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


Materials in development, excuse the dust

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 2018)

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
Lecture 2: Data exploration, probability density, assessing normality
Lab 1: Intro to R/RStudio, R projects, scripts
2 Lecture 3: Central limit theorem, confidence intervals, t-distribution
Lecture 4: The hypothesis test system
Lab 2: Basic wrangling and visualization, Rmarkdown intro
3 Lecture 5: Meaning of the p-value, 2-sample t-tests
Lecture 6: T-tests continued, p-value considerations, errors, power
Lab 3: Z-distribution probabilities, t-tests
4 Lecture 7: Power, effect size, communicating means differences
Lecture 8: Error recap, interpreting results, intro to one-way ANOVA
Lab 4: Exploratory graphs, wrangling, t-test, effect size, power
5 Lecture 9: One-way ANOVA, Tukey's HSD, no dynamite plots!
(Midterm Wednesday)
Lab 5: Wrangling, power, joins, viz
6 Lecture 10: Intro to rank-based tests
Lecture 11: Rank-based tests, Cliff's delta, chi-square intro
Lab 6: GitHub intro, wrangling, visualization, ANOVA
7 (Monday holiday)
Lecture 12: Chi-square, intro to OLS & simple linear regression
Lab 7: GitHub continued, chi-square, rank-based tests
8 Lecture 13: OLS & simple linear regression, diagnostics, correlation
(No lecture Wednesday)
Lab 8 (take home): Simple linear regression
9 Lecture 14: Multiple linear regression intro
Lecture 15: MLR continued, AIC, VIF, interactions, bias
Lab 9: Multiple linear regression
10 Lecture 16: Intro to logistic regression
Lecture 17: Course review and important things
Lab 10: Heatmaps, gganimate

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


Bren alumni R/RStudio refresher workshop (August 2019)

Click here for complete workshop materials on GitHub, or download materials below.

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)

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