Loading Libraries

The following R libraries are used in this report–please install them to be able to replicate the sections that follow. The tidyverse library is a metapackage–a package containing other packages–consisting of useful data management and visualization tools (https://www.tidyverse.org/). The library magrittr is used for its pipe operations, which facilitate readable code by being able to “pass” functions to other functions (https://magrittr.tidyverse.org). Finally, the packages knitr and flextable are used to present formatted tables (https://github.com/yihui/knitr) (https://davidgohel.github.io/flextable/).

# Libraries to use.
libs <- c('tidyverse', 'magrittr', 'knitr', 'flextable')

# For each library...
for (i in libs) { 
  
  # If it's not already installed...
  if (!require(i, character.only = TRUE)) { 
    
    # Install it...
    install.packages(i) 
    
    # Then load it.
    library(i, character.only = TRUE)  
    
  }
  
}

Importing the Data

The polling results are copied to a comma-separated values (CSV) file named sachse2020.csv and imported via the read_csv() function, presenting the results with the flextable() function to produce a formatted table.

data <- read_csv('sachse2020.csv')

data %>%
  arrange(desc(votes_total_pct)) %>%
  set_names(c('Candidate', 'Early Voting', 'Election Day', 
              'Total Votes', 'Total Votes %')) %>%
  flextable() %>%
  set_caption(caption = 'Table 1: Special Election Results (Pre-runoff)') %>%
  footnote(part = 'header',
           value = as_paragraph('Source: https://www.cityofsachse.com/365/Election-Results')) %>%
  autofit()

R Code: Graphing the Total Votes by Candidate

ggplot(data) + 
  aes(x = reorder(candidate, votes_total), 
      y = votes_total, 
      fill = reorder(candidate, votes_total),
      label = paste0(votes_total,'; ', votes_total_pct, '%')) + 
  geom_bar(stat = 'identity') + 
  geom_text(nudge_y = 36) +
  scale_fill_brewer(palette = 'Blues') + 
  ylim(0, 350) + 
  coord_flip() + 
  labs(x = '',
       y = 'Total Votes',
       title = 'Figure 2: Total Votes by Candidate') +
  theme_light() + 
  theme(panel.grid.minor = element_blank(),
        panel.grid.major.y = element_blank(),
        legend.position = 'none')

For the next graph, early voting numbers are plotted against Election Day votes to grasp their relationship more clearly. First, a function called theme_light2()–based on theme_light from ggplot2–is created in a way that emphasizes the data points by minimizing the number of non-data elements (e.g. background color).

R Code: Theme Function

theme_light2 <- function() {
  
  theme_light() + 
  theme(panel.grid.minor = element_blank(),
        panel.grid.major.x = element_blank(),
        legend.position = 'none')
  
}

To plot the names of the candidates more easily, we extract only their last names with a combination of gsub() from base R; str_split() from the stringr library within tidyverse; and map_chr() from the purrr package within tidyverse. A plot with text as the geometry is then plotted.

R Code: Graphing Early Voting and Election Day Votes

data$last_name <- data$candidate %>%
  gsub(' Jr\\.', '', .) %>%
  str_split(' ') %>% 
  map_chr(~ .x[length(.x)])

ggplot(data) + 
  aes(x = votes_early, y = votes_election_day, label = last_name) + 
  geom_text(col = 'cyan4', position = position_jitter()) + 
  labs(y = 'Votes on Election Day',
       x = 'Votes During the Early Voting Period',
       title = 'Figure 3: Early and Election Day Voting Behavior')  +
  theme_light2()

Limitations

One limitation of this work is that the dataset must be in a CSV format. Second, the dataset must be arranged such that there are five required columns: “Candidate,” “Early Voting,” “Election Day”, “Total Votes,” and “Total Votes %.” Third, it assumes that the link to the election results are fixed (i.e. does not change). Finally, some users may feel daunted by the amount of setup and programming required.

Future Work

In the future, this document will serve as a proof-of-concept for a web application via R Shiny in which users can upload polling results and said application would produce a basic written report with tables and graphs.