The Uneven Landscape of Asian Restaurants in the U.S.

Comparing restaurant presence with Asian American population distribution

This visualization explores the disparity between Asian restaurant types in the U.S. and their corresponding population demographics. Based on Pew Research Center data, it reveals how Chinese, Japanese, and Thai cuisines dominate the Asian restaurant landscape while Filipino and Indian cuisines remain significantly under-represented despite their larger population shares.

Author

Steven Ponce

Published

May 5, 2025

Original

The original visualization Asian Restaurant in the US comes from 71% of Asian restaurants in the U.S. serve Chinese, Japanese or Thai food

Makeover

Figure 1: A data visualization titled ‘The Uneven Landscape of Asian Restaurants in the U.S.’ showing two main charts. The top chart displays the composition of Asian restaurants by cuisine type, with Chinese (39%), Japanese (28%), and Thai (11%) dominating. The bottom chart shows representation gaps, highlighting how Japanese (+21%), Chinese (+15%), and Thai (+9%) cuisines are over-represented compared to their population percentages, while Filipino (-19%) and Indian (-14%) cuisines are significantly under-represented. Key insights explain that only 12% of U.S. restaurants serve Asian food and that the three dominant cuisines represent only one-third of Asian Americans.

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
    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
    readxl,         # Read Excel Files
    patchwork,      # The Composer of Plots
    camcorder       # Record Your Plot History 
  )
})

### |- figure size ----
gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  =  10,
    height =  10,
    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

## Original Chart
# Asian Restaurant in the US
# https://data.world/makeovermonday/2025-w19-asian-restaurants-in-the-us

##  Article
# Pew Research Center "71% of Asian restaurants in the U.S. serve Chinese, Japanese or Thai food"
# https://www.pewresearch.org/short-reads/2023/05/23/71-of-asian-restaurants-in-the-u-s-serve-chinese-japanese-or-thai-food/

sheet1_raw <- read_excel(
  here::here('data/MakeoverMonday/2025/MakeoverMonday 2025 W19_ Asian Restaurants in the US.xlsx'),
                                   sheet = 1) |> 
  clean_names()

sheet2_raw <- read_excel(
  here::here('data/MakeoverMonday/2025/MakeoverMonday 2025 W19_ Asian Restaurants in the US.xlsx'),
                                   sheet = 2) |> 
  clean_names()
```

3. Examine the Data

Show code

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

glimpse(sheet1_raw)
glimpse(sheet2_raw)
```

4. Tidy Data

Show code

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

### |-  tidy data ----
sheet2_clean <- sheet2_raw |>
  mutate(percent_of_asian_restaurants = case_when(
    percent_of_asian_restaurants == "<1" ~ "0.5",
    TRUE ~ percent_of_asian_restaurants
  )) |>
  mutate(percent_of_asian_restaurants = as.numeric(percent_of_asian_restaurants))

# Add Asian population percentages based on Pew Research Center data
# Source: "71% of Asian restaurants in the U.S. serve Chinese, Japanese or Thai food"
# Published by Pew Research Center on May 23, 2023
population_data <- tibble(
  category = c("Chinese", "Japanese", "Thai", "Indian", "Vietnamese", "Korean", 
               "Filipino", "Pakistani", "Mongolian", "Burmese", "Other Asian/Unspecified"),
  percent_of_asian_population = c(24, 7, 2, 21, 11, 9, 20, 3, 0.1, 0.9, 2)
)

# Combine datasets
sheet2_combined <- sheet2_clean |>
  left_join(population_data, by = "category") |> 
  mutate(
    percent_of_all_restaurants = percent_of_asian_restaurants * sheet1_raw$percent_of_all_us_restaurants[1] / 100,
    representation_ratio = percent_of_asian_restaurants / percent_of_asian_population,
    representation_diff = percent_of_asian_restaurants - percent_of_asian_population,
    representation_status = ifelse(representation_diff > 0, "over represented", "under represented")
  )

# plot 1 data: composition 
composition_data <- sheet2_combined |>
  filter(category != "Other Asian/Unspecified") |>
  arrange(desc(percent_of_asian_restaurants)) |>
  mutate(label_text = paste0(percent_of_asian_restaurants, "%"))

# plot 2 data: diverging bars
diverging_data <- sheet2_combined |>
  filter(
    category != "Other Asian/Unspecified",
    percent_of_asian_restaurants >= 1
    ) |>
  # Create a temporary data frame with the needed calculations
  mutate(
    hjust_value = ifelse(representation_diff > 0, -0.2, 1.2)
  )
```

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(
  "over represented" =  "#4CAF50",   
  "under represented" = "#FF9800", 
  "neutral" = "#BDBDBD" 
))
  
### |-  titles and caption ----
title_text <- str_wrap("The Uneven Landscape of Asian Restaurants in the U.S.", width = 80)
subtitle_text <- str_wrap("Comparing restaurant presence with Asian American population distribution",
                          width = 85)

# Create caption
caption_text <- create_mm_caption(
    mm_year = 2025,
    mm_week = 19,
    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(0.85)),
    axis.title.y = element_blank(),
    axis.text.y = element_text(face = "bold", size = rel(0.85)),
    
    # Grid lines
    panel.grid.major.y = element_blank(),
    panel.grid.minor = element_blank(),

    # Margin
    plot.margin = margin(10, 10, 10, 10)
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

Show code

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

### |-  Plot 1  ----
# Composition bar chart     
composition_plt <- composition_data |>
  ggplot(aes(x = reorder(category, percent_of_asian_restaurants), 
             y = percent_of_asian_restaurants,
             fill = representation_status)
         ) +
  # Geoms
  geom_col() +
  geom_text(aes(label = label_text), 
            hjust = -0.2, size = 3.5) +
  # Scales
  scale_fill_manual(
    values = colors$palette,
    name = "Representation"
  ) +
  scale_y_continuous(limits = c(0, 45), labels = function(x) paste0(x, "%")) +
  coord_flip() +
  # Labs
  labs(
    title = "Composition of Asian Restaurants in the U.S.",
    subtitle = "Percentage of Asian restaurants by cuisine type",
    x = NULL,
    y = "Percentage of Asian Restaurants"
  ) +
  # Theme
  theme(
    legend.position = "plot",
    panel.grid.major.y = element_blank(),
    plot.title = element_text(size = 12, face = "bold"),
    plot.subtitle = element_text(size = 9, color = "gray50")
  )

### |-  Plot 2  ----
# Diverging bar chart
diverging_plt <- diverging_data |>
  ggplot(aes(x = reorder(category, representation_diff), 
             y = representation_diff,
             fill = representation_status)
         ) +
  # Geoms
  geom_col() +
  geom_hline(yintercept = 0, linewidth = 0.5, color = 'gray20') +
  geom_text(
    aes(label = paste0(ifelse(representation_diff > 0, "+", ""), 
                       round(representation_diff, 1), "%"),
        hjust = hjust_value),
    size = 3
  ) +
  # Scales
  scale_fill_manual(
    values = colors$palette,
    name = "Representation"
  ) +
  scale_y_continuous(
    labels = function(x) paste0(x, "%"),
    limits = c(-23, 25)
  ) +
  coord_flip() +
  # Labs
  labs(
    title = "Over and Under-Representation of Asian Cuisines",
    subtitle = "Difference between restaurant percentage and population percentage",
    x = NULL,
    y = "Representation Gap (%)"
  ) +
  # Theme
  theme(
    legend.position = "top",
    legend.title = element_text(size = 9),
    legend.text = element_text(size = 8),
    panel.grid.major.y = element_blank(),
    plot.title = element_text(size = 12, face = "bold"),
    plot.subtitle = element_text(size = 9, color = "gray50")
  )

### |-  Plot 3  ----
# title panel
title_panel <- ggplot() + 
  # Scales
  xlim(0, 1) + ylim(0, 1) +
  # Annotate
  annotate(
    "text", x = 0, y = 0.6, 
    label = title_text,
    hjust = 0, vjust = 0,
    color = colors$title, size = 9, 
    fontface = "bold", family = fonts$title
  ) +
  annotate(
    "text", x = 0, y = 0.2, 
    label = subtitle_text,
    hjust = 0, vjust = 0,
    size = 5, color = "gray50", family = fonts$subtitle
  ) +
  # Theme
  theme_void() +
  theme(
    plot.margin = margin(20, 10, 10, 10),
    plot.background  = element_rect(fill = colors$background, color = colors$background),
    panel.background = element_rect(fill = colors$background, color = colors$background),
  )

### |-  Plot 4  ----
# empty panel
empty_panel <- ggplot() + 
  # Theme
  theme_void() 

### |-  Plot 5  ----
# insights panel
insights_panel <- ggplot() + 
  # Scales
  xlim(0, 1) + ylim(0, 1) +  
  # Annotate
  annotate(
    "text", x = 0.05, y = 0.95, 
    label = "Key Insights:",
    hjust = 0, vjust = 1,
    size = 4.5, fontface = "bold"
  ) +
  annotate(
    "text", x = 0.05, y = 0.82, 
    label = "• Only 12% of all U.S. restaurants serve Asian food",
    hjust = 0, vjust = 1,
    size = 3.2
  ) +
  annotate(
    "text", x = 0.05, y = 0.67, 
    label = "• Chinese, Japanese, and Thai cuisines dominate the Asian restaurant\n  scene (78%) despite representing only one-third of Asian Americans",
    hjust = 0, vjust = 1,
    size = 3.2
  ) +
  annotate(
    "text", x = 0.05, y = 0.47, 
    label = "• Japanese (+21%) and Thai (+9%) cuisines have the highest\n  over-representation compared to their population shares",
    hjust = 0, vjust = 1,
    size = 3.2
  ) +
  annotate(
    "text", x = 0.05, y = 0.32, 
    label = "• Filipino cuisine shows the largest representation gap: only 1% of Asian\n  restaurants despite Filipinos comprising 20% of Asian Americans",
    hjust = 0, vjust = 1,
    size = 3.2
  ) +
  annotate(
    "text", x = 0.05, y = 0.17, 
    label = "• Indian cuisine shows the second-largest representation gap (-14%),\n  with far fewer restaurants than their population would suggest",
    hjust = 0, vjust = 1,
    size = 3.2
  ) +
  # Theme
  theme_void() +
  theme(
    plot.margin = margin(0, 5, 0, 5),  
    plot.background  = element_rect(fill = colors$background, color = colors$background),
    panel.background = element_rect(fill = colors$background, color = colors$background),
  )

### |-  Final Plot  ----
# combined plot
combined_plot <- title_panel +   # P1
  insights_panel +               # P2
  composition_plt +              # P3
  diverging_plt +                # P4
  empty_panel +                  # P5
  plot_layout(
    design = "
    AAAAA
    BCCCC
    BDDDD
    EEEEE
    ",
    widths = c(4.8, 1, 1, 1, 1),
    heights = c(0.8, 2, 2, 0.4)
  ) 

combined_plot <- combined_plot +
  plot_annotation(
    caption = caption_text,
    theme = theme(
          panel.spacing = unit(15, "pt"),
          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

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

### |-  plot image ----  
save_plot_patchwork(
  combined_plot, 
  type = "makeovermonday", 
  year = 2025,
  week = 19,
  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 patchwork_1.3.0 readxl_1.4.3    here_1.0.1     
 [5] glue_1.8.0      scales_1.3.0    skimr_2.1.5     janitor_2.2.0  
 [9] showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2   
[13] lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4    
[17] purrr_1.0.2     readr_2.1.5     tidyr_1.3.1     tibble_3.2.1   
[21] 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] yulab.utils_0.1.8  vctrs_0.6.5        tools_4.4.0        generics_0.1.3    
 [9] curl_6.0.0         gifski_1.32.0-1    fansi_1.0.6        pkgconfig_2.0.3   
[13] ggplotify_0.1.2    lifecycle_1.0.4    compiler_4.4.0     farver_2.1.2      
[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        pillar_1.9.0       magick_2.8.5      
[25] commonmark_1.9.2   tidyselect_1.2.1   digest_0.6.37      stringi_1.8.4     
[29] labeling_0.4.3     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        timechange_0.3.0  
[41] rmarkdown_2.29     cellranger_1.1.0   hms_1.1.3          evaluate_1.0.1    
[45] knitr_1.49         markdown_1.13      gridGraphics_0.5-1 rlang_1.1.6       
[49] gridtext_0.1.5     Rcpp_1.0.13-1      xml2_1.3.6         renv_1.0.3        
[53] svglite_2.1.3      rstudioapi_0.17.1  jsonlite_1.8.9     R6_2.5.1          
[57] fs_1.6.5           systemfonts_1.1.0

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in mm_2025_19.qmd.

For the full repository, click here.

10. References

Expand for References

Data:

Makeover Monday 2025 Week 19: Asian Restaurant in the US

Article

Pew Research Center: 71% of Asian restaurants in the U.S. serve Chinese, Japanese or Thai food

--- title: "The Uneven Landscape of Asian Restaurants in the U.S." subtitle: "Comparing restaurant presence with Asian American population distribution" description: "This visualization explores the disparity between Asian restaurant types in the U.S. and their corresponding population demographics. Based on Pew Research Center data, it reveals how Chinese, Japanese, and Thai cuisines dominate the Asian restaurant landscape while Filipino and Indian cuisines remain significantly under-represented despite their larger population shares." author: "Steven Ponce" date: "2025-05-05" categories: ["MakeoverMonday", "Data Visualization", "R Programming", "2025"] tags: [ "Asian restaurants", "demographic analysis", "representation gap", "cuisine diversity", "population distribution", "data storytelling", "patchwork", "restaurant industry", "ethnic food", "cultural representation", "data journalism" ] image: "thumbnails/mm_2025_19.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_19.html" # description: "MakeoverMonday week 19: The Uneven Landscape of Asian Restaurants in the U.S. - Exploring how 78% of Asian restaurants serve just three cuisines, despite representing only 33% of the Asian American population" # twitter: true # linkedin: true # email: true # facebook: false # reddit: false # stumble: false # tumblr: false # mastodon: true # bsky: true --- ### Original The original visualization __Asian Restaurant in the US__ comes from [71% of Asian restaurants in the U.S. serve Chinese, Japanese or Thai food](https://www.pewresearch.org/short-reads/2023/05/23/71-of-asian-restaurants-in-the-u-s-serve-chinese-japanese-or-thai-food/) ![Original visualization](https://raw.githubusercontent.com/poncest/MakeoverMonday/master/2025/Week_19/original_chart.png) ### Makeover ![A data visualization titled 'The Uneven Landscape of Asian Restaurants in the U.S.' showing two main charts. The top chart displays the composition of Asian restaurants by cuisine type, with Chinese (39%), Japanese (28%), and Thai (11%) dominating. The bottom chart shows representation gaps, highlighting how Japanese (+21%), Chinese (+15%), and Thai (+9%) cuisines are over-represented compared to their population percentages, while Filipino (-19%) and Indian (-14%) cuisines are significantly under-represented. Key insights explain that only 12% of U.S. restaurants serve Asian food and that the three dominant cuisines represent only one-third of Asian Americans.](mm_2025_19.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 readxl, # Read Excel Files patchwork, # The Composer of Plots camcorder # Record Your Plot History ) }) ### |- figure size ---- gg_record( dir = here::here("temp_plots"), device = "png", width = 10, height = 10, 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 # Asian Restaurant in the US # https://data.world/makeovermonday/2025-w19-asian-restaurants-in-the-us ## Article # Pew Research Center "71% of Asian restaurants in the U.S. serve Chinese, Japanese or Thai food" # https://www.pewresearch.org/short-reads/2023/05/23/71-of-asian-restaurants-in-the-u-s-serve-chinese-japanese-or-thai-food/ sheet1_raw <- read_excel( here::here('data/MakeoverMonday/2025/MakeoverMonday 2025 W19_ Asian Restaurants in the US.xlsx'), sheet = 1) |> clean_names() sheet2_raw <- read_excel( here::here('data/MakeoverMonday/2025/MakeoverMonday 2025 W19_ Asian Restaurants in the US.xlsx'), sheet = 2) |> clean_names() ``` #### 3. Examine the Data ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(sheet1_raw) glimpse(sheet2_raw) ``` #### 4. Tidy Data ```{r} #| label: tidy #| warning: false ### |- tidy data ---- sheet2_clean <- sheet2_raw |> mutate(percent_of_asian_restaurants = case_when( percent_of_asian_restaurants == "<1" ~ "0.5", TRUE ~ percent_of_asian_restaurants )) |> mutate(percent_of_asian_restaurants = as.numeric(percent_of_asian_restaurants)) # Add Asian population percentages based on Pew Research Center data # Source: "71% of Asian restaurants in the U.S. serve Chinese, Japanese or Thai food" # Published by Pew Research Center on May 23, 2023 population_data <- tibble( category = c("Chinese", "Japanese", "Thai", "Indian", "Vietnamese", "Korean", "Filipino", "Pakistani", "Mongolian", "Burmese", "Other Asian/Unspecified"), percent_of_asian_population = c(24, 7, 2, 21, 11, 9, 20, 3, 0.1, 0.9, 2) ) # Combine datasets sheet2_combined <- sheet2_clean |> left_join(population_data, by = "category") |> mutate( percent_of_all_restaurants = percent_of_asian_restaurants * sheet1_raw$percent_of_all_us_restaurants[1] / 100, representation_ratio = percent_of_asian_restaurants / percent_of_asian_population, representation_diff = percent_of_asian_restaurants - percent_of_asian_population, representation_status = ifelse(representation_diff > 0, "over represented", "under represented") ) # plot 1 data: composition composition_data <- sheet2_combined |> filter(category != "Other Asian/Unspecified") |> arrange(desc(percent_of_asian_restaurants)) |> mutate(label_text = paste0(percent_of_asian_restaurants, "%")) # plot 2 data: diverging bars diverging_data <- sheet2_combined |> filter( category != "Other Asian/Unspecified", percent_of_asian_restaurants >= 1 ) |> # Create a temporary data frame with the needed calculations mutate( hjust_value = ifelse(representation_diff > 0, -0.2, 1.2) ) ``` #### 5. Visualization Parameters ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- # Get base colors with custom palette colors <- get_theme_colors(palette = c( "over represented" = "#4CAF50", "under represented" = "#FF9800", "neutral" = "#BDBDBD" )) ### |- titles and caption ---- title_text <- str_wrap("The Uneven Landscape of Asian Restaurants in the U.S.", width = 80) subtitle_text <- str_wrap("Comparing restaurant presence with Asian American population distribution", width = 85) # Create caption caption_text <- create_mm_caption( mm_year = 2025, mm_week = 19, 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(0.85)), axis.title.y = element_blank(), axis.text.y = element_text(face = "bold", size = rel(0.85)), # Grid lines panel.grid.major.y = element_blank(), panel.grid.minor = element_blank(), # Margin plot.margin = margin(10, 10, 10, 10) ) ) # Set theme theme_set(weekly_theme) ``` #### 6. Plot ```{r} #| label: plot #| warning: false ### |- Plot 1 ---- # Composition bar chart composition_plt <- composition_data |> ggplot(aes(x = reorder(category, percent_of_asian_restaurants), y = percent_of_asian_restaurants, fill = representation_status) ) + # Geoms geom_col() + geom_text(aes(label = label_text), hjust = -0.2, size = 3.5) + # Scales scale_fill_manual( values = colors$palette, name = "Representation" ) + scale_y_continuous(limits = c(0, 45), labels = function(x) paste0(x, "%")) + coord_flip() + # Labs labs( title = "Composition of Asian Restaurants in the U.S.", subtitle = "Percentage of Asian restaurants by cuisine type", x = NULL, y = "Percentage of Asian Restaurants" ) + # Theme theme( legend.position = "plot", panel.grid.major.y = element_blank(), plot.title = element_text(size = 12, face = "bold"), plot.subtitle = element_text(size = 9, color = "gray50") ) ### |- Plot 2 ---- # Diverging bar chart diverging_plt <- diverging_data |> ggplot(aes(x = reorder(category, representation_diff), y = representation_diff, fill = representation_status) ) + # Geoms geom_col() + geom_hline(yintercept = 0, linewidth = 0.5, color = 'gray20') + geom_text( aes(label = paste0(ifelse(representation_diff > 0, "+", ""), round(representation_diff, 1), "%"), hjust = hjust_value), size = 3 ) + # Scales scale_fill_manual( values = colors$palette, name = "Representation" ) + scale_y_continuous( labels = function(x) paste0(x, "%"), limits = c(-23, 25) ) + coord_flip() + # Labs labs( title = "Over and Under-Representation of Asian Cuisines", subtitle = "Difference between restaurant percentage and population percentage", x = NULL, y = "Representation Gap (%)" ) + # Theme theme( legend.position = "top", legend.title = element_text(size = 9), legend.text = element_text(size = 8), panel.grid.major.y = element_blank(), plot.title = element_text(size = 12, face = "bold"), plot.subtitle = element_text(size = 9, color = "gray50") ) ### |- Plot 3 ---- # title panel title_panel <- ggplot() + # Scales xlim(0, 1) + ylim(0, 1) + # Annotate annotate( "text", x = 0, y = 0.6, label = title_text, hjust = 0, vjust = 0, color = colors$title, size = 9, fontface = "bold", family = fonts$title ) + annotate( "text", x = 0, y = 0.2, label = subtitle_text, hjust = 0, vjust = 0, size = 5, color = "gray50", family = fonts$subtitle ) + # Theme theme_void() + theme( plot.margin = margin(20, 10, 10, 10), plot.background = element_rect(fill = colors$background, color = colors$background), panel.background = element_rect(fill = colors$background, color = colors$background), ) ### |- Plot 4 ---- # empty panel empty_panel <- ggplot() + # Theme theme_void() ### |- Plot 5 ---- # insights panel insights_panel <- ggplot() + # Scales xlim(0, 1) + ylim(0, 1) + # Annotate annotate( "text", x = 0.05, y = 0.95, label = "Key Insights:", hjust = 0, vjust = 1, size = 4.5, fontface = "bold" ) + annotate( "text", x = 0.05, y = 0.82, label = "• Only 12% of all U.S. restaurants serve Asian food", hjust = 0, vjust = 1, size = 3.2 ) + annotate( "text", x = 0.05, y = 0.67, label = "• Chinese, Japanese, and Thai cuisines dominate the Asian restaurant\n scene (78%) despite representing only one-third of Asian Americans", hjust = 0, vjust = 1, size = 3.2 ) + annotate( "text", x = 0.05, y = 0.47, label = "• Japanese (+21%) and Thai (+9%) cuisines have the highest\n over-representation compared to their population shares", hjust = 0, vjust = 1, size = 3.2 ) + annotate( "text", x = 0.05, y = 0.32, label = "• Filipino cuisine shows the largest representation gap: only 1% of Asian\n restaurants despite Filipinos comprising 20% of Asian Americans", hjust = 0, vjust = 1, size = 3.2 ) + annotate( "text", x = 0.05, y = 0.17, label = "• Indian cuisine shows the second-largest representation gap (-14%),\n with far fewer restaurants than their population would suggest", hjust = 0, vjust = 1, size = 3.2 ) + # Theme theme_void() + theme( plot.margin = margin(0, 5, 0, 5), plot.background = element_rect(fill = colors$background, color = colors$background), panel.background = element_rect(fill = colors$background, color = colors$background), ) ### |- Final Plot ---- # combined plot combined_plot <- title_panel + # P1 insights_panel + # P2 composition_plt + # P3 diverging_plt + # P4 empty_panel + # P5 plot_layout( design = " AAAAA BCCCC BDDDD EEEEE ", widths = c(4.8, 1, 1, 1, 1), heights = c(0.8, 2, 2, 0.4) ) combined_plot <- combined_plot + plot_annotation( caption = caption_text, theme = theme( panel.spacing = unit(15, "pt"), 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_patchwork( combined_plot, type = "makeovermonday", year = 2025, week = 19, 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_19.qmd`](https://github.com/poncest/personal-website/blob/master/data_visualizations/MakeoverMonday/2025/mm_2025_19.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 19: [Asian Restaurant in the US](https://data.world/makeovermonday/2025-w19-asian-restaurants-in-the-us) 2. Article - Pew Research Center: [71% of Asian restaurants in the U.S. serve Chinese, Japanese or Thai food](https://www.pewresearch.org/short-reads/2023/05/23/71-of-asian-restaurants-in-the-u-s-serve-chinese-japanese-or-thai-food/) :::