Data Visualization

class: center, middle, inverse, title-slide

.title[
# Data Visualization
]
.subtitle[
## Chapter 4. Data Communication in R
]
.author[
### Iñaki Úcar
]
.institute[
### Department of Statistics | uc3m-Santander Big Data Institute
]
.institute[
### Master in Computational Social Science
]
.date[
### Licensed under Creative Commons Attribution <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a> Last generated: 2023-12-09
]

---

class: base24, middle, clear

- [Version Control](ch4_1.html#3)
- [R Markdown](ch4_2.html#3)

---
class: inverse, center, middle

# R Markdown
## The source code for documents

---
class: base24

# Introduction

## Goals

- How to _communicate_ data with a visualization, i.e. how to effectively
  _encode_ a visual .blue[**message**], was covered in chapter 1.

- Here, we refer to the .red[**channel**] used to _share_ that message.

- We will talk about
  - Document formats (e.g.: article, report, book, presentation, website...)
  - File formats (e.g.: HTML, PDF, EPUB, DOCX, PPTX...)
  - **Reproducibility** (and automation)

---
class: base24

# Introduction

## Markup language

.pull-left[
- "a **text-encoding system** consisting of a set of symbols inserted in a text
  document to control its **structure**, formatting, or the relationship between
  its parts."

- "Markup is often used to control the **display** of the document or to
enrich its content to facilitating **automated processing**."
]
.pull-right[
```html
<!DOCTYPE html>
<html>
<body>

<h2>My First Heading</h2>
My first paragraph
<a href="#">This is a link</a>

</body>
</html>
```

<h2>My First Heading</h2>
My first paragraph.
<a href="#">This is a link</a>
]

---
class: base24

# Introduction

## Markdown

.pull-left[
- Gruber & Swartz, 2004

- A markup language **appealing to human readers**.

- Rise, divergence, then standardization in 2012.

- Many flavors still today.

- Key component: **pandoc**, by John MacFarlane.
]
.pull-right[
```markdown
## My First Heading

My first **paragraph**.

[This is a link](#)
```

.footnote[[Markdown Basics](https://rmarkdown.rstudio.com/lesson-8.html), [Pandoc's Markdown](https://pandoc.org/MANUAL.html#pandocs-markdown)]

## My First Heading

My first **paragraph**.

[This is a link](#)
]

---
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# Introduction

## YAML

.pull-left[
- _YAML Ain't Markup Language_

- Human-readable data-serialization language

- Configuration files, etc.

- Here, **document metadata**
]
.pull-right[
```yaml
title:  "Title: it contains a colon"
author:
- Author One
- Author Two
keywords: [nothing, nothingness]
abstract: |
  This is the abstract.

It consists of two paragraphs.
```
]

.footnote[[YAML syntax](https://en.wikipedia.org/wiki/YAML#Syntax)]

---
class: base24

# R Markdown

.footnote[[Reference guide](https://www.rstudio.com/wp-content/uploads/2015/03/rmarkdown-reference.pdf), [Cheatsheet](https://github.com/rstudio/cheatsheets/raw/main/rmarkdown-2.0.pdf)]

.pull-left[
- An (optional) YAML header surrounded by `---`s

- Text mixed with simple text formatting in Markdown

- **Executable code chunks** surrounded by ` ``` `s
]

.pull-right[
![](assets/img/ch4/rmarkdownwhatis.png)
]

.center[![](assets/img/ch4/rmarkdownflow.png)]

---
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# R Markdown

## Showcase

- [Code chunks](https://rmarkdown.rstudio.com/lesson-3.html)
  - Chunk options
  - Figure output

- [Inline code](https://rmarkdown.rstudio.com/lesson-4.html)

- [Code languages](https://rmarkdown.rstudio.com/lesson-5.html)

- [Parameters](https://rmarkdown.rstudio.com/lesson-6.html)

- [Tables](https://rmarkdown.rstudio.com/lesson-7.html)

- [Output formats](https://rmarkdown.rstudio.com/lesson-9.html)

---
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# Documents and Notebooks

## Showcase

- [HTML document](https://bookdown.org/yihui/rmarkdown/html-document.html)

- [Notebook](https://bookdown.org/yihui/rmarkdown/notebook.html)

- [PDF document](https://bookdown.org/yihui/rmarkdown/pdf-document.html)

- [Word document](https://bookdown.org/yihui/rmarkdown/word-document.html)

- Raw markup

---
class: base24

# Presentations

## Showcase

- [ioslides](https://bookdown.org/yihui/rmarkdown/ioslides-presentation.html)

- [slidy](https://bookdown.org/yihui/rmarkdown/slidy-presentation.html)

- [Beamer (PDF)](https://bookdown.org/yihui/rmarkdown/beamer-presentation.html)

- [PowerPoint](https://bookdown.org/yihui/rmarkdown/powerpoint-presentation.html)

- [reveal.js](https://bookdown.org/yihui/rmarkdown/revealjs.html)

- [xaringan](https://bookdown.org/yihui/rmarkdown/xaringan.html)

---
class: base24

# Other Formats

## Showcase

- [Tufte Handouts](https://bookdown.org/yihui/rmarkdown/tufte-handouts.html)

- [Websites](https://bookdown.org/yihui/rmarkdown/websites.html)

- [Books](https://bookdown.org/yihui/rmarkdown/books.html)

- [Journals](https://bookdown.org/yihui/rmarkdown/journals.html)

- [**Distill**](https://rstudio.github.io/distill/)

---
class: base24

# HTML Widgets

- The `htmlwidgets` package provides a framework for creating R bindings to
  create **web components**.

- Charts, but also maps, diagrams, tables, forms... and even mini-apps.
  Basically, any HTML component with optional **JavaScript**.

- You don't generally need to use the `htmlwidgets` package directly.
  Many packages use it to provide many components.

- They can be
  - used at the R console for data analysis, just like conventional R plots;
  - embedded within any **HTML document**;
  - saved as standalone web pages for ad-hoc sharing.

---

# HTML Widgets

.pull-left-mod[

```r
p <- ggplot(gapminder::gapminder) +
 aes(gdpPercap, lifeExp) +
 geom_point(aes(size=pop,
 color=continent),
 alpha=0.7) +
 scale_size(range=c(2, 12), guide=NULL) +
 scale_x_log10() +
 theme_gray(base_size=10) +
 aes(frame=year, text=country)

library(plotly)

ggplotly(p) |>
 layout(legend=list(
 x=1, y=0, xanchor="right"))
```
]
.pull-right-mod[
<div class="plotly html-widget html-fill-item" id="htmlwidget-f437f04a22a358376fae" style="width:100%;height:550px;"></div>
<script type="application/json" data-for="htmlwidget-f437f04a22a358376fae">{"x":{"data":[{"x":[3.3889902366128295,3.546617951893976,3.0264320126717377,2.9300526050205478,2.7350039250646971,2.5305793268568042,3.0691749464286691,3.029915447835549,3.0713907290818487,3.042571943572232,2.8923964580961394,3.3274859174242151,3.1425755133131092,3.4264347203837331,3.1519280501569278,2.5747754426179159,2.5171174236159333,2.5588840279669709,3.6328090876363524,2.6859482337800413,2.9596608637313313,2.7077374684251021,2.4769045152043669,2.9312243461536549,2.4754477554867704,2.7601004101431883,3.3779521224475015,3.1592698569002877,2.5672206138227596,2.6554620948372678,2.8710565594093267,3.2940153194906676,3.2274248143258331,2.6707337315928892,3.3844932769166256,2.8818862174230904,3.0323293453507603,3.4343908858518062,2.6931321343486228,2.9442771161033847,3.1614749102802429,2.9443779036382201,3.0552826850697294,3.6744289754841963,3.2084389723810651,3.0600843443064192,2.8553071488998873,2.9344018133538139,3.1668667439061169,2.8661416540551472,3.0597106180244795,2.609470734853601],"y":[43.076999999999998,30.015000000000001,38.222999999999999,47.622,31.975000000000001,39.030999999999999,38.523000000000003,35.463000000000001,38.091999999999999,40.715000000000003,39.143000000000001,42.110999999999997,40.476999999999997,34.811999999999998,41.893000000000001,34.481999999999999,35.927999999999997,34.078000000000003,37.003,30,43.149000000000001,33.609000000000002,32.5,42.270000000000003,42.137999999999998,38.479999999999997,42.722999999999999,36.680999999999997,36.256,33.685000000000002,40.542999999999999,50.985999999999997,42.872999999999998,31.286000000000001,41.725000000000001,37.444000000000003,36.323999999999998,52.723999999999997,40,46.470999999999997,37.277999999999999,30.331,32.978000000000002,45.009,38.634999999999998,41.406999999999996,41.215000000000003,38.595999999999997,44.600000000000001,39.978000000000002,42.037999999999997,48.451000000000001],"text":["year: 1952 Algeria gdpPercap: 2449.0082 lifeExp: 43.07700 pop: 9279525 continent: Africa","year: 1952 Angola gdpPercap: 3520.6103 lifeExp: 30.01500 pop: 4232095 continent: Africa","year: 1952 Benin gdpPercap: 1062.7522 lifeExp: 38.22300 pop: 1738315 continent: Africa","year: 1952 Botswana gdpPercap: 851.2411 lifeExp: 47.62200 pop: 442308 continent: Africa","year: 1952 Burkina Faso gdpPercap: 543.2552 lifeExp: 31.97500 pop: 4469979 continent: Africa","year: 1952 Burundi gdpPercap: 339.2965 lifeExp: 39.03100 pop: 2445618 continent: Africa","year: 1952 Cameroon gdpPercap: 1172.6677 lifeExp: 38.52300 pop: 5009067 continent: Africa","year: 1952 Central African Republic gdpPercap: 1071.3107 lifeExp: 35.46300 pop: 1291695 continent: Africa","year: 1952 Chad gdpPercap: 1178.6659 lifeExp: 38.09200 pop: 2682462 continent: Africa","year: 1952 Comoros gdpPercap: 1102.9909 lifeExp: 40.71500 pop: 153936 continent: Africa","year: 1952 Congo, Dem. 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1952 Guinea gdpPercap: 510.1965 lifeExp: 33.60900 pop: 2664249 continent: Africa","year: 1952 Guinea-Bissau gdpPercap: 299.8503 lifeExp: 32.50000 pop: 580653 continent: Africa","year: 1952 Kenya gdpPercap: 853.5409 lifeExp: 42.27000 pop: 6464046 continent: Africa","year: 1952 Lesotho gdpPercap: 298.8462 lifeExp: 42.13800 pop: 748747 continent: Africa","year: 1952 Liberia gdpPercap: 575.5730 lifeExp: 38.48000 pop: 863308 continent: Africa","year: 1952 Libya gdpPercap: 2387.5481 lifeExp: 42.72300 pop: 1019729 continent: Africa","year: 1952 Madagascar gdpPercap: 1443.0117 lifeExp: 36.68100 pop: 4762912 continent: Africa","year: 1952 Malawi gdpPercap: 369.1651 lifeExp: 36.25600 pop: 2917802 continent: Africa","year: 1952 Mali gdpPercap: 452.3370 lifeExp: 33.68500 pop: 3838168 continent: Africa","year: 1952 Mauritania gdpPercap: 743.1159 lifeExp: 40.54300 pop: 1022556 continent: Africa","year: 1952 Mauritius gdpPercap: 1967.9557 lifeExp: 50.98600 pop: 516556 continent: Africa","year: 1952 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1952 Argentina gdpPercap: 5911.3151 lifeExp: 62.48500 pop: 17876956 continent: Americas","year: 1952 Bolivia gdpPercap: 2677.3263 lifeExp: 40.41400 pop: 2883315 continent: Americas","year: 1952 Brazil gdpPercap: 2108.9444 lifeExp: 50.91700 pop: 56602560 continent: Americas","year: 1952 Canada gdpPercap: 11367.1611 lifeExp: 68.75000 pop: 14785584 continent: Americas","year: 1952 Chile gdpPercap: 3939.9788 lifeExp: 54.74500 pop: 6377619 continent: Americas","year: 1952 Colombia gdpPercap: 2144.1151 lifeExp: 50.64300 pop: 12350771 continent: Americas","year: 1952 Costa Rica gdpPercap: 2627.0095 lifeExp: 57.20600 pop: 926317 continent: Americas","year: 1952 Cuba gdpPercap: 5586.5388 lifeExp: 59.42100 pop: 6007797 continent: Americas","year: 1952 Dominican Republic gdpPercap: 1397.7171 lifeExp: 45.92800 pop: 2491346 continent: Americas","year: 1952 Ecuador gdpPercap: 3522.1107 lifeExp: 48.35700 pop: 3548753 continent: Americas","year: 1952 El Salvador gdpPercap: 3048.3029 lifeExp: 45.26200 pop: 2042865 continent: Americas","year: 1952 Guatemala gdpPercap: 2428.2378 lifeExp: 42.02300 pop: 3146381 continent: Americas","year: 1952 Haiti gdpPercap: 1840.3669 lifeExp: 37.57900 pop: 3201488 continent: Americas","year: 1952 Honduras gdpPercap: 2194.9262 lifeExp: 41.91200 pop: 1517453 continent: Americas","year: 1952 Jamaica gdpPercap: 2898.5309 lifeExp: 58.53000 pop: 1426095 continent: Americas","year: 1952 Mexico gdpPercap: 3478.1255 lifeExp: 50.78900 pop: 30144317 continent: Americas","year: 1952 Nicaragua gdpPercap: 3112.3639 lifeExp: 42.31400 pop: 1165790 continent: Americas","year: 1952 Panama gdpPercap: 2480.3803 lifeExp: 55.19100 pop: 940080 continent: Americas","year: 1952 Paraguay gdpPercap: 1952.3087 lifeExp: 62.64900 pop: 1555876 continent: Americas","year: 1952 Peru gdpPercap: 3758.5234 lifeExp: 43.90200 pop: 8025700 continent: Americas","year: 1952 Puerto Rico gdpPercap: 3081.9598 lifeExp: 64.28000 pop: 2227000 continent: Americas","year: 1952 Trinidad and Tobago 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82400996 continent: Europe","year: 2007 Greece gdpPercap: 27538.4119 lifeExp: 79.48300 pop: 10706290 continent: Europe","year: 2007 Hungary gdpPercap: 18008.9444 lifeExp: 73.33800 pop: 9956108 continent: Europe","year: 2007 Iceland gdpPercap: 36180.7892 lifeExp: 81.75700 pop: 301931 continent: Europe","year: 2007 Ireland gdpPercap: 40675.9964 lifeExp: 78.88500 pop: 4109086 continent: Europe","year: 2007 Italy gdpPercap: 28569.7197 lifeExp: 80.54600 pop: 58147733 continent: Europe","year: 2007 Montenegro gdpPercap: 9253.8961 lifeExp: 74.54300 pop: 684736 continent: Europe","year: 2007 Netherlands gdpPercap: 36797.9333 lifeExp: 79.76200 pop: 16570613 continent: Europe","year: 2007 Norway gdpPercap: 49357.1902 lifeExp: 80.19600 pop: 4627926 continent: Europe","year: 2007 Poland gdpPercap: 15389.9247 lifeExp: 75.56300 pop: 38518241 continent: Europe","year: 2007 Portugal gdpPercap: 20509.6478 lifeExp: 78.09800 pop: 10642836 continent: Europe","year: 2007 Romania gdpPercap: 10808.4756 lifeExp: 72.47600 pop: 22276056 continent: Europe","year: 2007 Serbia gdpPercap: 9786.5347 lifeExp: 74.00200 pop: 10150265 continent: Europe","year: 2007 Slovak Republic gdpPercap: 18678.3144 lifeExp: 74.66300 pop: 5447502 continent: Europe","year: 2007 Slovenia gdpPercap: 25768.2576 lifeExp: 77.92600 pop: 2009245 continent: Europe","year: 2007 Spain gdpPercap: 28821.0637 lifeExp: 80.94100 pop: 40448191 continent: Europe","year: 2007 Sweden gdpPercap: 33859.7484 lifeExp: 80.88400 pop: 9031088 continent: Europe","year: 2007 Switzerland gdpPercap: 37506.4191 lifeExp: 81.70100 pop: 7554661 continent: Europe","year: 2007 Turkey gdpPercap: 8458.2764 lifeExp: 71.77700 pop: 71158647 continent: Europe","year: 2007 United Kingdom gdpPercap: 33203.2613 lifeExp: 79.42500 pop: 60776238 continent: Europe"],"frame":"2007","type":"scatter","mode":"markers","marker":{"autocolorscale":false,"color":"rgba(0,176,246,1)","opacity":0.69999999999999996,"size":[9.5174948641712653,10.528554910231616,10.904654201271278,9.765058086345876,10.364040658262704,9.7505516541082944,10.87808071079187,9.9795261935646167,9.9275749792117498,15.68974088276282,17.003690756408272,10.95512095305982,10.833284872947766,8.0709880476974867,9.6534332262655109,15.491724456653538,8.3817174683878601,11.788259109226955,9.7835744592109002,14.013692150135013,10.944985204269464,12.464863177169198,10.865248319509385,9.9749078576061923,9.0122008415974619,14.173666299546225,10.676504935731618,10.408451425586405,16.335281425879582,15.669217824043105],"symbol":"circle","line":{"width":1.8897637795275593,"color":"rgba(0,176,246,1)"}},"hoveron":"points","name":"Europe","legendgroup":"Europe","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","visible":true},{"x":[4.5370047214526892,4.4011421122881123],"y":[81.234999999999999,80.203999999999994],"text":["year: 2007 Australia gdpPercap: 34435.3674 lifeExp: 81.23500 pop: 20434176 continent: Oceania","year: 2007 New Zealand gdpPercap: 25185.0091 lifeExp: 80.20400 pop: 4115771 continent: Oceania"],"frame":"2007","type":"scatter","mode":"markers","marker":{"autocolorscale":false,"color":"rgba(231,107,243,1)","opacity":0.69999999999999996,"size":[12.257099212248486,9.655161416476794],"symbol":"circle","line":{"width":1.8897637795275593,"color":"rgba(231,107,243,1)"}},"hoveron":"points","name":"Oceania","legendgroup":"Oceania","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","visible":true}],"traces":[0,1,2,3,4]}],"shinyEvents":["plotly_hover","plotly_click","plotly_selected","plotly_relayout","plotly_brushed","plotly_brushing","plotly_clickannotation","plotly_doubleclick","plotly_deselect","plotly_afterplot","plotly_sunburstclick"],"base_url":"https://plot.ly"},"evals":[],"jsHooks":[]}</script>
]

---

# HTML Widgets

.pull-left-mod[

```r
library(leaflet)

g <- c(
 "OpenStreetMap", "Stamen.Toner",
 "Stamen.TonerLite", "CartoDB.Positron")

leaflet() |>
 addProviderTiles(g[1], group=g[1]) |>
 addProviderTiles(g[2], group=g[2]) |>
 addProviderTiles(g[3], group=g[3]) |>
 addProviderTiles(g[4], group=g[4]) |>
 addLayersControl(g) |>
 addMarkers(lng=174.768, lat=-36.852,
 popup="The birthplace of R")
```
]
.pull-right-mod[
<div class="leaflet html-widget html-fill-item" id="htmlwidget-8a37aeb7ce9927437c21" style="width:100%;height:550px;"></div>
<script type="application/json" data-for="htmlwidget-8a37aeb7ce9927437c21">{"x":{"options":{"crs":{"crsClass":"L.CRS.EPSG3857","code":null,"proj4def":null,"projectedBounds":null,"options":{}}},"calls":[{"method":"addProviderTiles","args":["OpenStreetMap",null,"OpenStreetMap",{"errorTileUrl":"","noWrap":false,"detectRetina":false}]},{"method":"addProviderTiles","args":["Stamen.Toner",null,"Stamen.Toner",{"errorTileUrl":"","noWrap":false,"detectRetina":false}]},{"method":"addProviderTiles","args":["Stamen.TonerLite",null,"Stamen.TonerLite",{"errorTileUrl":"","noWrap":false,"detectRetina":false}]},{"method":"addProviderTiles","args":["CartoDB.Positron",null,"CartoDB.Positron",{"errorTileUrl":"","noWrap":false,"detectRetina":false}]},{"method":"addLayersControl","args":[["OpenStreetMap","Stamen.Toner","Stamen.TonerLite","CartoDB.Positron"],[],{"collapsed":true,"autoZIndex":true,"position":"topright"}]},{"method":"addMarkers","args":[-36.852,174.768,null,null,null,{"interactive":true,"draggable":false,"keyboard":true,"title":"","alt":"","zIndexOffset":0,"opacity":1,"riseOnHover":false,"riseOffset":250},"The birthplace of R",null,null,null,null,{"interactive":false,"permanent":false,"direction":"auto","opacity":1,"offset":[0,0],"textsize":"10px","textOnly":false,"className":"","sticky":true},null]}],"limits":{"lat":[-36.852,-36.852],"lng":[174.768,174.768]}},"evals":[],"jsHooks":[]}</script>
]

---

# HTML Widgets

.pull-left-mod[

```r
library(networkD3)

data(MisLinks)
data(MisNodes)

forceNetwork(
 Links = MisLinks, Nodes = MisNodes,
 Source = "source", Target = "target",
 Value = "value", NodeID = "name",
 Group = "group", opacity = 0.8)
```
]
.pull-right-mod[
<div class="forceNetwork html-widget html-fill-item" id="htmlwidget-03e00ad58c3a50f5c166" style="width:100%;height:550px;"></div>
<script type="application/json" data-for="htmlwidget-03e00ad58c3a50f5c166">{"x":{"links":{"source":[1,2,3,3,4,5,6,7,8,9,11,11,11,11,12,13,14,15,17,18,18,19,19,19,20,20,20,20,21,21,21,21,21,22,22,22,22,22,22,23,23,23,23,23,23,23,23,23,24,24,25,25,25,26,26,26,26,27,27,27,27,27,28,28,29,29,29,30,31,31,31,31,32,33,33,34,34,35,35,35,36,36,36,36,37,37,37,37,37,38,38,38,38,38,38,39,40,41,41,42,42,42,43,43,43,44,44,45,47,48,48,48,48,49,49,50,50,51,51,51,52,52,53,54,54,54,55,55,55,55,55,55,55,55,55,55,56,56,57,57,57,58,58,58,58,58,59,59,59,59,60,60,60,61,61,61,61,61,61,62,62,62,62,62,62,62,62,63,63,63,63,63,63,63,63,64,64,64,64,64,64,64,64,64,64,65,65,65,65,65,65,65,65,65,65,66,66,66,66,66,66,66,66,66,67,68,68,68,68,68,68,69,69,69,69,69,69,69,70,70,70,70,70,70,70,70,71,71,71,71,71,71,71,71,72,72,72,73,74,74,75,75,75,75,75,75,75,76,76,76,76,76,76,76],"target":[0,0,0,2,0,0,0,0,0,0,10,3,2,0,11,11,11,11,16,16,17,16,17,18,16,17,18,19,16,17,18,19,20,16,17,18,19,20,21,16,17,18,19,20,21,22,12,11,23,11,24,23,11,24,11,16,25,11,23,25,24,26,11,27,23,27,11,23,30,11,23,27,11,11,27,11,29,11,34,29,34,35,11,29,34,35,36,11,29,34,35,36,37,11,29,25,25,24,25,41,25,24,11,26,27,28,11,28,46,47,25,27,11,26,11,49,24,49,26,11,51,39,51,51,49,26,51,49,39,54,26,11,16,25,41,48,49,55,55,41,48,55,48,27,57,11,58,55,48,57,48,58,59,48,58,60,59,57,55,55,58,59,48,57,41,61,60,59,48,62,57,58,61,60,55,55,62,48,63,58,61,60,59,57,11,63,64,48,62,58,61,60,59,57,55,64,58,59,62,65,48,63,61,60,57,25,11,24,27,48,41,25,68,11,24,27,48,41,25,69,68,11,24,27,41,58,27,69,68,70,11,48,41,25,26,27,11,48,48,73,69,68,25,48,41,70,71,64,65,66,63,62,48,58],"value":[1,8,10,6,1,1,1,1,2,1,1,3,3,5,1,1,1,1,4,4,4,4,4,4,3,3,3,4,3,3,3,3,5,3,3,3,3,4,4,3,3,3,3,4,4,4,2,9,2,7,13,1,12,4,31,1,1,17,5,5,1,1,8,1,1,1,2,1,2,3,2,1,1,2,1,3,2,3,3,2,2,2,2,1,2,2,2,2,1,2,2,2,2,2,1,1,1,2,3,2,2,1,3,1,1,3,1,2,1,2,1,1,1,3,2,1,1,9,2,2,1,1,1,2,1,1,6,12,1,1,21,19,1,2,5,4,1,1,1,1,1,7,7,6,1,4,15,5,6,2,1,4,2,2,6,2,5,1,1,9,17,13,7,2,1,6,3,5,5,6,2,4,3,2,1,5,12,5,4,10,6,2,9,1,1,5,7,3,5,5,5,2,5,1,2,3,3,1,2,2,1,1,1,1,3,5,1,1,1,1,1,6,6,1,1,2,1,1,4,4,4,1,1,1,1,1,1,2,2,2,1,1,1,1,2,1,1,2,2,3,3,3,3,1,1,1,1,1,1,1,1,1,1,1],"colour":["#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666","#666"]},"nodes":{"name":["Myriel","Napoleon","Mlle.Baptistine","Mme.Magloire","CountessdeLo","Geborand","Champtercier","Cravatte","Count","OldMan","Labarre","Valjean","Marguerite","Mme.deR","Isabeau","Gervais","Tholomyes","Listolier","Fameuil","Blacheville","Favourite","Dahlia","Zephine","Fantine","Mme.Thenardier","Thenardier","Cosette","Javert","Fauchelevent","Bamatabois","Perpetue","Simplice","Scaufflaire","Woman1","Judge","Champmathieu","Brevet","Chenildieu","Cochepaille","Pontmercy","Boulatruelle","Eponine","Anzelma","Woman2","MotherInnocent","Gribier","Jondrette","Mme.Burgon","Gavroche","Gillenormand","Magnon","Mlle.Gillenormand","Mme.Pontmercy","Mlle.Vaubois","Lt.Gillenormand","Marius","BaronessT","Mabeuf","Enjolras","Combeferre","Prouvaire","Feuilly","Courfeyrac","Bahorel","Bossuet","Joly","Grantaire","MotherPlutarch","Gueulemer","Babet","Claquesous","Montparnasse","Toussaint","Child1","Child2","Brujon","Mme.Hucheloup"],"group":[1,1,1,1,1,1,1,1,1,1,2,2,3,2,2,2,3,3,3,3,3,3,3,3,4,4,5,4,0,2,3,2,2,2,2,2,2,2,2,4,6,4,4,5,0,0,7,7,8,5,5,5,5,5,5,8,5,8,8,8,8,8,8,8,8,8,8,9,4,4,4,4,5,10,10,4,8]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]

---

# HTML Widgets

```r
DT::datatable(iris, options=list(pageLength=6))
```

<div class="datatables html-widget html-fill-item" id="htmlwidget-a10174c17281dfdb5c59" style="width:100%;height:auto;"></div>
<script type="application/json" 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---

# HTML Widgets

---
class: base24

# Dashboards

- **Business-style reports** to highlight brief and key summaries.

- You may have heard of Salesforce's _Tableau_, Microsoft _Power BI_,
  Google _Data Studio_...

- Often, grid-based layout, components arranged in boxes.

- With the `flexdashboard` package, you can
  - use R Markdown to produce dashboards;
  - embed a wide variety of components, including HTML widgets;
  - specify flexible layouts that resize automatically;
  - use **Shiny** for dynamic content.

.footnote[[Documentation](https://pkgs.rstudio.com/flexdashboard/)]

---
class: inverse, center, middle

# Demo

---
class: base24

# Shiny

.footnote[[Documentation](https://shiny.rstudio.com/)]

## Static vs. Dynamic (!= interactive) content

.pull-left[
### Static HTML

<img src="assets/img/ch4/html-static.svg" width="40%" style="display: block; margin: auto;" />
]
.pull-right[
### Shiny

<img src="assets/img/ch4/html-dynamic.svg" width="83%" style="display: block; margin: auto;" />
]

---

# Shiny

## Your first Shiny app

.footnote[Example from `rmdexamples::kmeans_cluster()`]

.pull-left[
### ui.R
.code50[
```r
library(shiny)
vars <- names(iris)

ui <- fluidPage(
 fluidRow(style = "padding-bottom: 20px;",
 column(4, selectInput("xcol", "X Variable", vars)),
 column(4, selectInput("ycol", "Y Variable", vars,
 selected = vars[2])),
 column(4, numericInput("clusters", "Cluster count", 3,
 min = 1, max = 9))
 ),
 fluidRow(
 plotOutput("kmeans", height = "400px") 
 )
)
```
]]
.pull-right[
### server.R
.code50[
```r
library(shiny)

server <- function(input, output, session) {
 selectedData = reactive({
 iris[, c(input$xcol, input$ycol)]
 })

clusters = reactive({
    kmeans(selectedData(), input$clusters)
  })

output$kmeans = renderPlot(height = 400, {
    res = clusters()
    par(mar = c(5.1, 4.1, 0, 1))
    plot(selectedData(),
         col = res$cluster, pch = 20, cex = 3)
    points(res$centers, pch = 4, cex = 4, lwd = 4)
  })
}
```
]]

---

# Dashboards

## Shiny dashboards!

1. Add `runtime: shiny` to the options declared at the top of the document
  (YAML metadata).

2. (Optional) Add the `{.sidebar}` attribute to the first column of the
  dashboard to make it a host for Shiny input controls.

3. Add Shiny inputs and outputs as appropriate.

4. When including plots, be sure to wrap them in a call to `renderPlot()`.

.footnote[[shinyapps.io](https://www.shinyapps.io/) | [Example app](https://enchufa2.shinyapps.io/ls-springs/)]

---

# Dashboards