SEAStats

Stats and data science learning resources for researchers in the USDA ARS Southeast Area

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USDA-REE-ARS/SEAStats

Contents:

Contents:

This project is maintained by USDA-REE-ARS

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usda-ree-ars.github.io/SEAStats

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.

R Installation instructions

Instructions for installing R, RStudio, and R packages

Follow these instructions before starting any of the lessons.

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.

R Boot Camp lesson logo

R Boot Camp

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; virtually for Little Rock AR, June 2, 2025.

Full-text version Slides Worksheets Answers to Exercises
Lesson 1: R Boot Camp: the very basics Slides Worksheet / Worksheet filled in Answers
Lesson 2: R Boot Camp: working with data frames Slides Worksheet / Worksheet filled in Answers

A practical toolkit for mixed models lesson logo

A practical toolkit for mixed models in R

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
Lesson 3: From linear model to linear mixed model Slides Worksheet / Worksheet filled in Answers
Lesson 4: Going further with mixed models Slides Worksheet / Worksheet filled in Answers
Lesson 5: Generalized linear mixed models Slides Worksheet / Worksheet filled in Answers
Lesson 6: Estimating and comparing treatment means Slides Worksheet / Worksheet filled in Answers

Data visualization basics with R and ggplot2 lesson logo

Data visualization basics with R and ggplot2

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.

A crash course in Bayesian mixed models with brms lesson logo

A crash course in Bayesian mixed models with brms

Five multi-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, Lesson 3 introduces residual covariance structures and generalized additive mixed models, and Lesson 4 expands your toolkit with models that can handle proportion data, count data with zeros, and positive continuous data with zeros. Lesson 5 introduces you to multinomial mixed models for ordinal data and to nonlinear 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.
Lessons 2-3: Taught in hybrid format in Raleigh, NC, November 21-22, 2024.
Lessons 4 and 5 are still draft versions and haven’t yet been taught.

Full-text version Slides Worksheets Answers to Exercises
Lesson 1: Introduction to Bayes Slides Worksheet / Worksheet filled in Answers
Lesson 2: Bayesian GLMMs Slides Worksheet / Worksheet filled in Answers
Lesson 3: Bayesian space and time models Slides Worksheet / Worksheet filled in Answers
Lesson 4: Beta, zero-inflated, and hurdle models Slides Worksheet / Worksheet filled in Answers
Lesson 5: Ordinal and nonlinear models Slides Worksheet / Worksheet filled in Answers

R for SAS users lesson logo

R for SAS Users

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 1-3 taught in Stuttgart, AR, January 10-11, 2024. Lessons 2 and 3 taught in New Orleans, LA, March 6, 2024. Lessons 1 and 2 taught in Florence, SC, December 5, 2024. All lessons taught virtually for Starkville/Oxford, MS, March 4, 2025.

Full-text version Slides R Code Worksheet SAS Code from Lesson Answers to Exercises
Lesson 1: Basic R for SAS users Slides Worksheet / Worksheet filled in Lesson SAS code file Answers
Lesson 2: Intro to Mixed Models in R for SAS users Slides Worksheet / Worksheet filled in Lesson SAS code file Answers
Lesson 3: My first GLMMs in R for SAS users Slides Worksheet / Worksheet filled in Lesson SAS code file Answers

Machine learning lesson logo

Two-hour 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.
Taught in Florence, SC, December 6, 2024. Taught virtually for Starkville/Oxford, MS, March 6, 2025, and Little Rock, AR, June 2, 2025.

Power analysis lesson logo

Power Analysis Tutorial

Two-hour workshop going over the basics of power analysis using R, with an introductory lecture followed by hands-on examples. We go through how to estimate effect sizes from your own data or from the published literature, how to calculate power and sample size using formulas for simple study designs, and how to do simulation-based power analysis for more complex designs.
Taught virtually for Starkville/Oxford, MS, March 5, 2025.

Multiomics demo lesson logo

MultiOmics Demo

Day-long workshop on analyzing omics data consisting of introductory slides and five code demos showing two applications of the MOFA2 package for multi-omics factor analysis (MOFA), two applications of the mixOmics package for sparse partial least squares discriminant analysis (sPLS-DA) and N-integration with DIABLO, and one application of the DESeq2 package for differential expression analysis. These course materials were adapted from the ELIXIR Omics Integration and Systems Biology course developed by the National Bioinformatics Infrastructure, Sweden, and R package vignettes from mixOmics and DESeq2.

Click here for instructions on installing the software and packages you’ll need for this workshop.
Lessons 1-3 taught in Little Rock, AR, January 9, 2024; New Orleans, LA, March 5, 2024; Mayagüez, PR, June 12, 2024. Lessons 1, 3, and 5 taught virtually for Little Rock, AR, June 3, 2025.

Full-text version Worksheets
Demo 1: Multiomics Factor Analysis (MOFA) mouse single-cell omics case study Worksheet / Worksheet filled in
Demo 2: MOFA chronic leukemia case study Worksheet / Worksheet filled in
Demo 3: mixOmics sPLS-DA small round blue cell tumors case study Worksheet / Worksheet filled in
Demo 4: DESeq2 differential expression analysis, Drosophila Pasilla RNAseq case study Worksheet / Worksheet filled in
Demo 5: mixOmics DIABLO breast cancer TCGA case study Worksheet / Worksheet filled in

Stats and data science talks

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 or HTML slides) Date Venue
(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
Structural equation modeling in ag science December 5, 2024 PDREC, Florence, SC
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
  March 5, 2025 Starkville/Oxford, MS (virtual)
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
  March 5, 2025 Starkville/Oxford, MS (virtual)
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)
Experimental design matters! May 28, 2025 FFSRU seminar series, New Orleans, LA (virtual)

Course and workshop pages

These are the homepages for stats training events, past and present, 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 2025-06-04. This page’s contents were created by the author and do not reflect official USDA policy.