Introduction to Biostatistical Computing PHC 6937

Overview

Zhiguang Huo (Caleb)

Monday August 24, 2022

Basic description of this class

Lecturer: Zhiguang Huo (Caleb) zhuo@ufl.edu
TAs: None
Deliver format:
- On campus: Room: HPNP G210
Lecture times:
- Monday: 12:50-2:45pm
- Wednesday: 1:55-2:45pm
Office hours
- Caleb: Monday 3:00-4:30pm
Course Website:

https://caleb-huo.github.io/teaching/2022FALL/biostatisticalComputing.html

elearning: used to collect submissions, and keep track of grades

More description of this class

Will learning programming languages in the context of data science
- R
- Python
No prior programming knowledge assumed
Some statistics knowledge is preferred
- An introductory course in statistics that covers statistical inference, such as PHC6052 Introduction to Biostatistical Methods
Class will be cumulative, so keep up with the material and assignments!

Contents of this class

Lecture notes https://caleb-huo.github.io/teaching/2022FALL/biostatisticalComputing.html
- Former part: how to use R.
- Later part: how to use Python.
See syllabus https://caleb-huo.github.io/teaching/2022FALL/PHC6068_2022_Intro_Biostatistical_Computing_Syllabus.pdf
Homework assignment, Exams, Final projects and grades will be available on elearning

Textbook

No textbook is required
Recommanded reading:
- R
  - Hadley Wickham (2014). Advanced R. Taylor & Francis. http://adv-r.had.co.nz
  - Garrett Grolemund and Hadley Wickham (2016). R for Data Science. O’Reilly Media, Inc., https://r4ds.had.co.nz/
- Python
  - Wes McKinney (2012). Python for Data Analysis, https://www.oreilly.com/library/view/python-for- data/9781449323592/
  - Jake VanderPlas (2016). Python Data Science Handbook. https://jakevdp.github.io/PythonDataScienceHandbook/

Assignment

grading (total 100%)
Requirement	points
Homework1	5%
Homework2	5%
Mid-term exam	25%
Homework3	5%
Homework4	5%
Homework5	5%
Final project	25%
Final exam	25%

Exams

Mid-term exam:
- Take-home exam (1 week) on R programming.
Final exam:
- Take-home exam (1 week) on Python programming.
Format:
- Will be released/submitted in R markdown/Jupyter.
Due time:
- See course website, no late exam submissions.
Can I work with other people?
- No, you are not allowed to discuss with other people since this is exam.

Homework policy

Format: will be released/submitted in markdown format.
- R markdown for R programming.
- Jupyter for Python programming.
Due time:
- Two weeks after the homework is released, usually due on Monday 11:59 pm.
Late homework:
- 80% credit within one day late;
- 50% credit within two day late;
- 0% after two day’s late.
Grace period: totally 2 days.
- Can be applied to any homework (not exam or final project).
- Each time at least one day of grace period will be used.
Where to submit: electronically via elearning.
Can I work with other people?
- Yes. You are allowed to discuss how to do the Homework with your classmates.
- No. But you have to write your own solution.

Final project

Wrap up a R package/R Shiny app
- Complete documentation of all your functions.
- Apply R package to real data or synthetic data.
- Report using R markdown, include how to use the package.
- Pass all warning and error checks.
How to make an outstanding R package?
- Calling c++
- Make nice vignette
- A comprehensive package
- Host the package on GitHub
Instructions: will be released on Monday Oct 3rd.
Can work alone, or work in group with less than 3.

Trouble shooting

You may ask questions during in class.
You may ask questions during office hours
- Caleb: Monday 3:00-4:30pm, CTRB 5230
You may ask/answer questions in the canvas discussion section.
- If you have a question, other students are likely to have similar questions, so posting your question in the canvas discussion section will benefit other students.
- The instructor will also frequently check the canvas discussions. And please email the instructor if you didn’t get response within one day.
- The instructor will not answer questions by email, canvas discussion is preferred.

Other Policy

Attendance
- Attendance is encouraged but not required.
Recorded lecture video
- The lecture will be recorded via zoom.
- The recorded videos are available upon reasonable request (sick)
- Should only be used to review the lecture by yourself.
- Should not be distributed for any other purpose

Any question about administration?

What software do we need?

R is a programming language for statistical computing
- R Studio is an integrated development environment (IDE) for R programming
- R Markdown is a markup language for combining R code with text
Python is widely used for data science and machine learning
- Anaconda is an integrated development environment (IDE) for Python programming
- Jupyter is a markup language for combining Python code with text

All of these are free

Installing R

R can be downloaded from the ``Comprehensive R Archive Network" (CRAN).
R can be installed on Windows, Mac or Linux operating systems.
In this course, the instructors will run R on Mac.
To install R, visit the R website, http://www.r-project.org and follow the installation directions (see the FAQ section for more details).
R version 4.0 and above are required for this class.

R Console

open R console

RStudio

RStudio is a free and open source integrated development environment (IDE) designed specifically for R. http://www.rstudio.com
Runs on, Windows, Mac or Linux operating systems.
Useful productivity tools：
- Syntax highlighting, smart indention, parentheses matching
- Easily execute code from the source editor
- Integrates all of the tools (source editor, console, plots, documentation, and more) when working with R into a single environment
- Extensive tools for developing R packages
- Interactive debugging for diagnosing and fixing errors

R markdown

Will be introduced today if there are any remaining time
Otherwise will be introduced next Monday

Install Python

Python can be installed on Windows, Mac or Linux operating systems.
In this course, the instructors will run Python on Mac.
To install Python, visit the Python website, https://www.python.org/downloads/release/python-3106/.
Python version 3.8 and above are required for this class.

Python Console

open Python console via command line:
- MAC user:
  - Open Terminal
  - just typing python, python3, python3.8, or python3.9
- Windows user:
  - open the Start Menu via clicking the Start Button
  - Click Windows System, then select Command Prompt with the black icon.
  - just typing python, python3, python3.8, or python3.9

Anaconda

Anaconda is a free and open source integrated development environment (IDE) for python. https://www.anaconda.com
Runs on, Windows, Mac or Linux operating systems.
Useful productivity tools：
- Integrate many popular data science tools including Jupyter, PyCharm
- Allow creating user level environment independent of the operating system or admin libraries

Comparison of R and Python

Commonality:
- both are open source languages, free to use for everyone
- both are well suited for data science
  - data clearning
  - data manipulation
  - data visualization
Difference:
- R is rooted in statistical analysis.
- Python is general-purpose programming language, more widely used in machine learning and deep learning

Some drawbacks of R and Python

R and Python are slow in loop (e.g. for, while), so try to avoid loops if you run a large program and care about speed.
- Solution: vectorize your program.

Things you should have done, by the end of this week

Install R (>4.0) and R Studio on your laptop
Install Python (>3.8) and Anaconda on your laptop

HiPerGator

HiPerGator is high performance computing for UF.
High performance computing is widely used in both academy and industry
Will be introduced in later lectures.
Acceptable Use Policy:
- ACCEPTABLE USE I acknowledge that the access to the HPC resources operated by UF Research Computing is subject to the UF Acceptable Use Policy at https://it.ufl.edu/policies/acceptable-use/acceptable-use-policy/ and the Research Computing policies at http://www.rc.ufl.edu/services/procedures/ and that I am responsible for following these policies.
- RESTRICTED DATA I also certify that using restricted data and software on the HPC resources requires extra steps described at UFRC Policies and at UFRC Export Policies, and that I will notify both my account sponsor and the Office of Research (Research Compliance) and Research Computing at support.rc.ufl.edu when I am working with such data.