References

Additional Resources

Books and Textbooks

R Programming and Data Science: - Wickham, H., & Grolemund, G. (2017). R for Data Science. O’Reilly Media. Available online at: https://r4ds.had.co.nz/ - Wickham, H. (2019). Advanced R (2nd ed.). CRC Press. Available online at: https://adv-r.hadley.nz/ - Xie, Y., Allaire, J. J., & Grolemund, G. (2018). R Markdown: The Definitive Guide. CRC Press.

Statistical Modeling: - Kuhn, M., & Silge, J. (2022). Tidy Modeling with R. O’Reilly Media. Available online at: https://www.tmwr.org/ - James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning with Applications in R (2nd ed.). Springer. - McElreath, R. (2020). Statistical Rethinking: A Bayesian Course with Examples in R and Stan (2nd ed.). CRC Press.

Ecological Statistics: - Zuur, A. F., Ieno, E. N., & Elphick, C. S. (2010). A protocol for data exploration to avoid common statistical problems. Methods in Ecology and Evolution, 1(1), 3-14. - Bolker, B. M. (2008). Ecological Models and Data in R. Princeton University Press.

Online Resources

Official Documentation: - The Comprehensive R Archive Network (CRAN): https://cran.r-project.org/ - RStudio Education: https://education.rstudio.com/ - Tidyverse: https://www.tidyverse.org/ - Tidymodels: https://www.tidymodels.org/

Community Resources: - Stack Overflow (R tag): https://stackoverflow.com/questions/tagged/r - RStudio Community: https://community.rstudio.com/ - R-bloggers: https://www.r-bloggers.com/ - #rstats on Twitter/X

Cheat Sheets: - RStudio Cheat Sheets: https://www.rstudio.com/resources/cheatsheets/ - Data Wrangling with dplyr and tidyr - Data Visualization with ggplot2 - R Markdown - Tidymodels

R Packages

Core Tidyverse Packages: - dplyr: Data manipulation - ggplot2: Data visualization - tidyr: Data tidying - readr: Data import - purrr: Functional programming - tibble: Modern data frames - stringr: String manipulation - forcats: Factor handling

Tidymodels Packages: - parsnip: Model specification - recipes: Preprocessing - rsample: Resampling - tune: Hyperparameter tuning - workflows: Model workflows - yardstick: Model metrics - broom: Tidy model outputs

Statistical Analysis: - rstatix: Pipe-friendly statistical tests - car: Companion to Applied Regression - lme4: Linear mixed-effects models - performance: Model assessment - effectsize: Effect size calculations

Visualization: - patchwork: Combine plots - viridis: Colorblind-friendly palettes - plotly: Interactive graphics - ggrepel: Better plot labels

Spatial Analysis: - sf: Simple features for spatial data - terra: Spatial data analysis - leaflet: Interactive maps

Datasets

All datasets used in this book are available in the data/ directory of the GitHub repository. Citations for each dataset are provided in their respective subdirectories.

Dataset Sources: - Palmer Penguins: Palmer Station Antarctica LTER - Crop Yields: Our World in Data - Biodiversity: IUCN Red List of Threatened Species - Marine Data: Great Lakes Fishery Commission - Additional datasets from TidyTuesday and other open data sources

Software Versions

This book was developed using: - R version 4.3.0 or higher - RStudio 2023.06.0 or higher - Quarto 1.3.0 or higher

For reproducibility, consider using renv to manage package versions. See the install_packages.R script for the complete list of required packages.

Getting Help

When You Encounter Problems:

Read Error Messages Carefully: R’s error messages often provide helpful clues
Check Package Documentation: Use ?function_name or help(function_name)
Search Online: Many R problems have been solved before on Stack Overflow
Create Reproducible Examples: Use the reprex package to create minimal examples
Ask the Community: Post questions on RStudio Community or Stack Overflow

Creating Good Questions: - Provide a minimal, reproducible example - Include your R version and package versions - Describe what you expected vs. what actually happened - Show what you’ve already tried

Contributing to This Book

This book is open source and welcomes contributions: - GitHub Repository: https://github.com/jm0535/dains - Report Issues: Use the issue tracker for bugs or suggestions - Submit Improvements: Pull requests are welcome - Share Your Experience: Let us know how you’re using this book

Staying Current

The field of data science and R programming evolves rapidly. To stay updated:

Follow R-bloggers for the latest R news and tutorials
Subscribe to RStudio’s email newsletter
Attend useR! conferences and local R user group meetings
Explore TidyTuesday for weekly data visualization practice
Read package changelogs when updating

Citing This Book

If you use this book in your research or teaching, please cite as:

Moses, J. (2025). Data Analysis in Natural Sciences: An R-Based Approach. Retrieved from https://jm0535.github.io/dains/

BibTeX entry:

@book{moses2025data,
  title={Data Analysis in Natural Sciences: An R-Based Approach},
  author={Moses, Jimmy},
  year={2025},
  publisher={Self-published},
  url={https://jm0535.github.io/dains/}
}

License

This book is released under the MIT License. You are free to share, adapt, and build upon this work, provided you give appropriate credit.

Note: This references section is automatically populated with citations from the book chapters. The references listed above are automatically generated from the references.bib file using the APA citation style (apa.csl).

Elith, J., & Leathwick, J. R. (2009). Species distribution models: Ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics, 40, 677–697.

--- prefer-html: true --- # References {.unnumbered} ::: {#refs} ::: ## Additional Resources ### Books and Textbooks **R Programming and Data Science:** - Wickham, H., & Grolemund, G. (2017). *R for Data Science*. O'Reilly Media. Available online at: https://r4ds.had.co.nz/ - Wickham, H. (2019). *Advanced R* (2nd ed.). CRC Press. Available online at: https://adv-r.hadley.nz/ - Xie, Y., Allaire, J. J., & Grolemund, G. (2018). *R Markdown: The Definitive Guide*. CRC Press. **Statistical Modeling:** - Kuhn, M., & Silge, J. (2022). *Tidy Modeling with R*. O'Reilly Media. Available online at: https://www.tmwr.org/ - James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). *An Introduction to Statistical Learning with Applications in R* (2nd ed.). Springer. - McElreath, R. (2020). *Statistical Rethinking: A Bayesian Course with Examples in R and Stan* (2nd ed.). CRC Press. **Ecological Statistics:** - Zuur, A. F., Ieno, E. N., & Elphick, C. S. (2010). A protocol for data exploration to avoid common statistical problems. *Methods in Ecology and Evolution*, 1(1), 3-14. - Bolker, B. M. (2008). *Ecological Models and Data in R*. Princeton University Press. ### Online Resources **Official Documentation:** - The Comprehensive R Archive Network (CRAN): https://cran.r-project.org/ - RStudio Education: https://education.rstudio.com/ - Tidyverse: https://www.tidyverse.org/ - Tidymodels: https://www.tidymodels.org/ **Community Resources:** - Stack Overflow (R tag): https://stackoverflow.com/questions/tagged/r - RStudio Community: https://community.rstudio.com/ - R-bloggers: https://www.r-bloggers.com/ - #rstats on Twitter/X **Cheat Sheets:** - RStudio Cheat Sheets: https://www.rstudio.com/resources/cheatsheets/ - Data Wrangling with dplyr and tidyr - Data Visualization with ggplot2 - R Markdown - Tidymodels ### R Packages **Core Tidyverse Packages:** - **dplyr**: Data manipulation - **ggplot2**: Data visualization - **tidyr**: Data tidying - **readr**: Data import - **purrr**: Functional programming - **tibble**: Modern data frames - **stringr**: String manipulation - **forcats**: Factor handling **Tidymodels Packages:** - **parsnip**: Model specification - **recipes**: Preprocessing - **rsample**: Resampling - **tune**: Hyperparameter tuning - **workflows**: Model workflows - **yardstick**: Model metrics - **broom**: Tidy model outputs **Statistical Analysis:** - **rstatix**: Pipe-friendly statistical tests - **car**: Companion to Applied Regression - **lme4**: Linear mixed-effects models - **performance**: Model assessment - **effectsize**: Effect size calculations **Visualization:** - **patchwork**: Combine plots - **viridis**: Colorblind-friendly palettes - **plotly**: Interactive graphics - **ggrepel**: Better plot labels **Spatial Analysis:** - **sf**: Simple features for spatial data - **terra**: Spatial data analysis - **leaflet**: Interactive maps ### Datasets All datasets used in this book are available in the `data/` directory of the GitHub repository. Citations for each dataset are provided in their respective subdirectories. **Dataset Sources:** - Palmer Penguins: Palmer Station Antarctica LTER - Crop Yields: Our World in Data - Biodiversity: IUCN Red List of Threatened Species - Marine Data: Great Lakes Fishery Commission - Additional datasets from TidyTuesday and other open data sources ### Software Versions This book was developed using: - R version 4.3.0 or higher - RStudio 2023.06.0 or higher - Quarto 1.3.0 or higher For reproducibility, consider using **renv** to manage package versions. See the `install_packages.R` script for the complete list of required packages. ### Getting Help **When You Encounter Problems:** 1. **Read Error Messages Carefully**: R's error messages often provide helpful clues 2. **Check Package Documentation**: Use `?function_name` or `help(function_name)` 3. **Search Online**: Many R problems have been solved before on Stack Overflow 4. **Create Reproducible Examples**: Use the **reprex** package to create minimal examples 5. **Ask the Community**: Post questions on RStudio Community or Stack Overflow **Creating Good Questions:** - Provide a minimal, reproducible example - Include your R version and package versions - Describe what you expected vs. what actually happened - Show what you've already tried ### Contributing to This Book This book is open source and welcomes contributions: - **GitHub Repository**: https://github.com/jm0535/dains - **Report Issues**: Use the issue tracker for bugs or suggestions - **Submit Improvements**: Pull requests are welcome - **Share Your Experience**: Let us know how you're using this book ### Staying Current The field of data science and R programming evolves rapidly. To stay updated: - Follow R-bloggers for the latest R news and tutorials - Subscribe to RStudio's email newsletter - Attend useR! conferences and local R user group meetings - Explore TidyTuesday for weekly data visualization practice - Read package changelogs when updating ### Citing This Book If you use this book in your research or teaching, please cite as: Moses, J. (2025). *Data Analysis in Natural Sciences: An R-Based Approach*. Retrieved from https://jm0535.github.io/dains/ BibTeX entry: ``` @book{moses2025data, title={Data Analysis in Natural Sciences: An R-Based Approach}, author={Moses, Jimmy}, year={2025}, publisher={Self-published}, url={https://jm0535.github.io/dains/} } ``` ### License This book is released under the MIT License. You are free to share, adapt, and build upon this work, provided you give appropriate credit. --- **Note**: This references section is automatically populated with citations from the book chapters. The references listed above are automatically generated from the `references.bib` file using the APA citation style (`apa.csl`).