Important: This is a template repository to help you set up your team project.
You are free to modify it based on your needs. For example, if your data is downloaded using multiple scripts instead of a single one (as shown in
\data\), structure the code accordingly. The same applies to all other starter files—adapt or remove them as needed.Feel free to delete this text.
The goal of this project is to examine the relationship between movie running time and audience ratings, and how this relationship may differ between a selection of movie genres.
The movie industry continuously tries to understand what factors influence audience ratings, as they play an important role in determining commercial success and popularity. One characteristic that might impact viewers’ perceptions is running time. Longer films can allow for more extensive storytelling and character development; however, they may also demand higher mental effort and time commitment from the audience. As a consequence, the relationship between running time and audience ratings remains unclear and worth investigating.
Previous research in media and entertainment studies suggests that structural movie characteristics can influence audience evaluations and satisfaction (Cini & Abela, 2025).
Furthermore, audience expectations may differ between movie genres. For instance, viewers might expect longer runtimes in genres such as drama, whereas faster-paced runtimes may be expected in low cognitive effort genres such as comedies. Genre-based expectation theories propose that consumers develop category-specific expectations that influence their evaluations (Jeffres, Atkin, & Neuendorf, 2023).
In addition, theories related to cognitive load and information processing suggest that increased exposure time may influence judgments due to differences in mental effort.
Based on these notions, the main research question that will be examined is described as:
- What is the relationship between running time (IV) and audience ratings (DV) in movies, and how does this differ between movie genres?
To examine the research question, two publicly available datasets from the IMBD website were utilized and later merged.
The data sets used:
After merging, the final dataset contains approximately 140.000 observations after filtering for movies, their running time (<=360 minutes) and number of votes (>100), where missing values for genre's and audience ratings were filtered out.
- Include a table of variable description/operstionalisation.
The main variables that will be examined:
• Movie running time (IV) - primary runtime of the title, in minutes
• Audience rating (DV) - weighted average of all the individual user ratings
• Genre (Categorical Moderator) - includes up to three genres associated with the title
For data preparation, datawrangling principles were administered. Firstly the dataset title.basics.tsv.gz was filtered to only include movie titles, thereby excluding other included film-types in the dataset such as tv-shows and short-films. Next, the dataset title.ratings.tsv.gz was merged. This merged dataset was structured in ascending order of release year.
Moreover, next to flitering for missing values for genre and audience scores, the observations were filtered to have a running time of up to 360 minutes and a minimum number of 100 recorded audience votes.
As for the method of analysis, Multiple Linear Regression (MLR) with interaction terms will be used. MLR is an appropriate analysis method for a continuous IV and DV, allowing examination of interaction terms for movie genre as a moderator.
-
Describe the gist of your findings (save the details for the final paper!) ...
-
How are the findings/end product of the project deployed? ...
-
Explain the relevance of these findings/product. ...
team-projects-team-4/
│
├── data/ # Data-related scripts and raw data handling
│ └── download-data.R # Script to download and store raw datasets
│
├── reporting/ # Final report and application files
│ ├── report.Rmd # Main analysis report
│ └── start_app.R # Possible ption to run the report using a Shiny app
│
├── src/ # Source code
│ ├── analysis/ # Statistical modeling and analysis scripts
│ │ └── analysis.R # Main analysis script
│ │
│ └── data-preparation/ # Data cleaning and preparation scripts
│ └── data-cleaning.R # Data cleaning script
│
├── README.md # Project documentation
├── README.html # Rendered version of README
├── makefile # Workflow automation
├── .gitignore # Files excluded from version control
└── team-projects-team-4.Rproj # RStudio project file
To recreate and run this repository's workflow, a recent version of R Studio is necessary.
The main packages needed to run this workflow in R Studio:
- R Package: readr - used to read selected datasets
- R Package: tidyverse - used for data wrangling and analysis
To load these packages in R:
# Load libraries
library(readr)
library(tidyverse)
Provide step-by-step instructions that have to be followed to run this workflow.
To run this repository's workflow, you first need to clone the GitHub repository.
Using Command Prompt on Windows/ Terminal on Mac, clone this repository:
Git clone https://github.com/course-dprep/Team4_MovieLength_AudienceScore.git
After cloning the repository, the necessary folders will be copied to your pc/laptop.
Next, after opening the data folder, you will run into an R script file named "download-data.R". The first lines of code will allow you to create a new folder to store the raw datasets, followed by code to then download the datasets:
# Create raw data folder
dir.create("data/raw", showWarnings = FALSE, recursive = TRUE)
# Download data files
download.file("https://datasets.imdbws.com/title.basics.tsv.gz","data/raw/title.basics.tsv.gz")
download.file("https://datasets.imdbws.com/title.ratings.tsv.gz","data/raw/title.ratings.tsv.gz")
Finally, the appropriate packages need to be loaded and the datafiles need to be read in R Studio.
# Load libraries
library(readr)
library(tidyverse)
# Read data
title.basics <- read_tsv("data/raw/title.basics.tsv.gz", na = "\\N")
title.ratings <- read_tsv("data/raw/title.ratings.tsv.gz", na = "\\N")
After following these three steps, any interested people should be able to recreate, or further analyse the IMDB datasets appropriately.
This project is set up as part of the Master's course Data Preparation & Workflow Management at the Department of Marketing, Tilburg University, the Netherlands.
The project is implemented by team 4, members: <Dalyl Lachkar, ...>