---

title: “Netflix TV Show Actor Analysis” author: “Bayartsetseg” date: “2025-10-06” output: html_document —get

library(tidyverse)

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Netflix <- read_csv("Netflix.csv")

## Rows: 6234 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (10): type, title, director, cast, country, date_added, rating, duration...
## dbl  (2): show_id, release_year
## 
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## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Netflix_Actor <- Netflix %>%
  separate_rows(cast, sep = ", ") %>%
  drop_na(cast) %>%
  rename(actor = cast)

Top_Actors <- Netflix_Actor %>%
  select(type, actor) %>%
  filter(type == "TV Show") %>%
  group_by(actor) %>%
  count(sort = TRUE) %>%
  ungroup() %>%
  head(6)

Top_Actors

## # A tibble: 6 × 2
##   actor                  n
##   <chr>              <int>
## 1 Takahiro Sakurai      18
## 2 Yuki Kaji             16
## 3 Daisuke Ono           14
## 4 David Attenborough    14
## 5 Ashleigh Ball         12
## 6 Hiroshi Kamiya        12

Top_Actors %>%
  ggplot(aes(x = reorder(actor, n), y = n, fill = actor)) +
  geom_col(show.legend = FALSE) +
  coord_flip() +
  labs(
    title = "Top 6 Most Frequent TV Show Actors on Netflix",
    x = "Actor",
    y = "Number of Appearances"
  ) +
  theme_minimal()