Chapter 9 Guest Lecture and/or Examples

9.1 Guest Lecture by Zheng Nan

9.1.1 Exploring the bitcoin market efficiency using R

Data, methodology, and interpretations

1. Opening: Challenges in data analysis

   • Collecting data, defining data, and preprocessing data.
   • Methodology and models
   • Interpretations of the results

2. Background

   • Bitcoin
   • Efficient Market Hypothesis (EMH)
   • Random Walk Hypothesis
     • Augmented Dickey-Fuller (ADF) test
   • Bitcoin exchange (BX) rate
   • Methodologies

3. Practice - R Script and Data are in Moodle

4. Results

9.2 Topics of EDA

9.2.1 Contents

• Financial Data

  • Sites and Packages: Quandl, quanmod
  • Stock Market and Crypt Currency

• Model Analysis

  • Linear Regression Review
  • Stratification and Confounder
  • R squared and p-value
  • About Sixth Assignment - in Moodle

• Presentation on 2021-02-17 and 2021-02-24

• The Eighth Assignment

9.2.2 Financial Data

9.2.2.1 Quandl

• Quandl
• R package: https://cran.r-project.org/web/packages/Quandl/Quandl.pdf
• Quandl: Bitcoin Market Price USD

9.2.2.2 Quantmod

• quantmod: Quantitative Financial Modelling & Trading Framework for R
• R package `quantmod: https://cran.r-project.org/web/packages/quantmod/quantmod.pdf
• quantmod R documentation
• Yahoo Finance Data Using quantmod
• Reference: CryptCurrency Bitcoin Analysis Using quantmod

9.2.3 Your Course Project, Part I

All documents must contain ‘ID’, ‘Name’, ‘Date’ of submission

1. ‘Short Paper’ (that can be much longer than the Paper

• Due: 2021-02-09 for Interim Report (and 2021-03-03: supporting doc for Paper)
• Contents:
  1. Objective: What and Why
  2. Data
  3. Reproducible Exploratory Data Analysis with Explanations
  4. Questions based on your findings and technical quesitons
• Format: R Notebook (*.nb.html)

2. Presentation: 10 minutes (5-7 min. presentation and 3-5 min. QA)

• On 2021-02-17 [or 2021-02-24, a reserve]
• With a digital file (.nb.html, html, pdf, word, ppt, … ) by file share
• Note: Be ready to show your codes by R Notebook or R Scripts, when requested

9.2.4 Your Course Project, Part II

3. Paper: 5 to 10 pages

• Due: 2021-03-03
• Contents: Exploratory Data Analysis Using Public Data
  1. Introduction - include what and why
  2. Description of Data
  3. Exposition of Your Exploration with Visualization of Data
  4. Concluding Remarks
  5. References, if any
  6. Acknowledgements, if any (can give a credit to your classmate)
• Note:
  • Give logical explanations of your observations using data tables and charts
  • No need to include the whole process
  • Include codes only when necessary
• Format: pdf. (Rmd > pdf, Rmd > MS Word > pdf, Rmd > MS Word > Google Doc > pdf)

9.2.5 Presentation Format

9.2.5.1 10 minutes (5-7 min. presentation and 3-5 min. QA)

• On 2021-02-17 and 2021-02-24
• Revised: 15 minutes (7-10 min. presentation and 5-8 min. QA)
• With a digital file (.nb.html, html, pdf, word, ppt, … ) by file share
• Note: Be ready to show your codes by R Notebook or R Scripts, when requested

9.2.6 The Eighth Assignment (in Moodle)

A. Give your feedback to your classmates’ posts on the Forum, Seventh Assignment as [Reply] to keep in a thread.

1. Comments on the project?
2. Write your questions related to the topic?

B. Add explanations or responses to your topic.

1. Add questions to investigate.
2. Share the difficulties you are facing.
3. Respond to the comments of your classmates.

C. Option: Share the link(s) to your R Notebook(s) in RStudio.cloud

• Submit your response to Moodle (The Seventh Assignment) by 2021-02-16 23:59:00

9.2.7 Learning Resources, VIII

• R for Data Science, Part III Wrangle - Tidy and Relational
• R for Data Science, Part IV Model

9.2.7.1 RStudio Primers: See References in Moodle at the bottom

1. The Basics – r4ds: Explore, I
2. Work with Data – r4ds: Wrangle, I
3. Visualize Data – r4ds: Explore, II

• Exploratory Data Analysis, Bar Charts, Histograms
• Boxplots and Counts, Scatterplots, Line Plots
• Overplotting and Big Data, Customize Your Plots

4. Tidy Your Data – r4ds: Wrangle, II

• Reshape Data, Separate and Unite Columns, Join Data Sets

9.3 Part II. 2022-02-16 Presentation

Presentation

10 minutes (5-7 min. presentation and 3-5 min. QA)

• On 2021-02-17 and 2021-02-24
• Revised: 15 minutes (7-10 min. presentation and 5-8 min. QA)
• With a digital file (.nb.html, html, pdf, word, ppt, … ) by file share
• Note: Be ready to show your codes by R Notebook or R Scripts, when requested

Share Your Exploratory Data Analysis Experience

Presentation Example:

• Motivation and Questions: What and Why
• About the Data
• Findings
• Problems and Difficulties
• What Remains to be Done; Questions and/or Analysis

9.3.1 The Ninth Assignment (in Moodle)

Write your feedback to each of your classmates’ presentation on the Forum in Moodle.

• First, include the name of the presenter and/or the title of the presentation on the topic of your post

• Write on the following

  1. Comments on the presentation,
  2. Suggestions on the project, or
  3. Technical or general questions related to the presentation

• You can write comments on your own presentation, and/or responses to comments.

• Submit your response to Moodle (The Ninth Assignment) by 2021-02-23 23:59:00

9.3.2 Learning Resources, IX

• R for Data Science, Part III Wrangle
• R for Data Science, Part VI Communicate
• Data analysis write-ups
• Structure of a Data Analysis Report

9.3.2.1 RStudio Primers: See References in Moodle at the bottom

1. The Basics – r4ds: Explore, I
2. Work with Data – r4ds: Wrangle, I
3. Visualize Data – r4ds: Explore, II

• Exploratory Data Analysis, Bar Charts, Histograms
• Boxplots and Counts, Scatterplots, Line Plots
• Overplotting and Big Data, Customize Your Plots

4. Tidy Your Data – r4ds: Wrangle, II

• Reshape Data, Separate and Unite Columns, Join Data Sets