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PA.Rmd
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PA.Rmd
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---
title: "Pennsylvania 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")
PA_stats <- read_csv("D:/DropBox/Dropbox/Mail_Ballots_2020/markdown/2020G_Early_Vote_PA.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"
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"
age_shell_returned <- data.frame(Race=character(),
Count=integer(),
Frequency=double(),
Count2=integer(),
Rate=integer(),
stringsAsFactors=FALSE)
age_shell_returned[1,1] <- "18 to 24"
age_shell_returned[2,1] <- "25 to 34"
age_shell_returned[3,1] <- "35 to 44"
age_shell_returned[4,1] <- "45 and 55"
age_shell_returned[5,1] <- "56 and 65"
age_shell_returned[6,1] <- "66 and up"
age_shell_returned[7,1] <- "TOTAL"
# Pennsylvania
PA_req_send_party <- party_shell
PA_req_send_party[1,2] <- state_stats[39,10]
PA_req_send_party[2,2] <- state_stats[39,11]
PA_req_send_party[3,2] <- state_stats[39,12]
PA_req_send_party[4,2] <- state_stats[39,13]
PA_req_send_party[5,2] <- state_stats[39,5]
PA_req_send_party$Percent <- 100*PA_req_send_party$Count/PA_req_send_party[5,2]
PA_accept_party <- party_shell_returned
PA_accept_party[1,2] <- state_stats[39,29]
PA_accept_party[2,2] <- state_stats[39,30]
PA_accept_party[3,2] <- state_stats[39,31]
PA_accept_party[4,2] <- state_stats[39,32]
PA_accept_party[5,2] <- state_stats[39,6]
PA_accept_party[1,4] <- state_stats[39,10]
PA_accept_party[2,4] <- state_stats[39,11]
PA_accept_party[3,4] <- state_stats[39,12]
PA_accept_party[4,4] <- state_stats[39,13]
PA_accept_party[5,4] <- state_stats[39,5]
PA_accept_party$Frequency <- 100*PA_accept_party$Count/PA_accept_party[5,2]
PA_accept_party$Rate <- 100*PA_accept_party$Count/PA_accept_party$Count2
colnames(PA_accept_party) <- c("Party", "Returned Ballots", "Freq. Distribution", "Requested Ballots", "Return Rate")
PA_req_send_age <- age_shell
PA_req_send_age[1,2] <- state_stats[39,23]
PA_req_send_age[2,2] <- state_stats[39,24]
PA_req_send_age[3,2] <- state_stats[39,25]
PA_req_send_age[4,2] <- state_stats[39,26]
PA_req_send_age[5,2] <- state_stats[39,27]
PA_req_send_age[6,2] <- state_stats[39,28]
PA_req_send_age[7,2] <- state_stats[39,5]
PA_req_send_age$Percent <- 100*PA_req_send_age$Count/PA_req_send_age[7,2]
PA_accept_age <- age_shell_returned
PA_accept_age[1,2] <- sum(state_stats[39,42])
PA_accept_age[2,2] <- sum(state_stats[39,43])
PA_accept_age[3,2] <- sum(state_stats[39,44])
PA_accept_age[4,2] <- sum(state_stats[39,45])
PA_accept_age[5,2] <- sum(state_stats[39,46])
PA_accept_age[6,2] <- sum(state_stats[39,47])
PA_accept_age[7,2] <- sum(state_stats[39,6])
PA_accept_age[1,4] <- sum(state_stats[39,23])
PA_accept_age[2,4] <- sum(state_stats[39,24])
PA_accept_age[3,4] <- sum(state_stats[39,25])
PA_accept_age[4,4] <- sum(state_stats[39,26])
PA_accept_age[5,4] <- sum(state_stats[39,27])
PA_accept_age[6,4] <- sum(state_stats[39,28])
PA_accept_age[7,4] <- sum(state_stats[39,5])
PA_accept_age$Frequency <- 100 * PA_accept_age$Count/PA_accept_age[7,2]
PA_accept_age$Rate <- 100*PA_accept_age$Count/PA_accept_age$Count2
colnames(PA_accept_age) <- c("Age", "Returned Ballots", "Freq. Distribution", "Requested Ballots", "Return Rate")
PA_stats_requests <- select(PA_stats, County, Reg.Voters, Mail.Req.Tot, Pct.Request)
PA_stats_returns <- select(PA_stats, County, Mail.Req.Tot, Mail.Rtn.Tot, Pct.Accept)
PA_stats_rejected <- select(PA_stats, County, Mail.All.Tot, Mail.Rej.Tot, Pct.Reject)
```
## {.tabset}
Last Report: `r state_stats[39,9]`
Source: `r state_stats[39,2]`
### Mail Ballots Returned
Mail Ballots Returned: **`r format(as.numeric(state_stats[39,6]), big.mark =",")`**
#### **Mail Ballots Returned by Party Registration**
``` {r echo = FALSE}
kable(PA_accept_party, format.args = list(big.mark = ",",
scientific = FALSE), digits = 1) %>%
kable_styling(bootstrap_options = "striped", full_width = F, position = "left")
```
``` {r echo = FALSE}
PA_map_data <- PA_stats
PA_map_data <- mutate(PA_map_data, percent = round(100*(Mail.Rtn.Tot/Mail.Req.Tot), digits = 1))
PA_map_data <- mutate(PA_map_data, fips = as.character(fips))
mapfile <- download_map_data("countries/us/us-pa-all.js")
mapdata <- get_data_from_map(mapfile)
mapdata$row <- as.integer(rownames(mapdata))
PA_map_data <- left_join(PA_map_data, mapdata, by = "fips")
PA_map_data <- arrange(PA_map_data, row)
hcmap(map = "countries/us/us-pa-all", data = PA_map_data,
value = "percent", name = "Percent Returned", 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(PA_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.Rtn.Tot'), 0, mark = ",")
```
#### **Mail Ballots Returned by Age**
``` {r echo = FALSE}
kable(PA_accept_age, 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(state_stats[39,48]), big.mark =",")`**
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.
At this time I do not have party registration statistics for rejected ballots.
Pennsylvania election officials cannot begin to fully process mail ballots until Election Day. The few rejected ballots reported here are for first time voters who did not provide required id with their mail ballot or a missing signature.
``` {r echo = FALSE}
PA_map_data <- PA_stats
PA_map_data <- mutate(PA_map_data, percent = round(Pct.Reject, digits = 1))
PA_map_data <- mutate(PA_map_data, fips = as.character(fips))
mapfile <- download_map_data("countries/us/us-pa-all.js")
mapdata <- get_data_from_map(mapfile)
mapdata$row <- as.integer(rownames(mapdata))
PA_map_data <- left_join(PA_map_data, mapdata, by = "fips")
PA_map_data <- arrange(PA_map_data, row)
hcmap(map = "countries/us/us-pa-all", data = PA_map_data,
value = "percent", name = "Percent Rejected", joinBy = "fips") %>%
hc_title(text ="Mail Ballot Rejection Rates") %>%
hc_subtitle(text = "County plots may not be shaded using the same scale")
```
``` {r echo = FALSE}
datatable(PA_stats_rejected, colnames = c("County", "All Mail Ballots Returned", "Mail Ballots Rejected", "Percent Returned"), rownames = F) %>%
formatPercentage('Pct.Reject', 1) %>%
formatRound(c('Mail.All.Tot', 'Mail.Rej.Tot'), 0, mark = ",")
```
### Mail Ballot Requests
#### Mail Ballot Requests by Party Registration
``` {r echo = FALSE}
kable(PA_req_send_party, format.args = list(big.mark = ",",
scientific = FALSE), digits = 1) %>%
kable_styling(bootstrap_options = "striped", full_width = F, position = "left")
```
Pennsylvania registered Democrats have a **`r format(as.numeric(PA_req_send_party[1,2]-PA_req_send_party[2,2]), big.mark =",")`** ballot request lead over registered Republicans.
``` {r echo = FALSE}
PA_map_data <- PA_stats
PA_map_data <- mutate(PA_map_data, percent = round(100*(Mail.Req.Tot/Reg.Voters), digits = 1))
PA_map_data <- mutate(PA_map_data, fips = as.character(fips))
mapfile <- download_map_data("countries/us/us-pa-all.js")
mapdata <- get_data_from_map(mapfile)
mapdata$row <- as.integer(rownames(mapdata))
PA_map_data <- left_join(PA_map_data, mapdata, by = "fips")
PA_map_data <- arrange(PA_map_data, row)
hcmap(map = "countries/us/us-pa-all", data = PA_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(PA_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 Ballot Requests by Age
``` {r echo = FALSE}
kable(PA_req_send_age, format.args = list(big.mark = ",",
scientific = FALSE), digits = 1) %>%
kable_styling(bootstrap_options = "striped", full_width = F, position = "left")
```