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What’s in `episodes`?

glimpse(episodes)
#> Rows: 159
#> Columns: 38
#> $ overall_episode          <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14…
#> $ season                   <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
#> $ episode_in_season        <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14…
#> $ season_episode_code      <chr> "S01E01", "S01E02", "S01E03", "S01E04", "S01E…
#> $ title                    <chr> "Portugal's Heartland", "Paris: Grand and Int…
#> $ synopsis                 <chr> "Portugal; barnacles; fishing town; pilgrims;…
#> $ theme_tags               <chr> "Religion & Spirituality", "Art & Architectur…
#> $ episode_type             <chr> "Standard", "Standard", "Standard", "Standard…
#> $ is_retired               <chr> "Yes", "Yes", "Yes", "Yes", "Yes", "No", "Yes…
#> $ primary_country          <chr> "Portugal", "France", "United Kingdom", "Unit…
#> $ all_countries            <chr> "Portugal", "France", "United Kingdom", "Unit…
#> $ region                   <chr> "Iberia", "Western Europe", "British Isles", …
#> $ primary_destination      <chr> "Heartland Portugal", "Paris", "South England…
#> $ iso2                     <chr> "PT", "FR", "GB", "GB", "IT", "DE", "GB", "BG…
#> $ flag                     <chr> "🇵🇹", "🇫🇷", "🇬🇧", "🇬🇧", "🇮🇹", "🇩🇪", "🇬🇧", "🇧🇬", "🇮🇹", …
#> $ lat                      <dbl> 39.66216, 48.85350, 51.35474, 53.25199, 41.89…
#> $ long                     <dbl> -8.1353519, 2.3483915, -0.1353155, -3.0420904…
#> $ geo_match                <chr> "country", "full", "full", "full", "full", "s…
#> $ original_air_date        <date> 2000-09-03, 2000-09-10, 2000-09-17, 2000-09-…
#> $ air_year                 <int> 2000, 2000, 2000, 2000, 2000, 2000, 2000, 200…
#> $ air_month                <ord> Sep, Sep, Sep, Sep, Oct, Oct, Oct, Oct, Oct, …
#> $ air_weekday              <ord> Sun, Sun, Sun, Sun, Sun, Sun, Sun, Sun, Sun, …
#> $ days_since_premiere      <int> 0, 7, 14, 21, 28, 35, 42, 49, 56, 63, 70, 77,…
#> $ gap_since_prev_episode_d <int> NA, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,…
#> $ imdb_tconst              <chr> "tt1453776", "tt1453775", "tt1453778", "tt145…
#> $ imdb_url                 <chr> "https://www.imdb.com/title/tt1453776/", "htt…
#> $ imdb_rating              <dbl> 6.4, 7.2, 8.3, 8.0, 7.4, 7.8, 7.7, 7.2, 7.2, …
#> $ imdb_votes               <int> 18, 12, 15, 12, 12, 18, 15, 10, 10, 8, 11, NA…
#> $ imdb_rating_shrunk       <dbl> 7.168969, 7.563797, 7.958889, 7.830464, 7.630…
#> $ imdb_low_votes           <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FAL…
#> $ image_url                <chr> "https://i.ytimg.com/vi/0IvZOZtjMYM/maxresdef…
#> $ image_source             <chr> "ricksteves.com", "ricksteves.com", NA, "rick…
#> $ best_summary             <chr> "Portugal has an oversized history, fascinati…
#> $ summary_source           <chr> "ricksteves.com", "ricksteves.com", "tvmaze",…
#> $ best_runtime             <int> 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 2…
#> $ watch_url                <chr> "https://www.ricksteves.com/watch-read-listen…
#> $ tvmaze_url               <chr> "https://www.tvmaze.com/episodes/525238/rick-…
#> $ tvmaze_id                <int> 525238, 525239, 525241, 525242, 525243, 52524…

One row per episode, 13 seasons, 159 episodes spanning 2000–2025. Most columns fall into a few groups:

Identity: overall_episode, season, episode_in_season, season_episode_code.
Editorial: title, synopsis, theme_tags, region, primary_destination, episode_type, is_retired.
Geography: primary_country, all_countries, iso2, flag, lat, long, geo_match.
Dates: original_air_date, air_year, air_month, air_weekday, plus derived gap and span fields.
IMDB: imdb_rating, imdb_votes, imdb_rating_shrunk, imdb_low_votes, imdb_url, imdb_tconst.
Canonical bests: image_url, best_summary, best_runtime, plus *_source provenance flags.

How often does Rick Steves visit each country?

episodes |>
  count(primary_country, flag, sort = TRUE) |>
  head(10)
#> # A tibble: 10 × 3
#>    primary_country flag      n
#>    <chr>           <chr> <int>
#>  1 Italy           🇮🇹       29
#>  2 Multiple        🌍       20
#>  3 United Kingdom  🇬🇧       20
#>  4 France          🇫🇷       15
#>  5 Germany         🇩🇪        9
#>  6 Spain           🇪🇸        9
#>  7 Turkey          🇹🇷        6
#>  8 Austria         🇦🇹        4
#>  9 Greece          🇬🇷        4
#> 10 Portugal        🇵🇹        4

episodes |>
  count(primary_country) |>
  filter(primary_country != "Multiple") |>
  mutate(primary_country = forcats::fct_reorder(primary_country, n)) |>
  ggplot(aes(n, primary_country)) +
  geom_col(fill = "#1B3A6B") +
  labs(title = "Episodes per country",
       x = "Episodes", y = NULL) +
  theme_minimal()

Horizontal bar chart of the number of Rick Steves' Europe episodes set in each country, sorted from most to fewest.

Are highly-rated episodes a particular kind of place?

imdb_rating_shrunk pulls noisy ratings (some episodes have only 5 votes) toward the show mean. It is always populated, so it sorts cleanly.

episodes |>
  filter(!is.na(lat)) |>
  group_by(region) |>
  summarise(n = n(),
            median_rating = median(imdb_rating_shrunk)) |>
  arrange(desc(median_rating))
#> # A tibble: 11 × 3
#>    region             n median_rating
#>    <chr>          <int>         <dbl>
#>  1 Multi-Region       9          7.91
#>  2 British Isles     23          7.87
#>  3 Balkans            4          7.78
#>  4 Alpine             8          7.77
#>  5 Scandinavia        7          7.77
#>  6 Western Europe    26          7.77
#>  7 Beyond Europe     13          7.72
#>  8 Eastern Europe    10          7.70
#>  9 Mediterranean     34          7.70
#> 10 Iberia            13          7.59
#> 11 Baltic             1          7.50

Mapping the show

lat and long are filled for ~93% of episodes. Compilation episodes (“Travel Skills Special”, “Why We Travel”) are intentionally NA — there is no single coordinate.

library(leaflet)

episodes |>
  filter(!is.na(lat)) |>
  leaflet() |>
  addTiles() |>
  addCircleMarkers(
    ~long, ~lat,
    radius = ~ pmax(3, imdb_rating_shrunk - 5),
    popup  = ~ sprintf("<b>%s</b><br>%s %s<br>%s",
                       title, flag, primary_country, best_summary),
    color = "#1B3A6B", fillOpacity = 0.6, stroke = FALSE
  )

Production cadence

episodes |>
  count(air_year) |>
  ggplot(aes(air_year, n)) +
  geom_col(fill = "#FFC72C") +
  labs(title = "Episodes aired per year",
       x = NULL, y = "Episodes") +
  theme_minimal()