A Tale of Pixar’s Evolution: Duration and Reception

How Pixar films have evolved in length and how audiences vs critics perceive them.

An analysis of Pixar’s film evolution examining how movie durations have increased over time and comparing audience and critic reception patterns across their filmography. Visualizations reveal interesting patterns in run times and reception across different eras and franchises.

Author

Steven Ponce

Published

March 10, 2025

Figure 1: A two-panel visualization titled ‘A Tale of Pixar’s Evolution: Duration and Reception.’ The top panel shows ‘The Evolution of Pixar Film Durations (1995-2023)’ with a scatter plot and trend line demonstrating how Pixar films have increased in length from around 80 minutes to over 150 minutes, with points color-coded by film. The bottom panel displays ‘Audience vs. Critic Reception of Pixar Films’ comparing Cinema Score (audience ratings) on the y-axis with Rotten Tomatoes (critic consensus) on the x-axis. Films are grouped into four quadrants: Universal Acclaim, Critic Favorite, Audience Favorite, and Mixed Reception, revealing patterns in how different Pixar films are received by audiences versus critics.

Steps to Create this Graphic

1. Load Packages & Setup

Show code

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
if (!require("pacman")) install.packages("pacman")
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
    glue,           # Interpreted String Literals
    here,           # A Simpler Way to Find Your Files
    camcorder,      # Record Your Plot History 
    patchwork,      # The Composer of Plots # The Composer of Plots # The Composer of Plots
    ggrepel         # Position Non-Overlapping Text Labels with 'ggplot2'
    )
})

### |- figure size ----
gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  =  8,
    height =  10,
    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

tt <- tidytuesdayR::tt_load(2025, week = 10) 

pixar_films <- tt$pixar_films |> clean_names()
public_response <- tt$public_response |> clean_names()

tidytuesdayR::readme(tt)
rm(tt)

3. Examine the Data

Show code

glimpse(pixar_films)
skim(pixar_films)

glimpse(public_response)
skim(public_response)

4. Tidy Data

Show code

### |-  tidy data ----
# Combine the data for easier analysis
pixar_combined <- pixar_films |>
    left_join(public_response, by = "film")

# P1. Film Duration's ----

# Define eras for P1
p1_data <- pixar_combined |>
    mutate(
        era = case_when(
            release_date < as.Date("2001-01-01") ~ "Early Years (1995-2000)",
            release_date < as.Date("2006-01-01") ~ "Finding Nemo Era (2001-2005)",
            release_date < as.Date("2011-01-01") ~ "Golden Age (2006-2010)",
            release_date < as.Date("2016-01-01") ~ "Brave New World (2011-2015)",
            TRUE ~ "Modern Era (2016+)"
        )
    ) |> 
    filter(!is.na(film))

# Define era boundary lines
era_boundaries <- data.frame(
    x = as.Date(c("2001-01-01", "2006-01-01", "2011-01-01", "2016-01-01")),
    ymin = 70,
    ymax = 160
)

# Era labels coordinates
era_labels <- data.frame(
    x = as.Date(c("1997-06-01", "2003-06-01", "2008-06-01", "2013-06-01", "2020-06-01")),
    y = c(rep(160, 5)),
    label = c("Early Years", "Finding\nNemo Era", "Golden Age", "Brave New\nWorld", "Modern Era")
)

# P2. Audience vs. Critic Reception ----
p2_data <- pixar_combined |>  
    filter(!is.na(rotten_tomatoes) & !is.na(cinema_score)) |>
    mutate(
        # Convert Cinema Score to numeric
        cinema_score_numeric = case_when(
            cinema_score == "A+" ~ 10,
            cinema_score == "A" ~ 9,
            cinema_score == "A-" ~ 8.5,
            cinema_score == "B+" ~ 8,
            cinema_score == "B" ~ 7,
            cinema_score == "B-" ~ 6.5,
            TRUE ~ 5
        ),
        # Create release year groups
        release_decade = case_when(
            release_date < as.Date("2000-01-01") ~ "1995-1999",
            release_date < as.Date("2010-01-01") ~ "2000-2009",
            release_date < as.Date("2020-01-01") ~ "2010-2019",
            TRUE ~ "2020+"
        ),
        # Create selective labeling flag
        label_film = case_when(
            cinema_score_numeric >= 9 & rotten_tomatoes < 90 ~ TRUE,
            cinema_score_numeric < 10 & rotten_tomatoes >= 99 ~ TRUE,
            rotten_tomatoes < 50 ~ TRUE,
            film %in% c("Toy Story", "Inside Out", "Coco", "Soul") ~ TRUE,
            TRUE ~ FALSE
        )
    )

# Create a filtered dataframe for labels
p2_labels <- p2_data |> filter(label_film == TRUE)

# Define reception area dividing lines
reception_lines <- data.frame(
    type = c("vertical_1",  "horizontal"),
    x1 = c(90, 40),
    y1 = c(5, 9),
    x2 = c(90, 105),
    y2 = c(10.5, 9)
)

5. Visualization Parameters

Show code

### |-  plot aesthetics ----
# Color for P1
colors <- get_theme_colors(palette = c(
    "gray40", "gray60", "gray70", "gray80", "#87BBA2")
    )

### |-  titles and caption ----
title_text <- str_glue("A Tale of Pixar's Evolution: Duration and Reception")

subtitle_text <- str_glue("How Pixar films have evolved in length and how audiences vs critics perceive them.")

# Create caption
caption_text <- create_social_caption(
    tt_year = 2025,
    tt_week = 10,
    source_text =  "pixarfilms R package" 
)

### |-  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 = element_text(color = colors$text, face = "bold", size = rel(0.8)),
        axis.text = element_text(color = colors$text, size = rel(0.7)),
        
        # Grid elements
        panel.grid.minor = element_line(color = "gray80", linewidth = 0.05),
        panel.grid.major = element_line(color = "gray80", linewidth = 0.02),
        
        # Legend elements
        legend.position = "plot",
        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 = 10, r = 10, b = 10, l = 10),
    )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code

### |-  Plot  ----
# P1. Film Duration's ----
p1 <- ggplot(p1_data, aes(x = release_date, y = run_time)) +
  # Geoms
  geom_vline(
    data = era_boundaries,
    aes(xintercept = x),
    linetype = "dotted",
    color = colors$palette[2],
    alpha = 0.4
  ) +
  geom_smooth(
    method = "loess", se = FALSE, color = colors$palette[1],
    linewidth = 0.5, alpha = 0.6, linetype = "dashed", na.rm = TRUE
  ) +
  geom_text_repel(
    aes(label = film),
    size = 3,
    max.overlaps = 10,
    box.padding = 0.5,
    colour = colors$palette[1],
    segment.color = colors$palette[3],
    min.segment.length = 0.2,
    seed = 123
  ) +
  geom_point(size = 3, color = colors$palette[5], alpha = 0.9, na.rm = TRUE) +
  # Annotate
  annotate(
    "text",
    x = era_labels$x,
    y = era_labels$y,
    label = era_labels$label,
    alpha = 0.8,
    fontface = "bold",
    size = 3
  ) +
  # Scales
  scale_x_date(
    date_breaks = "5 years",
    date_labels = "%Y",
    limits = c(as.Date("1994-01-01"), NA)
  ) +
  scale_y_continuous(
    breaks = seq(80, 180, by = 10),
  ) +
  coord_cartesian(clip = "off") +
  # Labs
  labs(
    title = "The Evolution of Pixar Film Durations (1995-2023)",
    subtitle = "From 80-minute early films to 100+ minute modern features",
    x = "Release Date",
    y = "Run Time (minutes)"
  )

# P2. Audience vs. Critic Reception ----
p2 <- ggplot(p2_data, aes(x = rotten_tomatoes, y = cinema_score_numeric)) +
  # Geoms
  geom_segment(
    data = reception_lines,
    aes(x = x1, y = y1, xend = x2, yend = y2),
    linetype = "dotted", color = colors$palette[1], alpha = 0.8
  ) +
  geom_text_repel(
    data = p2_labels,
    aes(label = film),
    size = 3,
    box.padding = 0.8,
    point.padding = 0.5,
    colour = colors$palette[1],
    segment.color = colors$palette[3],
    min.segment.length = 0.2,
    max.overlaps = 30,
    force = 3,
    seed = 145
  ) +
  geom_point(
    aes(size = 3),
    color = colors$palette[5], alpha = 0.8
  ) +
  # Annotate
  annotate(
    "text",
    x = 105, y = 10.8,
    label = "Universal Acclaim",
    size = 3, fontface = "bold", alpha = 0.7,
    color = colors$palette[1],
    hjust = 1
  ) +
  annotate(
    "text",
    x = 105, y = 7,
    label = "Critic Favorite",
    size = 3, fontface = "bold", alpha = 0.7,
    color = colors$palette[1],
    hjust = 1
  ) +
  annotate(
    "text",
    x = 75, y = 10.8,
    label = "Audience Favorite",
    size = 3, fontface = "bold", alpha = 0.7,
    color = colors$palette[1],
    hjust = 0.5
  ) +
  annotate(
    "text",
    x = 75, y = 7,
    label = "Mixed Reception",
    size = 3, fontface = "bold", alpha = 0.7,
    color = colors$palette[1],
    hjust = 0.5
  ) +
  # Scales
  scale_color_brewer(
    palette = "Set1",
    name = "Film Era"
  ) +
  scale_size_continuous(
    range = c(2, 5),
    name = "Runtime (min)"
  ) +
  scale_y_continuous(
    breaks = c(10, 9, 8.5, 8, 7, 6.5),
    labels = c("A+", "A", "A-", "B+", "B", "B-"),
    limits = c(6, 10.8),
    minor_breaks = NULL,
    expand = expansion(mult = c(0.02, 0.02))
  ) +
  scale_x_continuous(
    breaks = seq(40, 100, by = 10),
    limits = c(40, 105),
    minor_breaks = NULL,
    expand = expansion(mult = c(0.02, 0.02))
  ) +
  coord_cartesian(clip = "off") +
  # Labs
  labs(
    title = "Audience vs. Critic Reception of Pixar Films",
    subtitle = "Comparing Cinema Score (audience ratings) with Rotten Tomatoes (critic consensus)",
    y = "Cinema Score (audience rating)",
    x = "Rotten Tomatoes Score (critic rating)"
  ) 

# Combined Plot -----
combined_plot <- (p1 / plot_spacer() / p2) +
  plot_layout(
    height = c(1, 0.005, 1),
    ncol = 1
  )

combined_plot <- combined_plot +
  plot_annotation(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    theme = theme(
      plot.title = element_text(
        size = rel(1.8),
        family = fonts$title,
        face = "bold",
        color = colors$title,
        lineheight = 1.1,
        margin = margin(t = 5, b = 5)
      ),
      plot.subtitle = element_text(
        size = rel(0.85),
        family = fonts$subtitle,
        color = colors$subtitle,
        lineheight = 1.2,
        margin = margin(t = 5, b = 10)
      ),
      plot.caption = element_markdown(
        size   = rel(0.65),
        family = fonts$caption,
        color  = colors$caption,
        hjust  = 0.5,
        margin = margin(t = 10)
      ),
      plot.margin = margin(t = 20, r = 10, b = 20, l = 10),
    )
  )

7. Save

Show code

### |-  plot image ----  
save_plot_patchwork(
  plot = combined_plot, 
  type = "tidytuesday", 
  year = 2025, 
  week = 10, 
  width = 8,
  height = 10
)

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] ggrepel_0.9.6   patchwork_1.3.0 camcorder_0.1.0 here_1.0.1     
 [5] glue_1.8.0      scales_1.3.0    skimr_2.1.5     janitor_2.2.0  
 [9] showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2   
[13] lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4    
[17] purrr_1.0.2     readr_2.1.5     tidyr_1.3.1     tibble_3.2.1   
[21] ggplot2_3.5.1   tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1   farver_2.1.2       fastmap_1.2.0      gh_1.4.1          
 [5] digest_0.6.37      timechange_0.3.0   lifecycle_1.0.4    rsvg_2.6.1        
 [9] magrittr_2.0.3     compiler_4.4.0     rlang_1.1.4        tools_4.4.0       
[13] utf8_1.2.4         yaml_2.3.10        knitr_1.49         htmlwidgets_1.6.4 
[17] bit_4.5.0          curl_6.0.0         xml2_1.3.6         repr_1.1.7        
[21] tidytuesdayR_1.1.2 withr_3.0.2        grid_4.4.0         fansi_1.0.6       
[25] colorspace_2.1-1   gitcreds_0.1.2     cli_3.6.3          rmarkdown_2.29    
[29] crayon_1.5.3       generics_0.1.3     rstudioapi_0.17.1  tzdb_0.4.0        
[33] commonmark_1.9.2   splines_4.4.0      parallel_4.4.0     ggplotify_0.1.2   
[37] base64enc_0.1-3    vctrs_0.6.5        yulab.utils_0.1.8  Matrix_1.7-0      
[41] jsonlite_1.8.9     gridGraphics_0.5-1 hms_1.1.3          bit64_4.5.2       
[45] systemfonts_1.1.0  magick_2.8.5       gifski_1.32.0-1    codetools_0.2-20  
[49] stringi_1.8.4      gtable_0.3.6       munsell_0.5.1      pillar_1.9.0      
[53] rappdirs_0.3.3     htmltools_0.5.8.1  R6_2.5.1           httr2_1.0.6       
[57] rprojroot_2.0.4    lattice_0.22-6     vroom_1.6.5        evaluate_1.0.1    
[61] markdown_1.13      gridtext_0.1.5     snakecase_0.11.1   renv_1.0.3        
[65] Rcpp_1.0.13-1      nlme_3.1-164       svglite_2.1.3      mgcv_1.9-1        
[69] xfun_0.49          fs_1.6.5           pkgconfig_2.0.3

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in tt_2025_10.qmd.

For the full repository, click here.

10. References

Expand for References

Data Sources:
- TidyTuesday 2025 Week 10: Pixar Films

--- title: "A Tale of Pixar's Evolution: Duration and Reception" subtitle: "How Pixar films have evolved in length and how audiences vs critics perceive them." description: "An analysis of Pixar's film evolution examining how movie durations have increased over time and comparing audience and critic reception patterns across their filmography. Visualizations reveal interesting patterns in run times and reception across different eras and franchises." author: "Steven Ponce" date: "2025-03-10" categories: ["TidyTuesday", "Data Visualization", "R Programming", "2025"] tags: [ "pixar", "film analysis", "data storytelling", "ggplot2", "animation", "movie ratings", "cinema", "audience reception", "film critics", "time series", "entertainment data", "patchwork" ] image: "thumbnails/tt_2025_10.png" format: html: toc: true toc-depth: 5 code-link: true code-fold: true code-tools: true code-summary: "Show code" self-contained: true theme: light: [flatly, assets/styling/custom_styles.scss] dark: [darkly, assets/styling/custom_styles_dark.scss] 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/TidyTuesday/2025/tt_2025_10.html" # description: "Exploring 25+ years of Pixar films: See how movie lengths have grown over time and discover which films critics loved but audiences didn't (and vice versa)! #TidyTuesday #DataViz" # # twitter: true # linkedin: true # email: true # facebook: false # reddit: false # stumble: false # tumblr: false # mastodon: true # bsky: true --- ![A two-panel visualization titled 'A Tale of Pixar's Evolution: Duration and Reception.' The top panel shows 'The Evolution of Pixar Film Durations (1995-2023)' with a scatter plot and trend line demonstrating how Pixar films have increased in length from around 80 minutes to over 150 minutes, with points color-coded by film. The bottom panel displays 'Audience vs. Critic Reception of Pixar Films' comparing Cinema Score (audience ratings) on the y-axis with Rotten Tomatoes (critic consensus) on the x-axis. Films are grouped into four quadrants: Universal Acclaim, Critic Favorite, Audience Favorite, and Mixed Reception, revealing patterns in how different Pixar films are received by audiences versus critics.](tt_2025_10.png){#fig-1} ### <mark> **Steps to Create this Graphic** </mark> #### 1. Load Packages & Setup ```{r} #| label: load #| warning: false #| message: false #| results: "hide" ## 1. LOAD PACKAGES & SETUP ---- suppressPackageStartupMessages({ if (!require("pacman")) install.packages("pacman") 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 glue, # Interpreted String Literals here, # A Simpler Way to Find Your Files camcorder, # Record Your Plot History patchwork, # The Composer of Plots # The Composer of Plots # The Composer of Plots ggrepel # Position Non-Overlapping Text Labels with 'ggplot2' ) }) ### |- figure size ---- gg_record( dir = here::here("temp_plots"), device = "png", width = 8, height = 10, 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 tt <- tidytuesdayR::tt_load(2025, week = 10) pixar_films <- tt$pixar_films |> clean_names() public_response <- tt$public_response |> clean_names() tidytuesdayR::readme(tt) rm(tt) ``` #### 3. Examine the Data ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(pixar_films) skim(pixar_films) glimpse(public_response) skim(public_response) ``` #### 4. Tidy Data ```{r} #| label: tidy #| warning: false ### |- tidy data ---- # Combine the data for easier analysis pixar_combined <- pixar_films |> left_join(public_response, by = "film") # P1. Film Duration's ---- # Define eras for P1 p1_data <- pixar_combined |> mutate( era = case_when( release_date < as.Date("2001-01-01") ~ "Early Years (1995-2000)", release_date < as.Date("2006-01-01") ~ "Finding Nemo Era (2001-2005)", release_date < as.Date("2011-01-01") ~ "Golden Age (2006-2010)", release_date < as.Date("2016-01-01") ~ "Brave New World (2011-2015)", TRUE ~ "Modern Era (2016+)" ) ) |> filter(!is.na(film)) # Define era boundary lines era_boundaries <- data.frame( x = as.Date(c("2001-01-01", "2006-01-01", "2011-01-01", "2016-01-01")), ymin = 70, ymax = 160 ) # Era labels coordinates era_labels <- data.frame( x = as.Date(c("1997-06-01", "2003-06-01", "2008-06-01", "2013-06-01", "2020-06-01")), y = c(rep(160, 5)), label = c("Early Years", "Finding\nNemo Era", "Golden Age", "Brave New\nWorld", "Modern Era") ) # P2. Audience vs. Critic Reception ---- p2_data <- pixar_combined |> filter(!is.na(rotten_tomatoes) & !is.na(cinema_score)) |> mutate( # Convert Cinema Score to numeric cinema_score_numeric = case_when( cinema_score == "A+" ~ 10, cinema_score == "A" ~ 9, cinema_score == "A-" ~ 8.5, cinema_score == "B+" ~ 8, cinema_score == "B" ~ 7, cinema_score == "B-" ~ 6.5, TRUE ~ 5 ), # Create release year groups release_decade = case_when( release_date < as.Date("2000-01-01") ~ "1995-1999", release_date < as.Date("2010-01-01") ~ "2000-2009", release_date < as.Date("2020-01-01") ~ "2010-2019", TRUE ~ "2020+" ), # Create selective labeling flag label_film = case_when( cinema_score_numeric >= 9 & rotten_tomatoes < 90 ~ TRUE, cinema_score_numeric < 10 & rotten_tomatoes >= 99 ~ TRUE, rotten_tomatoes < 50 ~ TRUE, film %in% c("Toy Story", "Inside Out", "Coco", "Soul") ~ TRUE, TRUE ~ FALSE ) ) # Create a filtered dataframe for labels p2_labels <- p2_data |> filter(label_film == TRUE) # Define reception area dividing lines reception_lines <- data.frame( type = c("vertical_1", "horizontal"), x1 = c(90, 40), y1 = c(5, 9), x2 = c(90, 105), y2 = c(10.5, 9) ) ``` #### 5. Visualization Parameters ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- # Color for P1 colors <- get_theme_colors(palette = c( "gray40", "gray60", "gray70", "gray80", "#87BBA2") ) ### |- titles and caption ---- title_text <- str_glue("A Tale of Pixar's Evolution: Duration and Reception") subtitle_text <- str_glue("How Pixar films have evolved in length and how audiences vs critics perceive them.") # Create caption caption_text <- create_social_caption( tt_year = 2025, tt_week = 10, source_text = "pixarfilms R package" ) ### |- 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$text, size = rel(0.78), margin = margin(b = 20)), # Axis elements axis.title = element_text(color = colors$text, face = "bold", size = rel(0.8)), axis.text = element_text(color = colors$text, size = rel(0.7)), # Grid elements panel.grid.minor = element_line(color = "gray80", linewidth = 0.05), panel.grid.major = element_line(color = "gray80", linewidth = 0.02), # Legend elements legend.position = "plot", legend.title = element_text(family = fonts$text, size = rel(0.8)), legend.text = element_text(family = fonts$text, size = rel(0.7)), # Plot margins plot.margin = margin(t = 10, r = 10, b = 10, l = 10), ) ) # Set theme theme_set(weekly_theme) ``` #### 6. Plot ```{r} #| label: plot #| warning: false ### |- Plot ---- # P1. Film Duration's ---- p1 <- ggplot(p1_data, aes(x = release_date, y = run_time)) + # Geoms geom_vline( data = era_boundaries, aes(xintercept = x), linetype = "dotted", color = colors$palette[2], alpha = 0.4 ) + geom_smooth( method = "loess", se = FALSE, color = colors$palette[1], linewidth = 0.5, alpha = 0.6, linetype = "dashed", na.rm = TRUE ) + geom_text_repel( aes(label = film), size = 3, max.overlaps = 10, box.padding = 0.5, colour = colors$palette[1], segment.color = colors$palette[3], min.segment.length = 0.2, seed = 123 ) + geom_point(size = 3, color = colors$palette[5], alpha = 0.9, na.rm = TRUE) + # Annotate annotate( "text", x = era_labels$x, y = era_labels$y, label = era_labels$label, alpha = 0.8, fontface = "bold", size = 3 ) + # Scales scale_x_date( date_breaks = "5 years", date_labels = "%Y", limits = c(as.Date("1994-01-01"), NA) ) + scale_y_continuous( breaks = seq(80, 180, by = 10), ) + coord_cartesian(clip = "off") + # Labs labs( title = "The Evolution of Pixar Film Durations (1995-2023)", subtitle = "From 80-minute early films to 100+ minute modern features", x = "Release Date", y = "Run Time (minutes)" ) # P2. Audience vs. Critic Reception ---- p2 <- ggplot(p2_data, aes(x = rotten_tomatoes, y = cinema_score_numeric)) + # Geoms geom_segment( data = reception_lines, aes(x = x1, y = y1, xend = x2, yend = y2), linetype = "dotted", color = colors$palette[1], alpha = 0.8 ) + geom_text_repel( data = p2_labels, aes(label = film), size = 3, box.padding = 0.8, point.padding = 0.5, colour = colors$palette[1], segment.color = colors$palette[3], min.segment.length = 0.2, max.overlaps = 30, force = 3, seed = 145 ) + geom_point( aes(size = 3), color = colors$palette[5], alpha = 0.8 ) + # Annotate annotate( "text", x = 105, y = 10.8, label = "Universal Acclaim", size = 3, fontface = "bold", alpha = 0.7, color = colors$palette[1], hjust = 1 ) + annotate( "text", x = 105, y = 7, label = "Critic Favorite", size = 3, fontface = "bold", alpha = 0.7, color = colors$palette[1], hjust = 1 ) + annotate( "text", x = 75, y = 10.8, label = "Audience Favorite", size = 3, fontface = "bold", alpha = 0.7, color = colors$palette[1], hjust = 0.5 ) + annotate( "text", x = 75, y = 7, label = "Mixed Reception", size = 3, fontface = "bold", alpha = 0.7, color = colors$palette[1], hjust = 0.5 ) + # Scales scale_color_brewer( palette = "Set1", name = "Film Era" ) + scale_size_continuous( range = c(2, 5), name = "Runtime (min)" ) + scale_y_continuous( breaks = c(10, 9, 8.5, 8, 7, 6.5), labels = c("A+", "A", "A-", "B+", "B", "B-"), limits = c(6, 10.8), minor_breaks = NULL, expand = expansion(mult = c(0.02, 0.02)) ) + scale_x_continuous( breaks = seq(40, 100, by = 10), limits = c(40, 105), minor_breaks = NULL, expand = expansion(mult = c(0.02, 0.02)) ) + coord_cartesian(clip = "off") + # Labs labs( title = "Audience vs. Critic Reception of Pixar Films", subtitle = "Comparing Cinema Score (audience ratings) with Rotten Tomatoes (critic consensus)", y = "Cinema Score (audience rating)", x = "Rotten Tomatoes Score (critic rating)" ) # Combined Plot ----- combined_plot <- (p1 / plot_spacer() / p2) + plot_layout( height = c(1, 0.005, 1), ncol = 1 ) combined_plot <- combined_plot + plot_annotation( title = title_text, subtitle = subtitle_text, caption = caption_text, theme = theme( plot.title = element_text( size = rel(1.8), family = fonts$title, face = "bold", color = colors$title, lineheight = 1.1, margin = margin(t = 5, b = 5) ), plot.subtitle = element_text( size = rel(0.85), family = fonts$subtitle, color = colors$subtitle, lineheight = 1.2, margin = margin(t = 5, b = 10) ), plot.caption = element_markdown( size = rel(0.65), family = fonts$caption, color = colors$caption, hjust = 0.5, margin = margin(t = 10) ), plot.margin = margin(t = 20, r = 10, b = 20, l = 10), ) ) ``` #### 7. Save ```{r} #| label: save #| warning: false ### |- plot image ---- save_plot_patchwork( plot = combined_plot, type = "tidytuesday", year = 2025, week = 10, width = 8, height = 10 ) ``` #### 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 [`tt_2025_10.qmd`](https://github.com/poncest/personal-website/blob/master/data_visualizations/TidyTuesday/2025/tt_2025_10.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: - TidyTuesday 2025 Week 10: [Pixar Films](https://github.com/rfordatascience/tidytuesday/blob/main/data/2025/2025-03-11) :::