NC.Rmd

---
title: "North Carolina 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")
NC_stats <- read_csv("D:/DropBox/Dropbox/Mail_Ballots_2020/markdown/2020G_Early_Vote_NC.csv")
NC_FIPS <- read_csv("D:/DropBox/Dropbox/Mail_Ballots_2020/markdown/NC_FIPS.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"

party_shell_returned <- data.frame(Party=character(),
                                  Count=integer(),
                                  Frequency=double(),
                                  Count2=integer(),
                                  Rate=integer(),
                                  stringsAsFactors=FALSE)
party_shell_returned[1,1] <- "Democrats"
party_shell_returned[2,1] <- "Republicans"
party_shell_returned[3,1] <- "Minor"
party_shell_returned[4,1] <- "No Party Affiliation"
party_shell_returned[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"

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"


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

NC_age_shell <- data.frame(Age=character(),
                                  Count=integer(),
                                  Percent=double(),
                                  stringsAsFactors=FALSE)
NC_age_shell[1,1] <- "18 to 25"
NC_age_shell[2,1] <- "26 to 40"
NC_age_shell[3,1] <- "41 to 65"
NC_age_shell[4,1] <- "66 and up"
NC_age_shell[5,1] <- "TOTAL"


NC_age_shell_returned <- data.frame(Race=character(),
                                  Count=integer(),
                                  Frequency=double(),
                                  Count2=integer(),
                                  Rate=integer(),
                                  stringsAsFactors=FALSE)
NC_age_shell_returned[1,1] <- "18 to 25"
NC_age_shell_returned[2,1] <- "26 to 40"
NC_age_shell_returned[3,1] <- "41 to 65"
NC_age_shell_returned[4,1] <- "66 and up"
NC_age_shell_returned[5,1] <- "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"

# North Carolina


NC_inperson_party <- party_shell

NC_inperson_party[1,2] <- state_stats[34,77] 
NC_inperson_party[2,2] <- state_stats[34,78] 
NC_inperson_party[3,2] <- state_stats[34,79] 
NC_inperson_party[4,2] <- state_stats[34,80] 
NC_inperson_party[5,2] <- state_stats[34,7] 

NC_inperson_party$Percent <- 100*NC_inperson_party$Count/NC_inperson_party[5,2]


NC_voted_party <- party_shell

NC_voted_party[1,2] <- state_stats[34,77] + state_stats[34,29] 
NC_voted_party[2,2] <- state_stats[34,78] + state_stats[34,30] 
NC_voted_party[3,2] <- state_stats[34,79] + state_stats[34,31]
NC_voted_party[4,2] <- state_stats[34,80] + state_stats[34,32] 
NC_voted_party[5,2] <- state_stats[34,7] + state_stats[34,6]

NC_voted_party$Percent <- 100*NC_voted_party$Count/NC_voted_party[5,2]


NC_req_send_party <- party_shell

NC_req_send_party[1,2] <- state_stats[34,10] 
NC_req_send_party[2,2] <- state_stats[34,11] 
NC_req_send_party[3,2] <- state_stats[34,12] 
NC_req_send_party[4,2] <- state_stats[34,13] 
NC_req_send_party[5,2] <- state_stats[34,5] 

NC_req_send_party$Percent <- 100*NC_req_send_party$Count/NC_req_send_party[5,2]

NC_accept_party <- party_shell_returned

NC_accept_party[1,2] <- state_stats[34,29] 
NC_accept_party[2,2] <- state_stats[34,30] 
NC_accept_party[3,2] <- state_stats[34,31] 
NC_accept_party[4,2] <- state_stats[34,32] 
NC_accept_party[5,2] <- state_stats[34,6] 

NC_accept_party[1,4] <- state_stats[34,10] 
NC_accept_party[2,4] <- state_stats[34,11] 
NC_accept_party[3,4] <- state_stats[34,12] 
NC_accept_party[4,4] <- state_stats[34,13] 
NC_accept_party[5,4] <- state_stats[34,5] 

NC_accept_party$Frequency <- 100*NC_accept_party$Count/NC_accept_party[5,2]
NC_accept_party$Rate <- 100*NC_accept_party$Count/NC_accept_party$Count2

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

NC_reject_party <- party_shell_returned

NC_reject_party[1,2] <- state_stats[34,49] 
NC_reject_party[2,2] <- state_stats[34,50] 
NC_reject_party[3,2] <- state_stats[34,51] 
NC_reject_party[4,2] <- state_stats[34,52] 
NC_reject_party[5,2] <- state_stats[34,48] 

NC_reject_party[1,4] <- state_stats[34,29] + state_stats[34,49] 
NC_reject_party[2,4] <- state_stats[34,30] + state_stats[34,50]
NC_reject_party[3,4] <- state_stats[34,31] + state_stats[34,51]
NC_reject_party[4,4] <- state_stats[34,32] + state_stats[34,52]
NC_reject_party[5,4] <- state_stats[34,6] + state_stats[34,48]

NC_reject_party$Frequency <- 100*NC_reject_party$Count/NC_reject_party[5,2]
NC_reject_party$Rate <- 100*NC_reject_party$Count/NC_reject_party$Count2
colnames(NC_reject_party) <- c("Party", "Rejected Ballots", "Freq. Distribution", "All Returned Ballots", "Rejection Rate")

NC_voted_race <- race_shell

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

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

NC_inperson_race <- race_shell

NC_inperson_race[1,2] <- sum(state_stats[34,81])
NC_inperson_race[2,2] <- sum(state_stats[34,82]) 
NC_inperson_race[3,2] <- sum(state_stats[34,85]) 
NC_inperson_race[4,2] <- sum(state_stats[34,83]) 
NC_inperson_race[5,2] <- sum(state_stats[34,84]) 
NC_inperson_race[6,2] <- sum(state_stats[34,86]) 
NC_inperson_race[7,2] <- sum(state_stats[34,7]) 

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


NC_req_send_race <- race_shell

NC_req_send_race[1,2] <- sum(state_stats[34,14])
NC_req_send_race[2,2] <- sum(state_stats[34,15]) 
NC_req_send_race[3,2] <- sum(state_stats[34,16]) 
NC_req_send_race[4,2] <- sum(state_stats[34,17]) 
NC_req_send_race[5,2] <- sum(state_stats[34,18]) 
NC_req_send_race[6,2] <- sum(state_stats[34,19]) 
NC_req_send_race[7,2] <- sum(state_stats[34,5]) 

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

NC_accept_race <- race_shell_returned

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

NC_accept_race[1,4] <- sum(state_stats[34,14])
NC_accept_race[2,4] <- sum(state_stats[34,15]) 
NC_accept_race[3,4] <- sum(state_stats[34,16]) 
NC_accept_race[4,4] <- sum(state_stats[34,17]) 
NC_accept_race[5,4] <- sum(state_stats[34,18]) 
NC_accept_race[6,4] <- sum(state_stats[34,19]) 
NC_accept_race[7,4] <- sum(state_stats[34,5]) 

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

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

NC_reject_race <- race_shell_returned

NC_reject_race[1,2] <- sum(state_stats[34,53])
NC_reject_race[2,2] <- sum(state_stats[34,54]) 
NC_reject_race[3,2] <- sum(state_stats[34,57]) 
NC_reject_race[4,2] <- sum(state_stats[34,55]) 
NC_reject_race[5,2] <- sum(state_stats[34,56]) 
NC_reject_race[6,2] <- sum(state_stats[34,58]) 
NC_reject_race[7,2] <- sum(state_stats[34,48]) 

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

NC_reject_race$Frequency <- 100 * NC_reject_race$Count/NC_reject_race[7,2]
NC_reject_race$Rate <- 100*NC_reject_race$Count/NC_reject_race$Count2
colnames(NC_reject_race) <- c("Race/Ethnicity", "Rejected Ballots", "Freq. Distribution", "All Returned Ballots", "Rejection Rate")


NC_voted_voted <- Voted_shell

NC_voted_voted[1,2] <- sum(state_stats[34,68]) + sum(state_stats[34,97])
NC_voted_voted[2,2] <- sum(state_stats[34,69]) + sum(state_stats[34,98])
NC_voted_voted[3,2] <- sum(state_stats[34,6]) + sum(state_stats[34,7])

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

NC_inperson_voted <- Voted_shell

NC_inperson_voted[1,2] <- sum(state_stats[34,97])
NC_inperson_voted[2,2] <- sum(state_stats[34,98])
NC_inperson_voted[3,2] <- sum(state_stats[34,7])

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


NC_accept_voted <- Voted_shell

NC_accept_voted[1,2] <- sum(state_stats[34,68])
NC_accept_voted[2,2] <- sum(state_stats[34,69])
NC_accept_voted[3,2] <- sum(state_stats[34,6])

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

NC_req_send_gender <- gender_shell

NC_req_send_gender[1,2] <- sum(state_stats[34,21])
NC_req_send_gender[2,2] <- sum(state_stats[34,20]) 
NC_req_send_gender[3,2] <- sum(state_stats[34,22]) 
NC_req_send_gender[4,2] <- sum(state_stats[34,5]) 

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

NC_accept_gender <- NC_gender_shell_returned

NC_accept_gender[1,2] <- sum(state_stats[34,39])
NC_accept_gender[2,2] <- sum(state_stats[34,40]) 
NC_accept_gender[3,2] <- sum(state_stats[34,41]) 
NC_accept_gender[4,2] <- sum(state_stats[34,6]) 

NC_accept_gender[1,4] <- sum(state_stats[34,21])
NC_accept_gender[2,4] <- sum(state_stats[34,20]) 
NC_accept_gender[3,4] <- sum(state_stats[34,22]) 
NC_accept_gender[4,4] <- sum(state_stats[34,5]) 

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

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

NC_req_send_age <- NC_age_shell

NC_req_send_age[1,2] <- sum(NC_stats$Mail.Req.Age.1825, na.rm = T)
NC_req_send_age[2,2] <- sum(NC_stats$Mail.Req.Age.2640, na.rm = T) 
NC_req_send_age[3,2] <- sum(NC_stats$Mail.Req.Age.4165, na.rm = T) 
NC_req_send_age[4,2] <- sum(NC_stats$Mail.Req.Age.66up, na.rm = T) 
NC_req_send_age[5,2] <- sum(NC_stats$Mail.Req.Tot, na.rm = T)

NC_req_send_age$Percent <- 100 * NC_req_send_age$Count/NC_req_send_age[5,2]

NC_accept_age <- NC_age_shell_returned

NC_accept_age[1,2] <- sum(NC_stats$Mail.Rtn.Age.1825, na.rm = T)
NC_accept_age[2,2] <- sum(NC_stats$Mail.Rtn.Age.2640, na.rm = T) 
NC_accept_age[3,2] <- sum(NC_stats$Mail.Rtn.Age.4165, na.rm = T) 
NC_accept_age[4,2] <- sum(NC_stats$Mail.Rtn.Age.66up, na.rm = T) 
NC_accept_age[5,2] <- sum(NC_stats$Mail.Rtn.Tot, na.rm = T)

NC_accept_age[1,4] <- sum(NC_stats$Mail.Req.Age.1825, na.rm = T)
NC_accept_age[2,4] <- sum(NC_stats$Mail.Req.Age.2640, na.rm = T) 
NC_accept_age[3,4] <- sum(NC_stats$Mail.Req.Age.4165, na.rm = T) 
NC_accept_age[4,4] <- sum(NC_stats$Mail.Req.Age.66up, na.rm = T) 
NC_accept_age[5,4] <- sum(NC_stats$Mail.Req.Tot, na.rm = T)

NC_accept_age$Frequency <- 100 * NC_accept_age$Count/NC_accept_age[5,2]
NC_accept_age$Rate <- 100*NC_accept_age$Count/NC_accept_age$Count2

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


NC_out_party <- party_shell_returned

NC_out_party[1,2] <- state_stats[34,10] - state_stats[34,29] 
NC_out_party[2,2] <- state_stats[34,11] - state_stats[34,30]
NC_out_party[3,2] <- state_stats[34,12] - state_stats[34,31]
NC_out_party[4,2] <- state_stats[34,13] - state_stats[34,32]
NC_out_party[5,2] <- state_stats[34,5] - state_stats[34,6]

NC_out_party[1,4] <- state_stats[34,29] 
NC_out_party[2,4] <- state_stats[34,30] 
NC_out_party[3,4] <- state_stats[34,31] 
NC_out_party[4,4] <- state_stats[34,32] 
NC_out_party[5,4] <- state_stats[34,6] 

NC_out_party[1,5] <- state_stats[34,10] 
NC_out_party[2,5] <- state_stats[34,11] 
NC_out_party[3,5] <- state_stats[34,12] 
NC_out_party[4,5] <- state_stats[34,13] 
NC_out_party[5,5] <- state_stats[34,5] 

NC_out_party$Frequency <- 100*NC_out_party$Count/NC_out_party[5,2]

colnames(NC_out_party) <- c("Party", "Outstanding Ballots", "Freq. Distribution", "Returned Ballots", "Requested Ballots")




NC_stats_voted <- select(NC_stats, County, Reg.Voters, Voted.Tot, Pct.Voted)

NC_stats_requests <- select(NC_stats, County, Reg.Voters, Mail.Req.Tot, Pct.Request)
NC_stats_requests_Dem <- select(NC_stats, County, Reg.Voters.Dem, Mail.Req.Dem, Pct.Request.Dem)
NC_stats_requests_Rep <- select(NC_stats, County, Reg.Voters.Rep, Mail.Req.Rep, Pct.Request.Rep)
NC_stats_requests_NPA <- select(NC_stats, County, Reg.Voters.NPA, Mail.Req.NPA, Pct.Request.NPA)
NC_stats_requests_Minor <- select(NC_stats, County, Reg.Voters.Minor, Mail.Req.Minor, Pct.Request.Minor)

NC_stats_returns <- select(NC_stats, County, Mail.Req.Tot, Mail.Rtn.Tot, Pct.Return)
NC_stats_returns_Dem <- select(NC_stats, County, Mail.Req.Dem, Mail.Rtn.Dem, Pct.Return.Dem)
NC_stats_returns_Rep <- select(NC_stats, County, Mail.Req.Rep, Mail.Rtn.Rep, Pct.Return.Rep)
NC_stats_returns_NPA <- select(NC_stats, County, Mail.Req.NPA, Mail.Rtn.NPA, Pct.Return.NPA)
NC_stats_returns_Minor <- select(NC_stats, County, Mail.Req.Minor, Mail.Rtn.Minor, Pct.Return.Minor)


NC_stats_rejects <- select(NC_stats, County, Mail.Rtn.Tot, Mail.Reject.Tot, Pct.Reject)
NC_stats_rejects <- mutate(NC_stats_rejects, Mail.Rtn.Tot = Mail.Rtn.Tot + Mail.Reject.Tot)

NC_stats_rejects_Dem <- select(NC_stats, County, Mail.Rtn.Dem, Mail.Reject.Dem, Pct.Reject.Dem)
NC_stats_rejects_Dem <- mutate(NC_stats_rejects_Dem, Mail.Rtn.Dem = Mail.Rtn.Dem + Mail.Reject.Dem)

NC_stats_rejects_Rep <- select(NC_stats, County, Mail.Rtn.Rep, Mail.Reject.Rep, Pct.Reject.Rep)
NC_stats_rejects_Rep <- mutate(NC_stats_rejects_Rep, Mail.Rtn.Rep = Mail.Rtn.Rep + Mail.Reject.Rep)

NC_stats_rejects_NPA <- select(NC_stats, County, Mail.Rtn.NPA, Mail.Reject.NPA, Pct.Reject.NPA)
NC_stats_rejects_NPA <- mutate(NC_stats_rejects_NPA, Mail.Rtn.NPA = Mail.Rtn.NPA + Mail.Reject.NPA)

NC_stats_rejects_Minor <- select(NC_stats, County, Mail.Rtn.Minor, Mail.Reject.Minor, Pct.Reject.Minor)
NC_stats_rejects_Minor <- mutate(NC_stats_rejects_Minor, Mail.Rtn.Minor = Mail.Rtn.Minor + Mail.Reject.Minor)

NC_stats_rejects_nh_white <- select(NC_stats, County, Mail.Rtn.nh_white, Mail.Reject.nh_white, Pct.Reject.nh_white)
NC_stats_rejects_nh_white <- mutate(NC_stats_rejects_nh_white, Mail.Rtn.nh_white = Mail.Rtn.nh_white + Mail.Reject.nh_white)

NC_stats_rejects_nh_black <- select(NC_stats, County, Mail.Rtn.nh_black, Mail.Reject.nh_black, Pct.Reject.nh_black)
NC_stats_rejects_nh_black <- mutate(NC_stats_rejects_nh_black, Mail.Rtn.nh_black = Mail.Rtn.nh_black + Mail.Reject.nh_black)

NC_stats_out <- select(NC_stats, County, Mail.Req.Tot, Mail.Rtn.Tot)

NC_stats_out <- NC_stats_out %>%
  mutate(Mail.Out.Tot = Mail.Req.Tot - Mail.Rtn.Tot)

NC_stats_out <- NC_stats_out[c(1,4,3,2)]

```
## {.tabset}
Last Report: `r state_stats[34,9]`  
Source:  `r state_stats[34,2]`

### Outstanding Mail Ballots

#### **Total Voted by Party Registration**

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

``` {r echo = FALSE}
datatable(NC_stats_out, colnames = c("County", "Outstanding Mail Ballots", "Returned Mail Ballots", "Requested Mail Ballots"), rownames = F) %>%
  formatRound(c('Mail.Out.Tot', 'Mail.Rtn.Tot', 'Mail.Req.Tot'), 0, mark = ",")
```


### Total Voted

Total Voted: **`r format(as.numeric(state_stats[34,8]), big.mark =",")`**   

``` {r echo = FALSE}

NC_2020g_map_data <- left_join(NC_stats, NC_FIPS, by = "County")

NC_2020g_map_data$fips <- as.character(NC_2020g_map_data$FIPS_NUM)

NC_2020g_map_data <- mutate(NC_2020g_map_data, percent_returned = round(100*Pct.Voted, digits = 1))

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

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

NC_2020g_map_data <- left_join(NC_2020g_map_data, mapdata, by = "fips")

NC_2020g_map_data <- arrange(NC_2020g_map_data, row)

hcmap(map = "countries/us/us-nc-all", data = NC_2020g_map_data,
  value = "percent_returned", name = "Percent voted", joinby = "fips") %>%
  hc_title(text ="Turnout Rate (as Percentage of Registered Voters)") %>%
  hc_subtitle(text = "County plots may not be shaded using the same scale")
```

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

#### **Total Voted by Party Registration**

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

#### **Total Voted by Race and Ethnicity**

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

#### **Total Voted by 2016 Vote History**
``` {r echo = FALSE}
kable(NC_voted_voted, format.args = list(big.mark = ",", 
  scientific = FALSE), digits = 1) %>%
  kable_styling(bootstrap_options = "striped", full_width = F, position = "left")
```

### In-Person Votes

In-Person Votes: **`r format(as.numeric(state_stats[34,7]), big.mark =",")`**    

#### **In-Person Votes by Party Registration**

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

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

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

#### **In-Person Votes by 2016 Vote History**
``` {r echo = FALSE}
kable(NC_inperson_voted, format.args = list(big.mark = ",", 
  scientific = FALSE), digits = 1) %>%
  kable_styling(bootstrap_options = "striped", full_width = F, position = "left")
```

### Returned and Accepted Mail Ballots

Ballots Returned: **`r format(as.numeric(sum(NC_stats$Mail.Rtn.Tot, na.rm = T)), big.mark =",")`**  
Return Rate: **`r paste(round(100*sum(NC_stats$Mail.Rtn.Tot, na.rm = T)/sum(NC_stats$Mail.Req.Tot, na.rm = T), digits = 1),"%", sep = "")`**

#### **Mail Ballots Returned and Accepted by Party Registration**

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

##### {.tabset .tabset-pills}

###### All

``` {r echo = FALSE}

NC_2020g_map_data <- left_join(NC_stats, NC_FIPS, by = "County")

NC_2020g_map_data$fips <- as.character(NC_2020g_map_data$FIPS_NUM)

NC_2020g_map_data <- mutate(NC_2020g_map_data, percent_returned = round(100*Pct.Return, digits = 1))

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

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

NC_2020g_map_data <- left_join(NC_2020g_map_data, mapdata, by = "fips")

NC_2020g_map_data <- arrange(NC_2020g_map_data, row)

hcmap(map = "countries/us/us-nc-all", data = NC_2020g_map_data,
  value = "percent_returned", name = "Percent Returned", joinby = "fips") %>%
  hc_title(text ="Mail Ballot Return Rates - All") %>%
  hc_subtitle(text = "County plots may not be shaded using the same scale")
```

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


#### Mail Ballots Returned and Accepted by 2016 Vote History
Note: North Carolina provides only aggregate request statistics, so it is impossible to generate ballot return rates by 2016 general vote.
``` {r echo = FALSE}
kable(NC_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 Race and Ethnicity

``` {r echo = FALSE}
kable(NC_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 Age

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

### Rejected

Ballots Rejected: **`r format(as.numeric(sum(NC_stats$Mail.Reject.Tot, na.rm = T)), big.mark =",")`**  
Rejection Rate: **`r paste(round(100*sum(NC_stats$Mail.Reject.Tot, na.rm = T)/(sum(NC_stats$Mail.Reject.Tot, na.rm = T)+sum(NC_stats$Mail.Rtn.Tot, na.rm = T)), digits = 1),"%", sep = "")`**

Most of these rejected ballots are for the ballot status reason of "WITNESS INFO INCOMPLETE".

North Carolina election officials are required by state law to follow up with these voters. If you are a North Carolina voter concerned about your mail ballot, you can track the status of your ballot at the [North Carolina State Board of Elections website](https://northcarolina.ballottrax.net/voter/). 

In the "All" mail ballot returned statistics, I add the mail ballots that are returned and accepted with the rejected ballots. Dividing the number of rejected by this quantity yields a rejection rate. 

#### **Mail Ballots Rejected by Party Registration**

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

##### {.tabset .tabset-pills}

###### All

``` {r echo = FALSE}

NC_2020g_map_data <- left_join(NC_stats, NC_FIPS, by = "County")

NC_2020g_map_data$fips <- as.character(NC_2020g_map_data$FIPS_NUM)

NC_2020g_map_data <- mutate(NC_2020g_map_data, percent_rejected = round(100*Pct.Reject, digits = 1))

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

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

NC_2020g_map_data <- left_join(NC_2020g_map_data, mapdata, by = "fips")

NC_2020g_map_data <- arrange(NC_2020g_map_data, row)

hcmap(map = "countries/us/us-nc-all", data = NC_2020g_map_data,
  value = "percent_rejected", name = "Percent Rejected", joinby = "fips") %>%
  hc_title(text ="Mail Ballot Rejection Rates - All") %>%
  hc_subtitle(text = "County plots may not be shaded using the same scale")
```

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

#### **Mail Ballots Rejected by Race and Ethnicity**

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

##### {.tabset .tabset-pills}

###### Non-Hispanic White

``` {r echo = FALSE}

NC_2020g_map_data <- left_join(NC_stats, NC_FIPS, by = "County")

NC_2020g_map_data$fips <- as.character(NC_2020g_map_data$FIPS_NUM)

NC_2020g_map_data <- mutate(NC_2020g_map_data, percent_rejected = round(100*Pct.Reject.nh_white, digits = 1))

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

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

NC_2020g_map_data <- left_join(NC_2020g_map_data, mapdata, by = "fips")

NC_2020g_map_data <- arrange(NC_2020g_map_data, row)

hcmap(map = "countries/us/us-nc-all", data = NC_2020g_map_data,
  value = "percent_rejected", name = "Percent Rejected", joinby = "fips") %>%
  hc_title(text ="Mail Ballot Rejection Rates - Non-Hispanic White") %>%
  hc_subtitle(text = "County plots may not be shaded using the same scale")
```

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

###### Non-Hispanic Black

``` {r echo = FALSE}

NC_2020g_map_data <- left_join(NC_stats, NC_FIPS, by = "County")

NC_2020g_map_data$fips <- as.character(NC_2020g_map_data$FIPS_NUM)

NC_2020g_map_data <- mutate(NC_2020g_map_data, percent_rejected = round(100*Pct.Reject.nh_black, digits = 1))

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

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

NC_2020g_map_data <- left_join(NC_2020g_map_data, mapdata, by = "fips")

NC_2020g_map_data <- arrange(NC_2020g_map_data, row)

hcmap(map = "countries/us/us-nc-all", data = NC_2020g_map_data,
  value = "percent_rejected", name = "Percent Rejected", joinby = "fips") %>%
  hc_title(text ="Mail Ballot Rejection Rates - Non-Hispanic Black") %>%
  hc_subtitle(text = "County plots may not be shaded using the same scale")
```

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

### Requested

Ballots Requested: **`r format(as.numeric(sum(NC_stats$Mail.Req.Tot)), big.mark =",")`**  
Request Rate: **`r paste(round(100*sum(NC_stats$Mail.Req.Tot)/(sum(NC_stats$Reg.Voters)), digits = 1),"%", sep = "")`**

#### **Mail Ballot Requests by Party Registration**

North Carolina registered Democrats have a **`r format(as.numeric(NC_req_send_party[1,2]-NC_req_send_party[2,2]), big.mark =",")`** ballot request lead over registered Republicans.

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

##### {.tabset .tabset-pills}
North Carolina releases an update to the voter registration file each Saturday. The voter registration numbers are from the most recent release and may be slightly dated.  

###### All 

``` {r echo = FALSE}

NC_2020g_map_data <- left_join(NC_stats, NC_FIPS, by = "County")

NC_2020g_map_data$fips <- as.character(NC_2020g_map_data$FIPS_NUM)

NC_2020g_map_data <- mutate(NC_2020g_map_data, percent_requested = round(100*Pct.Request, digits = 1))

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

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

NC_2020g_map_data <- left_join(NC_2020g_map_data, mapdata, by = "fips")

NC_2020g_map_data <- arrange(NC_2020g_map_data, row)

hcmap(map = "countries/us/us-nc-all", data = NC_2020g_map_data,
  value = "percent_requested", name = "Percent Requested", joinby = "fips")  %>%
  hc_title(text ="Mail Ballot Request Rates - All") %>%
  hc_subtitle(text = "County plots may not be shaded using the same scale")
```

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

#### Mail Ballot Requests by Race and Ethnicity

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

#### Mail Ballot Requests by Age
North Carolina provides mail ballot request data in age ranges inconsistent with other states' ranges. The age statistics presented here are not included in the national age statistics.

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

#### Mail Ballot Requests by Gender

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