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The proposed course uses interactive, hands-on instruction to develop coding skills for all ecologists, focusing on tools and techniques in the R programming language that are most relevant to the modern ecologist. No assumptions are made about the coding or training background of participants. The content for this course is based on the forthcoming textbooks R Crash Course for Biologists and R STATS Crash Course for Biologists, which will be made available electronically to course participants at no cost. Sessions will be recorded and posted online along with self-guided tutorials in multiple languages to accommodate participants with different coding competencies, learning styles, and language preferences.
This course is developed for ANY ecologist in the academic, public, and private sectors who has little to no prior experience in computer science or the R programming language. This short crash-course will include the following topics:
Installation and set-up of R Studio, R, and relevant packages
Introduction to the fundamentals R for coding and generating flexible, reproducible report
Working with data, including importing, merging, and organizing data
Distributions and basic statistical tests
Experimental design and model selection
A brief overview of more advanced modeling techniques, including generalized linear models, generalized linear mixed effects models, and generalized additive models.
General tips and techniques to create reproducible code and encourage open-source data science.
The teaching team include the author of R Crash Course for Biologists (Dr. Robert I. Colautti, Associate Professor, Queen's University) and ecology graduate students and professionals who are proficient in R and are familiar with the needs and challenges of working with real ecological data. The course will be offered concurrently in multiple languages, including English and Spanish, to accommodate attendees with different language preferences.
Overall, this course will provide a valuable introduction to coding in R for ecologists, empowering attendees to use this powerful tool to improve their research, develop reproducible data analyses, and collaborate with others.