Import, inspection, and cleaning of dataset-

From the dataset, we can see that: 1. There are no missing values in the dataset. 2. The movies with duplicated entries. There are Duplicates (2907 distinct titles in 2961 rows) The following piece of code shows how we have cleaned it.

movies <- read_csv(here::here("data", "movies.csv"))
glimpse(movies)

## Rows: 2,961
## Columns: 11
## $ title               <chr> "Avatar", "Titanic", "Jurassic World", "The Avenge…
## $ genre               <chr> "Action", "Drama", "Action", "Action", "Action", "…
## $ director            <chr> "James Cameron", "James Cameron", "Colin Trevorrow…
## $ year                <dbl> 2009, 1997, 2015, 2012, 2008, 1999, 1977, 2015, 20…
## $ duration            <dbl> 178, 194, 124, 173, 152, 136, 125, 141, 164, 93, 1…
## $ gross               <dbl> 760505847, 658672302, 652177271, 623279547, 533316…
## $ budget              <dbl> 2.37e+08, 2.00e+08, 1.50e+08, 2.20e+08, 1.85e+08, …
## $ cast_facebook_likes <dbl> 4834, 45223, 8458, 87697, 57802, 37723, 13485, 920…
## $ votes               <dbl> 886204, 793059, 418214, 995415, 1676169, 534658, 9…
## $ reviews             <dbl> 3777, 2843, 1934, 2425, 5312, 3917, 1752, 1752, 35…
## $ rating              <dbl> 7.9, 7.7, 7.0, 8.1, 9.0, 6.5, 8.7, 7.5, 8.5, 7.2, …

# no missing values. There are Duplicates (2907 distinct titles in 2961 rows). 
skim(movies)

Variable type: character

Variable type: numeric

# show the duplicate movies
movies %>% count(title, sort=T)

## # A tibble: 2,907 × 2
##    title                           n
##    <chr>                       <int>
##  1 Home                            3
##  2 A Nightmare on Elm Street       2
##  3 Across the Universe             2
##  4 Alice in Wonderland             2
##  5 Aloha                           2
##  6 Around the World in 80 Days     2
##  7 Brothers                        2
##  8 Carrie                          2
##  9 Chasing Liberty                 2
## 10 Cinderella                      2
## # … with 2,897 more rows

# to see what happens with the duplicates
movies %>% filter(title=="Homes")

## # A tibble: 0 × 11
## # … with 11 variables: title <chr>, genre <chr>, director <chr>, year <dbl>,
## #   duration <dbl>, gross <dbl>, budget <dbl>, cast_facebook_likes <dbl>,
## #   votes <dbl>, reviews <dbl>, rating <dbl>

# `distinct` function can only keep the first entry but not latest
# movies <- distinct(movies, title, .keep_all=T)
length(unique(movies$title))

## [1] 2907

movies <- movies %>% 
  group_by(title) %>% 
  filter(votes == max(votes)) %>%
  ungroup()

# there are still duplicates
movies %>% count(title, sort=T)

## # A tibble: 2,907 × 2
##    title                          n
##    <chr>                      <int>
##  1 Chasing Liberty                2
##  2 10 Cloverfield Lane            1
##  3 10 Days in a Madhouse          1
##  4 10 Things I Hate About You     1
##  5 102 Dalmatians                 1
##  6 10th & Wolf                    1
##  7 12 Rounds                      1
##  8 12 Years a Slave               1
##  9 127 Hours                      1
## 10 13 Going on 30                 1
## # … with 2,897 more rows

# to see what happens with the duplicates
movies %>% filter(title=="Chasing Liberty")

## # A tibble: 2 × 11
##   title    genre  director   year duration   gross budget cast_facebook_l… votes
##   <chr>    <chr>  <chr>     <dbl>    <dbl>   <dbl>  <dbl>            <dbl> <dbl>
## 1 Chasing… Comedy Andy Cad…  2004      101  1.22e7  2.3e7              842 30092
## 2 Chasing… Comedy Andy Cad…  2004      101  1.22e7  2.3e7              829 30092
## # … with 2 more variables: reviews <dbl>, rating <dbl>

# do the filter only for the entries of Chasing Liberty 
movies <- movies %>%
  group_by(title) %>% 
  filter(cast_facebook_likes==max(cast_facebook_likes)) %>%
  ungroup()

skim(movies)

Variable type: character

Variable type: numeric

The following table shows the count of movies by genre, ranked in descending order

movies %>% count(genre, sort = TRUE)

## # A tibble: 17 × 2
##    genre           n
##    <chr>       <int>
##  1 Comedy        844
##  2 Action        719
##  3 Drama         484
##  4 Adventure     281
##  5 Crime         198
##  6 Biography     135
##  7 Horror        128
##  8 Animation      35
##  9 Fantasy        26
## 10 Documentary    25
## 11 Mystery        15
## 12 Sci-Fi          7
## 13 Family          3
## 14 Musical         2
## 15 Romance         2
## 16 Western         2
## 17 Thriller        1

Here we have a table with the average gross earning and budget (gross and budget) by genre. We have calculated a variable return_on_budget which shows how many $ did a movie make at the box office for each $ of its budget. We have ranked genres by this return_on_budget in descending order-

movies %>% 
  mutate(movies_return = gross/budget ) %>%
  group_by(genre) %>%
  summarise(avg_gross = mean(gross),
            avg_budget = mean(budget),
            genre_return_on_budget = sum(gross)/sum(budget),
            movie_mean_return_on_budget = mean(movies_return)) %>%
  arrange(-movie_mean_return_on_budget)

## # A tibble: 17 × 5
##    genre        avg_gross avg_budget genre_return_on_budget movie_mean_return_o…
##    <chr>            <dbl>      <dbl>                  <dbl>                <dbl>
##  1 Horror       37782310.  13804379.                2.74                86.1    
##  2 Biography    45201805.  28543696.                1.58                22.3    
##  3 Musical      92084000    3189500                28.9                 18.8    
##  4 Family      149160478.  14833333.               10.1                 14.1    
##  5 Documentary  17353973.   5887852.                2.95                 8.70   
##  6 Western      20821884    3465000                 6.01                 7.06   
##  7 Fantasy      41902674.  18484615.                2.27                 6.10   
##  8 Animation    98433792.  61701429.                1.60                 5.01   
##  9 Comedy       42487808.  24458506.                1.74                 3.70   
## 10 Romance      31264848.  25107500                 1.25                 3.17   
## 11 Drama        36754959.  25832605.                1.42                 2.98   
## 12 Mystery      69117136.  41500000                 1.67                 2.90   
## 13 Adventure    94350236.  64692313.                1.46                 2.44   
## 14 Crime        37601525.  26527405.                1.42                 2.19   
## 15 Action       86270343.  70774558.                1.22                 1.93   
## 16 Sci-Fi       29788371.  27607143.                1.08                 1.58   
## 17 Thriller         2468     300000                 0.00823              0.00823

Here we have a table that shows the top 15 directors who have created the highest gross revenue in the box office. We have shown the total gross amount, the mean, median, and standard deviation per director.

movies %>%
  group_by(director) %>%
  summarise(total_gross = sum(gross),
            mean_gross = mean(gross),
            median_gross = median(gross),
            standard_dev_gross = sd(gross)) %>%
  slice_max ( order_by = total_gross, n = 15)

## # A tibble: 15 × 5
##    director          total_gross mean_gross median_gross standard_dev_gross
##    <chr>                   <dbl>      <dbl>        <dbl>              <dbl>
##  1 Steven Spielberg   4014061704 174524422.   164435221          101421051.
##  2 Michael Bay        2195443511 182953626.   168468240.         125789167.
##  3 James Cameron      1909725910 318287652.   175562880.         309171337.
##  4 Christopher Nolan  1813227576 226653447    196667606.         187224133.
##  5 George Lucas       1741418480 348283696    380262555          146193880.
##  6 Robert Zemeckis    1619309108 124562239.   100853835           91300279.
##  7 Tim Burton         1557078534 111219895.    69791834           99304293.
##  8 Sam Raimi          1443167519 180395940.   138480208          174705230.
##  9 Clint Eastwood     1378321100  72543216.    46700000           75487408.
## 10 Francis Lawrence   1358501971 271700394.   281666058          135437020.
## 11 Ron Howard         1335988092 111332341    101587923           81933761.
## 12 Gore Verbinski     1329600995 189942999.   123207194          154473822.
## 13 Andrew Adamson     1137446920 284361730    279680930.         120895765.
## 14 Shawn Levy         1129750988 102704635.    85463309           65484773.
## 15 Ridley Scott       1128857598  80632686.    47775715           68812285.

We have produced a table that describes how ratings are distributed by genre. The histogram visually shows how ratings are distributed.

movies_rating <- movies %>%
  group_by(genre) %>%
  summarise(mean_rating = mean(rating),
            min_rating = min(rating),
             max_rating = max(rating),
             sd_rating = sd(rating)) 
movies_rating

## # A tibble: 17 × 5
##    genre       mean_rating min_rating max_rating sd_rating
##    <chr>             <dbl>      <dbl>      <dbl>     <dbl>
##  1 Action             6.23        2.1        9       1.04 
##  2 Adventure          6.51        2.3        8.6     1.11 
##  3 Animation          6.65        4.5        8       0.968
##  4 Biography          7.11        4.5        8.9     0.760
##  5 Comedy             6.11        1.9        8.8     1.02 
##  6 Crime              6.92        4.8        9.3     0.853
##  7 Documentary        6.66        1.6        8.5     1.77 
##  8 Drama              6.74        2.1        8.8     0.915
##  9 Family             6.5         5.7        7.9     1.22 
## 10 Fantasy            6.08        4.3        7.9     0.953
## 11 Horror             5.79        3.6        8.5     0.987
## 12 Musical            6.75        6.3        7.2     0.636
## 13 Mystery            6.84        4.6        8.5     0.910
## 14 Romance            6.65        6.2        7.1     0.636
## 15 Sci-Fi             6.66        5          8.2     1.09 
## 16 Thriller           4.8         4.8        4.8    NA    
## 17 Western            5.7         4.1        7.3     2.26

movies %>%
  ggplot(mapping = aes(x = rating)) + 
  geom_histogram(bins=30) +
  facet_wrap(~genre)+
  labs(title = "Distribution of ratings in each genre",
       x = "Rating (1-10)",
       y = "Num of movies") +
  NULL

Using ggplot to find relationships between variables

Understanding the correlation between gross and cast_facebook_likes. We have produced a scatterplot with Facebook Likes on the X-Axis and Gross Revenue on the Y-Axis.

ggplot(movies, aes(x = cast_facebook_likes, y = gross)) +
  geom_point() +
  geom_smooth(method = "lm")+
   labs(
    title = "Relationship of Facebook Likes vs Gross Revenue of the Movie",
    x = "Facebook Likes",
    y = "Gross Revenue"
  )+
  NULL

## `geom_smooth()` using formula 'y ~ x'

We analyze the following from the graph below-

1. Facebook likes do not seem like a good indicator of the gross as there is no direct correlation as seen from the scatter plot.
2. We mapped gross to Y axes and number of facebook likes to X, because the gross is the final outcome of a movie, aka dependent variable.

Now we examine the relationship between gross and budget by creating a scatterplot.

ggplot(movies, aes(x = budget , y = gross)) +
  geom_point() +
  geom_smooth(method = "lm") +
   labs(
    title = "Relationship of Gross Revenue vs Budget of the Movie",
    x = "Movie Budget",
    y = "Gross Revenue"
  )+
  NULL

## `geom_smooth()` using formula 'y ~ x'

From the plot above we see that, the budget and gross do seem correlated. The higher the budget, it is more likely that the gross may be higher.

Furthermore, we examine the relationship between gross and rating. Segmenting the scatterplot by ‘genre’, we can see the following results-

ggplot(movies, aes(x = rating , y = gross)) +
  geom_point() +
  geom_smooth(method = "lm") +
  facet_wrap(~genre) +
  labs(title = "Gross vs Rating of Movies For Each Genre ",
       x = "Rating",
       y = "Gross") +
  NULL

## `geom_smooth()` using formula 'y ~ x'

## Warning in qt((1 - level)/2, df): NaNs produced

## Warning in qt((1 - level)/2, df): NaNs produced

## Warning in qt((1 - level)/2, df): NaNs produced

## Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf

## Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf

## Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf

We can see that:

• The higher the rating the more will be the gross for the most genres of movies.

• For movies of some genres like ‘Documentary’, ‘Mystery’, ‘Horror’ and ‘Sci-Fi’, the gross has a very less change with respect to rating. Documentaries certainly have a different business model.

• Negative correlation even appears.

• Sample size of genres like ‘Family’, ‘Romance’ , ‘Musical’ is very small with under three values.