Homework activities

Activity 1

Activity 1

General instructions

Complete this activity as a qmd file and commit the source code, along with the rendered pdf, to your course activities repository by 20 September

  • Save the .qmd source code for this file to your local directory.
  • Edit the document with your responses to the questions, and render the document into an pdf file.
  • Commit and push all materials to your “Activities” git repository.

Note: if all else fails, you can email this activity to me as a Word or PDF file.

Grading: Each paper (or dataset, for undergraduate students) you describe is worth 4 points. An additional 5 points are for writing your responses in a qmd file, rendering it to a PDF, and making it available in your course repository.

Instructions for graduate students

Identify five primary research papers that you feel are important for you to know inside-and-out as part of your dissertation research, and which you feel might be good candidates for our course semester project. For each paper, complete the following information:

  • Full article citation:

  • In your own words, describe (a) what was the motivation for this study; (b) what data the authors generated to address their question; (c) how the data were analyzed (both in terms of what approaches, and what code/statistical packages); and (d) how the results from this paper shape your work.

  • Have the authors made the data and/or code available as supplemental materials or through public repositories? If so, please include a link to the data.

  • What steps would you have to take to reproduce 1–2 key results from this paper?

Instructions for undergraduate students

Explore the data options available for your semester project through the LTER Data Sampler R package: https://lter.github.io/lterdatasampler/. Read each dataset’s vignette, which is available through the “Articles” option in the top menubar.

  • Name and source of dataset:

  • Why was this dataset originally collected? (I.e. what questions were the authors exploring when collecting these data?)

  • What is the type of data available? Describe both the technical features of the files (e.g. “a CSV table with 10 columns and 950 rows”), and the contents themselves (e.g. “columns 2 and 3 describe the latitude and longitude; column 4 describes the species name, […]”)

  • Brainstorm 1–2 figures that you would like to create with these data.1

Activity 2

Footnotes

  1. If you are stuck, consider exploring the R graph gallery to explore different ways to visualize data↩︎