Instrumentos de Análisis Urbanos II

# Instrumentos de Análisis Urbanos II
## Maestría en Economía Urbana
### 
### Universidad Torcuato Di Tella
### 15/08/2023

---

layout: true
  
  
<div class="my-footer"><span>Instrumentos de Análisis Urbanos II - <a href="https://tuqmano.github.io/geo_utdt/">https://tuqmano.github.io/geo_utdt/</a></span></div>

---
class: inverse, center, middle

# Intro DataViz

---

# Elegir el gráfico correcto para nuestros datos

---
class: center

## Elegir el gráfico correcto para nuestros datos

#### FUENTE: 
**https://www.data-to-viz.com/caveat/pie.html#**

---

## _Único caso útil para un gráfico de tortas_
<img src="https://raw.githubusercontent.com/TuQmano/geo_utdt2022/main/fig/piramide.png" width="80%" />

## Tampoco para tanto

### _A case for pie charts_ - [Fundamentals of Data Visualization, Claus Wilke](https://clauswilke.com/dataviz/visualizing-proportions.html#a-case-for-pie-charts)

---

## El problema de los mapas coropléticos

---
class:inverse

### #Election2020

#### > **"_Trump loves this 2016 election map because it suggests the majority of the country supports him (...)_"** **([NYT](https://www.nytimes.com/interactive/2020/10/30/opinion/election-results-maps.html))**

---

# El problema de los mapas coropléticos

---

## ARGENTINA 2015 ([repo](https://github.com/TuQmano/votemapARG/))

---

# Alternativas de visualización _geo_

---

# Mapa Coroplético

---

# Cartogramas

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# _Tilemaps_

---

# _Tilemaps_

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# _Faceted tilemaps_

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# Tilegrams

---

# Limitaciones varias

* Visualizar una variable / valor por unidad geográfica

* Solo hace uso del color para codificación visual (dificultad para calcular diferencias)

* Sesgo a favor de grandes unidades geográficas

---

background-image: url(https://hafen.github.io/geofacet/reference/figures/logo.png)
background-position: 95% 5%
background-size: 10%

# Grillas como (si fueran) mapas

**[<svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:#fdbd15;" xmlns="http://www.w3.org/2000/svg">  <path d="M326.612 185.391c59.747 59.809 58.927 155.698.36 214.59-.11.12-.24.25-.36.37l-67.2 67.2c-59.27 59.27-155.699 59.262-214.96 0-59.27-59.26-59.27-155.7 0-214.96l37.106-37.106c9.84-9.84 26.786-3.3 27.294 10.606.648 17.722 3.826 35.527 9.69 52.721 1.986 5.822.567 12.262-3.783 16.612l-13.087 13.087c-28.026 28.026-28.905 73.66-1.155 101.96 28.024 28.579 74.086 28.749 102.325.51l67.2-67.19c28.191-28.191 28.073-73.757 0-101.83-3.701-3.694-7.429-6.564-10.341-8.569a16.037 16.037 0 0 1-6.947-12.606c-.396-10.567 3.348-21.456 11.698-29.806l21.054-21.055c5.521-5.521 14.182-6.199 20.584-1.731a152.482 152.482 0 0 1 20.522 17.197zM467.547 44.449c-59.261-59.262-155.69-59.27-214.96 0l-67.2 67.2c-.12.12-.25.25-.36.37-58.566 58.892-59.387 154.781.36 214.59a152.454 152.454 0 0 0 20.521 17.196c6.402 4.468 15.064 3.789 20.584-1.731l21.054-21.055c8.35-8.35 12.094-19.239 11.698-29.806a16.037 16.037 0 0 0-6.947-12.606c-2.912-2.005-6.64-4.875-10.341-8.569-28.073-28.073-28.191-73.639 0-101.83l67.2-67.19c28.239-28.239 74.3-28.069 102.325.51 27.75 28.3 26.872 73.934-1.155 101.96l-13.087 13.087c-4.35 4.35-5.769 10.79-3.783 16.612 5.864 17.194 9.042 34.999 9.69 52.721.509 13.906 17.454 20.446 27.294 10.606l37.106-37.106c59.271-59.259 59.271-155.699.001-214.959z"></path></svg>`{geofacet}`](https://ryanhafen.com/blog/geofacet/)**

---

## Ventajas de `{geofacet}`

[<svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:#fdbd15;" xmlns="http://www.w3.org/2000/svg">  <path d="M326.612 185.391c59.747 59.809 58.927 155.698.36 214.59-.11.12-.24.25-.36.37l-67.2 67.2c-59.27 59.27-155.699 59.262-214.96 0-59.27-59.26-59.27-155.7 0-214.96l37.106-37.106c9.84-9.84 26.786-3.3 27.294 10.606.648 17.722 3.826 35.527 9.69 52.721 1.986 5.822.567 12.262-3.783 16.612l-13.087 13.087c-28.026 28.026-28.905 73.66-1.155 101.96 28.024 28.579 74.086 28.749 102.325.51l67.2-67.19c28.191-28.191 28.073-73.757 0-101.83-3.701-3.694-7.429-6.564-10.341-8.569a16.037 16.037 0 0 1-6.947-12.606c-.396-10.567 3.348-21.456 11.698-29.806l21.054-21.055c5.521-5.521 14.182-6.199 20.584-1.731a152.482 152.482 0 0 1 20.522 17.197zM467.547 44.449c-59.261-59.262-155.69-59.27-214.96 0l-67.2 67.2c-.12.12-.25.25-.36.37-58.566 58.892-59.387 154.781.36 214.59a152.454 152.454 0 0 0 20.521 17.196c6.402 4.468 15.064 3.789 20.584-1.731l21.054-21.055c8.35-8.35 12.094-19.239 11.698-29.806a16.037 16.037 0 0 0-6.947-12.606c-2.912-2.005-6.64-4.875-10.341-8.569-28.073-28.073-28.191-73.639 0-101.83l67.2-67.19c28.239-28.239 74.3-28.069 102.325.51 27.75 28.3 26.872 73.934-1.155 101.96l-13.087 13.087c-4.35 4.35-5.769 10.79-3.783 16.612 5.864 17.194 9.042 34.999 9.69 52.721.509 13.906 17.454 20.446 27.294 10.606l37.106-37.106c59.271-59.259 59.271-155.699.001-214.959z"></path></svg> _Voto Popular_ vs _Colegio Electoral_](https://github.com/TuQmano/popular_vote)

* se pueden graficar múltiples variables para cada unidad geográfica

* se pueden aplicar otros esquemas de codificación visual más allá del color

* cada unidad geográfica tiene asignada la misma proporción espacial

---
background-image: url(https://hafen.github.io/geofacet/reference/figures/logo.png)
background-position: 95% 5%
background-size: 10%

# `geofacet::grid_design()`

### <svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg">  <path d="M326.612 185.391c59.747 59.809 58.927 155.698.36 214.59-.11.12-.24.25-.36.37l-67.2 67.2c-59.27 59.27-155.699 59.262-214.96 0-59.27-59.26-59.27-155.7 0-214.96l37.106-37.106c9.84-9.84 26.786-3.3 27.294 10.606.648 17.722 3.826 35.527 9.69 52.721 1.986 5.822.567 12.262-3.783 16.612l-13.087 13.087c-28.026 28.026-28.905 73.66-1.155 101.96 28.024 28.579 74.086 28.749 102.325.51l67.2-67.19c28.191-28.191 28.073-73.757 0-101.83-3.701-3.694-7.429-6.564-10.341-8.569a16.037 16.037 0 0 1-6.947-12.606c-.396-10.567 3.348-21.456 11.698-29.806l21.054-21.055c5.521-5.521 14.182-6.199 20.584-1.731a152.482 152.482 0 0 1 20.522 17.197zM467.547 44.449c-59.261-59.262-155.69-59.27-214.96 0l-67.2 67.2c-.12.12-.25.25-.36.37-58.566 58.892-59.387 154.781.36 214.59a152.454 152.454 0 0 0 20.521 17.196c6.402 4.468 15.064 3.789 20.584-1.731l21.054-21.055c8.35-8.35 12.094-19.239 11.698-29.806a16.037 16.037 0 0 0-6.947-12.606c-2.912-2.005-6.64-4.875-10.341-8.569-28.073-28.073-28.191-73.639 0-101.83l67.2-67.19c28.239-28.239 74.3-28.069 102.325.51 27.75 28.3 26.872 73.934-1.155 101.96l-13.087 13.087c-4.35 4.35-5.769 10.79-3.783 16.612 5.864 17.194 9.042 34.999 9.69 52.721.509 13.906 17.454 20.446 27.294 10.606l37.106-37.106c59.271-59.259 59.271-155.699.001-214.959z"></path></svg> [Grillas colaborativas](https://github.com/hafen/geofacet/issues/102)

```r
library(geofacet)
grid_preview(argentina_grid2)
```

---

background-image: url(https://hafen.github.io/geofacet/reference/figures/logo.png)
background-position: 95% 5%
background-size: 10%

## **EJERCICIO**:

### Comunas de C.A.B.A

+ <svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:#fdbd15;" xmlns="http://www.w3.org/2000/svg">  <path d="M326.612 185.391c59.747 59.809 58.927 155.698.36 214.59-.11.12-.24.25-.36.37l-67.2 67.2c-59.27 59.27-155.699 59.262-214.96 0-59.27-59.26-59.27-155.7 0-214.96l37.106-37.106c9.84-9.84 26.786-3.3 27.294 10.606.648 17.722 3.826 35.527 9.69 52.721 1.986 5.822.567 12.262-3.783 16.612l-13.087 13.087c-28.026 28.026-28.905 73.66-1.155 101.96 28.024 28.579 74.086 28.749 102.325.51l67.2-67.19c28.191-28.191 28.073-73.757 0-101.83-3.701-3.694-7.429-6.564-10.341-8.569a16.037 16.037 0 0 1-6.947-12.606c-.396-10.567 3.348-21.456 11.698-29.806l21.054-21.055c5.521-5.521 14.182-6.199 20.584-1.731a152.482 152.482 0 0 1 20.522 17.197zM467.547 44.449c-59.261-59.262-155.69-59.27-214.96 0l-67.2 67.2c-.12.12-.25.25-.36.37-58.566 58.892-59.387 154.781.36 214.59a152.454 152.454 0 0 0 20.521 17.196c6.402 4.468 15.064 3.789 20.584-1.731l21.054-21.055c8.35-8.35 12.094-19.239 11.698-29.806a16.037 16.037 0 0 0-6.947-12.606c-2.912-2.005-6.64-4.875-10.341-8.569-28.073-28.073-28.191-73.639 0-101.83l67.2-67.19c28.239-28.239 74.3-28.069 102.325.51 27.75 28.3 26.872 73.934-1.155 101.96l-13.087 13.087c-4.35 4.35-5.769 10.79-3.783 16.612 5.864 17.194 9.042 34.999 9.69 52.721.509 13.906 17.454 20.446 27.294 10.606l37.106-37.106c59.271-59.259 59.271-155.699.001-214.959z"></path></svg> [App](https://hafen.github.io/grid-designer/#data=)

---

background-image: url(https://github.com/electorArg/geofaceteAR/blob/master/hex/ggplot2.png?raw=true)
background-position: 95% 5%
background-size: 10%

# Las grillas de Argentina

.pull-left[
### **LatinR 2018** - [Abstract](https://github.com/TuQmano/geofacet_ARG/blob/master/.LatinR/Geofaceting_Argentina_RuizNicolini.pdf)

#### [📊 Slides](https://www.researchgate.net/publication/327382101_Geofaceting_Argentina_LatinR_2018)

#### [📦 {geofaceteAR}](https://electorarg.github.io/geofaceteAR/)

#### [⌨️ blogpost](https://www.tuqmano.com/2020/05/22/empaquetar/)
]

<img src="sesion8_files/figure-html/unnamed-chunk-17-1.png" width="504" style="display: block; margin: auto auto auto 0;" />
]

---

background-image: url(https://github.com/PoliticaArgentina/geoAr/raw/main/man/figures/logo.png)
background-position: 95% 2%
background-size: 15%

## **_geofaceteAr_** en
## `{geoAr}`

#### Para descargar grillas: `get_grid()`

```r

(tucuman <- geoAr::get_grid(district = "TUCUMAN"))
##    name_provincia            name row col code
## 1         TUCUMAN       BURRUYACU   1   4  013
## 2         TUCUMAN         CAPITAL   2   4  001
## 3         TUCUMAN     CHICLIGASTA   4   2  005
## 4         TUCUMAN       CRUZ ALTA   2   5  012
## 5         TUCUMAN        FAMAILLA   3   3  003
## 6         TUCUMAN        GRANEROS   5   4  009
## 7         TUCUMAN JUAN B. ALBERDI   5   3  007
## 8         TUCUMAN        LA COCHA   6   3  008
## 9         TUCUMAN          LEALES   3   5  011
## 10        TUCUMAN           LULES   3   4  002
## 11        TUCUMAN        MONTEROS   3   2  004
## 12        TUCUMAN       RIO CHICO   4   3  006
## 13        TUCUMAN          SIMOCA   4   4  010
## 14        TUCUMAN  TAFI DEL VALLE   2   1  017
## 15        TUCUMAN      TAFI VIEJO   2   2  016
## 16        TUCUMAN         TRANCAS   1   3  014
## 17        TUCUMAN     YERBA BUENA   2   3  015
```

---

background-image: url(https://github.com/PoliticaArgentina/geoAr/raw/main/man/figures/logo.png)
background-position: 95% 2%
background-size: 15%

## **_geofaceteAr_** en
## `{geoAr}`

```r

geofacet::grid_preview(tucuman)
```

---

background-image: url(https://github.com/PoliticaArgentina/geoAr/raw/main/man/figures/logo.png)
background-position: 95% 2%
background-size: 15%

## **_geofaceteAr_** en
## `{geoAr}`

#### Para recodificar distritos: `recode_grid()`

```r

tucuman %>% 
geofacet::grid_preview() 
```

]
--

```r

tucuman %>% 
* recode_grid(type = "indec") %>%
geofacet::grid_preview()
```

<img src="sesion8_files/figure-html/unnamed-chunk-21-1.png" width="504" height="70%" />
]

---

## Data Viz (II)

### Las muchas capas de `ggplot2 +` extensiones

---

class:  middle
background-image: url(https://user-images.githubusercontent.com/520851/34887433-ce1d130e-f7c6-11e7-83fc-d60ad4fae6bd.gif)
background-position: 95% 5%
background-size: 10%

### GEOM integrado
.pull-left[

```r
library(tidyverse)

p <- geoAr::get_geo(geo = "TUCUMAN") %>% 
   ggplot2::ggplot() + #  SETTING color
*  ggplot2::geom_sf(color = "blue")

p   
```

]

]

---

background-image: url(https://user-images.githubusercontent.com/520851/34887433-ce1d130e-f7c6-11e7-83fc-d60ad4fae6bd.gif)
background-position: 95% 5%
background-size: 10%

## `aes()`

**Estéticas como capas**

* Extraemos valores de variable desde el objeto `ggplot2`

```r

p + 
* aes(fill = p$data$coddepto_censo)
```

---
class: inverse, center, middle

# Galería de extensiones a `ggplot2`

<https://exts.ggplot2.tidyverse.org/gallery/>

---

background-image: url(https://patchwork.data-imaginist.com/reference/figures/logo.png) 
background-position: 95% 5%
background-size: 10%

# Composición de gráficos

[<svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg">  <path d="M326.612 185.391c59.747 59.809 58.927 155.698.36 214.59-.11.12-.24.25-.36.37l-67.2 67.2c-59.27 59.27-155.699 59.262-214.96 0-59.27-59.26-59.27-155.7 0-214.96l37.106-37.106c9.84-9.84 26.786-3.3 27.294 10.606.648 17.722 3.826 35.527 9.69 52.721 1.986 5.822.567 12.262-3.783 16.612l-13.087 13.087c-28.026 28.026-28.905 73.66-1.155 101.96 28.024 28.579 74.086 28.749 102.325.51l67.2-67.19c28.191-28.191 28.073-73.757 0-101.83-3.701-3.694-7.429-6.564-10.341-8.569a16.037 16.037 0 0 1-6.947-12.606c-.396-10.567 3.348-21.456 11.698-29.806l21.054-21.055c5.521-5.521 14.182-6.199 20.584-1.731a152.482 152.482 0 0 1 20.522 17.197zM467.547 44.449c-59.261-59.262-155.69-59.27-214.96 0l-67.2 67.2c-.12.12-.25.25-.36.37-58.566 58.892-59.387 154.781.36 214.59a152.454 152.454 0 0 0 20.521 17.196c6.402 4.468 15.064 3.789 20.584-1.731l21.054-21.055c8.35-8.35 12.094-19.239 11.698-29.806a16.037 16.037 0 0 0-6.947-12.606c-2.912-2.005-6.64-4.875-10.341-8.569-28.073-28.073-28.191-73.639 0-101.83l67.2-67.19c28.239-28.239 74.3-28.069 102.325.51 27.75 28.3 26.872 73.934-1.155 101.96l-13.087 13.087c-4.35 4.35-5.769 10.79-3.783 16.612 5.864 17.194 9.042 34.999 9.69 52.721.509 13.906 17.454 20.446 27.294 10.606l37.106-37.106c59.271-59.259 59.271-155.699.001-214.959z"></path></svg> `{patchwork}`](https://patchwork.data-imaginist.com/)

```r
library(ggplot2)
*library(patchwork)
p1 <- ggplot(mtcars) + geom_point(aes(mpg, disp))
p2 <- ggplot(mtcars) + geom_boxplot(aes(gear, disp, group = gear))
*p1 + p2
```

---

background-image: url(https://patchwork.data-imaginist.com/reference/figures/logo.png) 
background-position: 95% 5%
background-size: 10%

# Composición de gráficos

```r
p3 <- ggplot(mtcars) + geom_smooth(aes(disp, qsec))
p4 <- ggplot(mtcars) + geom_bar(aes(carb))
*(p1 | p2 | p3) /
*     p4
```

---

background-image: url(https://patchwork.data-imaginist.com/reference/figures/logo.png) 
background-position: 95% 5%
background-size: 10%

# Composición de gráficos

```r

mapa <- p + 
  ggplot2::geom_sf_label(data = p$data %>% geoAr::add_geo_codes(), 
                         aes(label = nomdepto_censo))
mapa
```

---

background-image: url(https://patchwork.data-imaginist.com/reference/figures/logo.png) 
background-position: 95% 5%
background-size: 10%

# Composición de gráficos

```r

library(geoAr)
tucgrid <- geoAr::get_grid("TUCUMAN") %>% geofacet::grid_preview(label = "name")
```

```r

tucgrid
```

---

background-image: url(https://patchwork.data-imaginist.com/reference/figures/logo.png) 
background-position: 95% 45%
background-size: 10%

# Composición de gráficos

```r

library(patchwork)

mapa + tucgrid
```

---

background-image: url(https://wilkelab.org/cowplot/reference/figures/logo.png) 
background-position: 95% 3%
background-size: 10%

# Composición de gráficos

[<svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg">  <path d="M326.612 185.391c59.747 59.809 58.927 155.698.36 214.59-.11.12-.24.25-.36.37l-67.2 67.2c-59.27 59.27-155.699 59.262-214.96 0-59.27-59.26-59.27-155.7 0-214.96l37.106-37.106c9.84-9.84 26.786-3.3 27.294 10.606.648 17.722 3.826 35.527 9.69 52.721 1.986 5.822.567 12.262-3.783 16.612l-13.087 13.087c-28.026 28.026-28.905 73.66-1.155 101.96 28.024 28.579 74.086 28.749 102.325.51l67.2-67.19c28.191-28.191 28.073-73.757 0-101.83-3.701-3.694-7.429-6.564-10.341-8.569a16.037 16.037 0 0 1-6.947-12.606c-.396-10.567 3.348-21.456 11.698-29.806l21.054-21.055c5.521-5.521 14.182-6.199 20.584-1.731a152.482 152.482 0 0 1 20.522 17.197zM467.547 44.449c-59.261-59.262-155.69-59.27-214.96 0l-67.2 67.2c-.12.12-.25.25-.36.37-58.566 58.892-59.387 154.781.36 214.59a152.454 152.454 0 0 0 20.521 17.196c6.402 4.468 15.064 3.789 20.584-1.731l21.054-21.055c8.35-8.35 12.094-19.239 11.698-29.806a16.037 16.037 0 0 0-6.947-12.606c-2.912-2.005-6.64-4.875-10.341-8.569-28.073-28.073-28.191-73.639 0-101.83l67.2-67.19c28.239-28.239 74.3-28.069 102.325.51 27.75 28.3 26.872 73.934-1.155 101.96l-13.087 13.087c-4.35 4.35-5.769 10.79-3.783 16.612 5.864 17.194 9.042 34.999 9.69 52.721.509 13.906 17.454 20.446 27.294 10.606l37.106-37.106c59.271-59.259 59.271-155.699.001-214.959z"></path></svg> `{cowplot}`](https://wilkelab.org/cowplot/index.html)

```r
library(cowplot)
logo_file <- "https://github.com/electorArg/polAr/blob/master/hex/hex-polAr.png?raw=true"
ggdraw(cow_plot) +
  draw_label(label = "HECHO CON", color = "blue", size = 40, angle = 45 , alpha = .5) +
  draw_image(logo_file, x = 1, y = 1.1, hjust = 1, vjust = 1, width = 0.15)
```

---

background-image: url(https://dreamrs.github.io/esquisse/reference/figures/esquisse.gif) 
background-size: 60%

# [<svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg">  <path d="M326.612 185.391c59.747 59.809 58.927 155.698.36 214.59-.11.12-.24.25-.36.37l-67.2 67.2c-59.27 59.27-155.699 59.262-214.96 0-59.27-59.26-59.27-155.7 0-214.96l37.106-37.106c9.84-9.84 26.786-3.3 27.294 10.606.648 17.722 3.826 35.527 9.69 52.721 1.986 5.822.567 12.262-3.783 16.612l-13.087 13.087c-28.026 28.026-28.905 73.66-1.155 101.96 28.024 28.579 74.086 28.749 102.325.51l67.2-67.19c28.191-28.191 28.073-73.757 0-101.83-3.701-3.694-7.429-6.564-10.341-8.569a16.037 16.037 0 0 1-6.947-12.606c-.396-10.567 3.348-21.456 11.698-29.806l21.054-21.055c5.521-5.521 14.182-6.199 20.584-1.731a152.482 152.482 0 0 1 20.522 17.197zM467.547 44.449c-59.261-59.262-155.69-59.27-214.96 0l-67.2 67.2c-.12.12-.25.25-.36.37-58.566 58.892-59.387 154.781.36 214.59a152.454 152.454 0 0 0 20.521 17.196c6.402 4.468 15.064 3.789 20.584-1.731l21.054-21.055c8.35-8.35 12.094-19.239 11.698-29.806a16.037 16.037 0 0 0-6.947-12.606c-2.912-2.005-6.64-4.875-10.341-8.569-28.073-28.073-28.191-73.639 0-101.83l67.2-67.19c28.239-28.239 74.3-28.069 102.325.51 27.75 28.3 26.872 73.934-1.155 101.96l-13.087 13.087c-4.35 4.35-5.769 10.79-3.783 16.612 5.864 17.194 9.042 34.999 9.69 52.721.509 13.906 17.454 20.446 27.294 10.606l37.106-37.106c59.271-59.259 59.271-155.699.001-214.959z"></path></svg> `{esquisse}`](https://dreamrs.github.io/esquisse/)

---
class: inverse

## <svg viewBox="0 0 448 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:#fdbd15;" xmlns="http://www.w3.org/2000/svg">  <path d="M448 360V24c0-13.3-10.7-24-24-24H96C43 0 0 43 0 96v320c0 53 43 96 96 96h328c13.3 0 24-10.7 24-24v-16c0-7.5-3.5-14.3-8.9-18.7-4.2-15.4-4.2-59.3 0-74.7 5.4-4.3 8.9-11.1 8.9-18.6zM128 134c0-3.3 2.7-6 6-6h212c3.3 0 6 2.7 6 6v20c0 3.3-2.7 6-6 6H134c-3.3 0-6-2.7-6-6v-20zm0 64c0-3.3 2.7-6 6-6h212c3.3 0 6 2.7 6 6v20c0 3.3-2.7 6-6 6H134c-3.3 0-6-2.7-6-6v-20zm253.4 250H96c-17.7 0-32-14.3-32-32 0-17.6 14.4-32 32-32h285.4c-1.9 17.1-1.9 46.9 0 64z"></path></svg> Referencias

### Themes

* `{ggthemes}` <https://jrnold.github.io/ggthemes/index.html>

* `{hrbthemes}` <https://cinc.rud.is/web/packages/hrbrthemes/>

### Fonts

* `{extrafont}` <https://github.com/wch/extrafont>
* `{ggtext}` <https://wilkelab.org/ggtext/>

### `+`

* `{plotly}` <https://plotly-r.com/>

* **The `R` Graph Gallery** <https://www.r-graph-gallery.com/>