Welcome

This workshop is intended to provide an introduction to data science tools and approaches for instructors with a background in R. The goal is to help identify key capacities for instructors and students to "think with data" while answering a statistical question. Key topics include data wrangling (operations to get data in the right form for graphics), reproducibility, web-scraping, and constructing static and dynamic graphics from data.

  • Nicholas Horton, Amherst College
  • Danny Kaplan, Macalester College

Supported by NSF DUE # 0920350, Project MOSAIC.

USCOTS 2015, May 27-28, 2015

Basics of Data Science in R and RStudio

 

 

Workshop leaders:

  • Nicholas Horton, Amherst College
  • Danny Kaplan, Macalester College

 

Workshop Objectives

  • Introduce you to some key aspects of data science
    • enough for you to become a plausible, independent data scientist.
  • Help you become proficient with software and techniques for carrying out data science.
  • Help you, as educators to
    • use data science to enliven your statistics classes
    • make informed decisions about the extent to which data science can become part of your institution's curriculum

What the workshop covers

  • The structure of data [tidy data]
  • Data wrangling to condense and combine data sources to a form suitable for display
  • Visualization, one technique for displaying data informatively
  • "Machine learning"
  • Acquiring data: scraping and cleaning
  • An intro to dynamic, interactive graphics.
  • Reproducible workflow for communicating, updating, and revising your work.

Some specific systems

Each of the workshop topics is important enough that there is a variety of software implementing approaches to it.

We focus on a particular set of interrelated software tools based in R & RStudio. This software "stack" is already very widely used professionally and use is growing rapidly.

  1. Structure of data: tidyr et al.
  2. Wrangling: dplyr et al.
  3. Visualization: ggplot2 et al.
  4. Dynamics graphics: shiny
  5. Reproducible workflow: knitr & rmarkdown

Much less consensus on unified tools for machine learning or for acquiring data. So we use a hodgepodge. We expect that by USCOTS 2017 these will be fully integrated into the stack.

NB: (1), (2), and (3) are sometimes called the "Hadley Stack."

What we won't cover (1)

Statistics

There's a lot of overlap between statistics and data science. We figure you already know a lot about statistics so we don't cover it here.

  • Statistical tests, e.g., the "Stat 101 Stack"
  • Statistical models, e.g. generalized linear models

But we will do a bit with machine/statistical learning

What we won't cover (2)

Enterprise scale data

  • Database design
  • Unstructured data
  • Data large enough that special network techniques are needed.
  • Streaming data

Logistics and Community

Resources for this workshop

Forming teams with a mixture of skills

Power and internet connectivity

Getting the computer systems running