Biostatistical Computing, PHC 6068

Overview

Zhiguang Huo (Caleb)

Monday August 23, 2021

Basic description of this class

Lecturer: Zhiguang Huo (Caleb) zhuo@ufl.edu
TAs: Runzhi Zhang runzhi.zhang@ufl.edu
Deliver format: HyFlex (Stduents could choose one of these options)
- On campus: Room: HPNP G-301A
- Online synchronous: Check out canvas website https://elearning.ufl.edu/ for the Zoom link
- Online asynchronous: All lectures will be recorded, and the recorded videos will be made available
Lecture times:
- Monday: 12:50-2:45pm
- Wednesday: 1:55-2:45pm
Virtual office hours (https://ufl.zoom.us/j/94451437002):
- Caleb: Monday 3:00-4:30pm
- Runzhi: Wednesday 3:00-4:30pm
Course Website:

https://caleb-huo.github.io/teaching/2021FALL/biostatisticalComputing.html

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

More description of this class

Major targeted audience:
- First/Second year PhD students in Biostatistics department.
- Second year Master’s students in Biostatistics department.
No programming knowledge assumed
Some statistics knowledge assumed
- PHC 6092: Biostatistical Theory
- PHC 6050: Statistical Methods for Health Sciences Research I
- PHC 6051: Biostatistical Methods II
Focus almost entirely on R
- R usage
- Many statistical model applications
- Some algorithm implementations
Class will be cumulative, so keep up with the material and assignments!

Contents of this class

See syllabus https://caleb-huo.github.io/teaching/2021FALL/PHC6068_2021_Biostatistical_Computing_Syllabus.pdf
Lecture notes https://caleb-huo.github.io/teaching/2021FALL/biostatisticalComputing.html
- Former part: how to use R.
- Later part: how to perform statistical modeling using R.
Homework assignment, Exams, Final projects and grades will be available on elearning

Textbook

No textbook is required
If you never used R before
- Warm up here: https://www.datacamp.com/courses/free-introduction-to-r
Recommanded reading:
- Google’s R Style Guide. https://google.github.io/styleguide/Rguide.xml
- 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/

Assignment

grading (total 100%)
Requirement	points
Homework1	5%
Homework2	5%
Mid-term exam	20%
Homework3	5%
Homework4	5%
Homework5	5%
Homework6	5%
Final project	20%
Final exam	30%

Homework policy

Format: will be released/submitted in R markdown.
- Will introduce later in today’s class.
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.

Trouble shooting

You may ask questions during in class or zoom chat.
You may ask questions during the virtual office hours (https://ufl.zoom.us/j/94451437002):
- Caleb: Monday 3:00-4:30pm
- Runzhi: Wednesday 3:00-4:30pm
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 and the TA will also frequently check the canvas discussions.

Exams

Mid-term exam:
- 1 week take-home exam.
Final exam:
- 1 week take-home exam.
Format:
- will be released/submitted in R markdown.
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.

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 Sep 27.
Can work alone, or work in group with less than 3.

Any question about administration?

What software do we need?

R is a programming language for statistical computing
R Studio is an integrated development environment for R programming
R Markdown is a markup language for combining R code with text

All 3 are free, and all 3 will be used extensively in this course

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/Windows.
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 Console

When you first open R, you will see the R window. The console is the fundamental way for interacting with R.
In the console is a prompt, > this is where an expression, phrase of code that can be executed, is inputed into R (if entering lengthy expressions that require more than one line the prompt changes from > to +.
An expression is typed into the console at the prompt, press Enter, and the expression is evaluated.
Depending on the expression, the system may respond by outputting results to the console, creating a graph in a new window or performing some other task. Then another expression can be inputed and evaluated.
An R session is this interaction between you and the system.
Often included with outputted results in the console are numbers in [ ]. These numbers are used to reference locations in the output.

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 later today

HiPerGator

HiPerGator is high performance computing for UF.
We will use HiPerGator for jobs with heavy computation/large memory.
You have to agree with following 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.

Advantages of R

It’s free, open source, and available on every major platform.
A massive set of packges for statistical modeling, machine learning, visualization and importing and manipulating data.
Cutting edge tools.
A fantastic community.
Powerful tools for communicating your results. (html, pdf, interactive website).
An IDE (RStudio).
Connect to high-performance programming languages (C, C++, Java)
When you published a statistical methodology paper, often you will be asked to publish a R package as well.

Some drawbacks of R

R is not a particularly fast programming language, and poorly written R code can be terribly slow. R also uses lots of memory.
R is slow in loop (e.g. for, while) so try to avoid loop in R 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 and R Studio on your laptop
Get comfortable with R Studio, knitting Rmd documents into HTML files
Get comfortable with navigating R help files
Get comfortable with installing R packages
- below is an example how to install a R package
```
install.package("mclust")
```