EDA I: Introduction to R and Data Analysis

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---

# Analyse des Données : Introduction to visualization

### 2023-01-15

#### [Master I MIDS & MFA]()

#### [Analyse Exploratoire de Données](http://stephane-v-boucheron.fr/courses/eda/)

#### [Stéphane Boucheron](http://stephane-v-boucheron.fr)

---
template: inter-slide

## <svg aria-hidden="true" role="img" viewBox="0 0 576 512" style="height:1em;width:1.12em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:white;overflow:visible;position:relative;"><path d="M565.6 36.24C572.1 40.72 576 48.11 576 56V392C576 401.1 569.8 410.9 560.5 414.4L392.5 478.4C387.4 480.4 381.7 480.5 376.4 478.8L192.5 417.5L32.54 478.4C25.17 481.2 16.88 480.2 10.38 475.8C3.882 471.3 0 463.9 0 456V120C0 110 6.15 101.1 15.46 97.57L183.5 33.57C188.6 31.6 194.3 31.48 199.6 33.23L383.5 94.52L543.5 33.57C550.8 30.76 559.1 31.76 565.6 36.24H565.6zM48 421.2L168 375.5V90.83L48 136.5V421.2zM360 137.3L216 89.3V374.7L360 422.7V137.3zM408 421.2L528 375.5V90.83L408 136.5V421.2z"/></svg>

### [Grammar of Graphics illustrated](#gg)

### [Do It Yourself](#diy)

### [Books](#books)

---
template: inter-slide

## Grammar of Graphics

---

We will use the _Grammar of Graphics_ approach to visualization

The expression _Grammar of Graphics_ was coined by Leiland Wilkinson to describe a principled approach to visualization in Data Analysis (EDA)

A plot is organized around data (a table with rows (observations) and columns (variables))

A *plot* is a *graphical object* that can be built layer by layer

Building a graphical object consists in chaining elementary operations

The acclaimed TED presentation by [Hans Rosling](https://en.wikipedia.org/wiki/Hans_Rosling) illustrates the Grammar of Graphics approach

---
### Grammar of Graphics in Action <svg aria-hidden="true" role="img" viewBox="0 0 576 512" style="height:1em;width:1.12em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M0 128C0 92.7 28.7 64 64 64H320c35.3 0 64 28.7 64 64V384c0 35.3-28.7 64-64 64H64c-35.3 0-64-28.7-64-64V128zM559.1 99.8c10.4 5.6 16.9 16.4 16.9 28.2V384c0 11.8-6.5 22.6-16.9 28.2s-23 5-32.9-1.6l-96-64L416 337.1V320 192 174.9l14.2-9.5 96-64c9.8-6.5 22.4-7.2 32.9-1.6z"/></svg>

```r
knitr::include_url("https://www.youtube.com/embed/jbkSRLYSojo")
```

???

### [<svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M579.8 267.7c56.5-56.5 56.5-148 0-204.5c-50-50-128.8-56.5-186.3-15.4l-1.6 1.1c-14.4 10.3-17.7 30.3-7.4 44.6s30.3 17.7 44.6 7.4l1.6-1.1c32.1-22.9 76-19.3 103.8 8.6c31.5 31.5 31.5 82.5 0 114L422.3 334.8c-31.5 31.5-82.5 31.5-114 0c-27.9-27.9-31.5-71.8-8.6-103.8l1.1-1.6c10.3-14.4 6.9-34.4-7.4-44.6s-34.4-6.9-44.6 7.4l-1.1 1.6C206.5 251.2 213 330 263 380c56.5 56.5 148 56.5 204.5 0L579.8 267.7zM60.2 244.3c-56.5 56.5-56.5 148 0 204.5c50 50 128.8 56.5 186.3 15.4l1.6-1.1c14.4-10.3 17.7-30.3 7.4-44.6s-30.3-17.7-44.6-7.4l-1.6 1.1c-32.1 22.9-76 19.3-103.8-8.6C74 372 74 321 105.5 289.5L217.7 177.2c31.5-31.5 82.5-31.5 114 0c27.9 27.9 31.5 71.8 8.6 103.9l-1.1 1.6c-10.3 14.4-6.9 34.4 7.4 44.6s34.4 6.9 44.6-7.4l1.1-1.6C433.5 260.8 427 182 377 132c-56.5-56.5-148-56.5-204.5 0L60.2 244.3z"/></svg> Gapminder](https://www.gapminder.org)

[Gapminder](https://www.gapminder.org) is a Swedish foundation.

> Gapminder fights devastating misconceptions about global development. Gapminder produces free teaching resources making the world understandable based on reliable statistics. Gapminder promotes a fact-based worldview everyone can understand.

Hans Rosling was a founder of Gapminder

---

The TED presentation is an example of attractive data visualization

It raises a number of questions

- Where do the data come from?

- Are the data reliable?

- How can we *gather* data?

- How can we *tidy* data?

- How can we analyze/explore data?

- How can we present data (to a diverse audience)?

---
template: inter-slide
name: diy

## Do It Yourself with <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg>

---

We will reproduce the animated demonstration using

- `ggplot2`: an implementation of *grammar of graphics* in <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg>

- `plotly`: a bridge between <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> and the javascript library `D3.js`

- Using `plotly`, opting for `html` ouput, brings the possibility of interactivity and animation

---
### <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M78.6 5C69.1-2.4 55.6-1.5 47 7L7 47c-8.5 8.5-9.4 22-2.1 31.6l80 104c4.5 5.9 11.6 9.4 19 9.4h54.1l109 109c-14.7 29-10 65.4 14.3 89.6l112 112c12.5 12.5 32.8 12.5 45.3 0l64-64c12.5-12.5 12.5-32.8 0-45.3l-112-112c-24.2-24.2-60.6-29-89.6-14.3l-109-109V104c0-7.5-3.5-14.5-9.4-19L78.6 5zM19.9 396.1C7.2 408.8 0 426.1 0 444.1C0 481.6 30.4 512 67.9 512c18 0 35.3-7.2 48-19.9L233.7 374.3c-7.8-20.9-9-43.6-3.6-65.1l-61.7-61.7L19.9 396.1zM512 144c0-10.5-1.1-20.7-3.2-30.5c-2.4-11.2-16.1-14.1-24.2-6l-63.9 63.9c-3 3-7.1 4.7-11.3 4.7H352c-8.8 0-16-7.2-16-16V102.6c0-4.2 1.7-8.3 4.7-11.3l63.9-63.9c8.1-8.1 5.2-21.8-6-24.2C388.7 1.1 378.5 0 368 0C288.5 0 224 64.5 224 144l0 .8 85.3 85.3c36-9.1 75.8 .5 104 28.7L429 274.5c49-23 83-72.8 83-130.5zM104 432c0 13.3-10.7 24-24 24s-24-10.7-24-24s10.7-24 24-24s24 10.7 24 24z"/></svg> Install and load packages

.fl.w-50.pa2.f6[

In <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> The `gapminder` data can be loaded from *package* `gapminder`

]

.fl.w-50.pa2[

```r
pacman::p_load("tidyverse")
pacman::p_load("gapminder")
pacman::p_load("glue")
pacman::p_load("DT")
```

- Loading [`tidyverse`]() also loads parts of `magrittr`, notably the _pipe_ `%>%`

- <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M320 408c0-6.428-.8457-12.66-2.434-18.6C338.2 376.7 352 353.9 352 328c0-6.428-.8457-12.66-2.434-18.6C370.2 296.7 384 273.9 384 248c0-2.705-.1484-5.373-.4414-8H440C479.7 240 512 207.7 512 168S479.7 96 440 96H243.7C227.5 76.51 203.2 64 176 64H126.1C94.02 64 64.47 81.1 49 108.6L17.65 164.5C6.104 185.1 0 208.4 0 231.8v107.9C0 417.1 64.6 480 144 480h104C287.7 480 320 447.7 320 408zM280 304c13.23 0 24 10.78 24 24S293.2 352 280 352H232.1C218.9 352 208 341.2 208 328S218.8 304 232 304H280zM312 224c13.23 0 24 10.78 24 24S325.2 272 312 272h-48c-3.029 0-5.875-.7012-8.545-1.73C260.7 259.9 264 248.4 264 236V224H312zM440 144c13.23 0 24 10.78 24 24S453.2 192 440 192h-176V152c0-2.686-.5566-5.217-.793-7.84C263.5 144.2 263.7 144 264 144H440zM48 339.7V231.8c0-15.25 3.984-30.41 11.52-43.88l31.34-55.78C97.84 119.7 111.4 112 126.1 112H176c22.06 0 40 17.94 40 40v84c0 15.44-12.56 28-28 28S160 251.4 160 236V184C160 170.8 149.3 160 136 160S112 170.8 112 184v52c0 33.23 21.58 61.25 51.36 71.54C161.3 314 160 320.9 160 328c0 5.041 1.166 9.836 2.178 14.66C137.4 354 120 378.1 120 408c0 7.684 1.557 14.94 3.836 21.87C80.56 420.9 48 383.9 48 339.7zM192 432c-13.23 0-24-10.78-24-24S178.8 384 192 384h56c13.23 0 24 10.78 24 24s-10.77 24-24 24H192z"/></svg> <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> `4.xx` offers a new pipe `|>`

]

???

Insist on the difference between _installing_ and _loading_ a package

- How do we get the list of installed packages?
- How do we get the list of loaded packages?
- Which objects are made available by a package?

`pacman::p_load()` kills two birds with one stone

---

### Have a look at `gapminder` dataset

.fl.w-30.pa2[

- `gapminder` is a table, like a spreadsheet table, or a relational database table

- In <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> parlance, `gapminder` is a `data.frame`

- It is also a `tibble` in modern `tidyverse` parlance

]

.fl.w-70.pa2[

```r
gapminder %>% 
  glimpse(width=50)
```

```
## Rows: 1,704
## Columns: 6
## $ country <fct> "Afghanistan", "Afghanistan", …
## $ continent <fct> Asia, Asia, Asia, Asia, Asia, …
## $ year <int> 1952, 1957, 1962, 1967, 1972, …
## $ lifeExp <dbl> 28.801, 30.332, 31.997, 34.020…
## $ pop <int> 8425333, 9240934, 10267083, 11…
## $ gdpPercap <dbl> 779.4453, 820.8530, 853.1007, …
```

]

---

### What's in a data frame/tibble?

.fl.w-30.pa2[
- A table has a _schema_: a list of named _columns_, each  with a given type

- A table has a _content_: _rows_. Each row is a collection of items, corresponding
to the columns

- `glimpse()` allows to see the schema and the first rows

- `head()` allows to see the first rows

]

.fl.w-70.pa2.f6[

```r
gapminder %>% 
  head() %>% 
  knitr::kable(digits = 3)
```

]
---

### Get a feeling of the dataset

.fl.w-30.pa2.f6[

We pick two random rows for each continent

```r
tmp <- gapminder %>% 
* group_by(continent) %>%
* slice_sample(n=2) %>%
 knitr::kable(digits = 3)
```

]

.fl.w-70.pa2.f6[

<table>
 <thead>
 <tr>
 <th style="text-align:left;"> country </th>
 <th style="text-align:left;"> continent </th>
 <th style="text-align:right;"> year </th>
 <th style="text-align:right;"> lifeExp </th>
 <th style="text-align:right;"> pop </th>
 <th style="text-align:right;"> gdpPercap </th>
 </tr>
 </thead>
<tbody>
 <tr>
 <td style="text-align:left;"> Congo, Rep. </td>
 <td style="text-align:left;"> Africa </td>
 <td style="text-align:right;"> 1982 </td>
 <td style="text-align:right;"> 56.695 </td>
 <td style="text-align:right;"> 1774735 </td>
 <td style="text-align:right;"> 4879.508 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Morocco </td>
 <td style="text-align:left;"> Africa </td>
 <td style="text-align:right;"> 2007 </td>
 <td style="text-align:right;"> 71.164 </td>
 <td style="text-align:right;"> 33757175 </td>
 <td style="text-align:right;"> 3820.175 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Mexico </td>
 <td style="text-align:left;"> Americas </td>
 <td style="text-align:right;"> 1952 </td>
 <td style="text-align:right;"> 50.789 </td>
 <td style="text-align:right;"> 30144317 </td>
 <td style="text-align:right;"> 3478.126 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Guatemala </td>
 <td style="text-align:left;"> Americas </td>
 <td style="text-align:right;"> 1977 </td>
 <td style="text-align:right;"> 56.029 </td>
 <td style="text-align:right;"> 5703430 </td>
 <td style="text-align:right;"> 4879.993 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> China </td>
 <td style="text-align:left;"> Asia </td>
 <td style="text-align:right;"> 1977 </td>
 <td style="text-align:right;"> 63.967 </td>
 <td style="text-align:right;"> 943455000 </td>
 <td style="text-align:right;"> 741.237 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Hong Kong, China </td>
 <td style="text-align:left;"> Asia </td>
 <td style="text-align:right;"> 1957 </td>
 <td style="text-align:right;"> 64.750 </td>
 <td style="text-align:right;"> 2736300 </td>
 <td style="text-align:right;"> 3629.076 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Poland </td>
 <td style="text-align:left;"> Europe </td>
 <td style="text-align:right;"> 1962 </td>
 <td style="text-align:right;"> 67.640 </td>
 <td style="text-align:right;"> 30329617 </td>
 <td style="text-align:right;"> 5338.752 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Iceland </td>
 <td style="text-align:left;"> Europe </td>
 <td style="text-align:right;"> 1982 </td>
 <td style="text-align:right;"> 76.990 </td>
 <td style="text-align:right;"> 233997 </td>
 <td style="text-align:right;"> 23269.607 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Australia </td>
 <td style="text-align:left;"> Oceania </td>
 <td style="text-align:right;"> 1952 </td>
 <td style="text-align:right;"> 69.120 </td>
 <td style="text-align:right;"> 8691212 </td>
 <td style="text-align:right;"> 10039.596 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Australia </td>
 <td style="text-align:left;"> Oceania </td>
 <td style="text-align:right;"> 1977 </td>
 <td style="text-align:right;"> 73.490 </td>
 <td style="text-align:right;"> 14074100 </td>
 <td style="text-align:right;"> 18334.198 </td>
 </tr>
</tbody>
</table>

]

---
exclude: true

### A tidy table

???

What's make a table tidy?

-  No/Few redundancies (what does it mean)
  -  Is the `gapminder` table redundant?
  -  ....
-  Compare with Codd's principles from database theory

---
exclude: true

### Gapminder tibble  (extract)

```r
tt <- gapminder %>%
 filter(year==2002) %>%
 mutate(pop = stringr::str_c(round(pop/1e6,1), 'M')) %>%
 DT::datatable(options=list(pageLength=8,
 dom = 't'),
* rownames = FALSE) %>%
* DT::formatCurrency('gdpPercap', digits=0) %>%
* DT::formatRound('lifeExp', 1)
```

???

-  Formatting instruction should not be wired in the table fed to `DT`
-  Reorder columns
-  Order countries by decreasing population size
-  Right-align `pop` column
-  Make provision for caption

---
### <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M448 80v48c0 44.2-100.3 80-224 80S0 172.2 0 128V80C0 35.8 100.3 0 224 0S448 35.8 448 80zM393.2 214.7c20.8-7.4 39.9-16.9 54.8-28.6V288c0 44.2-100.3 80-224 80S0 332.2 0 288V186.1c14.9 11.8 34 21.2 54.8 28.6C99.7 230.7 159.5 240 224 240s124.3-9.3 169.2-25.3zM0 346.1c14.9 11.8 34 21.2 54.8 28.6C99.7 390.7 159.5 400 224 400s124.3-9.3 169.2-25.3c20.8-7.4 39.9-16.9 54.8-28.6V432c0 44.2-100.3 80-224 80S0 476.2 0 432V346.1z"/></svg> `$\sigma$`: Picking one year of data

.fl.w-30.pa2[

```r
gapminder_2002 <-
 gapminder %>%
* filter(year==2002)
```

]

.fl.w-70.pa2.f6[

<table>
 <thead>
 <tr>
 <th style="text-align:left;"> country </th>
 <th style="text-align:left;"> continent </th>
 <th style="text-align:right;"> year </th>
 <th style="text-align:right;"> lifeExp </th>
 <th style="text-align:right;"> pop </th>
 <th style="text-align:right;"> gdpPercap </th>
 </tr>
 </thead>
<tbody>
 <tr>
 <td style="text-align:left;"> Afghanistan </td>
 <td style="text-align:left;"> Asia </td>
 <td style="text-align:right;"> 2002 </td>
 <td style="text-align:right;"> 42.129 </td>
 <td style="text-align:right;"> 25268405 </td>
 <td style="text-align:right;"> 726.734 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Albania </td>
 <td style="text-align:left;"> Europe </td>
 <td style="text-align:right;"> 2002 </td>
 <td style="text-align:right;"> 75.651 </td>
 <td style="text-align:right;"> 3508512 </td>
 <td style="text-align:right;"> 4604.212 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Algeria </td>
 <td style="text-align:left;"> Africa </td>
 <td style="text-align:right;"> 2002 </td>
 <td style="text-align:right;"> 70.994 </td>
 <td style="text-align:right;"> 31287142 </td>
 <td style="text-align:right;"> 5288.040 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Angola </td>
 <td style="text-align:left;"> Africa </td>
 <td style="text-align:right;"> 2002 </td>
 <td style="text-align:right;"> 41.003 </td>
 <td style="text-align:right;"> 10866106 </td>
 <td style="text-align:right;"> 2773.287 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Argentina </td>
 <td style="text-align:left;"> Americas </td>
 <td style="text-align:right;"> 2002 </td>
 <td style="text-align:right;"> 74.340 </td>
 <td style="text-align:right;"> 38331121 </td>
 <td style="text-align:right;"> 8797.641 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Australia </td>
 <td style="text-align:left;"> Oceania </td>
 <td style="text-align:right;"> 2002 </td>
 <td style="text-align:right;"> 80.370 </td>
 <td style="text-align:right;"> 19546792 </td>
 <td style="text-align:right;"> 30687.755 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Austria </td>
 <td style="text-align:left;"> Europe </td>
 <td style="text-align:right;"> 2002 </td>
 <td style="text-align:right;"> 78.980 </td>
 <td style="text-align:right;"> 8148312 </td>
 <td style="text-align:right;"> 32417.608 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Bahrain </td>
 <td style="text-align:left;"> Asia </td>
 <td style="text-align:right;"> 2002 </td>
 <td style="text-align:right;"> 74.795 </td>
 <td style="text-align:right;"> 656397 </td>
 <td style="text-align:right;"> 23403.559 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Bangladesh </td>
 <td style="text-align:left;"> Asia </td>
 <td style="text-align:right;"> 2002 </td>
 <td style="text-align:right;"> 62.013 </td>
 <td style="text-align:right;"> 135656790 </td>
 <td style="text-align:right;"> 1136.390 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Belgium </td>
 <td style="text-align:left;"> Europe </td>
 <td style="text-align:right;"> 2002 </td>
 <td style="text-align:right;"> 78.320 </td>
 <td style="text-align:right;"> 10311970 </td>
 <td style="text-align:right;"> 30485.884 </td>
 </tr>
</tbody>
</table>

]

---

In the parlance of Relational Algebra, `filter` performs  a _selection_ of rows

[Package `dplyr` docs](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8/topics/filter)

> find rows/cases where conditions are true

---
exclude: true

---
template: inter-slide

## Static plotting

---

### First attempt

.fl.w-40.pa2[

- Define a plot with respect to `gapminder_2002`

- Map variables `gdpPercap` and `lifeExp` to axes `x` and `y`

- For each row, draw a point at coordinates defined by the mapping

```r
p <- gapminder_2002 %>%
* ggplot() +
* aes(x=gdpPercap) +
* aes(y=lifeExp) +
* geom_point()

p
```

]

.fl.w-60.pa2[

]

---

We are building a graphical object (a `ggplot` object) around a data frame (`gapminder`)

We supply _aesthetic mappings_ (`aes()`) that can be either global or bound to some _geometries_ (`geom_point()`) or _statistics_

The global aesthetic mapping defines which columns are

- mapped to which axes,

- possibly mapped to colours, linetypes, shapes, ...

Geometries and Statistics describe the building blocks of graphics

Aesthetic mappings are usually complemented by, possibly implicit, *scales*

---

### What's missing here?

when comparing to the Gapminder demonstration, we can spot  that

-  colors are missing

-  bubble sizes are all the same

-  titles and legends are missing

We will add *layers* to the graphical object to complete the plot

---

### Display more information

.fl.w-30.pa2[

- Map `continent` to color

- Map `pop` to bubble size

- Make point transparent by tuning `alpha` (avoid *overplotting*)

```r
p <- p +
* aes(size = pop,
* color = continent
* ) +
* geom_point(alpha=.75)

p
```

]

.fl.w-70.pa2[

]

---

### Scaling

In order to pay tribute to Hans Rosling, we need to take care of two _scaling_ issues:

- the gdp per capita axis should be _logarithmic_

- the _area_ of the point should be proportional to the population

---

### Fixing scales

.fl.w-40.pa2.f6[

```r
p <- p +
*scale_x_log10(
* limits=c(1e2,5e4)
* ) +
*scale_size_area()

p
```
]

.fl.w-60.pa2[

]

???

```r
zoom_continent <- 'Europe'
```

---

### <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M184 0c30.9 0 56 25.1 56 56V456c0 30.9-25.1 56-56 56c-28.9 0-52.7-21.9-55.7-50.1c-5.2 1.4-10.7 2.1-16.3 2.1c-35.3 0-64-28.7-64-64c0-7.4 1.3-14.6 3.6-21.2C21.4 367.4 0 338.2 0 304c0-31.9 18.7-59.5 45.8-72.3C37.1 220.8 32 207 32 192c0-30.7 21.6-56.3 50.4-62.6C80.8 123.9 80 118 80 112c0-29.9 20.6-55.1 48.3-62.1C131.3 21.9 155.1 0 184 0zM328 0c28.9 0 52.6 21.9 55.7 49.9c27.8 7 48.3 32.1 48.3 62.1c0 6-.8 11.9-2.4 17.4c28.8 6.2 50.4 31.9 50.4 62.6c0 15-5.1 28.8-13.8 39.7C493.3 244.5 512 272.1 512 304c0 34.2-21.4 63.4-51.6 74.8c2.3 6.6 3.6 13.8 3.6 21.2c0 35.3-28.7 64-64 64c-5.6 0-11.1-.7-16.3-2.1c-3 28.2-26.8 50.1-55.7 50.1c-30.9 0-56-25.1-56-56V56c0-30.9 25.1-56 56-56z"/></svg>

- Why is it important to use logarithmic scaling for gdp per capita?

- When is it important to use logarithmic scaling on some axis (in other contexts)?

- Why is it important to specify `scale_size_area()` ?

---

### In perspective

.fl.w-30.pa2[

- Add a plot title

- Make axes titles
  + explicit
  + readable

]

.fl.w-70.pa2.f6[

```r
p_titre <- glue("Gapminder {min(p$data$year)}-{max(p$data$year)}")

p_caption <- "From sick and poor (bottom left) to healthy and rich (top right)"

p <- p +
* labs(title=p_titre,
* x = "Yearly income per capita (US$)",
* y = "Life expectancy",
* caption=p_caption )

p
```

]

---

### In perspective (cont'd)

.fl.w-30.pa2[

- Add a plot title

- Make axes titles
  + explicit
  + readable

]

.fl.w-70.pa2[

]

---

What should be the purposes of

- Title

- Subtitle

- Caption

---

### Theming using `ggthemes`

.fl.w-40.pa2[

- Theming

```r
require("ggthemes")

p +
  theme_wsj()
```

A theme defines the _look and feel_ of plots

Within a single document, we should use only one theme

See [Getting the theme](https://ggplot2.tidyverse.org/reference/theme_get.html) for a gallery of available themes

]

.fl.w-60.pa2[

```
## Loading required package: ggthemes
```

]

???

- 
-

---

### Tuning scales

.fl.w-60.pa2[

```r
neat_color_scale <-
 c("Africa" = "#01d4e5",
 "Americas" = "#7dea01" ,
 "Asia" = "#fc5173",
 "Europe" = "#fde803",
 "Oceania" = "#536227")
p <- p +
* scale_size_area(max_size = 15) +
* scale_color_manual(values = neat_color_scale)

p
```

Choosing a color scale is a difficult task

`viridis` is often a good pick

]

.fl.w-40.pa2[

<img src="cm-1.2-EDA_files/figure-html/theme_scale-out-1.png" width="504" />
]

---

### Zooming on a continent

.fl.w-40.pa2[

```r
*require(ggforce)

z_in <- 'Africa'

p_africa <- p +
* facet_zoom(
* xy= continent==z_in,
* zoom.data=continent==z_in
* )

p_africa
```

]

.fl.w-60.pa2[

]

???

Look for journal themes/ WSJ themes

Wes Anderson themes

---

### A static plot for Gapminder (2002)

---
name: recap 
template: inter-slide

## Recap

???

---

### Building a graphical object layer by layer

```r
gapminder %>% 
  filter(year == 2002) %>%
   ggplot() +
   aes(x = gdpPercap) +
   aes(y = lifeExp) +
   aes(size = pop) +
   aes(color = continent) +
   geom_point(alpha=.5) +
   scale_x_log10() +
   scale_size_area(max_size = 15,
                  labels= scales::label_number(scale=1/1e6,
                                               suffix=" M")) +
   scale_color_manual(values = neat_color_scale) +
   theme_minimal() +
   theme(legend.position = "none") +
    labs(title= glue("Gapminder  {min(gapminder$year)}-{max(gapminder$year)}"),
         x = "Yearly Income per Capita",
         y = "Life Expectancy",
       caption="From sick  and poor (bottom left) to healthy and rich (top right)")
```

???

---

### In action

```r
*gapminder
```
]
 
.panel2-plot_gap_minder_2002-auto[

```
## # A tibble: 1,704 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
## 7 Afghanistan Asia 1982 39.9 12881816 978.
## 8 Afghanistan Asia 1987 40.8 13867957 852.
## 9 Afghanistan Asia 1992 41.7 16317921 649.
## 10 Afghanistan Asia 1997 41.8 22227415 635.
## # … with 1,694 more rows
```
]

---
count: false

```r
gapminder %>%
* filter(year == 2002)
```
]
 
.panel2-plot_gap_minder_2002-auto[

```
## # A tibble: 142 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 2002 42.1 25268405 727.
## 2 Albania Europe 2002 75.7 3508512 4604.
## 3 Algeria Africa 2002 71.0 31287142 5288.
## 4 Angola Africa 2002 41.0 10866106 2773.
## 5 Argentina Americas 2002 74.3 38331121 8798.
## 6 Australia Oceania 2002 80.4 19546792 30688.
## 7 Austria Europe 2002 79.0 8148312 32418.
## 8 Bahrain Asia 2002 74.8 656397 23404.
## 9 Bangladesh Asia 2002 62.0 135656790 1136.
## 10 Belgium Europe 2002 78.3 10311970 30486.
## # … with 132 more rows
```
]

---
count: false

```r
gapminder %>%
 filter(year == 2002) %>%
* ggplot()
```
]
 
.panel2-plot_gap_minder_2002-auto[
<img src="cm-1.2-EDA_files/figure-html/plot_gap_minder_2002_auto_03_output-1.png" width="504" />
]

---
count: false

```r
gapminder %>%
 filter(year == 2002) %>%
 ggplot() +
* aes(x = gdpPercap)
```
]
 
.panel2-plot_gap_minder_2002-auto[
<img src="cm-1.2-EDA_files/figure-html/plot_gap_minder_2002_auto_04_output-1.png" width="504" />
]

---
count: false

```r
gapminder %>%
 filter(year == 2002) %>%
 ggplot() +
 aes(x = gdpPercap) +
* aes(y = lifeExp)
```
]
 
.panel2-plot_gap_minder_2002-auto[
<img src="cm-1.2-EDA_files/figure-html/plot_gap_minder_2002_auto_05_output-1.png" width="504" />
]

---
count: false

```r
gapminder %>%
 filter(year == 2002) %>%
 ggplot() +
 aes(x = gdpPercap) +
 aes(y = lifeExp) +
* aes(size = pop)
```
]
 
.panel2-plot_gap_minder_2002-auto[
<img src="cm-1.2-EDA_files/figure-html/plot_gap_minder_2002_auto_06_output-1.png" width="504" />
]

---
count: false

```r
gapminder %>%
 filter(year == 2002) %>%
 ggplot() +
 aes(x = gdpPercap) +
 aes(y = lifeExp) +
 aes(size = pop) +
* aes(color = continent)
```
]
 
.panel2-plot_gap_minder_2002-auto[
<img src="cm-1.2-EDA_files/figure-html/plot_gap_minder_2002_auto_07_output-1.png" width="504" />
]

---
count: false

```r
gapminder %>%
 filter(year == 2002) %>%
 ggplot() +
 aes(x = gdpPercap) +
 aes(y = lifeExp) +
 aes(size = pop) +
 aes(color = continent) +
* geom_point(alpha=.5)
```
]
 
.panel2-plot_gap_minder_2002-auto[
<img src="cm-1.2-EDA_files/figure-html/plot_gap_minder_2002_auto_08_output-1.png" width="504" />
]

---
count: false

```r
gapminder %>%
 filter(year == 2002) %>%
 ggplot() +
 aes(x = gdpPercap) +
 aes(y = lifeExp) +
 aes(size = pop) +
 aes(color = continent) +
 geom_point(alpha=.5) +
* scale_x_log10()
```
]
 
.panel2-plot_gap_minder_2002-auto[
<img src="cm-1.2-EDA_files/figure-html/plot_gap_minder_2002_auto_09_output-1.png" width="504" />
]

---
count: false

---
count: false

---
count: false

---
count: false

```r
gapminder %>%
 filter(year == 2002) %>%
 ggplot() +
 aes(x = gdpPercap) +
 aes(y = lifeExp) +
 aes(size = pop) +
 aes(color = continent) +
 geom_point(alpha=.5) +
 scale_x_log10() +
 scale_size_area(max_size = 15,
 labels= scales::label_number(scale=1/1e6,
 suffix=" M")) +
 scale_color_manual(values = neat_color_scale) +
 theme_minimal() +
* theme(legend.position = "none")
```
]
 
.panel2-plot_gap_minder_2002-auto[
<img src="cm-1.2-EDA_files/figure-html/plot_gap_minder_2002_auto_13_output-1.png" width="504" />
]

---
count: false

```r
gapminder %>%
 filter(year == 2002) %>%
 ggplot() +
 aes(x = gdpPercap) +
 aes(y = lifeExp) +
 aes(size = pop) +
 aes(color = continent) +
 geom_point(alpha=.5) +
 scale_x_log10() +
 scale_size_area(max_size = 15,
 labels= scales::label_number(scale=1/1e6,
 suffix=" M")) +
 scale_color_manual(values = neat_color_scale) +
 theme_minimal() +
 theme(legend.position = "none") +
* labs(title= glue("Gapminder {min(gapminder$year)}-{max(gapminder$year)}"),
* x = "Yearly Income per Capita",
* y = "Life Expectancy",
* caption="From sick and poor (bottom left) to healthy and rich (top right)")
```
]
 
.panel2-plot_gap_minder_2002-auto[
<img src="cm-1.2-EDA_files/figure-html/plot_gap_minder_2002_auto_14_output-1.png" width="504" />
]

---
count: false

```r
gapminder %>%
 filter(year == 2002) %>%
 ggplot() +
 aes(x = gdpPercap) +
 aes(y = lifeExp) +
 aes(size = pop) +
 aes(color = continent) +
 geom_point(alpha=.5) +
 scale_x_log10() +
 scale_size_area(max_size = 15,
 labels= scales::label_number(scale=1/1e6,
 suffix=" M")) +
 scale_color_manual(values = neat_color_scale) +
 theme_minimal() +
 theme(legend.position = "none") +
 labs(title= glue("Gapminder {min(gapminder$year)}-{max(gapminder$year)}"),
 x = "Yearly Income per Capita",
 y = "Life Expectancy",
 caption="From sick and poor (bottom left) to healthy and rich (top right)")
```
]
 
.panel2-plot_gap_minder_2002-auto[
<img src="cm-1.2-EDA_files/figure-html/plot_gap_minder_2002_auto_15_output-1.png" width="504" />
]

???

---

### Adding labels

```r
*require(ggrepel)
 
gapminder %>% 
  filter(year == 2002) %>%
   ggplot() +
   aes(x = gdpPercap) +
   aes(y = lifeExp) +
   aes(size = pop) +
   aes(color = continent) +
*  aes(label = country) +
   geom_point(alpha=.5) +
*  ggrepel::geom_label_repel(max.overlaps = 15) +
   scale_x_log10() +
   scale_size_area(max_size = 15,
                  labels= scales::label_number(scale=1/1e6,
                                               suffix=" M")) +
   scale_color_manual(values = neat_color_scale) +
   theme_minimal() +
   theme(legend.position = "none") +
    labs(title= glue("Gapminder  {min(gapminder$year)}-{max(gapminder$year)}"),
         x = "Yearly Income per Capita",
         y = "Life Expectancy",
       caption="From sick  and poor (bottom left) to healthy and rich (top right)")
```

---

---
template: inter-slide

## Facetting

---

So far we have only presented one year of data (2002)

Rosling used an *animation* to display the flow of time

If we have to deliver a printable report, we cannot rely on animation, 
but we can rely on *facetting*

Facets are collections of small plots constructed in the same way 
on subsets of the data

We add a layer to the graphical object using `facet_wrap()`

As all rows in `gapminder_2002` are all related to `year` 2002

---

```r
p <- p +
 scale_x_log10(limits=c(100, 50000)) +
 scale_size_area(max_size = 15,
 labels= scales::label_number(scale=1/1e6, suffix=" M")) +
 scale_color_manual(values = neat_color_scale) +
 guides(color = guide_legend(title = "Continent",
 override.aes = list(size = 5),
 order = 1),
 size = guide_legend(title = "Population",
 order = 2)) +
 theme(axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
* facet_wrap(vars(year), ncol=6)

p
```

---

---

We need to rebuild the graphical object along the same lines (using the same graphical pipeline) but starting from the whole `gapminder` dataset

Should we do this using _cut and paste_?

---

### Don't Repeat Yoursel (DRY)

.f6[
> Abide to the DRY principle using operator `%+%`: the `ggplot2` object `p` can be fed with another dataframe and all you need is proper facetting.
]

```r
*p <- (p %+% gapminder)

p <- p + 
 labs(title=glue("Gapminder {min(p$data$year)}-{max(p$data$year)}"))

p
```

???

---

---
template: inter-slide

## Animate for free with plotly

---

### From <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> to `D3.js`

```r
q <- filter(gapminder, FALSE) %>%
 ggplot() +
 aes(x = gdpPercap) +
 aes(y = lifeExp) +
 aes(size = pop) +
* aes(text = country) +
 aes(fill = continent) +
* aes(frame = year) +
 geom_point(alpha=.5, colour='black') +
 scale_x_log10() +
 scale_size_area(max_size = 15,
 labels= scales::label_number(scale=1/1e6,
 suffix=" M")) +
 scale_fill_manual(values = neat_color_scale) +
 theme(legend.position = "none") +
 labs(title= glue("Gapminder {min(gapminder$year)}-{max(gapminder$year)}"),
 x = "Yearly Income per Capita",
 y = "Life Expectancy",
 caption="From sick and poor (bottom left) to healthy and rich (top right)")

(q %+% gapminder) %>%
* plotly::ggplotly(height = 500, width=750)
```

---

```r
(p +
 facet_null() +
 aes(frame=year)) %>%
 plotly::ggplotly(height = 500, width=750)
```

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---

### Zooming on Europe (tweaking code)

```r
target_continent <- 'Europe'

(
  q %+%
* filter(gapminder, continent==target_continent) +
* ggtitle(glue("Gapminder on {target_continent}"))
) %>%
  plotly::ggplotly(height = 500, width=750)
```

---

### Zooming on Europe

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---
template: inter-slide

```r
knitr::include_url("https://plotly.com/r/")
```

---
template: inter-slide

```r
knitr::include_url("https://www.youtube.com/embed/3n9nASHg9gc")
```

Slides created via the R package [**xaringan**](https://github.com/yihui/xaringan).

---
template: inter-slide

```r
knitr::include_url("https://www.youtube.com/embed/RPFh3y9UAX4")
```

---
exclude: true

- `ggplotly`

- `highcharter`

???

[raukr](https://nbisweden.github.io/raukrtemplate/presentation_demo.html#1)

---
exclude: true

### Under the hood

`htmltools`

`htmlwidgets`

???

- `facet_zoom()` on a continent (from `ggforce`)

- Animation is not interaction!

- `shiny/flex_dashboard`

---

background-image: url('./img/pexels-cottonbro-3171837.jpg')
background-size: cover

# The End