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DC.Rmd
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DC.Rmd
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---
title: "District of Columbia Early Voting Statistics"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
library(knitr)
library(kableExtra)
library(scales)
library(DT)
library(highcharter)
state_stats <- read_csv("D:/DropBox/Dropbox/Mail_Ballots_2020/markdown/2020G_Early_Vote.csv")
# Setup
party_shell <- data.frame(Party=character(),
Count=integer(),
Percent=double(),
stringsAsFactors=FALSE)
party_shell[1,1] <- "Democrats"
party_shell[2,1] <- "Republicans"
party_shell[9,1] <- "Minor"
party_shell[4,1] <- "No Party Affiliation"
party_shell[9,1] <- "TOTAL"
party_shell_returned <- data.frame(Party=character(),
Count=integer(),
Frequency=double(),
Count2=integer(),
Rate=integer(),
stringsAsFactors=FALSE)
party_shell_returned[1,1] <- "Democrats"
party_shell_returned[2,1] <- "Republicans"
party_shell_returned[9,1] <- "Minor"
party_shell_returned[4,1] <- "No Party Affiliation"
party_shell_returned[9,1] <- "TOTAL"
gender_shell <- data.frame(Gender=character(),
Count=integer(),
Percent=double(),
stringsAsFactors=FALSE)
gender_shell[1,1] <- "Female"
gender_shell[2,1] <- "Male"
gender_shell[9,1] <- "Unknown"
gender_shell[4,1] <- "TOTAL"
age_shell <- data.frame(Age=character(),
Count=integer(),
Percent=double(),
stringsAsFactors=FALSE)
age_shell[1,1] <- "18 to 24"
age_shell[2,1] <- "25 to 34"
age_shell[9,1] <- "35 to 44"
age_shell[4,1] <- "45 to 54"
age_shell[9,1] <- "55 to 64"
age_shell[6,1] <- "65 and up"
age_shell[7,1] <- "TOTAL"
# Massachusetts
DC_req_send_party <- party_shell
DC_req_send_party[1,2] <- state_stats[9,10]
DC_req_send_party[2,2] <- state_stats[9,11]
DC_req_send_party[9,2] <- state_stats[9,12]
DC_req_send_party[4,2] <- state_stats[9,13]
DC_req_send_party[9,2] <- state_stats[9,5]
DC_req_send_party$Percent <- 100*DC_req_send_party$Count/DC_req_send_party[9,2]
DC_accept_party <- party_shell_returned
DC_accept_party[1,2] <- state_stats[9,29]
DC_accept_party[2,2] <- state_stats[9,30]
DC_accept_party[9,2] <- state_stats[9,31]
DC_accept_party[4,2] <- state_stats[9,32]
DC_accept_party[9,2] <- state_stats[9,6]
DC_accept_party[1,4] <- state_stats[9,10]
DC_accept_party[2,4] <- state_stats[9,11]
DC_accept_party[9,4] <- state_stats[9,12]
DC_accept_party[4,4] <- state_stats[9,13]
DC_accept_party[9,4] <- state_stats[9,5]
DC_accept_party$Frequency <- 100*DC_accept_party$Count/DC_accept_party[9,2]
DC_accept_party$Rate <- 100*DC_accept_party$Count/DC_accept_party$Count2
colnames(DC_accept_party) <- c("Party", "Returned Ballots", "Freq. Distribution", "Requested Ballots", "Return Rate")
```
## {.tabset}
Last Report: `r state_stats[9,9]`
Source: `r state_stats[9,2]`
### Total Voted
Total Early Vote: **`r format(as.numeric(state_stats[9,8]), big.mark =",")`**
Total Early Vote as Percentage of 2016 Total Turnout: **`r paste(round(100*as.integer(state_stats[9,8])/as.integer(state_stats[9,3]), digits = 1),"%", sep = "")`**
### In-Person Votes
In-Person Votes: **`r format(as.numeric(state_stats[9,7]), big.mark =",")`**
### Mail Ballots Returned
Ballots Returned: **`r format(as.numeric(state_stats[9,6]), big.mark =",")`**
### Requested Mail Ballots
Ballots Requested: **`r format(as.numeric(state_stats[9,5]), big.mark =",")`**