outline.qmd

---
title: "Two-day R data-science workshop"

date-modified: 'today'
date-format: long

license: CC BY-NC 
bibliography: references.bib
---

## Day One:

### Part 1 - getting started

-   What is R

    -   data-first programming/coding language

    -   extend R with domain-specific **packages**. See: [MetaCran](https://www.r-pkg.org/), [CRAN](https://cran.r-project.org/), github, [bioconductor](https://www.bioconductor.org/)

-   What is the [Tidyverse](https://www.tidyverse.org/)

-   What is [Tidy data](https://r4ds.had.co.nz/tidy-data.html)

-   **Reproducibility**: data-first tools help leverage reproducible workflows throughout computational thinking

    -   blank-slate instead of .Rdata

    -   Restart instead of `rm(list = ls())`

    -   Intentional use of the RStudio projects feature

    -   Version Control: git and GitHub

    -   {`renv`} for archiving/documenting package environments

-   Tool suite

    -   RStudio

    -   Quarto Notebooks (Merge literate coding with report rendering)

    -   {`dplyr`} & {`tidyr`} - a grammar for data wrangling

    -   {`ggplot2`} - a grammar of graphics

    -   Other packages e.g. {`readr`}, {`forcats`}, {`stringr`}, {`purrr`}, {`tidymodels`}

-   Console

    -   basic computation

    -   vectors and vectorized math

    -   environment variables and data variables

    -   **assignment** variables: `<-` and `=` (not the same as equivalency `==`)

    -   **pipe**s = `|>` and `%>%`

-   RStudio [Projects](https://docs.posit.co/ide/user/ide/guide/code/projects.html)

    -   use projects instead of `setwd()` ; relative paths are vital to reproducibility
    -   Documents: Quarto document

-   Import CSV data

    -   `read_csv()` and the data import wizard found in *Files* tab and *Environment* tab

    -   Other data formats

        -   {`readxl`}, {`haven`}

    -   SQL

        -   Code-chunks can perform SQL

        -   {`dbplyr`}

        -   [Best Practices with Databases](https://solutions.posit.co/connections/db/)

    -   **Exercise**

        -   import a CSV file

        -   import a Stata categorical file using {*haven*}

-   {`dplyr`}

    -   select(), filter(), arrange(), mutate(), group_by(), summarize(), count()

    -   **Exercise**

    -   Semantic meaning in data 

    -   Codebooks in a literate coding context

\

### Part 2 - visualization plus EDA

-   {ggplot2} viz

    -   **Exercise**

-   basic interactivity - ggplotly()

    -   **Exercise**

-   {`broom`} and handling modeling outputs (e.g. linear regression)

    -   Advertise {`tidymodels`}

    -   **Exercise**

-   {skimr} and EDA 

    -   skim()

    -   Reprise group_by() and summarize() 

-   {`tidyr`} pivot_longer() and pivot_wider()

    -   **Tall data** - what is it?

    -   Tall data for iteration: computation 

    -   Tall data for visualization , facet_wrap()

    -   Tall data for modeling ; {purrr} a first look.

-   join with `dplyr::left_join()` or other {`dplyr`} join functions

-   Wrap-up and Summarize the day

\

## Day Two

### Part 3 Iteration and custom functions

-   Questions from yesterday

-   R is a functional programming language

    -   What is functional programing?

        -   Customizing functions and iterating without FOR loops

    -   FOR loops?

    -   Tidyverse preference for data frames

-   Vectorized math (review)

    -   Vectorized iteration ; environment variables and data variables

-   Case study - import multiple data files + data cleaning + analysis + visualization

    -   read_csv() again

-   Introduce {`stringr`} and regular expressions

-   Some nifty data wrangling functions

-   Composing custom functions

-   Nesting data for iteration

\

### Part 4 Review, questions, case study, reports

-   Questions / Review

-   Setup case study

-   Using R to orchestrate data workflows

-   Reproducible report rendering (slides, PDF, dashboards, etc.)

-   Summarize

-   Finding help

## Resources

-   R for Data Science: [book](https://r4ds.had.co.nz/) \| [slack](https://www.rfordatasci.com/)

-   Tidyverse [primers](https://posit.cloud/learn/primers)

-   RStudio IDE \> Help \> \[ Documentation \| Forum \| Cheatsheets \| Shortcuts \] & on-board *Help* tab

    -   [RStudio User Guide](https://docs.posit.co/ide/user/)

-   Tidyverse webpages: {`package_name`}.tidyverse.org (e.g. ggplot2.tidyverse.org)

-   Interactivity

    -   [HTMLWidgets](https://www.htmlwidgets.org)

    -   [ObservableJS](https://quarto.org/docs/interactive/ojs/)

    -   [Shiny](https://shiny.rstudio.com/)

## Recommendations

-   [Blank slate](https://docs.posit.co/ide/user/ide/get-started/#blank-slate)

-   Use the Projects in the RStudio IDE

-   Use version control (git and GitHub)

-   Use {[`renv`](https://rstudio.github.io/renv/index.html)} for package reproducible environments

-   Use [mybinder.org](https://mybinder.org/) for sharing containers in a reproducible/replicable state