M02 - In-depth Literate Programming

Author

Andrew Mao

Published

Invalid Date

1 # Overview

Quarto is an open-source scientific and technical publishing system that extends the capabilities of R Markdown and integrates seamlessly with Python, Julia, and JavaScript.

## Key Features of Quarto

- Supports multiple output formats (HTML, PDF, Word, slides).

- Works across R, Python, Julia, and Observable.

- Allows for interactive elements such as code folding, tables, and cross-referencing.

- Enhances reproducibility with YAML metadata and version control.

- Includes built-in support for dashboards and books.

# Summary of Instructor’s Lecture on Quarto

## Key Takeaways

Quarto’s Integration: Unlike R Markdown, Quarto natively supports multiple languages.

Document Publishing: Enables single-source publishing, allowing users to create reports, websites, books, and presentations from one file.

Cross-referencing: Figures, tables, and equations can be referenced dynamically.

HTML Widgets and Interactive Features: Supports interactive tables, collapsible sections, and embedded JavaScript.

Quarto vs. R Markdown

r # Quarto Code Chunk (YAML-based settings){r} summary(mtcars)

r # Equivalent R Markdown Code Chunk

{r echo=FALSE}

summary(mtcars)

:::

2 Summary of Posit Video on Quarto’s Capabilities

2.1 Quarto as a Universal Publishing Tool

The video from Posit (formerly RStudio) highlighted how Quarto is not just for R users but a universal publishing tool for data scientists and technical writers.

2.2 Key Features Highlighted

Supports R, Python, Julia, and JavaScript.
Built-in support for multi-document projects, including books and blogs.
Customizable templates and themes.
Advanced document structuring with fenced divs and callout blocks.
High extensibility with Lua filters and Pandoc enhancements.
Suitable for academic papers, technical reports, and websites.

Comparison: R Markdown vs. Quarto

Feature	R Markdown	Quarto
Language Support	R only	R, Python, Julia, JavaScript
Output Formats	HTML, PDF, Word	HTML, PDF, Word, Reveal.js, Dashboards
Interactive Elements	Limited	Extensive (JavaScript, Observable)
Cross-referencing	Limited	Dynamic
Publishing	Requires additional setup	Built-in (Quarto Pub, GitHub Pages)

3 Personal Reflection: Will I Use Quarto Daily?

Quarto’s ability to generate well-structured, cross-referenced reports makes it an ideal tool for academic and professional work.

Allows for structured lecture notes with embedded code outputs.
Facilitates the creation of presentation slides using Reveal.js.
Supports data science workflows by enabling reproducible reports hosted on GitHub Pages.

4 Formatting a Class Assignment in Quarto

4.1 Introduction

Streaming services have transformed the way people watch their favorite shows, movies, and sports through a Subscription Video on Demand (SVOD) model. The introduction of bundling subscriptions has gained prominence due to its convenience and perceived value. However, pricing these bundles poses challenges as consumer sensitivity and willingness to pay (WTP) vary across demographics and preferences. This study explores and compares pricing methodologies to identify optimal pricing for bundled streaming services.

4.2 Problem Statement

Netflix has faced public backlash over frequent price increases, which many subscribers feel are unjustified due to rising competition in the streaming industry.

Additionally, other platforms offer bundled services, such as:

Disney+, Hulu, and Max – a bundled package for a single fee.
Amazon Prime Video – offers add-ons like PBS Kids, AMC+, or other streaming services for an extra charge.

These bundles also vary in pricing depending on ad-supported vs. ad-free options.

Managerial Problem Statement

Streaming services struggle to find a balance between competitive pricing and profitability while addressing diverse consumer expectations.

5 Analytics Objectives

This project aims to analyze various pricing methodologies:

Contributor: Joshua Arias , Analyzes acceptable price ranges , thresholds, and psychological price points , Uses survey responses from Cal Poly Pomona students to determine , “Too expensive” and “too cheap” thresholds , Optimal price points and price sensitivity.

Contributor: Andrew Mao , Evaluates price sensitivity and its influence on consumer preferences , Factors analyzed: , Price, ad levels, and content quality , Feature trade-offs and customer segmentation , Competitor cross-price sensitivity.

Contributor: Andrea Aceves , Identifies price thresholds and likelihood of purchase , Cross-analyzes findings with Conjoint Analysis.

Contributor: Jonathan De La Torre , Compares expressed willingness to pay vs. actual price thresholds , Integrates findings across methodologies to refine pricing strategies.

6 Research Significance

This project addresses the need for data-driven pricing strategies in the highly competitive streaming industry.

6.1 Key Insights

Consumer Price Sensitivity
Understanding how consumers perceive pricing for bundled services.
Best Practices for Pricing Bundles
Strategies that maximize both consumer satisfaction and business profitability.
Practical & Academic Contributions
- Enhances understanding of pricing methodologies and data analysis.
- Provides insights for streaming services on pricing adjustments and market positioning.

7 Evaluation of Pricing Methodologies

7.1 Comparison of Van Westendorp, Gabor-Granger, and Conjoint Analysis

Methodology	Strengths	Limitations
Van Westendorp’s PSM	Identifies psychological price points	Subjective survey responses
Conjoint Analysis	Evaluates feature trade-offs and sensitivity	Requires well-structured survey design
Gabor-Granger	Determines actual purchase likelihood at set prices	May not account for competitor cross-effects

8 Concluding Thoughts

The findings from this study highlight the challenges and strategies involved in pricing bundled streaming services. By using a combination of Van Westendorp’s PSM, Conjoint Analysis, and Gabor-Granger Method, this research provides a comprehensive framework for analyzing consumer price sensitivity.

9 Publishing This Document to Quarto Pub

Render the document as HTML:
```
r
```
quarto render "M02-In-depth-Literate-Programming.qmd"

--- title: "M02 - In-depth Literate Programming" author: "Andrew Mao" date: "`r format(Sys.time())`" format: html: theme: cosmo toc: true toc-depth: 2 number-sections: true number-depth: 2 toc-expand: true self-contained: true code-fold: true code-tools: true code-overflow: wrap editor: visual execute: freeze: auto --- # \# Overview Quarto is an open-source scientific and technical publishing system that extends the capabilities of R Markdown and integrates seamlessly with Python, Julia, and JavaScript. \## Key Features of Quarto \- Supports multiple output formats (HTML, PDF, Word, slides). \- Works across R, Python, Julia, and Observable. \- Allows for interactive elements such as code folding, tables, and cross-referencing. \- Enhances reproducibility with YAML metadata and version control. \- Includes built-in support for dashboards and books. --- \# Summary of Instructor's Lecture on Quarto \## Key Takeaways Quarto’s Integration: Unlike R Markdown, Quarto natively supports multiple languages. Document Publishing: Enables single-source publishing, allowing users to create reports, websites, books, and presentations from one file. Cross-referencing: Figures, tables, and equations can be referenced dynamically. HTML Widgets and Interactive Features: Supports interactive tables, collapsible sections, and embedded JavaScript. Quarto vs. R Markdown r \# Quarto Code Chunk (YAML-based settings){r} summary(mtcars) r \# Equivalent R Markdown Code Chunk {r echo=FALSE} summary(mtcars) ::: # Summary of Posit Video on Quarto’s Capabilities ## Quarto as a Universal Publishing Tool The video from Posit (formerly RStudio) highlighted how Quarto is not just for R users but a universal publishing tool for data scientists and technical writers. ## Key Features Highlighted - **Supports R, Python, Julia, and JavaScript.** - **Built-in support** for multi-document projects, including books and blogs. - **Customizable templates and themes.** - **Advanced document structuring** with fenced divs and callout blocks. - **High extensibility** with Lua filters and Pandoc enhancements. - **Suitable for academic papers, technical reports, and websites.** ### Comparison: R Markdown vs. Quarto | Feature | R Markdown | Quarto | |----|----|----| | Language Support | R only | R, Python, Julia, JavaScript | | Output Formats | HTML, PDF, Word | HTML, PDF, Word, Reveal.js, Dashboards | | Interactive Elements | Limited | Extensive (JavaScript, Observable) | | Cross-referencing | Limited | Dynamic | | Publishing | Requires additional setup | Built-in (Quarto Pub, GitHub Pages) | ------------------------------------------------------------------------ # Personal Reflection: Will I Use Quarto Daily? Quarto's ability to generate well-structured, cross-referenced reports makes it an ideal tool for academic and professional work. - Allows for structured lecture notes with embedded code outputs. - Facilitates the creation of presentation slides using Reveal.js. - Supports data science workflows by enabling reproducible reports hosted on GitHub Pages. ------------------------------------------------------------------------ # Formatting a Class Assignment in Quarto ## Introduction Streaming services have transformed the way people watch their favorite shows, movies, and sports through a **Subscription Video on Demand (SVOD)** model. The introduction of bundling subscriptions has gained prominence due to its convenience and perceived value. However, pricing these bundles poses challenges as consumer sensitivity and willingness to pay (WTP) vary across demographics and preferences. This study explores and compares pricing methodologies to identify optimal pricing for bundled streaming services. ## Problem Statement Netflix has faced public backlash over frequent price increases, which many subscribers feel are unjustified due to rising competition in the streaming industry. Additionally, other platforms offer bundled services, such as: - **Disney+, Hulu, and Max** – a bundled package for a single fee. - **Amazon Prime Video** – offers add-ons like PBS Kids, AMC+, or other streaming services for an extra charge. These bundles also vary in pricing depending on **ad-supported vs. ad-free options**. ### Managerial Problem Statement Streaming services struggle to find a balance between competitive pricing and profitability while addressing diverse consumer expectations. ------------------------------------------------------------------------ # Analytics Objectives This project aims to analyze various pricing methodologies: ::: {.panel-tabset} ### Van Westendorp’s Price Sensitivity Meter Contributor: Joshua Arias , Analyzes acceptable price ranges , thresholds, and psychological price points , Uses survey responses from Cal Poly Pomona students to determine , “Too expensive” and “too cheap” thresholds , Optimal price points and price sensitivity. ### Conjoint Analysis Contributor: Andrew Mao , Evaluates price sensitivity and its influence on consumer preferences , Factors analyzed: , Price, ad levels, and content quality , Feature trade-offs and customer segmentation , Competitor cross-price sensitivity. ### Gabor-Granger Method Contributor: Andrea Aceves , Identifies price thresholds and likelihood of purchase , Cross-analyzes findings with Conjoint Analysis. ### Synthesis Across Methods Contributor: Jonathan De La Torre , Compares expressed willingness to pay vs. actual price thresholds , Integrates findings across methodologies to refine pricing strategies. ::: --- ------------------------------------------------------------------------ --- # Research Significance This project addresses the need for data-driven pricing strategies in the highly competitive streaming industry. ## Key Insights - **Consumer Price Sensitivity**\ Understanding how consumers perceive pricing for bundled services. - **Best Practices for Pricing Bundles**\ Strategies that maximize both consumer satisfaction and business profitability. - **Practical & Academic Contributions** - Enhances understanding of pricing methodologies and data analysis. - Provides insights for streaming services on pricing adjustments and market positioning. ------------------------------------------------------------------------ # Evaluation of Pricing Methodologies ## Comparison of Van Westendorp, Gabor-Granger, and Conjoint Analysis | Methodology | Strengths | Limitations | |----|----|----| | Van Westendorp’s PSM | Identifies psychological price points | Subjective survey responses | | Conjoint Analysis | Evaluates feature trade-offs and sensitivity | Requires well-structured survey design | | Gabor-Granger | Determines actual purchase likelihood at set prices | May not account for competitor cross-effects | ------------------------------------------------------------------------ # Concluding Thoughts The findings from this study highlight the challenges and strategies involved in pricing bundled streaming services. By using a combination of Van Westendorp’s PSM, Conjoint Analysis, and Gabor-Granger Method, this research provides a comprehensive framework for analyzing consumer price sensitivity. ------------------------------------------------------------------------ # Publishing This Document to Quarto Pub 1. Render the document as HTML: ``` r ``` `quarto render "M02-In-depth-Literate-Programming.qmd"`