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MI.Rmd
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---
title: "Michigan 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")
MI_stats <- read_csv("D:/DropBox/Dropbox/Mail_Ballots_2020/markdown/2020G_Early_Vote_MI.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"
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"
# Michigan
age_shell <- data.frame(Race=character(),
Count=integer(),
Frequency=double(),
Count2=integer(),
Rate=integer(),
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"
MI_accept_age <- age_shell
MI_accept_age[1,2] <- state_stats[23,42]
MI_accept_age[2,2] <- state_stats[23,43]
MI_accept_age[3,2] <- state_stats[23,44]
MI_accept_age[4,2] <- state_stats[23,45]
MI_accept_age[5,2] <- state_stats[23,46]
MI_accept_age[6,2] <- state_stats[23,47]
MI_accept_age[7,2] <- state_stats[23,6]
MI_accept_age[1,4] <- state_stats[23,23]
MI_accept_age[2,4] <- state_stats[23,24]
MI_accept_age[3,4] <- state_stats[23,25]
MI_accept_age[4,4] <- state_stats[23,26]
MI_accept_age[5,4] <- state_stats[23,27]
MI_accept_age[6,4] <- state_stats[23,28]
MI_accept_age[7,4] <- state_stats[23,5]
MI_accept_age$Frequency <- 100*MI_accept_age$Count/MI_accept_age[7,2]
MI_accept_age$Rate <- 100*MI_accept_age$Count/MI_accept_age$Count2
colnames(MI_accept_age) <- c("Age", "Returned Ballots", "Freq. Distribution", "Requested Ballots", "Return Rate")
MI_stats_requested <- MI_stats %>%
select(CountyName, Reg.Voters, Mail.Req.Tot, Pct.Request)
MI_stats_accepted <- MI_stats %>%
select(CountyName, Mail.Req.Tot, Mail.Accept.Tot, Pct.Accept)
MI_stats_rejected <- MI_stats %>%
select(CountyName, Mail.Reject.Tot, Mail.Returned.Tot, Pct.Reject)
```
## {.tabset}
Last Report: `r state_stats[23,9]`
Source: `r state_stats[23,2]`
These Michigan data are compiled from a file of absentee ballot requests purchased from the Michigan Secretary of State's office.
### Returned and Accepted Mail Ballots
Ballots Returned: **`r format(as.numeric(state_stats[23,6]), big.mark =",")`**
Return Rate: **`r paste(round(100*sum(state_stats[23,6], na.rm = T)/sum(state_stats[23,5], na.rm = T), digits = 1),"%", sep = "")`**
``` {r echo = FALSE}
MI_2020g_map_data <- MI_stats
MI_2020g_map_data$fips <- as.character(MI_2020g_map_data$FIPS_NUM)
MI_2020g_map_data <- mutate(MI_2020g_map_data, percent = round(100*Pct.Accept, digits = 1))
mapfile <- download_map_data("countries/us/us-mi-all.js")
mapdata <- get_data_from_map(mapfile)
mapdata$row <- as.integer(rownames(mapdata))
MI_2020g_map_data <- left_join(MI_2020g_map_data, mapdata, by = "fips")
MI_2020g_map_data <- arrange(MI_2020g_map_data, row)
hcmap(map = "countries/us/us-mi-all", data = MI_2020g_map_data,
value = "percent", name = "Percent Accepted", joinBy = "fips") %>%
hc_title(text ="Mail Ballot Return Rates") %>%
hc_subtitle(text = "County plots may not be shaded using the same scale")
```
``` {r echo = FALSE}
datatable(MI_stats_accepted, colnames = c("County", "Mail Ballots Requested", "Mail Ballots Accepted", "Percent Accepted"), rownames = F) %>%
formatPercentage('Pct.Accept', 1) %>%
formatRound(c('Mail.Req.Tot', 'Mail.Accept.Tot'), 0, mark = ",")
```
#### Mail Ballots Returned and Accepted by Age
``` {r echo = FALSE}
kable(MI_accept_age, format.args = list(big.mark = ",",
scientific = FALSE), digits = 1) %>%
kable_styling(bootstrap_options = "striped", full_width = F, position = "left")
```
### Rejected Mail Ballots
Ballots Rejected: **`r format(as.numeric(state_stats[23,48]), big.mark =",")`**
Rejection Rate: **`r paste(round(100*(state_stats[23,48])/(state_stats[23,48]+state_stats[23,6]), digits = 1),"%", sep = "")`**
Michigan election officials have rejected **`r format(as.numeric(state_stats[23,48]), big.mark =",")`** mail ballots.
To calculate mail ballot rejection rates, I divide the number of rejected ballots by the number of accepted ballots *plus* the number of rejected ballots.
``` {r echo = FALSE}
MI_2020g_map_data <- MI_stats
MI_2020g_map_data$fips <- as.character(MI_2020g_map_data$FIPS_NUM)
MI_2020g_map_data <- mutate(MI_2020g_map_data, percent = round(100*Pct.Reject, digits = 1))
mapfile <- download_map_data("countries/us/us-mi-all.js")
mapdata <- get_data_from_map(mapfile)
mapdata$row <- as.integer(rownames(mapdata))
MI_2020g_map_data <- left_join(MI_2020g_map_data, mapdata, by = "fips")
MI_2020g_map_data <- arrange(MI_2020g_map_data, row)
hcmap(map = "countries/us/us-mi-all", data = MI_2020g_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(MI_stats_rejected, colnames = c("County", "Mail Ballots Rejected", "Mail Ballots Returned (All)", "Percent Rejected"), rownames = F) %>%
formatPercentage('Pct.Reject', 1) %>%
formatRound(c('Mail.Reject.Tot', 'Mail.Returned.Tot'), 0, mark = ",")
```
### Requested Mail Ballots
Ballots Requested: **`r format(as.numeric(state_stats[23,5]), big.mark =",")`**
Request Rate: **`r paste(round(100*state_stats[23,5]/(sum(MI_stats$Reg.Voters)), digits = 1),"%", sep = "")`**
``` {r echo = FALSE}
MI_2020g_map_data <- MI_stats
MI_2020g_map_data$fips <- as.character(MI_2020g_map_data$FIPS_NUM)
MI_2020g_map_data <- mutate(MI_2020g_map_data, percent = round(100*Pct.Request, digits = 1))
mapfile <- download_map_data("countries/us/us-mi-all.js")
mapdata <- get_data_from_map(mapfile)
mapdata$row <- as.integer(rownames(mapdata))
MI_2020g_map_data <- left_join(MI_2020g_map_data, mapdata, by = "fips")
MI_2020g_map_data <- arrange(MI_2020g_map_data, row)
hcmap(map = "countries/us/us-mi-all", data = MI_2020g_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(MI_stats_requested, colnames = c("County", "Voter Registration", "Mail Ballots Requested", "Percent Requested"), rownames = F) %>%
formatPercentage('Pct.Request', 1) %>%
formatRound(c('Reg.Voters', 'Mail.Req.Tot'), 0, mark = ",")
```