Welcome & Introduction

class: inverse

# Welcome

***

## Resources
### Course Materials

#### [http://bit.ly/ACCE-book](http://bit.ly/ACCE-book)

### Course Collaborative Notepad 
#### [http://bit.ly/acce21-notepad](http://bit.ly/acce21-notepad)

_Please **Sign In** on the Notepad_

---
class: top, left, inverse

## ACCE DTP

### _Reproducible Research Data and Project Management in R_

***

.bottom[
# Introduction & Welcome

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</svg> April-May 2021**

**<svg viewBox="0 0 288 512" style="height:1em;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg">
 <path d="M112 316.94v156.69l22.02 33.02c4.75 7.12 15.22 7.12 19.97 0L176 473.63V316.94c-10.39 1.92-21.06 3.06-32 3.06s-21.61-1.14-32-3.06zM144 0C64.47 0 0 64.47 0 144s64.47 144 144 144 144-64.47 144-144S223.53 0 144 0zm0 76c-37.5 0-68 30.5-68 68 0 6.62-5.38 12-12 12s-12-5.38-12-12c0-50.73 41.28-92 92-92 6.62 0 12 5.38 12 12s-5.38 12-12 12z"></path>
</svg> Online** 
]

---

# 👋 Hello

### me: Dr Anna Krystalli

- **Research Software Engineer**, _University of Sheffield_
    + twitter **@annakrystalli**
    + github **@annakrystalli**
    + email **a.krystalli[at]sheffield.ac.uk**
    
- **Editor [rOpenSci](http://onboarding.ropensci.org/)**

--
## Course support

Also here: **David Wilby** & **Bob Turner**, fellow Sheffield RSEs

---
class: inverse

# Ice Breaker

### Split into break out rooms

### Introduce yourselves

### Q: Why did you decide to join this course?

---
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# Why are we here?

***

---

### The paper is the advertisement

> “an article about computational result is advertising, not scholarship. The actual scholarship is the **full software environment, code and data, that produced the result.**”

*John Claerbout paraphrased in [Buckheit and Donoho (1995)](https://statweb.stanford.edu/~wavelab/Wavelab_850/wavelab.pdf)*

### [The Scientific Paper Is Obsolete](https://www.theatlantic.com/science/archive/2018/04/the-scientific-paper-is-obsolete/556676/)
Here's what's next

*APR 5, 2018, The Atlantic*

---
### Lessons from the Reproducibility/Replicability crisis

- Many issues statistical and a results of broken Academic incentive systems.

- Much can be tackled by transparency and better computational literacy.

---

### [Reproducible Research in Computational Science](http://science.sciencemag.org/content/334/6060/1226)
ROGER D. PENG, SCIENCE 02 DEC 2011 : 1226-1227

> Reproducibility has the potential to serve as a minimum standard for judging scientific claims when full independent replication of a study is not possible.

---

## Reinventing discovery by open sourcing science

_Nielsen, Michael. Reinventing Discovery: The New Era of Networked Science. Princeton University Press, 2012. JSTOR, www.jstor.org/stable/j.ctt7s4vx._

.pull-left[
- Sharing resources
- Collective intelligence
- Mass collaboration
]

.pull-right[
<img src="assets/reinventing-innovation.png" height="300px">
]
---
## The internet was built for open science

### Key to next generation networked science

---
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# **The grand vision**

---

### Hans Rosling on open data (and data science) back in 2006

.center[
<iframe width="470" height="250" src="https://goo.gl/ry6AiG" frameborder="0" allowfullscreen></iframe>
]

> So how far have we come?

---
class: inverse

## gapminder.org: today

#### liberating stories from data

### www.gapminder.org

---

## gapmider at our fingertips

```r
library(ggplot2)

p <- ggplot(gapminder::gapminder, 
 aes(gdpPercap, lifeExp, size = pop, 
 color = continent, frame = year)) +
 geom_point() + scale_x_log10() + theme_bw()
```

```r
plotly::ggplotly(p)
```

<div id="htmlwidget-8ecdc4a4eb586684c920" style="width:504px;height:288px;" class="plotly html-widget"></div>
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---
class: top, right, inverse

# How do we get there?

***
---

## **Research meta-responsibilities**

We need better digital curation of the workhorses of modern science: **code** & **data**

> **aim to create secure materials that are [FAIR](https://www.nature.com/articles/sdata201618)**
> *findable, accessible, interoperable, reusable*

---

## **Research meta-responsibilities**

***

.pull-left[

- #### Think about traceablility and provencance.

- #### Follow community conventions.

- #### Prepare it to share it.

]
.pull-right.center[

### We all need to do our bit!

![](assets/CultureShift.jpg)

]

---

## **Drivers of better digital management**

- Funders: value for money, impact, reputation

- Publishers: many now require code and data.
    + Specialist journals have emerged for:
      + **software**: [Journal of Open Source Software](http://joss.theoj.org/), [MEE](https://besjournals.onlinelibrary.wiley.com/journal/2041210x) 
      + **data**: [Scientific Data](https://www.nature.com/sdata/))
 
- PIs, Supervisors and immediate research group

- Your wider scientific community

- The public

---

## **Yourselves!**

**Be your own best friend:**

.center[![](https://media.giphy.com/media/9Q249Qsl5cfLi/giphy.gif)]

---

### **Ultimately it's about getting a handle on our research materials**

> "Agree on a community convention...then follow it""

.centre[
<img src="assets/img/beer_messy_tidy.png" width="70%" />

]

---

## The concept of a Research Compendium
.pull-left[

>“
...We introduce the concept of a compendium
as both a container for the different elements
that make up the document and its computations (i.e. text, code, data, ...), and as a means for distributing, managing and updating the
collection."

[_Gentleman and Temple Lang, 2004_](https://biostats.bepress.com/bioconductor/paper2/)
]

.pull-right[

.centre[
<img src="assets/ResearchCompendium.jpg" width="50%" />

]

---

[_Kartik Ram: rstudio::conf 2019 talk_](https://github.com/karthik/rstudio2019)

---

[_Kartik Ram: rstudio::conf 2019 talk_](https://github.com/karthik/rstudio2019)

---

## R + Rstudio

### Next generation data science powerhouse

#### Backed by a diverse and active community of learners, users and developers

---

## Back to "Why are we here?"

- To show you howto use R + Rstudio to perform reproducible data analyses.

- To help you make the most of the real workhorses of your work, **YOUR CODE & DATA**!

- To help you be empowered by modern tools & technologies rather than be overwhelmed by them

- To help you lead the culture change rather than be burdened by increased requirements

- Ultimately, to **change how science works for better for everyone**!

---

- We'll do this by introducing you to **useful data and software tools and best practices**.

---

# Course Outline

.pull-left[
- ### Welcome

- ### Basics

- ### Project Management

- ### Data Munging
]

.pull-right[

- ### Metadata

- ### Analysing & presenting data

- ### Version Control

]

***

#### **We'll take regular breaks and aim to break for lunch between 12:00-13:00 for an hour**

---

# Before we dive in

- We'll exploring best practice in data and workflow management. I've tried to focus on concepts and tools that I wish I knew when I started

- We'll explore individual tools and concepts and show how they work nicely together.

- We'll be coding together and working in Rstudio Cloud.

- Feedback: After each day, let me know on the notepad:
  - 📗: somethind you liked
  - 🔴: somethind that could be improved

- Please feel free to ask questions if I use jargon you don't understand or need some clarification.  Questions are helpful for everyone! ✨

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## Working in Blackboard collaborate

- Have your mic on mute by default

- Please enter questions relevant to the course in the Collaborative notepad under **Participant Questions**

- Please ask technical questions in the chat

- Please try to help each other!

- Use status reaction emojis to communicate how it's going

- If you need to get my attention while speaking, raise your hand!

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# Let's go!

## Get back [home](https://annakrystalli.me/rrresearchACCE20/)