Coding workshop: Week 7

1. Summary

Packages

• tidyverse
  
• here
  
• ggeffects
  
• flextable

Operations

New functions

• organize file paths using here()
  
• look at data using str(), glimpse(), and class()
  
• construct a linear model using lm()
  
• visualize model predictions using ggpredict()
  
• saving image using ggsave()
  
• calculating correlation using cor.test()
  
• plot model diagnostics using plot()
  
• plot model 95% CI using geom_ribbon()
  
• plot model predictions using geom_line()

Review

• read in data using read_csv()
  
• visualize data using ggplot()
  
• create scatterplot using geom_point()

Data sources

The data from this workshop comes from two sources:

1. Hamilton et al. 2022. “Integrated multi-trophic aquaculture mitigates the effects of ocean acidification: Seaweeds raise system pH and improve growth of juvenile abalone.” https://doi.org/10.1016/j.aquaculture.2022.738571

2. Ramirez, A. 2024. Sonadora elevational plots: long-term monitoring of air temperature ver 877108. Environmental Data Initiative. https://doi.org/10.6073/pasta/6b66eecae3092d8f2340b5132dec38ab (Accessed 2025-05-14).

2. Code

All code is in this repository.

All GitHub/Git steps are in this guide. Videos to accompany this guide are on Canvas.