Gilmore Girls: Custom Themes and Netflix Data

Creating custom ggplot themes and color palettes inspired by Gilmore Girls, plus analyzing Netflix viewing data.

This post covers creating custom ggplot themes inspired by Gilmore Girls, extracting color palettes from show imagery, and visualizing Netflix engagement data.

NoteThings I played around with here!

Setup

library(here)
library(ggplot2)
library(showtext)
library(sysfonts)
library(tidyverse)
library(readxl)
library(ggimage)

# Set base directory
base_dir <- here::here()

# Load custom font
font_add("gg", regular = "assets/optiamadeus-solid.otf")

showtext_auto()

Creating a Gilmore Girls Theme

Color Palettes

First, let’s define color palettes inspired by the show’s aesthetic:

# Main Gilmore Girls palette
gg_pal <- c(
"#a1dce2", "#02365b", "#31a590", "#d0eeee", "#3eb4d2",
"#4E889D", "#2D637B", "#0077b6", "#0096c7", "#00b4d8",
"#48cae4", "#90e0ef", "#ADE8f4", "#caf0f8", "#8791A8", "#292F3A"
)

# Season-specific palettes
s1_palette <- c("#DF6D3B", "#F3A749", "#F5C371", "#F5D9AC")
s2_palette <- c("#125072", "#4C98A7", "#8DCBD2", "#CAE4E6")
s3_palette <- c("#4D306D", "#7F629B", "#BB9DC6", "#E2D0DD")
s4_palette <- c("#C1DAB9", "#89B997", "#518E66", "#205D3D")
s5_palette <- c("#992058", "#AF526F", "#E6AAB4", "#F7CFDF")
s6_palette <- c("#0A2965", "#3F4F7A", "#6877A6", "#C7C8E0")
s7_palette <- c("#782B2E", "#D47A73", "#EDA098", "#F9C6C1")

# Dark/winter palette
drk_palette <- c(
"#101422", "#273F68", "#4E6F9F", "#6083B5", "#ACB6CD",
"#8791A8", "#6A738B", "#4B5976", "#3C4459", "#242D40"
)

Palette Function

A function to generate palettes for discrete or continuous scales:

gg_palette <- function(num_cols, var_type = c("discrete", "continuous")) {
type <- match.arg(var_type)

if (missing(num_cols)) {
 num_cols <- length(gg_pal)
}

pal <- switch(
 type,
 "discrete" = rep(gg_pal, length.out = num_cols),
 "continuous" = grDevices::colorRampPalette(gg_pal)(num_cols)
)

structure(
 pal,
 name = "gg",
 class = "palette"
)
}

Font Check Helper

font_urls <- data.frame(
name = c("Architects Daughter", "Shadows Into Light Two", "Kalam"),
url = c(
 "https://fonts.google.com/specimen/Architects+Daughter/",
 "https://fonts.google.com/specimen/Shadows+Into+Light+Two/",
 "https://fonts.google.com/specimen/Kalam/"
)
)

check_font <- function(font_name) {
if (!requireNamespace("extrafont", quietly = TRUE)) {
 warning(
   "The font \"", font_name, "\" may or may not be installed on your system. ",
   "Please install the package `extrafont` if you'd like me to check for you.",
   call. = FALSE
 )
} else {
 if (!font_name %in% extrafont::fonts()) {
   if (font_name %in% font_urls$name) {
     warning(
       "Font '", font_name, "' isn't in the extrafont font list (but it may still work). ",
       "If recently installed, try running `extrafont::font_import()`. ",
       "To install, visit: ", font_urls[font_urls$name == font_name, "url"],
       call. = FALSE
     )
   } else {
     warning(
       "Font '", font_name, "' isn't in the extrafont font list (but it may still work). ",
       "If recently installed, try running `extrafont::font_import()`.",
       call. = FALSE
     )
   }
 }
}
}

Custom Theme

theme_gg <- function(
 base_family = "gg",
 base_size = 10.5,
 with.panel.grid = FALSE,
 text.color = "#000",
 title.color = "#D81F26",
 axis.text.color = "#2D637B",
 plot.background.color = "#fff",
 axis.text.size = base_size * 3/4,
 subtitle.text.size = base_size * 1.25,
 title.text.size = base_size * 1.75,
 caption_family = base_family,
 caption_size = 11,
 caption_face = "plain",
 caption_margin = 14,
 axis_title_just = "rt",
 plot_margin = ggplot2::margin(30, 30, 30, 30),
 base_theme = ggplot2::theme_minimal()
) {
if (!is.null(base_family)) check_font(base_family)

base_theme +
 ggplot2::theme(
   text = ggplot2::element_text(
     family = base_family,
     size = base_size,
     color = text.color
   ),
   plot.background = ggplot2::element_rect(
     fill = plot.background.color,
     color = "#fff"
   ),
   panel.background = ggplot2::element_blank(),
   panel.grid = ggplot2::element_line(
     color = if (with.panel.grid) axis.text.color else "transparent",
     linetype = 1
   ),
   axis.text = ggplot2::element_text(
     color = axis.text.color,
     size = axis.text.size
   ),
   legend.background = ggplot2::element_rect(fill = "transparent", color = NA),
   legend.key = ggplot2::element_rect(fill = "transparent", color = NA),
   legend.text = ggplot2::element_text(color = "#f8f8f2"),
   legend.title = ggplot2::element_text(face = "bold", color = "#000"),
   title = ggplot2::element_text(
     family = "gg",
     color = title.color,
     size = title.text.size
   )
 )
}

Test the Custom Font

set.seed(123)
hist(
rnorm(1000),
breaks = 30,
col = "#4E889D",
border = "white",
main = "",
xlab = "",
ylab = ""
)
title("Hey look it is the Gilmore font!", family = "gg", cex.main = 2)
text(2, 70, "N = 1000", family = "gg", cex = 2.5)

Extracting Color Palettes

Using the eyedroppeR package, we can create color palettes based on show imagery:

library(eyedroppeR)

# Extract colors from promotional images
path <- "https://media.glamour.com/photos/58280c860700a182135fdc61/master/w_2560%2Cc_limit/gilmore-girls-winter-alexis-bledel-lauren-graham-netflix-horizontal-2016.jpg"
extract_pal(10, path, label = "GG", sort = "auto")

Visualizing the Main Palette

cols <- c(
"#03045e", "#023e8a", "#0077b6", "#0096c7", "#00b4d8",
"#48cae4", "#90e0ef", "#ADE8f4", "#caf0f8"
) |>
fct_inorder()

tibble(x = 1:9, y = 1) |>
ggplot(aes(x, y, fill = cols)) +
geom_col(colour = "white") +
geom_label(
 aes(label = cols),
 nudge_y = -0.1,
 fill = "white",
 size = 3
) +
annotate(
 "label",
 x = 5, y = 0.5,
 label = "Gilmore Girls Colors",
 fill = "white",
 alpha = 0.8,
 size = 6,
 family = "gg"
) +
scale_fill_manual(values = as.character(cols)) +
theme_void() +
theme(legend.position = "none")

Fall Colors Palette

fall_cols <- c(
"#03071e", "#370617", "#6a040f", "#9d0208", "#d00000",
"#dc2f02", "#e85d04", "#f48c06", "#faa307", "#ffba08"
) |>
fct_inorder()

tibble(x = 1:10, y = 1) |>
ggplot(aes(x, y, fill = fall_cols)) +
geom_col(colour = "white") +
geom_label(
 aes(label = fall_cols),
 nudge_y = -0.1,
 fill = "white",
 size = 3
) +
annotate(
 "label",
 x = 5.5, y = 0.5,
 label = "Fall Colors",
 fill = "white",
 alpha = 0.8,
 size = 6,
 family = "gg"
) +
scale_fill_manual(values = as.character(fall_cols)) +
theme_void() +
theme(legend.position = "none")

Netflix Viewing Data

Let’s analyze how much people watched Gilmore Girls on Netflix using their engagement report.

Load and Prepare Data

library(tinytable)

netflix_raw <- read_excel("What_We_Watched_A_Netflix_Engagement_Report_2023Jan-Jun.xlsx")

# Set column names
cols <- c("title", "yes", "date", "hours")
names(netflix_raw) <- cols

# Filter for Gilmore Girls and add season info
netflix_data <- netflix_raw |> 
  filter(str_detect(title, "Gilmore Girls")) |> 
  mutate(
    # Convert hours to numeric (remove commas if present)
    hours = as.numeric(gsub(",", "", hours)),
    season = case_when(
      grepl("1", title) ~ 1,
      grepl("2", title) ~ 2,
      grepl("3", title) ~ 3,
      grepl("4", title) ~ 4,
      grepl("5", title) ~ 5,
      grepl("6", title) ~ 6,
      grepl("7", title) ~ 7,
      TRUE ~ 8
    ),
    season_label = if_else(season == 8, "A Year in the Life", paste("Season", season)),
    img = case_when(
      season == 1 ~ "assets/S1.png",
      season == 2 ~ "assets/S2.png",
      season == 3 ~ "assets/S3.png",
      season == 4 ~ "assets/S4.png",
      season == 5 ~ "assets/S5.png",
      season == 6 ~ "assets/S6.png",
      season == 7 ~ "assets/S7.png",
      season == 8 ~ "assets/AYITL.png"
    )
  )

netflix_data |>
  select(title, season, hours) |>
  tt(caption = "Gilmore Girls Netflix Viewing Hours (Jan-Jun 2023)") |>
  format_tt(j = "hours", fn = scales::comma) |>
  style_tt(bootstrap_class = "table table-striped table-hover")
Gilmore Girls Netflix Viewing Hours (Jan-Jun 2023)
title season hours
Gilmore Girls: Season 1 1 82,100,000
Gilmore Girls: Season 2 2 75,200,000
Gilmore Girls: Season 3 3 71,300,000
Gilmore Girls: Season 5 5 69,900,000
Gilmore Girls: Season 4 4 69,800,000
Gilmore Girls: Season 6 6 63,900,000
Gilmore Girls: Season 7 7 56,500,000
Gilmore Girls: A Year in the Life: Limited Series 8 17,100,000

Basic Plot

ggplot(netflix_data, aes(season, hours)) +
geom_point(size = 4, color = "#4E889D") +
geom_line(color = "#4E889D", alpha = 0.5) +
scale_x_continuous(breaks = 1:8, labels = c(paste("S", 1:7), "AYITL")) +
scale_y_continuous(labels = scales::comma) +
labs(
 title = "Gilmore Girls Netflix Viewing Hours",
 subtitle = "January - June 2023",
 x = "Season",
 y = "Hours Watched"
) +
theme_gg(base_size = 14) +
theme(
 panel.grid.major.y = element_line(color = "#ccc", linetype = "dotted"),
 axis.text = element_text(size = 12)
)

Adding Season Cover Images

Using geom_image() to replace points with season artwork:

netflix_data |> 
ggplot(aes(season, hours)) +
geom_image(aes(image = img), size = 0.12) +
scale_x_continuous(
 breaks = 1:8,
 labels = c(paste("Season", 1:7), "A Year in the Life")
) +
scale_y_continuous(labels = scales::comma) +
labs(
 title = "Gilmore Girls Netflix Viewing Hours",
 subtitle = "January - June 2023 Engagement Report",
 x = "",
 y = "Hours Watched",
 caption = "Data: Netflix Engagement Report"
) +
theme_gg(base_size = 14) +
theme(
 panel.grid.major.y = element_line(color = "#ccc", linetype = "dotted"),
 axis.text.x = element_text(angle = 45, hjust = 1, size = 10),
 axis.text.y = element_text(size = 12),
 plot.caption = element_text(size = 9, color = "#666")
)

Bar Chart with Images

# Define aspect ratio
asp_ratio <- 1.618

netflix_data |>
ggplot(aes(x = reorder(season_label, -hours), y = hours)) +
geom_col(fill = "#4E889D", alpha = 0.8, width = 0.3) +
geom_image(
 aes(image = img, y = hours + max(hours) * 0.05),
 size = 0.08,
 asp = asp_ratio
) +
geom_text(
 aes(label = scales::comma(hours)),
 vjust = -0.5,
 size = 4,
 family = "gg",
 color = "#02365b"
) +
scale_y_continuous(
 labels = scales::comma,
 expand = expansion(mult = c(0, 0.15))
) +
labs(
 title = "Which Season Do People Watch Most?",
 subtitle = "Netflix viewing hours, January - June 2023",
 x = "",
 y = "Hours Watched",
 caption = "Data: Netflix Engagement Report"
) +
theme_gg(base_size = 14) +
theme(
 panel.grid.major.y = element_line(color = "#ccc", linetype = "dotted"),
 axis.text.x = element_text(angle = 45, hjust = 1, size = 11),
 plot.caption = element_text(size = 9, color = "#666")
)

Making a raincloud plot?

devtools::source_gist("2a1bb0133ff568cbe28d", 
                      filename = "geom_flat_violin.R")

netflix_data <- netflix_data |>
  mutate(
    era = case_when(
      season %in% 1:3 ~ "Early Seasons (1–3)",
      season %in% 4:5 ~ "Middle Seasons (4–5)",
      season %in% 6:7 ~ "Late Seasons (6–7)",
      season == 8     ~ "A Year in the Life"
    ),
    era = factor(era, levels = c(
      "A Year in the Life",
      "Late Seasons (6–7)",
      "Middle Seasons (4–5)",
      "Early Seasons (1–3)"
    ))
  )

# Define era colors using your season palettes
era_fills  <- c(
  "Early Seasons (1–3)"  = "#DF6D3B",
  "Middle Seasons (4–5)" = "#518E66",
  "Late Seasons (6–7)"   = "#0A2965",
  "A Year in the Life"   = "#782B2E"
)

era_points <- c(
  "Early Seasons (1–3)"  = "#F3A749",
  "Middle Seasons (4–5)" = "#C1DAB9",
  "Late Seasons (6–7)"   = "#6877A6",
  "A Year in the Life"   = "#D47A73"
)

netflix_data |>
  ggplot(aes(x = era, y = hours, fill = era)) +
  geom_flat_violin(
    position = position_nudge(x = 0.15),
    trim = FALSE,
    alpha = 0.7,
    scale = "width",
    color = NA
  ) +
  geom_boxplot(
    width = 0.1,
    outlier.shape = NA,
    alpha = 0.5,
    color = "#02365b"
  ) +
  geom_point(
    aes(color = era),
    position = position_nudge(x = -0.15),
    size = 4,
    alpha = 0.8
  ) +
  # Label each point with its season number
  geom_text(
    aes(label = if_else(season == 8, "AYITL", paste0("S", season))),
    position = position_nudge(x = -0.25),
    size = 3,
    family = "Architects Daughter",
    color = "#02365b"
  ) +
  scale_fill_manual(values = era_fills) +
  scale_color_manual(values = era_points) +
  scale_y_continuous(labels = scales::comma) +
  coord_flip() +
  labs(
    title = "How Do Gilmore Girls Eras Compare?",
    subtitle = "Netflix viewing hours by era, January – June 2023",
    x = "",
    y = "Hours Watched",
    caption = "Data: Netflix Engagement Report"
  ) +
  theme_gg(base_size = 14) +
  theme(
    legend.position = "none",
    panel.grid.major.x = element_line(color = "#ccc", linetype = "dotted"),
    axis.text = element_text(size = 12)
  )

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