The Partisan Divide: Who Agrees More?m

Net percentage point difference between Biden and Trump supporters on cultural issues

MakeoverMonday
Data Visualization
R Programming
2025
Visualizing stark partisan divides on 8 cultural issues using data from Pew Research Center’s 2024 election survey of 7,166 registered voters.
Published

October 29, 2025

Original

The original visualization comes from Cultural Issues and the 2024 Election

Original visualization

Makeover

Figure 1: Bar chart showing percentage-point differences between Biden and Trump supporters on cultural issues. Blue bars extend to the right for Biden’s advantages (legacy of slavery +52pp, openness +51pp, gender identity +50pp). Red bars extend left for Trump advantages (gun ownership -63pp, criminal justice -41pp, marriage priority -40pp).

Steps to Create this Graphic

1. Load Packages & Setup

Show code 
```{r}
#| label: load
#| warning: false
#| message: false
#| results: "hide"

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
  if (!require("pacman")) install.packages("pacman")
  pacman::p_load(
    tidyverse,  # Easily Install and Load the 'Tidyverse'
    ggtext,     # Improved Text Rendering Support for 'ggplot2'
    showtext,   # Using Fonts More Easily in R Graphs
    scales,     # Scale Functions for Visualization
    glue,       # Interpreted String Literals
    patchwork   # Using Fonts More Easily in R Graphs
  )
})

### |- figure size ----
camcorder::gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  = 8,
    height = 8,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))
```

2. Read in the Data

Show code 
```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false
#|
pew_culture_wars_2024_raw <- read_csv(
  here::here('data/MakeoverMonday/2025/pew_culture_wars_2024.csv')) |> 
  janitor::clean_names()
```

3. Examine the Data

Show code 
```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(pew_culture_wars_2024_raw )
skimr::skim_without_charts(pew_culture_wars_2024_raw )
```

4. Tidy Data

Show code 
```{r}
#| label: tidy
#| warning: false

pew_tidy <- pew_culture_wars_2024_raw |>
  mutate(
    # Create concise, readable issue labels
    issue_label = case_when(
      str_detect(issue, "Gun ownership") ~ "Gun ownership increases safety",
      str_detect(issue, "legacy of slavery") ~ "Legacy of slavery affects Black Americans",
      str_detect(issue, "openness to people") ~ "America's openness is essential",
      str_detect(issue, "man or a woman") ~ "Gender identity beyond assigned sex",
      str_detect(issue, "criminal justice") ~ "Criminal justice not tough enough",
      str_detect(issue, "marriage and having children") ~ "Marriage and children should be priority",
      str_detect(issue, "Religion should be kept") ~ "Religion separate from government",
      str_detect(issue, "gains women have made") ~ "Women's gains at men's expense",
      TRUE ~ issue
    ),
    net_diff = biden_supporters_percent - trump_supporters_percent,
    advantage = if_else(net_diff > 0, "Biden", "Trump"),
    abs_diff = abs(net_diff),
    issue_label = str_wrap(issue_label, width = 25),
    issue_label = fct_reorder(issue_label, net_diff),
  ) 
```

5. Visualization Parameters

Show code 
```{r}
#| label: params
#| include: true
#| warning: false

### |-  plot aesthetics ----
# Get base colors with custom palette
colors <- get_theme_colors(
  palette = c(
    "Biden" = "#2C5F8D",   
    "Trump" = "#C7352E" 
    )
  ) 
### |-  titles and caption ----
title_text <- str_glue("The Partisan Divide: Who Agrees More?")

subtitle_text <- str_glue(
    "Net percentage point difference between Biden and Trump supporters on cultural issues"
  )

# Create caption
caption_text <- create_mm_caption(
  mm_year = current_year,
  mm_week = current_week,
  source_text = "Pew Research Center, April 2024 (n=7,166 registered voters)<br>Note: Positive values indicate Biden supporters agree more; negative values indicate Trump supporters agree more"
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    # # Text styling
    plot.title = element_text(
      size = rel(1.4), family = fonts$title, face = "bold",
      color = colors$title, lineheight = 1.1, hjust = 0,
      margin = margin(t = 5, b = 10)
    ),
    plot.subtitle = element_text(
      size = rel(0.9), family = fonts$subtitle, face = "italic",
      color = alpha(colors$subtitle, 0.9), lineheight = 1.1,
      margin = margin(t = 0, b = 20)
    ),
    
    # Legend formatting
    legend.position = "plot",
    legend.justification = "top",
    legend.margin = margin(l = 12, b = 5),
    legend.key.size = unit(0.8, "cm"),
    legend.box.margin = margin(b = 10),
    legend.title = element_text(face = "bold"),
    
    # Axis formatting
    axis.ticks.y = element_blank(),
    axis.ticks.x = element_line(color = "gray", linewidth = 0.5),
    axis.title.x = element_text(
      face = "bold", size = rel(0.85),
      margin = margin(t = 10)
    ),
    axis.title.y = element_text(
      face = "bold", size = rel(0.85),
      margin = margin(r = 10)
    ),
    axis.text.x = element_text(
      size = rel(0.85), family = fonts$subtitle,
      color = colors$text
    ),
    axis.text.y = element_text(
      size = rel(0.85), family = fonts$subtitle,
      color = colors$text
    ),
    
    # Grid lines
    panel.grid.minor = element_line(color = "#ecf0f1", linewidth = 0.2),
    panel.grid.major = element_line(color = "#ecf0f1", linewidth = 0.4),
    
    # Margin
    plot.margin = margin(20, 20, 20, 20)
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

Show code 
```{r}
#| label: plot
#| warning: false

p <- pew_tidy |>
  ggplot(aes(x = net_diff, y = issue_label, fill = advantage)) +
  # Geoms
  geom_col(width = 0.65, alpha = 0.9) +
  geom_vline(xintercept = 0, color = "#1a1a1a", linewidth = 1) +
  geom_text(
    aes(
      label = paste0(ifelse(net_diff > 0, "+", ""), net_diff, "pp"),
      hjust = ifelse(net_diff > 0, -0.15, 1.15)
    ),
    size = 3.5,
    fontface = "bold",
    color = "#1a1a1a"
  ) +
  # Scales
  scale_fill_manual(
    values = colors$palette
  ) +
  scale_x_continuous(
    breaks = seq(-70, 70, 20),
    labels = function(x) paste0(abs(x), "pp"),
    limits = c(-75, 75),
    expand = c(0, 0)
  ) +
  # Annotate
  annotate(
    "text",
    x = -37.5, y = 8.5,
    label = "← Trump supporters agree more",
    color = colors$palette[2],
    fontface = "bold",
    size = 4,
    hjust = 0.5
  ) +
  annotate(
    "text",
    x = 37.5, y = 8.5,
    label = "Biden supporters agree more →",
    color = colors$palette[1],
    fontface = "bold",
    size = 4,
    hjust = 0.5
  ) +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = "Percentage point difference",
    y = NULL,
  ) +
  # Theme
  theme(
    plot.title = element_text(
      size = rel(1.7),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      lineheight = 1.1,
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_markdown(
      size = rel(0.90),
      family = fonts$subtitle,
      color = alpha(colors$subtitle, 0.9),
      lineheight = 1.2,
      margin = margin(t = 5, b = 20)
    ),
    plot.caption = element_markdown(
      size = rel(0.65),
      family = fonts$caption,
      color = colors$caption,
      hjust = 0.5,
      margin = margin(t = 10)
    ),
    panel.grid.major.y = element_blank(),
    panel.grid.major.x = element_line(color = "#e5e5e5", linewidth = 0.3),
    panel.grid.minor = element_blank(),
  )
```

7. Save

Show code 
```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot(
  plot = p, 
  type = "makeovermonday", 
  year = current_year,
  week = current_week,
  width = 8, 
  height = 8
  )
```

8. Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1      patchwork_1.3.0 glue_1.8.0      scales_1.3.0   
 [5] showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2   
 [9] lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4    
[13] purrr_1.0.2     readr_2.1.5     tidyr_1.3.1     tibble_3.2.1   
[17] ggplot2_3.5.1   tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6      xfun_0.49         htmlwidgets_1.6.4 tzdb_0.5.0       
 [5] vctrs_0.6.5       tools_4.4.0       generics_0.1.3    curl_6.0.0       
 [9] parallel_4.4.0    gifski_1.32.0-1   fansi_1.0.6       pkgconfig_2.0.3  
[13] skimr_2.1.5       lifecycle_1.0.4   compiler_4.4.0    farver_2.1.2     
[17] textshaping_0.4.0 munsell_0.5.1     repr_1.1.7        janitor_2.2.0    
[21] codetools_0.2-20  snakecase_0.11.1  htmltools_0.5.8.1 yaml_2.3.10      
[25] crayon_1.5.3      pillar_1.9.0      camcorder_0.1.0   magick_2.8.5     
[29] commonmark_1.9.2  tidyselect_1.2.1  digest_0.6.37     stringi_1.8.4    
[33] rsvg_2.6.1        rprojroot_2.0.4   fastmap_1.2.0     grid_4.4.0       
[37] colorspace_2.1-1  cli_3.6.4         magrittr_2.0.3    base64enc_0.1-3  
[41] utf8_1.2.4        withr_3.0.2       bit64_4.5.2       timechange_0.3.0 
[45] rmarkdown_2.29    bit_4.5.0         ragg_1.3.3        hms_1.1.3        
[49] evaluate_1.0.1    knitr_1.49        markdown_1.13     rlang_1.1.6      
[53] gridtext_0.1.5    Rcpp_1.0.13-1     xml2_1.3.6        renv_1.0.3       
[57] svglite_2.1.3     rstudioapi_0.17.1 vroom_1.6.5       jsonlite_1.8.9   
[61] R6_2.5.1          systemfonts_1.1.0

9. GitHub Repository

The complete code for this analysis is available in mm_2025_42.qmd.

For the full repository, click here.

10. References

11. Custom Functions Documentation

This analysis uses custom functions from my personal module library for efficiency and consistency across projects.

Functions Used:

  • fonts.R: setup_fonts(), get_font_families() - Font management with showtext
  • social_icons.R: create_social_caption() - Generates formatted social media captions
  • image_utils.R: save_plot() - Consistent plot saving with naming conventions
  • base_theme.R: create_base_theme(), extend_weekly_theme(), get_theme_colors() - Custom ggplot2 themes

Why custom functions?
These utilities standardize theming, fonts, and output across all my data visualizations. The core analysis (data tidying and visualization logic) uses only standard tidyverse packages.

Source Code:
View all custom functions → GitHub: R/utils

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