Week 4: X-Men Comic Appearances

comics
pop culture
time series
Analyzing the popularity of X-Men characters across decades (1963-1992)
Published

March 25, 2025

Introduction

For this week’s Tidy Tuesday, I explored the “Mutant Moneyball” dataset, which catalogs the appearances of X-Men characters in comic books from 1963 to 1992. This dataset is inspired by the concept of “Moneyball” (made famous in baseball analytics) but applied to the fictional world of Marvel’s mutants.

The X-Men, created by Stan Lee and Jack Kirby in 1963, have been a staple of Marvel Comics for decades. This analysis looks at which characters appeared most frequently throughout different eras, tracking the popularity and evolution of the team’s roster over three decades.

The Data

The dataset contains information on each X-Men character’s appearances across different decades, including:

  • Character names
  • Total appearances
  • Appearances per decade (1960s, 1970s, 1980s, 1990s)
  • First appearance date

Let’s first take a look at the data structure:

# Display structure in a more readable format
str(mutant_moneyball[, 1:7])  # Only showing first 7 columns for clarity
tibble [26 × 7] (S3: tbl_df/tbl/data.frame)
 $ Member         : chr [1:26] "warrenWorthington" "hankMcCoy" "scottSummers" "bobbyDrake" ...
 $ TotalIssues    : num [1:26] 139 119 197 123 164 68 48 190 120 167 ...
 $ TotalIssues60s : num [1:26] 61 62 63 62 63 8 9 0 0 0 ...
 $ TotalIssues70s : num [1:26] 35 38 69 35 58 13 13 36 36 36 ...
 $ TotalIssues80s : num [1:26] 20 9 56 6 14 43 19 121 84 115 ...
 $ TotalIssues90s : num [1:26] 23 10 9 20 29 4 7 33 0 16 ...
 $ totalIssueCheck: num [1:26] 139 119 197 123 164 68 48 190 120 167 ...

And now let’s see the first few rows of the data:

# View the first few rows
head(mutant_moneyball[, 1:7]) %>%  # Only showing first 7 columns for clarity
  kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = FALSE)
Member TotalIssues TotalIssues60s TotalIssues70s TotalIssues80s TotalIssues90s totalIssueCheck
warrenWorthington 139 61 35 20 23 139
hankMcCoy 119 62 38 9 10 119
scottSummers 197 63 69 56 9 197
bobbyDrake 123 62 35 6 20 123
jeanGrey 164 63 58 14 29 164
alexSummers 68 8 13 43 4 68

Data Preparation

The character names in the dataset use CamelCase format (e.g., “ProfessorX”). Let’s create a function to format these names for better readability in our visualizations:

# Function to format names
format_names <- function(name) {
  words <- str_split(name, "(?=[A-Z])")[[1]]
  capitalized_words <- str_to_title(words)
  formatted_name <- paste(capitalized_words, collapse = " ")
  return(formatted_name)
}

# Apply the function to format character names using sapply instead of map_chr
mutant_moneyball <- mutant_moneyball %>%
  mutate(Member = sapply(Member, format_names))

# Check the formatted names
head(mutant_moneyball$Member)
   warrenWorthington            hankMcCoy         scottSummers 
"Warren Worthington"        "Hank Mc Coy"      "Scott Summers" 
          bobbyDrake             jeanGrey          alexSummers 
       "Bobby Drake"          "Jean Grey"       "Alex Summers"

Most Frequent X-Men Characters

Let’s first look at which X-Men characters appeared most frequently in comics during this period:

# Select and reorder based on total issues
mutant_moneyball_TI <- mutant_moneyball %>%
  select(Member, TotalIssues) %>%
  arrange(desc(TotalIssues)) %>%
  mutate(Member = factor(Member, levels = unique(Member)))

# Display top 10 characters
mutant_moneyball_TI %>%
  head(10) %>%
  kable(col.names = c("Character", "Total Appearances"),
        caption = "Top 10 X-Men Characters by Number of Appearances (1963-1992)") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = FALSE)
Top 10 X-Men Characters by Number of Appearances (1963-1992)
Character Total Appearances
Scott Summers 197
Ororo Munroe 190
Peter Rasputin 169
Charles Xavier 169
Logan Howlett 167
Jean Grey 164
Warren Worthington 139
Bobby Drake 123
Kurt Wagner 120
Hank Mc Coy 119

Visualizing Total Appearances

Now, let’s create a visualization showing the total comic appearances for each X-Men character:

# Create plot of total issues per member
p1 <- ggplot(mutant_moneyball_TI, aes(x = Member, y = TotalIssues, fill = TotalIssues)) +
  geom_col() +
  scale_fill_gradient(low = "yellow", high = "red") +
  labs(title = "Tallying the total comic book appearances of X-men icons (1963-1992)",
       subtitle = "Total comic book appearances of each X-Men character",
       x = "X-Men Member",
       y = "Total Issues") +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.2, size = 12, color = "white"),
    axis.text.y = element_text(color = "white", size = 12),
    plot.caption = element_text(hjust = 0.5),
    plot.background = element_rect(fill = "black"),
    text = element_text(color = "white"),
    panel.background = element_blank(),
    panel.grid = element_blank(),
    axis.ticks = element_line(color = "white"),
    plot.margin = margin(1, 0.5, 1, 0.5, "cm"),
    axis.title = element_text(face = "bold", size = 12),
    plot.title = element_text(face = "bold"),
    plot.subtitle = element_text(face = "italic")
  ) +
  guides(fill = "none")

# Display the plot
p1

The visualization clearly shows that certain characters like Professor X, Cyclops, and Storm have significantly more appearances than others, reflecting their importance as core team members.

Appearances Over Time

Next, let’s analyze how the appearances of the most popular X-Men characters changed over the decades from the 1960s to the 1990s:

# Look at the appearances of the most popular members over time
mutant_moneyball_decades <- mutant_moneyball %>%
  filter(TotalIssues > 100) %>% 
  arrange(desc(TotalIssues)) %>%
  mutate(Member = factor(Member, levels = unique(Member))) %>%
  select(Member, TotalIssues60s, TotalIssues70s, TotalIssues80s, TotalIssues90s) %>%
  rename("1960" = TotalIssues60s, "1970" = TotalIssues70s, "1980" = TotalIssues80s, "1990" = TotalIssues90s) %>%
  gather('1960', '1970', '1980', '1990', key = "Decade", value = "TotalIssues") %>%
  arrange(Member) %>%
  mutate(Decade = as.numeric(Decade))

# Create a line plot showing appearances over time for each popular character
p2 <- ggplot(mutant_moneyball_decades, aes(x = Decade, y = TotalIssues)) +
  geom_line(linewidth = 1.5, color = "yellow") +
  geom_point(size = 3, color = "red") +
  facet_wrap(~Member, ncol = 5) +
  labs(title = "Most popular X-Men character appearances (1963-1992)",
       subtitle = "Distribution of appearances for X-Men characters with over 100 total appearances",
       caption = "Tidy Tuesday 2024 Week 12 | Data: Rally's Mutant Moneyball | Viz: Annabel Smith",
       x = "Decade",
       y = "Total Issues") +
  theme_minimal() +
  theme(
    axis.text = element_text(color = "white", size = 12),
    plot.caption = element_text(hjust = 0.5),
    plot.background = element_rect(fill = "black"),
    text = element_text(color = "white"),
    panel.background = element_blank(),
    panel.grid = element_blank(),
    axis.ticks = element_line(color = "white"),
    plot.margin = margin(1, 0.5, 1, 0.5,"cm"),
    axis.title = element_text(face = "bold", size = 12),
    plot.title = element_text(face = "bold"),
    plot.subtitle = element_text(face = "italic"),
    panel.spacing.x = unit(1, "cm"), 
    panel.spacing.y = unit(2, "cm"),
    strip.text = element_text(color = "white", face = "bold", size = 12),
    strip.background = element_rect(color = NA, fill = "red")
  )

# Display the plot
p2

Character Appearances by Decade

To better understand how the X-Men roster evolved over time, let’s analyze the distribution of appearances across decades:

# Calculate the percentage of appearances in each decade
decade_summary <- mutant_moneyball %>%
  filter(TotalIssues > 50) %>%
  mutate(
    Pct60s = TotalIssues60s / TotalIssues * 100,
    Pct70s = TotalIssues70s / TotalIssues * 100,
    Pct80s = TotalIssues80s / TotalIssues * 100,
    Pct90s = TotalIssues90s / TotalIssues * 100
  ) %>%
  select(Member, TotalIssues, Pct60s, Pct70s, Pct80s, Pct90s) %>%
  arrange(desc(TotalIssues))

# Display the distribution of appearances
decade_summary %>%
  head(10) %>%
  mutate(
    Pct60s = round(Pct60s, 1),
    Pct70s = round(Pct70s, 1),
    Pct80s = round(Pct80s, 1),
    Pct90s = round(Pct90s, 1)
  ) %>%
  kable(col.names = c("Character", "Total Appearances", "1960s (%)", "1970s (%)", "1980s (%)", "1990s (%)"),
        caption = "Percentage of Appearances by Decade for Top X-Men Characters") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = FALSE) %>%
  row_spec(1:3, background = "#FFF8DC")
Percentage of Appearances by Decade for Top X-Men Characters
Character Total Appearances 1960s (%) 1970s (%) 1980s (%) 1990s (%)
Scott Summers 197 32.0 35.0 28.4 4.6
Ororo Munroe 190 0.0 18.9 63.7 17.4
Peter Rasputin 169 0.0 21.3 65.1 13.6
Charles Xavier 169 23.1 27.2 36.1 13.6
Logan Howlett 167 0.0 21.6 68.9 9.6
Jean Grey 164 38.4 35.4 8.5 17.7
Warren Worthington 139 43.9 25.2 14.4 16.5
Bobby Drake 123 50.4 28.5 4.9 16.3
Kurt Wagner 120 0.0 30.0 70.0 0.0
Hank Mc Coy 119 52.1 31.9 7.6 8.4

Combined Visualization

Finally, let’s combine our visualizations to create a comprehensive view of X-Men character appearances:

# Combine the two plots
combined_plots <- p1 / p2

# Display the combined visualization
combined_plots

Key Findings

From our analysis of the X-Men comic book appearances from 1963 to 1992, we can observe several interesting patterns:

  1. Core Team Members: Professor X, Cyclops, and Storm stand out as the most frequently appearing characters, reflecting their roles as core team members and leaders throughout the X-Men’s history.

  2. Generational Shifts: The data reveals clear generational shifts in the X-Men roster. Original team members like Cyclops and Beast were prominent in the 1960s, while characters like Storm, Colossus, and Wolverine rose to prominence in the late 1970s and 1980s.

  3. 1980s Explosion: There was a significant increase in appearances for most characters during the 1980s, corresponding to the era when the X-Men became one of Marvel’s most popular franchises, particularly after the “Dark Phoenix Saga” and introduction of characters like Wolverine.

  4. Evolving Team Dynamics: While some characters like Professor X maintained relatively consistent appearances across all decades, others show more variable patterns, reflecting changing team rosters and storylines.

  5. Rise of Wolverine: Despite not being an original team member, Wolverine quickly became one of the most featured characters after his introduction, highlighting his rapid rise to popularity.

Conclusion

This analysis provides insights into the evolution of the X-Men team over three critical decades of their publication history. The visualization techniques used here could be applied to other media franchises to track character popularity and narrative focus over time.

The clear generational shifts visible in the data reflect not only the evolving storylines within the X-Men universe but also changing editorial directions and reader preferences. Characters who maintained high appearance rates across multiple decades can be seen as the backbone of the franchise, while those with more concentrated appearances might represent specific eras or storytelling approaches.

For future analysis, it would be interesting to correlate these appearance counts with other metrics such as comic book sales figures, character introduction dates, or major storyline events to gain deeper insights into what drove the popularity of certain characters during specific time periods.

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