Note: this is leftover verbiage from a previous iteration of this lab. It can be adapted form the learning objectives.

The Stucture of Lab Assignments

Questions in Part I deal with the context in which Arbuthnot collected his data. These questions should should be answered before you have looked at the data itself. In general, in Part I you will identify the question of interest, consider the manner in which it arose, and set expectations for the shape and structure of the data.

Part II is where you get your hands on the data and consider where it aligns with and diverges from your expections from Part I. Part III features extensions of the ideas in Part I and Part II, often to a new data set.

Your work should feature writing that is clear and in full sentences. Your document should be formatted cleanly, with appropriate use of headers, body text, and lists. Your code should be clear and simple, with no extraneous code.

Certain questions on the labs in this class call for speculation or for your opinion. There may not be a single correct answer, but some are more reasonable and thoughtful than others. You’re encouraged to talk these questions through with your peers and course staff during lab sessions, evening study session, and office hours.

Concepts

  • Proposing a research question and articulating the evidence (data and other) that could bear on that question.
  • Identification of the unit of observation and the names and types of variables.
  • Creating a statistical graphic that can answer a research question and interpreting the observed structure in the data.
  • Assessing the degree to which a statistical graphic supports a claim / answers a question.

Skills from the R Workshop

RStudio Terminology

  • Console
  • Environment
  • Editor
  • File Directory

R/RStudio Concepts

  • Printing to the console vs saving to the environment
  • R scripts as final draft of code, console as the sandbox

R Functions

  • +, -, *, /, ^

  • <-

  • ?

  • library()

  • c()

  • class()

  • sum()

  • mean()

  • data()

  • tibble()

  • select()

  • arrange()

  • mutate()

Skills from Thursday lab

  • loading arbuthnot
  • loading tidyverse
  • practice with select(), mutate(), and arrange()
  • making a line plot using ggplot