We've seen a lot of computational tools this week. If you're confused, don't worry! These things will become more natural. I'd like to emphasize, though, that not all the tools we've seen are mandatory! Here's a summary of the minimum requirements:
Make a fresh new repo on GitHub for you to place your in-class activities.
Advanced (optional): instead of starting fresh, Fork the main BAIT509 repo. If you took this option and want to start fresh, feel free to do so. You won’t be penalized, just be sure to provide a link to your old repo in your new repo (and vice versa) (in a file called README.md).
Attempt in-class exercises using R, in an R script. They don't have to be correct in any way!
Advanced (optional): Write your R code in an RMarkdown script, and knit this to an output of your choice (pdf, html, md).
Put your attempts at in-class exercises on your GitHub repo by dragging and dropping the file onto GitHub.
Advanced, Part 1 (optional): commit your files locally using git, and push your attempts to your GitHub repo (requires you to set up git, which is optional).
Advanced, Part 2 (optional): use a git GUI like SourceTree to interact with git locally, instead of using the command line.
This is explained in more detail in the Class Meeting 01 notes, in these sections:
A note about R: The way we're using R in class is typically not how you'd go about running a machine learning algorithm, so there's no need to worry about details of the code (like sapply and map_chr). R is only being used in-class to manually explore certain aspects of ML algorithms. Tools for ML are elaborated in your assignments.
We've seen a lot of computational tools this week. If you're confused, don't worry! These things will become more natural. I'd like to emphasize, though, that not all the tools we've seen are mandatory! Here's a summary of the minimum requirements:
Forkthe main BAIT509 repo. If you took this option and want to start fresh, feel free to do so. You won’t be penalized, just be sure to provide a link to your old repo in your new repo (and vice versa) (in a file called README.md).This is explained in more detail in the Class Meeting 01 notes, in these sections:
A note about R: The way we're using R in class is typically not how you'd go about running a machine learning algorithm, so there's no need to worry about details of the code (like
sapplyandmap_chr). R is only being used in-class to manually explore certain aspects of ML algorithms. Tools for ML are elaborated in your assignments.