Introduction and Overview

Two important questions before we begin

Two important questions before we begin

video link

• We'll be doing some introductions shortly.

• You might have some clear ideas/goals – we want to hear them

• You might not know – you are welcome, too

• Your reasons might change as the week goes along

1. Shop #. Work

More details here

Shop

In the first 2.5 days, we will present you with some tools for learning from data and give you an opportunity to give them a test drive

* introduction to R and RStudio
* data visualization
* data manipulation (tidying, cleaining, summarizing, etc.)
* reproducible workflow with RMarkdown

You get to “try things on” and “see how they fit”

Work

In the second 2.5 days, you will have a chance to prepare something (some things) of your own choosing to use

* in a class
* in a lab
* in your research
* ???

• we will work with you to find and learn what you need

• collaboration is encouraged

We will having you working already in Part 1,

• Goal: By Wednesday lunch, everyone

  • have tried out a number of “exercises” in R/RStudio/RMarkdown
  • will have generated a document in RMarkdown that describes something of interest to you and includes at least one plot [a way to motivate learning RMarkdown]
  • have identified a project to work on in Part 2.

There will also be some shopping in Part 2

• we will be collecting up ideas based on your interests, needs, wishes

• some of these may be “optional” – those who are interested may join in, others may choose to keep working

1. You leave here having learned something of value to you.

#. You leave here equipped to continue learning after the workshop is over.

#. You got to know some interesting people (and what they are interested in).

#. You had an enjoyable week.

Our schedule does not include any times.

We can take the time we need before moving on * Be sure to ask questions and provide us feedback so we can adjust appropriately.

R is one tool for working with data, but many of the things we will talk about are more general than R and apply equally to other ways of working with data.

R's particular advantages:

• state of the art (high quality, wide scope)
  • widely used in academia and industry
• excellent data visualization
• reproducible workflow tools
• free!