Mini project 02

Introduction

Welcome to Mini-Project 02. This project aims to recommend successful movies to recreate by analyzing various data sources. We will look at movie ratings, genres, and the involvement of key actors and directors to understand what makes a film successful.

By calculating a success metric based on ratings and audience engagement, we can filter for films that have performed well. The goal is to create a data-driven list of films that have the potential for a successful recreation, ensuring they resonate with audiences.

Gathering the IMDB Databases

options(repos = c(CRAN = "https://cloud.r-project.org"))
get_imdb_file <- function(fname){
    BASE_URL <- "https://datasets.imdbws.com/"
    fname_ext <- paste0(fname, ".tsv.gz")
    if(!file.exists(fname_ext)){
        FILE_URL <- paste0(BASE_URL, fname_ext)
        download.file(FILE_URL, 
                      destfile = fname_ext)
    }
    as.data.frame(readr::read_tsv(fname_ext, lazy=FALSE))
}

NAME_BASICS      <- get_imdb_file("name.basics")
Rows: 13884604 Columns: 6
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr (6): nconst, primaryName, birthYear, deathYear, primaryProfession, known...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
TITLE_BASICS     <- get_imdb_file("title.basics")
Warning: One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
Rows: 11174655 Columns: 9
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr (8): tconst, titleType, primaryTitle, originalTitle, startYear, endYear,...
dbl (1): isAdult

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
TITLE_EPISODES   <- get_imdb_file("title.episode")
Rows: 8579083 Columns: 4
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr (4): tconst, parentTconst, seasonNumber, episodeNumber

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
TITLE_RATINGS    <- get_imdb_file("title.ratings")
Rows: 1489867 Columns: 3
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr (1): tconst
dbl (2): averageRating, numVotes

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
TITLE_CREW       <- get_imdb_file("title.crew")
Rows: 10514405 Columns: 3
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr (3): tconst, directors, writers

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
TITLE_PRINCIPALS <- get_imdb_file("title.principals")
Warning: One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
Rows: 86651930 Columns: 6
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr (5): tconst, nconst, category, job, characters
dbl (1): ordering

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Data Sub-Sampling

Restricting to people with at least two “know for”credits:

IMDB has a long tail of obscure movies.

# Install the ggplot2 package (only need to do this once)
install.packages("ggplot2")
Installing package into 'C:/Users/krisf/AppData/Local/R/win-library/4.4'
(as 'lib' is unspecified)
package 'ggplot2' successfully unpacked and MD5 sums checked

The downloaded binary packages are in
    C:\Users\krisf\AppData\Local\Temp\RtmpWQw1EN\downloaded_packages
library(ggplot2)
library(scales)


TITLE_RATINGS |> ggplot(aes(x=numVotes)) + geom_histogram(bins=30) + xlab("Number of IMDB Ratings") + ylab("Number of Titles") + ggtitle("Majority of IMDB Titles Have Less than 100 Ratings") + theme_bw() + scale_x_log10(label=scales::comma) + scale_y_continuous(label=scales::comma)

Throw out any tittle less than 100 ratings.

install.packages("dplyr")
Installing package into 'C:/Users/krisf/AppData/Local/R/win-library/4.4'
(as 'lib' is unspecified)
package 'dplyr' successfully unpacked and MD5 sums checked
Warning: cannot remove prior installation of package 'dplyr'
Warning in file.copy(savedcopy, lib, recursive = TRUE): problem copying
C:\Users\krisf\AppData\Local\R\win-library\4.4\00LOCK\dplyr\libs\x64\dplyr.dll
to C:\Users\krisf\AppData\Local\R\win-library\4.4\dplyr\libs\x64\dplyr.dll:
Permission denied
Warning: restored 'dplyr'

The downloaded binary packages are in
    C:\Users\krisf\AppData\Local\Temp\RtmpWQw1EN\downloaded_packages
library(dplyr)

Attaching package: 'dplyr'
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union
TITLE_RATINGS |> pull(numVotes) |> quantile()
     0%     25%     50%     75%    100% 
      5      11      26     100 2953131

Reducing the size of the datasetes.

TITLE_RATINGS <- TITLE_RATINGS |> filter(numVotes >= 100)

Applying filters with semi_join which returns only values which have match and doesn’t add columns

TITLE_EPISODES_1 <- TITLE_EPISODES |> semi_join(TITLE_RATINGS, join_by(tconst == tconst))
TITLE_EPISODES_2 <- TITLE_EPISODES |> semi_join(TITLE_RATINGS, join_by(parentTconst == tconst))

Initial exploration

Cleaning the NAMES_BASIC command

NAME_BASICS <- NAME_BASICS |> mutate(birthYear = as.numeric(birthYear), deathYear = as.numeric(deathYear))
Warning: There were 2 warnings in `mutate()`.
The first warning was:
ℹ In argument: `birthYear = as.numeric(birthYear)`.
Caused by warning:
! NAs introduced by coercion
ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.
head (NAME_BASICS)
     nconst     primaryName birthYear deathYear
1 nm0000001    Fred Astaire      1899      1987
2 nm0000002   Lauren Bacall      1924      2014
3 nm0000003 Brigitte Bardot      1934        NA
4 nm0000004    John Belushi      1949      1982
5 nm0000005  Ingmar Bergman      1918      2007
6 nm0000006  Ingrid Bergman      1915      1982
                   primaryProfession                          knownForTitles
1       actor,miscellaneous,producer tt0072308,tt0050419,tt0053137,tt0027125
2 actress,soundtrack,archive_footage tt0037382,tt0075213,tt0117057,tt0038355
3  actress,music_department,producer tt0057345,tt0049189,tt0056404,tt0054452
4      actor,writer,music_department tt0072562,tt0077975,tt0080455,tt0078723
5              writer,director,actor tt0050986,tt0083922,tt0050976,tt0069467
6        actress,producer,soundtrack tt0034583,tt0036855,tt0038109,tt0038787

Task 1: Column Type Correction

Correct the column types of the TITLE tables using a combination of mutate and the coercion functions as.numeric and as.logical.

head (TITLE_BASICS)
     tconst titleType           primaryTitle          originalTitle isAdult
1 tt0000001     short             Carmencita             Carmencita       0
2 tt0000002     short Le clown et ses chiens Le clown et ses chiens       0
3 tt0000003     short           Poor Pierrot         Pauvre Pierrot       0
4 tt0000004     short            Un bon bock            Un bon bock       0
5 tt0000005     short       Blacksmith Scene       Blacksmith Scene       0
6 tt0000006     short      Chinese Opium Den      Chinese Opium Den       0
  startYear endYear runtimeMinutes                   genres
1      1894     \\N              1        Documentary,Short
2      1892     \\N              5          Animation,Short
3      1892     \\N              5 Animation,Comedy,Romance
4      1892     \\N             12          Animation,Short
5      1893     \\N              1             Comedy,Short
6      1894     \\N              1                    Short
  1. Getting data types for each data set, to evaulate which information needs to be updated with the coercion functions.
str (TITLE_BASICS) 
'data.frame':   11174655 obs. of  9 variables:
 $ tconst        : chr  "tt0000001" "tt0000002" "tt0000003" "tt0000004" ...
 $ titleType     : chr  "short" "short" "short" "short" ...
 $ primaryTitle  : chr  "Carmencita" "Le clown et ses chiens" "Poor Pierrot" "Un bon bock" ...
 $ originalTitle : chr  "Carmencita" "Le clown et ses chiens" "Pauvre Pierrot" "Un bon bock" ...
 $ isAdult       : num  0 0 0 0 0 0 0 0 0 0 ...
 $ startYear     : chr  "1894" "1892" "1892" "1892" ...
 $ endYear       : chr  "\\N" "\\N" "\\N" "\\N" ...
 $ runtimeMinutes: chr  "1" "5" "5" "12" ...
 $ genres        : chr  "Documentary,Short" "Animation,Short" "Animation,Comedy,Romance" "Animation,Short" ...
str (TITLE_EPISODES)
'data.frame':   8579083 obs. of  4 variables:
 $ tconst       : chr  "tt0031458" "tt0041951" "tt0042816" "tt0042889" ...
 $ parentTconst : chr  "tt32857063" "tt0041038" "tt0989125" "tt0989125" ...
 $ seasonNumber : chr  "\\N" "1" "1" "\\N" ...
 $ episodeNumber: chr  "\\N" "9" "17" "\\N" ...
str (TITLE_RATINGS)
'data.frame':   374436 obs. of  3 variables:
 $ tconst       : chr  "tt0000001" "tt0000002" "tt0000003" "tt0000004" ...
 $ averageRating: num  5.7 5.6 6.5 5.4 6.2 5 5.4 5.4 5.4 6.8 ...
 $ numVotes     : num  2096 283 2104 183 2841 ...
str (TITLE_CREW)
'data.frame':   10514405 obs. of  3 variables:
 $ tconst   : chr  "tt0000001" "tt0000002" "tt0000003" "tt0000004" ...
 $ directors: chr  "nm0005690" "nm0721526" "nm0721526" "nm0721526" ...
 $ writers  : chr  "\\N" "\\N" "\\N" "\\N" ...
str (TITLE_PRINCIPALS)
'data.frame':   86651930 obs. of  6 variables:
 $ tconst    : chr  "tt0000001" "tt0000001" "tt0000001" "tt0000001" ...
 $ ordering  : num  1 2 3 4 1 2 1 2 3 4 ...
 $ nconst    : chr  "nm1588970" "nm0005690" "nm0005690" "nm0374658" ...
 $ category  : chr  "self" "director" "producer" "cinematographer" ...
 $ job       : chr  "\\N" "\\N" "producer" "director of photography" ...
 $ characters: chr  "[\"Self\"]" "\\N" "\\N" "\\N" ...
  1. Replacing “N” with NA before executinf the Coercion functions
TITLE_BASICS[TITLE_BASICS == "N"] <- NA 
TITLE_EPISODES[TITLE_EPISODES == "N"] <- NA 
TITLE_RATINGS[TITLE_RATINGS == "N"] <- NA 
TITLE_CREW[TITLE_CREW == "N"] <- NA 
TITLE_PRINCIPALS[TITLE_PRINCIPALS == "N"] <- NA

head (TITLE_BASICS)
     tconst titleType           primaryTitle          originalTitle isAdult
1 tt0000001     short             Carmencita             Carmencita       0
2 tt0000002     short Le clown et ses chiens Le clown et ses chiens       0
3 tt0000003     short           Poor Pierrot         Pauvre Pierrot       0
4 tt0000004     short            Un bon bock            Un bon bock       0
5 tt0000005     short       Blacksmith Scene       Blacksmith Scene       0
6 tt0000006     short      Chinese Opium Den      Chinese Opium Den       0
  startYear endYear runtimeMinutes                   genres
1      1894     \\N              1        Documentary,Short
2      1892     \\N              5          Animation,Short
3      1892     \\N              5 Animation,Comedy,Romance
4      1892     \\N             12          Animation,Short
5      1893     \\N              1             Comedy,Short
6      1894     \\N              1                    Short

C: Using a combination of mutate and the coercion functions as.numeric and as.logical.

TITLE_BASICS <- TITLE_BASICS |> mutate(isAdult = as.logical(isAdult), startYear = as.numeric(startYear), endYear = as.numeric(endYear), runtimeMinutes = as.numeric(runtimeMinutes))
Warning: There were 3 warnings in `mutate()`.
The first warning was:
ℹ In argument: `startYear = as.numeric(startYear)`.
Caused by warning:
! NAs introduced by coercion
ℹ Run `dplyr::last_dplyr_warnings()` to see the 2 remaining warnings.
str (TITLE_BASICS)
'data.frame':   11174655 obs. of  9 variables:
 $ tconst        : chr  "tt0000001" "tt0000002" "tt0000003" "tt0000004" ...
 $ titleType     : chr  "short" "short" "short" "short" ...
 $ primaryTitle  : chr  "Carmencita" "Le clown et ses chiens" "Poor Pierrot" "Un bon bock" ...
 $ originalTitle : chr  "Carmencita" "Le clown et ses chiens" "Pauvre Pierrot" "Un bon bock" ...
 $ isAdult       : logi  FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ startYear     : num  1894 1892 1892 1892 1893 ...
 $ endYear       : num  NA NA NA NA NA NA NA NA NA NA ...
 $ runtimeMinutes: num  1 5 5 12 1 1 1 1 45 1 ...
 $ genres        : chr  "Documentary,Short" "Animation,Short" "Animation,Comedy,Romance" "Animation,Short" ...
TITLE_EPISODES <- TITLE_EPISODES |> mutate(seasonNumber = as.numeric(seasonNumber), episodeNumber = as.numeric(episodeNumber))
Warning: There were 2 warnings in `mutate()`.
The first warning was:
ℹ In argument: `seasonNumber = as.numeric(seasonNumber)`.
Caused by warning:
! NAs introduced by coercion
ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.
str (TITLE_EPISODES)
'data.frame':   8579083 obs. of  4 variables:
 $ tconst       : chr  "tt0031458" "tt0041951" "tt0042816" "tt0042889" ...
 $ parentTconst : chr  "tt32857063" "tt0041038" "tt0989125" "tt0989125" ...
 $ seasonNumber : num  NA 1 1 NA 3 2 2 3 1 2 ...
 $ episodeNumber: num  NA 9 17 NA 42 16 8 3 6 16 ...
glimpse(NAME_BASICS) 
Rows: 13,884,604
Columns: 6
$ nconst            <chr> "nm0000001", "nm0000002", "nm0000003", "nm0000004", …
$ primaryName       <chr> "Fred Astaire", "Lauren Bacall", "Brigitte Bardot", …
$ birthYear         <dbl> 1899, 1924, 1934, 1949, 1918, 1915, 1899, 1924, 1925…
$ deathYear         <dbl> 1987, 2014, NA, 1982, 2007, 1982, 1957, 2004, 1984, …
$ primaryProfession <chr> "actor,miscellaneous,producer", "actress,soundtrack,…
$ knownForTitles    <chr> "tt0072308,tt0050419,tt0053137,tt0027125", "tt003738…
install.packages("tidyr") 
Installing package into 'C:/Users/krisf/AppData/Local/R/win-library/4.4'
(as 'lib' is unspecified)
package 'tidyr' successfully unpacked and MD5 sums checked

The downloaded binary packages are in
    C:\Users\krisf\AppData\Local\Temp\RtmpWQw1EN\downloaded_packages
library(tidyr)

NAME_BASICS |> separate_longer_delim(knownForTitles, ",") |> slice_head(n=10)
      nconst     primaryName birthYear deathYear
1  nm0000001    Fred Astaire      1899      1987
2  nm0000001    Fred Astaire      1899      1987
3  nm0000001    Fred Astaire      1899      1987
4  nm0000001    Fred Astaire      1899      1987
5  nm0000002   Lauren Bacall      1924      2014
6  nm0000002   Lauren Bacall      1924      2014
7  nm0000002   Lauren Bacall      1924      2014
8  nm0000002   Lauren Bacall      1924      2014
9  nm0000003 Brigitte Bardot      1934        NA
10 nm0000003 Brigitte Bardot      1934        NA
                    primaryProfession knownForTitles
1        actor,miscellaneous,producer      tt0072308
2        actor,miscellaneous,producer      tt0050419
3        actor,miscellaneous,producer      tt0053137
4        actor,miscellaneous,producer      tt0027125
5  actress,soundtrack,archive_footage      tt0037382
6  actress,soundtrack,archive_footage      tt0075213
7  actress,soundtrack,archive_footage      tt0117057
8  actress,soundtrack,archive_footage      tt0038355
9   actress,music_department,producer      tt0057345
10  actress,music_department,producer      tt0049189

Task 2: Instructor provided questions

  1. How many movies are in our data set? How many TV series? How many TV episodes?

With the following code you will fin the count for all the Types availables in the dataset in descendent order:

TITLE_BASICS|> count (titleType)|>arrange(desc(n))
      titleType       n
1     tvEpisode 8579083
2         short 1023102
3         movie  694834
4         video  299504
5      tvSeries  271690
6       tvMovie  148593
7  tvMiniSeries   57396
8     tvSpecial   49811
9     videoGame   40203
10      tvShort   10438
11      tvPilot       1

Movies = 132,320 TV Series= 30,013 TV Episodes = 156,860

Now, we’re filtering to get the count for the 3 specific types requested:

filtered_data <- TITLE_BASICS |> filter(titleType %in% c("movie", "tvSeries", "tvEpisode"))

filtered_data |> ggplot(aes(x = titleType)) + geom_bar() + xlab("Title Types") + ylab("Number of Titles") + ggtitle("Number of Types per Titles") + theme_bw() + scale_x_discrete() + scale_y_continuous(labels = scales::comma)

2.Who is the oldest living person in our data set?

For this question, we will analyze two different scenarios.

Scenario 1: We’ll calculate the Äge, assuming that NA is a missing value, so we’re excluding those rows.

NAME_BASICS <- NAME_BASICS |> mutate(age = ifelse(!is.na(deathYear) & !is.na(birthYear), deathYear - birthYear, NA)) |>arrange(desc(10))

Then, we’ll arrange by the oldest person by age,having the year of death.

Under this analysis, the oldest person alive was: Jeanne Louis Calment, who died at 122 years old.

NAME_BASICS |> arrange(desc(age)) |> select(primaryName, birthYear, deathYear, age) |> head() |> print()
            primaryName birthYear deathYear  age
1          Rosita Royce         9      1954 1945
2 Jeanne Louise Calment      1875      1997  122
3       Tatzumbia Dupea      1849      1970  121
4             Kabir Das      1398      1518  120
5             Sankardev      1449      1568  119
6        Durgabai Kamat      1879      1997  118

Scenario 2: Assuming that the NA values on year of death are missing because the person is still alive, then we’ll use the following code to calculate the age.

current_year <- 2024 

NAME_BASICS |> 
  mutate(age = ifelse(!is.na(deathYear), deathYear - birthYear, current_year - birthYear)) |> 
  arrange(desc(age)) |> 
  select(primaryName, birthYear, deathYear, age) |> 
  head() |> 
  print()
        primaryName birthYear deathYear  age
1      Danial Shaki        18        NA 2006
2      Rosita Royce         9      1954 1945
3         Donatello      1386        NA  638
4         Meera Bai      1498        NA  526
5    Hans Neusidler      1508        NA  516
6 Ivan the Terrible      1530        NA  494

As we can see, under this scenario, we can’t take the results as possible answers, because it’s impossible that a person can live for 399 years.

3.There is one TV Episode in this data set with a perfect 10/10 rating and at least 200,000 IMDb ratings. What is it? What series does it belong to?

With the following code we will filter by the tittle with a 10/10 ranking and equal or more than 200,00 votes: We knot that the title ID number is: “tt2301451”, now we need to identify the title name on the table “TABLE_BASICS”.

TITLE_RATINGS |> arrange(desc(averageRating)) |> filter(numVotes >= 200000) |> select(tconst, averageRating, numVotes) |> head() |> print()
     tconst averageRating numVotes
1 tt2301451          10.0   230087
2 tt4283088           9.9   232830
3 tt0903747           9.5  2220756
4 tt0185906           9.4   542837
5 tt0795176           9.4   222817
6 tt0111161           9.3  2953131
TITLE_BASICS |> filter (tconst == "tt2301451")|> select (tconst, titleType, primaryTitle)
     tconst titleType primaryTitle
1 tt2301451 tvEpisode   Ozymandias
TITLE_EPISODES |> filter (tconst == "tt2301451")|> select (tconst, seasonNumber, episodeNumber)
     tconst seasonNumber episodeNumber
1 tt2301451            5            14

The TV Episode with the perfect ranking and 230,087 votes is the episode number 14, of season 5 from the TV Series called “Ozymandias”.

4.What four projects is the actor Mark Hamill most known for?

NAME_BASICS |> filter (primaryName == "Mark Hamill")|> select (primaryName, knownForTitles)
  primaryName                          knownForTitles
1 Mark Hamill tt0076759,tt2527336,tt0080684,tt0086190
2 Mark Hamill                              tt12402330
3 Mark Hamill                               tt0389413
4 Mark Hamill                               tt1008054
5 Mark Hamill                               tt1694520
6 Mark Hamill                               tt0202206
library(dplyr)

TITLE_BASICS |> filter(tconst %in% c("tt0076759", "tt2527336", "tt0080684", "tt0086190")) |> select(tconst, titleType, primaryTitle) |> print()
     tconst titleType                                   primaryTitle
1 tt0076759     movie             Star Wars: Episode IV - A New Hope
2 tt0080684     movie Star Wars: Episode V - The Empire Strikes Back
3 tt0086190     movie     Star Wars: Episode VI - Return of the Jedi
4 tt2527336     movie        Star Wars: Episode VIII - The Last Jedi

The actor Mark Hamill is most known for the Star Wars movies, these are the primary titles:

5.What TV series, with more than 12 episodes, has the highest average rating?

episode_ratings <- TITLE_EPISODES |> inner_join(TITLE_RATINGS, by = "tconst") |> inner_join(TITLE_BASICS, by = c("parentTconst" = "tconst"))

# Count episodes per series and filter for series with more than 12 episodes


top_rated_series <- episode_ratings |> 
  group_by(parentTconst, primaryTitle) |> 
  summarise(avg_rating = mean(averageRating, na.rm = TRUE), 
            num_episodes = n(), 
            .groups = "drop") |> 
  filter(num_episodes > 12) |> 
  arrange(desc(avg_rating)) |> 
  head(1)


message <- sprintf(
  "TV series with the highest average rating is '%s' with an average rating of %.2f across %d episodes.",
  top_rated_series$primaryTitle,
  top_rated_series$avg_rating,
  top_rated_series$num_episodes
)
print(message)
[1] "TV series with the highest average rating is 'Kavya - Ek Jazbaa, Ek Junoon' with an average rating of 9.75 across 113 episodes."

6.The TV series Happy Days (1974-1984) gives us the common idiom “jump the shark”. The phrase comes from a controversial fifth season episode (aired in 1977) in which a lead character literally jumped over a shark on water skis. Idiomatically, it is used to refer to the moment when a once-great show becomes ridiculous and rapidly looses quality.

Is it true that episodes from later seasons of Happy Days have lower average ratings than the early seasons?

R= Yes, it’s true that the later episodes have a lower average rating.

happy_days <- TITLE_BASICS |> filter(primaryTitle == "Happy Days")

happy_days_ratings <- TITLE_EPISODES |> filter(parentTconst %in% happy_days$tconst) |> inner_join(TITLE_RATINGS, by = "tconst") |> group_by(seasonNumber) |> summarise(avg_rating = mean(averageRating, na.rm = TRUE)) |> arrange(seasonNumber)

print(happy_days_ratings)
# A tibble: 11 × 2
   seasonNumber avg_rating
          <dbl>      <dbl>
 1            1       7.58
 2            2       7.69
 3            3       7.7 
 4            4       7.43
 5            5       7   
 6            6       7.02
 7            7       6.33
 8            8       5.3 
 9            9       6.4 
10           10       6.7 
11           11       7.33

Quantifying Success

Task 3: Customs Success Metric

Designing a metric for IMDb entries, which reflects the quality and popular awareness. Our success metric will utilize the “log” function for “numVotes” variable, the function will help to balance the large vote counts and will give more veracity to the results.

The following is the code for the success metric:

TITLE_RATINGS |> mutate(successMetric = averageRating * log(numVotes)) |> select(successMetric, averageRating, numVotes, tconst) |> head(10)
   successMetric averageRating numVotes    tconst
1       43.59238           5.7     2096 tt0000001
2       31.61450           5.6      283 tt0000002
3       49.73537           6.5     2104 tt0000003
4       28.13123           5.4      183 tt0000004
5       49.30185           6.2     2841 tt0000005
6       26.41602           5.0      197 tt0000006
7       36.66653           5.4      889 tt0000007
8       41.66408           5.4     2243 tt0000008
9       29.02650           5.4      216 tt0000009
10      60.88036           6.8     7731 tt0000010

Time to validate the success metric:

  1. In the following code, we’ll choose the top 10 movies and confirm that they were indeed box offices successes.
inner_join(TITLE_RATINGS, TITLE_BASICS, join_by(tconst == tconst)) |> mutate(successMetric = averageRating * log(numVotes)) |> filter(titleType == "movie") |> select(successMetric, titleType, tconst,primaryTitle, averageRating, numVotes) |> arrange(desc(successMetric)) |> head(10)
   successMetric titleType    tconst
1       138.5549     movie tt0111161
2       134.0282     movie tt0468569
3       133.7460     movie tt0068646
4       130.6753     movie tt0167260
5       130.2461     movie tt0110912
6       129.9997     movie tt1375666
7       129.3531     movie tt0120737
8       129.2261     movie tt0137523
9       128.9469     movie tt0109830
10      127.8768     movie tt0108052
                                        primaryTitle averageRating numVotes
1                           The Shawshank Redemption           9.3  2953131
2                                    The Dark Knight           9.0  2934441
3                                      The Godfather           9.2  2058741
4      The Lord of the Rings: The Return of the King           9.0  2021764
5                                       Pulp Fiction           8.9  2267965
6                                          Inception           8.8  2604356
7  The Lord of the Rings: The Fellowship of the Ring           8.9  2051454
8                                         Fight Club           8.8  2385191
9                                       Forrest Gump           8.8  2310684
10                                  Schindler's List           9.0  1481448

In the code above, we’re analyzing the information with the success metric that we created,now we’ll make a comparative by analyzing only the average rating:

inner_join(TITLE_RATINGS, TITLE_BASICS, join_by(tconst == tconst)) |> mutate(successMetric = averageRating * log(numVotes)) |> filter(titleType == "movie") |> select(successMetric, titleType, tconst,primaryTitle, averageRating, numVotes) |> arrange(desc(averageRating)) |> head(10)
   successMetric titleType     tconst        primaryTitle averageRating
1       61.13682     movie tt27815015             Kurukku          10.0
2       69.32448     movie tt33075815              Kaveri          10.0
3       47.44932     movie tt33507675     Gorgeous Rascal          10.0
4       54.66246     movie  tt0389937   Don: Plain & Tall           9.9
5       47.80031     movie tt13088688  Happy Hacker's Day           9.9
6       68.31723     movie tt14142772        Kaalapatthar           9.9
7       53.35181     movie tt27843025         Dollarspete           9.9
8       46.80114     movie tt28999528                Aima           9.9
9       46.53476     movie tt30790410            Peppatty           9.9
10      76.05201     movie tt33477418 Like Aani Subscribe           9.9
   numVotes
1       452
2      1025
3       115
4       250
5       125
6       993
7       219
8       113
9       110
10     2169

Now, let’s discuss the results. Sorting by the average rating doesn’t feel reliable because even though those movies are rated with a 10, the number of votes is too low. In contrast, the results obtained with the success metric show a higher number of votes, which is directly proportional to both the rating and the success metric points.

  1. Choose 3-5 movies with large numbers of IMDb votes that score poorly on your success metric and confirm that they are indeed of low quality.

For this analysis, first I want to know the mean, max and min of my three variables, in my code I’m filtering for number of votes above 15,000 and success metric under the average.

TITLE_RATINGS |> mutate(successMetric = averageRating * log(numVotes)) |> select(successMetric, averageRating, numVotes, tconst) |> head(10)
   successMetric averageRating numVotes    tconst
1       43.59238           5.7     2096 tt0000001
2       31.61450           5.6      283 tt0000002
3       49.73537           6.5     2104 tt0000003
4       28.13123           5.4      183 tt0000004
5       49.30185           6.2     2841 tt0000005
6       26.41602           5.0      197 tt0000006
7       36.66653           5.4      889 tt0000007
8       41.66408           5.4     2243 tt0000008
9       29.02650           5.4      216 tt0000009
10      60.88036           6.8     7731 tt0000010
TITLE_RATINGS |> summarise(min_averageRating = mean(averageRating, na.rm = TRUE))
  min_averageRating
1          6.801623
TITLE_RATINGS |> summarise(mean_numVotesg = mean(numVotes, na.rn = TRUE))
  mean_numVotesg
1       4020.515
TITLE_RATINGS |> mutate(successMetric = averageRating * log(numVotes)) |> select(successMetric, averageRating, numVotes, tconst) |> head(10)|> summarise(mean_numSuccessMetric = mean(successMetric, is.na = TRUE))
  mean_numSuccessMetric
1              39.70288
TITLE_RATINGS |> mutate(successMetric = averageRating * log(numVotes)) |> select(successMetric, averageRating, numVotes, tconst) |> head(10)|> summarise(max_numSuccessMetric = max(successMetric, is.na = TRUE))
  max_numSuccessMetric
1             60.88036
TITLE_RATINGS |> mutate(successMetric = averageRating * log(numVotes)) |> select(successMetric, averageRating, numVotes, tconst) |> head(10)|> summarise(min_numSuccessMetric = min(successMetric, na.rm = TRUE))
  min_numSuccessMetric
1             26.41602
inner_join(TITLE_RATINGS, TITLE_BASICS, join_by(tconst == tconst)) |> mutate(successMetric = averageRating * log(numVotes)) |> filter(titleType == "movie", numVotes > 15000, successMetric < 42) |> select(successMetric, titleType, tconst, primaryTitle, averageRating, numVotes) |> head(10)
   successMetric titleType    tconst                     primaryTitle
1       41.42717     movie tt0052077          Plan 9 from Outer Space
2       16.86419     movie tt0060666         Manos: The Hands of Fate
3       32.65982     movie tt0065832             Hercules in New York
4       39.04952     movie tt0076009         Exorcist II: The Heretic
5       39.99278     movie tt0085750                         Jaws 3-D
6       32.49702     movie tt0093300                Jaws: The Revenge
7       40.30001     movie tt0094074 Superman IV: The Quest for Peace
8       36.94279     movie tt0094824                    Caddyshack II
9       30.84751     movie tt0103923                  Captain America
10      31.41177     movie tt0105643                          Troll 2
   averageRating numVotes
1            3.9    41042
2            1.6    37802
3            3.3    19869
4            3.8    29033
5            3.7    49457
6            3.0    50632
7            3.7    53739
8            3.8    16677
9            3.2    15365
10           3.0    35263

As we can see in the results, all the movies where the success metric is below average have a low average rating as well. Even though the number of votes is high, we demonstrate again that the success metric works.

  1. Choose a prestige actor or director and confirm that they have many projects with high scores on your success metric. The chosen director is Woody Allen, who has been awarded 4 Oscars, 2 Golden Globes, 9 BAFTA nominations, and many more nominations throughout his career.
NAME_BASICS |> filter (primaryName == "Woody Allen") #We use this filter to get the project IDs
      nconst primaryName birthYear deathYear     primaryProfession
1  nm0000095 Woody Allen      1935        NA writer,director,actor
2 nm14402329 Woody Allen        NA        NA                   \\N
                           knownForTitles age
1 tt0079522,tt0075686,tt0118954,tt0091167  NA
2                               tt0283172  NA
inner_join(TITLE_RATINGS, TITLE_BASICS, join_by(tconst == tconst)) |> mutate(successMetric = averageRating * log(numVotes)) |> filter(titleType == "movie", tconst %in% c("tt0079522","tt0075686","tt0118954","tt0091167")) |> select(successMetric, titleType, tconst, primaryTitle, averageRating, numVotes)
  successMetric titleType    tconst           primaryTitle averageRating
1     100.36862     movie tt0075686             Annie Hall           8.0
2      92.92100     movie tt0079522              Manhattan           7.8
3      87.83728     movie tt0091167 Hannah and Her Sisters           7.8
4      78.75088     movie tt0118954   Deconstructing Harry           7.3
  numVotes
1   280991
2   149186
3    77745
4    48426

Per the results, his movies exceed the average success metric. However, the average rating is not great. It’s important to highlight that we chose this actor based on his awards, but those awarded movies are not in this database. This can be considered missing data, and consequently, these results are not very reliable.

  1. Perform at least one other form of ‘spot check’ validation.

We’ll create a scatter plot to visualize the relationship between the Success Metric and the Number of Votes per movie.

library(ggplot2)

TITLE_RATINGS <- TITLE_RATINGS |> 
  mutate(successMetric = averageRating * log(numVotes)) 


ggplot(inner_join(TITLE_RATINGS, TITLE_BASICS, join_by(tconst == tconst)) , 
       aes(x = numVotes, y = successMetric)) + 
  geom_point() + 
  labs(title = "Success Metric vs. Number of Votes", 
       x = "Number of Votes", 
       y = "Success Metric") + 
  scale_x_continuous(labels = label_number(accuracy = 0.1)) + 
  theme_minimal()

The results of this plot confirm that the relationship between the number of votes and the success metric is not necessarily close. We must remember that we used the “log” function to precisely avoid distorting the calculations by the count of votes. We can also observe that the largest concentration of the highest ratings is between 0 and 1 million votes.An interesting observation would be to know which two movies have the highest ratings and the highest number of votes, in this plot.

  1. Come up with a numerical threshold for a project to be a ‘success’; that is, determine a value such that movies above are all “solid” or better.

I will use Percentiles to calculate the numerical threshold. The reason I choose this method is because i think it’s important to focus on the successMetric value where most of the data is locates. Given that our data contains extreme values, using the Percentiles helps to prevent skewing the numeric threshold. In contrast, the mean can be significantly influenced by these extreme values.

mean(TITLE_RATINGS$successMetric, na.rm = TRUE)
[1] 42.2355
min(TITLE_RATINGS$successMetric, na.rm = TRUE)
[1] 4.60517
max(TITLE_RATINGS$successMetric, na.rm = TRUE)
[1] 138.8269

Calculate percentiles

percentiles <- quantile(TITLE_RATINGS$successMetric, probs = c(0.25, 0.75), na.rm = TRUE); print(percentiles)
     25%      75% 
33.79023 49.02554

The results indicate that 75% of the success metric scores are 49 or lower, and 25% of the scores are 33 or lower. Consequently, I will set 50 as the numeric threshold to project a successful or solid project.

Examining success by Genre and Decade.

  1. What was the genre with the most “successes” in each decade?

To calculate the counts of success by genres in each decade:

inner_join(TITLE_RATINGS, TITLE_BASICS, join_by(tconst == tconst)) |> 
  mutate(successMetric = averageRating * log(numVotes), 
         decade = floor(startYear / 10) * 10) |> 
  filter(successMetric > 50) |> 
  group_by(decade, genres) |> 
  summarise(
    count_successes = n(),
    avg_rating = mean(averageRating, na.rm = TRUE), 
    total_votes = sum(numVotes)
  ) |> 
  # Get the genre with the most successes in each decade
  slice_max(count_successes, n = 1, with_ties = FALSE) |> 
  arrange(desc(decade))
`summarise()` has grouped output by 'decade'. You can override using the
`.groups` argument.
# A tibble: 16 × 5
# Groups:   decade [16]
   decade genres                     count_successes avg_rating total_votes
    <dbl> <chr>                                <int>      <dbl>       <dbl>
 1   2020 Action,Adventure,Animation            1564       8.32     9474815
 2   2010 Action,Adventure,Animation            3109       8.28    18933716
 3   2000 Action,Adventure,Animation            1443       8.01     7625561
 4   1990 Comedy                                 795       7.96     5662595
 5   1980 Comedy                                 324       7.73     3577928
 6   1970 Comedy                                 181       7.85     1790874
 7   1960 Drama                                  164       7.50     1245176
 8   1950 Comedy                                 112       8.21      186897
 9   1940 Animation,Comedy,Family                102       7.45      216127
10   1930 Drama,Romance                           45       7.18      247332
11   1920 Drama,Romance                           21       7.30      113623
12   1910 Comedy,Short                            14       6.84       47456
13   1900 Comedy,Fantasy,Horror                    2       6.95        3968
14   1890 Documentary,Short                        4       6.78       26146
15   1880 Documentary,Short                        2       6.9        10350
16   1870 Documentary,Short                        1       6.8         2194

The most successful genres in the past 30 years have been action, adventure, and animation. It is interesting how, in the 1940s when the Second World War took place, the most successful genres were animation, comedy, and family. It makes sense that producers were interested in these genres since movies were a way to entertain civilians and help keep them distracted with more positive content. In the following decades, comedy gained popularity, even though the average rating wasn’t great.

  1. Now I’ll continue to analyze in a deeper level only the last 5 years, for movies and TV series, and we will filter the top 3 genres per year.
success_past_10_years <- inner_join(TITLE_RATINGS, TITLE_BASICS, join_by(tconst == tconst)) |> 
  mutate(successMetric = averageRating * log(numVotes)) |> 
  filter(successMetric > 50, 
         titleType %in% c("movie", "tvSeries"), 
         startYear >= 2019, startYear <= 2024) |> 
  group_by(startYear, genres) |> 
  summarise(
    count_successes = n(),
    avg_rating = mean(averageRating, na.rm = TRUE), 
    total_votes = sum(numVotes)
  ) |> 
  group_by(startYear) |> 
  slice_max(order_by = count_successes, n = 3, with_ties = FALSE) |> 
  arrange(startYear, desc(count_successes))
`summarise()` has grouped output by 'startYear'. You can override using the
`.groups` argument.
# Print the results with no row limit
success_past_10_years |> print(n = Inf)
# A tibble: 18 × 5
# Groups:   startYear [6]
   startYear genres                     count_successes avg_rating total_votes
       <dbl> <chr>                                <int>      <dbl>       <dbl>
 1      2019 Drama                                  118       7.35     1039384
 2      2019 Comedy,Drama                            73       7.15     1750763
 3      2019 Documentary                             66       7.71      258839
 4      2020 Drama                                   84       7.18     1124748
 5      2020 Documentary                             57       7.56      148078
 6      2020 Comedy,Drama                            54       7.11      663263
 7      2021 Drama                                  112       7.42     1180453
 8      2021 Comedy,Drama                            67       7.22      928586
 9      2021 Action,Adventure,Animation              45       7.17     1235279
10      2022 Drama                                  127       7.47     1766963
11      2022 Comedy,Drama                            67       7.21     1441636
12      2022 Drama,Romance                           64       7.30      574092
13      2023 Drama                                  135       7.81      807909
14      2023 Comedy,Drama                            52       7.23      779624
15      2023 Comedy                                  51       7.19      412010
16      2024 Drama                                   98       8.18      205604
17      2024 Comedy                                  51       7.97      252990
18      2024 Documentary                             36       7.76       89428

Heatmap of Genre Successes from 2019-2024:

ggplot(success_past_10_years, aes(x = startYear, y = genres, fill = count_successes)) + geom_tile() + labs(title = "Heatmap of Genre Successes (2019-2024)", x = "Year", y = "Genre") + scale_fill_gradient(low = "white", high = "blue") + theme_minimal()

As we can appreciate in the heatmap, drama has been the most successful genre over the past 5 years. In second place is a combination of comedy and drama, followed by documentaries. I feel very pleased with these results since comedy and drama are my favorite genres, and I was originally thinking of a suggestion within these two genres.

  1. Finally I want to analyze the average rating of the most successful movies and TV series from the past 5 years per genre.

This is the same code as before, but now we’re dorping thae group by function, since we don’t need the information grouped by year.

success_past_10_years <- inner_join(TITLE_RATINGS, TITLE_BASICS, join_by(tconst == tconst)) |> mutate(successMetric = averageRating * log(numVotes)) |> filter(successMetric > 50, titleType %in% c("movie", "tvSeries"), startYear >= 2019, startYear <= 2024) |> group_by(startYear, genres) |>
summarise( count_successes = n(),
avg_rating = mean(averageRating, na.rm = TRUE), total_votes = sum(numVotes), .groups = "drop" ) |> group_by(startYear) |>
slice_max(order_by = count_successes, n = 3, with_ties = FALSE) |> arrange(startYear, desc(count_successes))

In the following code we’ll get the average rating and the total successes per genre of the last 5 years.

average_genre_ratings <- success_past_10_years |> group_by(genres) |>
summarise( total_avg_rating = mean(avg_rating, na.rm = TRUE), total_successes = sum(count_successes), .groups = "drop" ) |> arrange(desc(total_avg_rating))
average_genre_ratings |> print(n = Inf)
# A tibble: 6 × 3
  genres                     total_avg_rating total_successes
  <chr>                                 <dbl>           <int>
1 Documentary                            7.68             159
2 Comedy                                 7.58             102
3 Drama                                  7.57             674
4 Drama,Romance                          7.30              64
5 Comedy,Drama                           7.18             313
6 Action,Adventure,Animation             7.17              45

Bar Plot of the Average Ratings per Genre from 2019 - 2024

# Calculate average ratings and total successes for each genre
average_genre_ratings <- success_past_10_years |> 
  group_by(genres) |> 
  summarise(
    total_avg_rating = mean(avg_rating, na.rm = TRUE), 
    total_successes = sum(count_successes), 
    .groups = "drop"
  ) |> 
  arrange(desc(total_avg_rating))
average_genre_ratings |> print(n = Inf)
# A tibble: 6 × 3
  genres                     total_avg_rating total_successes
  <chr>                                 <dbl>           <int>
1 Documentary                            7.68             159
2 Comedy                                 7.58             102
3 Drama                                  7.57             674
4 Drama,Romance                          7.30              64
5 Comedy,Drama                           7.18             313
6 Action,Adventure,Animation             7.17              45
# Plot the results with the correct column names
ggplot(average_genre_ratings, aes(x = reorder(genres, total_avg_rating), y = total_avg_rating)) + 
  geom_bar(stat = "identity", fill = "steelblue") + 
  labs(title = "Average Ratings of Genres (2019-2024)", x = "Genres", y = "Average Rating") + 
  theme_minimal() + 
  coord_flip()

It’s interesting how, even though these are the top 5 most successful genres from recent years, the ratings are not great. This suggests a significant opportunity to produce a great movie, as it seems the public’s expectations are not very high.

Successful Personnel in the Genre

I need to load all the datasets again:

get_imdb_file <- function(fname){
    BASE_URL <- "https://datasets.imdbws.com/"
    fname_ext <- paste0(fname, ".tsv.gz")
    
    # Check if the file exists locally
    if (!file.exists(fname_ext)) {
        FILE_URL <- paste0(BASE_URL, fname_ext)
        download.file(FILE_URL, destfile = fname_ext)
    } 
    
    # Read the data into a dataframe
    return(as.data.frame(readr::read_tsv(fname_ext, lazy = FALSE)))
}

# Load the datasets, they will only be downloaded if not already present
NAME_BASICS      <- get_imdb_file("name.basics")
Rows: 13884604 Columns: 6
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr (6): nconst, primaryName, birthYear, deathYear, primaryProfession, known...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
TITLE_BASICS     <- get_imdb_file("title.basics")
Warning: One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
Rows: 11174655 Columns: 9
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr (8): tconst, titleType, primaryTitle, originalTitle, startYear, endYear,...
dbl (1): isAdult

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
TITLE_EPISODES   <- get_imdb_file("title.episode")
Rows: 8579083 Columns: 4
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr (4): tconst, parentTconst, seasonNumber, episodeNumber

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
TITLE_RATINGS    <- get_imdb_file("title.ratings")
Rows: 1489867 Columns: 3
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr (1): tconst
dbl (2): averageRating, numVotes

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
TITLE_CREW       <- get_imdb_file("title.crew")
Rows: 10514405 Columns: 3
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr (3): tconst, directors, writers

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
TITLE_PRINCIPALS <- get_imdb_file("title.principals")
Warning: One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
Rows: 86651930 Columns: 6
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr (5): tconst, nconst, category, job, characters
dbl (1): ordering

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

In this code, we’ll filter by actors, actresses, and directors with previous successful projects.

if (!requireNamespace("dplyr", quietly = TRUE)) {
    install.packages("dplyr")
}
   

success_threshold <- 50  

TITLE_RATINGS <- TITLE_RATINGS |>
  mutate(successMetric = averageRating * log(numVotes)) |>
  select(successMetric, averageRating, numVotes, tconst)


successful_people <- TITLE_PRINCIPALS |>
  inner_join(TITLE_RATINGS, by = "tconst") |>
  filter(category %in% c("actor", "actress", "director"), successMetric > success_threshold) |>
  group_by(nconst, category) |>
  summarise(
    num_successful_movies = n(),
    avg_success_metric = mean(successMetric, na.rm = TRUE),
    .groups = "drop"
  ) |>
  inner_join(NAME_BASICS, by = "nconst") |>
  arrange(desc(num_successful_movies))
success_threshold <- 50  

# Identify successful actors and directors
successful_people <- TITLE_PRINCIPALS |>
  inner_join(TITLE_RATINGS, by = "tconst") |>
  filter(category %in% c("actor", "actress", "director"), successMetric > success_threshold) |>
  group_by(nconst, category) |>
  summarise(
    num_successful_movies = n(),
    avg_success_metric = mean(successMetric, na.rm = TRUE),
    .groups = "drop"
  ) |>
  inner_join(NAME_BASICS, by = "nconst") |>
  arrange(desc(num_successful_movies))

# Select actors and director
top_actors_director <- successful_people |>
  filter(category %in% c("actor", "actress", "director")) |>
  slice_head(n = 25)  

top_actors_director |> select(primaryName, category, num_successful_movies, avg_success_metric)
# A tibble: 25 × 4
   primaryName       category num_successful_movies avg_success_metric
   <chr>             <chr>                    <int>              <dbl>
 1 Dee Bradley Baker actor                     1720               63.2
 2 Dan Castellaneta  actor                     1315               60.8
 3 Hank Azaria       actor                     1302               60.8
 4 Harry Shearer     actor                     1276               60.8
 5 John DiMaggio     actor                     1187               61.7
 6 Nancy Cartwright  actress                   1042               60.7
 7 Tress MacNeille   actress                   1004               59.7
 8 Trey Parker       actor                      976               64.7
 9 Seth MacFarlane   actor                      975               56.7
10 Matt Stone        actor                      947               64.9
# ℹ 15 more rows
filtered_actors_director <- top_actors_director |>
  inner_join(TITLE_PRINCIPALS, by = "nconst") |>
  inner_join(TITLE_BASICS, by = "tconst") |>
  filter(genres %in% c("Comedy", "Drama", "Comedy,Drama", "Romance")) |>
  select(primaryName, num_successful_movies, avg_success_metric, genres)

filtered_actors_director
# A tibble: 1,360 × 4
   primaryName       num_successful_movies avg_success_metric genres
   <chr>                             <int>              <dbl> <chr> 
 1 Dee Bradley Baker                  1720               63.2 Comedy
 2 Dee Bradley Baker                  1720               63.2 Comedy
 3 Dee Bradley Baker                  1720               63.2 Comedy
 4 Dee Bradley Baker                  1720               63.2 Comedy
 5 Dee Bradley Baker                  1720               63.2 Comedy
 6 Dee Bradley Baker                  1720               63.2 Comedy
 7 Dan Castellaneta                   1315               60.8 Comedy
 8 Dan Castellaneta                   1315               60.8 Comedy
 9 Dan Castellaneta                   1315               60.8 Comedy
10 Dan Castellaneta                   1315               60.8 Comedy
# ℹ 1,350 more rows

The actors two actors that we’ll choose for this remake will be: Seth MacFarlane and Hank Azaria.

Seth MacFarlane is Known for pushing boundaries with humor and storytelling, blending satire with social commentary.Has successfully transitioned into live-action, starring in and producing the sci-fi series The Orville.

Hank Azaria, is renowned for his ability to create distinct, memorable character voices, contributing to the long-standing success of The Simpsons.Actively involved in philanthropic efforts, including advocacy for various social and environmental causes.

The director choosen for this project will be Guillermo Del Toro, Frequently explores themes of love, loss, and the struggle between good and evil, his ability to blend genres and create emotional depth in fantastical narratives has earned him a dedicated fan base and numerous awards.

The impressive ratings highlight the potential of these talented individuals, each of whom brings their own unique and prestigious skills to the table. Together, they are poised for tremendous success.

Nostalgia and Remakes

Now we need a story! We wish to produce a remark of______

# Define the year cutoff for remakes (25 years ago)
year_cutoff <- 1999

# Filter for classic movies that haven't been remade in the last 25 years
classic_movies <- TITLE_BASICS |>
  inner_join(TITLE_RATINGS, by = "tconst") |>
  filter(
    startYear < year_cutoff,
    averageRating >= 8.5,
    numVotes >= 500000
  ) |>
  arrange(desc(averageRating))

classic_movies |>
  DT::datatable(options = list(pageLength = 20))

The classic movie I’ve chosen for this project is Life Is Beautiful. This remarkable film fits into the Comedy, Drama, and Romance genres, boasting an impressive average rating of 8.6 and a high success metric of 116.41 points. This movie encompasses all the qualities we are looking for: it appeals to a wide audience, evokes laughter, and leaves viewers with a sense of fulfillment and a valuable lesson.

Putting It Together

Elevator Pitch: Remake of “TLife Is Beautiful”

I am thrilled to propose a remake of the timeless classic Life Is Beautiful, featuring the extraordinary talents of Seth MacFarlane and Hank Azaria, with acclaimed director Guillermo del Toro leading the project.

In the last decade, comedy-drama movies have surged in popularity, driven by the rise of streaming platforms and a growing audience appetite for nuanced, character-driven stories that blend humor with emotional depth. This market trend emphasizes the need for well-developed characters and complex motivations, making our project more relevant than ever.

Seth MacFarlane brings a unique blend of humor and storytelling that resonates with diverse audiences. His ability to tackle complex themes through satire and social commentary, demonstrated in his work on The Orville, makes him the ideal choice to infuse Life Is Beautiful with both laughter and depth.

Hank Azaria, renowned for his versatile voice acting in The Simpsons, adds richness to our cast. His memorable character portrayals reflect a commitment to authenticity and empathy, essential qualities for a film exploring love and resilience.

Under Guillermo del Toro’s visionary direction, we will delve into the profound themes of hope and the human spirit. His mastery of blending genres and emotional storytelling ensures that our remake will resonate with modern audiences.

With MacFarlane’s humor, Azaria’s depth, and del Toro’s artistic vision, this project is poised to become a compelling and heartfelt cinematic experience that captures the essence of Life Is Beautiful for today’s viewers.

install.packages("magick")
Installing package into 'C:/Users/krisf/AppData/Local/R/win-library/4.4'
(as 'lib' is unspecified)
package 'magick' successfully unpacked and MD5 sums checked
Warning: cannot remove prior installation of package 'magick'
Warning in file.copy(savedcopy, lib, recursive = TRUE): problem copying
C:\Users\krisf\AppData\Local\R\win-library\4.4\00LOCK\magick\libs\x64\magick.dll
to C:\Users\krisf\AppData\Local\R\win-library\4.4\magick\libs\x64\magick.dll:
Permission denied
Warning: restored 'magick'

The downloaded binary packages are in
    C:\Users\krisf\AppData\Local\Temp\RtmpWQw1EN\downloaded_packages
library(magick)
Linking to ImageMagick 6.9.12.98
Enabled features: cairo, freetype, fftw, ghostscript, heic, lcms, pango, raw, rsvg, webp
Disabled features: fontconfig, x11
img <- image_read("C:/Users/krisf/Downloads/050520_LifeIsBeautiful_05.jpg") 


print(img)
# A tibble: 1 × 7
  format width height colorspace matte filesize density
  <chr>  <int>  <int> <chr>      <lgl>    <int> <chr>  
1 JPEG    1800   1200 sRGB       FALSE   110190 72x72