GitHub Repository for STAA 577

Slides, code, cheat sheets, and RStudio lab notebooks for "Applied Machine Learning" course Spring 2018

Datasets for STAA 577

• nyflights13
  • new york city airport flight data from 2013 (must install)
  • install with install.packages("nyflights13", repos="http://cran.rstudio.com")
• iris
  • classic iris flower data set from Fisher (comes with R installed)
• mtcars & mtcars2
  • mtcars: USA motor trend cannonical data set (comes with R installed)
  • mtcars2: An extension of mtcars by Max Kuhn with updated data and more features (see Labs/data/mtcars2.rda)
• Ames Data
  • An extension of Ames, Iowa housing data set by Max Kuhn (must install)
  • install with install.packages("AmesHousing", repos="http://cran.rstudio.com")
• OkC
  • predicting STEM and other for various predictors (see Labs/data/okc.rda)
  • Contains several types of fields:
    • a number of open text essays related to interests and personal descriptions
    • single choice type fields, such as profession, diet, gender, body type, etc.
    • multiple choice data, including languages spoken, etc.
    • We will try to predict whether someone has a profession in the STEM fields (science, technology, engineering, and math) using a random sample of the overall dataset.

Cheatsheets:

RStudio cheatsheets

GitHub HTML Preview

• Fairly useful tool to preview HTML docs without having to clone the repository
• Right-click the *.html file, copy the link, then go here, paste the GitHub specific HTML link

Sad But True

Stu's Looping Rules for R

1. Always use a vectorized solution over iteration when possible, otherwise ... go to #2.
2. Use a functional. Since R is a functional language and for readability, usually of the apply() family, or a loop-wrapper function, unless ...
   1. modifying in place: if you are modifying or transforming certain subsets (columns) of a data frame.
   2. recursive problems: whenever an iteration depends on the previous iteration, a loop is better suited because a functional does not have access to variables outside the present lexical scope.
   3. while loops: in problems where it is unknown how many iterations will be performed, while-loops are well suited and preferred over a functional.
3. If you must use a loop, ensure the following:
   1. Initialize new objects: prior to the loop, allocate the necessary space ahead of time. Do NOT "grow" a vector on-the-fly within a loop (this is terribly slow).
   2. Optimize operations: do NOT perform operations inside the loop that could be done either up front of applied in a vectorized fashion following the loop. Enter the loop, do the bare minimum, then get out.

Hadley Wickham Links

• Advanced R
• R Packages
• R for Data Science Book
• Twitter: @hadleywickham
• GitHub: https://github.com/hadley

Max Kuhn's Links

• Applied Predictive Modeling Book
• Twitter: @topepo
• GitHub: https://github.com/topepo

Modeling Framework (thx Max Kuhn)

Memory Usage and rsample:

The rsample package is smarter than you might think.

Vignettes:

• dplyr
• tidyr

What is the Tidyverse?

Information about the

• The tidyverse
• The tidyverse packages
• Converting to the Tidyverse