Allison Horst - Course & Workshop Materials

Bren School of Environmental Science and Management (UC Santa Barbara)

ESM 206: Introduction to environmental data analysis & stats in R



Course description: Develop critical thinking, technical and communication skills to successfully approach and answer environmental questions using quantitative and qualitative data. Topics include: data wrangling using the tidyverse and tidy data principles, exploratory data analysis and visualization, descriptive statistics, uncertainty, hypothesis testing, data visualization and communication. Best practices for reproducibility, version control and collaboration are emphasized throughout. Skills will be developed through wrangling, analysis and communication of datasets using R, RStudio and GitHub.

Course goals:

ESM 206 course syllabus (Fall 2019)

ESM 206 Code of Conduct

0 During orientation: get started with R, RStudio and GitHub Get R/RStudio and the tidyverse
Get started with Git/GitHub
1 Lecture 1: Course intro; computational reproducibility; R & RStudio

Lecture 2: Naming things; tidy data; basic troubleshooting and getting help with R
Lab 1: Meet RStudio, projects, scripts, basic reading & wrangling, intro to ggplot2Broman & Woo "Data organization in spreadsheets

Lowndes et al. "Our path to better science in less time using open data science tools"
2 Lecture 3: Code style tips; intro to R Markdown

Lecture 4: Version control with Git & GitHub overview; exploratory data visualization
Lab 2: Intro to R Markdown, data wrangling continued, tidyr::pivot_*, joins, and customizing a ggplot graphWilson et al. "Good enough practices in scientific computing"

Chapters 4 - 7 in "Fundamentals of Data Visualization" by Claus O. Wilke
3 Lecture 5: Data visualization Part 1
Lecture 6: Data visualization Part 2
Lab 3: Data visualization, dplyr::case_when(), purrr::modify_if(), writing files & saving figures, {here} for file pathsChapters 9 - 11 in "Fundamentals of Data Visualization" by Claus O. Wilke
4 Lecture 7:
Lecture 8:
Lab 4: Week 4 reading
5 Lecture 9:
Lecture 10:
Lab 5: Week 5 reading
6 Lecture 11:
Lecture 12:
Lab 6: Week 6 reading
7 (Monday holiday)
Lecture 13:
Lab 7: Week 7 reading
8 Lecture 14:
Lecture 15:
Lab 8: Week 8 reading
9 Lecture 16:
(No lecture Wednesday)
Lab 9: Take-homeWeek 9 reading
10 Lecture 17:
Lecture 18:
Lab 10:

Course resources:


ESM 244: Advanced methods for environmental data analysis in R


Materials in development, excuse the dust

ESM 244 course description: A survey course in advanced topics in statistics and data analysis for environmental scientists (ordinal and multinomial logistic regression, bootstrapping, non-linear models, intro to time-series analysis, spatial data analysis and interpolation, ordination methods, cluster analysis, text mining, etc.) while continuing to build skills and habits in data science (data wrangling, analysis & computational reproducibility in R/RStudio, version control and collaboration with R and GitHub). Term project: build a Shiny app.

ESM 244 course syllabus (Winter 2019)

ESM 244 Code of Conduct

1 Lecture 1: Course intro + 206 review
Lecture 2: Binary + ordinal logistic regression
Lab 1: data wrangling + visualization review
2 Lecture 3: Logistic regression, PCA intro
Lecture 4: PCA/RDA continued
Lab 2: Ordinal logistic regression, PCA, Shiny example
3 Lecture 5: Bootstrapping
Lecture 6: Exploring missingness
Lab 3: Bootstrap, assessing missingness
4 Lecture 7: Nonlinear least squares
Lecture 8: Longitudinal data
Lab 4: NLS, panel regression example
5 Lecture 9: Exploring time series data
Lecture 10: Autocorrelation, MA, ARIMA intro
Lab 5: Intro to time series analysis
6 Lecture 11: Spatial data, projections, variograms
Lecture 12: Kriging
Lab 6: Getting, wrangling and viewing spatial data
7 Lecture 13: Point pattern analysis, k-means clustering Lab 7: Rasters, Kriging, PPA
8 Lecture 14: Hierarchical clustering, Bayesian thinking intro
Lecture 15: Bayesian + text analysis case studies
Lab 8: Cluster analysis (k-means + hierarchical), + some text wrangling/sentiment analysis
9 Lecture 16: Graph theory terms, data sci collaboration Lab 9: Network analysis + cool extras


About Allison

Allison is a lecturer at the Bren School of Environmental Science and Management (UC Santa Barbara), where she has been teaching data analysis, statistics, and presentation skills courses in an applied environmental graduate program since 2012. In addition, she teaches introductory and refresher workshops in R for incoming graduate students and alumni. She is a co-founder of R-Ladies Santa Barbara, and recently co-founded a #tidytuesday coding club at UCSB. Allison completed her studies in engineering (BS Chemical Engineering, MS Mechanical Engineering) before earning her PhD in Environmental Nanotoxicology from UCSB.


  • ESM 206: Introductory data analysis and statistics in environmental science and management (2012 - present)
  • ESM 244: Advanced methods in environmental data science (2012 - present)
  • ESM 438: Presentation skills for environmental professionals (2013 - present)
  • Calculus intensive workshop: a 2-week calculus refresher course for incoming graduate students at the Bren School (2008 - present)
  • Data analysis workshop: a 2-week quantitative methods and introduction to R workshop for incoming PhD students at UCSB (2016 - present)


  • Bren School Distinguished Teaching Award (2018)
  • UC Santa Barbara Distinguished Teaching Award (2019)

Allison is also a fine artist, graphic designer, fly-fisher, hiker, backpacker, wanderer, and proud aunt to three nieces and two nephews. She splits time between Santa Barbara, CA, and Aspendell, CA, where she lives with her partner Greg and their silly rescue dog, Teddy.

We're proud Openscapes Champions

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