnames(GA_reject_gender) <- c("Age", "Rejected Ballots", "Freq. Distribution", "Returned Ballots (All)", "Rejection Rate")

GA_stats_voted <- select(GA_stats, County, Reg.Voters, Voted.Tot, Pct.Voted)
GA_stats_requests <- select(GA_stats, County, Reg.Voters, Mail.Req.Tot, Pct.Request)
GA_stats_returns <- select(GA_stats, County, Mail.Req.Tot, Mail.Accept.Tot, Pct.Accept)
GA_stats_rejects <- select(GA_stats, County, Mail.Reject.Tot, Mail.Return.Tot, Pct.Reject)
```
## {.tabset}
Last Report: `r state_stats[11,9]`  
Source:  `r state_stats[11,2]`

### Total Voted  

Total Voted: **`r format(as.numeric(state_stats[11,8]), big.mark =",")`**  
Turnout Rate (of Registered Voters): **`r paste(round(100*sum(GA_stats$Voted.Tot)/sum(GA_stats$Reg.Voters), digits = 1),"%", sep = "")`**  

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

GA_map_data <- mutate(GA_map_data, percent = round(100*(Voted.Tot/Reg.Voters), 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 Voted", joinBy = "fips") %>%
  hc_title(text ="Total Turnout Rate 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", "Registered Voters", "Total Voted", "Percent Voted"), rownames = F) %>%
  formatPercentage('Pct.Voted', 1) %>%
  formatRound(c('Voted.Tot', 'Reg.Voters'), 0, mark = ",")
```

#### Total Voted 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 Voted by 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")
```

#### Total Voted by by Gender

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

### In-Person Early Votes  

In-Person Early Votes: **`r format(as.numeric(state_stats[11,7]), big.mark =",")`**  

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

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

#### In-Person Early Votes by Age

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

#### In-Person Early Votes by Gender

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

### Mail Ballots Returned and Accepted
Mail Ballots Returned and Accepted: **`r format(as.numeric(sum(GA_stats$Mail.Accept.Tot)), big.mark =",")`**  
Return Rate: **`r paste(round(100*sum(GA_stats$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*(Mail.Accept.Tot/Mail.Req.Tot), 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 Return Rates") %>%
  hc_subtitle(text = "County plots may not be shaded using the same scale")
```

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

#### Mail Ballots Returned and Accepted by Race and Ethnicity

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

#### Mail Ballots Returned and Accepted by Vote in 2016 General Election

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

#### Mail Ballots Returned and Accepted by Age

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

#### Mail Ballots Returned and Accepted by Gender

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

### Mail Ballots Rejected

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

To calculate mail ballot rejection rates, I divide the number of rejected ballots by the number of accepted ballots *plus* the number of rejected ballots. 

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

GA_map_data <- mutate(GA_map_data, percent = round(100*(Mail.Reject.Tot/Mail.Return.Tot), digits = 1))
GA_map_data <- mutate(GA_map_data, percent = replace_na(percent, 0))

# 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 Rates") %>%
  hc_subtitle(text = "County plots may not be shaded using the same scale")
```

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

#### Mail Ballots Rejected by Race and Ethnicity

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

#### Mail Ballots Rejected by Vote in 2016 General Election

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

#### Mail Ballots Rejected by Age

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

#### Mail Ballots Rejected by Gender

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

### Mail Ballots Requested

Ballots Requested: **`r format(as.numeric(sum(GA_stats$Mail.Req.Tot)), big.mark =",")`**  
Request Rate: **`r paste(round(100*sum(GA_stats$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*(Mail.Req.Tot/Reg.Voters), 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 Rates") %>%
  hc_subtitle(text = "County plots may not be shaded using the same scale")
```

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

#### Mail Ballots Requested by Race and Ethnicity

``` {r echo = FALSE}
kable(GA_request_race, 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 2016 General Election

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

#### Mail Ballots Requested by Gender

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