Visitor Distribution by Start Month at World’s Fairs

Density of visitor counts across fairs held from 1851 to 2021

#TidyTuesday
Author

Steven Ponce

Published

August 13, 2024

Figure 1: This is a ridge chart showing the distribution of visitors to World’s Fairs, organized by the month they started from January to December between 1851 and 2021. Each ridge represents a different month, with varying peaks indicating the number of visitors in millions. The chart uses shades of purple for higher visitor months and warmer colors for lower ones.

Steps to Create this Graphic

1. Load Packages & Setup

Code 
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
  MetBrewer,     # Color Palettes Inspired by Works at the Metropolitan Museum of Art
  ggridges       # Ridgeline Plots in 'ggplot2' # Ridgeline Plots in 'ggplot2'
 )  

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  =  8,
  height =  6,
  units  = "in",
  dpi    = 320
)

### |- resolution ----
showtext_opts(dpi = 320, regular.wt = 300, bold.wt = 800)

2. Read in the Data

Code 
tt <-tidytuesdayR::tt_load(2024, week = 33) 

worlds_fairs <- tt$worlds_fairs |> clean_names() |> glimpse()

tidytuesdayR::readme(tt)
rm(tt)

3. Examing the Data

Code 
glimpse(worlds_fairs)
skim(worlds_fairs)

4. Tidy Data

Code 
# Tidy
worlds_fairs <- worlds_fairs |>
    mutate(start_month = factor(start_month, levels = 1:12, labels = month.abb))

# Create a data frame with all months
all_months <- data.frame(start_month = factor(month.abb, levels = month.abb))

# Left join with plot_data
data_plot <- all_months |> 
    left_join(worlds_fairs, by = "start_month")

5. Visualization Parameters

Code 
### |- plot aesthetics ----
bkg_col      <- colorspace::lighten('#f7f5e9', 0.05)    
title_col    <- "gray20"           
subtitle_col <- "gray20"     
caption_col  <- "gray30"   
text_col     <- "gray20"    

### |-  titles and caption ----
# icons
tt <- str_glue("#TidyTuesday: { 2024 } Week { 33 } &bull; Source: List of world expositions (Wikipedia)<br>")
li <- str_glue("<span style='font-family:fa6-brands'>&#xf08c;</span>")
gh <- str_glue("<span style='font-family:fa6-brands'>&#xf09b;</span>")
mn <- str_glue("<span style='font-family:fa6-brands'>&#xf4f6;</span>")

# text
title_text    <- str_glue("Visitor Distribution by Start Month at World\\'s Fairs")
subtitle_text <- str_glue("Density of visitor counts across fairs held from 1851 to 2021")
caption_text  <- str_glue("{tt} {li} stevenponce &bull; {mn} @sponce1(graphic.social) {gh} poncest &bull; #rstats #ggplot2")


### |-  fonts ----
font_add("fa6-brands", "fonts/6.4.2/Font Awesome 6 Brands-Regular-400.otf")
font_add_google("Oswald", regular.wt = 400, family = "title")
font_add_google("Merriweather Sans", regular.wt = 400, family = "subtitle")
font_add_google("Merriweather Sans", regular.wt = 400, family = "text")
font_add_google("Noto Sans", regular.wt = 400, family = "caption")
showtext_auto(enable = TRUE)


### |-  plot theme ----
theme_set(theme_minimal(base_size = 12, base_family = "text"))                

theme_update(
    plot.title.position   = "plot",
    plot.caption.position = "plot",
    legend.position       = "top",
    
    legend.justification  = "right",
    legend.title.position = "top",
    legend.title.align    = 1,  
    legend.box.just       = "right",
    
    legend.margin         = margin(5, 10, -65, 0),
    
    plot.background       = element_rect(fill = bkg_col, color = bkg_col),
    panel.background      = element_rect(fill = bkg_col, color = bkg_col),
    plot.margin           = margin(t = 20, r = 25, b = 20, l = 25),
    axis.title.x          = element_text(margin = margin(10, 0, 0, 0), size = rel(1.2), 
                                         color = text_col, family = "text", face = "bold", hjust = 0.5),
    axis.title.y          = element_text(margin = margin(0, 10, 0, 0), size = rel(1.2), 
                                         color = text_col, family = "text", face = "bold", vjust = 0.5),
    axis.text             = element_text(size = rel(0.95), color = text_col, family = "text"),
    axis.line.x           = element_line(color = "gray40", linewidth = 0.12),
    panel.grid.minor.x    = element_blank(),
    panel.grid.major.x    = element_blank(),
    panel.grid.major.y    = element_line(linetype = "dotted", linewidth = 0.15, color = 'gray'),
    panel.grid.minor.y    = element_blank(),
)

6. Plot

Code 
### |-  final plot ----  
p <- data_plot |>
    ggplot(aes(x = visitors, y = start_month, fill = start_month)) +
    
    # Geom
    geom_density_ridges_gradient(
        scale = 3,
        rel_min_height = 0.01,
        gradient_lwd = 1.0,
        aes(fill = ..x..), 
        color = "gray10", 
        linewidth = .25
    ) +
    
    # Scales
    scale_y_discrete() +
    scale_x_continuous(labels = label_number(suffix = " M")) +
    scale_fill_viridis_c(
        name = "Number of Visitors",
        option = "C",
        direction = -1,
        begin = 0.2,
        end = 0.9,
        guide = guide_colourbar(
            title = "Visitors (in millions)",
            title.position = "top",
            barwidth = 12,
            draw.ulim = 75,
            barheight = 1,
            frame.colour = NA)) +
    coord_cartesian(clip = 'off') +
    
    # Labs
    labs(
        x = "Number of Visitors",
        y = "Start Month",
        fill = "Number of Visitors",
        title    = title_text,
        subtitle = subtitle_text,
        caption  = caption_text,
    ) +
    
    # Theme
    theme(
        plot.title      = element_markdown(
            size        = rel(1.7),
            family      = "title",
            color       = title_col,
            face        = "bold",
            lineheight  = 0.85,
            margin      = margin(t = 5, b = 5)
        ),
        plot.subtitle   = element_markdown(
            size        = rel(1.1),
            family      = "subtitle",
            color       = title_col,
            lineheight  = 1,
            margin      = margin(t = 5, b = 15)
        ),
        plot.caption    = element_markdown(
            size        = rel(.65),
            family      = "caption",
            color       = caption_col,
            lineheight  = 0.6,
            hjust       = 0,
            halign      = 0,
            margin      = margin(t = 10, b = 0)
        )
    )

7. Save

Code 
### |-  plot image ----  
ggsave(
  filename = here::here("data_visualizations/TidyTuesday/2024/tt_2024_33.png"),
  plot = p,
  width  =  8,
  height =  6,
  units  = "in",
  dpi    = 320
)

### |-  plot thumbnail----  
magick::image_read(here::here("data_visualizations/TidyTuesday/2024/tt_2024_33.png")) |> 
  magick::image_resize(geometry = "400") |> 
  magick::image_write(here::here("data_visualizations/TidyTuesday/2024/thumbnails/tt_2024_33.png"))

8. Session Info

Code 
sessionInfo()
R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 10 x64 (build 19045)

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     

loaded via a namespace (and not attached):
 [1] htmlwidgets_1.6.4 compiler_4.4.0    fastmap_1.2.0     cli_3.6.3        
 [5] htmltools_0.5.8.1 tools_4.4.0       rstudioapi_0.16.0 yaml_2.3.10      
 [9] rmarkdown_2.28    knitr_1.48        jsonlite_1.8.8    xfun_0.47        
[13] digest_0.6.37     rlang_1.1.4       renv_1.0.3        evaluate_0.24.0
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