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

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ESM 206 MATERIALS (FALL 2020)


DISCLAIMER: 2020 is weird and so is the course structure. If you're looking for normal-ish materials, see 2018 / 2019 archives below.



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. Good enough 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 2020)

ESM 206 Code of Conduct


WEEK LECTURES LAB MATERIALS READING
0 During orientation: get started with R, RStudio and GitHub Get R/RStudio and the tidyverse
Get started with Git/GitHub
None
1 Week 1 Slides

Coming soonBroman & Woo (2018): Data organization in spreadsheets

Lowndes et al. (2017): Our path to better science in less time using open data science tools
2 Week 2 Slides Coming soonWilson et al. (2017): Good enough practices in scientific computing

Ch. 1 - 5 in Claus Wilke's Fundamentals of Data Visualization
3 Week 3 Slides Coming soonNo reading
4 Week 4 Slides Coming soonSullivan & Feinn (2012). Using effect size - or why the p-value is not enough.
5 Week 5 Slides Coming soonCheruvelil, KS and PA Soranno (2018). Data-intensive ecological research is catalyzed by open science and team science. BioScience 68 (10): 813 - 822

Bahlai, C et al. (2019). Open science isn’t always open to all scientists. American Scientist 107 (2): 78
6 Week 6 Slides Coming soonNo reading
7 Week 7: TA Teaching Week! (see GauchoSpace) Coming soonHampton et al. (2015). The Tao of open science for ecology. Ecosphere 6 (7): 1 - 13

Couture JL, Blake RE, McDonald G, Ward CL (2018) A funder-imposed data publication requirement seldom inspired data sharing. PLoS ONE 13(7): e0199789
8 Week 8 SlidesComing soonCh 14 in Indigenous Data Sovereignty, Building a data revolution in Indian Country by Dr. Desi Rodriguez-Lonebear
9 Week 9 Slides Coming soonNo reading
10 Week 10 Slides Coming soonMah, Alice. (2016) Environmental justice in the age of big data : challenging toxic blind spots of voice, speed, and expertise. Environmental Sociology.doi: 10.1080/23251042.2016.1220849

Course resources:

ESM 206 ARCHIVES


ESM 244: Advanced methods for environmental data analysis in R

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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 2020)

ESM 244 Code of Conduct


WEEK LECTURES LAB MATERIALS
1 Lecture 1: Course welcome, 206 review, PCA intro Lab 1: data wrangling + visualization review, get going with blogdown
2 Lecture 2: PCA continued, RDA intro
Getting started with Shiny teaching key
Lab 2: Getting started in Shiny, data wrangling continued, exploring missings, and a PCA example
3 Lecture 3: Binary logistic regression intro
Lecture 4: Logistic regression continued
Lab 3: Binary logistic regression, some maps
4 Take-home labs (`sf` + `Shiny`) Take-home (no in-class labs)
5 Lecture 5: Intro to time-series data exploration and analysis
Lecture 6: Decomposition and forecasting overview
Lab 5: Time series + gh-pages!
6 Lecture 7: Intro to spatial data, variograms and kriging
Lecture 8: Kriging continued, intro to point pattern analysis
Lab 6: Check out a GeoTIFF, variograms, kriging
7 Lecture 9: Point pattern analysis, kernel density, intro to cluster analysis
Lecture 10: Hierarchical clustering methods
Lab 7: Point pattern analysis, k-means, hierarchical clustering
8 Lecture 11: Bayesian case studies, intro to text mining and analysis
Lecture 12: Sentiment analysis
Lab 8: Text mining and sentiment analysis with `tidytext`!
9 Lecture 13: Bootstrapping
Lecture 14: Nonlinear least squares
Lab 9: Bootstrapping & nonlinear least squares
10 Lecture 15: Becoming a nimble modern data scientist - interfacing with other languages & tools
Lecture 16: Course review and where you should go from here
Lab 10: R and SQL and Python, oh my!


ESM 244 ARCHIVES


Workshops


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.

Teaching

  • 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)

Distinctions

  • 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.


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