Building Shiny Apps: With Great Power Comes Great Responsibility

# Building Shiny Apps: With Great Power Comes Great Responsibility
## CSP 2018
### Jessica Minnier, PhD Oregon Health & Science University Twitter: @datapointier Slides available at <a href="http://bit.ly/shiny-csp18" class="uri">http://bit.ly/shiny-csp18</a>
### February 16, 2018

---

# This talk:

## 1. Motivation & Uses

## 2. Considerations & Challenges

## 3. Responsibilities

## 4. Tips on Development

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class: center, middle

# Why Shiny?

---

# Motivation

## Too many spreadsheets!

---

## Motivation

"Can you make me this plot? Ok now this plot? Wait one more plot..."

---

# Motivation

## Too many plots!

---
background-image: url(figs/shinyhex.png)
background-position: 50% 50%
background-size: 50%

# Enter, Shiny!

---

# My motivation

### (First step) Help collaborator: app to display one data set

- Explore, visualize, share data
- Generate hypotheses

### (Now) Help everyone: general app for uploading/analyzing data sets

- Implement existing R tools to analyze data
- Explore, visualize, share data
- Generate hypotheses

---
class: middle, center, inverse

# Motivation For All:

## Distribute your hard work!

---

#  *Collaborator:* "How can I share my data?"

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# *Developer/Statistician/Data Scientist*: "How can I get people to use my new method/package?"

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# *Developer/Statistician/Data Scientist*: Look at this analysis I did!

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# *Everyone:* Teach! (`learnr`)

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# Shiny basics

```r
# what you see, client side
ui <- fluidPage(
 
 
 
)
```

```r
# computations here!
server <- function(input, output) {
 
 
 
 
}
```

```r
# run the app!
shinyApp(ui = ui, server = server)
```

---
# Shiny basics

```r
# what you see, client side
ui <- fluidPage(
 # Title, panels
 plotOutput("myplot"), #from output$myplot
 selectInput("foo",choices=allthechoices)
)
```

```r
# computations here!
server <- function(input, output) {
 
 
 
 
}
```

```r
# run the app!
shinyApp(ui = ui, server = server)
```

---
# Shiny basics

```r
# what you see, client side
ui <- fluidPage(
 # Title, panels
 plotOutput("myplot"), #from output$myplot
 selectInput("foo",choices=allthechoices)
)
```

```r
# computations here!
server <- function(input, output) {
 # reactive plots
 output$myplot <- renderPlot({ ggplot(yay) + geom_point() })
 # use input$foo here, maybe observe changes in UI
 observe({ dostuff(input$foo) })
}
```

```r
# run the app!
shinyApp(ui = ui, server = server)
```

---

# An example: START

Shiny 
Transcriptome
Analysis
Resource
Tool

Github: [https://github.com/jminnier/STARTapp](https://github.com/jminnier/STARTapp)

DATA = RNA-seq gene expression

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# Very quick tour

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# Very quick tour

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# Data distributions, interactive

![start_boxplot](figs/start_boxplots.png)

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# Analysis results, interactive

![start_volcano](figs/start_volcano.png)

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# Data exploration, interactive

![start_scatter](figs/start_scatterplot.png)

---
class: inverse, middle, center

# Considerations

---

# Who is your audience?

## Example: START app

- likely biologists
- may not be used to coding or large data sets

<img src="figs/noun_1067136_cc.png" width="300px"></img>
---

# Challenges: *User*

Data is larger than they are used to + Cannot query or share results easily
 
 
Interactive search boxes/filtering + Reactive visualization

---

# Challenges, Input/Ouput Data

- Input data ~ multiple formats: Special input formats
- Interface with other software? Export special formats

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# Challenges, Input/Ouput Data

- Input data ~ multiple formats: Special input formats

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# Challenges, Input/Ouput Data

- Input data ~ multiple formats: Special input formats

<img src="figs/clustvis_inputdata2.png"></img>
<https://biit.cs.ut.ee/clustvis/>
---

# Challenges, Analysis

## Many possible ways to analyze data:

- RNA-seq data has many analysis pipelines (edgeR, DESeq2, limma)
- Complex study designs
 + repeated measures
 + multiple pairwise comparisons vs overall group comparisons
- How to allow for so many possibilities?

### Balance flexibility/simplicity/**CORRECT**ness

- Too many options `$\rightarrow$` high likelihood of incorrect use of analysis tools

---

# Challenges, Analysis

### Many possible ways to analyze data:

- Restrict analyses performed
- Allow for uploading of analysis results

---
class: center, middle

# Challenges, Analysis

## Look at the data! (QA/QC)

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# Challenges, Analysis: QA/QC

### Present QA plots up front

<https://github.com/joey711/shiny-phyloseq>
<img src="figs/phyloseq_qa.png" width="800px"></img>

---

# Challenges, Analysis: QA/QC

### Does this data look "weird"?

---
class: center, middle, inverse

# Responsibilities

## How do we avoid "data fishing"?

## (Food for thought)

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# Responsibilities

## - Are resulting analyses correct?

## - Are we encouraging "torturing the data until it speaks"?

## - Misuse of p-values?

---

# Responsibilities: Visual inference

### Plots = statistics

-- [Di Cook's](https://twitter.com/visnut) keynote at [rstudio::conf 2018](http://www.dicook.org/files/rstudio/#1)

### Solution?: Rorschach image tests

Generate null data sets and null plots using permutation and simulation

Can you pick out your data from a line up?

R package [`nullabor`](https://cran.r-project.org/web/packages/nullabor/vignettes/nullabor.html)

---

background-image: url(http://visiphilia.github.io/assets/skittles-lineup1.png)
background-position: 50% 90%
background-size: 80%

# Visual inference: Apophenia

http://visiphilia.org/2014/11/03/nullabor

---
class: inverse
background-image: url(figs/start_heatmap.png)

# Too many heatmaps?

---

#  Room for misuse, possible solutions:

### Documentation
#### - Use instructions and data inputs wisely.
#### - Provide examples of when advanced analyses are needed.
#### - Provide examples of statistical write ups based on app's results.

### Analysis
#### - Avoid too many hypothesis tests.
#### - Avoid p-values (Bayesian posterior probabilities?).
#### - With collaborators: stay involved!

---

# Landing page - Where am I?

- Show what the app is for/can do
- Point to instructions and guidelines

Bonus: [Jasmine Dumas](https://jasdumas.github.io/) has a [`shinyLP`](https://github.com/jasdumas/shinyLP) package to help!
<img src="figs/shinylp.jpg"></img>

---

# Instructions, Please

- Extremely difficult.
- Not just a vignette of a package (users may not be coders)
-	Written instructions vs. video demos, gif instructions

No one will use it if they don't know **how** to use it!
<img src="figs/start_instruct.png"></img>
---
# Instructions

Tauno Metsalu and Jaak Vilo: "Clustvis"
<img src="figs/clustvis1.png"></img>
---

# Instructions

Tauno Metsalu and Jaak Vilo: "Clustvis"
<img src="figs/clustvis_instruct.gif"></img>

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# But Does It work?

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# Debugging Tips

Debugging is notoriously a pain here

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# Thanks, stack trace:

---

# Did you update a package?

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# Old fashioned methods work best

Breakpoints and tracing:

- `browser()` - I didn't say it was fun
- `print()` - every single function/observe statement gets a print

---

# New methods are useful

`runApp(..., display.mode="showcase")`

But, doesn't show why something broke.

---

# Final Tips

## Put on your Software Development Hat!

- Test it first
    + Unit testing can save you so much headache (packages: [`testthat`](http://testthat.r-lib.org/), [`shinytest`](https://rstudio.github.io/shinytest/))
    + Run tests on example data
    + Create .Md file of test plots
- Security
    + Are users uploading sensitive data?
    + Security review of public apps vs commercial version
- Speed: [Why so slow?](https://support.rstudio.com/hc/en-us/articles/115000171848-Why-are-my-Shiny-apps-are-running-slowly-)
    + Rprofiler in Rstudio
    + Reduce start up time
    + Newer tools: `future` and `promises` packages for [async programming](https://medium.com/@joe.cheng/an-informal-intro-to-async-shiny-cbf01c85c4c5)

---

# Be Creative!

[papr by Lucy D'Agostino McGowan, Nick Strayer, Jeff Leek](https://jhubiostatistics.shinyapps.io/papr/)
<img src="figs/papr.png"></img>

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# Have fun!

[homebrewR by David Stephens](https://davesteps.shinyapps.io/homebrewR/)
<img src="figs/homebrewr.png"></img>

---

# Thank you! Go forth and shiny!

Contact: @ [datapointier](https://twitter.com/datapointier), email: minnier-[at]-ohsu.edu

Github: <https://github.com/jminnier/STARTapp>

Slides available at <http://bit.ly/shiny-csp18>

Code for slides available at <https://github.com/jminnier/talks>

Slides created via the R package [xaringan](https://github.com/yihui/xaringan) by [Yihui Xie](https://twitter.com/xieyihui?lang=en) with the [Rladies](https://alison.rbind.io/post/r-ladies-slides/) theme by [Alison Hill](https://twitter.com/apreshill).