Trump Approval Ratings Across Selected Demographic Groups

Net approval and disapproval percentages from Pew Research survey, April 7-13, 2025

A visual analysis of Trump approval ratings across key demographic groups based on Pew Research Center data. This visualization reveals significant disparities in approval ratings, with substantial gaps between political affiliations, racial/ethnic groups, education levels, age groups, and genders.

Author

Steven Ponce

Published

April 28, 2025

Original

The original visualization Trump Approval Ratings comes from Pew Research Center “Evaluations of Trump: Job approval and confidence on issues”

Makeover

Figure 1: Bar chart titled ‘Trump Approval Ratings Across Selected Demographic Groups’ showing approval and disapproval percentages across different demographics. Black Americans show highest disapproval (82%) with lowest approval (14%), while Republicans show highest approval (75%) with lowest disapproval (24%). Other groups including Hispanic, Postgrad, Ages 18-29, Women, Total population, Men, and White Americans all show higher disapproval than approval, with White Americans being closest to parity (49% approval, 51% disapproval). Data from Pew Research survey, April 7-13, 2025.

Steps to Create this Graphic

1. Load Packages & Setup

Show code

## 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
    janitor,        # Simple Tools for Examining and Cleaning Dirty Data
    skimr,          # Compact and Flexible Summaries of Data
    scales,         # Scale Functions for Visualization
    glue,           # Interpreted String Literals
    here,           # A Simpler Way to Find Your Files
    lubridate,      # Make Dealing with Dates a Little Easier
    camcorder       # Record Your Plot History 
  )
})

### |- figure size ----
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

## Original Chart
# Trump Approval Ratings
# https://data.world/makeovermonday/2025w18-trump-approval-ratings

##  Article
# Pew Research Center "Evaluations of Trump: Job approval and confidence on issues"
# https://www.pewresearch.org/politics/2025/04/23/evaluations-of-trump-job-approval-and-confidence-on-issues/

approval_ratings_raw <- read_csv(
  here::here('data/MakeoverMonday/2025/Trump Approval Ratings - Response.csv')) |> 
    clean_names()

3. Examine the Data

Show code

glimpse(approval_ratings_raw)
skim(approval_ratings_raw)

4. Tidy Data

Show code

### |-  tidy data ----

lollipop_data <- approval_ratings_raw |>
  # Select key demographics but categorize them
  filter(demographic %in% c(
    "Total", "Men", "Women", "White", "Black", "Hispanic", 
    "Ages 18-29", "Postgrad", "Rep/Lean Rep", "Dem/Lean Dem"
  )) |>
  # Add proper categorization
  mutate(                       
    category = case_when(
      demographic %in% c("Men", "Women") ~ "Gender",
      demographic %in% c("White", "Black", "Hispanic") ~ "Race/Ethnicity",
      demographic == "Ages 18-29" ~ "Age",
      demographic == "Postgrad" ~ "Education",
      demographic %in% c("Rep/Lean Rep", "Dem/Lean Dem") ~ "Political Affiliation",
      TRUE ~ "Overall"
    ),
    # Order demographics by approval gap (disapproval - approval)
    approval_gap = net_disapprove - net_approve,
    demographic = reorder(demographic, approval_gap)
  ) |>
  select(demographic, category, net_approve, net_disapprove, approval_gap)

5. Visualization Parameters

Show code

### |-  plot aesthetics ----
# Get base colors with custom palette
colors <- get_theme_colors(palette = c(
  "#53917E", "#6D1A36"
))
  
### |-  titles and caption ----
title_text <- str_wrap("Trump Approval Ratings Across Selected Demographic Groups", width = 80)
subtitle_text <- str_wrap("Net approval and disapproval percentages from Pew Research survey, April 7-13, 2025\nNote: Selected representative groups shown from original dataset of 23 demographic categories",
                          width = 85)

# Create caption
caption_text <- create_mm_caption(
    mm_year = 2025,
    mm_week = 18,
    source_text = "Pew Research Center"
)

### |-  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(

    # Legend formatting
    legend.position = "top",
    legend.title = element_text(face = "bold"),

    axis.ticks.y = element_blank(),
    axis.ticks.x = element_line(color = "gray", linewidth = 0.5), 
    axis.ticks.length = unit(0.2, "cm"), 

    # Axis formatting
    axis.title.x = element_text(face = "bold", size = rel(1.14)),
    axis.title.y = element_blank(),
    axis.text.y = element_text(face = "bold", size = rel(1)),
    
    # Grid lines
    panel.grid.major.y = element_blank(),
    panel.grid.minor = element_blank(),

    # Margin
    plot.margin = margin(20, 30, 20, 20)
  )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code

### |-  Plot  ----

p <- ggplot(lollipop_data) +
  # Geoms
  geom_vline(xintercept = 50, color = "gray90", linetype = "dashed") +
  geom_segment(
    aes(
      x = demographic, xend = demographic,
      y = net_approve, yend = net_disapprove
    ),
    color = "gray50",
    linewidth = 0.6
  ) +
  geom_point(aes(x = demographic, y = net_approve),
    size = 4,
    shape = 16,
    color = colors$palette[1]
  ) +
  geom_point(aes(x = demographic, y = net_disapprove),
    size = 4,
    shape = 17,
    color = colors$palette[2]
  ) +
  geom_text(
    aes(
      x = demographic, y = net_approve,
      label = paste0(net_approve, "%")
    ),
    nudge_y = -5, nudge_x = 0.1, size = 3.2,
    color = colors$palette[1]
  ) +
  geom_text(
    aes(
      x = demographic, y = net_disapprove,
      label = paste0(net_disapprove, "%")
    ),
    nudge_y = 5, nudge_x = 0.1, size = 3.2,
    color = colors$palette[2]
  ) +
  geom_text(
    data = distinct(lollipop_data, demographic, category),
    aes(x = demographic, y = -10, label = category),
    hjust = 0, size = 3, color = "gray30", fontface = "italic"
  ) +
  # Annotate
  annotate(
    "point",
    x = 1.5, y = 90,
    shape = 16, size = 4, color = colors$palette[1]
  ) +
  annotate(
    "text",
    x = 1.5, y = 91,
    label = "  Approval", hjust = 0, size = 3.5, color = colors$palette[1]
  ) +
  annotate(
    "point",
    x = 1, y = 90,
    shape = 17, size = 4, color = colors$palette[2]
  ) +
  annotate(
    "text",
    x = 1, y = 91,
    label = "  Disapproval", hjust = 0, size = 3.5, color = colors$palette[2]
  ) +
  # Scales
  scale_y_continuous(
    limits = c(-10, 100),
    breaks = seq(0, 100, 25)
  ) +
  coord_flip() +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = NULL,
    y = "Percentage"
  ) +
  # Theme
  theme(
    plot.title = element_text(
      size = rel(1.5),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      lineheight = 1.1,
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_text(
      size = rel(0.85),
      family = fonts$subtitle,
      color = colors$subtitle,
      lineheight = 1.2,
      margin = margin(t = 5, b = 15)
    ),
    plot.caption = element_markdown(
      size   = rel(0.65),
      family = fonts$caption,
      color  = colors$caption,
      hjust  = 0.5,
      margin = margin(t = 10)
    )
  )

7. Save

Show code

### |-  plot image ----  
save_plot(
  p, 
  type = "makeovermonday", 
  year = 2025,
  week = 18,
  width = 10, 
  height = 10
  )

8. Session Info

Expand for Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

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] camcorder_0.1.0 here_1.0.1      glue_1.8.0      scales_1.3.0   
 [5] skimr_2.1.5     janitor_2.2.0   showtext_0.9-7  showtextdb_3.0 
 [9] sysfonts_0.8.9  ggtext_0.1.2    lubridate_1.9.3 forcats_1.0.0  
[13] stringr_1.5.1   dplyr_1.1.4     purrr_1.0.2     readr_2.1.5    
[17] tidyr_1.3.1     tibble_3.2.1    ggplot2_3.5.1   tidyverse_2.0.0
[21] 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] lifecycle_1.0.4   farver_2.1.2      compiler_4.4.0    textshaping_0.4.0
[17] munsell_0.5.1     repr_1.1.7        codetools_0.2-20  snakecase_0.11.1 
[21] htmltools_0.5.8.1 yaml_2.3.10       crayon_1.5.3      pillar_1.9.0     
[25] magick_2.8.5      commonmark_1.9.2  tidyselect_1.2.1  digest_0.6.37    
[29] stringi_1.8.4     rsvg_2.6.1        rprojroot_2.0.4   fastmap_1.2.0    
[33] grid_4.4.0        colorspace_2.1-1  cli_3.6.4         magrittr_2.0.3   
[37] base64enc_0.1-3   utf8_1.2.4        withr_3.0.2       bit64_4.5.2      
[41] timechange_0.3.0  rmarkdown_2.29    bit_4.5.0         ragg_1.3.3       
[45] hms_1.1.3         evaluate_1.0.1    knitr_1.49        markdown_1.13    
[49] rlang_1.1.6       gridtext_0.1.5    Rcpp_1.0.13-1     xml2_1.3.6       
[53] renv_1.0.3        svglite_2.1.3     rstudioapi_0.17.1 vroom_1.6.5      
[57] jsonlite_1.8.9    R6_2.5.1          systemfonts_1.1.0

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in mm_2025_18.qmd.

For the full repository, click here.

10. References

Expand for References

Data:

Makeover Monday 2025 Week 18: [Work Productivity]https://data.world/makeovermonday/2025w18-trump-approval-ratings)

Article

Pew Research Center: Evaluations of Trump: Job approval and confidence on issues

--- title: "Trump Approval Ratings Across Selected Demographic Groups" subtitle: "Net approval and disapproval percentages from Pew Research survey, April 7-13, 2025" description: "A visual analysis of Trump approval ratings across key demographic groups based on Pew Research Center data. This visualization reveals significant disparities in approval ratings, with substantial gaps between political affiliations, racial/ethnic groups, education levels, age groups, and genders." author: "Steven Ponce" date: "2025-04-28" categories: ["MakeoverMonday", "Data Visualization", "R Programming", "2025"] tags: [ "approval ratings", "political polling", "demographic analysis", "data visualization", "Pew Research", "lollipop chart", "ggplot2", "trump", "politics", "opinion polling" ] image: "thumbnails/mm_2025_18.png" format: html: toc: true toc-depth: 5 code-link: true code-fold: true code-tools: true code-summary: "Show code" self-contained: true theme: light: [flatly, assets/styling/custom_styles.scss] dark: [darkly, assets/styling/custom_styles_dark.scss] editor_options: chunk_output_type: inline execute: freeze: true cache: true error: false message: false warning: false eval: true # filters: # - social-share # share: # permalink: "https://stevenponce.netlify.app/data_visualizations/MakeoverMonday/2025/mm_2025_18.html" # description: "MakeoverMonday week 18: Visualizing Trump approval ratings across demographic groups using Pew Research Center data, highlighting the stark contrasts in political support among different segments of the population." # twitter: true # linkedin: true # email: true # facebook: false # reddit: false # stumble: false # tumblr: false # mastodon: true # bsky: true --- ### Original The original visualization __Trump Approval Ratings__ comes from [Pew Research Center "Evaluations of Trump: Job approval and confidence on issues"](https://www.pewresearch.org/politics/2025/04/23/evaluations-of-trump-job-approval-and-confidence-on-issues/) ![Original visualization](https://raw.githubusercontent.com/poncest/MakeoverMonday/master/2025/Week_18/original_chart.png) ### Makeover ![Bar chart titled 'Trump Approval Ratings Across Selected Demographic Groups' showing approval and disapproval percentages across different demographics. Black Americans show highest disapproval (82%) with lowest approval (14%), while Republicans show highest approval (75%) with lowest disapproval (24%). Other groups including Hispanic, Postgrad, Ages 18-29, Women, Total population, Men, and White Americans all show higher disapproval than approval, with White Americans being closest to parity (49% approval, 51% disapproval). Data from Pew Research survey, April 7-13, 2025.](mm_2025_18.png){#fig-1} ### <mark> **Steps to Create this Graphic** </mark> #### 1. Load Packages & Setup ```{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 janitor, # Simple Tools for Examining and Cleaning Dirty Data skimr, # Compact and Flexible Summaries of Data scales, # Scale Functions for Visualization glue, # Interpreted String Literals here, # A Simpler Way to Find Your Files lubridate, # Make Dealing with Dates a Little Easier camcorder # Record Your Plot History ) }) ### |- figure size ---- 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 ```{r} #| label: read #| include: true #| eval: true #| warning: false ## Original Chart # Trump Approval Ratings # https://data.world/makeovermonday/2025w18-trump-approval-ratings ## Article # Pew Research Center "Evaluations of Trump: Job approval and confidence on issues" # https://www.pewresearch.org/politics/2025/04/23/evaluations-of-trump-job-approval-and-confidence-on-issues/ approval_ratings_raw <- read_csv( here::here('data/MakeoverMonday/2025/Trump Approval Ratings - Response.csv')) |> clean_names() ``` #### 3. Examine the Data ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(approval_ratings_raw) skim(approval_ratings_raw) ``` #### 4. Tidy Data ```{r} #| label: tidy #| warning: false ### |- tidy data ---- lollipop_data <- approval_ratings_raw |> # Select key demographics but categorize them filter(demographic %in% c( "Total", "Men", "Women", "White", "Black", "Hispanic", "Ages 18-29", "Postgrad", "Rep/Lean Rep", "Dem/Lean Dem" )) |> # Add proper categorization mutate( category = case_when( demographic %in% c("Men", "Women") ~ "Gender", demographic %in% c("White", "Black", "Hispanic") ~ "Race/Ethnicity", demographic == "Ages 18-29" ~ "Age", demographic == "Postgrad" ~ "Education", demographic %in% c("Rep/Lean Rep", "Dem/Lean Dem") ~ "Political Affiliation", TRUE ~ "Overall" ), # Order demographics by approval gap (disapproval - approval) approval_gap = net_disapprove - net_approve, demographic = reorder(demographic, approval_gap) ) |> select(demographic, category, net_approve, net_disapprove, approval_gap) ``` #### 5. Visualization Parameters ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- # Get base colors with custom palette colors <- get_theme_colors(palette = c( "#53917E", "#6D1A36" )) ### |- titles and caption ---- title_text <- str_wrap("Trump Approval Ratings Across Selected Demographic Groups", width = 80) subtitle_text <- str_wrap("Net approval and disapproval percentages from Pew Research survey, April 7-13, 2025\nNote: Selected representative groups shown from original dataset of 23 demographic categories", width = 85) # Create caption caption_text <- create_mm_caption( mm_year = 2025, mm_week = 18, source_text = "Pew Research Center" ) ### |- 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( # Legend formatting legend.position = "top", legend.title = element_text(face = "bold"), axis.ticks.y = element_blank(), axis.ticks.x = element_line(color = "gray", linewidth = 0.5), axis.ticks.length = unit(0.2, "cm"), # Axis formatting axis.title.x = element_text(face = "bold", size = rel(1.14)), axis.title.y = element_blank(), axis.text.y = element_text(face = "bold", size = rel(1)), # Grid lines panel.grid.major.y = element_blank(), panel.grid.minor = element_blank(), # Margin plot.margin = margin(20, 30, 20, 20) ) ) # Set theme theme_set(weekly_theme) ``` #### 6. Plot ```{r} #| label: plot #| warning: false ### |- Plot ---- p <- ggplot(lollipop_data) + # Geoms geom_vline(xintercept = 50, color = "gray90", linetype = "dashed") + geom_segment( aes( x = demographic, xend = demographic, y = net_approve, yend = net_disapprove ), color = "gray50", linewidth = 0.6 ) + geom_point(aes(x = demographic, y = net_approve), size = 4, shape = 16, color = colors$palette[1] ) + geom_point(aes(x = demographic, y = net_disapprove), size = 4, shape = 17, color = colors$palette[2] ) + geom_text( aes( x = demographic, y = net_approve, label = paste0(net_approve, "%") ), nudge_y = -5, nudge_x = 0.1, size = 3.2, color = colors$palette[1] ) + geom_text( aes( x = demographic, y = net_disapprove, label = paste0(net_disapprove, "%") ), nudge_y = 5, nudge_x = 0.1, size = 3.2, color = colors$palette[2] ) + geom_text( data = distinct(lollipop_data, demographic, category), aes(x = demographic, y = -10, label = category), hjust = 0, size = 3, color = "gray30", fontface = "italic" ) + # Annotate annotate( "point", x = 1.5, y = 90, shape = 16, size = 4, color = colors$palette[1] ) + annotate( "text", x = 1.5, y = 91, label = " Approval", hjust = 0, size = 3.5, color = colors$palette[1] ) + annotate( "point", x = 1, y = 90, shape = 17, size = 4, color = colors$palette[2] ) + annotate( "text", x = 1, y = 91, label = " Disapproval", hjust = 0, size = 3.5, color = colors$palette[2] ) + # Scales scale_y_continuous( limits = c(-10, 100), breaks = seq(0, 100, 25) ) + coord_flip() + # Labs labs( title = title_text, subtitle = subtitle_text, caption = caption_text, x = NULL, y = "Percentage" ) + # Theme theme( plot.title = element_text( size = rel(1.5), family = fonts$title, face = "bold", color = colors$title, lineheight = 1.1, margin = margin(t = 5, b = 5) ), plot.subtitle = element_text( size = rel(0.85), family = fonts$subtitle, color = colors$subtitle, lineheight = 1.2, margin = margin(t = 5, b = 15) ), plot.caption = element_markdown( size = rel(0.65), family = fonts$caption, color = colors$caption, hjust = 0.5, margin = margin(t = 10) ) ) ``` #### 7. Save ```{r} #| label: save #| warning: false ### |- plot image ---- save_plot( p, type = "makeovermonday", year = 2025, week = 18, width = 10, height = 10 ) ``` #### 8. Session Info ::: {.callout-tip collapse="true"} ##### Expand for Session Info ```{r, echo = FALSE} #| eval: true #| warning: false sessionInfo() ``` ::: #### 9. GitHub Repository ::: {.callout-tip collapse="true"} ##### Expand for GitHub Repo The complete code for this analysis is available in [`mm_2025_18.qmd`](https://github.com/poncest/personal-website/blob/master/data_visualizations/MakeoverMonday/2025/mm_2025_18.qmd). For the full repository, [click here](https://github.com/poncest/personal-website/). ::: #### 10. References ::: {.callout-tip collapse="true"} ##### Expand for References 1. Data: - Makeover Monday 2025 Week 18: [Work Productivity]https://data.world/makeovermonday/2025w18-trump-approval-ratings) 2. Article - Pew Research Center: [Evaluations of Trump: Job approval and confidence on issues](https://www.pewresearch.org/politics/2025/04/23/evaluations-of-trump-job-approval-and-confidence-on-issues/) :::