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Zoom Meeting Agenda (2021 03 15)

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Clarke Iakovakis edited this page Mar 15, 2021 · 9 revisions

Proposed Agenda

Goal: Reach for consensus on creating a plan draft for the next round of edits to the lesson

  1. Discuss Agenda. Modify as necessary.
  2. Announcements and Comments
  3. Review Feedback
  4. Review Issues - https://github.com/LibraryCarpentry/lc-r/issues
    • numerous
  5. Prioritize Next Steps
  6. Subgroups
  7. Next Meeting(s) and meeting frequency

Minutes

March 15, 2021 Present: Clarke, John Little, Sarah Lin, Dough Joubert, Annajiat Alim A list of maintainers is found in the readme

 - Clarke] comments on fundamentally restructuring the lesson and leading with Tidyverse.  
 - Clarke] Links are broken and exercises that talk about vectors that are not actually created
 - Doug] confirmed that starting with the Tidyverse is a good approach.
 - Clarke] review the issues scheme and give people permission to assign themselves as an assignee
 - Annajiat] suggests we encourage issue-submitters to perform their own changes and submit as a PR. As a reply to the issues, "would you be interested in submitting a PR?"
 - Clarke] will give a first pass to all the issue.
 - Sarah] Agrees we should suggest that people make issues as PRs
 - Doug] Reviews the process as an end-users/submitter.  top-right corner (improve this page). end-users types
 - Clarke] -- suggest "starting with data" is the great place
 - Sarah] -- read in a CSV, what are the common error message (what if you have spaces in names, need to change columns).  Don't get too deep in character vectors.
 - All] -- tips, tricks, and shared experiences in teaching
       - janitor::clean_names()
       - example: leading0 issue with ISBNs
       - data vectors and data types is found in the _introduction_
       - import data wizard is useful for empowering our users
       - making R a "tear-free experience"
       - Promoting the gratifying experience of having a lot of modules in the advanced section 
  - for **NEXT Time**
      - look closely at [before we start](https://librarycarpentry.org/lc-r/00-before-we-start/index.html)
      - Clarke] will make the site up to date and (push changes and rebuild).  Clarke will notify us.

Sarah's proposed outline :

  1. Before we start (all)
  2. Starting with Data
  • What is a data.frame?
  • How can I read a complete csv file into R?
  • How can I get basic summary information about my dataset?
  1. Data cleaning & transformation
  • How can I select specific rows and/or columns from a data frame?
  • How can I combine multiple commands into a single command?
  • How can I create new columns or remove existing columns from a data frame?
  1. Data Visualization with ggplot2 (all)
  2. Functions (or a much better name) What is a function and how can we pass arguments to functions?
  • How can values be initially assigned to variables of different data types?
  • How can a vector be created?
  • What are the available data types?
  • How can subsets be extracted from vectors?
  • How does R treat missing values?
  • How can we deal with missing values in R?
  • How can I change the way R treats strings in my dataset?
  • Why would I want strings to be treated differently?
  • How are dates represented in R and how can I change the format?

Caveats:

  • My goal with this was the idea that with sections 1-4, a person could leave actually doing something simple in R that was useful in the real world, and if they got section 5 afterward, they'd be able to handle some fairly common problems/use cases (like dates). The first 4 sections would be pretty fast, I think, with 5 possibly just as long as the first 4 in total.
  • I didn't check the existing examples and assume that a lesson like this would need great care in making sure the code & examples didn't surface any of the tasks that come up in section 5 and that datasets would be curated to bring up on the commands/ideas being taught
  • I think things like strings, vectors, and subsets are more easily understood if you're already mastered a couple of actions and the need naturally arises to do more with the data, rather than front-loading tasks that are eventually useful.
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