This page hosts learning materials for USDA ARS SEA statistics and data science trainings created by SEA area statistician Quentin Read. Please contact me if you have issues with accessibility of any of the content on these pages, or if you would like the source code of any of these materials.
For scientists interested in contacting me for a consultation, check out the frequently asked questions page!
To see a list of my favorite stats and R learning resources, check out the learning resources page.
For USDA personnel only: to access video recordings of previous training sessions, visit the SEA Statistics Training page on USDA Axon+ intranet.
Instructions for installing R, RStudio, and R packages
Follow these instructions before starting any of the lessons if you do not have R and RStudio installed on your laptop.
Instructions for setting up a Posit Cloud account
Follow these instructions if you are participating in a workshop using Posit Cloud to run R on a cloud server.
Two 90-minute lessons on basic R programming for complete beginners (Lessons 1 and 2 in a six-lesson series).
Taught in hybrid format in Raleigh, NC, December 8-9, 2022, and in person in Fayetteville, AR, January 9, 2023; Booneville, AR, January 12, 2023; Stoneville, MS, June 14, 2023; New Orleans, LA, March 4, 2024.
Full-text version | Slides | Worksheets | Answers to Exercises |
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Lesson 1: R Boot Camp: the very basics | Slides | Worksheet | Answers |
Lesson 2: R Boot Camp: working with data frames | Slides | Worksheet | Answers |
Video recordings of R Boot Camp lessons (USDA Axon) (listed as lessons 1 and 2 under “A practical toolkit for mixed models in R”)
GitHub repository with source code of course materials (USDA internal)
Four 90-minute lessons on fitting simple linear mixed models in R (Lessons 3-6 in a six-lesson series).
Taught in hybrid format in Raleigh, NC, December 8-9, 2022.
Full-text version | Slides | Worksheets | Answers to Exercises |
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Lesson 3: From linear model to linear mixed model | Slides | Worksheet | Answers |
Lesson 4: Going further with mixed models | Slides | Worksheet | Answers |
Lesson 5: Generalized linear mixed models | Slides | Worksheet | Answers |
Lesson 6: Estimating and comparing treatment means | Slides | Worksheet | Answers |
90-minute session introducing students to visualizing data with R and ggplot2.
Taught in Fayetteville, AR, January 10, 2023; Athens, GA, February 8, 2023; Raleigh, NC, March 22, 2024.
Three three-hour sessions introducing students to Bayesian inference, and showing how to fit Bayesian models with brms. Lesson 1 gives a conceptual intro to Bayesian inference, and demonstrates how to fit a Bayesian mixed model and use it to make predictions and test hypotheses. Lesson 2 introduces generalized linear mixed models, and Lesson 3 introduces residual covariance structures and generalized additive mixed models. Throughout, there is a focus on practical skills: producing figures, tables, and verbal descriptions that you could put in a publication or presentation.
Lesson 1: Older versions taught in Fayetteville, AR, January 10, 2023; Athens, GA, February 9, 2023; and Stoneville, MS, June 16, 2023. Updated version taught in hybrid format in Raleigh, NC, December 8, 2023.
Full-text version | Slides | Worksheets | Answers to Exercises |
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Lesson 1: Introduction to Bayes | Slides | Worksheet | Answers |
Lesson 2: Bayesian GLMMs | Slides | Worksheet | Answers |
Lesson 3: Bayesian space and time models | Slides | Worksheet | Answers |
Video recordings of Bayesian mixed models lessons (USDA Axon)
GitHub repository with source code of course materials and older versions (USDA internal)
Three 90-minute lessons showing basic data processing and model fitting procedures in R and SAS side-by-side.
Lesson 1 taught in Athens, GA, February 7, 2023. Lessons 1 and 2 taught in Stoneville, MS, June 14-15, 2023. Lessons 2 and 3 taught in New Orleans, LA, March 6, 2024.
Full-text version | Slides | R Code Worksheet | SAS Code from Lesson | Answers to Exercises |
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Lesson 1: Basic R for SAS users | Slides | Worksheet | Lesson SAS code file | Answers |
Lesson 2: Intro to Mixed Models in R for SAS users | Slides | Worksheet | Lesson SAS code file | Answers |
Lesson 3: My first GLMMs in R for SAS users | Slides | Worksheet | Lesson SAS code file | Answers |
Video recordings of Bayesian mixed models lessons (USDA Axon) (Lessons 2-3 not yet available)
GitHub repository with source code of course materials (USDA internal)
90-minute session beginning with an introduction explaining what machine learning is and what it can do, in simple terms. There are walkthroughs of two machine learning models: one for a classification task (random forest) and one for a regression task (lasso). Code is in R.
Video recordings of Machine learning demystified lesson not yet available
Three-hour workshop on analyzing omics data consisting of introductory slides and three code demos showing two applications of multi-omics factor analysis (MOFA) and one of sparse partial least squares discriminant analysis (sPLS-DA). These course materials were adapted from the ELIXIR Omics Integration and Systems Biology course developed by the National Bioinformatics Infrastructure, Sweden.
Taught in Little Rock, AR, January 9, 2024; New Orleans, LA, March 5, 2024; Mayagüez, PR, June 12, 2024.
I have given talks on a wide range of topics in stats and data science to different scientific audiences. I’ve provided the lecture slides as PDF or HTML files here. Video recordings of some of these talks are available on the Axon+ page (USDA personnel only).
Talk (PDF slides) | Date | Venue |
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(Un)ethical practices in biostatistics | February 28, 2023 | NCSU Biotechnology guest lecture, Raleigh, NC |
Power analysis: bureaucratic busywork or critical part of the scientific method? | July 25, 2023 | IACUC workshop, Athens, GA (virtual) |
March 7, 2024 | HBBGPR, Baton Rouge, LA | |
Structural equation modeling in food science | August 1, 2023 | FSMQHRU unit meeting, Raleigh, NC |
Statistical interactions: what are they and what do they mean, anyway? | January 8, 2024 | MMRU & Arkansas Children’s Nutrition Center, Little Rock, AR |
March 4, 2024 | SRRC, New Orleans, LA | |
Everything you ever wanted to know about means comparisons but were afraid to ask | January 10, 2024 | DBNRRC, Stuttgart, AR |
March 5, 2024 | SRRC, New Orleans, LA | |
Troubleshooting common errors and warnings in (G)L(M)Ms | January 11, 2024 | DBNRRC, Stuttgart, AR |
March 7, 2024 | HBBGPR, Baton Rouge, LA | |
Bayesian p-values!?!? | January 18, 2024 | USDA ARS statisticians’ monthly meeting |
A smorgasbord of options for multiomics data analysis | January 9, 2024 | MMRU & Arkansas Children’s Nutrition Center, Little Rock, AR |
March 5, 2024 | SRRC, New Orleans, LA | |
Analyzing ordered categorical phenotypes: challenges and pitfalls | March 22, 2024 | SIBS workshop, Raleigh, NC |
June 12, 2024 | TARS, Mayagüez, PR | |
Doing more with less: using prior knowledge and creative experimental design to get more results from fewer animals | July 18, 2024 | IACUC workshop, Stuttgart, AR (virtual) |
These are the homepages for stats trainings I have held in the past in case anyone needs to refer to them. They include the syllabi for the workshops, with links to the text and slides versions of the lessons I taught, as well as the worksheets and example datasets.
This page last updated by QDR on 2024-09-17. This page’s contents were created by the author and do not reflect official USDA policy.