Speed Distribution by Pokémon Type

Electric and Flying types tend to be fastest, while Rock types are slowest

TidyTuesday

2025

This visualization explores the speed distribution across different Pokémon types using data from the {pokemon} R package. The beeswarm plot reveals distinct patterns in how speed stats vary between types, with Electric and Flying Pokémon showcasing the highest average speeds while Rock types tend to be the slowest. Diamond markers indicate mean values for each type, providing insight into competitive gameplay advantages.

Author

Steven Ponce

Published

March 23, 2025

Figure 1: A beeswarm plot showing speed distribution across Pokémon types. Types are ordered by average speed, with Flying and Electric types at the bottom (fastest) and Rock near the top (slowest). Purple diamonds mark the mean speed for each type, and a vertical dashed line at 100 indicates the “Fast” threshold. Most Pokémon fall between 50-100 speed, with outliers reaching up to 150.

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
    ggbeeswarm,     # Categorical Scatter (Violin Point) Plots 
    camcorder       # Record Your Plot History 
    )
})

### |- figure size ----
gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  =  7,
    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 = 13) 

pokemon_raw <- tt$pokemon_df |> clean_names()

tidytuesdayR::readme(tt)
rm(tt)

3. Examine the Data

Show code

glimpse(pokemon_raw)
skim(pokemon_raw)

4. Tidy Data

Show code

### |-  tidy data ----
pokemon_clean <- pokemon_raw |>
    select(pokemon, type_1, speed, generation_id, color_1) |>
    filter(!is.na(speed), !is.na(type_1)) |>
    mutate(generation = as.factor(generation_id))

# Summarize speed stats 
ordered_types <- pokemon_clean |>
    group_by(type_1) |>
    summarize(
        across(
            speed,
            list(
                mean = ~mean(.x, na.rm = TRUE),
                median = ~median(.x, na.rm = TRUE),
                min = ~min(.x, na.rm = TRUE),
                max = ~max(.x, na.rm = TRUE)
            ),
            .names = "{fn}_{.col}"
        ),
        count = n(),
        .groups = "drop"
    ) |>
    arrange(desc(mean_speed)) |>
    pull(type_1)

# plot data
pokemon_clean <- pokemon_clean |>
    mutate(type_1 = factor(type_1, levels = ordered_types))

5. Visualization Parameters

Show code

### |-  plot aesthetics ----
colors <- get_theme_colors(
    palette = c(rep("#7570B3", 18))
)

### |-  titles and caption ----
title_text <- str_glue("Speed Distribution by Pokémon Type")
subtitle_text <- str_glue("Electric and Flying types tend to be fastest, while Rock types are slowest")

# Create caption
caption_text <- create_social_caption(
    tt_year = 2025,
    tt_week = 13,
    source_text =  "{ pokemon 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.direction = "horizontal",
        legend.title = element_text(family = fonts$text, size = rel(0.8), face = "bold"),
        legend.text = element_text(family = fonts$text, size = rel(0.7)),
        
        # two-row legend
        legend.box.spacing = unit(0.4, "cm"),
        legend.key.width = unit(1.5, "cm"),
        legend.spacing.x = unit(0.2, "cm"),
 
        legend.box = "horizontal",
        legend.box.just = "left",
        
        # Plot margins 
        plot.margin = margin(t = 20, r = 20, b = 20, l = 20),
    )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code

### |-  Plot  ----
p <- pokemon_clean |>
    ggplot(aes(x = type_1, y = speed, color = type_1)) +
    # Geoms
    geom_beeswarm(cex = 1, size = 1.2, alpha = 0.75, shape = 21, na.rm = TRUE) +
    stat_summary(   # mean indicator
        fun = mean, 
        geom = "point", 
        shape = 18, 
        size = 3, 
        color =  alpha(colors$palette[1], 0.9),
    ) +
    geom_hline(
        yintercept = 100, 
        linetype = "dashed",
        color = "darkgray", 
        ) +
    # Annotate
    annotate(
        "text", x = length(ordered_types), y = 102, 
        label = "Fast (Speed ≥ 100)", hjust = 0, vjust = -2,
        size = 3, color = "dimgray"
        ) +
    annotate(
        "point",  
        x = "steel", 
        y = 140, 
        shape = 18, 
        size = 4, 
        color = alpha(colors$palette[1], 0.9)
    ) +
    annotate(
        "text",
        x = "steel", y = 141, label = "  = Mean",
        color =  alpha(colors$palette[1], 0.9),
        size = 4.2,
        angle = 0,
        hjust = 0,
        fontface = "bold"
    ) +
    
    # Scales
    scale_color_manual(values = colors$palette) +
    coord_flip() +
    # labs
    labs(
        title = title_text,
        subtitle = subtitle_text,
        caption = caption_text,
        x = NULL,
        y = "Base Speed Stat"
    ) +
    # Theme
    theme(
        plot.title = element_text(
            size = rel(2),
            family = fonts$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 = alpha(colors$subtitle, 0.9),
            lineheight = 1.2,
            margin = margin(t = 5, b = 10)
        ),
        plot.caption = element_markdown(
            size = rel(0.65),
            family = fonts$caption,
            color = colors$caption,
            hjust = 0.5,
            margin = margin(t = 10)
        )
    )

7. Save

Show code

### |-  plot image ----  
save_plot_patchwork(
  plot = p, 
  type = "tidytuesday", 
  year = 2025, 
  week = 13, 
  width = 7,
  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] camcorder_0.1.0  ggbeeswarm_0.7.2 here_1.0.1       glue_1.8.0      
 [5] scales_1.3.0     skimr_2.1.5      janitor_2.2.0    showtext_0.9-7  
 [9] showtextdb_3.0   sysfonts_0.8.9   ggtext_0.1.2     lubridate_1.9.3 
[13] forcats_1.0.0    stringr_1.5.1    dplyr_1.1.4      purrr_1.0.2     
[17] readr_2.1.5      tidyr_1.3.1      tibble_3.2.1     ggplot2_3.5.1   
[21] tidyverse_2.0.0  pacman_0.5.1    

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1   vipor_0.4.7        farver_2.1.2       fastmap_1.2.0     
 [5] gh_1.4.1           digest_0.6.37      timechange_0.3.0   lifecycle_1.0.4   
 [9] rsvg_2.6.1         magrittr_2.0.3     compiler_4.4.0     rlang_1.1.4       
[13] tools_4.4.0        utf8_1.2.4         yaml_2.3.10        knitr_1.49        
[17] labeling_0.4.3     htmlwidgets_1.6.4  bit_4.5.0          curl_6.0.0        
[21] xml2_1.3.6         repr_1.1.7         tidytuesdayR_1.1.2 withr_3.0.2       
[25] grid_4.4.0         fansi_1.0.6        colorspace_2.1-1   gitcreds_0.1.2    
[29] cli_3.6.3          rmarkdown_2.29     crayon_1.5.3       generics_0.1.3    
[33] rstudioapi_0.17.1  tzdb_0.4.0         commonmark_1.9.2   parallel_4.4.0    
[37] ggplotify_0.1.2    base64enc_0.1-3    vctrs_0.6.5        yulab.utils_0.1.8 
[41] jsonlite_1.8.9     gridGraphics_0.5-1 hms_1.1.3          bit64_4.5.2       
[45] beeswarm_0.4.0     systemfonts_1.1.0  magick_2.8.5       gifski_1.32.0-1   
[49] codetools_0.2-20   stringi_1.8.4      gtable_0.3.6       munsell_0.5.1     
[53] pillar_1.9.0       rappdirs_0.3.3     htmltools_0.5.8.1  R6_2.5.1          
[57] httr2_1.0.6        rprojroot_2.0.4    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      svglite_2.1.3      xfun_0.49          fs_1.6.5          
[69] pkgconfig_2.0.3

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in tt_2025_13.qmd.

For the full repository, click here.

10. References

Expand for References

Data Sources:
- TidyTuesday 2025 Week 13: Pokemon

--- title: "Speed Distribution by Pokémon Type" subtitle: "Electric and Flying types tend to be fastest, while Rock types are slowest" description: "This visualization explores the speed distribution across different Pokémon types using data from the {pokemon} R package. The beeswarm plot reveals distinct patterns in how speed stats vary between types, with Electric and Flying Pokémon showcasing the highest average speeds while Rock types tend to be the slowest. Diamond markers indicate mean values for each type, providing insight into competitive gameplay advantages." author: "Steven Ponce" date: "2025-03-23" categories: ["TidyTuesday", "2025"] tags: [ "pokemon", "ggplot2", "ggbeeswarm", "MetBrewer", "data-visualization", "gaming-data", "speed-stats", "type-analysis", "beeswarm-plot" ] image: "thumbnails/tt_2025_13.png" format: html: toc: true toc-depth: 5 code-link: true code-fold: true code-tools: true code-summary: "Show code" 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_13.html" # description: "Exploring Pokémon speed stats across different types: which are fastest and which are slowest? A #TidyTuesday data visualization showing why Electric and Flying types have the competitive edge" # # twitter: true # linkedin: true # email: true # facebook: false # reddit: false # stumble: false # tumblr: false # mastodon: true # bsky: true --- ![A beeswarm plot showing speed distribution across Pokémon types. Types are ordered by average speed, with Flying and Electric types at the bottom (fastest) and Rock near the top (slowest). Purple diamonds mark the mean speed for each type, and a vertical dashed line at 100 indicates the "Fast" threshold. Most Pokémon fall between 50-100 speed, with outliers reaching up to 150.](tt_2025_13.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 ggbeeswarm, # Categorical Scatter (Violin Point) Plots camcorder # Record Your Plot History ) }) ### |- figure size ---- gg_record( dir = here::here("temp_plots"), device = "png", width = 7, 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 = 13) pokemon_raw <- tt$pokemon_df |> clean_names() tidytuesdayR::readme(tt) rm(tt) ``` #### 3. Examine the Data ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(pokemon_raw) skim(pokemon_raw) ``` #### 4. Tidy Data ```{r} #| label: tidy #| warning: false ### |- tidy data ---- pokemon_clean <- pokemon_raw |> select(pokemon, type_1, speed, generation_id, color_1) |> filter(!is.na(speed), !is.na(type_1)) |> mutate(generation = as.factor(generation_id)) # Summarize speed stats ordered_types <- pokemon_clean |> group_by(type_1) |> summarize( across( speed, list( mean = ~mean(.x, na.rm = TRUE), median = ~median(.x, na.rm = TRUE), min = ~min(.x, na.rm = TRUE), max = ~max(.x, na.rm = TRUE) ), .names = "{fn}_{.col}" ), count = n(), .groups = "drop" ) |> arrange(desc(mean_speed)) |> pull(type_1) # plot data pokemon_clean <- pokemon_clean |> mutate(type_1 = factor(type_1, levels = ordered_types)) ``` #### 5. Visualization Parameters ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- colors <- get_theme_colors( palette = c(rep("#7570B3", 18)) ) ### |- titles and caption ---- title_text <- str_glue("Speed Distribution by Pokémon Type") subtitle_text <- str_glue("Electric and Flying types tend to be fastest, while Rock types are slowest") # Create caption caption_text <- create_social_caption( tt_year = 2025, tt_week = 13, source_text = "{ pokemon R package }" ) ### |- fonts ---- setup_fonts() fonts <- get_font_families() ### |- plot theme ---- # Start with base theme base_theme <- create_base_theme(colors) # Add weekly-specific theme elements weekly_theme <- extend_weekly_theme( base_theme, theme( # Text styling plot.title = element_text(face = "bold", family = fonts$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.direction = "horizontal", legend.title = element_text(family = fonts$text, size = rel(0.8), face = "bold"), legend.text = element_text(family = fonts$text, size = rel(0.7)), # two-row legend legend.box.spacing = unit(0.4, "cm"), legend.key.width = unit(1.5, "cm"), legend.spacing.x = unit(0.2, "cm"), legend.box = "horizontal", legend.box.just = "left", # Plot margins plot.margin = margin(t = 20, r = 20, b = 20, l = 20), ) ) # Set theme theme_set(weekly_theme) ``` #### 6. Plot ```{r} #| label: plot #| warning: false ### |- Plot ---- p <- pokemon_clean |> ggplot(aes(x = type_1, y = speed, color = type_1)) + # Geoms geom_beeswarm(cex = 1, size = 1.2, alpha = 0.75, shape = 21, na.rm = TRUE) + stat_summary( # mean indicator fun = mean, geom = "point", shape = 18, size = 3, color = alpha(colors$palette[1], 0.9), ) + geom_hline( yintercept = 100, linetype = "dashed", color = "darkgray", ) + # Annotate annotate( "text", x = length(ordered_types), y = 102, label = "Fast (Speed ≥ 100)", hjust = 0, vjust = -2, size = 3, color = "dimgray" ) + annotate( "point", x = "steel", y = 140, shape = 18, size = 4, color = alpha(colors$palette[1], 0.9) ) + annotate( "text", x = "steel", y = 141, label = " = Mean", color = alpha(colors$palette[1], 0.9), size = 4.2, angle = 0, hjust = 0, fontface = "bold" ) + # Scales scale_color_manual(values = colors$palette) + coord_flip() + # labs labs( title = title_text, subtitle = subtitle_text, caption = caption_text, x = NULL, y = "Base Speed Stat" ) + # Theme theme( plot.title = element_text( size = rel(2), 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$subtitle, color = alpha(colors$subtitle, 0.9), 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) ) ) ``` #### 7. Save ```{r} #| label: save #| warning: false ### |- plot image ---- save_plot_patchwork( plot = p, type = "tidytuesday", year = 2025, week = 13, width = 7, 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_13.qmd`](https://github.com/poncest/personal-website/blob/master/data_visualizations/TidyTuesday/2025/tt_2025_13.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 13: [Pokemon](https://github.com/rfordatascience/tidytuesday/blob/main/data/2025/2025-04-01) :::