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agenda.Rmd
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---
title: "R tools for a code-based data workflow"
output:
bookdown::pdf_document2:
toc: true
number_sections: false
urlcolor: blue
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Webinar Information
## Description
After a brief review of the advantages of a code-based workflow for ecological survey data, we introduce participants to some useful tools available via the R programming language for moving data along the data life cycle. We suggest some accessible tools in R for each step of the life cycle, and conclude with a walk through of how the functionality available in R can increase the reliability, efficiency, and transparency of scientific data management.
## Presenters
- [McCrea Cobb](mailto:[email protected]) (Refuge Inventory and Monitoring Program, Alaska) and [Adam Smith](mailto:[email protected]) (Refuge Inventory and Monitoring Program, IR2/4)
## When
June 24, 2020 (3:00-4:30 EST)
## Location
- [DOI Talent](https://doitalent.ibc.doi.gov/mod/facetoface/view.php?id=23143)
- [Webinar slides](https://usfws.github.io/data-mgt-with-r/) (use arrow keys to advance)
## Additional resources
[GitHub repository](https://github.com/USFWS/data-mgt-with-r)
# Outline
## Introduction (*McCrea, 10 min*)
## Planning (*McCrea, 10 min*)
## Documenting (*Adam, 10 min*)
## Acquiring (*Adam, 10 min*)
## Processing (*Adam, 10 min*)
## Analyzing (*Adam, 5 min*)
## Sharing (*McCrea, 10 min*)
## Archiving (*McCrea, 5 min*)
## An example R project / Live demo (*10 min*)
## Questions (*10 min*)
# Resources (Links)
**Introduction to R**
- [An Introduction to R book](https://intro2r.com/)
- [R for Excel Users](https://rstudio-conf-2020.github.io/r-for-excel/)
**Resources for Teaching R**
- [DataCamp's tidyverse course](https://learn.datacamp.com/courses/working-with-data-in-the-tidyverse)
- [learnr package](https://rstudio.github.io/learnr/)
- [RStudio teaching resources](https://education.rstudio.com/teach/materials/)
- [Data Wrangling, Exploration and Analysis with R "STAT 545"](https://stat545.com/)
- [Learn the tidyverse](https://www.tidyverse.org/learn/)
- [Geocomputation with R](https://geocompr.robinlovelace.net/)
**R Resources**
- [Why learn R](https://datacarpentry.org/R-ecology-lesson/00-before-we-start.html#r_code_is_great_for_reproducibility)
- [What they forgot to teach you about R](https://rstats.wtf/)
- [R cheatsheets](https://rstudio.com/resources/cheatsheets/)
- [Project-oriented workflow](https://www.tidyverse.org/blog/2017/12/workflow-vs-script/)
**Style Guides**
- [Tidyverse style guide](https://style.tidyverse.org/)
- [DataNovia R style guide](https://www.datanovia.com/en/blog/r-coding-style-best-practices/)
**R Packages**
- [Packaging your reproducible analysis](https://thomasleeper.com/2016/11/analysis-as-package/)
- [R packages](http://r-pkgs.had.co.nz/)
- [Packaging data analytical work reproducibly using R (and friends)](https://peerj.com/preprints/3192.pdf)
**Project management**
- [Stop working directory insanity!](https://gist.github.com/jennybc/362f52446fe1ebc4c49f)
- [A minimal project tree in R](https://talesofr.wordpress.com/2017/12/12/a-minimal-project-tree-in-r/)
- [Organizing the project directory](https://nicercode.github.io/blog/2013-05-17-organising-my-project/)
- [Designing projects](https://nicercode.github.io/blog/2013-04-05-projects/)
- [Project management with RStudio](https://swcarpentry.github.io/r-novice-gapminder/02-project-intro/)
- [File structure for data management](https://r-dir.com/blog/2013/11/folder-structure-for-data-analysis.html)
- [Organizing files for data analysis](https://github.com/AndersenLab/IBiS-Bootcamp/wiki/Organizing-files-for-data-analysis)
- [A meaningful file structure for R projects](https://www.r-bloggers.com/a-meaningful-file-structure-for-r-projects/)
- [An introduction to Docker for R users](https://colinfay.me/docker-r-reproducibility/)
- [R Docker tutorial](https://ropenscilabs.github.io/r-docker-tutorial/)
**Project Directory Templates**
- [MakeProject package](https://cran.r-project.org/web/packages/makeProject/index.html)
- [rrtools package](https://github.com/benmarwick/rrtools)
- [prodigenr package](https://cran.r-project.org/web/packages/prodigenr/readme/README.html)
**General Coding Best Practices**
- [What's in a name? The concepts and language of replication and reproducibility](https://thomasleeper.com/2015/05/open-science-language/)
- [Best practices for scientific computing](https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001745)
- [Good enough practices in scientific computing](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005510)
- [Ten simple rules for documenting scientific software](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006561)
- [Art of README](https://github.com/noffle/art-of-readme#bonus-exemplars) - see examples and checklist
- [Introduction to `roxygen2` vignette](https://cran.r-project.org/web/packages/roxygen2/vignettes/roxygen2.html)
**Version Control**
- [Happy Git with R](https://happygitwithr.com/)
**Other**
- [How to share your data with a statistician](https://github.com/jtleek/datasharing)
- [Tools for reproducible research](http://kbroman.org/Tools4RR/assets/lectures/06_org_eda_withnotes.pdf)
- [Reproducibility vs. replicability: a brief history of a confused terminology](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5778115/)