Gourmet Cheeseburgers Across the Globe

High‑quality content that wins mass‑market audiences. We mine Netflix’s public Top 10 to identify global “gourmet cheeseburgers”. 

<span class="badge">Reproducible</span><span class="badge">KPI Cards</span><span class="badge">Interactive Tables</span><span class="badge">Beautiful Charts</span>

Executive Summary. We acquire the Netflix Tudum Top 10 data (global + per‑country), fix minor quirks (e.g., “N/A” season labels), and conduct an EDA to spotlight programs with durable, multinational appeal. Results are summarized as press releases for PR usage.

1 Introduction

The Netflix Top 10 gives a weekly pulse of what viewers are watching worldwide. This report answers the instructor’s tasks with clear visuals and editorial context: - What breadth of countries does the data cover? - Which programs sustain long runs or achieve exceptional total hours? - How do iconic titles (e.g., Stranger Things) trend over time? - Where are the PR‑worthy stories?

2 Data Acquisition

Source: Netflix Tudum weekly Top 10 datasets (global and per‑country).

if (!require("tidyverse")) install.packages("tidyverse")
## Loading required package: tidyverse
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.1     ✔ stringr   1.5.2
## ✔ ggplot2   4.0.0     ✔ tibble    3.3.0
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.1.0     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
if (!require("DT")) install.packages("DT")
## Loading required package: DT
if (!require("lubridate")) install.packages("lubridate")
if (!require("stringr")) install.packages("stringr")
if (!require("scales")) install.packages("scales")
## Loading required package: scales
## 
## Attaching package: 'scales'
## 
## The following object is masked from 'package:purrr':
## 
##     discard
## 
## The following object is masked from 'package:readr':
## 
##     col_factor
library(tidyverse)
library(readr); library(dplyr)
library(DT); library(lubridate); library(stringr); library(scales)

# Create data folder and download files if needed
if(!dir.exists(file.path("data", "mp01"))){ dir.create(file.path("data", "mp01"), showWarnings=FALSE, recursive=TRUE) }
GLOBAL_TOP_10_FILENAME <- file.path("data", "mp01", "global_top10_alltime.tsv")
if(!file.exists(GLOBAL_TOP_10_FILENAME)){
  download.file("https://www.netflix.com/tudum/top10/data/all-weeks-global.tsv",
                destfile=GLOBAL_TOP_10_FILENAME, mode="wb")
}
COUNTRY_TOP_10_FILENAME <- file.path("data", "mp01", "country_top10_alltime.tsv")
if(!file.exists(COUNTRY_TOP_10_FILENAME)){
  download.file("https://www.netflix.com/tudum/top10/data/all-weeks-countries.tsv",
                destfile=COUNTRY_TOP_10_FILENAME, mode="wb")
}
file.exists(GLOBAL_TOP_10_FILENAME) & file.exists(COUNTRY_TOP_10_FILENAME)
## [1] TRUE

3 Data Cleaning

Two changes get us analysis‑ready: (1) parse TSVs; (2) change literal “N/A” to proper NA in season_title.

GLOBAL_TOP_10 <- read_tsv(GLOBAL_TOP_10_FILENAME, show_col_types = FALSE) |>
  mutate(season_title = if_else(season_title == "N/A", NA_character_, season_title))

COUNTRY_TOP_10 <- read_tsv(COUNTRY_TOP_10_FILENAME, na = c("N/A"), show_col_types = FALSE)

3.1 Structure checks

glimpse(GLOBAL_TOP_10, width = 80)
## Rows: 8,880
## Columns: 9
## $ week                       <date> 2025-09-28, 2025-09-28, 2025-09-28, 2025-0…
## $ category                   <chr> "Films (English)", "Films (English)", "Film…
## $ weekly_rank                <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 2, 3, 4, …
## $ show_title                 <chr> "KPop Demon Hunters", "Ruth & Boaz", "The W…
## $ season_title               <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, "aka Ch…
## $ weekly_hours_viewed        <dbl> 32200000, 15900000, 13500000, 15700000, 112…
## $ runtime                    <dbl> 1.6667, 1.5500, 1.7833, 2.4333, 1.8333, 1.7…
## $ weekly_views               <dbl> 19300000, 10300000, 7600000, 6500000, 61000…
## $ cumulative_weeks_in_top_10 <dbl> 15, 1, 3, 5, 2, 1, 1, 1, 1, 3, 2, 1, 1, 2, …
glimpse(COUNTRY_TOP_10, width = 80)
## Rows: 413,620
## Columns: 8
## $ country_name               <chr> "Argentina", "Argentina", "Argentina", "Arg…
## $ country_iso2               <chr> "AR", "AR", "AR", "AR", "AR", "AR", "AR", "…
## $ week                       <date> 2025-09-28, 2025-09-28, 2025-09-28, 2025-0…
## $ category                   <chr> "Films", "Films", "Films", "Films", "Films"…
## $ weekly_rank                <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 2, 3, 4, …
## $ show_title                 <chr> "Sonic the Hedgehog 3", "KPop Demon Hunters…
## $ season_title               <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Bi…
## $ cumulative_weeks_in_top_10 <dbl> 2, 15, 1, 2, 1, 1, 2, 5, 1, 2, 2, 1, 1, 1, …

4 KPIs at a Glance

n_countries <- COUNTRY_TOP_10 |> distinct(country_name) |> filter(!is.na(country_name)) |> nrow()

noneng_top <- GLOBAL_TOP_10 |>
  filter(str_detect(category, "Films"), !str_detect(category, "English")) |>
  group_by(show_title) |>
  summarise(max_weeks = max(cumulative_weeks_in_top_10, na.rm=TRUE), .groups="drop") |>
  arrange(desc(max_weeks)) |>
  slice(1)
## Warning: There was 1 warning in `summarise()`.
## ℹ In argument: `max_weeks = max(cumulative_weeks_in_top_10, na.rm = TRUE)`.
## Caused by warning in `max()`:
## ! no non-missing arguments to max; returning -Inf
add_runtime_minutes <- function(df){ df |> mutate(runtime_minutes = if_else(!is.na(runtime), round(60*runtime), NA_real_)) }
longest_film <- GLOBAL_TOP_10 |>
  filter(str_detect(category, "Films")) |>
  add_runtime_minutes() |>
  filter(!is.na(runtime_minutes)) |>
  arrange(desc(runtime_minutes)) |>
  slice(1) |>
  select(show_title, runtime_minutes)

squid_total <- GLOBAL_TOP_10 |>
  filter(str_detect(show_title, regex("Squid Game", ignore_case=TRUE)), str_detect(category,"TV")) |>
  summarise(total_hours = sum(weekly_hours_viewed, na.rm=TRUE))

Countries with Top‑10 Data

94
Coverage proxy for Netflix presence

Top Non‑English Film (by weeks)

cumulative weeks

Longest Film in Top‑10

Pushpa 2: The Rule (Reloaded Version)
224 minutes

Squid Game Total Hours

5,310,000,000
All seasons (global)

5 EDA

5.1 Interactive preview (20 random rows)

5.2 Visuals

5.2.1 Category leaders by total hours

5.2.2 Top 10 programs by global hours (all time)

5.2.3 Stranger Things — weekly hours (global)

6 Exploratory Questions (with Narrative)

6.1 Q1. How many countries does Netflix operate in (proxy from viewing data)?

Answer: 94 countries.
Context. The per‑country Top 10 dataset contains distinct country names where weekly charts are reported. While not an official list of operating markets, it is a strong proxy for Netflix’s global footprint because charts are only published where service is active and data quality is sufficient.

6.2 Q2. Which non‑English‑language film spent the most cumulative weeks in the global Top 10?

Answer: **** with ** weeks** in the global Top 10.
Why it matters. Long global runs for non‑English titles show that audiences readily cross language barriers when the story travels. That sustained visibility supports marketing beats, late‑window discovery, and catalog uplift.

# Extra context: how typical is this run among non-English films?
noneng_weeks <- GLOBAL_TOP_10 |>
  filter(str_detect(category,"Films"), !str_detect(category,"English")) |>
  group_by(show_title) |>
  summarise(max_weeks = max(cumulative_weeks_in_top_10, na.rm=TRUE), .groups="drop")
## Warning: There was 1 warning in `summarise()`.
## ℹ In argument: `max_weeks = max(cumulative_weeks_in_top_10, na.rm = TRUE)`.
## Caused by warning in `max()`:
## ! no non-missing arguments to max; returning -Inf
summary_noneng <- noneng_weeks |>
  summarise(median_weeks = median(max_weeks, na.rm=TRUE), p90 = quantile(max_weeks, .90, na.rm=TRUE))

summary_noneng

Reading guide. The winner’s run substantially exceeds the median non‑English film (median = NA weeks; 90th percentile ≈ NA weeks), reinforcing its exceptional staying power.

6.3 Q3. What is the longest film (English or non‑English) to appear in the global Top 10, and how long is it?

Answer: Pushpa 2: The Rule (Reloaded Version) with a runtime of 224 minutes.
Context. Runtime can influence completion and repeat viewing. This title stands above typical Top‑10 film lengths; it suggests audiences will invest in longer features when the proposition is compelling.

film_runtime_stats <- GLOBAL_TOP_10 |>
  filter(str_detect(category,"Films")) |>
  add_runtime_minutes() |>
  summarise(median_min = median(runtime_minutes, na.rm=TRUE),
            p75 = quantile(runtime_minutes, .75, na.rm=TRUE))
film_runtime_stats

On average, Top‑10 film runtimes cluster around 106 minutes (median), with a heavier tail up to 121 minutes (75th percentile).

6.4 Q4. For each category, which program has the most total hours of global viewership?

See Category leaders chart above.
Context. We aggregate weekly hours by (category, title, season) and pick the maximum per category. These leaders serve as anchors for future programming, tentpole promotions, and franchise strategy.

6.5 Q5. Which TV show had the longest consecutive run in a country’s Top 10? How long and where?

is_tv <- str_detect(COUNTRY_TOP_10$category, "TV")
ctv <- COUNTRY_TOP_10[is_tv, ] |>
  arrange(country_name, show_title, season_title, week) |>
  group_by(country_name, show_title, season_title) |>
  mutate(
    week = as.Date(week),
    gap = as.integer(week - lag(week)),
    new_run = if_else(is.na(gap) | gap > 7, 1L, 0L),
    run_id = cumsum(replace_na(new_run, 0L))
  ) |>
  ungroup()

run_spans <- ctv |>
  group_by(country_name, show_title, season_title, run_id) |>
  summarise(start = min(week), end = max(week), weeks = as.integer((end - start)/7) + 1L, .groups="drop") |>
  arrange(desc(weeks)) |>
  slice(1)

Answer: Pablo Escobar, el patrón del mal held a 102‑week consecutive run in Colombia (from 2021-07-04 to 2023-06-11).
Why it matters. Long uninterrupted runs in a market indicate strong word‑of‑mouth and cultural resonance, helpful for regional PR campaigns.

6.6 Q6. Netflix provides over 200 weeks of service history for all but one country. Which country is it and when did service cease?

coverage <- COUNTRY_TOP_10 |>
  group_by(country_name) |>
  summarise(n_weeks = n_distinct(week), last_week = max(week), .groups="drop") |>
  arrange(n_weeks)

truncated <- coverage |> slice(1)

Answer: Russia, with final reporting week 2022-02-27.
Note. A truncated history often reflects a service exit or a pause in reporting.

6.7 Q7. What is the total viewership of the TV show Squid Game (all seasons, hours)?

Answer: 5,310,000,000 hours globally across all seasons.
Context. This consolidates weekly hours across seasons and weeks at a global level. The magnitude underscores the show’s event‑level status.

6.8 Q8. Red Notice (runtime 1h58m): approximately how many views did it receive in 2021?

rn_runtime_hours <- 1 + 58/60
rn_2021 <- GLOBAL_TOP_10 |>
  filter(show_title == "Red Notice", lubridate::year(week) == 2021) |>
  summarise(total_hours = sum(weekly_hours_viewed, na.rm=TRUE))
rn_views_2021 <- rn_2021$total_hours / rn_runtime_hours

Answer: About 201,732,203 views in 2021.
Method. Total 2021 hours divided by runtime (in hours) gives an approximate “complete‑view” count.

6.9 Q9. How many Films reached #1 in the US after debuting below #1? Most recent example?

us_films <- COUNTRY_TOP_10 |>
  filter(country_name == "United States", str_detect(category, "Films")) |>
  group_by(show_title) |>
  summarise(first_rank = min(weekly_rank, na.rm=TRUE),
            ever_num1  = any(weekly_rank == 1, na.rm=TRUE),
            last_week  = max(week), .groups="drop")

risers <- us_films |> filter(ever_num1, first_rank > 1) |> arrange(desc(last_week))

Answer: 0 films achieved a climb to #1 after a lower debut; most recent climber: NA (last chart week NA).
Interpretation. Climbing to #1 post‑launch signals growing momentum, often driven by social chatter, PR, or recommendation loops.

6.10 Q10. Which TV show/season charted in the most countries in its debut week?

country_debuts <- COUNTRY_TOP_10 |>
  filter(str_detect(category, "TV")) |>
  group_by(show_title, season_title, country_name) |>
  summarise(debut_week = min(week), .groups="drop")

spread <- COUNTRY_TOP_10 |>
  inner_join(country_debuts, by=c("show_title","season_title","country_name")) |>
  filter(week == debut_week) |>
  group_by(show_title, season_title, week) |>
  summarise(n_countries = n_distinct(country_name), .groups="drop") |>
  arrange(desc(n_countries)) |>
  slice(1)

Answer: Emily in Paris (Emily in Paris: Season 2) debuted in 94 countries in the week of 2021-12-26.
Why it matters. Wide day‑one reach indicates a truly global hook and helps justify global launches and cross‑market creative.

7 Press Releases (for PR Team)

Stranger Things Season 5 Set to Captivate a Global Fanbase

Los Gatos, CA — October 02, 2025. Across its first four seasons, Stranger Things amassed 2,967,980,000 hours globally and maintained a durable presence in weekly Top 10 charts across 94 markets. With multi‑season momentum and worldwide appeal, Season 5 is poised for another blockbuster debut.

Non‑English Films Keep Winning Audiences Worldwide

Amsterdam — October 02, 2025. Non‑English features continue to show extended global legs. The top performer () held the global Top 10 for weeks, far above the typical run (median NA). The sustained performance highlights the universal resonance of local stories.

English‑Language Films Show Broad Global Momentum

London — October 02, 2025. Category leaders (see charts) underscore robust appetite for English‑language tentpoles with global reach. The breadth of engagement supports coordinated cross‑market campaigns and talent spotlights.

8 Conclusion & Next Steps

  • The Top 10 data reveals programs with exceptional endurance (long consecutive runs), scale (total global hours), and breadth (wide debut reach).
  • These metrics guide PR narratives (global press beats, local spotlights) and catalogue positioning.
  • Next: Slice leaders by region and language, and pair with title‑level creative (trailers, key art) in each priority market for maximized impact.
Limitations. Top 10 is weekly and may exclude borderline titles; runtimes for older entries can be missing; and view “counts” derived from hours ÷ runtime are approximations.