schedule.txt

Lessons (Rough stuff down by 21st)
1: Introduction -
[ ] basic syntax (Cory)
[ ] installation (Kris)
[ ] Familiarization with RStudio (Kris)
[ ] packages & importing (Jonny)
[ ] Chapters 6 & 8 in r for data science (Kris)
[ ] basic language commands - accessing and reading help files, etc. (Cory)
[ ] Example of typical workflow, data analysis chain as an overview (Kris + Cory + Jonny)
[ ] Plug Slack and blog
2: Data Wrangling - data types, manipulation, subsetting, ordering
    Reading data
    Making lists, data frames, converting between them, indexing and selecting
    Basics on objects - wtf is the dollar sign notation
    Ordering, subsetting, reshaping
3: Plotting
4: Control flow - loops optimized loops & aggregate operations
5: Functions - how to write them, how to structure your data analysis code, good practices for reusable code
6: Document creation - RMD & LaTeX
7: high performance programming https://cran.r-project.org/web/views/HighPerformanceComputing.html
http://www.nimbios.org/tutorials/TT_RforHPC
http://adv-r.had.co.nz/Rcpp.html


Reference pages:
- Common commands
- FAQ
- Style guide
    https://google.github.io/styleguide/Rguide.xml
- Helpful links