James Beard Foundation – Andres Gonzalez

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

Show code

# 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

Show code

# 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

Show code

# View data structure
glimpse(broadcast_media)

# Summary statistics
skim(broadcast_media)

4. 🤼 Wrangle Data

Show code

# 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

Show code

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

Show code

# 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

Show code

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

8. 🚀 GitHub Repository

Expand 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.

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--- title: "James Beard Foundation" description: "The James Beard Foundation Awards are annual honors recognizing excellence in the culinary, hospitality, and food media industries across the United States." lightbox: true date: December 30, 2024 categories: [TidyTuesday, R Programming, Data Visualization] tags: [ggplot2, data-visualization, tidyverse] image: "tt_53_2024.png" editor: markdown: wrap: sentence --- ![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](tt_53_2024.png){#fig-1} ### <mark> **How This Graphic Was Made** </mark> #### 1. 📦 Load Packages & Setup ```{r} #| label: load #| warning: false #| message: false #| results: hide # 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 ```{r} #| label: read #| include: true #| eval: true #| warning: false # 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 ```{r} #| label: examine #| include: true #| eval: true #| results: hide #| warning: false # View data structure glimpse(broadcast_media) # Summary statistics skim(broadcast_media) ``` #### 4. 🤼 Wrangle Data ```{r} #| label: wrangle #| warning: false #| results: hide # 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 ```{r} #| label: text #| include: true #| warning: false 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 ```{r} #| label: plot #| warning: false # 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 ```{r} #| label: save #| warning: false # Save plot with dimensions andresutils::save_plot(p2, type = "tidytuesday", year = 2024, week = 53, width = 8, height = 4.5) ``` #### 8. 🚀 GitHub Repository ::: {.callout-tip collapse="true"} ##### Expand for GitHub Repo Explore the complete code for this visualization in the following Quarto file: [`tt_53_2024.qmd`](https://github.com/OKcomputer626/Andres-Portfolio/blob/master/visualization/TidyTuesday/2024/Week_53/tt_53_2024.qmd). For additional visualizations and projects, [click here](https://github.com/OKcomputer626/Andres-Portfolio). :::