Comments (1)
Intro I
- Remove slides: 5, 7, 8, 10
- Slide 9: free, open-source software that can do most data science tasks (give more general examples if any. last time we spent too long naming things most people didn't ever hear of)
- Remove the GUI loading of data -- this can confuse people. We can mention that there is a package to create a GUI, but that we recommend writing code and will not focus on the GUI. Maybe add material on the appendix on why we recommend using code
- The joke on slide 31 was not particularly well-received last time
- Develop a bit the idea of "building blocks" in slide 40
- Joke on slide 51 was also not a big hit
Intro II
- Remove slides: 8, 9, 10, 11, 12, 54
- File paths are ALWAYS the hardest part of these trainings.
- Slide 38: remove reference to shortcut
- Consider moving loops and indentation to the appendix, depending on the level of familiarity of participants with coding in general
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Related Issues (20)
- May 2022 - R Markdown HOT 4
- Resources
- Rewrite repo to use qmd
- Add final code from sessions to GitHub HOT 1
- Bypass WB firewall in installation of packages from CRAN
- documentation: add mention of R and Rstudio setup in main README
- Mar 2023 - Descriptive analysis HOT 3
- March 2023 - Geospatial HOT 3
- March 2023 - R Markdown HOT 2
- March 2023 - Data vis
- March 2023 - Recommendations from participants
- Evaluate the option of `WebR` instead of using `learnr` HOT 1
- Evaluate the option of using `use_course` HOT 3
- Explore teaching how to use `quarto` rather than `markdown`
- R Markdown - Feedback based on CE session 03-Feb-2022
- May 2022 - Introduction to R HOT 6
- May 2022 - Intro to R Programming HOT 6
- May 2022 - Data Visualization HOT 1
- May 2022 - Descriptive Analysis HOT 1
- May 2022 - Descriptive statistics HOT 5
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from dime-r-training.