external_resources.qmd

# External Resources {.unnumbered}

*Here, you will find various valuable resources, to aid your bio data science workflow*

## A few quick ones...

- A very handy `ggplot` cheat-sheet can be found [here](https://rstudio.github.io/cheatsheets/data-visualization.pdf)
- So which plot to choose? Check this handy [guide](https://www.data-to-viz.com/)
- Explore ways of plotting [here](https://www.r-graph-gallery.com/)
- There is a nice tool to aid in choosing colours for visualisations [here](https://www.sessions.edu/color-calculator/)
- The [Posit community](https://community.rstudio.com) pages is a very nice place to get help if you're stuck

## Open source data science books

- [Hands-On Programming with R by Garrett Grolemund](https://rstudio-education.github.io/hopr/)
- [Statistical Inference via Data Science - A moderndive into R and the tidyverse by Chester Ismay and Albert Y. Kim](https://moderndive.com/)
- [Introduction to Data Science, Data Analysis and Prediction Algorithms with R by Rafael A. Irizarry](https://rafalab.github.io/dsbook/)
- [Mastering Shiny by Hadley Wickham](https://mastering-shiny.org/)
- [An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani](http://faculty.marshall.usc.edu/gareth-james/ISL/)
- [STAT 545 - Data wrangling, exploration, and analysis with R by Jenny Bryan](https://stat545.com/)
- [Happy Git and GitHub for the useR by Jenny Bryan, the STAT 545 TAs, Jim Hester](https://happygitwithr.com)

## Software Links

- [The R Project for Statistical Computing](https://www.r-project.org/)
- [RStudio - Open Source and Enterprise-ready professional software for R](https://rstudio.com/products/rstudio/download/?utm_source=downloadrstudio)
- [Tidyverse website](https://www.tidyverse.org/)

## Some Useful Links

- [From data to Viz: Find the graphic you need](https://www.data-to-viz.com)
- [R for Data Science: Exercise Solutions](https://jrnold.github.io/r4ds-exercise-solutions/)
- [R colour guide by Tian Zheng](http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf)
- [The tidyverse style guide - By Hadley Wickham](https://style.tidyverse.org/)
- [RStudio Primers - Learn data science basics with the interactive tutorials](https://rstudio.cloud/learn/primers)
- [The tidyverse style guide](https://style.tidyverse.org/documentation.html)
- [swirl - Learn R, in R](http://swirlstats.com/)
- [RStudio Cheat Sheets](https://www.rstudio.com/resources/cheatsheets/)
- [RStudio Community - Stuck? Ask a question and get help moving on](https://community.rstudio.com/)
- [HarvardX Biomedical Data Science Open Online Training](https://rafalab.github.io/pages/harvardx.html)
- [Data Analysis Playlist](https://www.youtube.com/playlist?list=PLzH6n4zXuckpfMu_4Ff8E7Z1behQks5ba)

## On Data Science

- [The Role of Academia in Data Science Education](https://hdsr.mitpress.mit.edu/pub/gg6swfqh/)

## Guides on Good Data Practices

- [A Guide to Reproducible Code by the British Ecology Society](https://www.britishecologicalsociety.org/wp-content/uploads/2017/12/guide-to-reproducible-code.pdf)
- [A Quick Guide to Organizing Computational Biology Projects](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000424)
- [How to pick more beautiful colors for your data visualizations](https://blog.datawrapper.de/beautifulcolors/)
- [Talk: Steps toward reproducible research by Karl Broman](https://www.youtube.com/watch?v=rNQ-RlG3JnQ)