index.Rmd

---
title: "2020 General Election Early Vote 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"

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"

# National Statistics

US_req_send_party <- party_shell

# Returned or Accepted (depends on how states report data)

US_accept_party <- party_shell_returned

US_accept_party[1,2] <- sum(state_stats$mail_accept_2020_dem) 
US_accept_party[2,2] <- sum(state_stats$mail_accept_2020_rep) 
US_accept_party[3,2] <- sum(state_stats$mail_accept_2020_minor)
US_accept_party[4,2] <- sum(state_stats$mail_accept_2020_npa) 
US_accept_party[5,2] <- sum(state_stats$mail_accept_2020_dem)+sum(state_stats$mail_accept_2020_rep)+sum(state_stats$mail_accept_2020_minor)+sum(state_stats$mail_accept_2020_npa)

US_accept_party[1,4] <- sum(state_stats$mail_sent_req_2020_dem) 
US_accept_party[2,4] <- sum(state_stats$mail_sent_req_2020_rep) 
US_accept_party[3,4] <- sum(state_stats$mail_sent_req_2020_minor)
US_accept_party[4,4] <- sum(state_stats$mail_sent_req_2020_npa) 
US_accept_party[5,4] <- sum(state_stats$mail_sent_req_2020_dem)+sum(state_stats$mail_sent_req_2020_rep)+sum(state_stats$mail_sent_req_2020_minor)+sum(state_stats$mail_sent_req_2020_npa) 

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

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

# Voted

US_voted_party <- party_shell

US_voted_party[1,2] <- sum(state_stats$mail_accept_2020_dem) + sum(state_stats$inperson_2020_dem)
US_voted_party[2,2] <- sum(state_stats$mail_accept_2020_rep) + sum(state_stats$inperson_2020_rep)
US_voted_party[3,2] <- sum(state_stats$mail_accept_2020_minor) + sum(state_stats$inperson_2020_minor)
US_voted_party[4,2] <- sum(state_stats$mail_accept_2020_npa) + sum(state_stats$inperson_2020_npa)
US_voted_party[5,2] <- sum(state_stats$mail_accept_2020_dem)+sum(state_stats$mail_accept_2020_rep)+sum(state_stats$mail_accept_2020_minor)+sum(state_stats$mail_accept_2020_npa) + sum(state_stats$inperson_2020_dem)+sum(state_stats$inperson_2020_rep)+sum(state_stats$inperson_2020_minor)+sum(state_stats$inperson_2020_npa)

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

# Inperson

US_inperson_party <- party_shell

US_inperson_party[1,2] <- sum(state_stats$inperson_2020_dem) 
US_inperson_party[2,2] <- sum(state_stats$inperson_2020_rep) 
US_inperson_party[3,2] <- sum(state_stats$inperson_2020_minor)
US_inperson_party[4,2] <- sum(state_stats$inperson_2020_npa) 
US_inperson_party[5,2] <- sum(state_stats$inperson_2020_dem)+sum(state_stats$inperson_2020_rep)+sum(state_stats$inperson_2020_minor)+sum(state_stats$inperson_2020_npa)

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

# Requested

US_req_send_party <- party_shell

US_req_send_party[1,2] <- sum(state_stats$mail_sent_req_2020_dem) 
US_req_send_party[2,2] <- sum(state_stats$mail_sent_req_2020_rep) 
US_req_send_party[3,2] <- sum(state_stats$mail_sent_req_2020_minor)
US_req_send_party[4,2] <- sum(state_stats$mail_sent_req_2020_npa) 
US_req_send_party[5,2] <- sum(state_stats$mail_sent_req_2020_dem)+sum(state_stats$mail_sent_req_2020_rep)+sum(state_stats$mail_sent_req_2020_minor)+sum(state_stats$mail_sent_req_2020_npa)

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

US_req_send_race <- race_shell

US_req_send_race[1,2] <- sum(state_stats$mail_sent_req_2020_nh_white) 
US_req_send_race[2,2] <- sum(state_stats$mail_sent_req_2020_nh_black) 
US_req_send_race[3,2] <- sum(state_stats$mail_sent_req_2020_hispanic)
US_req_send_race[4,2] <- sum(state_stats$mail_sent_req_2020_nh_asian) 
US_req_send_race[5,2] <- sum(state_stats$mail_sent_req_2020_nh_native_american)
US_req_send_race[6,2] <- sum(state_stats$mail_sent_req_2020_other) 
US_req_send_race[7,2] <- sum(state_stats$mail_sent_req_2020_nh_white) +
  sum(state_stats$mail_sent_req_2020_nh_black) +
  sum(state_stats$mail_sent_req_2020_hispanic) +
  sum(state_stats$mail_sent_req_2020_nh_asian) +
  sum(state_stats$mail_sent_req_2020_nh_native_american) +
  sum(state_stats$mail_sent_req_2020_other)

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

US_req_send_age <- age_shell

US_req_send_age[1,2] <- sum(state_stats$mail_sent_req_2020_age1824) 
US_req_send_age[2,2] <- sum(state_stats$mail_sent_req_2020_age2534) 
US_req_send_age[3,2] <- sum(state_stats$mail_sent_req_2020_age3544)
US_req_send_age[4,2] <- sum(state_stats$mail_sent_req_2020_age4554)
US_req_send_age[5,2] <- sum(state_stats$mail_sent_req_2020_age5564)
US_req_send_age[6,2] <- sum(state_stats$mail_sent_req_2020_age65up)
US_req_send_age[7,2] <- sum(state_stats$mail_sent_req_2020_age1824) +
  sum(state_stats$mail_sent_req_2020_age2534) +
  sum(state_stats$mail_sent_req_2020_age3544) +
  sum(state_stats$mail_sent_req_2020_age4554) +
  sum(state_stats$mail_sent_req_2020_age5564) +
  sum(state_stats$mail_sent_req_2020_age65up)

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

US_req_send_gender <- gender_shell

US_req_send_gender[1,2] <- sum(state_stats$mail_sent_req_2020_female) 
US_req_send_gender[2,2] <- sum(state_stats$mail_sent_req_2020_male) 
US_req_send_gender[3,2] <- sum(state_stats$mail_sent_req_2020_unk)
US_req_send_gender[4,2] <- sum(state_stats$mail_sent_req_2020_female) +
  sum(state_stats$mail_sent_req_2020_male) +
  sum(state_stats$mail_sent_req_2020_unk)

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

US_state_data <- state_stats %>%
  select(state, total_ballots_2016, mail_sent_req_2020, mail_accept_2020)
```
Last updated: `r format(as.POSIXct(Sys.time(), format = "%y%m%d %H:%M"), "%m/%d/%Y %I:%M %p")` Eastern Time

Michael McDonald • Professor, University of Florida  
Web: [United States Elections Project](http://www.electproject.org/) • Twitter: [\@ElectProject](https://twitter.com/ElectProject)

Detailed state statistics and analyses for reporting states  
[AK](https://electproject.github.io/Early-Vote-2020G/AK.html) |  [AL](https://electproject.github.io/Early-Vote-2020G/AL.html) |  [AZ](https://electproject.github.io/Early-Vote-2020G/AZ.html) |  [CA](https://electproject.github.io/Early-Vote-2020G/CA.html) |  [CO](https://electproject.github.io/Early-Vote-2020G/CO.html) |  [DC](https://electproject.github.io/Early-Vote-2020G/DC.html) |  [FL](https://electproject.github.io/Early-Vote-2020G/FL.html) |  [GA](https://electproject.github.io/Early-Vote-2020G/GA.html) |  [HI](https://electproject.github.io/Early-Vote-2020G/HI.html) |  [IA](https://electproject.github.io/Early-Vote-2020G/IA.html) |  [IL](https://electproject.github.io/Early-Vote-2020G/IL.html) |  [IN](https://electproject.github.io/Early-Vote-2020G/IN.html) |  [KS](https://electproject.github.io/Early-Vote-2020G/KS.html) |  [KY](https://electproject.github.io/Early-Vote-2020G/KY.html) |  [LA](https://electproject.github.io/Early-Vote-2020G/LA.html) |  [MA](https://electproject.github.io/Early-Vote-2020G/MA.html) |  [MD](https://electproject.github.io/Early-Vote-2020G/MD.html) |  [ME](https://electproject.github.io/Early-Vote-2020G/ME.html) |  [MI](https://electproject.github.io/Early-Vote-2020G/MI.html) |  [MN](https://electproject.github.io/Early-Vote-2020G/MN.html) |  [MS](https://electproject.github.io/Early-Vote-2020G/MS.html) |  [MT](https://electproject.github.io/Early-Vote-2020G/MT.html) |  [NC](https://electproject.github.io/Early-Vote-2020G/NC.html) |  [ND](https://electproject.github.io/Early-Vote-2020G/ND.html) |  [NE](https://electproject.github.io/Early-Vote-2020G/NE.html) |  [NH](https://electproject.github.io/Early-Vote-2020G/NH.html) |  [NJ](https://electproject.github.io/Early-Vote-2020G/NJ.html) |  [NM](https://electproject.github.io/Early-Vote-2020G/NM.html) |  [NV](https://electproject.github.io/Early-Vote-2020G/NV.html) |  [OH](https://electproject.github.io/Early-Vote-2020G/OH.html) |  [OK](https://electproject.github.io/Early-Vote-2020G/OK.html) |  [OR](https://electproject.github.io/Early-Vote-2020G/OR.html) |  [PA](https://electproject.github.io/Early-Vote-2020G/PA.html) |  [RI](https://electproject.github.io/Early-Vote-2020G/RI.html) |  [SC](https://electproject.github.io/Early-Vote-2020G/SC.html) | 
[SD](https://electproject.github.io/Early-Vote-2020G/SD.html) | 
[TN](https://electproject.github.io/Early-Vote-2020G/TN.html) | 
[TX](https://electproject.github.io/Early-Vote-2020G/TX.html) | 
[UT](https://electproject.github.io/Early-Vote-2020G/UT.html) | 
[VA](https://electproject.github.io/Early-Vote-2020G/VA.html) | 
[VT](https://electproject.github.io/Early-Vote-2020G/VT.html) | 
[WA](https://electproject.github.io/Early-Vote-2020G/WA.html) | 
[WI](https://electproject.github.io/Early-Vote-2020G/WI.html) |
[WV](https://electproject.github.io/Early-Vote-2020G/WV.html) | 
[WY](https://electproject.github.io/Early-Vote-2020G/WY.html)  

These states are those where I have recent data on mail ballot activity. More states will be added as state reports become available.

Read my analysis for the week ending:  

* [Sunday, Oct.18](https://electproject.github.io/Early-Vote-2020G/Early_Vote_Analysis_10_18.html)  
* [Sunday, Oct.11](https://electproject.github.io/Early-Vote-2020G/Early_Vote_Analysis_10_11.html)  
* [Sunday, Oct. 4](https://electproject.github.io/Early-Vote-2020G/Early_Vote_Analysis_10_4.html)  
* [Sunday, Sept. 27](https://electproject.github.io/Early-Vote-2020G/Early_Vote_Analysis_9_27.html)  

## Summary Statistics for Reporting States {.tabset} 

### Total Voted

Voters have cast a total of **`r format(as.numeric(sum(state_stats$total_early_2020)), big.mark =",")`** ballots in the reporting states.

``` {r echo = FALSE}
US_2020g_map_data <- state_stats

US_2020g_map_data <- arrange(US_2020g_map_data, hc_order)

hcoptslang <- getOption("highcharter.lang")
hcoptslang$thousandsSep <- ","
options(highcharter.lang = hcoptslang)

US_2020g_map_data <- US_2020g_map_data %>%
  mutate(display_data = total_early_2020) %>%
  mutate(display_data = na_if(display_data, 0))

hcmap(map = "countries/us/us-all", data = US_2020g_map_data,
  value = "display_data", name = "Voted") %>%
  hc_title(text ="Total Early Vote") %>%
  hc_subtitle(text = "State plots may not be shaded using the same scale")
```

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

###### Total Voted  

**Total Voted by Party Registration**  

Reporting states with party registration data: CA, CO, FL, IA, KS, KY, LA, MD, ME, NC, NE, NJ, NM, NV, OK, PA, SD    
``` {r echo = FALSE}

options(knitr.kable.NA = '')

kable(US_voted_party, valign = 't', 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 by Party Registration**  

Reporting states with party registration data: CO, KS, KY, LA, MD, NC, NM, NV, SD  
``` {r echo = FALSE}

options(knitr.kable.NA = '')

kable(US_inperson_party, valign = 't', format.args = list(big.mark = ",", scientific = FALSE), digits = 1) %>%
  kable_styling(bootstrap_options = "striped", full_width = F, position = "left")
```

###### Returned Mail Ballots  

**Returned Mail Ballots by Party Registration**

Reporting states with party registration data: CA, CO, FL, IA, KS, KY, MD, ME, NC, NE, NJ, NM, NV, OK, PA, SD  

``` {r echo = FALSE}

options(knitr.kable.NA = '')

kable(US_accept_party, valign = 't', format.args = list(big.mark = ",", scientific = FALSE), digits = 1) %>%
  kable_styling(bootstrap_options = "striped", full_width = F, position = "left")
```

### Turnout as Percent of 2016 Total Turnout

Nationally, voters have cast **`r paste(round(100*sum(state_stats$total_early_2020)/sum(state_stats$total_ballots_2016), digits = 1),"%", sep = "")`** of the total votes counted in the 2016 general election.

``` {r echo = FALSE}
US_2020g_map_data <- state_stats

US_2020g_map_data <- arrange(US_2020g_map_data, hc_order)

US_2020g_map_data <- US_2020g_map_data %>%
  mutate(display_data = round(100*total_early_2020/total_ballots_2016, digits = 1)) %>%
  mutate(display_data = na_if(display_data, 0))

hcmap(map = "countries/us/us-all", data = US_2020g_map_data,
  value = "display_data", name = "Percent") %>%
  hc_title(text ="Total Votes Cast as Percent of 2016 Total Turnout") %>%
  hc_subtitle(text = "State plots may not be shaded using the same scale")
```

### Mail Ballots Requested

Voters have requested a total of **`r format(as.numeric(sum(state_stats$mail_sent_req_2020)), big.mark =",")`** ballots in the reporting states.

``` {r echo = FALSE}
US_2020g_map_data <- state_stats

US_2020g_map_data <- arrange(US_2020g_map_data, hc_order)

hcoptslang <- getOption("highcharter.lang")
hcoptslang$thousandsSep <- ","
options(highcharter.lang = hcoptslang)

US_2020g_map_data <- US_2020g_map_data %>%
  mutate(display_data = mail_sent_req_2020) %>%
  mutate(display_data = na_if(display_data, 0))

hcmap(map = "countries/us/us-all", data = US_2020g_map_data,
  value = "display_data", name = "Requested Ballots") %>%
    hc_title(text ="Mail Ballots Requested") %>%
  hc_subtitle(text = "State plots may not be shaded using the same scale")
```

#### Mail Ballots Requested by Party Registration

Reporting states with party registration data: CA, CO, DC, FL, IA, MD, ME, NC, NJ, NM, NV, OR, PA, UT  

``` {r echo = FALSE}

options(knitr.kable.NA = '')

kable(US_req_send_party, valign = 't', format.args = list(big.mark = ",", scientific = FALSE), digits = 1) %>%
  kable_styling(bootstrap_options = "striped", full_width = F, position = "left")
```
Registered Democrats have a **`r format(as.numeric(US_req_send_party[1,2]-US_req_send_party[2,2]), big.mark =",")`** ballot request lead over registered Republicans.

*Note:* Party registration statistics are for states that have party registration. These statistics are not actual votes. By federal law, election officials do not begin counting ballots until Election Day, although they may start the process of preparing ballots for counting in advance. 

#### Mail Ballots Requested by Race and Ethnicity

Reporting states with race or ethnicity data: NC, SC   

``` {r echo = FALSE}

options(knitr.kable.NA = '')

kable(US_req_send_race, valign = 't', format.args = list(big.mark = ",", scientific = FALSE), digits = 1) %>%
  kable_styling(bootstrap_options = "striped", full_width = F, position = "left")
```

*Note:* Race and ethnicity statistics are for states that ask registered voters to provide their race and ethnicity. 

#### Mail Ballots Requested by Age

Reporting states with age data: CO, OH (partial), PA   

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

#### Mail Ballots Requested by Gender

Reporting states with gender data: CO, NC   

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