RaukR | Reproducible Research

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# RaukR | Reproducible Research
## Advanced R for Bioinformatics. Visby, 2018.
### Roy Francis
### 10 June, 2018

---

name: intro
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## What's the fuss about?

<img src="rr_presentation_assets/nature-reproducibility.jpg" class="fancyimage size-75">  
.small[<https://www.nature.com/collections/prbfkwmwvz/>]

<img src="rr_presentation_assets/nature-reproducibility-2.jpg" class="fancyimage size-75">  
.small[<https://www.nature.com/news/1-500-scientists-lift-the-lid-on-reproducibility-1.19970>]

???

A large percentage of research is not reproducible by other researchers or by the original researchers themselves. This concern has been lately addressed by several high profile journals.

---

---

.large["The difference between a scientist and a crazy person is that a scientist takes notes."]

---
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## Typical workflow

.pull-left-60[
1. Get data
2. Clean, transform data in spreadsheet
3. Copy-paste, copy-paste, copy-paste
4. Run analysis & export figures using A
5. Write up report using B
6. Import figures from A to B
7. Realises a sample was mislabelled
8. Go back to step 2, Repeat
]

.pull-right-40[
<img src="rr_presentation_assets/picard.jpg" class="fancyimage">
]

???

Manually handling workflow is hard to reproduce because it is hard to know the exact step carried out. A programmatic workflow allows full transparency to the exact steps followed.

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## Benefits of reproducibility

- Rerunning workflow
- Additional data/New data
- Returning to a project
- Transferring projects
- Collaborative work
- Easy to make changes
- Eliminate copy-paste errors

???

A reproducible workflow allows a lot of convenience.

- It's easy to automate re-running of analysis when earlier steps have changed such as new input data, code or assumptions. 
- Useful for an investigator returning to an analyses after a period of time.
- Useful when a project is transferred to a new investigator.
- Useful when working collaboratively.
- When you are asked to modify or change a parameter.

---
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## Solutions

![](rr_presentation_assets/rr-solutions.jpg)

* Containerized computing environment. Eg: *Docker*
* Workflow manager Eg: *Snakemake, Nextflow*
* Package and environment manager. Eg: *Packrat, Conda*
* Track edits and collaborate coding. Eg: *Git*
* Share and track code. Eg: *GitHub*
* Notebooks to document ongoing analyses. Eg: *Jupyter*
* Analyse and generate reports. Eg: *R Markdown*

???

Reproducible projects can be performed at different levels. Reproducibility is the ability for a work to be reproduced by an independently working third-party.

---
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## Steps to reproducibility

* Single document containing analysis, code and results
* Self-contained portable project
* Avoid manual steps
* Results are directly linked to code used to generate them
* Contexual narrative to why a certain step was performed
* Version control of documents

???

Reproducible programming is not an R specific issue. R offers a set of tools and an environment that is conducive to reproducible research.

---

## Automate workflow

* Install packages from repositories

```
install.packages(), devtools::install_github()
```  
* Read data and scripts

```
read.delim(), source(), readr::read_tsv()
```
* Reorganise data

```
dplyr, tidyr
```

* Create figures

```
ggplot2
```

* Run statistics

```
lm(), wilcox.test()
```

* Run external programs

```
system("./plink --file --flag1 --flag2 --out bla")
```

---

## RStudio | IDE

* Code completion
* Syntax highlighting (for many languages)
* R Notebook
* Debugging
* Useful GUI elements

---

## RStudio | Project

.small[**Create a new project**]
<img src="rr_presentation_assets/new-project.gif" class="fancyimage">

* Portable project (.Rproj)
* Dynamic reports
* Version control (git)
* Package control (packrat)

---

## Project Structure

Organise data, scripts and results sensibly in the same project directory. Use relative links.

???

Try to organise all material related to a project in a common directory. Organise the directory in a sensible manner. Use relative links to refer to files. Consider raw as read-only content.

---

## RMarkdown | Intro

### Markdown
- Plain text format for readability
- Support pure HTML for complex formatting

### RMarkdown
- Markdown + embedded R chunks
- Combine text and code in one file

---

## RMarkdown | Guide

.pull-left-50[
```
## Heading 2
### Heading 3
#### Heading 4

_italic text_ __bold text__ `code text`

- bullet point

Link to [this](somewhere.com)

![](rr_presentation_assets/summer.jpg)

```
]

.pull-right-50[

## Heading 2
### Heading 3
#### Heading 4

*italic text* **bold text** `code text`

* bullet point

Link to [this](somewhere.com)

![](rr_presentation_assets/summer.jpg)
]

R Markdown reference
https://rmarkdown.rstudio.com/

---

## RStudio | Notebook

.small[**Create a new .Rmd document**]
<img src="rr_presentation_assets/new-rmarkdown.gif" class="fancyimage">

* Text and code can be written together
* Inline R output (text and figures)

???

R Notebook demonstration.

---

## RStudio | Report

![](rr_presentation_assets/knit.png)

- Rmd > md > docx|HTML|PDF
- PDF needs Latex
- Handouts
- Scientific Articles
- Presentations
  - beamer
  - ioslides
  - slidy
  - xaringan

---

## RStudio | Export

- Simple websites
  - [Rmd](https://rmarkdown.rstudio.com/lesson-13.html)
- Book
  - [bookdown](https://bookdown.org/yihui/bookdown/)
- Package documentation
  - [pkgdown](http://pkgdown.r-lib.org/)
- Complex websites
  - [blogdown](https://bookdown.org/yihui/blogdown/)
- Dashboards
  - [flexdashboard](https://rmarkdown.rstudio.com/flexdashboard/)
- Web applications
  - [Shiny](https://www.rstudio.com/products/shiny/)

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## Acknowledgements

* [**Reproducible Research in R and RStudio**](https://www.slideshare.net/SusanJohnston3/reproducible-research-in-r-and-r-studio) - Susan Johnston  
* [**New Tools for Reproducible Research with R**](https://slides.yihui.name/2012-knitr-RStudio.html) - JJ Allaire and Yihui Xie  
* [**Reproducible research with R**](http://www.hafro.is/~einarhj/education/tcrenv2016/pre/r-markdown.pdf) - Bjarki Thor Elvarsson and Einar Hjorleifsson  
* [**Reproducible Research Workshop**](http://www.geo.uzh.ch/microsite/reproducible_research/post/rr-r-publication/) - University of Zurich

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## Session

.small[This presentation was created in RStudio using [`remarkjs`](https://github.com/gnab/remark) framework through R package [`xaringan`](https://github.com/yihui/xaringan).]

```r
getS3method("print","sessionInfo")(sessionInfo()[-7])
```

```
## R version 3.4.3 (2017-11-30)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows >= 8 x64 (build 9200)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=English_United Kingdom.1252 
## [2] LC_CTYPE=English_United Kingdom.1252   
## [3] LC_MONETARY=English_United Kingdom.1252
## [4] LC_NUMERIC=C                           
## [5] LC_TIME=English_United Kingdom.1252    
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] gridExtra_2.3    kableExtra_0.7.0 forcats_0.3.0    stringr_1.3.0   
##  [5] dplyr_0.7.4      purrr_0.2.4      readr_1.1.1      tidyr_0.8.1     
##  [9] tibble_1.4.2     ggplot2_2.2.1    tidyverse_1.2.1  captioner_2.2.3 
## [13] bookdown_0.7     knitr_1.20
```

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# Thank you