Common tools and functions to help clean data sets and prepare them for analysis
A step-by-step quick start tutorial for ggplot2
R GUI's allow you to get things done right away! While they sacrifice the full power of R for ease of usage, for many, the gain in usability is well worth this loss of advanced R functionality.
R is notoriously difficult, finicky and at times, needlessly challenging. Many resources have been created to help new users learn and become competent at using R.
A guide to existing packages that directly support agricultural research
How to find help when you run into trouble using R
If you have to repeat an action in R, functions are a great way to automate this process and avoid erross from cut-paste-replaCE. Here is a short introduction on how to write these functions.
ANOVA in R is a unfortunately a bit complicated. Unlike SAS, ANOVA functions in R lack a consistent structure, consistent output and the accessory packages for ANOVA display a patchwork of compatibility. The result is that it is easy to misspecify a model or make other mistakes.
Some instructions for R installation and your R setup to support reproducible research.
A few steps you can take to make your workflow in R more reproducible and less painful for you to deal with.