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CT.Rmd
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CT.Rmd
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---
title: "Connecticut Early Voting Statistics"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
library(knitr)
library(kableExtra)
library(scales)
library(DT)
library(highcharter)
state_stats <- read_csv("D:/DropBox/Dropbox/Mail_Ballots_2020/markdown/2020G_Early_Vote.csv")
# Setup
party_shell <- data.frame(Party=character(),
Count=integer(),
Percent=double(),
stringsAsFactors=FALSE)
party_shell[1,1] <- "Democrats"
party_shell[2,1] <- "Republicans"
party_shell[3,1] <- "Minor"
party_shell[4,1] <- "No Party Affiliation"
party_shell[5,1] <- "TOTAL"
party_shell_returned <- data.frame(Party=character(),
Count=integer(),
Frequency=double(),
Count2=integer(),
Rate=integer(),
stringsAsFactors=FALSE)
party_shell_returned[1,1] <- "Democrats"
party_shell_returned[2,1] <- "Republicans"
party_shell_returned[3,1] <- "Minor"
party_shell_returned[4,1] <- "No Party Affiliation"
party_shell_returned[5,1] <- "TOTAL"
gender_shell <- data.frame(Gender=character(),
Count=integer(),
Percent=double(),
stringsAsFactors=FALSE)
gender_shell[1,1] <- "Female"
gender_shell[2,1] <- "Male"
gender_shell[3,1] <- "Unknown"
gender_shell[4,1] <- "TOTAL"
age_shell <- data.frame(Age=character(),
Count=integer(),
Percent=double(),
stringsAsFactors=FALSE)
age_shell[1,1] <- "18 to 24"
age_shell[2,1] <- "25 to 34"
age_shell[3,1] <- "35 to 44"
age_shell[4,1] <- "45 to 54"
age_shell[5,1] <- "55 to 64"
age_shell[6,1] <- "65 and up"
age_shell[7,1] <- "TOTAL"
# Connecticut
CT_req_send_party <- party_shell
CT_req_send_party[1,2] <- state_stats[7,10]
CT_req_send_party[2,2] <- state_stats[7,11]
CT_req_send_party[3,2] <- state_stats[7,12]
CT_req_send_party[4,2] <- state_stats[7,13]
CT_req_send_party[5,2] <- state_stats[7,5]
CT_req_send_party$Percent <- 100*CT_req_send_party$Count/CT_req_send_party[5,2]
CT_inperson_party <- party_shell
CT_inperson_party[1,2] <- state_stats[7,77]
CT_inperson_party[2,2] <- state_stats[7,78]
CT_inperson_party[3,2] <- state_stats[7,79]
CT_inperson_party[4,2] <- state_stats[7,80]
CT_inperson_party[5,2] <- state_stats[7,7]
CT_inperson_party$Percent <- 100*CT_inperson_party$Count/CT_inperson_party[5,2]
CT_voted_party <- party_shell
CT_voted_party[1,2] <- state_stats[7,77] + state_stats[7,29]
CT_voted_party[2,2] <- state_stats[7,78] + state_stats[7,30]
CT_voted_party[3,2] <- state_stats[7,79] + state_stats[7,31]
CT_voted_party[4,2] <- state_stats[7,80] + state_stats[7,32]
CT_voted_party[5,2] <- state_stats[7,7] + state_stats[7,6]
CT_voted_party$Percent <- 100*CT_voted_party$Count/CT_voted_party[5,2]
CT_accept_party <- party_shell_returned
CT_accept_party[1,2] <- state_stats[7,29]
CT_accept_party[2,2] <- state_stats[7,30]
CT_accept_party[3,2] <- state_stats[7,31]
CT_accept_party[4,2] <- state_stats[7,32]
CT_accept_party[5,2] <- state_stats[7,6]
CT_accept_party[1,4] <- state_stats[7,10]
CT_accept_party[2,4] <- state_stats[7,11]
CT_accept_party[3,4] <- state_stats[7,12]
CT_accept_party[4,4] <- state_stats[7,13]
CT_accept_party[5,4] <- state_stats[7,5]
CT_accept_party$Frequency <- 100*CT_accept_party$Count/CT_accept_party[5,2]
CT_accept_party$Rate <- 100*CT_accept_party$Count/CT_accept_party$Count2
colnames(CT_accept_party) <- c("Party", "Returned Ballots", "Freq. Distribution", "Requested Ballots", "Return Rate")
```
## {.tabset}
Last Report: `r state_stats[7,9]`
Source: `r state_stats[7,2]`
### Mail Ballots Returned
Ballots Returned: **`r format(as.numeric(state_stats[7,6]), big.mark =",")`**
#### **Mail Ballots Returned and Accepted by Party Registration**
``` {r echo = FALSE}
kable(CT_accept_party, format.args = list(big.mark = ",",
scientific = FALSE), digits = 1) %>%
kable_styling(bootstrap_options = "striped", full_width = F, position = "left")
```
### Requested Mail Ballots
Ballots Requested: **`r format(as.numeric(state_stats[7,5]), big.mark =",")`**