class: center, middle, inverse, title-slide # Meet the toolkit --- layout: true <div class="my-footer"> <span> <img src = "img/dsbox-logo.png" width = "30"> </img> Slides adapted from <a href="https://datasciencebox.org" target="_blank">datasciencebox.org</a> by Dr. Lucy D'Agostino McGowan </span> </div> --- class: middle, center # ✉️ Accept your GitHub invitation --- ## Agenda - Demo: A reproducible data analysis - R and RStudio - R Markdown - Git and GitHub - Recap --- class: center, middle # A reproducible data analysis --- ## Let's talk about the Oscar nominations!  --- ## Recap: What did we just do? - Locate a repository on **GitHub** - Clone and open the project in **RStudio** - Run the analysis using **R Markdown** - Edit the analysis to exclude movie titles - Re-run the analysis using **R Markdown** --- ## Toolkit  - Scriptability `\(\rightarrow\)` R - Literate programming (code, narrative, output in one place) `\(\rightarrow\)` R Markdown - Version control `\(\rightarrow\)` Git / GitHub --- class: center, middle # R and RStudio --- ## What is R/RStudio? - R is a statistical programming language - RStudio is a convenient interface for R (an integreated development environment, IDE) - At its simplest:^➥ - R is like a car’s engine - RStudio is like a car’s dashboard <img src="img/02/engine-dashboard.png" width="420" style="display: block; margin: auto;" /> .footnote[ ➥ Source: [Modern Dive](https://moderndive.com/) ] --- ## Let's take a tour - R / RStudio .center[  ] Concepts introduced: - Console - Using R as a calculator - Environment - Loading and viewing a data frame - Accessing a variable in a data frame - R functions --- ## R essentials A short list (for now): - Functions are (most often) verbs, followed by what they will be applied to in parantheses: ```r do_this(to_this) do_that(to_this, to_that, with_those) ``` -- - Columns (variables) in data frames are accessed with `$`: ```r dataframe$var_name ``` -- - Packages are installed with the `install.packages` function and loaded with the `library` function, once per session: ```r install.packages("package_name") library(package_name) ``` --- ## tidyverse .pull-left[  ] .pull-right[ .center[ [tidyverse.org](https://www.tidyverse.org/) ] - The tidyverse is an opinionated collection of R packages designed for data analysis and data science. - All packages share an underlying philosophy and a common grammar. ] --- ## tidymodels .pull-left[  ] .pull-right[ .center[ [tidyverse.org](https://www.tidyverse.org/) ] - tidymodels is an opinionated collection of R packages designed for modeling and statistical analysis. - All packages share an underlying philosophy and a common grammar. ] --- class: center, middle # R Markdown --- ## R Markdown - Fully reproducible reports -- each time you knit the analysis is run from the beginning - Simple markdown syntax for text - Code goes in chunks, defined by three backticks, narrative goes outside of chunks --- ## Let's take a tour - R Markdown .center[  ] Concepts introduced: - Knitting documents - R Markdown and (some) R syntax --- ## R Markdown help .pull-left[ .center[ [R Markdown cheat sheet](https://github.com/rstudio/cheatsheets/raw/master/rmarkdown-2.0.pdf) ]  ] .pull-right[ .center[ Markdown Quick Reference `Help -> Markdown Quick Reference` ]  ] --- ## Workspaces Remember this, and expect it to bite you a few times as you're learning to work with R Markdown: The workspace of your R Markdown document is separate from the Console! - If I run the following in the console .small[ ```r x <- 2 x * 3 ``` ] -- .question[ All looks good, eh? ] -- - Then, if I add the following chunk in my R Markdown document .small[ ```r x * 3 ``` ] -- .question[ What happens? Why the error? ] --- ## How will we use R Markdown? - Every assignment / report / project / etc. is an R Markdown document - You'll always have a template R Markdown document to start with - The amount of scaffolding in the template will decrease over the semester --- class: center, middle # Git and GitHub --- ## How do we collaborate? - The statistical programming language we'll use is R - The software we use to interface with R is RStudio - But how do I get you the course materials that you can build on for your assignments? + Hint: I'm not going to email you documents, that would be a mess! --- ## Version control - We introduced GitHub as a platform for collaboration - But it's much more than that... - It's actually desiged for version control --- ## Versioning  --- ## Versioning with human readable messages  --- ## Why do we need version control?  --- # Git and GitHub tips - Git is a version control system -- like “Track Changes” features from Microsoft Word on steroids. GitHub is the home for your Git-based projects on the internet -- like DropBox but much, much better). -- - There are millions of git commands -- ok, that's an exaggeration, but there are a lot of them -- and very few people know them all. 99% of the time you will use git to add, commit, push, and pull. -- - We will be doing Git things and interfacing with GitHub through RStudio, but if you google for help you might come accross methods for doing these things in the command line -- skip that and move on to the next resource unless you feel comfortable trying it out. -- - There is a great resource for working with git and R: [happygitwithr.com](http://happygitwithr.com/). Some of the content in there is beyond the scope of this course, but it's a good place to look for help. --- ## Let's take a tour -- Git / GitHub .center[  ] Concepts introduced: - Connect an R project to Github repository - Working with a local and remote repository - Committing, Pushing and Pulling There is just a bit more of GitHub that we'll use in this class, but for today this is enough. --- class: center, middle # Recap --- ## Recap Can you answer these questions? - What is reproducible data analysis, and why do we care? - What is version control, and why do we care? - What is R vs RStudio? - What is git vs GitHub (and do I need to care)?