ASMS 2021 R Short Course: Getting Started with R for Mass Spectrometry Data Analysis

Course Logistics

Date: October 30 - 31, 2021

Time: 9AM -- 5PM

Location: ASMS 2021 Annual Conference, Philadelphia (see conference program for location)

Course Description

This course is designed to help participants develop a solid foundation in the basics of the R programming language and understand how it can be used to perform the types of data analysis tasks scientists frequently encounter in their daily work. While the focus of the course will be on learning practical fundamentals of R for data analysis in general, mass spectrometry specific examples will be used during the course, making it well suited for ASMS members interested in adding data analysis skills to their scientific toolbelt. The main goal of the course is to help scientists, who are new to R, and perhaps coding in general, get to the point where they can begin to perform basic data analysis tasks and produce data visualizations on their own, as well as to provide the basis for further study on the road to R fluency.

The course will focus on three main topics:

1. Introduction to R and RStudio: A high-level and practical introduction to R, covering the essential fundamentals that one needs to start doing basic data analysis tasks.
2. Working with tidy Data: An introduction to the tidyverse ecosystem of R packages and how they can be used to facilitate efficient and organized data analyses. In particular, dplyr package will be covered and how it can be used to perform powerful data analysis tasks.
3. Visualization with ggplot2: An introduction to the ggplot2 data visualization package, with an emphasis on practical fundamentals, so participants can start making basic plots and visualizations from their own data.

Please note that this course will not focus on the details of specific analysis and statistical methods, or how they can be applied to mass spectrometry data. Rather, the focus of the course is on helping new R users learn the essential fundamentals so they can start using R in their daily work. While one cannot expect to master R in one day, this course will help participants get over some of the initial hurdles that new R users often face. Additional learning resources and recommendations will also be provided at the end of the course to help participants continue their study of R.

Prerequisites

All participants will need a laptop in order to view the online course materials and perform the example exercises. Instruction will be provided at the beginning to assist with software set-up.

Participants should have some experience performing basic data analysis tasks, for example, using Excel and/or MS vendor software to process and review data.

While previous programming experience can be useful for learning R, it is not specifically required for this course since the focus will be on the use of R for data analysis as opposed to teaching participants how to program specifically. Those new to R, and programming in general, are welcome!

Instructors

Ryan Benz, Seer, Inc.

Jeffrey Jones, CalTech

N. Heath Patterson, Vanderbilt University, Frontier Diagnostics

Course Schedule

Saturday

Start Time	End Time	Duration	Session
9:00 AM	9:15 AM	15 min	Short course introduction
9:15 AM	10:15 AM	1 hr	Getting Set-up
10:15 AM	10:45 AM	30 min	Coffee Break
10:45 AM	11:30 AM	45 min	Introduction to R and R Studio
11:30 AM	12:00 PM	30 min	Practice session: RStudio Projects
12:00 PM	1:00 PM	1 hr	Lunch
1:00 PM	1:45 PM	45 min	Basic R Data Structures
1:45 PM	2:15 PM	30 min	Reading Data into R
2:15 PM	2:30 PM	15 min	Practice session: Reading & Working with Data
2:30 PM	3:00 PM	30 min	Coffee Break
3:00 PM	3:30 PM	30 min	Useful R Functions
3:30 PM	4:00 PM	30 min	Practice session: Working with R Functions
4:00 PM	5:00 PM	1 hr	Practice session: MS Data Exercises

Sunday

Start Time	End Time	Duration	Session
9:00 AM	9:30 AM	30 min	Recap, Day 2 Overview, Review of MS Data Exercises
9:30 AM	10:15 AM	45 min	Introduction to Tidy Data & the Tidyverse
10:15 AM	10:45 AM	30 min	Coffee Break
10:45 AM	11:30 AM	45 min	Data Manipulation with dplyr
11:30 AM	12:00 PM	30 min	Practice session: tidyr & dplyr examples
12:00 PM	1:00 PM	1 hr	Lunch
1:00 PM	1:15 PM	15 min	Practice session: tidyr & dplyr examples solutions
1:15 PM	2:00 PM	45 min	Visualization Basics with GGplot2
2:00 PM	2:30 PM	30 min	GGplot2 Extended Syntax
2:30 PM	3:00 PM	30 min	Coffee Break
3:00 PM	3:30 PM	30 min	Practice session: GGplot2 examples
3:30 PM	4:00 PM	30 min	Wrap-up & Next Steps
4:00 PM	5:00 PM	1 hr	General Questions, Office Hours