index.Rmd

---
title: "Research pipelines"
author:
  - George G Vega Yon
institute: "USC IMAGE project"
date: "2019/10/24"
output:
  xaringan::moon_reader:
    lib_dir: libs
    nature:
      highlightStyle: github
      highlightLines: true
      countIncrementalSlides: false
---

# Today's talk

## Reproducible Research

## Tools to eaze your work

We will spend most time looking at other people's work

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## Reproducible research (RR) (or how to avoid "it works on my computer!")

--

**A major new issue in sciences (overall)**

* Accessible Reproducible Research ([Mesirov, __Science__ 2010](http://science.sciencemag.org/content/327/5964/415))

* Again, and Again, and Again, ... ([Jasny et al., __Science__ 2011](http://science.sciencemag.org/content/334/6060/1225))

* Challenges in Irreproducible Research ([__nature__ topic](http://www.nature.com/news/reproducibility-1.17552))

* Reproducibility of computational workflows is automated using continuous analysis ([Beaulieu-Jones & Greene, __nature biotechnology__ 2017](https://www.nature.com/articles/nbt.3780?foxtrotcallback=true))

--

**A Major productivity problem for researchers**

* It is not only a good idea for science, but also for saving you time!


---

# Reproducible research

--

**Polished paper**

-  Ready to be submitted

-  Written using your fav doc editor

--

**Intermediate report**

-  Not ready to be published

-  Not necesarily using your fav doc editor

What do these two have in common?

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... both should have pretty figures AND be
reproducible


---

# The minimum

**Data** people must have a way to get your data

-  Include it with the paper.

-  Put it on a repo online like [zenodo](https://zenodo.org) (see for example
the [Gene Ontology's profile](https://zenodo.org/communities/gene-ontology/?page=1&size=20)) .

-  Include instructions about how to get the data (e.g. in an experimental
   setting, how did you get the samples).

--

**Analysis** source code/steps of your analysis

-  Include it with the paper

-  Put it on a repo online like [Github](https://github.com) or [GitLab](https://gitlab.com)

--

**Pro tip:** *Avoid the "contact the corresponding author for..." lines.*

---

# Tiers of reproducibility

### ~~minimum~~ Basic

*  **Data** Use public dataset (or at least shareable), or publish your dataset.

*  **Analysis** source code/steps of your analysis.


### Plus


*  **Tools** Use open source software (like R, python, etc.).

*  **Decency** Write your code neatly (like Emil does)

*  **Tidy** Organize your work in a structured way (folders+readme files,
    [here](https://github.com/gvegayon/fctc) is one example)


### Premium

*  **plug-n-play** Use a container (like [docker](https://www.docker.com/) or
[singularity](https://sylabs.io/))


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class: center, middle

# Research Pipeline

![](fig/pipeline.svg)

Source: Diagram by [문건웅](https://www.linkedin.com/in/%EA%B1%B4%EC%9B%85-%EB%AC%B8-5ab72599/?locale=en_US) showed [here](https://www.slideshare.net/ssuser7e30b2/reproducible-research1)

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Now, a lot of it has to do with automatization...
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## Automatizing your research: automatic reports

- For quick reports use tools like [**rmarkdown**](https://cran.r-project.org/package=rmarkdown) which is based on
[**pandoc**](https://pandoc.org)

- Automate tabular outputs using [**kableExtra**](https://cran.r-project.org/package=kableExtra) or
[**pander**](https://cran.r-project.org/package=pander)

- Automate tables: In R, you can use [**texreg**](https://cran.r-project.org/package=texreg), [**xtable**](https://cran.r-project.org/package=xtable)

---

## Automatizing your research: automatic reports (example) 

.pull-left[

```r
data(anorexia, package = "MASS")

anorex.1 <- glm(
  Postwt ~ Treat,
  data = anorexia
  )
  
anorex.2 <- glm(
  Postwt ~ Prewt + Treat,
  data = anorexia
  )

library(texreg)
htmlreg(
  list(anorex.1, anorex.2),
  doctype = FALSE
  )
```
]

<div style="font-size: small;" class="pull-right">

```{r texreg-tab, echo=FALSE, results='asis'}
data(anorexia, package = "MASS")

anorex.1 <- glm(Postwt ~ Treat, data = anorexia)
anorex.2 <- glm(Postwt ~ Prewt + Treat, data = anorexia)

texreg::htmlreg(list(anorex.1, anorex.2), doctype = FALSE)
```

</div>

--

Checkout the R CRAN Task View for [Reproducible Research](https://cran.r-project.org/web/views/ReproducibleResearch.html)

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<!-- ![](http://www.phdcomics.com/comics/archive/phd101212s.gif){style="width:50%" } -->

Version control to the rescue...
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# Git: The stupid version control system

```{r, echo=FALSE, out.width="600px"}
knitr::include_graphics("fig/git.svg")
```

(Live demo now)

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# Thank you!



## Some other resources

 rOpenSci's ["Reproduciblity in Science"](https://ropensci.github.io/reproducibility-guide/)

 Karl Broman's ["Tools for Reproducible Research Spring, 2016"](http://kbroman.org/Tools4RR/)

 The [**workflowr**](https://github.com/jdblischak/workflowr) package

Colin Fay's ["An introduction to Docker for R Users"](https://colinfay.me/docker-r-reproducibility/)

 R packages that work with Docker/Singularity [**babelwhale**](https://cran.r-project.org/package=babelwhale) and 
[**dockerfiler**](https://cran.r-project.org/package=dockerfiler)