James Beard Foundation

Figure 1: The plot shows the total wins and nominations for the top four broadcast media platforms—Instagram, iTunes, Netflix, and YouTube—at the James Beard Awards from 2019 to 2024. Opaque dots represent wins, while transparent dots indicate nominations, with Netflix and YouTube leading in overall recognition. Source: James Beard Foundation

How This Graphic Was Made

1. 📦 Load Packages & Setup

# Load packages
pacman::p_load(
  tidyverse,
  showtext,
  ggtext,
  skimr,
  glue
)

# Add Google fonts
font_add_google("Roboto Condensed")
font_add_google("Oswald")

# Add local font
font_add("Font Awesome 6 Brands", here::here("fonts/otfs/Font Awesome 6 Brands-Regular-400.otf"))

# Enable custom fonts
showtext_auto()
showtext_opts(dpi = 300)

2. 📖 Read in the Data

# Load the TidyTuesday data
tuesdata <- tidytuesdayR::tt_load(2024, week = 53)

broadcast_media <- tuesdata$broadcast_media

tidytuesdayR::readme(tuesdata)

# Remove the original list (tuesdata) from the environment to free up space
rm(tuesdata)

3. 🕵️ Examine the Data

# View data structure
glimpse(broadcast_media)

# Summary statistics
skim(broadcast_media)

4. 🤼 Wrangle Data

# Step 1: Get top 4 affiliations by frequency
top_affiliations <- broadcast_media %>%
  filter(year >= max(year) - 5) %>%
  mutate(affiliation = str_split(affiliation, ",")) %>%
  unnest(affiliation) %>%
  mutate(affiliation = str_trim(affiliation),
         affiliation = str_to_lower(affiliation)) %>%
  group_by(affiliation) %>%
  count() %>%
  ungroup() %>%
  slice_max(order_by = n, n = 4, with_ties = FALSE) %>%
  pull(affiliation)

# Step 2: Filter rows with top affiliations
df <- broadcast_media %>%
  filter(year >= max(year) - 5) %>%
  mutate(affiliation = str_split(affiliation, ",")) %>%
  unnest(affiliation) %>%
  mutate(affiliation = str_trim(affiliation),
         affiliation = str_to_lower(affiliation)) %>%
  filter(str_detect(affiliation, paste(top_affiliations, collapse = "|"))) %>%
  mutate(affiliation = str_extract(affiliation, paste(top_affiliations, collapse = "|")))

# Step 3: Map logos to affiliations
logo_df <- tribble(
  ~affiliation, ~logo,
  "youtube", "youtube_logo.png",
  "netflix", "netflix_logo.png",
  "itunes", "itunes_logo.png",
  "instagram", "instagram_logo.png"
) |>
  mutate(
    logo = here::here("visualization", "TidyTuesday", "2024", "Week_53", "logo", logo)
  )

df <- df %>%
  left_join(logo_df, by = "affiliation") %>%
  mutate(
    logo_label = glue("<img src='{logo}' width='{ifelse(affiliation == 'itunes', 30, 50)}'/>")
  )

# Step 5: Count rank occurrences per affiliation
results <- df %>%
  group_by(affiliation, rank) %>%
  count() %>%
  arrange(desc(n))

# Step 6: Create summary text for plot facets
results_text <- results %>%
  mutate(text = glue("{rank}: {n}")) %>%
  group_by(affiliation) %>%
  summarise(summary = paste(text, collapse = "\n"), .groups = "drop")

# Step 7: Attach summary text to data for plotting
df <- df %>%
  left_join(results_text, by = "affiliation")

# Step 8: Print dataframe for validation
print(df)

5. 🔤 Text

title <- "James Beard Awards Wins and Nominations Between 2019 to 2024"

subtitle <- "Total Wins and Nominations for the Top Four Broadcast Media Across All Subcategories"

# Create a social media caption with customized colors and font for consistency in visualization
social <- andresutils::social_caption(font_family = "Roboto Condensed", icon_color = "grey25") 

# Construct the final plot caption by combining TidyTuesday details, data source, and the social caption
cap <- paste0(
  "#TidyTuesday: Week 53, 2024 | **Source**: James Beard Foundation, Wikipedia, curated by Jon Harmon | **Graphic**: ", social
)

6. 📊 Plot

# Step 1: Create base plot with dot plot, facet by logo, and summary text
p <- df %>%
  ggplot(aes(x = factor(year), alpha = rank, fill = factor(year))) +
  geom_dotplot(method = "histodot", binwidth = 1, stackdir = "up",
               stackgroups = TRUE, color="grey50", stackratio = 0.75, dotsize = .8) +
  facet_wrap(vars(logo_label), nrow = 1) +
  scale_alpha_manual(values = c(0.45, 1)) +
  scale_fill_manual(values = c("#ef476f", "#ffd166", "#06d6a0", "#118ab2", "#073b4c")) +
  labs(title = title, subtitle = subtitle, caption = cap) + 
   geom_text(data = df %>% distinct(affiliation, summary, .keep_all = TRUE),
            aes(x = -Inf, y = Inf, label = summary),
            hjust = 0, vjust = 1,
            inherit.aes = FALSE,
            family = "Roboto Condensed", size = 2, color = "grey25") +
  theme_void() +
  theme(
    text = element_text(family = "Roboto Condensed"),
    plot.title = element_text(hjust = 0.5, family = "Oswald", face = "bold", margin = margin(b = 5, t = 10)),
    plot.subtitle = element_text(hjust = 0.5, margin = margin(b = 10, t = 0)),
    plot.margin = margin(5, 10, 5, 10),
    plot.background = element_rect(fill = "#ffffff", colour = "#ffffff"),
    panel.background = element_rect(fill = "#ffffff", colour = "#ffffff"),
    plot.caption = element_markdown(hjust = 0.5, size = 5, margin = margin(b = 5, t = 10)),
    axis.text.x = element_text(size = 7),
    legend.position = "none",
    strip.text = element_markdown(hjust = 0.5),
    panel.spacing = unit(1.5, "cm")
  ) +
  coord_cartesian(clip = "off")

# Step 2: Add curve annotations and text for the Instagram facet
p2 <- p +
  geom_curve(data = df %>% filter(affiliation == "instagram"),
             aes(x = 2, y = 0.4, xend = 3, yend = 0.3),
             arrow = arrow(length = unit(0.15, "cm"), type = "closed"),
             color = "grey25", linewidth = 0.3, curvature = -0.3, inherit.aes = FALSE) +
  geom_text(data = df %>% filter(affiliation == "instagram"),
            aes(x = 2, y = 0.4, label = "Opaque dots\nshow wins"),
            color = "grey25", family = "Roboto Condensed", size = 1.9, hjust = 1) +
  geom_curve(data = df %>% filter(affiliation == "instagram"),
             aes(x = 4.4, y = 0.5, xend = 4.4, yend = 0.24),
             arrow = arrow(length = unit(0.15, "cm"), type = "closed"),
             color = "grey25", linewidth = 0.3, curvature = 0.8, inherit.aes = FALSE) +
  geom_text(data = df %>% filter(affiliation == "instagram"),
            aes(x = 4.4, y = 0.5, label = "Transparent dots\nshow nominations"),
            color = "grey25", family = "Roboto Condensed", size = 1.9, hjust = 0)

7. 💾 Save

# Save plot with dimensions
andresutils::save_plot(p2, type = "tidytuesday", year = 2024, week = 53, width = 8, height = 4.5)

8. 🚀 GitHub Repository

TipExpand for GitHub Repo

Explore the complete code for this visualization in the following Quarto file: tt_53_2024.qmd.

For additional visualizations and projects, click here.