In DataScienceSalon/movies: Movie Rating Model and Predictor
options(knitr.table.format = "html")
options(max.print=100, scipen=999, width = 800)
knitr::opts_chunk$set(echo=FALSE,
cache=FALSE,
prompt=FALSE,
eval = TRUE,
tidy=TRUE,
root.dir = "..",
fig.height = 8,
fig.width = 20,
comment=NA,
message=FALSE,
warning=FALSE)
knitr::opts_knit$set(width=100, figr.prefix = T, figr.link = T)
knitr::knit_hooks$set(inline = function(x) {
prettyNum(x, big.mark=",")
})
load("../data/movies.Rdata")
load("../data/mdb2.Rdata")
library(dplyr)
library(extrafont)
library(ggplot2)
dataSets <- movies::preprocess(movies, mdb2)
mdb1 <- dataSets$mdb
mdb2 <- dataSets$mdbBox
Part 1: Data
The data were comprised of audience and critics opinions, awards, studio, and actor information from Rotten Tomatoes, imdb, and BoxOfficeMojo.com for a random sample of 651 movies produced and released prior to 2016.
Data Sources
Rotten Tomatoes
Launched in August 1998 by Senh Duong, Rotten Tomatoes is an American review aggregation website for film and television.
IMDB
Generalizability
Selected Features
The full codebook for the data set can be found in Appendix A. r kfigr::figr(label = "selected", prefix = TRUE, link = TRUE, type="Table") lists the data variables selected from the raw data that were included at this stage in this study.
r kfigr::figr(label = "selected", prefix = TRUE, link = TRUE, type="Table"): Selected features
raw <- openxlsx::read.xlsx("../data/features.xlsx", sheet = 1)
selected <- raw %>% filter(uni == "yes" & Source == "IMDB/RT/BO") %>% arrange(Group, No) %>% select(Type, Variable, Description)
knitr::kable(selected, align = 'l') %>%
kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center") %>%
kableExtra::group_rows("Overview", 1,3) %>%
kableExtra::group_rows("Organization", 4,5) %>%
kableExtra::group_rows("Dates", 6,6) %>%
kableExtra::group_rows("Performance", 7,16) %>%
kableExtra::group_rows("Box Office", 17,18)
Note that the values for the box office variables were obtained for a random sample of 100 reviews from the BoxOfficeMojo.com website.
Derived Features
The following additional features (r kfigr::figr(label = "derived", prefix = TRUE, link = TRUE, type="Table")) were derived from selected features and are as follows:
r kfigr::figr(label = "derived", prefix = TRUE, link = TRUE, type="Table"): Derived features
derived <- raw %>% filter(uni == "yes" & Source == "Derived") %>% arrange(Group, No) %>% select(Type, Variable, Description)
knitr::kable(derived, align = 'l') %>%
kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center") %>%
kableExtra::group_rows("Dates", 1,1) %>%
kableExtra::group_rows("Experience", 2,4) %>%
kableExtra::group_rows("Performance", 5,9) %>%
kableExtra::group_rows("Interaction", 10,19)
Cast experience and cast votes for each film were computed as follows:
Cast Experience
Cast experience for each film was defined by:
$$e = \displaystyle\sum_{i=1}^{5} N_i$$
where:
$e$ is the total cast experience for the film
$N_i$ is the total number of films in which actor $i$ was involved
Cast Votes
Cast votes for each film was defined by:
$$v = \displaystyle\sum_{i=1}^{5} V_i$$
where:
$v$ is the sum of IMDB cast votes for the film
$V_i$ is the sum of allocated IMDB cast votes for actor $i$
imdb votes were allocated to cast members as follows:
40% of total film IMDB votes for actor1
30% of total film IMDB votes for actor2
15% of total film IMDB votes for actor3
10% of total film IMDB votes for actor4
* 5% of total film IMDB votes for actor5
Each actor was allocated points accordingly, then the votes were aggregated for each film in which the cast member appeared. The IMDB votes were counted without regard for date to compensate for the limitations imposed by the sample size as movegoers had access to the population of reviews and director, studio, and actor performance data when making their purchase decision.
Omitted Features
The features listed in r kfigr::figr(label = "omitted", prefix = TRUE, link = TRUE, type="Table") were not included for redundancy reasons or due to the lack of direct relevance to the research question. Some variables, such as the actor variables, were used to derive other variables which are further described below.
r kfigr::figr(label = "omitted", prefix = TRUE, link = TRUE, type="Table"): Omitted features
omitted <- raw %>% filter(uni == "no") %>% select(Variable, Description)
knitr::kable(omitted) %>%
kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
Data Cleaning
The variables of interest were obtained from the data and complete cases were extracted reducing the number of observations from 651 to r nrow(mdb1).
Part 2: Research question
The underlying intent of this analysis was to determine the factors that most influence box office success for a film. Since box office revenue was not among the variables included in the raw data set, the first task was to determine which of the selected (or derived) variables would stand as a proxy for box office success. As such the first research question is concretely stated as follows:
Which of the selected or derived variables is most highly associated / correlated with total lifetime box office revenue
Once this proxy response variable was determined, the features that are most highly associated / correlated with this response variable were examined via the following research question.
Which features are most highly associated / correlated with the proxy response for box office success
Part 3: Exploratory data analysis
The exploratory data analysis began with a data preprocessing step to extract complete cases, and to create the response and two additional explanatory variables. Next, a univariate analysis examined each variable on a univariate basis. Lastly, a bivariate analysis explored the relationships between the response variable and various candidate predictors.
Univariate Analysis
edaUni <- movies::univariate(mdb1, mdb2)
Univariate Analysis of Categorical Variables
The purpose of the univariate analysis of categorical variables was to examine the relative frequencies and proportions of observations for each level of the categorical level. Categorical levels with fewer than five observations were removed from further analysis.
The categorical variables included at this stage of the analysis are indicated in r kfigr::figr(label = "uni_cat", prefix = TRUE, link = TRUE, type="Table").
r kfigr::figr(label = "uni_cat", prefix = TRUE, link = TRUE, type="Table"): Categorical Variables
uniCat <- raw %>% filter(uni == "yes" & Type == "Categorical") %>% select(Variable, Description)
knitr::kable(uniCat) %>%
kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
Title Type
Feature films constituted r edaUni$type$stats$Proportion[1] * 100% of the films in the sample. Since the focus of this study was theatrical releases, TV movies, which were included in the raw data were excluded from this analysis.
edaUni$type$plot
r kfigr::figr(label = "title_type", prefix = TRUE, link = TRUE, type="Figure"): Films by title type
Genre
The drama genre represented a plurality of the releases in the sample, followed by comedy action & adventure then mystery & suspense. The top four genres account for nearly r round(sum(head(edaUni$genre$stats %>% arrange(desc(Proportion)) %>% select(Proportion), 4)) * 100, -1)% of the films in the sample.
edaUni$genre$plot
r kfigr::figr(label = "genre", prefix = TRUE, link = TRUE, type="Figure"): Films by genre
MPAA Rating
Rated R films accounted for over r round(edaUni$mpaa$stats$Proportion[5] * 100, -1)% of the releases, followed by PG and PG-13. Collectively, R, PG, and PG-13 rated films represent r round(sum(head(edaUni$mpaa$stats %>% arrange(desc(Proportion)) %>% select(Proportion), 3)) * 100, -1)% of the films in the sample. NC-17 films were excluded from this analysis.
edaUni$mpaa$plot
r kfigr::figr(label = "mpaa", prefix = TRUE, link = TRUE, type="Figure"): Films by MPAA Rating
Studio
The data included films from r length(unique(mdb1$studio)) studios. Data with respect to the number of films in the sample per studio are captured in the studio experience variable below.
Director
The work of r length(unique(mdb1$director)) directors was included in the sample provided for this project. Data with respect to the number of films in the sample per director are captured in the director experience variable below.
Season of Theatrical Release
The plurality of features in the sample were released during the fall and summer months with over r round(edaUni$season$stats$Proportion[3] *100, -1)% opening in the month of December alone.
edaUni$season$plot
r kfigr::figr(label = "season", prefix = TRUE, link = TRUE, type="Figure"): Theatrical releases by season
Month of Theatrical Release
The plurality of features in the sample (r sum(edaUni$month$stats$Proportion[c(1,6,10,12)])*100%) were released during the months of January, June, October and December.
edaUni$month$plot
r kfigr::figr(label = "month", prefix = TRUE, link = TRUE, type="Figure"): Theatrical releases by month
Best Picture
Since the proportion of films nominated for and winning best picture were so small, this variable was not likely to be a good predictor of movie popularity. The bivariate analysis below will illuminate this further.
gridExtra::grid.arrange(edaUni$bestPicNom$plot, edaUni$bestPicWin$plot, ncol = 2)
r kfigr::figr(label = "best_picture", prefix = TRUE, link = TRUE, type="Figure"): Best picture nominations and wins
Best Director / Actor / Actress
As indicated in r kfigr::figr(label = "best_actor", prefix = TRUE, link = TRUE, type="Figure"), the percentages of films with best director, actor and actress oscars were r edaUni$bestDirWin$stats$Proportion[2] * 100%, r edaUni$bestActorWin$stats$Proportion[2] * 100%, and r edaUni$bestActressWin$stats$Proportion[2] * 100%, respectively. Again, these proportions indicate that oscar awards would not be a good predictor of movie popularity. The bivariate analysis will explore this further.
gridExtra::grid.arrange(edaUni$bestDirWin$plot, edaUni$bestActorWin$plot, edaUni$bestActressWin$plot, ncol = 3)
r kfigr::figr(label = "best_actor", prefix = TRUE, link = TRUE, type="Figure"): Best director/actor/actress
Top 200 Box Office
Again, the proportion of films in the Top 200 Box Office list was miniscule indicating that inclusion in the top 200 box office list was not likely to be a good predictor of movie popularity.
edaUni$top200Box$plot
r kfigr::figr(label = "top_200", prefix = TRUE, link = TRUE, type="Figure"): Frequency and proportion of movies by top 200 box office earnings
Univariate Analysis of Quantitative Variables
The primary aim of this analysis was to examine the distribution of the variables vis-a-vis a normal distribution, and to identify potential outliers. Summary statistics, histograms, boxplots, normal quantile-quantile plots were rendered for each variable. The quantitative variables included at this stage of the analysis are indicated in r kfigr::figr(label = "uni_quant", prefix = TRUE, link = TRUE, type="Table").
r kfigr::figr(label = "uni_quant", prefix = TRUE, link = TRUE, type="Table"): Quantitative Variables
uniQuant <- raw %>% filter(uni == "yes" & Type == "Numeric") %>% select(Variable, Description)
knitr::kable(uniQuant) %>%
kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
Studio Experience
This derived variable measured the relative experience of a given studio and was defined as the sum of the observations for the studio associated with each film.
r kfigr::figr(label = "studio_experience_stats", prefix = TRUE, link = TRUE, type="Table"): Studio experience summary statistics
knitr::kable(edaUni$studioExperience$stats, digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$studioExperience$hist, edaUni$studioExperience$qq, ncol = 2)
r kfigr::figr(label = "studio_experience_dist", prefix = TRUE, link = TRUE, type="Figure"): Studio experience histogram and QQ Plot
edaUni$studioExperience$box
r kfigr::figr(label = "studio_experience_box", prefix = TRUE, link = TRUE, type="Figure"): Studio experience boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "studio_experience_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$studioExperience$central
Dispersion: r edaUni$studioExperience$disp
Shape of Distribution: r edaUni$studioExperience$skew r edaUni$studioExperience$kurt The histogram and QQ plot in r kfigr::figr(label = "studio_experience_dist", prefix = TRUE, link = TRUE, type="Figure") reveal a distribution which departs significantly from normality.
Outliers: The boxplot in r kfigr::figr(label = "studio_experience_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$studioExperience$outliers) == 0, "no", " ") outliers were extant. r edaUni$studioExperience$out
Director Experience
This derived variable measured the relative experience of a given director and was defined as the sum of the observations for the director associated with each film.
r kfigr::figr(label = "director_experience_stats", prefix = TRUE, link = TRUE, type="Table"): Director experience summary statistics
knitr::kable(edaUni$directorExperience$stats, digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$directorExperience$hist, edaUni$directorExperience$qq, ncol = 2)
r kfigr::figr(label = "director_experience_dist", prefix = TRUE, link = TRUE, type="Figure"): Director experience histogram and QQ Plot
edaUni$directorExperience$box
r kfigr::figr(label = "director_experience_box", prefix = TRUE, link = TRUE, type="Figure"): Director experience boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "director_experience_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$directorExperience$central
Dispersion: r edaUni$directorExperience$disp
Shape of Distribution: r edaUni$directorExperience$skew r edaUni$directorExperience$kurt The histogram and QQ plot in r kfigr::figr(label = "director_experience_dist", prefix = TRUE, link = TRUE, type="Figure") reveal a distribution which departs significantly from normality.
Outliers: The boxplot in r kfigr::figr(label = "director_experience_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$directorExperience$outliers) == 0, "no", " ") outliers were extant. r edaUni$directorExperience$out Given the proximity of the outliers to the 1.5xIQR, no effort was made to remove them.
Cast Experience
This derived variable measured the relative experience of a given cast and was defined as the sum of the observations for the cast associated with each film.
r kfigr::figr(label = "cast_experience_stats", prefix = TRUE, link = TRUE, type="Table"): Cast experience summary statistics
knitr::kable(edaUni$castExperience$stats, digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$castExperience$hist, edaUni$castExperience$qq, ncol = 2)
r kfigr::figr(label = "cast_experience_dist", prefix = TRUE, link = TRUE, type="Figure"): Cast experience histogram and QQ Plot
edaUni$castExperience$box
r kfigr::figr(label = "cast_experience_box", prefix = TRUE, link = TRUE, type="Figure"): Cast experience boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "cast_experience_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$castExperience$central
Dispersion: r edaUni$castExperience$disp
Shape of Distribution: r edaUni$castExperience$skew r edaUni$castExperience$kurt The histogram and QQ plot in r kfigr::figr(label = "cast_experience_dist", prefix = TRUE, link = TRUE, type="Figure") reveal a distribution which departs significantly from normality.
Outliers: The boxplot in r kfigr::figr(label = "cast_experience_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$castExperience$outliers) == 0, "no", " ") outliers were extant. r edaUni$castExperience$out Given the proximity of the outliers to the 1.5xIQR, no effort was made to remove them.
Number of IMDB Votes
This variable captured the number of IMDB votes cast for each film.
r kfigr::figr(label = "imdb_votes_stats", prefix = TRUE, link = TRUE, type="Table"): IMDB votes summary statistics
knitr::kable(edaUni$imdbVotes$stats, digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$imdbVotes$hist, edaUni$imdbVotes$qq, ncol = 2)
r kfigr::figr(label = "imdb_votes_dist", prefix = TRUE, link = TRUE, type="Figure"): IMDB votes histogram and QQ Plot
edaUni$imdbVotes$box
r kfigr::figr(label = "imdb_votes_box", prefix = TRUE, link = TRUE, type="Figure"): IMDB votes boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "imdb_votes_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$imdbVotes$central
Dispersion: r edaUni$imdbVotes$disp
Shape of Distribution: r edaUni$imdbVotes$skew r edaUni$imdbVotes$kurt The histogram and QQ plot in r kfigr::figr(label = "imdb_votes_dist", prefix = TRUE, link = TRUE, type="Figure") reveal a distribution which departs significantly from normality.
Outliers: The boxplot in r kfigr::figr(label = "imdb_votes_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$imdbVotes$outliers) == 0, "no", " ") outliers were extant. r edaUni$imdbVotes$out
Log Number of IMDB Votes
This was a log transformation of the IMDB votes variable.
r kfigr::figr(label = "imdb_votes_log_stats", prefix = TRUE, link = TRUE, type="Table"): Log IMDB votes summary statistics
knitr::kable(edaUni$imdbVotesLog$stats, digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$imdbVotesLog$hist, edaUni$imdbVotesLog$qq, ncol = 2)
r kfigr::figr(label = "imdb_votes_log_dist", prefix = TRUE, link = TRUE, type="Figure"): Log IMDB votes histogram and QQ Plot
edaUni$imdbVotesLog$box
r kfigr::figr(label = "imdb_votes_log_box", prefix = TRUE, link = TRUE, type="Figure"): Log IMDB votes boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "imdb_votes_log_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$imdbVotesLog$central
Dispersion: r edaUni$imdbVotesLog$disp
Shape of Distribution: r edaUni$imdbVotesLog$skew r edaUni$imdbVotesLog$kurt The histogram and QQ plot in r kfigr::figr(label = "imdb_votes_log_dist", prefix = TRUE, link = TRUE, type="Figure") reveal a nearly normal distribution.
Outliers: The boxplot in r kfigr::figr(label = "imdb_votes_log_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$imdbVotesLog$outliers) == 0, "no", " ") outliers were extant. r edaUni$imdbVotesLog$out
IMDB Ratings
This variable captured the IMDB rating for each film
r kfigr::figr(label = "imdb_rating_stats", prefix = TRUE, link = TRUE, type="Table"): IMDB rating summary statistics
knitr::kable(edaUni$imdbRating$stats, digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$imdbRating$hist, edaUni$imdbRating$qq, ncol = 2)
r kfigr::figr(label = "imdb_rating_dist", prefix = TRUE, link = TRUE, type="Figure"): IMDB rating histogram and QQ Plot
edaUni$imdbRating$box
r kfigr::figr(label = "imdb_rating_box", prefix = TRUE, link = TRUE, type="Figure"): IMDB rating boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "imdb_rating_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$imdbRating$central
Dispersion: r edaUni$imdbRating$disp
Shape of Distribution: r edaUni$imdbRating$skew r edaUni$imdbRating$kurt The histogram and QQ plot in r kfigr::figr(label = "imdb_rating_dist", prefix = TRUE, link = TRUE, type="Figure") reveal a nearly normal distribution.
Outliers: The boxplot in r kfigr::figr(label = "imdb_rating_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$imdbRating$outliers) == 0, "no", " ") outliers were extant. r edaUni$imdbRating$out
Critics Scores
This variable captured the critics scores for each film
r kfigr::figr(label = "critics_scores_stats", prefix = TRUE, link = TRUE, type="Table"): Critics score summary statistics
knitr::kable(edaUni$criticsScores$stats, digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$criticsScores$hist, edaUni$criticsScores$qq, ncol = 2)
r kfigr::figr(label = "critics_scores_dist", prefix = TRUE, link = TRUE, type="Figure"): Critics score histogram and QQ Plot
edaUni$criticsScores$box
r kfigr::figr(label = "critics_scores_box", prefix = TRUE, link = TRUE, type="Figure"): Critics score boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "critics_scores_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$criticsScores$central
Dispersion: r edaUni$criticsScores$disp
Shape of Distribution: r edaUni$criticsScores$skew r edaUni$criticsScores$kurt The histogram and QQ plot in r kfigr::figr(label = "critics_scores_dist", prefix = TRUE, link = TRUE, type="Figure") reveals a left skewed distribution that departs from normality.
Outliers: The boxplot in r kfigr::figr(label = "critics_scores_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$criticsScores$outliers) == 0, "no", " ") outliers were extant. r edaUni$criticsScores$out
Audience Scores
This variable captured the audience scores for each film
r kfigr::figr(label = "audience_scores_stats", prefix = TRUE, link = TRUE, type="Table"): Audience score summary statistics
knitr::kable(edaUni$audienceScores$stats, digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$audienceScores$hist, edaUni$audienceScores$qq, ncol = 2)
r kfigr::figr(label = "audience_scores_dist", prefix = TRUE, link = TRUE, type="Figure"): Audience score histogram and QQ Plot
edaUni$audienceScores$box
r kfigr::figr(label = "audience_scores_box", prefix = TRUE, link = TRUE, type="Figure"): Audience score boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "audience_scores_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$audienceScores$central
Dispersion: r edaUni$audienceScores$disp
Shape of Distribution: r edaUni$audienceScores$skew r edaUni$audienceScores$kurt The histogram and QQ plot in r kfigr::figr(label = "audience_scores_dist", prefix = TRUE, link = TRUE, type="Figure") reveals a left skewed distribution that departs from normality.
Outliers: The boxplot in r kfigr::figr(label = "audience_scores_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$audienceScores$outliers) == 0, "no", " ") outliers were extant. r edaUni$audienceScores$out
Studio Votes
This variable captured the studio votes for each film
r kfigr::figr(label = "studio_votes_stats", prefix = TRUE, link = TRUE, type="Table"): Studio votes summary statistics
knitr::kable(edaUni$studioVotes$stats, digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$studioVotes$hist, edaUni$studioVotes$qq, ncol = 2)
r kfigr::figr(label = "studio_votes_dist", prefix = TRUE, link = TRUE, type="Figure"): Studio votes histogram and QQ Plot
edaUni$studioVotes$box
r kfigr::figr(label = "studio_votes_box", prefix = TRUE, link = TRUE, type="Figure"): Studio votes boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "studio_votes_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$studioVotes$central
Dispersion: r edaUni$studioVotes$disp
Shape of Distribution: r edaUni$studioVotes$skew r edaUni$studioVotes$kurt The histogram and QQ plot in r kfigr::figr(label = "studio_votes_dist", prefix = TRUE, link = TRUE, type="Figure") reveals a left skewed distribution that departs significantly from normality.
Outliers: The boxplot in r kfigr::figr(label = "studio_votes_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$studioVotes$outliers) == 0, "no", " ") outliers were extant. r edaUni$studioVotes$out
Log Studio Votes
This is a log transformation of the studio votes variable.
r kfigr::figr(label = "studio_votes_log_stats", prefix = TRUE, link = TRUE, type="Table"): Log studio votes summary statistics
knitr::kable(edaUni$studioVotesLog$stats, digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$studioVotesLog$hist, edaUni$studioVotesLog$qq, ncol = 2)
r kfigr::figr(label = "studio_votes_log_dist", prefix = TRUE, link = TRUE, type="Figure"): Log studio votes histogram and QQ Plot
edaUni$studioVotesLog$box
r kfigr::figr(label = "studio_votes_log_box", prefix = TRUE, link = TRUE, type="Figure"): Log studio votes boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "studio_votes_log_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$studioVotesLog$central
Dispersion: r edaUni$studioVotesLog$disp
Shape of Distribution: r edaUni$studioVotesLog$skew r edaUni$studioVotesLog$kurt The histogram and QQ plot in r kfigr::figr(label = "studio_votes_log_dist", prefix = TRUE, link = TRUE, type="Figure") reveals a left skewed distribution that approximates normality.
Outliers: The boxplot in r kfigr::figr(label = "studio_votes_log_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$studioVotesLog$outliers) == 0, "no", " ") outliers were extant. r edaUni$studioVotesLog$out
Cast Votes
This variable captured the total number of votes allocated to each cast member for a film.
r kfigr::figr(label = "cast_votes_stats", prefix = TRUE, link = TRUE, type="Table"): Cast votes summary statistics
knitr::kable(edaUni$castVotes$stats, digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$castVotes$hist, edaUni$castVotes$qq, ncol = 2)
r kfigr::figr(label = "cast_votes_dist", prefix = TRUE, link = TRUE, type="Figure"): Cast votes histogram and QQ Plot
edaUni$castVotes$box
r kfigr::figr(label = "cast_votes_box", prefix = TRUE, link = TRUE, type="Figure"): Cast votes boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "cast_votes_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$castVotes$central
Dispersion: r edaUni$castVotes$disp
Shape of Distribution: r edaUni$castVotes$skew r edaUni$castVotes$kurt The histogram and QQ plot in r kfigr::figr(label = "cast_votes_dist", prefix = TRUE, link = TRUE, type="Figure") reveals a left skewed distribution that departs significantly from normality.
Outliers: The boxplot in r kfigr::figr(label = "cast_votes_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$castVotes$outliers) == 0, "no", " ") outliers were extant. r edaUni$castVotes$out
Log Cast Votes
This is a log transformation of the cast votes variable.
r kfigr::figr(label = "cast_votes_log_stats", prefix = TRUE, link = TRUE, type="Table"): Log cast votes summary statistics
knitr::kable(edaUni$castVotesLog$stats, digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$castVotesLog$hist, edaUni$castVotesLog$qq, ncol = 2)
r kfigr::figr(label = "cast_votes_log_dist", prefix = TRUE, link = TRUE, type="Figure"): Log cast votes histogram and QQ Plot
edaUni$castVotesLog$box
r kfigr::figr(label = "cast_votes_log_box", prefix = TRUE, link = TRUE, type="Figure"): Log cast votes boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "cast_votes_log_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$castVotesLog$central
Dispersion: r edaUni$castVotesLog$disp
Shape of Distribution: r edaUni$castVotesLog$skew r edaUni$castVotesLog$kurt The histogram and QQ plot in r kfigr::figr(label = "cast_votes_log_dist", prefix = TRUE, link = TRUE, type="Figure") reveals a left skewed distribution that approximates normality.
Outliers: The boxplot in r kfigr::figr(label = "cast_votes_log_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$castVotesLog$outliers) == 0, "no", " ") outliers were extant. r edaUni$castVotesLog$out
Scores
This variable captured the total score for each film defined as 10 * IMDB Rating + critics score + audience_score.
r kfigr::figr(label = "scores_stats", prefix = TRUE, link = TRUE, type="Table"): Scores summary statistics
knitr::kable(edaUni$scores$stats, digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$scores$hist, edaUni$scores$qq, ncol = 2)
r kfigr::figr(label = "scores_dist", prefix = TRUE, link = TRUE, type="Figure"): Scores histogram and QQ Plot
edaUni$scores$box
r kfigr::figr(label = "scores_box", prefix = TRUE, link = TRUE, type="Figure"): Scores boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "scores_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$scores$central
Dispersion: r edaUni$scores$disp
Shape of Distribution: r edaUni$scores$skew r edaUni$scores$kurt The histogram and QQ plot in r kfigr::figr(label = "scores_dist", prefix = TRUE, link = TRUE, type="Figure") reveals a left skewed distribution that approximates normality.
Outliers: The boxplot in r kfigr::figr(label = "scores_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$scores$outliers) == 0, "no", " ") outliers were extant. r edaUni$scores$out
Log Scores
This is a log transformation of scores variable.
r kfigr::figr(label = "scores_log_stats", prefix = TRUE, link = TRUE, type="Table"): Log scores summary statistics
knitr::kable(edaUni$scoresLog$stats, digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$scoresLog$hist, edaUni$scoresLog$qq, ncol = 2)
r kfigr::figr(label = "scores_log_dist", prefix = TRUE, link = TRUE, type="Figure"): Log scores histogram and QQ Plot
edaUni$scoresLog$box
r kfigr::figr(label = "scores_log_box", prefix = TRUE, link = TRUE, type="Figure"): Log scores boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "scores_log_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$scoresLog$central
Dispersion: r edaUni$scoresLog$disp
Shape of Distribution: r edaUni$scoresLog$skew r edaUni$scoresLog$kurt The histogram and QQ plot in r kfigr::figr(label = "scores_log_dist", prefix = TRUE, link = TRUE, type="Figure") reveals a left skewed distribution that departs rather significantly from normality.
Outliers: The boxplot in r kfigr::figr(label = "scores_log_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$scoresLog$outliers) == 0, "no", " ") outliers were extant. r edaUni$scoresLog$out
IMDB Votes * Rating
This interaction variable is defined as the product of IMDB votes and IMDB ratings.
r kfigr::figr(label = "votes_imdb_rating_stats", prefix = TRUE, link = TRUE, type="Table"): IMDB Votes * Rating summary statistics
knitr::kable(edaUni$votesImdbRating$stats, digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$votesImdbRating$hist, edaUni$votesImdbRating$qq, ncol = 2)
r kfigr::figr(label = "votes_imdb_rating_dist", prefix = TRUE, link = TRUE, type="Figure"): IMDB Votes * Rating histogram and QQ Plot
edaUni$votesImdbRating$box
r kfigr::figr(label = "votes_imdb_rating_box", prefix = TRUE, link = TRUE, type="Figure"): IMDB Votes * Rating votes boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "votes_imdb_rating_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$votesImdbRating$central
Dispersion: r edaUni$votesImdbRating$disp
Shape of Distribution: r edaUni$votesImdbRating$skew r edaUni$votesImdbRating$kurt The histogram and QQ plot in r kfigr::figr(label = "votes_imdb_rating_dist", prefix = TRUE, link = TRUE, type="Figure") reveals a left skewed distribution that departs significantly from normality.
Outliers: The boxplot in r kfigr::figr(label = "votes_imdb_rating_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$votesImdbRating$outliers) == 0, "no", " ") outliers were extant. r edaUni$votesImdbRating$out
Log IMDB Votes * Rating
This is a log transformation of IMDB Votes * Rating variable.
r kfigr::figr(label = "votes_imdb_rating_log_stats", prefix = TRUE, link = TRUE, type="Table"): Log IMDB Votes * Rating summary statistics
knitr::kable(edaUni$votesImdbRatingLog$stats, digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$votesImdbRatingLog$hist, edaUni$votesImdbRatingLog$qq, ncol = 2)
r kfigr::figr(label = "votes_imdb_rating_log_dist", prefix = TRUE, link = TRUE, type="Figure"): Log IMDB Votes * Rating histogram and QQ Plot
edaUni$votesImdbRatingLog$box
r kfigr::figr(label = "votes_imdb_rating_log_box", prefix = TRUE, link = TRUE, type="Figure"): Log IMDB Votes * Rating boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "votes_imdb_rating_log_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$votesImdbRatingLog$central
Dispersion: r edaUni$votesImdbRatingLog$disp
Shape of Distribution: r edaUni$votesImdbRatingLog$skew r edaUni$votesImdbRatingLog$kurt The histogram and QQ plot in r kfigr::figr(label = "votes_imdb_rating_log_dist", prefix = TRUE, link = TRUE, type="Figure") reveals a left skewed distribution that approximates normality.
Outliers: The boxplot in r kfigr::figr(label = "votes_imdb_rating_log_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$votesImdbRatingLog$outliers) == 0, "no", " ") outliers were extant. r edaUni$votesImdbRatingLog$out
IMDB Votes * Critics Score
This interaction variable is defined as the product of IMDB votes and critics score.
r kfigr::figr(label = "votes_critics_score_stats", prefix = TRUE, link = TRUE, type="Table"): IMDB Votes * Critics Score summary statistics
knitr::kable(edaUni$votesCriticsScore$stats, digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$votesCriticsScore$hist, edaUni$votesCriticsScore$qq, ncol = 2)
r kfigr::figr(label = "votes_critics_score_dist", prefix = TRUE, link = TRUE, type="Figure"): IMDB Votes * Critics Score histogram and QQ Plot
edaUni$votesCriticsScore$box
r kfigr::figr(label = "votes_critics_score_box", prefix = TRUE, link = TRUE, type="Figure"): IMDB Votes * Critics Score votes boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "votes_critics_score_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$votesCriticsScore$central
Dispersion: r edaUni$votesCriticsScore$disp
Shape of Distribution: r edaUni$votesCriticsScore$skew r edaUni$votesCriticsScore$kurt The histogram and QQ plot in r kfigr::figr(label = "votes_critics_score_dist", prefix = TRUE, link = TRUE, type="Figure") reveals a left skewed distribution that departs significantly from normality.
Outliers: The boxplot in r kfigr::figr(label = "votes_critics_score_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$votesCriticsScore$outliers) == 0, "no", " ") outliers were extant. r edaUni$votesCriticsScore$out
Log IMDB Votes * Critics Score
This is a log transformation of IMDB Votes * Critics Score variable.
r kfigr::figr(label = "votes_critics_score_log_stats", prefix = TRUE, link = TRUE, type="Table"): Log IMDB Votes * Critics Score summary statistics
knitr::kable(edaUni$votesCriticsScoreLog$stats, digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$votesCriticsScoreLog$hist, edaUni$votesCriticsScoreLog$qq, ncol = 2)
r kfigr::figr(label = "votes_critics_score_log_dist", prefix = TRUE, link = TRUE, type="Figure"): Log IMDB Votes * Critics Score histogram and QQ Plot
edaUni$votesCriticsScoreLog$box
r kfigr::figr(label = "votes_critics_score_log_box", prefix = TRUE, link = TRUE, type="Figure"): Log IMDB Votes * Critics Score boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "votes_critics_score_log_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$votesCriticsScoreLog$central
Dispersion: r edaUni$votesCriticsScoreLog$disp
Shape of Distribution: r edaUni$votesCriticsScoreLog$skew r edaUni$votesCriticsScoreLog$kurt The histogram and QQ plot in r kfigr::figr(label = "votes_critics_score_log_dist", prefix = TRUE, link = TRUE, type="Figure") reveals a left skewed distribution that approximates normality.
Outliers: The boxplot in r kfigr::figr(label = "votes_critics_score_log_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$votesCriticsScoreLog$outliers) == 0, "no", " ") outliers were extant. r edaUni$votesCriticsScoreLog$out
IMDB Votes * Audience Score
This interaction variable is defined as the product of IMDB votes and audience score.
r kfigr::figr(label = "votes_audience_score_stats", prefix = TRUE, link = TRUE, type="Table"): IMDB Votes * Audience Score summary statistics
knitr::kable(edaUni$votesAudienceScore$stats, digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$votesAudienceScore$hist, edaUni$votesAudienceScore$qq, ncol = 2)
r kfigr::figr(label = "votes_audience_score_dist", prefix = TRUE, link = TRUE, type="Figure"): IMDB Votes * Audience Score histogram and QQ Plot
edaUni$votesAudienceScore$box
r kfigr::figr(label = "votes_audience_score_box", prefix = TRUE, link = TRUE, type="Figure"): IMDB Votes * Audience Score votes boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "votes_audience_score_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$votesAudienceScore$central
Dispersion: r edaUni$votesAudienceScore$disp
Shape of Distribution: r edaUni$votesAudienceScore$skew r edaUni$votesAudienceScore$kurt The histogram and QQ plot in r kfigr::figr(label = "votes_audience_score_dist", prefix = TRUE, link = TRUE, type="Figure") reveals a left skewed distribution that departs significantly from normality.
Outliers: The boxplot in r kfigr::figr(label = "votes_audience_score_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$votesAudienceScore$outliers) == 0, "no", " ") outliers were extant. r edaUni$votesAudienceScore$out
Log IMDB Votes * Audience Score
This is a log transformation of IMDB Votes * Audience Score variable.
r kfigr::figr(label = "votes_audience_score_log_stats", prefix = TRUE, link = TRUE, type="Table"): Log IMDB Votes * Audience Score summary statistics
knitr::kable(edaUni$votesAudienceScoreLog$stats, digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$votesAudienceScoreLog$hist, edaUni$votesAudienceScoreLog$qq, ncol = 2)
r kfigr::figr(label = "votes_audience_score_log_dist", prefix = TRUE, link = TRUE, type="Figure"): Log IMDB Votes * Audience Score histogram and QQ Plot
edaUni$votesAudienceScoreLog$box
r kfigr::figr(label = "votes_audience_score_log_box", prefix = TRUE, link = TRUE, type="Figure"): Log IMDB Votes * Audience Score boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "votes_audience_score_log_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$votesAudienceScoreLog$central
Dispersion: r edaUni$votesAudienceScoreLog$disp
Shape of Distribution: r edaUni$votesAudienceScoreLog$skew r edaUni$votesAudienceScoreLog$kurt The histogram and QQ plot in r kfigr::figr(label = "votes_audience_score_log_dist", prefix = TRUE, link = TRUE, type="Figure") reveals a left skewed distribution that approximates normality.
Outliers: The boxplot in r kfigr::figr(label = "votes_audience_score_log_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$votesAudienceScoreLog$outliers) == 0, "no", " ") outliers were extant. r edaUni$votesAudienceScoreLog$out
IMDB Votes * Total Score
This interaction variable is defined as the product of IMDB votes and total score.
r kfigr::figr(label = "votes_scores_stats", prefix = TRUE, link = TRUE, type="Table"): IMDB Votes * Total Score summary statistics
knitr::kable(edaUni$votesScores$stats, digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$votesScores$hist, edaUni$votesScores$qq, ncol = 2)
r kfigr::figr(label = "votes_scores_dist", prefix = TRUE, link = TRUE, type="Figure"): IMDB Votes * Total Score histogram and QQ Plot
edaUni$votesScores$box
r kfigr::figr(label = "votes_scores_box", prefix = TRUE, link = TRUE, type="Figure"): IMDB Votes * Total Score votes boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "votes_scores_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$votesScores$central
Dispersion: r edaUni$votesScores$disp
Shape of Distribution: r edaUni$votesScores$skew r edaUni$votesScores$kurt The histogram and QQ plot in r kfigr::figr(label = "votes_scores_dist", prefix = TRUE, link = TRUE, type="Figure") reveals a left skewed distribution that departs significantly from normality.
Outliers: The boxplot in r kfigr::figr(label = "votes_scores_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$votesScores$outliers) == 0, "no", " ") outliers were extant. r edaUni$votesScores$out
Log IMDB Votes * Total Score
This is a log transformation of IMDB Votes * Total Score variable.
r kfigr::figr(label = "votes_scores_log_stats", prefix = TRUE, link = TRUE, type="Table"): Log IMDB Votes * Total Score summary statistics
knitr::kable(edaUni$votesScoresLog$stats, digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$votesScoresLog$hist, edaUni$votesScoresLog$qq, ncol = 2)
r kfigr::figr(label = "votes_scores_log_dist", prefix = TRUE, link = TRUE, type="Figure"): Log IMDB Votes * Total Score histogram and QQ Plot
edaUni$votesScoresLog$box
r kfigr::figr(label = "votes_scores_log_box", prefix = TRUE, link = TRUE, type="Figure"): Log IMDB Votes * Total Score boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "votes_scores_log_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$votesScoresLog$central
Dispersion: r edaUni$votesScoresLog$disp
Shape of Distribution: r edaUni$votesScoresLog$skew r edaUni$votesScoresLog$kurt The histogram and QQ plot in r kfigr::figr(label = "votes_scores_log_dist", prefix = TRUE, link = TRUE, type="Figure") reveals a left skewed distribution that approximates normality.
Outliers: The boxplot in r kfigr::figr(label = "votes_scores_log_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$votesScoresLog$outliers) == 0, "no", " ") outliers were extant. r edaUni$votesScoresLog$out
Box Office
Total lifetime box office revenue was obtained for a subset of 100 randomly selected films from the movie data set. This is an analysis of box office revenue for this random sampling.
r kfigr::figr(label = "box_office_stats", prefix = TRUE, link = TRUE, type="Table"): Box office revenue summary statistics
knitr::kable(edaUni$boxOffice$stats, digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$boxOffice$hist, edaUni$boxOffice$qq, ncol = 2)
r kfigr::figr(label = "box_office_dist", prefix = TRUE, link = TRUE, type="Figure"): Box office revenue histogram and QQ Plot
edaUni$boxOffice$box
r kfigr::figr(label = "box_office_box", prefix = TRUE, link = TRUE, type="Figure"): Box office revenue boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "box_office_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$boxOffice$central
Dispersion: r edaUni$boxOffice$disp
Shape of Distribution: r edaUni$boxOffice$skew r edaUni$boxOffice$kurt The histogram and QQ plot in r kfigr::figr(label = "box_office_dist", prefix = TRUE, link = TRUE, type="Figure") reveals a left skewed distribution that departs significantly from normality.
Outliers: The boxplot in r kfigr::figr(label = "box_office_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$boxOffice$outliers) == 0, "no", " ") outliers were extant. r edaUni$boxOffice$out
Log Box Office
This is a log transformation of the box office variable.
r kfigr::figr(label = "box_office_log_stats", prefix = TRUE, link = TRUE, type="Table"): Log box office revenue summary statistics
knitr::kable(edaUni$boxOfficeLog$stats, digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$boxOfficeLog$hist, edaUni$boxOfficeLog$qq, ncol = 2)
r kfigr::figr(label = "box_office_log_dist", prefix = TRUE, link = TRUE, type="Figure"): Log box office revenue histogram and QQ Plot
edaUni$boxOfficeLog$box
r kfigr::figr(label = "box_office_log_box", prefix = TRUE, link = TRUE, type="Figure"): Log box office revenue boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "box_office_log_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$boxOfficeLog$central
Dispersion: r edaUni$boxOfficeLog$disp
Shape of Distribution: r edaUni$boxOfficeLog$skew r edaUni$boxOfficeLog$kurt The histogram and QQ plot in r kfigr::figr(label = "box_office_log_dist", prefix = TRUE, link = TRUE, type="Figure") reveals a left skewed distribution that approximates normality.
Outliers: The boxplot in r kfigr::figr(label = "box_office_log_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$boxOfficeLog$outliers) == 0, "no", " ") outliers were extant. r edaUni$boxOfficeLog$out
Bivariate Analysis
# dataSets <- list()
# dataSets[["mdb1"]] <- movies::process(data = mdb1)
# dataSets[["mdb2"]] <- movies::process(data = mdb2)
# edaBi1 <- bivariate(dataSets)
The objective at this stage is to ascertain the correlation (quantiative independent variable) or the association (categorical independent variable) between movie popularity and the following candidate predictors. To ascertain the suitability of a candidate predictor, statistical inference (i.e., hypothesis testing) was conducted to draw conclusions about how movie popularity relates to various factors, based on the sample of popularity and the explanatory variables. Once conditions were checked, the appropriate Anova / Regression (parametric) or Mann–Whitney U test/ Kruskal-Wallis (non-parametric) tests were conducted. The confidence level for all tests was 95%, yielding a two-sided $\alpha = 0.05$. Decisions were made w.r.t. the relationship between movie popularity and the following factors based upon the probability of observing a test statistic as extreme as the one observed, given the null hypothesis (equal means/ zero slope) was true.
Having introduced each of the variables and created new ones, twelve independent variables were selected for this next stage bivariate analysis and they are listed in r kfigr::figr(label = "predictors", prefix = TRUE, link = TRUE, type="Table").
r kfigr::figr(label = "predictors", prefix = TRUE, link = TRUE, type="Table"): Candidate predictors
predictors <- openxlsx::read.xlsx("../data/features.xlsx", sheet = 1)
predictors <- predictors %>% filter(bi == "yes") %>% select(Variable, Description)
knitr::kable(predictors) %>%
kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
Certain variables such as website addresses, film titles and runtimes provided no popularity predictive value. Similarly, studio, director, and actor variables were excluded in favor of their popularity and experience measures. The day of theatrical release as well as DVD release dates were not of interest for this analysis. Categorical scoring variables were excluded in favor of numeric measures. Lastly, dichotomous variables such as the oscar wins and inclusion in the box off top 200 provided insufficient sample size for one (or more) of these levels, as such they were excluded.
Genre
The hypothesis for the association between genre and movie popularity was as follows:
$H_0$:
Part 4: Modeling
To ascertain the suitability of a candidate predictor, statistical inference (i.e., hypothesis testing) was conducted to draw conclusions about how movie popularity relates to various factors, based on the sample of popularity and the explanatory values.
The relationship between movie popularity and an explanatory variable can be described by the equation $Y=β0+β1x$ where:
$Y$ is the movie popularity score
$β0$ is the $y$-intercept of the regression line
$β1$ is the slope of the regression line
$x$ is the coded value for the title type
The following analysis is only interested in the statistical significance of the slope, $β1$, whereas $β1 \neq 0$ indicates that the explanatory variable $x$ can be used to predict $Y$, movie popularity.
Before making any inferences, the conditions for inference were checked. For categorical variables,
linearity, independence of errors, normality of errors, and equal error variance was checked. Next, hypotheses statements were tested whereby $H_0$: $β1 = 0$ and $H_a$: $β1 \neq 0$. The confidence level for all tests was 95%, with a two-tailed $\alhha = 0.05$. Two test statistics were used: (1) the $t$-statistic and (2) the $F$ statistic for analysis of variance.
Observations included/omitted
- title_type == TV removed
r kfigr::figr(label = "forward", prefix = TRUE, link = TRUE, type="Table"): Forward Selection Prediction Model
#forwardSelection <- movies::forward(movies)
#summary(forwardSelection)
r kfigr::figr(label = "back", prefix = TRUE, link = TRUE, type="Table"): Backward Elimination Prediction Model
#backStep <- movies::back(movies)
#summary(backStep)
Part 5: Prediction
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Part 6: Conclusion
Appendix
Appendix A: Codebook
r kfigr::figr(label = "codebook", prefix = TRUE, link = TRUE, type="Table"): Movie data set codebook
codebook <- openxlsx::read.xlsx("../data/features.xlsx", sheet = 1)
codebook <- codebook %>% select(Source, Type, Variable, Description)
knitr::kable(codebook, align = 'l') %>%
kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center") %>%
kableExtra::group_rows("General", 1,7) %>%
kableExtra::group_rows("Organization", 8,14) %>%
kableExtra::group_rows("Dates", 15,21) %>%
kableExtra::group_rows("Experience", 22,24) %>%
kableExtra::group_rows("Performance", 25,41) %>%
kableExtra::group_rows("Interaction", 42,51) %>%
kableExtra::group_rows("Box Office", 52,53)
References
DataScienceSalon/movies documentation built on May 28, 2019, 12:24 p.m.
R Package Documentation
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options(knitr.table.format = "html") options(max.print=100, scipen=999, width = 800) knitr::opts_chunk$set(echo=FALSE, cache=FALSE, prompt=FALSE, eval = TRUE, tidy=TRUE, root.dir = "..", fig.height = 8, fig.width = 20, comment=NA, message=FALSE, warning=FALSE) knitr::opts_knit$set(width=100, figr.prefix = T, figr.link = T) knitr::knit_hooks$set(inline = function(x) { prettyNum(x, big.mark=",") })
load("../data/movies.Rdata") load("../data/mdb2.Rdata")
library(dplyr) library(extrafont) library(ggplot2)
dataSets <- movies::preprocess(movies, mdb2) mdb1 <- dataSets$mdb mdb2 <- dataSets$mdbBox
Part 1: Data
The data were comprised of audience and critics opinions, awards, studio, and actor information from Rotten Tomatoes, imdb, and BoxOfficeMojo.com for a random sample of 651 movies produced and released prior to 2016.
Data Sources
Rotten Tomatoes
Launched in August 1998 by Senh Duong, Rotten Tomatoes is an American review aggregation website for film and television.
IMDB
Generalizability
Selected Features
The full codebook for the data set can be found in Appendix A. r kfigr::figr(label = "selected", prefix = TRUE, link = TRUE, type="Table") lists the data variables selected from the raw data that were included at this stage in this study.
r kfigr::figr(label = "selected", prefix = TRUE, link = TRUE, type="Table"): Selected features
raw <- openxlsx::read.xlsx("../data/features.xlsx", sheet = 1) selected <- raw %>% filter(uni == "yes" & Source == "IMDB/RT/BO") %>% arrange(Group, No) %>% select(Type, Variable, Description) knitr::kable(selected, align = 'l') %>% kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center") %>% kableExtra::group_rows("Overview", 1,3) %>% kableExtra::group_rows("Organization", 4,5) %>% kableExtra::group_rows("Dates", 6,6) %>% kableExtra::group_rows("Performance", 7,16) %>% kableExtra::group_rows("Box Office", 17,18)
Note that the values for the box office variables were obtained for a random sample of 100 reviews from the BoxOfficeMojo.com website.
Derived Features
The following additional features (r kfigr::figr(label = "derived", prefix = TRUE, link = TRUE, type="Table")) were derived from selected features and are as follows:
r kfigr::figr(label = "derived", prefix = TRUE, link = TRUE, type="Table"): Derived features
derived <- raw %>% filter(uni == "yes" & Source == "Derived") %>% arrange(Group, No) %>% select(Type, Variable, Description) knitr::kable(derived, align = 'l') %>% kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center") %>% kableExtra::group_rows("Dates", 1,1) %>% kableExtra::group_rows("Experience", 2,4) %>% kableExtra::group_rows("Performance", 5,9) %>% kableExtra::group_rows("Interaction", 10,19)
Cast experience and cast votes for each film were computed as follows:
Cast Experience
Cast experience for each film was defined by:
$$e = \displaystyle\sum_{i=1}^{5} N_i$$
where:
$e$ is the total cast experience for the film
$N_i$ is the total number of films in which actor $i$ was involved
Cast Votes
Cast votes for each film was defined by:
$$v = \displaystyle\sum_{i=1}^{5} V_i$$
where:
$v$ is the sum of IMDB cast votes for the film
$V_i$ is the sum of allocated IMDB cast votes for actor $i$
imdb votes were allocated to cast members as follows:
40% of total film IMDB votes for actor1
30% of total film IMDB votes for actor2
15% of total film IMDB votes for actor3
10% of total film IMDB votes for actor4
* 5% of total film IMDB votes for actor5
Each actor was allocated points accordingly, then the votes were aggregated for each film in which the cast member appeared. The IMDB votes were counted without regard for date to compensate for the limitations imposed by the sample size as movegoers had access to the population of reviews and director, studio, and actor performance data when making their purchase decision.
Omitted Features
The features listed in r kfigr::figr(label = "omitted", prefix = TRUE, link = TRUE, type="Table") were not included for redundancy reasons or due to the lack of direct relevance to the research question. Some variables, such as the actor variables, were used to derive other variables which are further described below.
r kfigr::figr(label = "omitted", prefix = TRUE, link = TRUE, type="Table"): Omitted features
omitted <- raw %>% filter(uni == "no") %>% select(Variable, Description) knitr::kable(omitted) %>% kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
Data Cleaning
The variables of interest were obtained from the data and complete cases were extracted reducing the number of observations from 651 to r nrow(mdb1).
Part 2: Research question
The underlying intent of this analysis was to determine the factors that most influence box office success for a film. Since box office revenue was not among the variables included in the raw data set, the first task was to determine which of the selected (or derived) variables would stand as a proxy for box office success. As such the first research question is concretely stated as follows:
Which of the selected or derived variables is most highly associated / correlated with total lifetime box office revenue
Once this proxy response variable was determined, the features that are most highly associated / correlated with this response variable were examined via the following research question.
Which features are most highly associated / correlated with the proxy response for box office success
Part 3: Exploratory data analysis
The exploratory data analysis began with a data preprocessing step to extract complete cases, and to create the response and two additional explanatory variables. Next, a univariate analysis examined each variable on a univariate basis. Lastly, a bivariate analysis explored the relationships between the response variable and various candidate predictors.
Univariate Analysis
edaUni <- movies::univariate(mdb1, mdb2)
Univariate Analysis of Categorical Variables
The purpose of the univariate analysis of categorical variables was to examine the relative frequencies and proportions of observations for each level of the categorical level. Categorical levels with fewer than five observations were removed from further analysis.
The categorical variables included at this stage of the analysis are indicated in r kfigr::figr(label = "uni_cat", prefix = TRUE, link = TRUE, type="Table").
r kfigr::figr(label = "uni_cat", prefix = TRUE, link = TRUE, type="Table"): Categorical Variables
uniCat <- raw %>% filter(uni == "yes" & Type == "Categorical") %>% select(Variable, Description) knitr::kable(uniCat) %>% kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
Title Type
Feature films constituted r edaUni$type$stats$Proportion[1] * 100% of the films in the sample. Since the focus of this study was theatrical releases, TV movies, which were included in the raw data were excluded from this analysis.
edaUni$type$plot
r kfigr::figr(label = "title_type", prefix = TRUE, link = TRUE, type="Figure"): Films by title type
Genre
The drama genre represented a plurality of the releases in the sample, followed by comedy action & adventure then mystery & suspense. The top four genres account for nearly r round(sum(head(edaUni$genre$stats %>% arrange(desc(Proportion)) %>% select(Proportion), 4)) * 100, -1)% of the films in the sample.
edaUni$genre$plot
r kfigr::figr(label = "genre", prefix = TRUE, link = TRUE, type="Figure"): Films by genre
MPAA Rating
Rated R films accounted for over r round(edaUni$mpaa$stats$Proportion[5] * 100, -1)% of the releases, followed by PG and PG-13. Collectively, R, PG, and PG-13 rated films represent r round(sum(head(edaUni$mpaa$stats %>% arrange(desc(Proportion)) %>% select(Proportion), 3)) * 100, -1)% of the films in the sample. NC-17 films were excluded from this analysis.
edaUni$mpaa$plot
r kfigr::figr(label = "mpaa", prefix = TRUE, link = TRUE, type="Figure"): Films by MPAA Rating
Studio
The data included films from r length(unique(mdb1$studio)) studios. Data with respect to the number of films in the sample per studio are captured in the studio experience variable below.
Director
The work of r length(unique(mdb1$director)) directors was included in the sample provided for this project. Data with respect to the number of films in the sample per director are captured in the director experience variable below.
Season of Theatrical Release
The plurality of features in the sample were released during the fall and summer months with over r round(edaUni$season$stats$Proportion[3] *100, -1)% opening in the month of December alone.
edaUni$season$plot
r kfigr::figr(label = "season", prefix = TRUE, link = TRUE, type="Figure"): Theatrical releases by season
Month of Theatrical Release
The plurality of features in the sample (r sum(edaUni$month$stats$Proportion[c(1,6,10,12)])*100%) were released during the months of January, June, October and December.
edaUni$month$plot
r kfigr::figr(label = "month", prefix = TRUE, link = TRUE, type="Figure"): Theatrical releases by month
Best Picture
Since the proportion of films nominated for and winning best picture were so small, this variable was not likely to be a good predictor of movie popularity. The bivariate analysis below will illuminate this further.
gridExtra::grid.arrange(edaUni$bestPicNom$plot, edaUni$bestPicWin$plot, ncol = 2)
r kfigr::figr(label = "best_picture", prefix = TRUE, link = TRUE, type="Figure"): Best picture nominations and wins
Best Director / Actor / Actress
As indicated in r kfigr::figr(label = "best_actor", prefix = TRUE, link = TRUE, type="Figure"), the percentages of films with best director, actor and actress oscars were r edaUni$bestDirWin$stats$Proportion[2] * 100%, r edaUni$bestActorWin$stats$Proportion[2] * 100%, and r edaUni$bestActressWin$stats$Proportion[2] * 100%, respectively. Again, these proportions indicate that oscar awards would not be a good predictor of movie popularity. The bivariate analysis will explore this further.
gridExtra::grid.arrange(edaUni$bestDirWin$plot, edaUni$bestActorWin$plot, edaUni$bestActressWin$plot, ncol = 3)
r kfigr::figr(label = "best_actor", prefix = TRUE, link = TRUE, type="Figure"): Best director/actor/actress
Top 200 Box Office
Again, the proportion of films in the Top 200 Box Office list was miniscule indicating that inclusion in the top 200 box office list was not likely to be a good predictor of movie popularity.
edaUni$top200Box$plot
r kfigr::figr(label = "top_200", prefix = TRUE, link = TRUE, type="Figure"): Frequency and proportion of movies by top 200 box office earnings
Univariate Analysis of Quantitative Variables
The primary aim of this analysis was to examine the distribution of the variables vis-a-vis a normal distribution, and to identify potential outliers. Summary statistics, histograms, boxplots, normal quantile-quantile plots were rendered for each variable. The quantitative variables included at this stage of the analysis are indicated in r kfigr::figr(label = "uni_quant", prefix = TRUE, link = TRUE, type="Table").
r kfigr::figr(label = "uni_quant", prefix = TRUE, link = TRUE, type="Table"): Quantitative Variables
uniQuant <- raw %>% filter(uni == "yes" & Type == "Numeric") %>% select(Variable, Description) knitr::kable(uniQuant) %>% kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
Studio Experience
This derived variable measured the relative experience of a given studio and was defined as the sum of the observations for the studio associated with each film.
r kfigr::figr(label = "studio_experience_stats", prefix = TRUE, link = TRUE, type="Table"): Studio experience summary statistics
knitr::kable(edaUni$studioExperience$stats, digits = 2) %>% kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$studioExperience$hist, edaUni$studioExperience$qq, ncol = 2)
r kfigr::figr(label = "studio_experience_dist", prefix = TRUE, link = TRUE, type="Figure"): Studio experience histogram and QQ Plot
edaUni$studioExperience$box
r kfigr::figr(label = "studio_experience_box", prefix = TRUE, link = TRUE, type="Figure"): Studio experience boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "studio_experience_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$studioExperience$central
Dispersion: r edaUni$studioExperience$disp
Shape of Distribution: r edaUni$studioExperience$skew r edaUni$studioExperience$kurt The histogram and QQ plot in r kfigr::figr(label = "studio_experience_dist", prefix = TRUE, link = TRUE, type="Figure") reveal a distribution which departs significantly from normality.
Outliers: The boxplot in r kfigr::figr(label = "studio_experience_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$studioExperience$outliers) == 0, "no", " ") outliers were extant. r edaUni$studioExperience$out
Director Experience
This derived variable measured the relative experience of a given director and was defined as the sum of the observations for the director associated with each film.
r kfigr::figr(label = "director_experience_stats", prefix = TRUE, link = TRUE, type="Table"): Director experience summary statistics
knitr::kable(edaUni$directorExperience$stats, digits = 2) %>% kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$directorExperience$hist, edaUni$directorExperience$qq, ncol = 2)
r kfigr::figr(label = "director_experience_dist", prefix = TRUE, link = TRUE, type="Figure"): Director experience histogram and QQ Plot
edaUni$directorExperience$box
r kfigr::figr(label = "director_experience_box", prefix = TRUE, link = TRUE, type="Figure"): Director experience boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "director_experience_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$directorExperience$central
Dispersion: r edaUni$directorExperience$disp
Shape of Distribution: r edaUni$directorExperience$skew r edaUni$directorExperience$kurt The histogram and QQ plot in r kfigr::figr(label = "director_experience_dist", prefix = TRUE, link = TRUE, type="Figure") reveal a distribution which departs significantly from normality.
Outliers: The boxplot in r kfigr::figr(label = "director_experience_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$directorExperience$outliers) == 0, "no", " ") outliers were extant. r edaUni$directorExperience$out Given the proximity of the outliers to the 1.5xIQR, no effort was made to remove them.
Cast Experience
This derived variable measured the relative experience of a given cast and was defined as the sum of the observations for the cast associated with each film.
r kfigr::figr(label = "cast_experience_stats", prefix = TRUE, link = TRUE, type="Table"): Cast experience summary statistics
knitr::kable(edaUni$castExperience$stats, digits = 2) %>% kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$castExperience$hist, edaUni$castExperience$qq, ncol = 2)
r kfigr::figr(label = "cast_experience_dist", prefix = TRUE, link = TRUE, type="Figure"): Cast experience histogram and QQ Plot
edaUni$castExperience$box
r kfigr::figr(label = "cast_experience_box", prefix = TRUE, link = TRUE, type="Figure"): Cast experience boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "cast_experience_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$castExperience$central
Dispersion: r edaUni$castExperience$disp
Shape of Distribution: r edaUni$castExperience$skew r edaUni$castExperience$kurt The histogram and QQ plot in r kfigr::figr(label = "cast_experience_dist", prefix = TRUE, link = TRUE, type="Figure") reveal a distribution which departs significantly from normality.
Outliers: The boxplot in r kfigr::figr(label = "cast_experience_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$castExperience$outliers) == 0, "no", " ") outliers were extant. r edaUni$castExperience$out Given the proximity of the outliers to the 1.5xIQR, no effort was made to remove them.
Number of IMDB Votes
This variable captured the number of IMDB votes cast for each film.
r kfigr::figr(label = "imdb_votes_stats", prefix = TRUE, link = TRUE, type="Table"): IMDB votes summary statistics
knitr::kable(edaUni$imdbVotes$stats, digits = 2) %>% kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$imdbVotes$hist, edaUni$imdbVotes$qq, ncol = 2)
r kfigr::figr(label = "imdb_votes_dist", prefix = TRUE, link = TRUE, type="Figure"): IMDB votes histogram and QQ Plot
edaUni$imdbVotes$box
r kfigr::figr(label = "imdb_votes_box", prefix = TRUE, link = TRUE, type="Figure"): IMDB votes boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "imdb_votes_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$imdbVotes$central
Dispersion: r edaUni$imdbVotes$disp
Shape of Distribution: r edaUni$imdbVotes$skew r edaUni$imdbVotes$kurt The histogram and QQ plot in r kfigr::figr(label = "imdb_votes_dist", prefix = TRUE, link = TRUE, type="Figure") reveal a distribution which departs significantly from normality.
Outliers: The boxplot in r kfigr::figr(label = "imdb_votes_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$imdbVotes$outliers) == 0, "no", " ") outliers were extant. r edaUni$imdbVotes$out
Log Number of IMDB Votes
This was a log transformation of the IMDB votes variable.
r kfigr::figr(label = "imdb_votes_log_stats", prefix = TRUE, link = TRUE, type="Table"): Log IMDB votes summary statistics
knitr::kable(edaUni$imdbVotesLog$stats, digits = 2) %>% kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$imdbVotesLog$hist, edaUni$imdbVotesLog$qq, ncol = 2)
r kfigr::figr(label = "imdb_votes_log_dist", prefix = TRUE, link = TRUE, type="Figure"): Log IMDB votes histogram and QQ Plot
edaUni$imdbVotesLog$box
r kfigr::figr(label = "imdb_votes_log_box", prefix = TRUE, link = TRUE, type="Figure"): Log IMDB votes boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "imdb_votes_log_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$imdbVotesLog$central
Dispersion: r edaUni$imdbVotesLog$disp
Shape of Distribution: r edaUni$imdbVotesLog$skew r edaUni$imdbVotesLog$kurt The histogram and QQ plot in r kfigr::figr(label = "imdb_votes_log_dist", prefix = TRUE, link = TRUE, type="Figure") reveal a nearly normal distribution.
Outliers: The boxplot in r kfigr::figr(label = "imdb_votes_log_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$imdbVotesLog$outliers) == 0, "no", " ") outliers were extant. r edaUni$imdbVotesLog$out
IMDB Ratings
This variable captured the IMDB rating for each film
r kfigr::figr(label = "imdb_rating_stats", prefix = TRUE, link = TRUE, type="Table"): IMDB rating summary statistics
knitr::kable(edaUni$imdbRating$stats, digits = 2) %>% kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$imdbRating$hist, edaUni$imdbRating$qq, ncol = 2)
r kfigr::figr(label = "imdb_rating_dist", prefix = TRUE, link = TRUE, type="Figure"): IMDB rating histogram and QQ Plot
edaUni$imdbRating$box
r kfigr::figr(label = "imdb_rating_box", prefix = TRUE, link = TRUE, type="Figure"): IMDB rating boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "imdb_rating_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$imdbRating$central
Dispersion: r edaUni$imdbRating$disp
Shape of Distribution: r edaUni$imdbRating$skew r edaUni$imdbRating$kurt The histogram and QQ plot in r kfigr::figr(label = "imdb_rating_dist", prefix = TRUE, link = TRUE, type="Figure") reveal a nearly normal distribution.
Outliers: The boxplot in r kfigr::figr(label = "imdb_rating_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$imdbRating$outliers) == 0, "no", " ") outliers were extant. r edaUni$imdbRating$out
Critics Scores
This variable captured the critics scores for each film
r kfigr::figr(label = "critics_scores_stats", prefix = TRUE, link = TRUE, type="Table"): Critics score summary statistics
knitr::kable(edaUni$criticsScores$stats, digits = 2) %>% kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$criticsScores$hist, edaUni$criticsScores$qq, ncol = 2)
r kfigr::figr(label = "critics_scores_dist", prefix = TRUE, link = TRUE, type="Figure"): Critics score histogram and QQ Plot
edaUni$criticsScores$box
r kfigr::figr(label = "critics_scores_box", prefix = TRUE, link = TRUE, type="Figure"): Critics score boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "critics_scores_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$criticsScores$central
Dispersion: r edaUni$criticsScores$disp
Shape of Distribution: r edaUni$criticsScores$skew r edaUni$criticsScores$kurt The histogram and QQ plot in r kfigr::figr(label = "critics_scores_dist", prefix = TRUE, link = TRUE, type="Figure") reveals a left skewed distribution that departs from normality.
Outliers: The boxplot in r kfigr::figr(label = "critics_scores_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$criticsScores$outliers) == 0, "no", " ") outliers were extant. r edaUni$criticsScores$out
Audience Scores
This variable captured the audience scores for each film
r kfigr::figr(label = "audience_scores_stats", prefix = TRUE, link = TRUE, type="Table"): Audience score summary statistics
knitr::kable(edaUni$audienceScores$stats, digits = 2) %>% kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$audienceScores$hist, edaUni$audienceScores$qq, ncol = 2)
r kfigr::figr(label = "audience_scores_dist", prefix = TRUE, link = TRUE, type="Figure"): Audience score histogram and QQ Plot
edaUni$audienceScores$box
r kfigr::figr(label = "audience_scores_box", prefix = TRUE, link = TRUE, type="Figure"): Audience score boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "audience_scores_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$audienceScores$central
Dispersion: r edaUni$audienceScores$disp
Shape of Distribution: r edaUni$audienceScores$skew r edaUni$audienceScores$kurt The histogram and QQ plot in r kfigr::figr(label = "audience_scores_dist", prefix = TRUE, link = TRUE, type="Figure") reveals a left skewed distribution that departs from normality.
Outliers: The boxplot in r kfigr::figr(label = "audience_scores_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$audienceScores$outliers) == 0, "no", " ") outliers were extant. r edaUni$audienceScores$out
Studio Votes
This variable captured the studio votes for each film
r kfigr::figr(label = "studio_votes_stats", prefix = TRUE, link = TRUE, type="Table"): Studio votes summary statistics
knitr::kable(edaUni$studioVotes$stats, digits = 2) %>% kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$studioVotes$hist, edaUni$studioVotes$qq, ncol = 2)
r kfigr::figr(label = "studio_votes_dist", prefix = TRUE, link = TRUE, type="Figure"): Studio votes histogram and QQ Plot
edaUni$studioVotes$box
r kfigr::figr(label = "studio_votes_box", prefix = TRUE, link = TRUE, type="Figure"): Studio votes boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "studio_votes_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$studioVotes$central
Dispersion: r edaUni$studioVotes$disp
Shape of Distribution: r edaUni$studioVotes$skew r edaUni$studioVotes$kurt The histogram and QQ plot in r kfigr::figr(label = "studio_votes_dist", prefix = TRUE, link = TRUE, type="Figure") reveals a left skewed distribution that departs significantly from normality.
Outliers: The boxplot in r kfigr::figr(label = "studio_votes_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$studioVotes$outliers) == 0, "no", " ") outliers were extant. r edaUni$studioVotes$out
Log Studio Votes
This is a log transformation of the studio votes variable.
r kfigr::figr(label = "studio_votes_log_stats", prefix = TRUE, link = TRUE, type="Table"): Log studio votes summary statistics
knitr::kable(edaUni$studioVotesLog$stats, digits = 2) %>% kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$studioVotesLog$hist, edaUni$studioVotesLog$qq, ncol = 2)
r kfigr::figr(label = "studio_votes_log_dist", prefix = TRUE, link = TRUE, type="Figure"): Log studio votes histogram and QQ Plot
edaUni$studioVotesLog$box
r kfigr::figr(label = "studio_votes_log_box", prefix = TRUE, link = TRUE, type="Figure"): Log studio votes boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "studio_votes_log_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$studioVotesLog$central
Dispersion: r edaUni$studioVotesLog$disp
Shape of Distribution: r edaUni$studioVotesLog$skew r edaUni$studioVotesLog$kurt The histogram and QQ plot in r kfigr::figr(label = "studio_votes_log_dist", prefix = TRUE, link = TRUE, type="Figure") reveals a left skewed distribution that approximates normality.
Outliers: The boxplot in r kfigr::figr(label = "studio_votes_log_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$studioVotesLog$outliers) == 0, "no", " ") outliers were extant. r edaUni$studioVotesLog$out
Cast Votes
This variable captured the total number of votes allocated to each cast member for a film.
r kfigr::figr(label = "cast_votes_stats", prefix = TRUE, link = TRUE, type="Table"): Cast votes summary statistics
knitr::kable(edaUni$castVotes$stats, digits = 2) %>% kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$castVotes$hist, edaUni$castVotes$qq, ncol = 2)
r kfigr::figr(label = "cast_votes_dist", prefix = TRUE, link = TRUE, type="Figure"): Cast votes histogram and QQ Plot
edaUni$castVotes$box
r kfigr::figr(label = "cast_votes_box", prefix = TRUE, link = TRUE, type="Figure"): Cast votes boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "cast_votes_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$castVotes$central
Dispersion: r edaUni$castVotes$disp
Shape of Distribution: r edaUni$castVotes$skew r edaUni$castVotes$kurt The histogram and QQ plot in r kfigr::figr(label = "cast_votes_dist", prefix = TRUE, link = TRUE, type="Figure") reveals a left skewed distribution that departs significantly from normality.
Outliers: The boxplot in r kfigr::figr(label = "cast_votes_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$castVotes$outliers) == 0, "no", " ") outliers were extant. r edaUni$castVotes$out
Log Cast Votes
This is a log transformation of the cast votes variable.
r kfigr::figr(label = "cast_votes_log_stats", prefix = TRUE, link = TRUE, type="Table"): Log cast votes summary statistics
knitr::kable(edaUni$castVotesLog$stats, digits = 2) %>% kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$castVotesLog$hist, edaUni$castVotesLog$qq, ncol = 2)
r kfigr::figr(label = "cast_votes_log_dist", prefix = TRUE, link = TRUE, type="Figure"): Log cast votes histogram and QQ Plot
edaUni$castVotesLog$box
r kfigr::figr(label = "cast_votes_log_box", prefix = TRUE, link = TRUE, type="Figure"): Log cast votes boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "cast_votes_log_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$castVotesLog$central
Dispersion: r edaUni$castVotesLog$disp
Shape of Distribution: r edaUni$castVotesLog$skew r edaUni$castVotesLog$kurt The histogram and QQ plot in r kfigr::figr(label = "cast_votes_log_dist", prefix = TRUE, link = TRUE, type="Figure") reveals a left skewed distribution that approximates normality.
Outliers: The boxplot in r kfigr::figr(label = "cast_votes_log_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$castVotesLog$outliers) == 0, "no", " ") outliers were extant. r edaUni$castVotesLog$out
Scores
This variable captured the total score for each film defined as 10 * IMDB Rating + critics score + audience_score.
r kfigr::figr(label = "scores_stats", prefix = TRUE, link = TRUE, type="Table"): Scores summary statistics
knitr::kable(edaUni$scores$stats, digits = 2) %>% kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$scores$hist, edaUni$scores$qq, ncol = 2)
r kfigr::figr(label = "scores_dist", prefix = TRUE, link = TRUE, type="Figure"): Scores histogram and QQ Plot
edaUni$scores$box
r kfigr::figr(label = "scores_box", prefix = TRUE, link = TRUE, type="Figure"): Scores boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "scores_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$scores$central
Dispersion: r edaUni$scores$disp
Shape of Distribution: r edaUni$scores$skew r edaUni$scores$kurt The histogram and QQ plot in r kfigr::figr(label = "scores_dist", prefix = TRUE, link = TRUE, type="Figure") reveals a left skewed distribution that approximates normality.
Outliers: The boxplot in r kfigr::figr(label = "scores_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$scores$outliers) == 0, "no", " ") outliers were extant. r edaUni$scores$out
Log Scores
This is a log transformation of scores variable.
r kfigr::figr(label = "scores_log_stats", prefix = TRUE, link = TRUE, type="Table"): Log scores summary statistics
knitr::kable(edaUni$scoresLog$stats, digits = 2) %>% kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$scoresLog$hist, edaUni$scoresLog$qq, ncol = 2)
r kfigr::figr(label = "scores_log_dist", prefix = TRUE, link = TRUE, type="Figure"): Log scores histogram and QQ Plot
edaUni$scoresLog$box
r kfigr::figr(label = "scores_log_box", prefix = TRUE, link = TRUE, type="Figure"): Log scores boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "scores_log_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$scoresLog$central
Dispersion: r edaUni$scoresLog$disp
Shape of Distribution: r edaUni$scoresLog$skew r edaUni$scoresLog$kurt The histogram and QQ plot in r kfigr::figr(label = "scores_log_dist", prefix = TRUE, link = TRUE, type="Figure") reveals a left skewed distribution that departs rather significantly from normality.
Outliers: The boxplot in r kfigr::figr(label = "scores_log_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$scoresLog$outliers) == 0, "no", " ") outliers were extant. r edaUni$scoresLog$out
IMDB Votes * Rating
This interaction variable is defined as the product of IMDB votes and IMDB ratings.
r kfigr::figr(label = "votes_imdb_rating_stats", prefix = TRUE, link = TRUE, type="Table"): IMDB Votes * Rating summary statistics
knitr::kable(edaUni$votesImdbRating$stats, digits = 2) %>% kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$votesImdbRating$hist, edaUni$votesImdbRating$qq, ncol = 2)
r kfigr::figr(label = "votes_imdb_rating_dist", prefix = TRUE, link = TRUE, type="Figure"): IMDB Votes * Rating histogram and QQ Plot
edaUni$votesImdbRating$box
r kfigr::figr(label = "votes_imdb_rating_box", prefix = TRUE, link = TRUE, type="Figure"): IMDB Votes * Rating votes boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "votes_imdb_rating_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$votesImdbRating$central
Dispersion: r edaUni$votesImdbRating$disp
Shape of Distribution: r edaUni$votesImdbRating$skew r edaUni$votesImdbRating$kurt The histogram and QQ plot in r kfigr::figr(label = "votes_imdb_rating_dist", prefix = TRUE, link = TRUE, type="Figure") reveals a left skewed distribution that departs significantly from normality.
Outliers: The boxplot in r kfigr::figr(label = "votes_imdb_rating_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$votesImdbRating$outliers) == 0, "no", " ") outliers were extant. r edaUni$votesImdbRating$out
Log IMDB Votes * Rating
This is a log transformation of IMDB Votes * Rating variable.
r kfigr::figr(label = "votes_imdb_rating_log_stats", prefix = TRUE, link = TRUE, type="Table"): Log IMDB Votes * Rating summary statistics
knitr::kable(edaUni$votesImdbRatingLog$stats, digits = 2) %>% kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$votesImdbRatingLog$hist, edaUni$votesImdbRatingLog$qq, ncol = 2)
r kfigr::figr(label = "votes_imdb_rating_log_dist", prefix = TRUE, link = TRUE, type="Figure"): Log IMDB Votes * Rating histogram and QQ Plot
edaUni$votesImdbRatingLog$box
r kfigr::figr(label = "votes_imdb_rating_log_box", prefix = TRUE, link = TRUE, type="Figure"): Log IMDB Votes * Rating boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "votes_imdb_rating_log_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$votesImdbRatingLog$central
Dispersion: r edaUni$votesImdbRatingLog$disp
Shape of Distribution: r edaUni$votesImdbRatingLog$skew r edaUni$votesImdbRatingLog$kurt The histogram and QQ plot in r kfigr::figr(label = "votes_imdb_rating_log_dist", prefix = TRUE, link = TRUE, type="Figure") reveals a left skewed distribution that approximates normality.
Outliers: The boxplot in r kfigr::figr(label = "votes_imdb_rating_log_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$votesImdbRatingLog$outliers) == 0, "no", " ") outliers were extant. r edaUni$votesImdbRatingLog$out
IMDB Votes * Critics Score
This interaction variable is defined as the product of IMDB votes and critics score.
r kfigr::figr(label = "votes_critics_score_stats", prefix = TRUE, link = TRUE, type="Table"): IMDB Votes * Critics Score summary statistics
knitr::kable(edaUni$votesCriticsScore$stats, digits = 2) %>% kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$votesCriticsScore$hist, edaUni$votesCriticsScore$qq, ncol = 2)
r kfigr::figr(label = "votes_critics_score_dist", prefix = TRUE, link = TRUE, type="Figure"): IMDB Votes * Critics Score histogram and QQ Plot
edaUni$votesCriticsScore$box
r kfigr::figr(label = "votes_critics_score_box", prefix = TRUE, link = TRUE, type="Figure"): IMDB Votes * Critics Score votes boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "votes_critics_score_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$votesCriticsScore$central
Dispersion: r edaUni$votesCriticsScore$disp
Shape of Distribution: r edaUni$votesCriticsScore$skew r edaUni$votesCriticsScore$kurt The histogram and QQ plot in r kfigr::figr(label = "votes_critics_score_dist", prefix = TRUE, link = TRUE, type="Figure") reveals a left skewed distribution that departs significantly from normality.
Outliers: The boxplot in r kfigr::figr(label = "votes_critics_score_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$votesCriticsScore$outliers) == 0, "no", " ") outliers were extant. r edaUni$votesCriticsScore$out
Log IMDB Votes * Critics Score
This is a log transformation of IMDB Votes * Critics Score variable.
r kfigr::figr(label = "votes_critics_score_log_stats", prefix = TRUE, link = TRUE, type="Table"): Log IMDB Votes * Critics Score summary statistics
knitr::kable(edaUni$votesCriticsScoreLog$stats, digits = 2) %>% kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$votesCriticsScoreLog$hist, edaUni$votesCriticsScoreLog$qq, ncol = 2)
r kfigr::figr(label = "votes_critics_score_log_dist", prefix = TRUE, link = TRUE, type="Figure"): Log IMDB Votes * Critics Score histogram and QQ Plot
edaUni$votesCriticsScoreLog$box
r kfigr::figr(label = "votes_critics_score_log_box", prefix = TRUE, link = TRUE, type="Figure"): Log IMDB Votes * Critics Score boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "votes_critics_score_log_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$votesCriticsScoreLog$central
Dispersion: r edaUni$votesCriticsScoreLog$disp
Shape of Distribution: r edaUni$votesCriticsScoreLog$skew r edaUni$votesCriticsScoreLog$kurt The histogram and QQ plot in r kfigr::figr(label = "votes_critics_score_log_dist", prefix = TRUE, link = TRUE, type="Figure") reveals a left skewed distribution that approximates normality.
Outliers: The boxplot in r kfigr::figr(label = "votes_critics_score_log_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$votesCriticsScoreLog$outliers) == 0, "no", " ") outliers were extant. r edaUni$votesCriticsScoreLog$out
IMDB Votes * Audience Score
This interaction variable is defined as the product of IMDB votes and audience score.
r kfigr::figr(label = "votes_audience_score_stats", prefix = TRUE, link = TRUE, type="Table"): IMDB Votes * Audience Score summary statistics
knitr::kable(edaUni$votesAudienceScore$stats, digits = 2) %>% kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$votesAudienceScore$hist, edaUni$votesAudienceScore$qq, ncol = 2)
r kfigr::figr(label = "votes_audience_score_dist", prefix = TRUE, link = TRUE, type="Figure"): IMDB Votes * Audience Score histogram and QQ Plot
edaUni$votesAudienceScore$box
r kfigr::figr(label = "votes_audience_score_box", prefix = TRUE, link = TRUE, type="Figure"): IMDB Votes * Audience Score votes boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "votes_audience_score_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$votesAudienceScore$central
Dispersion: r edaUni$votesAudienceScore$disp
Shape of Distribution: r edaUni$votesAudienceScore$skew r edaUni$votesAudienceScore$kurt The histogram and QQ plot in r kfigr::figr(label = "votes_audience_score_dist", prefix = TRUE, link = TRUE, type="Figure") reveals a left skewed distribution that departs significantly from normality.
Outliers: The boxplot in r kfigr::figr(label = "votes_audience_score_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$votesAudienceScore$outliers) == 0, "no", " ") outliers were extant. r edaUni$votesAudienceScore$out
Log IMDB Votes * Audience Score
This is a log transformation of IMDB Votes * Audience Score variable.
r kfigr::figr(label = "votes_audience_score_log_stats", prefix = TRUE, link = TRUE, type="Table"): Log IMDB Votes * Audience Score summary statistics
knitr::kable(edaUni$votesAudienceScoreLog$stats, digits = 2) %>% kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$votesAudienceScoreLog$hist, edaUni$votesAudienceScoreLog$qq, ncol = 2)
r kfigr::figr(label = "votes_audience_score_log_dist", prefix = TRUE, link = TRUE, type="Figure"): Log IMDB Votes * Audience Score histogram and QQ Plot
edaUni$votesAudienceScoreLog$box
r kfigr::figr(label = "votes_audience_score_log_box", prefix = TRUE, link = TRUE, type="Figure"): Log IMDB Votes * Audience Score boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "votes_audience_score_log_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$votesAudienceScoreLog$central
Dispersion: r edaUni$votesAudienceScoreLog$disp
Shape of Distribution: r edaUni$votesAudienceScoreLog$skew r edaUni$votesAudienceScoreLog$kurt The histogram and QQ plot in r kfigr::figr(label = "votes_audience_score_log_dist", prefix = TRUE, link = TRUE, type="Figure") reveals a left skewed distribution that approximates normality.
Outliers: The boxplot in r kfigr::figr(label = "votes_audience_score_log_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$votesAudienceScoreLog$outliers) == 0, "no", " ") outliers were extant. r edaUni$votesAudienceScoreLog$out
IMDB Votes * Total Score
This interaction variable is defined as the product of IMDB votes and total score.
r kfigr::figr(label = "votes_scores_stats", prefix = TRUE, link = TRUE, type="Table"): IMDB Votes * Total Score summary statistics
knitr::kable(edaUni$votesScores$stats, digits = 2) %>% kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$votesScores$hist, edaUni$votesScores$qq, ncol = 2)
r kfigr::figr(label = "votes_scores_dist", prefix = TRUE, link = TRUE, type="Figure"): IMDB Votes * Total Score histogram and QQ Plot
edaUni$votesScores$box
r kfigr::figr(label = "votes_scores_box", prefix = TRUE, link = TRUE, type="Figure"): IMDB Votes * Total Score votes boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "votes_scores_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$votesScores$central
Dispersion: r edaUni$votesScores$disp
Shape of Distribution: r edaUni$votesScores$skew r edaUni$votesScores$kurt The histogram and QQ plot in r kfigr::figr(label = "votes_scores_dist", prefix = TRUE, link = TRUE, type="Figure") reveals a left skewed distribution that departs significantly from normality.
Outliers: The boxplot in r kfigr::figr(label = "votes_scores_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$votesScores$outliers) == 0, "no", " ") outliers were extant. r edaUni$votesScores$out
Log IMDB Votes * Total Score
This is a log transformation of IMDB Votes * Total Score variable.
r kfigr::figr(label = "votes_scores_log_stats", prefix = TRUE, link = TRUE, type="Table"): Log IMDB Votes * Total Score summary statistics
knitr::kable(edaUni$votesScoresLog$stats, digits = 2) %>% kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$votesScoresLog$hist, edaUni$votesScoresLog$qq, ncol = 2)
r kfigr::figr(label = "votes_scores_log_dist", prefix = TRUE, link = TRUE, type="Figure"): Log IMDB Votes * Total Score histogram and QQ Plot
edaUni$votesScoresLog$box
r kfigr::figr(label = "votes_scores_log_box", prefix = TRUE, link = TRUE, type="Figure"): Log IMDB Votes * Total Score boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "votes_scores_log_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$votesScoresLog$central
Dispersion: r edaUni$votesScoresLog$disp
Shape of Distribution: r edaUni$votesScoresLog$skew r edaUni$votesScoresLog$kurt The histogram and QQ plot in r kfigr::figr(label = "votes_scores_log_dist", prefix = TRUE, link = TRUE, type="Figure") reveals a left skewed distribution that approximates normality.
Outliers: The boxplot in r kfigr::figr(label = "votes_scores_log_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$votesScoresLog$outliers) == 0, "no", " ") outliers were extant. r edaUni$votesScoresLog$out
Box Office
Total lifetime box office revenue was obtained for a subset of 100 randomly selected films from the movie data set. This is an analysis of box office revenue for this random sampling.
r kfigr::figr(label = "box_office_stats", prefix = TRUE, link = TRUE, type="Table"): Box office revenue summary statistics
knitr::kable(edaUni$boxOffice$stats, digits = 2) %>% kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$boxOffice$hist, edaUni$boxOffice$qq, ncol = 2)
r kfigr::figr(label = "box_office_dist", prefix = TRUE, link = TRUE, type="Figure"): Box office revenue histogram and QQ Plot
edaUni$boxOffice$box
r kfigr::figr(label = "box_office_box", prefix = TRUE, link = TRUE, type="Figure"): Box office revenue boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "box_office_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$boxOffice$central
Dispersion: r edaUni$boxOffice$disp
Shape of Distribution: r edaUni$boxOffice$skew r edaUni$boxOffice$kurt The histogram and QQ plot in r kfigr::figr(label = "box_office_dist", prefix = TRUE, link = TRUE, type="Figure") reveals a left skewed distribution that departs significantly from normality.
Outliers: The boxplot in r kfigr::figr(label = "box_office_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$boxOffice$outliers) == 0, "no", " ") outliers were extant. r edaUni$boxOffice$out
Log Box Office
This is a log transformation of the box office variable.
r kfigr::figr(label = "box_office_log_stats", prefix = TRUE, link = TRUE, type="Table"): Log box office revenue summary statistics
knitr::kable(edaUni$boxOfficeLog$stats, digits = 2) %>% kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
gridExtra::grid.arrange(edaUni$boxOfficeLog$hist, edaUni$boxOfficeLog$qq, ncol = 2)
r kfigr::figr(label = "box_office_log_dist", prefix = TRUE, link = TRUE, type="Figure"): Log box office revenue histogram and QQ Plot
edaUni$boxOfficeLog$box
r kfigr::figr(label = "box_office_log_box", prefix = TRUE, link = TRUE, type="Figure"): Log box office revenue boxplot
Central Tendency: The summary statistics (r kfigr::figr(label = "box_office_log_stats", prefix = TRUE, link = TRUE, type="Table")) r edaUni$boxOfficeLog$central
Dispersion: r edaUni$boxOfficeLog$disp
Shape of Distribution: r edaUni$boxOfficeLog$skew r edaUni$boxOfficeLog$kurt The histogram and QQ plot in r kfigr::figr(label = "box_office_log_dist", prefix = TRUE, link = TRUE, type="Figure") reveals a left skewed distribution that approximates normality.
Outliers: The boxplot in r kfigr::figr(label = "box_office_log_box", prefix = TRUE, link = TRUE, type="Figure"), which graphically depicts the median, the IQR, and maximum and minimum values, suggested that r ifelse(nrow(edaUni$boxOfficeLog$outliers) == 0, "no", " ") outliers were extant. r edaUni$boxOfficeLog$out
Bivariate Analysis
# dataSets <- list() # dataSets[["mdb1"]] <- movies::process(data = mdb1) # dataSets[["mdb2"]] <- movies::process(data = mdb2) # edaBi1 <- bivariate(dataSets)
The objective at this stage is to ascertain the correlation (quantiative independent variable) or the association (categorical independent variable) between movie popularity and the following candidate predictors. To ascertain the suitability of a candidate predictor, statistical inference (i.e., hypothesis testing) was conducted to draw conclusions about how movie popularity relates to various factors, based on the sample of popularity and the explanatory variables. Once conditions were checked, the appropriate Anova / Regression (parametric) or Mann–Whitney U test/ Kruskal-Wallis (non-parametric) tests were conducted. The confidence level for all tests was 95%, yielding a two-sided $\alpha = 0.05$. Decisions were made w.r.t. the relationship between movie popularity and the following factors based upon the probability of observing a test statistic as extreme as the one observed, given the null hypothesis (equal means/ zero slope) was true.
Having introduced each of the variables and created new ones, twelve independent variables were selected for this next stage bivariate analysis and they are listed in r kfigr::figr(label = "predictors", prefix = TRUE, link = TRUE, type="Table").
r kfigr::figr(label = "predictors", prefix = TRUE, link = TRUE, type="Table"): Candidate predictors
predictors <- openxlsx::read.xlsx("../data/features.xlsx", sheet = 1) predictors <- predictors %>% filter(bi == "yes") %>% select(Variable, Description) knitr::kable(predictors) %>% kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center")
Certain variables such as website addresses, film titles and runtimes provided no popularity predictive value. Similarly, studio, director, and actor variables were excluded in favor of their popularity and experience measures. The day of theatrical release as well as DVD release dates were not of interest for this analysis. Categorical scoring variables were excluded in favor of numeric measures. Lastly, dichotomous variables such as the oscar wins and inclusion in the box off top 200 provided insufficient sample size for one (or more) of these levels, as such they were excluded.
Genre
The hypothesis for the association between genre and movie popularity was as follows:
$H_0$:
Part 4: Modeling
To ascertain the suitability of a candidate predictor, statistical inference (i.e., hypothesis testing) was conducted to draw conclusions about how movie popularity relates to various factors, based on the sample of popularity and the explanatory values.
The relationship between movie popularity and an explanatory variable can be described by the equation $Y=β0+β1x$ where:
$Y$ is the movie popularity score
$β0$ is the $y$-intercept of the regression line
$β1$ is the slope of the regression line
$x$ is the coded value for the title type
The following analysis is only interested in the statistical significance of the slope, $β1$, whereas $β1 \neq 0$ indicates that the explanatory variable $x$ can be used to predict $Y$, movie popularity.
Before making any inferences, the conditions for inference were checked. For categorical variables, linearity, independence of errors, normality of errors, and equal error variance was checked. Next, hypotheses statements were tested whereby $H_0$: $β1 = 0$ and $H_a$: $β1 \neq 0$. The confidence level for all tests was 95%, with a two-tailed $\alhha = 0.05$. Two test statistics were used: (1) the $t$-statistic and (2) the $F$ statistic for analysis of variance.
Observations included/omitted - title_type == TV removed
r kfigr::figr(label = "forward", prefix = TRUE, link = TRUE, type="Table"): Forward Selection Prediction Model
#forwardSelection <- movies::forward(movies) #summary(forwardSelection)
r kfigr::figr(label = "back", prefix = TRUE, link = TRUE, type="Table"): Backward Elimination Prediction Model
#backStep <- movies::back(movies) #summary(backStep)
Part 5: Prediction
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Part 6: Conclusion
Appendix
Appendix A: Codebook
r kfigr::figr(label = "codebook", prefix = TRUE, link = TRUE, type="Table"): Movie data set codebook
codebook <- openxlsx::read.xlsx("../data/features.xlsx", sheet = 1) codebook <- codebook %>% select(Source, Type, Variable, Description) knitr::kable(codebook, align = 'l') %>% kableExtra::kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = T, position = "center") %>% kableExtra::group_rows("General", 1,7) %>% kableExtra::group_rows("Organization", 8,14) %>% kableExtra::group_rows("Dates", 15,21) %>% kableExtra::group_rows("Experience", 22,24) %>% kableExtra::group_rows("Performance", 25,41) %>% kableExtra::group_rows("Interaction", 42,51) %>% kableExtra::group_rows("Box Office", 52,53)
References
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