Digital Divide Narrows: Internet Adoption Across Generations

The 65+ age group showed the steepest adoption curve, dramatically narrowing what was once a 56-point divide

30DayChartChallenge

2025

Exploring the dramatic narrowing of the digital divide in the United States from 2000 to 2024, with a focus on how the 65+ age group showed the steepest adoption curve, dramatically closing what was once a significant gap in internet usage between generations.

Author

Steven Ponce

Published

April 2, 2025

Figure 1: A slope chart showing internet adoption rates across age groups from 2000 to 2024. The 65+ age group shows the steepest slope, rising from 14% to 90%, dramatically narrowing the gap with younger groups. Other age groups (18-29, 30-49, and 50-64) all reached nearly 99% adoption by 2024, despite starting at different levels (70%, 61%, and 46% respectively).

Steps to Create this Graphic

1. Load Packages & Setup

Show code

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
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
  lubridate,      # Make Dealing with Dates a Little Easier
  ggrepel,        # Automatically Position Non-Overlapping Text Labels with 'ggplot2'
  camcorder       # Record Your Plot History
)
})

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

internet_raw <- read_delim(
  here::here('data/30DayChartChallenge/2025/internet_use_by_age_data_2024-11-13.csv'),
  skip = 3) |> clean_names() |> 
    head(-2)

3. Examine the Data

Show code

glimpse(internet_raw)
skim(internet_raw)

4. Tidy Data

Show code

### |- Tidy ----
internet_clean <- internet_raw |>
  # Remove whitespace from year and convert to numeric
  mutate(
    year = as.numeric(year),
    across(
      c(ages_18_29, x30_49, x50_64, x65),
      ~ as.numeric(str_remove(., "%")) / 100
    )
  ) |>
  rename(
    "18-29" = ages_18_29,
    "30-49" = x30_49,
    "50-64" = x50_64,
    "65+" = x65
  ) |>
  pivot_longer(
    cols = c("18-29", "30-49", "50-64", "65+"),
    names_to = "age_group",
    values_to = "internet_usage"
  ) |>
  mutate(
    age_group = factor(age_group,
                       levels = c("18-29", "30-49", "50-64", "65+")
    )
  ) |>
  filter(year %in% c(2000, 2024))

5. Visualization Parameters

Show code

### |- plot aesthetics ---- 
colors <- get_theme_colors(palette = c(
  "18-29" = "gray", 
  "30-49" = "gray", 
  "50-64" = "gray", 
  "65+" = "#8856a7"      
  ))  

### |-  titles and caption ----
# text
title_text    <- str_glue("Digital Divide Narrows: Internet Adoption Across Generations") 
subtitle_text <- str_glue("The __65+ age group__ showed the steepest adoption curve, dramatically narrowing what was<br> 
                          once a 56-point divide")

# Create caption
caption_text <- create_dcc_caption(
  dcc_year = 2025,
  dcc_day = 02,
  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(
    # Text styling 
    plot.title = element_text(face = "bold", family = fonts$title, size = rel(1.14), margin = margin(b = 10)),
    plot.subtitle = element_text(family = fonts$subtitle, color = colors$text, size = rel(0.78), margin = margin(b = 20)),
    
    # Axis elements
    axis.title.y = element_text(color = colors$text, size = rel(0.8),
                              hjust = 1, vjust = 0.5, angle = 90),
    axis.title.x = element_blank(),
    axis.text.x = element_text(color = colors$text, size = rel(0.7)),
    axis.text.y = ggtext::element_markdown(),
  
    # Grid elements
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
 
    # Legend elements
    legend.position = "top",
    legend.title = element_text(family = fonts$text, size = rel(0.8)),
    legend.text = element_text(family = fonts$text, size = rel(0.7)),
    
    # Plot margins 
    plot.margin = margin(t = 20, r = 20, b = 20, l = 60),
  )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code

### |-  Plot ----
p <- ggplot(data = internet_clean) +
  # Geoms
  geom_hline(
    yintercept = seq(0, 1, 0.25), 
    color = "gray90", 
    linetype = "dashed"
  ) +
  geom_vline(
    xintercept = c(2000, 2024),
    color = '#333333',
    linewidth = 0.4
  ) +
  geom_line(                            # gray lines
    data = internet_clean %>% filter(age_group != "65+"), 
    aes(x = year, y = internet_usage, group = age_group),
    color = "gray80", 
    linewidth = 0.4
  ) + 
  geom_line(                           # selected line
    data = internet_clean %>% filter(age_group == "65+"), 
    aes(x = year, y = internet_usage, group = age_group, color = age_group),
    linewidth = 1.0
  ) +
  geom_point(
    aes(x = year, y = internet_usage, color = age_group),
    size = 2
  ) +
  geom_text(                           # 2000 labels         
    data = internet_clean %>% filter(year == 2000),
    aes(x = year, y = internet_usage, 
        label = paste0(age_group, ": ", percent(internet_usage, accuracy = 1)),
        color = age_group),
    hjust = 1.2,
    fontface = "bold",
    size = 4
  ) +
  geom_text_repel(                     # 2024 labels 
    data = internet_clean %>% filter(year == 2024),
    aes(x = year, y = internet_usage, 
        label = paste0(age_group, ": ", percent(internet_usage, accuracy = 1)),
        color = age_group),
    hjust = 0,
    direction = "y",
    nudge_x = 1,
    segment.size = 0.2,
    segment.color = "gray70",
    min.segment.length = 0,
    fontface = "bold",
    size = 3.5,
    box.padding = 0.4,
    point.padding = 0.1,
    force = 2
  ) +
  annotate(                           # note
    "text", 
    x = 2012, 
    y = 0.2, 
    label = "65+ group shows\nthe steepest slope",
    color = colors$palette[4], 
    fontface = "bold", 
    size = 4.5,
    alpha = 0.8
  ) +
  # Scales
  scale_y_continuous(
    limits = c(0, 1.05),
    breaks = c(0, 0.25, 0.50, 0.75, 1.00),
    labels = scales::label_percent(
      suffix = '<span style="font-size:6pt;"> %</span>'
    )
  ) +
  scale_x_continuous(
    breaks = c(2000, 2024),
    labels = c("2000", "2024"),
    limits = c(1992, 2032),
    expand = c(0, 0),  
  ) +
  scale_color_manual(
    values = colors$palette, 
    guide = "none"
  ) +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = NULL,
    y = "Percentage of Adults Using the Internet"
  ) +
  # Theme
  theme(
    plot.title = element_text(
      size          = rel(1.75),
      family        = fonts$title,
      face          = "bold",
      color         = colors$title,
      margin        = margin(t = 5, b = 5)
    ),
    plot.subtitle   = element_markdown(
      size          = rel(1),
      family        = fonts$subtitle,
      color         = colors$subtitle,
      lineheight    = 1.1,
      margin        = margin(t = 5, b = 20)
    ),
    plot.caption    = element_markdown(
      size          = rel(.65),
      family        = fonts$caption,
      color         = colors$caption,
      lineheight    = 0.65,
      hjust         = 0.5,
      halign        = 0.5,
      margin        = margin(t = 10, b = 5)
    ),
  )

7. Save

Show code

### |-  plot image ----  

save_plot(
  p, 
  type = "30daychartchallenge", 
  year = 2025, 
  day = 02, 
  width = 10, 
  height = 8
  )

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] here_1.0.1      camcorder_0.1.0 ggrepel_0.9.6   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

loaded via a namespace (and not attached):
 [1] gtable_0.3.6      xfun_0.49         htmlwidgets_1.6.4 tzdb_0.4.0       
 [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       pacman_0.5.1     
[13] pkgconfig_2.0.3   lifecycle_1.0.4   farver_2.1.2      compiler_4.4.0   
[17] textshaping_0.4.0 munsell_0.5.1     repr_1.1.7        codetools_0.2-20 
[21] snakecase_0.11.1  htmltools_0.5.8.1 yaml_2.3.10       crayon_1.5.3     
[25] pillar_1.9.0      magick_2.8.5      commonmark_1.9.2  tidyselect_1.2.1 
[29] digest_0.6.37     stringi_1.8.4     rsvg_2.6.1        rprojroot_2.0.4  
[33] fastmap_1.2.0     grid_4.4.0        colorspace_2.1-1  cli_3.6.3        
[37] magrittr_2.0.3    base64enc_0.1-3   utf8_1.2.4        withr_3.0.2      
[41] bit64_4.5.2       timechange_0.3.0  rmarkdown_2.29    bit_4.5.0        
[45] ragg_1.3.3        hms_1.1.3         evaluate_1.0.1    knitr_1.49       
[49] markdown_1.13     rlang_1.1.4       gridtext_0.1.5    Rcpp_1.0.13-1    
[53] glue_1.8.0        xml2_1.3.6        renv_1.0.3        svglite_2.1.3    
[57] rstudioapi_0.17.1 vroom_1.6.5       jsonlite_1.8.9    R6_2.5.1         
[61] systemfonts_1.1.0

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in 30dcc_2025_02.qmd.

For the full repository, click here.

10. References

Expand for References

Data Sources:
- Internet, Broadband Fact Sheet: Fact Sheets: Tech Adoption Trends, Pew Research Center, published November 13, 2024 Internet use by age

--- title: "Digital Divide Narrows: Internet Adoption Across Generations" subtitle: "The 65+ age group showed the steepest adoption curve, dramatically narrowing what was once a 56-point divide" description: "Exploring the dramatic narrowing of the digital divide in the United States from 2000 to 2024, with a focus on how the 65+ age group showed the steepest adoption curve, dramatically closing what was once a significant gap in internet usage between generations." author: "Steven Ponce" date: "2025-04-02" categories: ["30DayChartChallenge", "2025"] tags: [ "ggplot2", "slope chart", "digital divide", "internet adoption", "technology trends", "data journalism", "generational analysis", "Pew Research", "time series", "social change" ] image: "thumbnails/30dcc_2025_02.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 # filters: # - social-share # share: # permalink: "https://stevenponce.netlify.app/data_visualizations/30DayChartChallenge/2025/30dcc_2025_02.html" # description: "The 65+ age group showed the most dramatic internet adoption rate of any generation, rising from just 14% in 2000 to 90% in 2024. This #30DayChartChallenge visualization highlights how seniors closed a once-significant digital divide. #DataViz #rstats" # twitter: true # linkedin: true # email: true # facebook: false # reddit: false # stumble: false # tumblr: false # mastodon: true # bsky: true --- ![A slope chart showing internet adoption rates across age groups from 2000 to 2024. The 65+ age group shows the steepest slope, rising from 14% to 90%, dramatically narrowing the gap with younger groups. Other age groups (18-29, 30-49, and 50-64) all reached nearly 99% adoption by 2024, despite starting at different levels (70%, 61%, and 46% respectively).](30dcc_2025_02.png){#fig-1} ### **Steps to Create this Graphic** #### 1. Load Packages & Setup ```{r} #| label: load #| warning: false #| message: false #| results: "hide" ## 1. LOAD PACKAGES & SETUP ---- suppressPackageStartupMessages({ 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 lubridate, # Make Dealing with Dates a Little Easier ggrepel, # Automatically Position Non-Overlapping Text Labels with 'ggplot2' camcorder # Record Your Plot History ) }) ### |- figure size ---- gg_record( dir = here::here("temp_plots"), device = "png", width = 10, 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 internet_raw <- read_delim( here::here('data/30DayChartChallenge/2025/internet_use_by_age_data_2024-11-13.csv'), skip = 3) |> clean_names() |> head(-2) ``` #### 3. Examine the Data ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(internet_raw) skim(internet_raw) ``` #### 4. Tidy Data ```{r} #| label: tidy #| warning: false ### |- Tidy ---- internet_clean <- internet_raw |> # Remove whitespace from year and convert to numeric mutate( year = as.numeric(year), across( c(ages_18_29, x30_49, x50_64, x65), ~ as.numeric(str_remove(., "%")) / 100 ) ) |> rename( "18-29" = ages_18_29, "30-49" = x30_49, "50-64" = x50_64, "65+" = x65 ) |> pivot_longer( cols = c("18-29", "30-49", "50-64", "65+"), names_to = "age_group", values_to = "internet_usage" ) |> mutate( age_group = factor(age_group, levels = c("18-29", "30-49", "50-64", "65+") ) ) |> filter(year %in% c(2000, 2024)) ``` #### 5. Visualization Parameters ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- colors <- get_theme_colors(palette = c( "18-29" = "gray", "30-49" = "gray", "50-64" = "gray", "65+" = "#8856a7" )) ### |- titles and caption ---- # text title_text <- str_glue("Digital Divide Narrows: Internet Adoption Across Generations") subtitle_text <- str_glue("The __65+ age group__ showed the steepest adoption curve, dramatically narrowing what was once a 56-point divide") # Create caption caption_text <- create_dcc_caption( dcc_year = 2025, dcc_day = 02, 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( # Text styling plot.title = element_text(face = "bold", family = fonts$title, size = rel(1.14), margin = margin(b = 10)), plot.subtitle = element_text(family = fonts$subtitle, color = colors$text, size = rel(0.78), margin = margin(b = 20)), # Axis elements axis.title.y = element_text(color = colors$text, size = rel(0.8), hjust = 1, vjust = 0.5, angle = 90), axis.title.x = element_blank(), axis.text.x = element_text(color = colors$text, size = rel(0.7)), axis.text.y = ggtext::element_markdown(), # Grid elements panel.grid.major = element_blank(), panel.grid.minor = element_blank(), # Legend elements legend.position = "top", legend.title = element_text(family = fonts$text, size = rel(0.8)), legend.text = element_text(family = fonts$text, size = rel(0.7)), # Plot margins plot.margin = margin(t = 20, r = 20, b = 20, l = 60), ) ) # Set theme theme_set(weekly_theme) ``` #### 6. Plot ```{r} #| label: plot #| warning: false ### |- Plot ---- p <- ggplot(data = internet_clean) + # Geoms geom_hline( yintercept = seq(0, 1, 0.25), color = "gray90", linetype = "dashed" ) + geom_vline( xintercept = c(2000, 2024), color = '#333333', linewidth = 0.4 ) + geom_line( # gray lines data = internet_clean %>% filter(age_group != "65+"), aes(x = year, y = internet_usage, group = age_group), color = "gray80", linewidth = 0.4 ) + geom_line( # selected line data = internet_clean %>% filter(age_group == "65+"), aes(x = year, y = internet_usage, group = age_group, color = age_group), linewidth = 1.0 ) + geom_point( aes(x = year, y = internet_usage, color = age_group), size = 2 ) + geom_text( # 2000 labels data = internet_clean %>% filter(year == 2000), aes(x = year, y = internet_usage, label = paste0(age_group, ": ", percent(internet_usage, accuracy = 1)), color = age_group), hjust = 1.2, fontface = "bold", size = 4 ) + geom_text_repel( # 2024 labels data = internet_clean %>% filter(year == 2024), aes(x = year, y = internet_usage, label = paste0(age_group, ": ", percent(internet_usage, accuracy = 1)), color = age_group), hjust = 0, direction = "y", nudge_x = 1, segment.size = 0.2, segment.color = "gray70", min.segment.length = 0, fontface = "bold", size = 3.5, box.padding = 0.4, point.padding = 0.1, force = 2 ) + annotate( # note "text", x = 2012, y = 0.2, label = "65+ group shows\nthe steepest slope", color = colors$palette[4], fontface = "bold", size = 4.5, alpha = 0.8 ) + # Scales scale_y_continuous( limits = c(0, 1.05), breaks = c(0, 0.25, 0.50, 0.75, 1.00), labels = scales::label_percent( suffix = ' %' ) ) + scale_x_continuous( breaks = c(2000, 2024), labels = c("2000", "2024"), limits = c(1992, 2032), expand = c(0, 0), ) + scale_color_manual( values = colors$palette, guide = "none" ) + # Labs labs( title = title_text, subtitle = subtitle_text, caption = caption_text, x = NULL, y = "Percentage of Adults Using the Internet" ) + # Theme theme( plot.title = element_text( size = rel(1.75), family = fonts$title, face = "bold", color = colors$title, margin = margin(t = 5, b = 5) ), plot.subtitle = element_markdown( size = rel(1), family = fonts$subtitle, color = colors$subtitle, lineheight = 1.1, margin = margin(t = 5, b = 20) ), plot.caption = element_markdown( size = rel(.65), family = fonts$caption, color = colors$caption, lineheight = 0.65, hjust = 0.5, halign = 0.5, margin = margin(t = 10, b = 5) ), ) ``` #### 7. Save ```{r} #| label: save #| warning: false ### |- plot image ---- save_plot( p, type = "30daychartchallenge", year = 2025, day = 02, width = 10, height = 8 ) ``` #### 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 [`30dcc_2025_02.qmd`](https://github.com/poncest/personal-website/blob/master/data_visualizations/TidyTuesday/2025/30dcc_2025_02.qmd). For the full repository, [click here](https://github.com/poncest/personal-website/). ::: #### 10. References ::: {.callout-tip collapse="true"} ##### Expand for References 1. Data Sources: - Internet, Broadband Fact Sheet: Fact Sheets: Tech Adoption Trends, Pew Research Center, published November 13, 2024 [Internet use by age](https://www.pewresearch.org/internet/fact-sheet/internet-broadband/) :::