The original thirteen didn’t fade equally

Share of U.S. population at each census, 1790–1900. New York and Pennsylvania held about a fifth of the nation; the other eleven original states fell from 71.5% to nearly the same level.

MakeoverMonday

2026

A two-line chart of national population share, 1790–1900, shows New York and Pennsylvania holding about a fifth of the U.S. while the other eleven original states fell from 71.5% to 19.9%. Grouped shares on modern state boundaries recast the 1883 atlas’s rank chart as magnitude. Built in R with ggplot2, ggtext, and showtext.

Author

Steven Ponce

Published

October 18, 2026

Original

The original visualization comes from The First Century of the U.S. Census

Makeover

Figure 1: Line chart titled “The original thirteen didn’t fade equally,” showing each group’s share of the U.S. population at every census from 1790 to 1900, using modern state boundaries. The other eleven original states fell from 71.5% of the nation in 1790 to 19.9% in 1900, with the steepest losses in the early decades. New York and Pennsylvania combined started at 19.7%, peaked at 25.4% in 1830, and eased to 17.9% by 1900, so at the final census the two groups were two percentage points apart. The chart shows shares, not population decline: both groups grew in population at every census. The eleven lost share as a group, though Georgia’s share rose. Data: U.S. Census Bureau, Working Paper No. 56 (Gibson & Jung, 2002).

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, ggtext, showtext, scales, glue, 
  janitor, ggview, readxl
)
})

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

### |- figure size ----
fig_w <- 10
fig_h <- 6.5
fig_dpi <- 320

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()
title_family <- fonts$title_1
text_family  <- fonts$text
```

2. Read in the Data

Show code

```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false
#| 

df_raw <- read_csv(
  here::here("data/MakeoverMonday/2026/us_state_population_1790_1990.csv"),
  col_types = cols(.default = col_character()),
  na = character()
) |>
  clean_names() |>
  mutate(
    year = as.integer(year),
    population = as.numeric(population)
  )
```

3. Examine the Data

Show code

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

glimpse(df_raw)
skimr::skim_without_charts(df_raw)
```

4. Tidy Data

Show code

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

two_states <- c("New York", "Pennsylvania")
original_13 <- c(
  "Connecticut", "Delaware", "Georgia", "Maryland", "Massachusetts",
  "New Hampshire", "New Jersey", "New York", "North Carolina",
  "Pennsylvania", "Rhode Island", "South Carolina", "Virginia"
)
grp_two <- "New York + Pennsylvania"
grp_eleven <- "Other 11 original states"

shares <- df_raw |>
  filter(year <= 1900) |>
  mutate(group = case_when(
    state %in% two_states ~ grp_two,
    state %in% original_13 ~ grp_eleven,
    .default = "Rest of U.S."
  )) |>
  summarise(pop = sum(population), .by = c(year, group)) |>
  mutate(share = pop / sum(pop), .by = year)

plot_data <- shares |> filter(group != "Rest of U.S.")

### |- values used in the copy ----
get_share <- function(g, y) {
  shares |>
    filter(group == g, year == y) |>
    pull(share)
}

two_1790 <- get_share(grp_two, 1790)
two_1900 <- get_share(grp_two, 1900)
eleven_1790 <- get_share(grp_eleven, 1790)
eleven_1900 <- get_share(grp_eleven, 1900)

peak <- plot_data |>
  filter(group == grp_two) |>
  slice_max(share, with_ties = FALSE)

orig13_by_year <- shares |>
  filter(group != "Rest of U.S.") |>
  summarise(share = sum(share), .by = year)

year_below_half <- min(orig13_by_year$year[orig13_by_year$share < 0.5])

state_delta <- df_raw |>
  filter(year %in% c(1790, 1900)) |>
  mutate(share = population / sum(population), .by = year) |>
  filter(state %in% original_13) |>
  select(state, year, share) |>
  pivot_wider(names_from = year, values_from = share, names_prefix = "y") |>
  mutate(delta_pp = 100 * (y1900 - y1790))

### |- end labels at fixed positions ----
end_labels <- plot_data |>
  filter(year == 1900) |>
  mutate(
    label_x = 1905,
    label_y = if_else(group == grp_eleven, share + 0.025, share - 0.025),
    label_vjust = if_else(group == grp_eleven, 0, 1),
    label_name = if_else(group == grp_eleven, "Other 11<br>original states", "New York +<br>Pennsylvania"),
    label = glue("**{label_name}**<br>{percent(share, 0.1)}")
  )
```

5. Visualization Parameters

Show code

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

### |- plot aesthetics ----
colors <- get_theme_colors(
  palette = list(
    paper       = "#f4f1ea",
    accent      = "#722F37",
    eleven_line = "#8A8780",
    eleven_text = "#6f6a63",
    ink         = "#2b2b2b",
    grid        = "#dedad1"
  )
)
clrs <- colors$palette
col_paper <- clrs$paper
col_accent <- clrs$accent
col_line11 <- clrs$eleven_line
col_text11 <- clrs$eleven_text
col_ink <- clrs$ink
col_grid <- clrs$grid

### |- titles and caption ----
title_text <- "The original thirteen didn't fade equally"

subtitle_text <- glue(
  "Share of U.S. population at each census, 1790–1900. ",
  "<b><span style='color:{col_accent}'>New York and Pennsylvania </span></b>",
  "held about a fifth of the nation; ",
  "<b><span style='color:{col_text11}'>the other eleven original states </span></b>",
  "fell from {percent(eleven_1790, 0.1)} to nearly the same level."
)

note_text <- glue(
  "Shares, not population counts: every group grew in population at every census. ",
  "The other eleven lost share collectively, though Georgia's share rose."
)
method_text <- "Modern state boundaries; U.S. totals include territories and D.C."

caption_text <- paste0(
  note_text, "<br><br>", method_text, "<br>",
  create_mm_caption(
    mm_year = 2026,
    mm_week = 42,
    source_text = "U.S. Census Bureau, Working Paper No. 56 (Gibson & Jung, 2002)"
  )
)

### |- plot theme ----
base_theme <- create_base_theme(colors)

weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    plot.background = element_rect(fill = col_paper, color = NA),
    panel.background = element_rect(fill = col_paper, color = NA),
    plot.title = element_text(
      family = title_family, size = rel(1.9), color = col_ink,
      margin = margin(b = 8)
    ),
    plot.subtitle = element_textbox_simple(
      family = text_family, size = rel(0.95), color = col_ink,
      lineheight = 1.25, margin = margin(b = 18)
    ),
    plot.caption = element_textbox_simple(
      family = text_family, size = rel(0.62), color = col_text11,
      lineheight = 1.2, margin = margin(t = 14)
    ),
    plot.title.position = "plot",
    plot.caption.position = "plot",
    panel.grid.major.y = element_line(color = col_grid, linewidth = 0.3),
    panel.grid.major.x = element_blank(),
    panel.grid.minor = element_blank(),
    axis.ticks = element_blank(),
    axis.title.y = element_text(
      family = text_family, size = rel(0.8),
      color = col_text11, margin = margin(r = 8)
    ),
    axis.text = element_text(family = text_family, size = rel(0.8), color = col_text11),
    legend.position = "none",
    plot.margin = margin(20, 45, 14, 20)
  )
)

theme_set(weekly_theme)
```

6. Plot

Show code

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

### |-  main plot ----
p <- ggplot(plot_data, aes(year, share, color = group)) +
  geom_line(linewidth = 1.3, lineend = "round") +
  geom_point(data = peak, size = 3, color = col_accent) +
  annotate(
    "text",
    x = peak$year, y = peak$share + 0.04,
    label = glue("{peak$year} peak · {percent(peak$share, 0.1)}"),
    family = text_family, color = col_accent, size = 3.8
  ) +
  geom_segment(
    data = end_labels,
    aes(x = 1900.8, xend = label_x - 0.6, y = share, yend = label_y),
    linewidth = 0.35
  ) +
  geom_richtext(
    data = end_labels,
    aes(x = label_x, y = label_y, label = label, vjust = label_vjust),
    hjust = 0, size = 3.8, lineheight = 1.05,
    family = text_family, fill = NA, label.color = NA,
    label.padding = unit(0, "pt")
  ) +
  scale_color_manual(
    values = c(col_accent, col_line11) |> set_names(c(grp_two, grp_eleven)),
    limits = c(grp_two, grp_eleven)
  ) +
  scale_x_continuous(
    breaks = seq(1790, 1900, 10),
    expand = expansion(mult = c(0.02, 0.20))
  ) +
  scale_y_continuous(
    labels = label_percent(), limits = c(0, 0.8),
    breaks = seq(0, 0.8, 0.2), expand = expansion(mult = c(0, 0.02))
  ) +
  coord_cartesian(clip = "off") +
  labs(
    x = NULL, y = "Share of U.S. population",
    title = title_text, subtitle = subtitle_text, caption = caption_text
  )
```

7. Save

Show code

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

### |- save ----
main_path  <- here::here("data_visualizations", "MakeoverMonday", "2026", "mm_2026_42.png")
thumb_path <- here::here("data_visualizations", "MakeoverMonday", "2026", "thumbnails", "mm_2026_42.png")

# Full-size version, for the QMD figure
save_ggplot(
  plot = p,
  file = main_path,
  width = fig_w, height = fig_h,
  units = "in", dpi = 320,
)

# Reduced-size thumbnail
fs::dir_create(dirname(thumb_path))
magick::image_read(main_path) |>
  magick::image_resize("400") |>
  magick::image_write(thumb_path)
```

8. Session Info

Expand for Session Info

R version 4.6.1 (2026-06-24)
Platform: aarch64-apple-darwin23
Running under: macOS Tahoe 26.6.2

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.6/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.6/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/New_York
tzcode source: internal

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

other attached packages:
 [1] here_1.0.2      readxl_1.5.0    ggview_0.2.2    janitor_2.2.1  
 [5] glue_1.8.1      scales_1.4.0    showtext_0.9-8  showtextdb_3.0 
 [9] sysfonts_0.8.9  ggtext_0.2.0    lubridate_1.9.5 forcats_1.0.1  
[13] stringr_1.6.0   dplyr_1.2.1     purrr_1.2.2     readr_2.2.0    
[17] tidyr_1.3.2     tibble_3.3.1    ggplot2_4.0.3   tidyverse_2.0.0
[21] pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.60          htmlwidgets_1.6.4  tzdb_0.5.0        
 [5] vctrs_0.7.3        tools_4.6.1        generics_0.1.4     curl_7.1.0        
 [9] parallel_4.6.1     pkgconfig_2.0.3    RColorBrewer_1.1-3 skimr_2.2.2       
[13] S7_0.2.2           lifecycle_1.0.5    compiler_4.6.1     farver_2.1.2      
[17] textshaping_1.0.5  repr_1.1.7         codetools_0.2-20   snakecase_0.11.1  
[21] litedown_0.10      htmltools_0.5.9    yaml_2.3.12        pillar_1.11.1     
[25] crayon_1.5.3       magick_2.9.1       commonmark_2.0.0   tidyselect_1.2.1  
[29] digest_0.6.39      stringi_1.8.7      rprojroot_2.1.1    fastmap_1.2.0     
[33] grid_4.6.1         cli_3.6.6          magrittr_2.0.5     base64enc_0.1-6   
[37] withr_3.0.3        bit64_4.8.2        timechange_0.4.0   rmarkdown_2.31    
[41] bit_4.6.0          otel_0.2.0         cellranger_1.1.0   ragg_1.5.2        
[45] hms_1.1.4          evaluate_1.0.5     knitr_1.51         markdown_2.0      
[49] rlang_1.3.0        gridtext_0.1.6     Rcpp_1.1.2         xml2_1.6.0        
[53] rstudioapi_0.19.0  vroom_1.7.1        jsonlite_2.0.0     R6_2.6.1          
[57] fs_2.1.0           systemfonts_1.3.2

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in mm_2026_42.qmd.

For the full repository, click here.

10. References

Expand for References

Primary Data (Makeover Monday): 1. Makeover Monday 2026 Week 42: The First Century of the U.S. Census - CSV: 886 rows × 3 columns (state, year, population) covering 51 units (50 states plus D.C.) across 21 censuses, 1790–1990. This makeover uses 1790–1900. No duplicates and no gaps within any unit’s series. - Boundaries: the CSV projects modern state boundaries backward and includes counts taken before statehood (81 rows, e.g., Arkansas 1810). The 1883 plate uses boundaries as enumerated: - 1790 Massachusetts (475,327) is the CSV’s Massachusetts plus Maine. - Virginia (747,610 in 1790; 1,596,318 in 1860) is the CSV’s Virginia plus West Virginia. - All seven checked 1880 values match exactly. - Modern boundaries are used here so each group covers a fixed territory. Under the plate’s boundaries, Virginia still led New York in 1810 by 15,551; in the CSV, New York leads. - Totals: row sums match the published U.S. totals in most censuses. - 1870 is the original count (38.56M), not the revised 39.82M. That count is known to undercount the South, which may slightly lower the eleven’s 1870 point. - The remaining gaps (1830–40, 1880–1950) appear to be people not assigned to any state (Navy personnel; Alaska and Hawaii as territories). They affect no claim in the chart. - Method: each group’s share of all reported population, including territories and D.C. Dropping D.C. moves any share by 0.2 points or less. Shares, not people: both groups grew in population at every census, although individual states did not (Virginia fell in 1840, Maine and New Hampshire in 1870). - Key figures: - New York + Pennsylvania: 19.7% in 1790, a 25.4% peak in 1830, 17.9% in 1900. - Other 11 original states: 71.5% in 1790, 19.9% in 1900. They declined as a group, not uniformly: Georgia’s share rose (+0.8 points) while Virginia’s fell 15.2 points. - The thirteen together fell below half of the U.S. by the 1860 census, under every boundary and denominator tested.

Source Data: 2. Gibson, C. and Jung, K. (2002). Historical Census Statistics on Population Totals by Race, 1790 to 1990. U.S. Census Bureau, Population Division Working Paper No. 56.

Original Visualization: 3. Hewes, F. W. and Gannett, H. (1883). Scribner’s Statistical Atlas of the United States, Plate 18, “Rank of States in Population at Each United States Census.” Discussed in Library of Congress, Gannett and Hewes: Visualizations of the 1880 Census (2022).

11. Custom Functions Documentation

📦 Custom Helper Functions

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

Citation

BibTeX citation:

@online{ponce2026,
  author = {Ponce, Steven},
  title = {The Original Thirteen Didn’t Fade Equally},
  date = {2026-10-18},
  url = {https://stevenponce.netlify.app/data_visualizations/MakeoverMonday/2026/mm_2026_42.html},
  langid = {en}
}

For attribution, please cite this work as:

Ponce, Steven. 2026. “The Original Thirteen Didn’t Fade Equally.” October 18. https://stevenponce.netlify.app/data_visualizations/MakeoverMonday/2026/mm_2026_42.html.

--- title: "The original thirteen didn't fade equally" subtitle: "Share of U.S. population at each census, 1790–1900. New York and Pennsylvania held about a fifth of the nation; the other eleven original states fell from 71.5% to nearly the same level." description: "A two-line chart of national population share, 1790–1900, shows New York and Pennsylvania holding about a fifth of the U.S. while the other eleven original states fell from 71.5% to 19.9%. Grouped shares on modern state boundaries recast the 1883 atlas's rank chart as magnitude. Built in R with ggplot2, ggtext, and showtext." date: "2026-10-18" author: - name: "Steven Ponce" url: "https://stevenponce.netlify.app" citation: url: "https://stevenponce.netlify.app/data_visualizations/MakeoverMonday/2026/mm_2026_42.html" categories: ["MakeoverMonday", "2026"] tags: [ "makeover-monday", "data-visualization", "ggplot2", "line-chart", "census", "us-history", "population", "population-share", "historical-data", "original-thirteen", "ggtext", "direct-labeling", "2026" ] image: "thumbnails/mm_2026_42.png" format: html: toc: true toc-depth: 5 code-link: true code-fold: true code-tools: true code-summary: "Show code" editor_options: chunk_output_type: inline execute: freeze: true cache: true error: false message: false warning: false eval: true --- ```{r} #| label: setup-links #| include: false # CENTRALIZED LINK MANAGEMENT ## Project-specific info current_year <- 2026 current_week <- 42 project_file <- "mm_2026_42.qmd" project_image <- "mm_2026_42.png" ## Data Sources data_main <- "https://www.census.gov/library/working-papers/2002/demo/POP-twps0056.html" data_secondary <- "https://www.census.gov/library/working-papers/2002/demo/POP-twps0056.html" ## Repository Links repo_main <- "https://github.com/poncest/personal-website/" repo_file <- paste0("https://github.com/poncest/personal-website/blob/master/data_visualizations/MakeoverMonday/", current_year, "/", project_file) ## External Resources/Images chart_original <- "https://raw.githubusercontent.com/poncest/MakeoverMonday/refs/heads/master/2026/Week_42/original_chart.png" ## Organization/Platform Links org_primary <- "https://blogs.loc.gov/maps/2022/12/gannett-and-hewes-visualizations-of-the-1880-census/" org_secondary <- "https://blogs.loc.gov/maps/2022/12/gannett-and-hewes-visualizations-of-the-1880-census/" # Helper function to create markdown links create_link <- function(text, url) { paste0("[", text, "](", url, ")") } # Helper function for citation-style links create_citation_link <- function(text, url, title = NULL) { if (is.null(title)) { paste0("[", text, "](", url, ")") } else { paste0("[", text, "](", url, ' "', title, '")') } } ``` ### Original The original visualization comes from `r create_link("The First Century of the U.S. Census", org_primary)` ![Original visualization](https://raw.githubusercontent.com/poncest/MakeoverMonday/refs/heads/master/2026/Week_42/original_chart.png) ### Makeover ![Line chart titled "The original thirteen didn't fade equally," showing each group's share of the U.S. population at every census from 1790 to 1900, using modern state boundaries. The other eleven original states fell from 71.5% of the nation in 1790 to 19.9% in 1900, with the steepest losses in the early decades. New York and Pennsylvania combined started at 19.7%, peaked at 25.4% in 1830, and eased to 17.9% by 1900, so at the final census the two groups were two percentage points apart. The chart shows shares, not population decline: both groups grew in population at every census. The eleven lost share as a group, though Georgia's share rose. Data: U.S. Census Bureau, Working Paper No. 56 (Gibson & Jung, 2002).](mm_2026_42.png){#fig-1} ### [**Steps to Create this Graphic**]{.mark} #### [1. Load Packages & Setup]{.smallcaps} ```{r} #| label: load #| warning: false #| message: false #| results: "hide" ## 1. LOAD PACKAGES & SETUP ---- suppressPackageStartupMessages({ if (!require("pacman")) install.packages("pacman") pacman::p_load( tidyverse, ggtext, showtext, scales, glue, janitor, ggview, readxl ) }) # Source utility functions suppressMessages( source(here::here("R/utils/fonts.R"))) source(here::here("R/utils/social_icons.R")) source(here::here("R/themes/base_theme.R")) ### |- figure size ---- fig_w <- 10 fig_h <- 6.5 fig_dpi <- 320 ### |- fonts ---- setup_fonts() fonts <- get_font_families() title_family <- fonts$title_1 text_family <- fonts$text ``` #### [2. Read in the Data]{.smallcaps} ```{r} #| label: read #| include: true #| eval: true #| warning: false #| df_raw <- read_csv( here::here("data/MakeoverMonday/2026/us_state_population_1790_1990.csv"), col_types = cols(.default = col_character()), na = character() ) |> clean_names() |> mutate( year = as.integer(year), population = as.numeric(population) ) ``` #### [3. Examine the Data]{.smallcaps} ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(df_raw) skimr::skim_without_charts(df_raw) ``` #### [4. Tidy Data]{.smallcaps} ```{r} #| label: tidy #| warning: false two_states <- c("New York", "Pennsylvania") original_13 <- c( "Connecticut", "Delaware", "Georgia", "Maryland", "Massachusetts", "New Hampshire", "New Jersey", "New York", "North Carolina", "Pennsylvania", "Rhode Island", "South Carolina", "Virginia" ) grp_two <- "New York + Pennsylvania" grp_eleven <- "Other 11 original states" shares <- df_raw |> filter(year <= 1900) |> mutate(group = case_when( state %in% two_states ~ grp_two, state %in% original_13 ~ grp_eleven, .default = "Rest of U.S." )) |> summarise(pop = sum(population), .by = c(year, group)) |> mutate(share = pop / sum(pop), .by = year) plot_data <- shares |> filter(group != "Rest of U.S.") ### |- values used in the copy ---- get_share <- function(g, y) { shares |> filter(group == g, year == y) |> pull(share) } two_1790 <- get_share(grp_two, 1790) two_1900 <- get_share(grp_two, 1900) eleven_1790 <- get_share(grp_eleven, 1790) eleven_1900 <- get_share(grp_eleven, 1900) peak <- plot_data |> filter(group == grp_two) |> slice_max(share, with_ties = FALSE) orig13_by_year <- shares |> filter(group != "Rest of U.S.") |> summarise(share = sum(share), .by = year) year_below_half <- min(orig13_by_year$year[orig13_by_year$share < 0.5]) state_delta <- df_raw |> filter(year %in% c(1790, 1900)) |> mutate(share = population / sum(population), .by = year) |> filter(state %in% original_13) |> select(state, year, share) |> pivot_wider(names_from = year, values_from = share, names_prefix = "y") |> mutate(delta_pp = 100 * (y1900 - y1790)) ### |- end labels at fixed positions ---- end_labels <- plot_data |> filter(year == 1900) |> mutate( label_x = 1905, label_y = if_else(group == grp_eleven, share + 0.025, share - 0.025), label_vjust = if_else(group == grp_eleven, 0, 1), label_name = if_else(group == grp_eleven, "Other 11 original states", "New York + Pennsylvania"), label = glue("**{label_name}** {percent(share, 0.1)}") ) ``` #### [5. Visualization Parameters]{.smallcaps} ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- colors <- get_theme_colors( palette = list( paper = "#f4f1ea", accent = "#722F37", eleven_line = "#8A8780", eleven_text = "#6f6a63", ink = "#2b2b2b", grid = "#dedad1" ) ) clrs <- colors$palette col_paper <- clrs$paper col_accent <- clrs$accent col_line11 <- clrs$eleven_line col_text11 <- clrs$eleven_text col_ink <- clrs$ink col_grid <- clrs$grid ### |- titles and caption ---- title_text <- "The original thirteen didn't fade equally" subtitle_text <- glue( "Share of U.S. population at each census, 1790–1900. ", "New York and Pennsylvania ", "held about a fifth of the nation; ", "the other eleven original states ", "fell from {percent(eleven_1790, 0.1)} to nearly the same level." ) note_text <- glue( "Shares, not population counts: every group grew in population at every census. ", "The other eleven lost share collectively, though Georgia's share rose." ) method_text <- "Modern state boundaries; U.S. totals include territories and D.C." caption_text <- paste0( note_text, " ", method_text, " ", create_mm_caption( mm_year = 2026, mm_week = 42, source_text = "U.S. Census Bureau, Working Paper No. 56 (Gibson & Jung, 2002)" ) ) ### |- plot theme ---- base_theme <- create_base_theme(colors) weekly_theme <- extend_weekly_theme( base_theme, theme( plot.background = element_rect(fill = col_paper, color = NA), panel.background = element_rect(fill = col_paper, color = NA), plot.title = element_text( family = title_family, size = rel(1.9), color = col_ink, margin = margin(b = 8) ), plot.subtitle = element_textbox_simple( family = text_family, size = rel(0.95), color = col_ink, lineheight = 1.25, margin = margin(b = 18) ), plot.caption = element_textbox_simple( family = text_family, size = rel(0.62), color = col_text11, lineheight = 1.2, margin = margin(t = 14) ), plot.title.position = "plot", plot.caption.position = "plot", panel.grid.major.y = element_line(color = col_grid, linewidth = 0.3), panel.grid.major.x = element_blank(), panel.grid.minor = element_blank(), axis.ticks = element_blank(), axis.title.y = element_text( family = text_family, size = rel(0.8), color = col_text11, margin = margin(r = 8) ), axis.text = element_text(family = text_family, size = rel(0.8), color = col_text11), legend.position = "none", plot.margin = margin(20, 45, 14, 20) ) ) theme_set(weekly_theme) ``` #### [6. Plot]{.smallcaps} ```{r} #| label: plot #| warning: false ### |- main plot ---- p <- ggplot(plot_data, aes(year, share, color = group)) + geom_line(linewidth = 1.3, lineend = "round") + geom_point(data = peak, size = 3, color = col_accent) + annotate( "text", x = peak$year, y = peak$share + 0.04, label = glue("{peak$year} peak · {percent(peak$share, 0.1)}"), family = text_family, color = col_accent, size = 3.8 ) + geom_segment( data = end_labels, aes(x = 1900.8, xend = label_x - 0.6, y = share, yend = label_y), linewidth = 0.35 ) + geom_richtext( data = end_labels, aes(x = label_x, y = label_y, label = label, vjust = label_vjust), hjust = 0, size = 3.8, lineheight = 1.05, family = text_family, fill = NA, label.color = NA, label.padding = unit(0, "pt") ) + scale_color_manual( values = c(col_accent, col_line11) |> set_names(c(grp_two, grp_eleven)), limits = c(grp_two, grp_eleven) ) + scale_x_continuous( breaks = seq(1790, 1900, 10), expand = expansion(mult = c(0.02, 0.20)) ) + scale_y_continuous( labels = label_percent(), limits = c(0, 0.8), breaks = seq(0, 0.8, 0.2), expand = expansion(mult = c(0, 0.02)) ) + coord_cartesian(clip = "off") + labs( x = NULL, y = "Share of U.S. population", title = title_text, subtitle = subtitle_text, caption = caption_text ) ``` #### [7. Save]{.smallcaps} ```{r} #| label: save #| warning: false ### |- save ---- main_path <- here::here("data_visualizations", "MakeoverMonday", "2026", "mm_2026_42.png") thumb_path <- here::here("data_visualizations", "MakeoverMonday", "2026", "thumbnails", "mm_2026_42.png") # Full-size version, for the QMD figure save_ggplot( plot = p, file = main_path, width = fig_w, height = fig_h, units = "in", dpi = 320, ) # Reduced-size thumbnail fs::dir_create(dirname(thumb_path)) magick::image_read(main_path) |> magick::image_resize("400") |> magick::image_write(thumb_path) ``` #### [8. Session Info]{.smallcaps} ::: {.callout-tip collapse="true"} ##### Expand for Session Info ```{r, echo = FALSE} #| eval: true #| warning: false sessionInfo() ``` ::: #### [9. GitHub Repository]{.smallcaps} ::: {.callout-tip collapse="true"} ##### Expand for GitHub Repo The complete code for this analysis is available in `r create_link(project_file, repo_file)`. For the full repository, `r create_link("click here", repo_main)`. ::: #### [10. References]{.smallcaps} ::: {.callout-tip collapse="true"} ##### Expand for References **Primary Data (Makeover Monday):** 1. Makeover Monday 2026 Week 42: `r create_link("The First Century of the U.S. Census", "https://blogs.loc.gov/maps/2022/12/gannett-and-hewes-visualizations-of-the-1880-census/")` - CSV: 886 rows × 3 columns (state, year, population) covering 51 units (50 states plus D.C.) across 21 censuses, 1790–1990. This makeover uses 1790–1900. No duplicates and no gaps within any unit's series. - **Boundaries:** the CSV projects modern state boundaries backward and includes counts taken before statehood (81 rows, e.g., Arkansas 1810). The 1883 plate uses boundaries as enumerated: - 1790 Massachusetts (475,327) is the CSV's Massachusetts plus Maine. - Virginia (747,610 in 1790; 1,596,318 in 1860) is the CSV's Virginia plus West Virginia. - All seven checked 1880 values match exactly. - Modern boundaries are used here so each group covers a fixed territory. Under the plate's boundaries, Virginia still led New York in 1810 by 15,551; in the CSV, New York leads. - **Totals:** row sums match the published U.S. totals in most censuses. - 1870 is the original count (38.56M), not the revised 39.82M. That count is known to undercount the South, which may slightly lower the eleven's 1870 point. - The remaining gaps (1830–40, 1880–1950) appear to be people not assigned to any state (Navy personnel; Alaska and Hawaii as territories). They affect no claim in the chart. - **Method:** each group's share of all reported population, including territories and D.C. Dropping D.C. moves any share by 0.2 points or less. Shares, not people: both groups grew in population at every census, although individual states did not (Virginia fell in 1840, Maine and New Hampshire in 1870). - **Key figures:** - New York + Pennsylvania: 19.7% in 1790, a 25.4% peak in 1830, 17.9% in 1900. - Other 11 original states: 71.5% in 1790, 19.9% in 1900. They declined as a group, not uniformly: Georgia's share rose (+0.8 points) while Virginia's fell 15.2 points. - The thirteen together fell below half of the U.S. by the 1860 census, under every boundary and denominator tested. **Source Data:** 2. Gibson, C. and Jung, K. (2002). `r create_link("Historical Census Statistics on Population Totals by Race, 1790 to 1990", "https://www.census.gov/library/working-papers/2002/demo/POP-twps0056.html")`. U.S. Census Bureau, Population Division Working Paper No. 56. **Original Visualization:** 3. Hewes, F. W. and Gannett, H. (1883). *Scribner's Statistical Atlas of the United States*, Plate 18, "Rank of States in Population at Each United States Census." Discussed in Library of Congress, `r create_link("Gannett and Hewes: Visualizations of the 1880 Census", "https://blogs.loc.gov/maps/2022/12/gannett-and-hewes-visualizations-of-the-1880-census/")` (2022). ::: #### [11. Custom Functions Documentation]{.smallcaps} ::: {.callout-note collapse="true"} ##### 📦 Custom Helper Functions 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](https://github.com/poncest/personal-website/tree/master/R) :::