This is a guide for acquiring the course materials for ATS 2020 PG9: A HANDS-ON INTRODUCTION TO STUDYING THE LUNG MICROBIOME
Prior to starting the section entitled Hands-On: A Crash Course in Microbiome Analysis: Part 1 several pieces of software need to be acquired. Completing these first three steps is required in order to be able to follow along during the live hands-on presentation. It should take between 15-30 minutes to complete these preparatory tasks.
First you'll need to acquire R itself. This can be done directly from CRAN. If R is already installed, we recommend updating to the current version. You can check your R verison with getRversion().
Secondly, install the free desktop version of RStudio. We also recommend updating RStudio if a prior installation exists.
Next, the course materials need to be acquired and loaded. First, locate the green Clone or download button at the top right of this page, then download the ZIP contents. From your Downloads folder, extract the files from ATS2020_PG9-master.zip. We recommend moving this unzipped/extracted directory either to your desktop or preferred workspace. You will need to navigate to this extracted directory in the next step.
Next, launch RStudio. Then create a new project, by clicking File > New Project, in the upper left of the RStudio interface. Click on Existing Directory and then browse to find the ATS2020_PG9-master directory that was extracted. Finally, left click the file entitled PG9_Lung_Microbiome_Tutorial.Rmd from the lower right pane in the RStudio inferface.
This extracted directory includes all of the data and code necessary to follow along during the hands-on session, though the only file you'll need to directly interact with is PG9_Lung_Microbiome_Tutorial.Rmd. Completing these first three steps is required in order to be able to follow along with the hands-on presentation. It should take between 15-30 minutes to complete these tasks.
If you wish to explore at your own pace, you can review the code and run it chunk by chunk, or create the report for a more guided experience (see Step V. below).
It would also be highly beneficial to be familiar with the basics of R programming prior to participating in the course, though it is not required. A great resource is R for Data Science. Reading chapters 1 through 5 will familiarize you with the R design philosophy and syntax you'll see during the hands-on presentation.
Finally, the .Rmd filed included with these course materials can be assembled into an html report by cliking the Knit button inside RStudio.
