Instrumentos de Análisis Urbanos II

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

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<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>

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# Data Viz I

## La _Gramática de los Gráficos_

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# Garmática de los Gráficos

>**Un conjunto formal de reglas para la producción de gráficos estadísticos**

> **Se basa en la definición de capas**

**- _Leland Wilkinson_**

* Estadística y Ciencia de Computación  **+**

* Experto en Viz (_SPSS, Tableau_) **+**

**->**  [_**Grammar of Graphics**_ (1999)](https://www.springer.com/gp/book/9780387245447)

---

## <svg viewBox="0 0 448 512" style="height:1em;position:relative;display:inline-block;top:.1em;" 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 (I)

Tres libros relevantes:

- [`ggplot2`: _**Elegant Graphics for Data Analysis**_](https://ggplot2-book.org/) (**H. Wickham**) es el material principal con definiciones de la aplicación de la _gramática de gráficos_ en `R`.

- [_**Data Visualization: A Pracitcal Introduction**_](https://socviz.co/index.html#preface) (**K. Heley**) disute principios sobre visualización de datos, y consejos práctivos de su aplicación acompañado de código en `R` par reproducirlos.

- En [_**Fundamentals of Data Visualization**_](https://clauswilke.com/dataviz/) (**Claus Wilke**) presentan argumentos y consejos para realizar visualizaciones profesionales que representen correctamente los datos.

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1. _Visualización de Datos (Intro)_, en "[R para Ciencia de Datos](https://es.r4ds.hadley.nz/visualizaci%C3%B3n-de-datos.html#introducci%C3%B3n-1)" (Wickham y Grolemnud).

2. [_ModernDive_](https://moderndive.com/2-viz.html)

3. [(a) Urdinez y Cruz](https://arcruz0.github.io/libroadp/dataviz.html); [(b) Montané](https://martinmontane.github.io/CienciaDeDatosBook/visualizaciones-de-datos-en-r.html); y [(c) Vázquez Brust](https://bitsandbricks.github.io/ciencia_de_datos_gente_sociable/visualizaci%C3%B3n.html).

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background-image: url(https://github.com/rstudio/hex-stickers/raw/master/PNG/ggplot2.png)
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# Data Viz

## La **g**ramática de los **g**ráficos en `R`

#### _Dibujando por capas_ con [<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>`{ggplot2}`](https://ggplot2.tidyverse.org/index.html)

**<svg viewBox="0 0 576 512" style="height:1em;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg">  <path d="M549.655 124.083c-6.281-23.65-24.787-42.276-48.284-48.597C458.781 64 288 64 288 64S117.22 64 74.629 75.486c-23.497 6.322-42.003 24.947-48.284 48.597-11.412 42.867-11.412 132.305-11.412 132.305s0 89.438 11.412 132.305c6.281 23.65 24.787 41.5 48.284 47.821C117.22 448 288 448 288 448s170.78 0 213.371-11.486c23.497-6.321 42.003-24.171 48.284-47.821 11.412-42.867 11.412-132.305 11.412-132.305s0-89.438-11.412-132.305zm-317.51 213.508V175.185l142.739 81.205-142.739 81.201z"></path></svg>** [_Plotting Anything with `ggplot2`_](https://www.youtube.com/watch?v=h29g21z0a68) - Thomas Lin Pedersen.

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# Data Viz

## RECETA BÁSICA

```r
ggplot(data = <DATA>) + 
  <GEOM_FUNCTION>(mapping = aes(<MAPPINGS>))
```

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background-image: url(https://github.com/rstudio/hex-stickers/raw/master/PNG/ggplot2.png)
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# Data Viz

### `millas` del   [paquete <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>`{datos}`](https://cienciadedatos.github.io/datos/)

```r
library(tidyverse)
library(datos)

dplyr::as_tibble(millas)
## # A tibble: 234 x 11
##    fabricante modelo     cilindrada  anio cilindros transmision traccion ciudad
##    <chr>      <chr>           <dbl> <int>     <int> <chr>       <chr>     <int>
##  1 audi       a4                1.8  1999         4 auto(l5)    d            18
##  2 audi       a4                1.8  1999         4 manual(m5)  d            21
##  3 audi       a4                2    2008         4 manual(m6)  d            20
##  4 audi       a4                2    2008         4 auto(av)    d            21
##  5 audi       a4                2.8  1999         6 auto(l5)    d            16
##  6 audi       a4                2.8  1999         6 manual(m5)  d            18
##  7 audi       a4                3.1  2008         6 auto(av)    d            18
##  8 audi       a4 quattro        1.8  1999         4 manual(m5)  4            18
##  9 audi       a4 quattro        1.8  1999         4 auto(l5)    4            16
## 10 audi       a4 quattro        2    2008         4 manual(m6)  4            20
## # i 224 more rows
## # i 3 more variables: autopista <int>, combustible <chr>, clase <chr>
```

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background-image: url(https://github.com/rstudio/hex-stickers/raw/master/PNG/ggplot2.png)
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background-size: 10%

# Data Viz: _ggplot2_

### Receta básica

```r
ggplot(data = millas) +  # DATOS 
  geom_point(mapping = aes(x = cilindrada,      
                           y = autopista)) # ESTETICAS
```

---

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background-position: 95% 15%
background-size: 10%

# _Dibujando por capas_

### mapeo y estéticas

---

## Una capa estética extra con **_aes()_**

```r
ggplot(data = millas) +   # 'Esteticas'
  geom_point(mapping = aes(x = cilindrada,      
                           y = autopista,         
*                          colour = clase))
```

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## _mapping_ VS _setting_

```r
*# Que pasa acá?
ggplot(data = millas) +  
  geom_point(mapping = aes(x = cilindrada,      
                           y = autopista, 
*                          colour = "blue"))
```

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## Data Viz: _ggplot2_

```r
ggplot(data = millas) +  
  geom_point(mapping = aes(x = cilindrada,      
                           y = autopista), 
*            colour = "blue")
```

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## Data Viz: _ggplot2_

Cada **_geometry_ tiene parámetros específicos** que pueden ser ajustados dentro de la capa _estética_.  El **color** es uno de ellos.

A `geom_point` podemos asignarle una forma particular en función de valores de alguna variable, por ejemplo:

* `shape`

* `size`

* `alpha`

* `fill`

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## _Dibujando por capas_ : **_facets_**

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## Data Viz: _ggplot2_: **facetas**

##### _small multiples_

```r
ggplot(data = millas) +
  geom_point(mapping = aes(x = cilindrada, y = autopista)) +
* facet_wrap(~ clase, nrow = 2)
```

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background-image: url(https://github.com/rstudio/hex-stickers/raw/master/PNG/ggplot2.png)
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background-size: 10%

## _Dibujando por capas_: **_geometry_**

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## Data Viz: _ggplot2_:

variedad de **geometrías** posibles

```r
# izquierda
ggplot(data = millas) +
* geom_point(mapping = aes(x = cilindrada, y = autopista))
# derecha
ggplot(data = millas) +
* geom_smooth(mapping = aes(x = cilindrada, y = autopista))
```

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## Data Viz: _ggplot2_

> Integramos los dos `geom_` como capas de un mismo gráfico

```r
ggplot(data = millas) +
  geom_point(mapping = aes(x = cilindrada, y = autopista)) +
  geom_smooth(mapping = aes(x = cilindrada, y = autopista))
```

#### **Que notan en el código?**

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## Data Viz: _ggplot2_

**Podemos asignar parametros _globales_ para todo el gráfico (que pueden ser sobrescritos en capas siguientes)**

```r
ggplot(data = millas, aes(x = cilindrada, y = autopista)) +
  geom_point() +
  geom_smooth()
```

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## Data Viz: _ggplot2_

```r
ggplot(millas, aes(cilindrada, autopista)) +
  geom_point() +
  geom_smooth()
```

> **Se pueden elidir los nombres de parámetros**

**<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>** Más detalle en esta [Intro a ggplot]("https://es.r4ds.hadley.nz/visualización-de-datos.html")

---

background-image: url(https://github.com/rstudio/hex-stickers/raw/master/PNG/ggplot2.png)
background-position: 95% 15%
background-size: 10%

## _Dibujando por capas_: **_theme_**

---

## Data Viz: _ggplot2_: **_theme()_**

```r
ggplot(millas, aes(cilindrada, autopista)) +
  geom_point() +
  geom_point(data = millas %>% 
               filter(fabricante == "audi"), color = "blue", size =3) +
  geom_smooth(se = FALSE) +
  labs(title = "Performance de los AUDI",  subtitle = "Un gráfico del TuQmano", 
        y = "Etiqueta Y",  x = "Etiqueta X", 
        caption = "FUENTE: {datos} 'R Para Ciencia de Datos'") +
* ggthemes::theme_wsj()
```

---

# Graficando Properati

[`sesiones/scripts/primer_ggplot.R`](https://github.com/TuQmano/geo_utdt/blob/master/sesiones/scripts/primer_ggplot.R)