A good rule of thumb: imagine somebody reading over your shoulder who is not familiar with your work. Would they be able to interpret your filenames, meta-data, README files, data field names, documentation, etc? A bonus is that you will be able to understand what you did when you revisit your data months or years later.
Organize and label samples carefully
install.packages(baRcodeR)
. Use it!
Create a Data Management Plan (DMP) * Know this now
– you will have to publish your data publicly, so take a bit of extra
time now to make sure it is understandable. * Using short-hand names
(e.g. Wt
instead of total dry biomass
) will
save a lot of coding headaches. * Create a metadata.txt
file describing the basics of your data. A short paragraph of where it
comes from, and then a line describing each file and variable in your
dataset.
Collect and Manage Data CAREFULLY
Publish your data
READ Google’s R Style Guide
Set up R Studio (Free Desktop Version) with a GitHub account on your computer.
Conventional Superpower | Coding Superpower |
---|---|
Enhanced abilities | Fundamentals of R and Python |
Mind Control | Bash and Linux Command Line Computing |
Robot Army | Flow Control in R and Python |
Clone backup | Git and GitHub |
Illusionist | Custom Visualizations with qplot and ggplot |
Telekenesis | Custom Functions in R and Python |
Transmutation | Regular Expresssions in R and Python |
Mega-mind | Resampling & Simulations in R Pt.1 Pt.2 Pt.3 |
Master Builder | Custom R Packages |
Here is a current, comprehensive list of guided self-tutorials, organized by theme.
NOTE: Many of these links include multiple tutorials, which are linked at the top of the webpage.
Public Website
Publication Record
Public Engagement
Private Lab Website