Meet the toolkit:
programming

Data Science in a Box

datasciencebox.org

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Course toolkit

Course operation

introds.org
Learn
Zoom
Teams
Piazza

Doing data science

Programming:
- R
- RStudio
- tidyverse
- R Markdown
Version control and collaboration:
- Git
- GitHub

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Learning goals

By the end of the course, you will be able to...

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Learning goals

By the end of the course, you will be able to...

gain insight from data

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Learning goals

By the end of the course, you will be able to...

gain insight from data
gain insight from data, reproducibly

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Learning goals

By the end of the course, you will be able to...

gain insight from data
gain insight from data, reproducibly
gain insight from data, reproducibly, using modern programming tools and techniques

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Learning goals

By the end of the course, you will be able to...

gain insight from data
gain insight from data, reproducibly
gain insight from data, reproducibly, using modern programming tools and techniques
gain insight from data, reproducibly and collaboratively, using modern programming tools and techniques

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Learning goals

By the end of the course, you will be able to...

gain insight from data
gain insight from data, reproducibly
gain insight from data, reproducibly, using modern programming tools and techniques
gain insight from data, reproducibly and collaboratively, using modern programming tools and techniques
gain insight from data, reproducibly (with literate programming and version control) and collaboratively, using modern programming tools and techniques

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Reproducible data analysis

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Reproducibility checklist

What does it mean for a data analysis to be "reproducible"?

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Reproducibility checklist

What does it mean for a data analysis to be "reproducible"?

Near-term goals:

Are the tables and figures reproducible from the code and data?
Does the code actually do what you think it does?
In addition to what was done, is it clear why it was done?

Long-term goals:

Can the code be used for other data?
Can you extend the code to do other things?

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Toolkit for reproducibility

Scriptability R
Literate programming (code, narrative, output in one place) R Markdown
Version control Git / GitHub

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R and RStudio

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R and RStudio

R is an open-source statistical programming language
R is also an environment for statistical computing and graphics
It's easily extensible with packages

RStudio is a convenient interface for R called an IDE (integrated development environment), e.g. "I write R code in the RStudio IDE"
RStudio is not a requirement for programming with R, but it's very commonly used by R programmers and data scientists

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R packages

Packages are the fundamental units of reproducible R code. They include reusable R functions, the documentation that describes how to use them, and sample data¹
As of September 2020, there are over 16,000 R packages available on CRAN (the Comprehensive R Archive Network)²
We're going to work with a small (but important) subset of these!

¹ Wickham and Bryan, R Packages.

² CRAN contributed packages.

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Tour: R and RStudio

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A short list (for now) of R essentials

Functions are (most often) verbs, followed by what they will be applied to in parentheses:

do_this(to_this)
do_that(to_this, to_that, with_those)

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A short list (for now) of R essentials

Functions are (most often) verbs, followed by what they will be applied to in parentheses:

do_this(to_this)
do_that(to_this, to_that, with_those)

Packages are installed with the install.packages function and loaded with the library function, once per session:

install.packages("package_name")
library(package_name)

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R essentials (continued)

Columns (variables) in data frames are accessed with $:

dataframe$var_name

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R essentials (continued)

Columns (variables) in data frames are accessed with $:

dataframe$var_name

Object documentation can be accessed with ?

?mean

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tidyverse

tidyverse.org

The tidyverse is an opinionated collection of R packages designed for data science
All packages share an underlying philosophy and a common grammar

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rmarkdown

rmarkdown.rstudio.com

rmarkdown and the various packages that support it enable R users to write their code and prose in reproducible computational documents
We will generally refer to R Markdown documents (with .Rmd extension), e.g. "Do this in your R Markdown document" and rarely discuss loading the rmarkdown package

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R Markdown

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R Markdown

Fully reproducible reports -- each time you knit the analysis is ran from the beginning
Simple markdown syntax for text
Code goes in chunks, defined by three backticks, narrative goes outside of chunks

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Tour: R Markdown

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Environments

The environment of your R Markdown document is separate from the Console!

Remember this, and expect it to bite you a few times as you're learning to work with R Markdown!

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Environments

First, run the following in the console

x <- 2
x * 3

All looks good, eh?

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Environments

First, run the following in the console

x <- 2
x * 3

All looks good, eh?

Then, add the following in an R chunk in your R Markdown document

x * 3

What happens? Why the error?

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R Markdown help

R Markdown Cheat Sheet
Help -> Cheatsheets

Markdown Quick Reference
Help -> Markdown Quick Reference

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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

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What's with all the hexes?

Mitchell O'Hara-Wild, useR! 2018 feature wall

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Your turn: AE 02 - Bechdel + R Markdown

The Bechdel test asks whether a work of fiction features at least two women who talk to each other about something other than a man, and there must be two women named characters.
Go to RStudio Cloud and start the assignment AE 02 - Bechdel + R Markdown.
Open and knit the R Markdown document bechdel.Rmd, review the document, and fill in the blanks.

↑, ←, Pg Up, k	Go to previous slide
↓, →, Pg Dn, Space, j	Go to next slide
Home	Go to first slide
End	Go to last slide
Number + Return	Go to specific slide
b / m / f	Toggle blackout / mirrored / fullscreen mode
c	Clone slideshow
p	Toggle presenter mode
t	Restart the presentation timer
?, h	Toggle this help

Meet the toolkit:programming

Data Science in a Box

datasciencebox.org

Course toolkit

Course operation

Doing data science

Learning goals

Learning goals

Learning goals

Learning goals

Learning goals

Learning goals

Reproducible data analysis

Reproducibility checklist

Reproducibility checklist

Toolkit for reproducibility

R and RStudio

R and RStudio

R packages

Tour: R and RStudio

A short list (for now) of R essentials

A short list (for now) of R essentials

R essentials (continued)

R essentials (continued)

tidyverse

rmarkdown

R Markdown

R Markdown

Tour: R Markdown

Environments

Environments

Environments

R Markdown help

How will we use R Markdown?

What's with all the hexes?

Course toolkit

Course operation

Doing data science

Help

Meet the toolkit:
programming