Exploring Individual Word Trends Across 27 Seasons of Power Rangers

Tracking the top 10 words and how they evolve independently over time in episode descriptions

Figure 1: The faceted area chart displays the frequency of the top 10 words used in Power Rangers episode descriptions over 27 seasons. Each word is plotted separately, with its frequency on the y-axis and the season number on the x-axis. This highlights individual word trends over time.

Steps to Create this Graphic

1. Load Packages & Setup

```{r}
#| label: load

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
  lubridate,     # Make Dealing with Dates a Little Easier
  MetBrewer,     # Color Palettes Inspired by Works at the Metropolitan Museum of Art
  tidytext       # Text Mining using 'dplyr', 'ggplot2', and Other Tidy Tools
 )   

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  =  7.77,
  height =  8,
  units  = "in",
  dpi    = 320
)

### |- resolution ----
showtext_opts(dpi = 320, regular.wt = 300, bold.wt = 800)
```

2. Read in the Data

```{r}
#| label: read

tt <-tidytuesdayR::tt_load(2024, week = 35) 

episodes <- tt$power_rangers_episodes |> clean_names() |> glimpse()
seasons  <- tt$power_rangers_seasons |> clean_names() |> glimpse()

tidytuesdayR::readme(tt)
rm(tt)
```

3. Examing the Data

```{r}
#| label: examine

glimpse(episodes)
glimpse(seasons)
```

4. Tidy Data

```{r}
#| label: tidy

# Tidy
joined_data <- episodes |> 
    left_join(y = seasons, by = "season_title") |> 
    rename(
        imdb_rating_episode = imdb_rating.x,
        imdb_rating_season = imdb_rating.y,
    ) |> 
    mutate(air_date_last_ep = ymd(air_date_last_ep)) 

# Unnest tokens from the 'desc' column, remove stop words, and calculate word frequency
word_frequency_over_time <- joined_data |>
    unnest_tokens(word, desc) |>
    anti_join(stop_words, by = "word") |>
    count(season_num, word, sort = TRUE) |>
    group_by(season_num) |>
    mutate(frequency = n / sum(n)) |>
    ungroup()

# Select the top 10 words by total frequency across all seasons
top_words <- word_frequency_over_time |>
    group_by(word) |>
    summarise(total_frequency = sum(frequency)) |>
    top_n(10, total_frequency) |>
    pull(word)

# Filter for top words 
data_plot <- word_frequency_over_time |>
    filter(word %in% top_words) |>
    mutate(word = fct_reorder(word, -frequency))
```

5. Visualization Parameters

```{r}
#| label: params

### |- plot aesthetics ----
bkg_col      <- colorspace::lighten('#f7f5e9', 0.05)    
title_col    <- "gray20"           
subtitle_col <- "gray20"     
caption_col  <- "gray30"   
text_col     <- "gray20"    

### |-  titles and caption ----
# icons
tt <- str_glue("#TidyTuesday: { 2024 } Week { 35 } &bull; Source: Power Rangers: Seasons and episodes data<br>")
li <- str_glue("<span style='font-family:fa6-brands'>&#xf08c;</span>")
gh <- str_glue("<span style='font-family:fa6-brands'>&#xf09b;</span>")
mn <- str_glue("<span style='font-family:fa6-brands'>&#xf4f6;</span>")

# text
title_text    <- str_glue("Exploring Individual Word Trends Across 27 Seasons of Power Rangers")
subtitle_text <- str_glue("Tracking the top 10 words and how they evolve independently over time in\nepisode descriptions")
caption_text  <- str_glue("{tt} {li} stevenponce &bull; {mn} @sponce1(graphic.social) {gh} poncest &bull; #rstats #ggplot2")

### |-  fonts ----
font_add("fa6-brands", "fonts/6.4.2/Font Awesome 6 Brands-Regular-400.otf")
font_add_google("Oswald", regular.wt = 400, family = "title")
font_add_google("Merriweather Sans", regular.wt = 400, family = "subtitle")
font_add_google("Merriweather Sans", regular.wt = 400, family = "text")
font_add_google("Noto Sans", regular.wt = 400, family = "caption")
showtext_auto(enable = TRUE)

### |-  plot theme ----
theme_set(theme_minimal(base_size = 14, base_family = "text"))                

theme_update(
    plot.title.position   = "plot",
    plot.caption.position = "plot",
    legend.position       = 'plot',
    plot.background       = element_rect(fill = bkg_col, color = bkg_col),
    panel.background      = element_rect(fill = bkg_col, color = bkg_col),
    plot.margin           = margin(t = 20, r = 20, b = 20, l = 20),
    axis.title.x          = element_text(margin = margin(10, 0, 0, 0), size = rel(1.1), color = text_col, family = "text", face = "bold", hjust = 0.5),
    axis.title.y          = element_text(margin = margin(0, 10, 0, 0), size = rel(1.1), color = text_col, family = "text", face = "bold", hjust = 0.5),
    axis.text             = element_text(size = rel(0.8), color = text_col, family = "text"),
    axis.line.x           = element_line(color = "gray40", linewidth = .15),
    panel.grid.minor.y    = element_blank(),
    panel.grid.major.y    = element_line(linetype = "dotted", linewidth = 0.1, color = 'gray'),
    panel.grid.minor.x    = element_blank(),
    panel.grid.major.x    = element_blank(),
    
    strip.text            = element_textbox(size     = rel(1),
                                            face     = 'bold',
                                            color    = text_col,
                                            hjust    = 0.5,
                                            halign   = 0.5,
                                            r        = unit(5, "pt"),
                                            width    = unit(5.5, "npc"),
                                            padding  = margin(3, 0, 3, 0),
                                            margin   = margin(3, 3, 3, 3),
                                            fill     = "transparent"),
    panel.spacing       = unit(1, 'lines')
)  
```

6. Plot

```{r}
#| label: plot

### |-  final plot ----  
p <- data_plot |> 
    ggplot(aes(x = season_num, y = frequency, color = word, fill = word)) +
    
    # Geoms
    geom_line(linewidth = 0.6) +  
    geom_area(alpha = 0.5) +
    
    # Scales
    scale_x_continuous(breaks = pretty_breaks()) +
    scale_y_continuous(breaks = pretty_breaks(n = 2)) +
    scale_color_viridis_d(option = "F", begin = 0.05, end = .85) + 
    scale_fill_viridis_d(option = "F", begin = 0.05, end = .85) + 
    coord_cartesian(clip = 'off') +
    
    # Labs
    labs(
        x = "Season Number",
        y = "Word Frequency",
        title = title_text,
        subtitle = subtitle_text,
        caption = caption_text
    ) +
    
    # Facets
    facet_wrap(~ word, scales = "free_y", ncol = 2) +  
    
    # Theme
    theme(
        plot.title = element_text(
            size = rel(1.3),
            family = "title",
            color = title_col,
            face = "bold",
            lineheight = 0.85,
            margin = margin(t = 5, b = 5)
        ),
        plot.subtitle = element_text(
            size = rel(1),
            family = "subtitle",
            color = title_col,
            lineheight = 1,
            margin = margin(t = 5, b = 15)
        ),
        plot.caption = element_markdown(
            size = rel(.5),
            family = "caption",
            color = caption_col,
            lineheight = 0.6,
            hjust = 0,
            halign = 0,
            margin = margin(t = 10, b = 0)
        )
    )
```

7. Save

```{r}
#| label: save

### |-  plot image ----  
ggsave(
  filename = here::here("data_visualizations/TidyTuesday/2024/tt_2024_35.png"),
  plot = p,
  width  =  7.77,
  height =  8,
  units  = "in",
  dpi    = 320
)

### |-  plot thumbnail----  
magick::image_read(here::here("data_visualizations/TidyTuesday/2024/tt_2024_35.png")) |> 
  magick::image_resize(geometry = "400") |> 
  magick::image_write(here::here("data_visualizations/TidyTuesday/2024/thumbnails/tt_2024_35.png"))
```

8. Session Info

```{r, eval=TRUE}
sessionInfo()
```

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     

loaded via a namespace (and not attached):
 [1] htmlwidgets_1.6.4 compiler_4.4.0    fastmap_1.2.0     cli_3.6.4        
 [5] htmltools_0.5.8.1 tools_4.4.0       rstudioapi_0.17.1 yaml_2.3.10      
 [9] rmarkdown_2.29    knitr_1.49        jsonlite_1.8.9    xfun_0.49        
[13] digest_0.6.37     rlang_1.1.6       renv_1.0.3        evaluate_1.0.1