outline.Rmd

---
title: Outline for modules
author: John Little
date: "`r Sys.Date()`"
output: github_document
---
<!-- READ THIS -->
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```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## Introduction to R - Refactoring the Workshop

Refactor existing workshop for smaller video segments and flipped presentation

1. Download software & run locally
    - [R](https://cran.r-project.org/) & [RStudio](https://rstudio.com/products/rstudio/download/) are not the same thing
        - [RStudio keyboard Shortcuts](https://support.rstudio.com/hc/en-us/articles/200711853-Keyboard-Shortcuts)
    - Cloud Options 
        - https://rstudio.cloud
        - https://vm-manage.oit.duke.edu/containers
    - R v Python
        - R is a _data first_ (i.e. analysis) programming language
        - Python is a general programming language
        - Are there libraries/packages relevant to your work?
        - Is there a supportive community?
            - [Rfun](https://rfun.library.duke.edu/)
            - [R Community](https://community.rstudio.com/)
            - [R Ladies](https://rladies.org/) | Locally, [R Ladies, RTP](https://www.meetup.com/rladies-rtp/) 
            - [TidyTuesday](https://github.com/rfordatascience/tidytuesday) weekly practice sessions (+ David Robinson's [recorded live feed of TT on YouTube](https://www.youtube.com/watch?v=NY0-IFet5AM&list=PL19ev-r1GBwkuyiwnxoHTRC8TTqP8OEi8) )
        - ML leans towards Python, [or does it](https://www.tidymodels.org/learn/)?
        - Programmer/Coder v Analyst
            - [how to choose a programming language](https://medium.com/better-programming/how-to-choose-a-programming-language-for-a-project-7c7a3e5a4de6)
            - [how to choose a religion](https://www.wikihow.com/Find-the-Right-Religion-for-You)
1. An RStudio project & reproducibility
    - **You are your most frequent collaborator** separated by time
        - **A simple test**:  Identify specific computational steps from a six-month old project?
        - **A simple goal**: Reproduce your computation on a different computer
        - Initial **Reproducibility in a nutshell**
            - Do everything with a script
            - Avoid point & click
            - Use relative paths
            - Write your code to run on any similar environment
            - Read more: [Initial steps toward reproducible research.](https://kbroman.org/steps2rr/) Karl Broman 
    - **RStudio Projects enable _Reproducibility_** 
        1. **Relative files paths**
            - `read_csv("data_raw/raw_data.csv")`
                - ProTip: `..` to move up one level in the directory structure
                - Avoid absolute paths
                    - avoid `setwd()`
                    - e.g. `setwd("d:/rfiles/myrproject")`
        4. **_Restart R and run all chunks_**
            - avoid:  `rm(list=ls())` 
        6. R Markdown & **literate coding** 
            - A script integrates code and natural language
            - Explain and describe your analysis within your workflow
            - Render reports in multiple formats
                - Notebooks, slide decks, web pages, dashboards, e-books, journal articles
        7. **File structure** matters
            - [_Practice of Reproducible Research_](https://www.practicereproducibleresearch.org/) by Kitzes, Turek, Deniz


```
EXAMPLE File Structure...

project_name (folder)
|-- project_name.Rproj
|-- README.md
|-- license.txt
|  data_raw
|  |-- raw_data.csv
|  |-- README.txt
|  data_clean
|  code_source
|  |--data_cleaning.Rmd
|  |--analysis.Rmd
|  images
|  reports_results
```

3. Get Data & Code Repository
    - Access your own data file (e.g. CSV)
    - [Download](https://github.com/libjohn/intro2r-code) & Expand a GitHub repository
    - Click on *.Rproj
4. Tour of the RStudio environment
    - Create a blank project
    - Console | Files / Packages / Help | Environment | Script Editor
    - R Markdown
    - Switch to your other project (from Section 2)
    - [Keyboard Shortcuts](https://support.rstudio.com/hc/en-us/articles/200711853-Keyboard-Shortcuts)

1. Tidyverse & other library packages
    - Packages extend the functionality of base R into your domain

      Practice | Frequency | Command
      -------- | --------- | -------
      Install  | once      |  `install.packages("tidyverse")`
      Load     | each time | `library(tidyverse)`
    - [**Tidyverse**](https://tidyverse.org):  
        1. an opinionated collection of packages with consistent web-based documentation and a supportive community
        2. a Meta-package that loads 8 helper packages and installs many consistent utilities

         Name | Purpose
         ---  | ---
         readr | importing CSV data
         dplyr | transforming data 
         ggplot2 | visualizing
         tibble  | rectangular grid / data frame
         tidyr   | pivot 
         forcats | categorical data / factors
         stringr | string data / manipulate natural language
         purrr   | iteration
         
    - **Other package repositories**
        - [CRAN](https://cran.r-project.org/web/packages/)
        - [Tidyverse Packages](https://www.tidyverse.org/packages/)
        - [BioConductor](https://www.bioconductor.org/packages/release/BiocViews.html#___Software)
        - [ROpenSci](https://ropensci.org/)
        - [MetaCran](https://www.r-pkg.org/)
        
1. Assignment `<-`  & pipe `%>%`

1. R, RStudio IDE, R Markdown
    - Base R, in the console
        - A big calculator
    - RStudio & Tidyverse
    - RMarkdown Notebook: reproducible literate coding = Prose + Coding + Reports + RStudio projects + version control (git/GitHub) 
    - [R Markdown](https://rmarkdown.rstudio.com/lesson-1.html) 
        - Code Chunks:  Separate prose from code
            - [Literate Programming](https://en.wikipedia.org/wiki/Literate_programming)  (e.g. Jupyter notebooks, R notebooks)
            - [R Markdown Cheatsheet](https://rmarkdown.library.duke.edu/slides/index.html#5)
        - Integrate a natural language explanation of your analysis along with your code snippets.  An approach used within computational sciences to create a functional record of *reproducible* research. You are your most frequent collaborator, six months from now, or six months ago. 
        -  Create a new, blank R Notebook
            -  R **Notebook** v. R Markdown **Document[s]**
        -  Many types of R Reports
            -  **slides**, documents, web pages, **e-books**, journal articles, web sites, **dashboards**, interactive **HTML widgets**, etc.
        -  Save the file  (`*.Rmd`)
        -  Preview the file 
            -  (Share with others, so they don't have to recreate your compute environment.)

1. [`dplyr`](https://dplyr.tidyverse.org/) package
    - "A grammar of data manipulation, providing a consistent set of verbs that help you solve the most common data manipulation challenges"
        - `mutate()` adds new variables 
        - `select()` pick variables / columns
        - `filter()` subset data by row
        - `summarise()` reduces multiple values into a summary
        - `count()` a special case of `summarize()` to tally occurrences
        - `arrange()` sort rows
    - [RStudio Keyboard Shortcuts](https://support.rstudio.com/hc/en-us/articles/200711853-Keyboard-Shortcuts)
 
1. [`tidyr`](https://tidyr.tidyverse.org/) package
    -  Make messy data into **tidy data**
        -  Every variable is a column
        -  Every row is an observation
        -  Every cell is a single value
    -  i.e. **pivots**

1. dplyr revisited
    -  People who like `pivot_longer()` also like `dplyr::left_joint()`

1. Exploratory Data Analysis (EDA): `ggplot2()` & `skimr`
    - `skimr::skim()`   from library[(skimr)](https://docs.ropensci.org/skimr/)
    - ggplot2(): a brief overview of visualization
 
1. [`ggplot2()`](https://ggplot2.tidyverse.org/): an introduction to the grammar of graphics, & interactive plots via `plotly` 

1. R Markdown




---

## Future modules

20. Large Data

25. Regression

30. Dashboards

35. Slides (Xaringan)

40. Mapping and Geocomputation / Spatial Analysis

45. Version Control:  git and GitHub

## Quick Start - Demonstration

1. Make a data folder
1. Drag fav.csv into the data folder
1. Make existing folder and RStudio project
1. Open an R Markdown Notebook
1. `library(tidyverses)`  plus other libraries
1. IMPORT data 

    - See Also [_RStudio data import wizard_](https://support.rstudio.com/hc/en-us/articles/218611977-Importing-Data-with-RStudio)
    - ATTACH data
  
1. EDA: Visualize `ggplot(data = starwars, aes(hair_color)) + geom_bar()` 
1. EDA: `skimr::skim(starwars)`
1. EDA: summary(fav_rating)
1. `left_join(starwars, fivethirtyeight)`
1. Transform data: five dplyr verbs ...

    - `filter`, `select`, `arrange`, `mutate`
    - `count` / `group_by` & `summarize`
        
1. Interactive visualization: `ggplotly`
1. linear regression / models (quick syntax introduction)
1. Reports:  notebooks, slides, dashboards, word document, PDF, book, etc.


## Resources
- [RStudio Primers](https://rstudio.cloud/learn/primers)
- [DataQuest.io](https://www.dataquest.io/data-science-courses-directory/)
- [Tidy Tuesday](https://github.com/rfordatascience/tidytuesday) practice
- [Rfun](https://rfun.library.duke.edu/) - **R we having fun yet‽**
- [ReMaster the Tidyverse](https://github.com/rstudio-education/remaster-the-tidyverse)

    - Update of [Master the Tidyverse](https://github.com/rstudio/master-the-tidyverse)

- [_R for Data Science_](https://r4ds.had.co.nz/) by Wickham and Grolemund
- [Introduction to Data Science](https://introds.org/) by Çetinkaya-Rundel
- [_Hands-On programming_](https://rstudio-education.github.io/hopr/) by Grolemund
- [_ggplot2: Elegant Graphics for Data Analysis_](https://ggplot2-book.org/) by Wickham
- [_Interactive web-based data visualization with R, plotly, and shiny_](https://plotly-r.com/) by Sievert
- [_Data Visualization A practical introduction_](https://socviz.co/makeplot.html#makeplot) by Healy
- [_Text Mining with R_](https://www.tidytextmining.com/) by Silge & Robinson
- [Shiny](https://shiny.rstudio.com/)
- [_Statistical Inference via Data Science: A ModernDive into R and the Tidyverse_](https://moderndive.com/) by Ismay and Kim
- [Tidymodels](https://www.tidymodels.org/) _packages for modeling and machine learning_