Unlike most technical guides, this project is specifically designed for academic and enterprise users who are abruptly confronted with need to use R for a project. Perhaps you reached a point in your thesis that required some statistical analysis. Or maybe you were assigned to a project at work that required you to chart and analyze production data. Or perhaps you even scored an internship with the right company. In any event, there’s this thing on your desk called “R” and you need to learn it. Welcome!
This is a “quick and dirty” guide to getting productive in R. We’re going to skip over some of the technical details (data types, etc.) in favor of simple “plug and play” examples that will work for most users. The intent here is for you to read the example, think about your project, and use the code from the example to “get going” down the path. If you’re going to spend a lot of time with the language, it would be helpful to have a good cookbook of R examples available for deeper reading – our desk reference is the R Cookbook from O’Reilly publishing (available on Amazon) .
Here’s a good walk through for your first 30 minutes with R….
Now that we’ve had an appetizer, lets move onto the main course. While it’s fun to use the data sets which come with the R package to play with the system, you’ve got your own information to work with! Here’s some deeper guidance on how to get started… (You may want to bookmark this page)
Step 1 – Getting Your Data Into R
Step 2 – Data Manipulation
But wait… as they say on late night TV, There’s MORE….
All this key bashing is rather fun, but what if there was a way to automate this into a set of scripts, whereby you could issue a single command and “stuff gets done”. While you get down to the more serious business of coffee and chitchat. That’s much better than keying commands line by line.
Fortunately… there is….
- Step 4 – Automation!
- Creating Functions in R
- Meta: Creating Empty Data Frames & Recycling Specifications