JJ Ying

For all but the most trivial data sets, we must rely upon distributed content container such as databases and oneline sources. Working with, and joining data, from distributed sources forms the foundation for most DataBase activities. It is also central to analysis of spatial data as well. In this topic we’ll use some complicated airline data to learn joins.

Lecture Content

For this topic, I will be spending a bit of time covering the broad theory as well as giving some concrete examples. To do so, you have access to the following slide set1.

The content of the slides are based upon a larger narrative document, which is embedded below2. You can use this narrative document as a general work-flow for this topic. It has all the code necessary to perform the basic operations covered in the talk and reinforced in the activity included below.

Here is the video recording of the short lecture.


Supplemental Materials

In addition to this lecture content, you may find the following information helpful as you go though these exercises.


Here is the activity associated with this particular project

If you would like to see how I answered these quiestions, here is a link to the notebook.

  1. This is a presentation writing in Xaringan, an RMarkdown presentation type. It is an active embed in this page (and hosted elsewhere). It can be extracted my clicking on the slide and hitting the c key on your keyboard—to clone it into its own window (more commands available using ? for viewing).↩︎

  2. This is an R-Notebook document you can download directly to your computer. To do so, pull down the Code menu on the top right and right-click the Download RMD option and save it to your Landscape Genetics Project folder.↩︎