some work...
# remove samples 4 and 5 that may have
# been compromised due to the monsters
chemistry_data %>%
dplyr::filter(!sampleID %in% c(4,5))
...more work
reproducibility for scientific reasons
- increased trustworthiness
- more rigorous, reliable science
- learning more from other works
“The climate scientists at the centre of a media storm over leaked emails were yesterday cleared of accusations that they fudged their results and silenced critics, but a review found they had failed to be open enough about their work.”1
reproducibility for personal reasons
- what if something needs to be redone, how to you manage that?
- how do you keep track of data sources, versions?
- how did I make that figure?
Lowndes, J., Best, B., Scarborough, C. et al. Our path to better science in less time using open data science tools. Nat Ecol Evol 1, 0160 (2017)
on the importance of scripting
consider, we want to remove samples that we feel may have been compromised…
in a R script
in a spreadsheet 
Lowndes, J., Best, B., Scarborough, C. et al. Our path to better science in less time using open data science tools. Nat Ecol Evol 1, 0160 (2017)
it is all about taming the chaos
Lowndes, J., Best, B., Scarborough, C. et al. Our path to better science in less time using open data science tools. Nat Ecol Evol 1, 0160 (2017)
tidy data
Open an intro to ANY statistics textbook and you will find that statistics (analysis, plotting - anything, really) starts once you have tidy data. Dr. Dianne Cook
project organization
If I could go back to my grad student self and tell her one thing, it would be: have a system to organize your files, data, and project notes. You’re thinking a lot about how to generate data, and what those data mean in the broader body of literature, but this is as important. Dr. Jacquelyn Gill
- use one folder per project
- separation of data, methods, output, etc.
- craft informative names
- include README files
standardization
comprehensive map of all countries in the world that use MMDDYYYY format
good coding practices
A recent study aiming to run 2,000 project’s worth of R code found that 74% of the associated R files failed to complete without error (Trisovic et al. 2022)1. Many of those errors involve coding practices that hinder reproducibility but are easily preventable by the original code authors.
publishing data (and code)
work with your mentor to determine the best way to publish your data and code
- 🔗 Milotić 2018 Good enough practices in data management
ASU research data management services
