Evidence
Grupo 4: Federico Zorrilla Jasso A00836629
Dereck Iker VIllafaña Romero A00573756
Carolina Suárez Rodríguez A00837922
Oscar Daniel Retes Torres A01383653
Kevin Meza Medina A00836113
Pablo Mendez Ortiz A01722656
Jesús Alberto Moreno Marroquin A01178032
2024-05-03
Executive summmary
The movies dataset from Kaggle provides a long list of movies from several different countries. Other than the names of the movies, the dataset includes the run time, original language, status, tagline, etc, and our most important variables, revenue and budget. We believe these are our critical values because with these we can create, or mutate a new column called profit. With profit declared, we can begin to analyze our problem setup question:
What makes a movie successful or unsuccessful?
We analyzed the data using statistical tools like Pearson’s
Coefficient of Correlation to compare various variables.
1. Words in the title - Profit, r^2 = 0.03, “Extremely low/null”
2. Budget - Profit, r^2 = 0.48, “Moderate”
3. Revenue - Profit, r^2 = 0.97, “Very strong”
3. Popularity - Profit, r^2 = “NULL”
4. Run time(<250minutes) - Profit, r^2 = “NULL”
The year with the most profit.
1. 1902
2. 2017
3. 1905,
while the lowest was 1988.
The genres with the most profit
1. Adventure
2. Fantasy
3. Animation,
while the lowest was History.
Vote Averages with the most revenue
1. 7.9
2. 8.1
3. 9.1
Interestingly, 10.0 vote is in 43rd place.
Word count in titles with the most profit
1. 10
2. 8
3. 7
Introduction.
This evidence is focused on the analysis of the Kaggle database (movie database), this dataset provided us with certain variables that we will be working with such as the duration of the movies, their income, budgets, etc. . Our main objective would be to know the reason why films fail or become commercially successful. This can be measured with the profit variable that is calculated as the difference between income and budget. In order to achieve this, we will have to use certain data cleaning and preparation techniques, which will be followed by extensive analysis. In addition to examining trends by genre and year, in order to recognize the patterns of success, with the purpose of understanding what elements contribute to the commercial success of films within the film industry.
Objectives.
The creation of our profit variable with the intention of being able to determine what the profit of each film would be.
Doing a genre and year analysis in order to determine which genres and years were the most profitable.
Clean and prepare the data to be able to handle missing values and errors, in order to make sure that the data is in the right format for analysis.
Run an in-depth analysis to recognize what patterns, anomalies and trends are in the data.
Progress setup 1 - Data Wrangling/Cleaning
To answer the question of what makes a movie successful or
unsuccesful, we first cleaned the data using functions like -
filter
- as.factor/character/numeric etc.
- vis_miss
- group_by
- mutate
First, we read the CSV provided for the problem setup.
moviespersonal <- read.csv("/Users/fedezorrilla/Desktop/Programación semestre 4/Business Analytics/movies_metadata.csv")Calling Packages
We call the packages we plan to use to clean the data provided.
#install.packages("jsonlite")
library("dplyr", quietly = TRUE)
library("stringr", quietly = TRUE)
# We also install the packages we will use to create graphs later
#install.packages("esquisse")
#install.packages("ggplot2")
library("ggplot2", quietly = TRUE)
#install.packages("tidyverse")
library("tidyverse", quietly = TRUE)Summary observations
We run a summary on the dataset to see if we can identify anything that we need to “clean”
summary(moviespersonal)## adult belongs_to_collection budget genres
## Length:45466 Length:45466 Length:45466 Length:45466
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## homepage id imdb_id original_language
## Length:45466 Length:45466 Length:45466 Length:45466
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## original_title overview popularity poster_path
## Length:45466 Length:45466 Length:45466 Length:45466
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## production_companies production_countries release_date
## Length:45466 Length:45466 Length:45466
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## revenue runtime spoken_languages status
## Min. :0.000e+00 Min. : 0.00 Length:45466 Length:45466
## 1st Qu.:0.000e+00 1st Qu.: 85.00 Class :character Class :character
## Median :0.000e+00 Median : 95.00 Mode :character Mode :character
## Mean :1.121e+07 Mean : 94.13
## 3rd Qu.:0.000e+00 3rd Qu.: 107.00
## Max. :2.788e+09 Max. :1256.00
## NA's :6 NA's :263
## tagline title video vote_average
## Length:45466 Length:45466 Length:45466 Min. : 0.000
## Class :character Class :character Class :character 1st Qu.: 5.000
## Mode :character Mode :character Mode :character Median : 6.000
## Mean : 5.618
## 3rd Qu.: 6.800
## Max. :10.000
## NA's :6
## vote_count
## Min. : 0.0
## 1st Qu.: 3.0
## Median : 10.0
## Mean : 109.9
## 3rd Qu.: 34.0
## Max. :14075.0
## NA's :6At first glance, we see a couple of columns that are in the wrong
data class.
adult, character -> factor. (True/False)
budget, chr -> numeric/integer
id, chr -> integer
popularity, chr -> numeric
release_date, chr -> date
revenue, chr -> numeric
runtime, chr -> numeric
status, chr -> factor (Released/Unreleased)
We need to change these into their correct class to avoid any problem in the analysis.
Furthermore, we see some outliers in the data that don’t allow for a
correct analysis.
Lines 19731, 29504, and 35588 have errors in every single column. Seeing
that we have 45,466 rows of data, our findings will not be affected if
we remove these 3 errors.
moviespersonal <- moviespersonal[-c(19731, 29504, 35588), ]Changing Data Types
Once this is done, we can finally turn the columns into their correct data class.
moviespersonal$adult <- as.factor(moviespersonal$adult)
moviespersonal$budget <- as.numeric(moviespersonal$budget)
moviespersonal$id <- as.integer(moviespersonal$id)
moviespersonal$popularity <- as.numeric(moviespersonal$popularity)
moviespersonal$release_date <- as.Date(moviespersonal$release_date)
moviespersonal$revenue <- as.numeric(moviespersonal$revenue)
moviespersonal$runtime <- as.numeric(moviespersonal$runtime)
moviespersonal$status <- as.factor(moviespersonal$status)Now that they are in their correct data type/class, we can begin to
change the way the data is shown.
For example, the column “genres” not only contains the genres of the
movie, but also a lot of seemingly random punctuation marks. “[{‘id’:
28, ‘name’: ‘Action’}, {‘id’: 12, ‘name’: ‘Adventure’},
The punctuation makes the data seem messy and not easy to read.
Now we are creating a new column to only show the genres to make it easy to read.
pattern <- "'name':\\s*'([^']+)'"
# Use sapply with str_extract_all to extract all genre names and collapse them into a single string
moviespersonal$genre_names <- sapply(str_extract_all(moviespersonal$genres, pattern), function(x) paste(x, collapse = ", "))
head(moviespersonal$genre_names)## [1] "'name': 'Animation', 'name': 'Comedy', 'name': 'Family'"
## [2] "'name': 'Adventure', 'name': 'Fantasy', 'name': 'Family'"
## [3] "'name': 'Romance', 'name': 'Comedy'"
## [4] "'name': 'Comedy', 'name': 'Drama', 'name': 'Romance'"
## [5] "'name': 'Comedy'"
## [6] "'name': 'Action', 'name': 'Crime', 'name': 'Drama', 'name': 'Thriller'"We can now see the genres on a cleaner format but it is also showing name, now to get rid of this unnecessary text we run the following code.
moviespersonal$genre_names <- gsub("'name':\\s'", "", moviespersonal$genre_names)
# Remove the ending single quotes and any commas directly following it
moviespersonal$genre_names <- gsub("',\\s", " ", moviespersonal$genre_names)
# You may also want to remove the single quotes at the very end of each string if they exist
moviespersonal$genre_names <- gsub("'$", "", moviespersonal$genre_names)
# Check the result
head(moviespersonal$genre_names)## [1] "Animation Comedy Family" "Adventure Fantasy Family"
## [3] "Romance Comedy" "Comedy Drama Romance"
## [5] "Comedy" "Action Crime Drama Thriller"Now we can clearly read the genres of the movie on a new column called genre_names. but they still are as chr, we need to convert them to factor to properly analyze the data.
## (Other) Drama Comedy Documentary
## 13695 5000 3621 2723Problem Setup 2 - NA’s
Now as we read the values from the dataframe we notice that on revenue there are a lot of values as 0, which are possibly registered as such because the revenue data was not possible to find or because of a different error. This error can mess up the statistics and drop the mean value to a lower number and affect the analysis of this year movies.
To solve this we will replace the values from 0 to the Revenue’s mean
mean_revenue <- mean(moviespersonal$revenue[moviespersonal$revenue > 0])
# Replace the zero values with the mean revenue
moviespersonal$revenue[moviespersonal$revenue == 0] <- mean_revenueNow we also replace NA values with the column’s mean
# Check if there are any NA values in the revenue column
any_na <- any(is.na(moviespersonal$revenue))
# If there are NA values, replace them with the mean revenue (excluding NA and zero values)
if(any_na) {
# Recalculate the mean in case the zero replacement affected it, excluding NA values
mean_revenue <- mean(moviespersonal$revenue[moviespersonal$revenue > 0], na.rm = TRUE)
# Replace NA values with the new mean revenue
moviespersonal$revenue[is.na(moviespersonal$revenue)] <- mean_revenue
}We repeat the same process now on the column Budget.
# Calculate the budget's mean value excluding NA's and zero's
mean_budget <- mean(moviespersonal$budget[moviespersonal$budget > 0], na.rm = TRUE)
# Replace zero's with the budget's mean value
moviespersonal$budget[moviespersonal$budget == 0] <- mean_budget
# Replace NA's with the budget's mean value
moviespersonal$budget[is.na(moviespersonal$budget)] <- mean_budgetData Explorer
For this next part, we are doing a DataExplorer report of the Movies database.
# We first install and call the DataExplorer library
#install.packages("DataExplorer")
library(DataExplorer)
# Now we can create a report using the library and the database
create_report(moviespersonal)This past function gives a general overview of the data contained in the database: basic statistics of the values in the database, data structures, histograms, etc. The general overview will be displayed in html format in a browser tab.
As shown in the principal component analysis, there are 43050 categories in the original title column, since shouldn’t be possible since there shouldn’t be repeat movies in the database.
The principal component analysis shows us how there is only 45089 categories in the IMDb id column, which may indicate that some movies may not be in the International Movie Database, thus these movies may lack information such as average score (based on vote_average) and how many reviews it has (based on vote_count)
There is also some movies which are lacking an overview (or a synopsis). This particular column however may not result as efficient to analize in the longrun since it would mean to analize each overview individually, and with more than 40 thousand objects this task results extremely difficult.
Problem Setup 3
Analayzing missing values
In our DataExplorer report we found out that runtime is the only column that still has has missing values that can actually be corrected, we will now analyze the data and use the package Mice to fill out missing values.
Function to visualize missing data
# Function to visualize missing data
library(visdat)
summary(moviespersonal$runtime)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 85.00 95.00 93.78 107.00 1256.00 251vis_miss(moviespersonal, warn_large_data = FALSE)
Create a variable to identify missing values in ‘runtime’
moviespersonal <- moviespersonal %>%
mutate(missing_inf = is.na(runtime))Analyze if there’s any relationship with popularity
moviespersonal %>%
group_by(missing_inf) %>%
summarize(avg_popularity = mean(popularity))## # A tibble: 2 × 2
## missing_inf avg_popularity
## <lgl> <dbl>
## 1 FALSE 2.93
## 2 TRUE NAUse MICE to impute missing values in ‘runtime’
library(mice)##
## Attaching package: 'mice'## The following object is masked from 'package:stats':
##
## filter## The following objects are masked from 'package:base':
##
## cbind, rbindlibrary(VIM)## Loading required package: colorspace## Loading required package: grid## VIM is ready to use.## Suggestions and bug-reports can be submitted at: https://github.com/statistikat/VIM/issues##
## Attaching package: 'VIM'## The following object is masked from 'package:datasets':
##
## sleepmethod_list <- rep("", ncol(moviespersonal))
names(method_list) <- colnames(moviespersonal)
method_list["runtime"] <- "pmm"
# Not executed due to the small sample size
# We will be using KNN imputation since it handles multicollinearity, which lowers the chance of error like having linearly dependent columns in a matrix or having multicollinearity in a dataset.
accounts_mice <- kNN(moviespersonal, k = 5)## Warning in `[<-.data.table`(`*tmp*`, indexNA2s[, variable[j]], variable[j], :
## Coercing 'character' RHS to 'double' to match the type of column 15 named
## 'release_date'.## Warning in `[<-.data.table`(`*tmp*`, indexNA2s[, variable[j]], variable[j], :
## NAs introduced by coercion# Now we strip columns 32 to 62
accounts_mice <- subset(accounts_mice, select = -c(32:62))Calculate the mean of ‘runtime’ to impute missing values
runtime_mean <- mean(moviespersonal$runtime, na.rm = TRUE)Replace missing values in ‘runtime’ with the mean
moviespersonal <- moviespersonal %>%
mutate(runtime = ifelse(is.na(runtime), runtime_mean, runtime))Visualize again the distribution of missing values of ‘runtime’ sorted by popularity
moviespersonal %>%
arrange(popularity) %>%
vis_miss(warn_large_data = FALSE)
Interpretation of the Report
In the project we carried out, we began by analyzing the information, cleaning it and deleting null values, or duplicates, since having the data clean is the longest and most important part so that the information we analyze next is of good quality. We also identified the incorrect data types and their outliers and removed the anomalies. Gender data is specifically addressed, cleaning it from a JSON-like string format to a more readable form, which is crucial for any further analysis.
It also handles zero values in the revenue and budget columns, and replaces them with the mean of the non-zero values, ensuring that no NA values are left untreated. This step is intended to prevent the analysis from being biased due to missing or unreported financial data.
An important part of the process we perform is ensuring data uniqueness by removing duplicate movie titles, which is crucial for accurate movie counts in your analysis.
In summary the work indicates a meticulous and systematic approach to prepare the data set for robust statistical analysis and possible model construction. However, the method we chose to address missing and zero values could be further examined to ensure it is the most appropriate approach, as it may have implications for the integrity of the analysis, especially if the zeros have real meaning within the set. of data. After this initial data preparation, you will be ready for exploratory data analysis to uncover insights and inform any hypotheses or models you plan to build.
Problem Setup 4: Creating tables and graphs
Tables
table1 <- table(moviespersonal$status, moviespersonal$adult)
prop.table(table1) * 100##
## False True
## 0.182378020 0.000000000
## Canceled 0.004737091 0.000000000
## In Production 0.042633823 0.002368546
## Planned 0.033159640 0.000000000
## Post Production 0.215537660 0.000000000
## Released 99.005210801 0.018948366
## Rumored 0.495026054 0.000000000table2 <- table(moviespersonal$video, moviespersonal$adult)
prop.table(table2) * 100##
## False True
## 0.002368546 0.000000000
## False 99.760776883 0.021316911
## True 0.215537660 0.000000000Bar plot using ggplot2
library(ggplot2)
ggplot(moviespersonal, aes(x = adult, fill = status)) + geom_bar() + facet_wrap(~adult)
# Esquisse usage omitted for creating more detailed graphicsAlso, for this particular part of the setup, we will be using the Esquisse package, which allows us to create more detailed and, in all fairness, prettier graphics than ggplot2
It is important to note that rather than different than calling a library, Esquisse is used by interacting with the Addins menu on the top of the screen
Add a column with only the release year
moviespersonal$year <- substr(moviespersonal$release_date, 1, 4)Scatter plot of average vote vs. revenue
# This graph shows us the movies between 1950 and 2023, comparing the average score they got on IMDb and how much money they made
moviespersonal %>%
filter(release_date >= "1950-05-03" & release_date <= "2023-04-01") %>%
ggplot() +
aes(x = vote_average, y = revenue) +
geom_point(shape = "circle", size = 1.5, colour = "#0C4C8A") +
theme_classic()
Scatter plot of budget vs. revenue
# This graph shows us the movies between 1950 and 2023, comparing between budget and revenue, which tells us that not necessarily a movie with a high production value will have significant returns, not even to break even
moviespersonal %>%
filter(release_date >= "1950-05-04" & release_date <= "2023-04-01") %>%
ggplot() +
aes(x = budget, y = revenue) +
geom_point(shape = "circle", size = 1.5, colour = "#112446") +
labs(caption = "For reference, 2E+08 equals 200 Million") +
theme_minimal()
Interpretation of the Report
In this section, we focused on generating tables and visualizations to glean insights from the dataset. Our analysis began with the creation of contingency tables to explore the relationship between movie status and adult content, as well as between video availability and adult content. These tables provide a comprehensive overview of the distribution of these variables within the dataset.
Furthermore, we leveraged the power of data visualization to gain deeper insights. Using the ggplot2 package, we crafted a bar plot to visually represent the distribution of movie status across different adult content categories. This graphical representation offers a clear depiction of how movie status varies with respect to adult content.
Additionally, we utilized scatter plots to examine relationships between key variables. Specifically, we explored the relationship between average vote and revenue, as well as between budget and revenue. These scatter plots enable us to visualize potential trends or patterns within the data, shedding light on any underlying relationships between these variables.
In summary, our analysis in this section underscores the importance of both tabular and graphical representations in uncovering insights from the dataset. Through careful examination of tables and visualizations, we gain a deeper understanding of the relationships and trends present in the data, laying the foundation for further analysis and interpretation.
Problem setup 5
# With this particular line of code, we are calculating the profit of each movie
moviespersonal <- moviespersonal %>%
mutate(profit= revenue - budget)Vote Average/Genre vs. Revenue
# With this code, we are computing means by groups, in which we are checking the mean revenue based on the score on the Internet Movie Database (IMDb). This with the intention to prove that not necessarily because a movie has a high score online it's going to perform well in the box office regard.
# This line of code helps us with stopping the numbers from appearing in scientific notation
options(scipen = 999)
# Now we code the mean revenue based on the average score on IMDb
mean_revenue <- moviespersonal %>%
group_by(vote_average) %>%
summarize(mean_revenue = mean(revenue, na.rm = TRUE)) %>%
arrange(desc(mean_revenue))
# With this we print the function
print(mean_revenue)## # A tibble: 92 × 2
## vote_average mean_revenue
## <dbl> <dbl>
## 1 7.9 87140789.
## 2 8.1 87125416.
## 3 9.1 84393695.
## 4 7.6 82704986.
## 5 8.2 79617362.
## 6 8.3 78486765.
## 7 7.5 77187057.
## 8 7.4 75553881.
## 9 7.7 73748020.
## 10 6.7 72205531.
## # ℹ 82 more rows# With this code we are going to find out the mean revenue per genre, since the genres are summed to each other as movies can have more than one genre we first separate de genres an then count the revenue and get the mean if the genre is in the movie.
# Create a new column with separated genres into new rows, keeping the original 'genre_names' column unchanged
movies_with_separated_genres <- moviespersonal %>%
mutate(separated_genres = genre_names) %>%
separate_rows(separated_genres, sep = " ") %>%
select(genre_names, separated_genres, revenue, profit)
# Get unique genres from the new 'separated_genres' column
unique_genres <- movies_with_separated_genres %>%
select(separated_genres) %>%
distinct() %>%
pull(separated_genres)
# Initialize a dataframe to store the average revenue for each unique genre
mean_revenue_by_genre <- data.frame(genre = character(), mean_revenue = numeric(), stringsAsFactors = FALSE)
# Iterate over each unique genre to calculate the average revenue
for(genre in unique_genres) {
mean_revenue <- movies_with_separated_genres %>%
# Select rows where the new 'separated_genres' column contains the current genre
filter(str_detect(separated_genres, fixed(genre))) %>%
# Calculate the average revenue for the selected genre
summarize(mean_revenue = mean(revenue, na.rm = TRUE)) %>%
# Extract the average revenue value
pull(mean_revenue)
# Append the genre and its average revenue to the dataframe
mean_revenue_by_genre <- rbind(mean_revenue_by_genre, data.frame(genre = genre, mean_revenue = mean_revenue))
}
# Display the result
print(mean_revenue_by_genre)## genre mean_revenue
## 1 Animation 89334110
## 2 Comedy 67805376
## 3 Family 89513534
## 4 Adventure 103618594
## 5 Fantasy 94402872
## 6 Romance 64738395
## 7 Drama 63925945
## 8 Action 81127887
## 9 Crime 66435133
## 10 Thriller 68885257
## 11 Horror 64464103
## 12 History 64717137
## 13 Science 83469618
## 14 Fiction 83469618
## 15 Mystery 67625498
## 16 War 68262127
## 17 Foreign 65258394
## 18 NaN
## 19 Music 65440574
## 20 Documentary 65198337
## 21 Western 65258973
## 22 TV 68747587
## 23 Movie 68747587Statistical observations
library("e1071")
mean(moviespersonal$profit)## [1] 47103572var(moviespersonal$profit)## [1] 2482584026357099sd(moviespersonal$profit)## [1] 49825536median(moviespersonal$profit)## [1] 47183112quantile(moviespersonal$profit)## 0% 25% 50% 75% 100%
## -111007242 47183112 47183112 47183112 2550965087min(moviespersonal$profit)## [1] -111007242max(moviespersonal$profit)## [1] 2550965087range(moviespersonal$profit)## [1] -111007242 2550965087skewness(moviespersonal$profit)## [1] 13.28206We now know that the distribution is skewed to the right. With this data, we can compute the confidence interval and correlation coefficient.
profit_mean <-mean(moviespersonal$profit)
profit_sd <-sd(moviespersonal$profit)
profit_dev <- c(profit_mean - profit_sd, profit_mean + profit_sd)
profit_dev## [1] -2721964 96929108The profit +- the standard deviation is saved in the object “profit_dev”
#Coefficient of variation
CV <- sd(moviespersonal$profit)/mean(moviespersonal$profit)
CV## [1] 1.057787#Trimmed mean
mean(moviespersonal$profit, trim=.1)## [1] 46343198#Z scores
#"scale" automatically gives you the zscores
#moviespersonal <- moviespersonal %>%
#mutate(zscore = scale(moviespersonal$profit))
#moviespersonal %>%
#filter(zscore < 3)
#We removed all rows with more than 3 zscores (438 rows)Analyzing statistical observations
Now, we will add a new column called words_title which will tell us how many words are in the title. This column will then be grouped and compared with the profit per each different word count
moviespersonal$words_title <- str_count(moviespersonal$original_title, "\\w+")
moviespersonal %>%
group_by(words_title) %>%
summarize(mean_profit = mean(profit),
sd_profit = sd(profit),
median_profit = median(profit),
quantile_profit = quantile(profit, 0.90),
count = n()) %>%
arrange(desc(mean_profit))## # A tibble: 20 × 6
## words_title mean_profit sd_profit median_profit quantile_profit count
## <int> <dbl> <dbl> <dbl> <dbl> <int>
## 1 10 64071620. 117714391. 47183112. 49509867. 127
## 2 8 57643350. 102907506. 47183112. 47183112. 510
## 3 7 54891330. 78345824. 47183112. 52947905 896
## 4 11 54448920. 62437300. 47183112. 60466534. 73
## 5 16 53851205. 11549475. 47183112. 63186534. 3
## 6 12 51830574. 33066573. 47183112. 47183112. 41
## 7 9 50407416. 49948258. 47183112. 47183112. 245
## 8 14 48806518. 5853273. 47183112. 47183112. 13
## 9 5 48545257. 56870932. 47183112. 47183112. 3575
## 10 6 48448381. 51368166. 47183112. 47183112. 1792
## 11 4 47456572. 44430688. 47183112. 47183112. 6254
## 12 15 47183112. 0 47183112. 47183112. 9
## 13 17 47183112. 0 47183112. 47183112. 2
## 14 18 47183112. 0 47183112. 47183112. 2
## 15 19 47183112. 0 47183112. 47183112. 3
## 16 3 46681369. 42229807. 47183112. 48787390. 9280
## 17 1 45982691. 54139116. 47183112. 60787390. 8056
## 18 2 45970169. 44364092. 47183112. 60927390. 11314
## 19 13 44236877. 10057832. 47183112. 47183112. 24
## 20 20 24350000 NA 24350000 24350000 1Now we will group by year to see which years were the most profitable for cinema
moviespersonal %>%
group_by(year) %>%
summarize(mean_profit = mean(profit),
sd_profit = sd(profit),
median_profit = median(profit),
quantile_profit = quantile(profit, 0.90),
count = n()) %>%
arrange(desc(mean_profit))## # A tibble: 136 × 6
## year mean_profit sd_profit median_profit quantile_profit count
## <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 1902 68781405. NA 68781405. 68781405. 1
## 2 2017 56221083. 81988788. 47183112. 64627390. 469
## 3 1905 51503961. 9661710. 47183112. 60145657. 5
## 4 2016 51400780. 69440583. 47183112. 63787390. 1442
## 5 2015 50584676. 81454255. 47183112. 58487390. 1744
## 6 1977 50302191. 46357949. 47183112. 47183112. 312
## 7 1904 50268366. 8162815. 47183112. 55821823. 7
## 8 2018 49824823. 4193441. 47183112. 54387390. 5
## 9 2012 49425227. 60546406. 47183112. 65787390. 1589
## 10 2011 49410075. 59204531. 47183112. 66987390. 1535
## # ℹ 126 more rowsThe year with the most mean profit was 1902, the 2017, then 1905.
ggplot(moviespersonal, aes(x = year, y = profit)) + geom_density()+ theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
Analyzing correlation between profit and other variables.
We will continue using the word count vs profit to find the correlation between them (if any)
ggplot(moviespersonal, aes(y = profit, x = words_title)) + geom_point() + geom_smooth(method = "lm", se = TRUE)## `geom_smooth()` using formula = 'y ~ x'
ggplot(moviespersonal, aes(x = cut(words_title, breaks = 5), y = profit)) + geom_boxplot() + geom_smooth(method = "lm", se = TRUE)## `geom_smooth()` using formula = 'y ~ x'
Now, we compute the Pearson coefficient of correlation
moviespersonal %>%
summarize(N = n(), r = cor(profit, words_title))## N r
## 1 42220 0.03440706The correlation between word count in the title and profit by the movie is extemely low / null, 0.03.
We will do this with the budget and the popularity to see if there are any positive correlations.
ggplot(moviespersonal, aes(y = profit, x = budget)) + geom_point() + geom_smooth(method = "lm", se = TRUE)## `geom_smooth()` using formula = 'y ~ x'
moviespersonal %>%
summarize(N = n(), r = cor(budget,profit))## N r
## 1 42220 0.4841505Although still not a very strong correlation, budget and profit have a moderate correlation of 0.48.
ggplot(moviespersonal, aes(y = profit, x = revenue)) + geom_point() + geom_smooth(method = "lm", se = TRUE)## `geom_smooth()` using formula = 'y ~ x'
moviespersonal %>%
summarize(N = n(), r = cor(revenue, profit))## N r
## 1 42220 0.9745676ggplot(moviespersonal, aes(y = profit, x = popularity)) + geom_point() + geom_smooth(method = "lm", se = TRUE)## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).
moviespersonal %>%
summarize(N = n(), r = cor(popularity, profit))## N r
## 1 42220 NAFor some odd reason, we cannot see the correlation coefficient for popularity, so we will try with another variable.
moviespersonal <- moviespersonal %>% filter(runtime <= 250)
ggplot(moviespersonal, aes(y = profit, x = runtime)) + geom_point() + geom_smooth(method = "lm", se = TRUE)## `geom_smooth()` using formula = 'y ~ x'
moviespersonal %>%
summarize(N = n(), r = cor(popularity, runtime))## N r
## 1 42067 NATo get rid of movies that are longer than 4 hours in runtime, we used the filter function. Again, we cannot see the value for the coefficient, but from seeing the line and the points, we can predict that this positive correlation has a moderate correlation.
ggplot(movies_with_separated_genres, aes(x = separated_genres, y = revenue)) +
geom_boxplot() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) + # Ajustar el texto del eje x para mejor visualización
labs(x = "Genre", y = "Revenue", title = "Boxplot of Revenue by Genre")
We can see that the revenues per genre are really spread, that’s because of the data quantity. We can also notice that Foreign, Movie and TV having really low revenue and Action, adventure, fantasy fiction and science have extreme outliers since the movie avatar has this genres and is the highest revenue of this year.
Now we analize the new column that we created named profit
movies_with_separated_genres %>%
group_by(separated_genres) %>%
summarize(
mean_profit = mean(profit, na.rm = TRUE),
sd_profit = sd(profit, na.rm = TRUE),
median_profit = median(profit, na.rm = TRUE),
quantile_90 = quantile(profit, 0.90, na.rm = TRUE),
count = n()
) %>%
arrange(desc(mean_profit))## # A tibble: 23 × 6
## separated_genres mean_profit sd_profit median_profit quantile_90 count
## <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 "Adventure" 71428203. 134477234. 47183112. 99600000 3231
## 2 "Fantasy" 65023884. 124170024. 47183112. 68739390. 2119
## 3 "Animation" 62345097. 95348486. 47183112. 67987390. 1846
## 4 "Family" 61621163. 97836888. 47183112. 67507390. 2590
## 5 "Fiction" 57536350. 104793345. 47183112. 68193990. 2829
## 6 "Science" 57536350. 104793345. 47183112. 68193990. 2829
## 7 "Action" 54819226. 91432796. 47183112. 66787390. 6119
## 8 "Movie" 47984308. 3848747. 47183112. 47183112. 673
## 9 "TV" 47984308. 3848747. 47183112. 47183112. 673
## 10 "" 47093271. 6241410. 47183112. 47183112. 2280
## # ℹ 13 more rowsSkewness and Log Transformation Analysis
## Skewness of revenue: 12.78445## Skewness of budget: 7.167064Distribution of original and transformed Budget
Original Budget
Density of Original Budget The original budget distribution is highly right-skewed, indicating that most movies have a low budget, while a few movies have a significantly higher budget. The long tail to the right shows that there are outliers with exceptionally large budgets compared to the rest.
Transformed Budget
Density of Log-Transformed Budget The log transformation has normalized the distribution of budget values, as evidenced by the peak around 15 to 17 on the log_budget scale. This suggests that a majority of movies have a budget within a mid-range when the scale is logarithmic, reducing the influence of extreme values.
Distribution of original and transformed Revenue
Original Revenue
Density of Original Revenue The density plot for the original revenue values demonstrates extreme right skewness, where the majority of movies earn revenue in the lower range, while very few have extraordinarily high revenues, creating a long right tail. This skewness could be due to blockbuster hits which are rare but generate a massive amount of revenue.
Transformed Revenue
Density of Log-Transformed Revenue After the logarithmic transformation, the revenue data shows a more bell-shaped distribution, though there is still a slight right skew. This indicates a more normalized distribution, but with a few outliers with exceptionally high revenue, visible from the tail extending to the right.
Summarized data
We now show the summarized and transformed data to see the central tendency an variability from Revenue and Budget
## mean_log_revenue median_log_revenue iqr_log_revenue mean_log_budget
## 1 17.70161 18.04653 0 16.58778
## median_log_budget iqr_log_budget
## 1 16.8884 0# The following code is used to examine the distribution of revenues produced by the movies through the simulations. We visualize this distribution as a histogram to make it easier to understand the data and to calculate the probabilities that the movies generate less than a certain amount of revenues, assuming that these revenues are distributed in a regular way.
mean_revenue <- mean(moviespersonal$revenue, na.rm = TRUE)
sd_revenue <- sd(moviespersonal$revenue, na.rm = TRUE)
n10 <- rnorm(10, mean = mean_revenue, sd = sd_revenue)
n100 <- rnorm(100, mean = mean_revenue, sd = sd_revenue)
n1000 <- rnorm(1000, mean = mean_revenue, sd = sd_revenue)
n10000 <- rnorm(10000, mean = mean_revenue, sd = sd_revenue)
hist(n10, breaks = 5, main = "Histogram of n10")
hist(n100, breaks = 30, main = "Histogram of n100")
hist(n1000, breaks = 40, main = "Histogram of n1000")
hist(n10000, breaks = 100, main = "Histogram of n10000")
value_to_evaluate <- 5
probability <- pnorm(value_to_evaluate, mean = mean_revenue, sd = sd_revenue, lower.tail = TRUE)
print(probability)## [1] 0.1210488The code identifies on average how much the films earn and how much the earnings of each film vary, with some films earning a lot and others not generating much revenue. This is followed by the simulation of how the earnings would be in small groups of movies in order to see if the variation and the average remain steady. Once we have this information we create the histograms to show graphically how the earnings will be distributed depending on the simulated group, i.e. which are the movies that have a lot of earnings and which are the ones that have few. Finally, we calculate how likely it is that a film can generate less than 5 million dollars, based on the calculated variation and the average.
Final Conclusion
To conclude this final analysis we made a comprehensive analysis of the Kaggle movie database in which we embarked on a systematic development to prepare, clean, and analyze the dataset to find key factors to actors that influence the success or failure of movies. The first move made was cleaning the data, a big issue to resolve due to incorrect types, outliers, and missing values etc. Then the statistical analysis and exploratory data examination had to be made to develop the employed to delve into revenue, budget, genre, and release year, using tables, visuals, and correlation analysis. WIth this we found a moderate relationship between budget, revenue, and profit which then applied to logarithmic transformations to normalize skewed distributions. Also, we inspected the factors such as movie title word count and release year on profitability, which showed us minimal correlation with word count but insights into profitable years. Throughout this long and meticulous analysis and visualization, our analisis offers valuable insights into the complicated dynamics of the movie industry, laying a foundation for further research and decision-making processes within this field.
Personal Analysis (Federico Zorrilla Jasso)
## randomForest 4.7-1.1## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:ggplot2':
##
## margin## The following object is masked from 'package:dplyr':
##
## combine## Loading required package: lattice##
## Attaching package: 'caret'## The following object is masked from 'package:purrr':
##
## lift## n= 31551
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 31551 110610200000000000000 68783410
## 2) runtime< 126.5 29128 54151270000000000000 66778720 *
## 3) runtime>=126.5 2423 54934680000000000000 92882690
## 6) words_title< 6.5 2324 42226380000000000000 87459310 *
## 7) words_title>=6.5 99 11035310000000000000 220195200 *##
## Call:
## randomForest(formula = revenue ~ words_title + runtime, data = trainData)
## Type of random forest: regression
## Number of trees: 500
## No. of variables tried at each split: 1
##
## Mean of squared residuals: 3463582381678326
## % Var explained: 1.2## 11 17 23 26 31 37 51 53
## 65492071 80334607 71497673 72237064 64998120 67790960 65635969 68447886
## 56 61 79 80 81 82 90 94
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## 99 102 104 108 109 113 114 123
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## 1889 1897 1899 1901 1902 1904 1906 1909
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## 1918 1923 1926 1927 1930 1932 1937 1942
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## 1947 1955 1956 1962 1969 1979 1989 1990
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## 2000 2003 2004 2009 2010 2012 2013 2017
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## 65045577 68001459 68001459 67321527The output provided details a Random Forest regression model designed to predict the revenue of movies, using predictors words_title and runtime. The model consists of 500 trees and demonstrates a very limited effectiveness, explaining only 1.22% of the variance in movie revenues. This suggests that the predictors used, words_title and runtime, do not have a strong relationship with movie revenue in the context of this model.
In terms of the relationship between movie runtime and revenue, the model’s decision nodes show that movies with a runtime of less than 122.5 minutes tend to have a lower average revenue of approximately $66,458,690, while movies with a runtime of 122.5 minutes or more have a significantly higher average revenue of around $90,864,760. Despite this observed pattern, the overall predictive power of the model remains low, as indicated by a very high mean of squared residuals (3535173783222618). This metric, which measures the average of the squares of the model’s prediction errors, highlights the model’s poor fit to the data. To enhance predictive accuracy, incorporating additional relevant features, refining the feature engineering process, or exploring alternative predictive modeling techniques may be necessary.