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
Wt instead of
total dry biomass) will
save a lot of coding headaches. * Create a
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
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.
Graham Coop’s PopGen notes
Private Lab Website