Visualización estática e interactiva con ggplot2 y plotly

class: center, middle, inverse, title-slide

# Visualización estática e interactiva con ggplot2 y plotly
### Natalia da Silva Instituto de Estadística-UDELAR

---

## Sobre mi 
- Profesora en el Instituto de Estadística de la Universidad de la República en Montevideo (UDELAR-IESTA)

- Doctorado y Maestría en Estadística: Iowa State University, USA

- Investigación: aprendizaje automático, estadística computacional, visualización estadística y meta-análisis

- Chair de LatinR2018, Co-fundadora de R-Ladies Ames y R-Ladies Montevideo

---

## Sobre el taller 
- Porqué y para qué visualizar

- Visualización estadística

- Ideas para una visualización efectiva

- ¿Porqué usar ggplot2?

- Gramática de gráficos

- Qué es la visualización interactiva

- Como hacer visualización interactiva con plotly

---

## Importancia de la visulización

"The greatest value of a picture is when it forces us to notice what we never expected to see." <a name=cite-tukey77></a>[Tukey (1977)](#bib-tukey77)
---

## Pocos se escapan a este pensamiento

- Los cálculos numéricos son exactos pero los gráficos son toscos o aproximados

- Para cada tipo de datos estadísticos hay un conjunto de cálculos que constituyen un análisis estadístico correcto

- Hacer cálculos intrincados es virtuoso mientras que mirar los datos es hacer trampa, <a name=cite-anscombe></a>[Anscombe
F (1973)](#bib-anscombe)
---

## Visualización Estadística

La visualización juega un rol importante en todas las etapas del análisis estadístico.

- **Exploración:** Encontrar patrones generales y específicos en los datos.

- **Modelado:** Chequear supuestos sobre los datos antes de modelar.
Se puede hacer inferencia gráfica (nueva linea).

- **Diagnóstico:** Visualizar el modelo en el espacio de los datos ó los datos en el espacio del modelo.
---

## ¿Cuál es la relación entre X e Y?
<table class="table table-bordered" style="font-size: 15px; width: auto !important; margin-left: auto; margin-right: auto;">
 <thead>
 <tr>
 <th style="text-align:right;"> X </th>
 <th style="text-align:right;"> Y </th>
 </tr>
 </thead>
<tbody>
 <tr>
 <td style="text-align:right;"> 1.972 </td>
 <td style="text-align:right;"> 1.236 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 1.112 </td>
 <td style="text-align:right;"> 1.994 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 0.000 </td>
 <td style="text-align:right;"> 1.009 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 0.665 </td>
 <td style="text-align:right;"> 1.942 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 0.235 </td>
 <td style="text-align:right;"> 0.356 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 0.247 </td>
 <td style="text-align:right;"> 1.658 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 1.275 </td>
 <td style="text-align:right;"> 1.961 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 0.702 </td>
 <td style="text-align:right;"> 0.045 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 1.760 </td>
 <td style="text-align:right;"> 0.350 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 1.691 </td>
 <td style="text-align:right;"> 0.277 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 1.628 </td>
 <td style="text-align:right;"> 1.778 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 1.957 </td>
 <td style="text-align:right;"> 1.290 </td>
 </tr>
</tbody>
</table>
---

## ¿Porqué usamos visualización?

<a name=cite-gelman2002let></a>[Gelman, Pasarica, and Dodhia (2002)](#bib-gelman2002let)
---

## ¿Porqué usamos visualización? 
.pull-left[ 
- Los gráficos proveen más información que los resúmenes numéricos
- Anscombe’s quartet [Anscombe
F (1973)](#bib-anscombe)
- `$n = 11$` 
- `$\bar x= 9.0$` 
- `$\bar y = 7.5$` 
- `$\hat \beta_1= 0.5$` 
- `$y = 3 + 0.5 x$` 
- `$R^2 = 0.667$` 
- `$....$`
]
.pull-right[
<img src = "Quartet.png" width = "400" class = "center">
]

---

## ¿Porqué usamos visualización? 
.pull-left[ 
<img src = "dino.png" width = 400 class = "center">

https://github.com/stephlocke/datasauRus

- <a name=cite-matejka2017same></a>[Matejka and Fitzmaurice (2017)](#bib-matejka2017same)
]

.pull-right[

- Mismos resúmenes estadísticos

- Distribuciones muy diferentes
 
- [Link algoritmo](https://www.autodeskresearch.com/publications/samestats).

]
---

## Visualizaciones efectivas
- No todas las visualizaciones son igualmente efectivas

- Hay diferentes criterios para evaluar gráficos (Cleveland, Tufte, Car, Wainer, etc  )

- Basado en el estudio de la percepción gráfica

- Las mejores visualizaciones son aquellas que requieren el uso de la visión "pre-attentive" (instantáneo, sin aparente esfuerzo visual) <a name=cite-cleveland></a>[Cleveland
William and McGuill (1985)](#bib-cleveland).

---

## Cleveland, Percepción gráfica

<img src = "cleveland.png" width = 600 class = "center">
---

## ¿Cómo decodificamos un gráfico?

- Cuando miramos un gráfico la información es visualmente decodificada por el sistema visual de la persona

- Un método gráfico es efectivo solamente si la decodificación lo es

- Buenas visualizaciones son las que optimizan el sistema visual humano

- Si esto es verdad tendríamos que saber cómo el sistema humano decodifica un gráfico

---

## Comparar variables cuantitativas

Cleveland ordena la dificultad de los elementos
gráficos basado en la percepción para estimar variables cuantitativas

- Posición a lo largo de una escala común
- Posición en escalas no alineadas pero idénticas
- Longitud
- Ángulo o pendiente
- Área
- Volumen, densidad o saturación del color
- Tono del color
---

## ¿Cómo uso esto?
- Cuál es la comparación más importante que quiero hacer cuando tengo variables cuantitativas

- Tenemos que codificar eso que quiero comparar usando los elementos gráficos en la tabla en orden ( 1. posiciones en la misma escala )
---

## Comparar millas por galón (mpg)

<img src = "clplot.png" width = 800>
---

## Ángulo ó pendiente

- Gráficos de torta son siempre un error!!!

- Pero ahora sabemos porqué

- Porque decodificar variables cuantitativas usando ángulos es más difícil que con otros elementos gráficos

- Siempre es preferible un gráfico de barras a uno de torta

---

## Visualización para comunicar...

---

## ¿Porqué usar ggplot2 ?

- **ggplot2** es un paquete para producir gráficos estadísticos o de datos desarrollado por <a name=cite-wickham2016ggplot2></a>[Wickham (2016)](#bib-wickham2016ggplot2).

- Tiene una teoría que lo sustenta basada en el Grammar of Graphics <a name=cite-wilkinson2006grammar></a>[Wilkinson (2006)](#bib-wilkinson2006grammar).

- Es el paquete de gráficos estadísticos más dominante en R, tiene ya 10 años!

- [ggplot2: Elegant Graphics for Data Analysis](https://github.com/hadley/ggplot2-book)

---
 
## Grammar of Graphics

La **Gramática de gráficos** nos responde las preguntas:

- ¿Qué es un gráfico?

- ¿Como describir un gráfico?

- ¿Como crear un gráfico?

Gráfico: mapeo de los datos a atributos estéticos de objetos geométricos
---

## ¿Porqué usar una gramática gráfica ? 
.pull-left[ 
- Graficar cosas que sabés

- Crear nuevas visualizaciones

- Tal vez encontrar mejores gráficos para visualizar tus datos
]
.pull-right[

El límite es tu imaginación!

<img src = "imaginacion.png" width =200>
]
---

## `ggplot2`

- Conjunto de componentes independientes, le da flexibilidad

- No limitado a gráficos pre determinados, crear lo que quieras

- Definido en base a un conjunto de principios, fácil de aprender

- Podés producir gráficos estandard de calidad publicable en poco tiempo

- Diseñado para trabajar iterativamente agregando capas

---

## Gramática de gráficos

- **data**: con mapeos (aes) entre los datos y los elementos gráficos

- **layers**: elementos geométricos (**geoms**, son puntos, lineas, rectángulos, texto, ...) y transformaciones estadísticas (**stats**, son identidades, conteos, bins,...)

- **scales**: mapea valores en el espacio de los datos a valores en el espacio estético (ej. color, tamaño, forma o tb posición)

- **coord**: normalmente Cartesianas, pero ej pie charts ej; polar

- **facetas**: como se arregla el display cuando son muchos gráficos

- **theme** items para mejorar el dibujo como fuente, tamaño color, background...
---

## Instalar ggplot2

- Instalar ggplot2

```r
install.packages("ggplot2") 
```

Versión en desarrollo: https://github.com/tidyverse/ggplot2

```r
install.packages("devtools")
library(devtools)
install_github("tidyverse/ggplot2")
```
---

## Ejemplo propina

```
## # A tibble: 6 x 7
## total propina sexo fuma dia momento cantidad
## <dbl> <dbl> <chr> <chr> <chr> <chr> <int>
## 1 17.0 1.01 F No Do Noche 2
## 2 10.3 1.66 M No Do Noche 3
## 3 21.0 3.5 M No Do Noche 3
## 4 23.7 3.31 M No Do Noche 2
## 5 24.6 3.61 F No Do Noche 4
## 6 25.3 4.71 M No Do Noche 4
```

---

## Tres componentes de todo plot

- **data**: datos

- **aes**: un conjunto de mapeos estéticos entre las variables en los datos y las propiedades visuales.

- **layer**: al menos una capa que describe como representar cada observación. Las capas son creadas con la función **geom**.
---

## Tres componentes de todo plot
- **data**: propina
- **aes**: total mapeado a la posición x , propina a la posición y.
- **layer**: puntos mediante geom_point.

```r
ggplot(data = propinas, aes(x = total, y = propina)) +  
  geom_point() +
  theme(aspect.ratio = 1)
```

<img src="index_files/figure-html/unnamed-chunk-5-1.png" style="display: block; margin: auto;" />
---

## Ejemplo propina
 
 ¿Qué vemos?

- Hay una relación lineal débil entre propina y la cuenta total

- Hay mucha variabilidad

- Las lineas horizontales indican la preferencia de la gente a dar de a 1 dólar de propina c/u

---

## Color, tamaño, forma y otros aes

Para agregar otras variables al gráfico podemos usar otros **aes** como color, forma o tamaño

```r
aes(x = total, y = propina, colour = sexo)

aes(x = total, y = propina, shape = sexo)

aes(x = total, y = propina, size = cantidad)
```
---

## Color

```r
ggplot(data = propinas, aes(x = total, y = propina, colour = sexo)) +
  geom_point() + theme(aspect.ratio = 1)
```

<img src="index_files/figure-html/unnamed-chunk-6-1.png" style="display: block; margin: auto;" />
---

## Color fijo 
Si queremos fijar un elemento estético a un valor fijo hay que hacerlo en la capa de afuera sin aes o usar `I('blue')` en aes

```r
ggplot(data = propinas, aes(x = total, y = propina) )+
  geom_point(colour = "blue") + theme(aspect.ratio = 1)
```

<img src="index_files/figure-html/unnamed-chunk-7-1.png" style="display: block; margin: auto;" />
---

## Forma

```r
ggplot(data = propinas, aes(x = total, y = propina, shape = sexo)) +
  geom_point() + theme(aspect.ratio = 1)
```

<img src="index_files/figure-html/unnamed-chunk-8-1.png" style="display: block; margin: auto;" />
---

## Tamaño

```r
ggplot(data = propinas, aes(x = total, y = propina, size = cantidad)) +
  geom_point(alpha = 1 / 3) + theme(aspect.ratio = 1)
```

<img src="index_files/figure-html/unnamed-chunk-9-1.png" style="display: block; margin: auto;" />
---

## Tamaño

```r
ggplot(data = propinas, aes(x = total, y = propina) ) +
  geom_point(size = 5, alpha = 1 / 3) + theme(aspect.ratio = 1)
```

---

## scales

- Los datos hay que convertirlos desde unidades datos (moneda, sexo, etc) a unidades gráficas (color,forma, etc) que se pueden representar en la computadora

- La conversión es llamada escalado y es realizada por `scales`

- `color` son representados por una cadena de seis letras y números, `sizes` por números y `shape` por enteros

- Estas especificaciones estéticas que tienen sentido para R se describen en ` vignette("ggplot2-specs")`
---

## scales 
- Mapeo estético solamente dice que una variable debe ser mapeada a un elemento estético pero no dice cómo debe ser.

- Cuando mapeo una variable a `shape` usando  `aes(shape = x)`  no especifico la forma (`shape`) específica que debe tomar.

- Cuando uso  `aes(color = z)` no digo de que color debe ser

- Describir el color, la forma, el tamaño etc (color, shape, size) a usar se hace mediante transformaciones en `scale`
---

## scales
- `color` y `fill`
- `size`
- `shape`
- `linetype`

Las `scales` se modifican con una serie de funciones con el siguiente esquema de nombrado `scale_<aesthetic>_<type>`. Mirar `scale_<tab>` ver la lista de las funciones de `scale`.
---

## `scales` disponibles

<img src="summary.png" width="700">
---

## Gramática de gráficos

```r
ggplot(propinas, aes(total, propina, colour = fuma)) +
  geom_point()
```

<img src="index_files/figure-html/unnamed-chunk-11-1.png" style="display: block; margin: auto;" />
---

## ¿Qué hay atrás de este plot?

- Cada observación representada como un punto cuya posición está de acuerdo a dos variables (posición horizontal y vertical)

- Cada punto tiene tamaño, color y forma estos atributos son llamados elementos estéticos (inglés aesthetics `aes` )

-  Los `aes` son propiedades que pueden ser percibidas en el gráfico
cada `aes` puede ser mapeado a una variable o fijado en un valor constante

- `total` es mapeado a la posición horizontal, `propina`  a la posición vertical y `fuma` al color. Tamaño y forma no son mapeados a variables (valor por defecto) 
---

## ¿Qué hay atrás de este plot?

```
## # A tibble: 3 x 3
## total propina fuma 
## <dbl> <dbl> <chr>
## 1 17.0 1.01 No 
## 2 10.3 1.66 No 
## 3 21.0 3.5 No
```
- Nuevos datos, mapeo de los elementos estéticos a los datos originales

|x  | y | colour|
|---|---|-------|
|17.0|	1.01|	     No|		
|10.3|	1.66|	     No|		
|21.0|	3.5|	     No|		
---

## Capas de un gráfico

- Los datos, mapeos estéticos, objetos geométricos y las transformaciones estadísticas  forman una **capa**

- Podemos tener un gráfico con muchas capas
---

## Capas de un gráfico
 La gramática de capas define componentes de un gráfico:
 
- datos y conjunto de mapeos de variables a elementos estéticos
- una o más capas, cada capa tiene un elemento geométrico, una transformación estadística, una posición y opcional datos y aes 
---

## Capas de un gráfico

```r
ggplot() +
  layer(
    data = propinas, mapping = aes(x = total, y = propina), 
    geom = "point", stat = "identity", position = "identity"
  ) +
  scale_x_continuous() +
  scale_y_continuous() +
  coord_cartesian()
```
Equivalente a :

```r
ggplot(data = propinas, aes(x = total, y = propina)) +
  geom_point() +
```
---

## Capas de un gráfico

Varios conjuntos de datos:

```r
ggplot() +
  geom_point(data = propinas, aes(x = total, y = propina)) +
  geom_point(data = data.frame(x = 30, y = 6), aes(x, y), 
             color = "red", size = 10)
```

<img src="index_files/figure-html/unnamed-chunk-15-1.png" style="display: block; margin: auto;" />
---

## Capas de un gráfico

Varios conjuntos de datos:

```r
p1 <- ggplot() +
 layer(
 data = propinas, mapping = aes(x = total, y = propina), 
 geom = "point", stat = "identity", position = "identity"
 ) +
 scale_x_continuous() +
 scale_y_continuous() +
 coord_cartesian()

p1 + layer(data = data.frame(x = 30, y = 6), mapping = aes(x = x, y = y, color = I("red"), size = I(10)), 
             geom = 'point', stat = 'identity', position ='identity'
             ) +
  scale_x_continuous() +
  scale_y_continuous() +
  coord_cartesian()
```
---

## Paneles

- Se pueden desplegar variables categóricas adicionales en un gráfico particionando el panel.

- Crea tablas de gráficos partiendo los datos en subconjuntos y mostrándolos el mismo gráfico para cada subconjunto.

- Dos tipos:  `facet_grid` y `facet_wrap`

---

## `facet_wrap`

```r
ggplot(data = propinas, aes(x = total, y = propina)) +
  geom_point() +  theme(aspect.ratio = 1) +
 facet_wrap( ~sexo)
```

<img src="index_files/figure-html/unnamed-chunk-17-1.png" style="display: block; margin: auto;" />
---

## `facet_grid`

```r
ggplot(data = propinas, aes(x = total, y = propina)) +
  geom_point() + theme(aspect.ratio = 1) +
 facet_grid(sexo ~fuma)
```

<img src="index_files/figure-html/unnamed-chunk-18-1.png" style="display: block; margin: auto;" />
---

## Otros geoms
 
 Cuando substituimos `geom_pont()` por otro `geom` obtenemos un gráfico distinto.
 Los `geom` más comunes:
 
- `geom_smooth()` 
- `geom_boxplot()`
- `geom_histogram()` 
- `geom_bar()`
- `geom_path()` y `geom_lines()`

cada `geom` tiene asociados elementos estéticos particulares
---

## Incluir labs

```r
ggplot(data = propinas, aes(x = total, y = propina, color = sexo)) +
  geom_point() +  theme(aspect.ratio = 1) +
  labs(x = "Total de la cuenta", y = "Propina", color = "Sexo")
```

<img src="index_files/figure-html/unnamed-chunk-19-1.png" style="display: block; margin: auto;" />
---

## Incluir labs

```r
ggplot(data = propinas, aes(x = total, y = propina, colour = sexo)) +
  geom_point() + theme(aspect.ratio = 1) + 
  scale_x_continuous(name ="Total de la cuenta") +
  scale_y_continuous(name = "Propina") +
  scale_color_discrete("Sexo")
```

<img src="index_files/figure-html/unnamed-chunk-20-1.png" style="display: block; margin: auto;" />
---

## theme

- `theme` en un gráfico permite controlar los elementos que no son datos en el mismo.

- Ayuda a hacer tu gráfico estéticamente como lo querés no afecta el mapeo de datos.

- `theme` te da control sobre las fuente, el fondo, ticks, etc.

---

## Tu turno

- Hacer un gráfico de dispersión: componentes estéticos `x` total, `y`  propina, `color` fuma

- Cambiá el nombre de los ejes: `x = "Total gastado en dólares" `, `y = "Propina en dólares"`

- Cambiá el nombre de la leyenda de color a "Fumdador""

- Cambiá la posición de la leyenda en la parte inferior del gráfico usando `theme`

- Cambiar la paleta de colores a Dark2 usando `scale_color_brewer`

---

## Tu turno: solución

```r
ggplot(data = propinas, aes(x = total, y = propina, color = fuma)) + 
  geom_point( )+ scale_color_brewer(palette = "Dark2") +
  labs(x = "Total gastado en dólares", y = "Propina en dólares", 
       color =  "Fumandor") +
  theme(aspect.ratio = 1, legend.position = "bottom") 
```

<img src="index_files/figure-html/unnamed-chunk-21-1.png" style="display: block; margin: auto;" />
---

## Tu turno

- Incluir un suavizado lineal usando geom_smooth

- Cambiar el color de la linea a negro

---

## Tu turno: solución

```r
ggplot(data = propinas) +
  geom_point( aes(x = total, y = propina, color = fuma) )+
  scale_color_brewer(palette = "Dark2") +
  labs(x = "Total gastado en dólares", y = "Propina en dólares", 
       color = "Fumador") +
  theme(aspect.ratio = 1, legend.position = "bottom")  + 
  geom_smooth(aes(x = total, y = propina), method = "lm",
              color ="black")
```

<img src="index_files/figure-html/unnamed-chunk-22-1.png" style="display: block; margin: auto;" />
---

## Tu turno: solución

```r
propina.lm <- lm(propina ~ total, data = propinas)
coef <- propina.lm$coefficients
ggplot(data = propinas) + geom_point( aes(x = total, y = propina,
 color = fuma) ) +
 scale_color_brewer(palette = "Dark2") +
 labs(x = "Total gastado en dólares", y = "Propina en dólares",
 color = "Fumador") + theme(aspect.ratio = 1, legend.position = "bottom")+
 geom_abline(intercept = coef[[1]] , slope = coef[[2]] )
```

<img src="index_files/figure-html/unnamed-chunk-23-1.png" style="display: block; margin: auto;" />
---

## Tu turno

- Hacer un gráfico de barras para la variable `dia` usando `geom_bar`
 
 - Colorear por día de la semana las barras usar el elemento estético `fill`
 
 - Ordena los días según frecuencia (usá `fct_infreq()`)
 
 - Rotá el gráfico usando `coord_flip` 
 
---

## Tu turno: sol

```r
 ggplot(data = propinas, aes(x = fct_infreq(dia), fill = dia)) + 
   geom_bar() + coord_flip()
```

<img src="index_files/figure-html/unnamed-chunk-24-1.png" style="display: block; margin: auto;" />
---

## Tu turno

- Incluir en el gráfico anterior la variable `sexo` para tener  
un gráfico de barras apiladas al 100% para la variable `dia` según `sexo` 
 
- Cambiar la paleta de colores a Dark2 usando `scale_fill_discrete`

- Cambiar las etiquetas de días usando `scale_x_discrete` a Jueves, Viernes, Sábado y Domingo.

---

## Tu turno: sol

```r
 ggplot(data = propinas, aes(x = fct_infreq(dia), fill = sexo)) + 
   geom_bar(position = "fill") +
   scale_x_discrete( labels = c('Vi'='Viernes',   'Ju'= 'Jueves',
                                'Sa' = 'Sábado', 'Do' = 'Domingo' )) +
  scale_fill_brewer(palette = "Dark2") +
  coord_flip() 
```

<img src="index_files/figure-html/unnamed-chunk-25-1.png" style="display: block; margin: auto;" />
---

## Más geoms

http://ggplot2.tidyverse.org/reference/
---

## Extensiones

Hay 40 extensiones de `ggplot2`

http://www.ggplot2-exts.org/gallery/

---

## `ggplot2` ayuda

Lista de mails: http://groups.google.com/group/ggplot2

stackoverflow: http://stackoverflow.com

---
## Visualización interactiva

Hay dos componentes claves que deben ser cumplidos para una visualización interactiva:

- Interacciones en cada visualización individual (mouse sobre etiquetas e información, zoom, mover)
 
 
 - Links entre distintos gráficos

Adicionalmente ser capaz de controlar rotaciones dinámicas en altas dimensiones

---

## Porqué usar Visualización interactiva

- Podemos encontrar conexiones que no pueden ser vistas en un gráfico estático

- Permite comprometernos con los datos de una forma que no es posible con estáticos

- Podemos enfocarnos en áreas que nos interesan más (zoom)

- Resaltar los datos que más nos interesan y ocultar los que no

---

## `plotly`

- plotly es una empresa <a name=cite-plotly></a>[Inc. (2015)](https://plot.ly) que mantiene el paquete `plotly` de R para hacer visualización interactiva

- Mantenido por Carson Sievert <a name=cite-sievert2016plotly></a>[Sievert, Parmer, Hocking, Chamberlain, Ram, Corvellec, and Despouy (2016)](#bib-sievert2016plotly)

Versión en desarrollo

```r
if (!require("devtools")) install.packages("devtools")
devtools::install_github("ropensci/plotly")
```
---

## Como iniciar un objeto plotly

- `ggplotly` transforma un objeto `ggplot` en un objeto `plotly`

- `plot_ly` transforma datos en un objeto `plotly`

Ambos producen una visualización interactiva que incluye por defecto ( tooltips, zooming y  panning)

---

## `ggplot2` y `plotly`

- Con `plotly` podemos transformar un gráfico estático generado con 
`ggplot2` a uno interactivo usando `ggplotly`

```r
library(plotly)

pl <- ggplot(propinas, aes(total, propina, colour = sexo)) +
 geom_point()

ggplotly(pl)
```
---
 
## Iniciar plotly con `ggplotly`
 
<div id="htmlwidget-cbaaad6f7921bb1bb67f" style="width:432px;height:360px;" class="plotly html-widget"></div>
<script type="application/json" data-for="htmlwidget-cbaaad6f7921bb1bb67f">{"x":{"data":[{"x":[16.99,24.59,35.26,14.83,10.33,16.97,20.29,15.77,19.65,15.06,20.69,16.93,10.29,34.81,26.41,16.45,3.07,17.07,26.86,25.28,14.73,10.07,34.83,5.75,16.32,22.75,11.35,15.38,44.3,22.42,20.92,14.31,7.25,25.71,17.31,10.65,12.43,24.08,13.42,12.48,29.8,14.52,11.38,20.27,11.17,12.26,18.26,8.51,10.33,14.15,13.16,17.47,27.05,16.43,8.35,18.64,11.87,29.85,25,13.39,16.21,17.51,10.59,10.63,9.6,20.9,18.15,19.81,43.11,13,12.74,13,16.4,16.47,12.76,13.27,28.17,12.9,30.14,13.42,15.98,16.27,10.09,22.12,35.83,27.18,18.78],"y":[1.01,3.61,5,3.02,1.67,3.5,2.75,2.23,3,3,2.45,3.07,2.6,5.2,1.5,2.47,1,3,3.14,5,2.2,1.83,5.17,1,4.3,3.25,2.5,3,2.5,3.48,4.08,4,1,4,3.5,1.5,1.8,2.92,1.68,2.52,4.2,2,2,2.83,1.5,2,3.25,1.25,2,2,2.75,3.5,5,2.3,1.5,1.36,1.63,5.14,3.75,2.61,2,3,1.61,2,4,3.5,3.5,4.19,5,2,2.01,2,2.5,3.23,2.23,2.5,6.5,1.1,3.09,3.48,3,2.5,2,2.88,4.67,2,3],"text":["total: 16.99 propina: 1.01 sexo: F","total: 24.59 propina: 3.61 sexo: F","total: 35.26 propina: 5.00 sexo: F","total: 14.83 propina: 3.02 sexo: F","total: 10.33 propina: 1.67 sexo: F","total: 16.97 propina: 3.50 sexo: F","total: 20.29 propina: 2.75 sexo: F","total: 15.77 propina: 2.23 sexo: F","total: 19.65 propina: 3.00 sexo: F","total: 15.06 propina: 3.00 sexo: F","total: 20.69 propina: 2.45 sexo: F","total: 16.93 propina: 3.07 sexo: F","total: 10.29 propina: 2.60 sexo: F","total: 34.81 propina: 5.20 sexo: F","total: 26.41 propina: 1.50 sexo: F","total: 16.45 propina: 2.47 sexo: F","total: 3.07 propina: 1.00 sexo: F","total: 17.07 propina: 3.00 sexo: F","total: 26.86 propina: 3.14 sexo: F","total: 25.28 propina: 5.00 sexo: F","total: 14.73 propina: 2.20 sexo: F","total: 10.07 propina: 1.83 sexo: F","total: 34.83 propina: 5.17 sexo: F","total: 5.75 propina: 1.00 sexo: F","total: 16.32 propina: 4.30 sexo: F","total: 22.75 propina: 3.25 sexo: F","total: 11.35 propina: 2.50 sexo: F","total: 15.38 propina: 3.00 sexo: F","total: 44.30 propina: 2.50 sexo: F","total: 22.42 propina: 3.48 sexo: F","total: 20.92 propina: 4.08 sexo: F","total: 14.31 propina: 4.00 sexo: F","total: 7.25 propina: 1.00 sexo: F","total: 25.71 propina: 4.00 sexo: F","total: 17.31 propina: 3.50 sexo: F","total: 10.65 propina: 1.50 sexo: F","total: 12.43 propina: 1.80 sexo: F","total: 24.08 propina: 2.92 sexo: F","total: 13.42 propina: 1.68 sexo: F","total: 12.48 propina: 2.52 sexo: F","total: 29.80 propina: 4.20 sexo: F","total: 14.52 propina: 2.00 sexo: F","total: 11.38 propina: 2.00 sexo: F","total: 20.27 propina: 2.83 sexo: F","total: 11.17 propina: 1.50 sexo: F","total: 12.26 propina: 2.00 sexo: F","total: 18.26 propina: 3.25 sexo: F","total: 8.51 propina: 1.25 sexo: F","total: 10.33 propina: 2.00 sexo: F","total: 14.15 propina: 2.00 sexo: F","total: 13.16 propina: 2.75 sexo: F","total: 17.47 propina: 3.50 sexo: F","total: 27.05 propina: 5.00 sexo: F","total: 16.43 propina: 2.30 sexo: F","total: 8.35 propina: 1.50 sexo: F","total: 18.64 propina: 1.36 sexo: F","total: 11.87 propina: 1.63 sexo: F","total: 29.85 propina: 5.14 sexo: F","total: 25.00 propina: 3.75 sexo: F","total: 13.39 propina: 2.61 sexo: F","total: 16.21 propina: 2.00 sexo: F","total: 17.51 propina: 3.00 sexo: F","total: 10.59 propina: 1.61 sexo: F","total: 10.63 propina: 2.00 sexo: F","total: 9.60 propina: 4.00 sexo: F","total: 20.90 propina: 3.50 sexo: F","total: 18.15 propina: 3.50 sexo: F","total: 19.81 propina: 4.19 sexo: F","total: 43.11 propina: 5.00 sexo: F","total: 13.00 propina: 2.00 sexo: F","total: 12.74 propina: 2.01 sexo: F","total: 13.00 propina: 2.00 sexo: F","total: 16.40 propina: 2.50 sexo: F","total: 16.47 propina: 3.23 sexo: F","total: 12.76 propina: 2.23 sexo: F","total: 13.27 propina: 2.50 sexo: F","total: 28.17 propina: 6.50 sexo: F","total: 12.90 propina: 1.10 sexo: F","total: 30.14 propina: 3.09 sexo: F","total: 13.42 propina: 3.48 sexo: F","total: 15.98 propina: 3.00 sexo: F","total: 16.27 propina: 2.50 sexo: F","total: 10.09 propina: 2.00 sexo: F","total: 22.12 propina: 2.88 sexo: F","total: 35.83 propina: 4.67 sexo: F","total: 27.18 propina: 2.00 sexo: F","total: 18.78 propina: 3.00 sexo: F"],"type":"scatter","mode":"markers","marker":{"autocolorscale":false,"color":"rgba(248,118,109,1)","opacity":1,"size":5.66929133858268,"symbol":"circle","line":{"width":1.88976377952756,"color":"rgba(248,118,109,1)"}},"hoveron":"points","name":"F","legendgroup":"F","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","frame":null},{"x":[10.34,21.01,23.68,25.29,8.77,26.88,15.04,14.78,10.27,15.42,18.43,21.58,16.29,20.65,17.92,39.42,19.82,17.81,13.37,12.69,21.7,9.55,18.35,17.78,24.06,16.31,18.69,31.27,16.04,17.46,13.94,9.68,30.4,18.29,22.23,32.4,28.55,18.04,12.54,9.94,25.56,19.49,38.01,11.24,48.27,20.29,13.81,11.02,18.29,17.59,20.08,20.23,15.01,12.02,10.51,17.92,27.2,22.76,17.29,19.44,16.66,32.68,15.98,13.03,18.28,24.71,21.16,28.97,22.49,40.17,27.28,12.03,21.01,12.46,15.36,20.49,25.21,18.24,14,38.07,23.95,29.93,11.69,14.26,15.95,8.52,22.82,19.08,16,34.3,41.19,9.78,7.51,14.07,13.13,17.26,24.55,19.77,48.17,16.49,21.5,12.66,13.81,24.52,20.76,31.71,50.81,15.81,7.25,31.85,16.82,32.9,17.89,14.48,34.63,34.65,23.33,45.35,23.17,40.55,20.69,30.46,23.1,15.69,28.44,15.48,16.58,7.56,10.34,13.51,18.71,20.53,26.59,38.73,24.27,30.06,25.89,48.33,28.15,11.59,7.74,12.16,8.58,13.42,20.45,13.28,24.01,15.69,11.61,10.77,15.53,10.07,12.6,32.83,29.03,22.67,17.82],"y":[1.66,3.5,3.31,4.71,2,3.12,1.96,3.23,1.71,1.57,3,3.92,3.71,3.35,4.08,7.58,3.18,2.34,2,2,4.3,1.45,2.5,3.27,3.6,2,2.31,5,2.24,2.54,3.06,1.32,5.6,3,5,6,2.05,3,2.5,1.56,4.34,3.51,3,1.76,6.73,3.21,2,1.98,3.76,2.64,3.15,2.01,2.09,1.97,1.25,3.08,4,3,2.71,3,3.4,5,2.03,2,4,5.85,3,3,3.5,4.73,4,1.5,3,1.5,1.64,4.06,4.29,3.76,3,4,2.55,5.07,2.31,2.5,2,1.48,2.18,1.5,2,6.7,5,1.73,2,2.5,2,2.74,2,2,5,2,3.5,2.5,2,3.48,2.24,4.5,10,3.16,5.15,3.18,4,3.11,2,2,3.55,3.68,5.65,3.5,6.5,3,5,2,4,1.5,2.56,2.02,4,1.44,2,2,4,4,3.41,3,2.03,2,5.16,9,3,1.5,1.44,2.2,1.92,1.58,3,2.72,2,3,3.39,1.47,3,1.25,1,1.17,5.92,2,1.75],"text":["total: 10.34 propina: 1.66 sexo: M","total: 21.01 propina: 3.50 sexo: M","total: 23.68 propina: 3.31 sexo: M","total: 25.29 propina: 4.71 sexo: M","total: 8.77 propina: 2.00 sexo: M","total: 26.88 propina: 3.12 sexo: M","total: 15.04 propina: 1.96 sexo: M","total: 14.78 propina: 3.23 sexo: M","total: 10.27 propina: 1.71 sexo: M","total: 15.42 propina: 1.57 sexo: M","total: 18.43 propina: 3.00 sexo: M","total: 21.58 propina: 3.92 sexo: M","total: 16.29 propina: 3.71 sexo: M","total: 20.65 propina: 3.35 sexo: M","total: 17.92 propina: 4.08 sexo: M","total: 39.42 propina: 7.58 sexo: M","total: 19.82 propina: 3.18 sexo: M","total: 17.81 propina: 2.34 sexo: M","total: 13.37 propina: 2.00 sexo: M","total: 12.69 propina: 2.00 sexo: M","total: 21.70 propina: 4.30 sexo: M","total: 9.55 propina: 1.45 sexo: M","total: 18.35 propina: 2.50 sexo: M","total: 17.78 propina: 3.27 sexo: M","total: 24.06 propina: 3.60 sexo: M","total: 16.31 propina: 2.00 sexo: M","total: 18.69 propina: 2.31 sexo: M","total: 31.27 propina: 5.00 sexo: M","total: 16.04 propina: 2.24 sexo: M","total: 17.46 propina: 2.54 sexo: M","total: 13.94 propina: 3.06 sexo: M","total: 9.68 propina: 1.32 sexo: M","total: 30.40 propina: 5.60 sexo: M","total: 18.29 propina: 3.00 sexo: M","total: 22.23 propina: 5.00 sexo: M","total: 32.40 propina: 6.00 sexo: M","total: 28.55 propina: 2.05 sexo: M","total: 18.04 propina: 3.00 sexo: M","total: 12.54 propina: 2.50 sexo: M","total: 9.94 propina: 1.56 sexo: M","total: 25.56 propina: 4.34 sexo: M","total: 19.49 propina: 3.51 sexo: M","total: 38.01 propina: 3.00 sexo: M","total: 11.24 propina: 1.76 sexo: M","total: 48.27 propina: 6.73 sexo: M","total: 20.29 propina: 3.21 sexo: M","total: 13.81 propina: 2.00 sexo: M","total: 11.02 propina: 1.98 sexo: M","total: 18.29 propina: 3.76 sexo: M","total: 17.59 propina: 2.64 sexo: M","total: 20.08 propina: 3.15 sexo: M","total: 20.23 propina: 2.01 sexo: M","total: 15.01 propina: 2.09 sexo: M","total: 12.02 propina: 1.97 sexo: M","total: 10.51 propina: 1.25 sexo: M","total: 17.92 propina: 3.08 sexo: M","total: 27.20 propina: 4.00 sexo: M","total: 22.76 propina: 3.00 sexo: M","total: 17.29 propina: 2.71 sexo: M","total: 19.44 propina: 3.00 sexo: M","total: 16.66 propina: 3.40 sexo: M","total: 32.68 propina: 5.00 sexo: M","total: 15.98 propina: 2.03 sexo: M","total: 13.03 propina: 2.00 sexo: M","total: 18.28 propina: 4.00 sexo: M","total: 24.71 propina: 5.85 sexo: M","total: 21.16 propina: 3.00 sexo: M","total: 28.97 propina: 3.00 sexo: M","total: 22.49 propina: 3.50 sexo: M","total: 40.17 propina: 4.73 sexo: M","total: 27.28 propina: 4.00 sexo: M","total: 12.03 propina: 1.50 sexo: M","total: 21.01 propina: 3.00 sexo: M","total: 12.46 propina: 1.50 sexo: M","total: 15.36 propina: 1.64 sexo: M","total: 20.49 propina: 4.06 sexo: M","total: 25.21 propina: 4.29 sexo: M","total: 18.24 propina: 3.76 sexo: M","total: 14.00 propina: 3.00 sexo: M","total: 38.07 propina: 4.00 sexo: M","total: 23.95 propina: 2.55 sexo: M","total: 29.93 propina: 5.07 sexo: M","total: 11.69 propina: 2.31 sexo: M","total: 14.26 propina: 2.50 sexo: M","total: 15.95 propina: 2.00 sexo: M","total: 8.52 propina: 1.48 sexo: M","total: 22.82 propina: 2.18 sexo: M","total: 19.08 propina: 1.50 sexo: M","total: 16.00 propina: 2.00 sexo: M","total: 34.30 propina: 6.70 sexo: M","total: 41.19 propina: 5.00 sexo: M","total: 9.78 propina: 1.73 sexo: M","total: 7.51 propina: 2.00 sexo: M","total: 14.07 propina: 2.50 sexo: M","total: 13.13 propina: 2.00 sexo: M","total: 17.26 propina: 2.74 sexo: M","total: 24.55 propina: 2.00 sexo: M","total: 19.77 propina: 2.00 sexo: M","total: 48.17 propina: 5.00 sexo: M","total: 16.49 propina: 2.00 sexo: M","total: 21.50 propina: 3.50 sexo: M","total: 12.66 propina: 2.50 sexo: M","total: 13.81 propina: 2.00 sexo: M","total: 24.52 propina: 3.48 sexo: M","total: 20.76 propina: 2.24 sexo: M","total: 31.71 propina: 4.50 sexo: M","total: 50.81 propina: 10.00 sexo: M","total: 15.81 propina: 3.16 sexo: M","total: 7.25 propina: 5.15 sexo: M","total: 31.85 propina: 3.18 sexo: M","total: 16.82 propina: 4.00 sexo: M","total: 32.90 propina: 3.11 sexo: M","total: 17.89 propina: 2.00 sexo: M","total: 14.48 propina: 2.00 sexo: M","total: 34.63 propina: 3.55 sexo: M","total: 34.65 propina: 3.68 sexo: M","total: 23.33 propina: 5.65 sexo: M","total: 45.35 propina: 3.50 sexo: M","total: 23.17 propina: 6.50 sexo: M","total: 40.55 propina: 3.00 sexo: M","total: 20.69 propina: 5.00 sexo: M","total: 30.46 propina: 2.00 sexo: M","total: 23.10 propina: 4.00 sexo: M","total: 15.69 propina: 1.50 sexo: M","total: 28.44 propina: 2.56 sexo: M","total: 15.48 propina: 2.02 sexo: M","total: 16.58 propina: 4.00 sexo: M","total: 7.56 propina: 1.44 sexo: M","total: 10.34 propina: 2.00 sexo: M","total: 13.51 propina: 2.00 sexo: M","total: 18.71 propina: 4.00 sexo: M","total: 20.53 propina: 4.00 sexo: M","total: 26.59 propina: 3.41 sexo: M","total: 38.73 propina: 3.00 sexo: M","total: 24.27 propina: 2.03 sexo: M","total: 30.06 propina: 2.00 sexo: M","total: 25.89 propina: 5.16 sexo: M","total: 48.33 propina: 9.00 sexo: M","total: 28.15 propina: 3.00 sexo: M","total: 11.59 propina: 1.50 sexo: M","total: 7.74 propina: 1.44 sexo: M","total: 12.16 propina: 2.20 sexo: M","total: 8.58 propina: 1.92 sexo: M","total: 13.42 propina: 1.58 sexo: M","total: 20.45 propina: 3.00 sexo: M","total: 13.28 propina: 2.72 sexo: M","total: 24.01 propina: 2.00 sexo: M","total: 15.69 propina: 3.00 sexo: M","total: 11.61 propina: 3.39 sexo: M","total: 10.77 propina: 1.47 sexo: M","total: 15.53 propina: 3.00 sexo: M","total: 10.07 propina: 1.25 sexo: M","total: 12.60 propina: 1.00 sexo: M","total: 32.83 propina: 1.17 sexo: M","total: 29.03 propina: 5.92 sexo: M","total: 22.67 propina: 2.00 sexo: M","total: 17.82 propina: 1.75 sexo: M"],"type":"scatter","mode":"markers","marker":{"autocolorscale":false,"color":"rgba(0,191,196,1)","opacity":1,"size":5.66929133858268,"symbol":"circle","line":{"width":1.88976377952756,"color":"rgba(0,191,196,1)"}},"hoveron":"points","name":"M","legendgroup":"M","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","frame":null}],"layout":{"margin":{"t":24.7671232876712,"r":7.30593607305936,"b":38.7214611872146,"l":48.9497716894977},"plot_bgcolor":"rgba(235,235,235,1)","paper_bgcolor":"rgba(255,255,255,1)","font":{"color":"rgba(0,0,0,1)","family":"","size":14.6118721461187},"xaxis":{"domain":[0,1],"automargin":true,"type":"linear","autorange":false,"range":[0.683,53.197],"tickmode":"array","ticktext":["10","20","30","40","50"],"tickvals":[10,20,30,40,50],"categoryorder":"array","categoryarray":["10","20","30","40","50"],"nticks":null,"ticks":"outside","tickcolor":"rgba(51,51,51,1)","ticklen":3.65296803652968,"tickwidth":0.66417600664176,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"","size":11.689497716895},"tickangle":-0,"showline":false,"linecolor":null,"linewidth":0,"showgrid":true,"gridcolor":"rgba(255,255,255,1)","gridwidth":0.66417600664176,"zeroline":false,"anchor":"y","title":"total","titlefont":{"color":"rgba(0,0,0,1)","family":"","size":14.6118721461187},"hoverformat":".2f"},"yaxis":{"domain":[0,1],"automargin":true,"type":"linear","autorange":false,"range":[0.55,10.45],"tickmode":"array","ticktext":["2.5","5.0","7.5","10.0"],"tickvals":[2.5,5,7.5,10],"categoryorder":"array","categoryarray":["2.5","5.0","7.5","10.0"],"nticks":null,"ticks":"outside","tickcolor":"rgba(51,51,51,1)","ticklen":3.65296803652968,"tickwidth":0.66417600664176,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"","size":11.689497716895},"tickangle":-0,"showline":false,"linecolor":null,"linewidth":0,"showgrid":true,"gridcolor":"rgba(255,255,255,1)","gridwidth":0.66417600664176,"zeroline":false,"anchor":"x","title":"propina","titlefont":{"color":"rgba(0,0,0,1)","family":"","size":14.6118721461187},"hoverformat":".2f"},"shapes":[{"type":"rect","fillcolor":null,"line":{"color":null,"width":0,"linetype":[]},"yref":"paper","xref":"paper","x0":0,"x1":1,"y0":0,"y1":1}],"showlegend":true,"legend":{"bgcolor":"rgba(255,255,255,1)","bordercolor":"transparent","borderwidth":1.88976377952756,"font":{"color":"rgba(0,0,0,1)","family":"","size":11.689497716895},"y":0.913385826771654},"annotations":[{"text":"sexo","x":1.02,"y":1,"showarrow":false,"ax":0,"ay":0,"font":{"color":"rgba(0,0,0,1)","family":"","size":14.6118721461187},"xref":"paper","yref":"paper","textangle":-0,"xanchor":"left","yanchor":"bottom","legendTitle":true}],"hovermode":"closest","barmode":"relative"},"config":{"doubleClick":"reset","modeBarButtonsToAdd":[{"name":"Collaborate","icon":{"width":1000,"ascent":500,"descent":-50,"path":"M487 375c7-10 9-23 5-36l-79-259c-3-12-11-23-22-31-11-8-22-12-35-12l-263 0c-15 0-29 5-43 15-13 10-23 23-28 37-5 13-5 25-1 37 0 0 0 3 1 7 1 5 1 8 1 11 0 2 0 4-1 6 0 3-1 5-1 6 1 2 2 4 3 6 1 2 2 4 4 6 2 3 4 5 5 7 5 7 9 16 13 26 4 10 7 19 9 26 0 2 0 5 0 9-1 4-1 6 0 8 0 2 2 5 4 8 3 3 5 5 5 7 4 6 8 15 12 26 4 11 7 19 7 26 1 1 0 4 0 9-1 4-1 7 0 8 1 2 3 5 6 8 4 4 6 6 6 7 4 5 8 13 13 24 4 11 7 20 7 28 1 1 0 4 0 7-1 3-1 6-1 7 0 2 1 4 3 6 1 1 3 4 5 6 2 3 3 5 5 6 1 2 3 5 4 9 2 3 3 7 5 10 1 3 2 6 4 10 2 4 4 7 6 9 2 3 4 5 7 7 3 2 7 3 11 3 3 0 8 0 13-1l0-1c7 2 12 2 14 2l218 0c14 0 25-5 32-16 8-10 10-23 6-37l-79-259c-7-22-13-37-20-43-7-7-19-10-37-10l-248 0c-5 0-9-2-11-5-2-3-2-7 0-12 4-13 18-20 41-20l264 0c5 0 10 2 16 5 5 3 8 6 10 11l85 282c2 5 2 10 2 17 7-3 13-7 17-13z m-304 0c-1-3-1-5 0-7 1-1 3-2 6-2l174 0c2 0 4 1 7 2 2 2 4 4 5 7l6 18c0 3 0 5-1 7-1 1-3 2-6 2l-173 0c-3 0-5-1-8-2-2-2-4-4-4-7z m-24-73c-1-3-1-5 0-7 2-2 3-2 6-2l174 0c2 0 5 0 7 2 3 2 4 4 5 7l6 18c1 2 0 5-1 6-1 2-3 3-5 3l-174 0c-3 0-5-1-7-3-3-1-4-4-5-6z"},"click":"function(gd) { \n // is this being viewed in RStudio?\n if (location.search == '?viewer_pane=1') {\n alert('To learn about plotly for collaboration, visit:\\n https://cpsievert.github.io/plotly_book/plot-ly-for-collaboration.html');\n } else {\n window.open('https://cpsievert.github.io/plotly_book/plot-ly-for-collaboration.html', '_blank');\n }\n }"}],"cloud":false},"source":"A","attrs":{"71f66206c3b6":{"x":{},"y":{},"colour":{},"type":"scatter"}},"cur_data":"71f66206c3b6","visdat":{"71f66206c3b6":["function (y) ","x"]},"highlight":{"on":"plotly_click","persistent":false,"dynamic":false,"selectize":false,"opacityDim":0.2,"selected":{"opacity":1},"debounce":0},"base_url":"https://plot.ly"},"evals":["config.modeBarButtonsToAdd.0.click"],"jsHooks":[]}</script>

Por defecto utiliza todos los elementos estéticos (`aes`) en las etiquetas 
---

## `ggplotly`: personalizar `tooltip`
 
- Argumento `tooltip` para restringir `aes`

```r
pl <- ggplot(propinas, aes(total, propina, colour = sexo)) +
 geom_point()

ggplotly(pl, tooltip = c("total", "propinas"))
```
---

## `ggplotly`: personalizar `tooltip`
 
- Argumento `tooltip` para restringir `aes`

<div id="htmlwidget-09ceebec251eb2d68ebd" style="width:432px;height:360px;" class="plotly html-widget"></div>
<script type="application/json" data-for="htmlwidget-09ceebec251eb2d68ebd">{"x":{"data":[{"x":[16.99,24.59,35.26,14.83,10.33,16.97,20.29,15.77,19.65,15.06,20.69,16.93,10.29,34.81,26.41,16.45,3.07,17.07,26.86,25.28,14.73,10.07,34.83,5.75,16.32,22.75,11.35,15.38,44.3,22.42,20.92,14.31,7.25,25.71,17.31,10.65,12.43,24.08,13.42,12.48,29.8,14.52,11.38,20.27,11.17,12.26,18.26,8.51,10.33,14.15,13.16,17.47,27.05,16.43,8.35,18.64,11.87,29.85,25,13.39,16.21,17.51,10.59,10.63,9.6,20.9,18.15,19.81,43.11,13,12.74,13,16.4,16.47,12.76,13.27,28.17,12.9,30.14,13.42,15.98,16.27,10.09,22.12,35.83,27.18,18.78],"y":[1.01,3.61,5,3.02,1.67,3.5,2.75,2.23,3,3,2.45,3.07,2.6,5.2,1.5,2.47,1,3,3.14,5,2.2,1.83,5.17,1,4.3,3.25,2.5,3,2.5,3.48,4.08,4,1,4,3.5,1.5,1.8,2.92,1.68,2.52,4.2,2,2,2.83,1.5,2,3.25,1.25,2,2,2.75,3.5,5,2.3,1.5,1.36,1.63,5.14,3.75,2.61,2,3,1.61,2,4,3.5,3.5,4.19,5,2,2.01,2,2.5,3.23,2.23,2.5,6.5,1.1,3.09,3.48,3,2.5,2,2.88,4.67,2,3],"text":["total: 16.99 propina: 1.01","total: 24.59 propina: 3.61","total: 35.26 propina: 5.00","total: 14.83 propina: 3.02","total: 10.33 propina: 1.67","total: 16.97 propina: 3.50","total: 20.29 propina: 2.75","total: 15.77 propina: 2.23","total: 19.65 propina: 3.00","total: 15.06 propina: 3.00","total: 20.69 propina: 2.45","total: 16.93 propina: 3.07","total: 10.29 propina: 2.60","total: 34.81 propina: 5.20","total: 26.41 propina: 1.50","total: 16.45 propina: 2.47","total: 3.07 propina: 1.00","total: 17.07 propina: 3.00","total: 26.86 propina: 3.14","total: 25.28 propina: 5.00","total: 14.73 propina: 2.20","total: 10.07 propina: 1.83","total: 34.83 propina: 5.17","total: 5.75 propina: 1.00","total: 16.32 propina: 4.30","total: 22.75 propina: 3.25","total: 11.35 propina: 2.50","total: 15.38 propina: 3.00","total: 44.30 propina: 2.50","total: 22.42 propina: 3.48","total: 20.92 propina: 4.08","total: 14.31 propina: 4.00","total: 7.25 propina: 1.00","total: 25.71 propina: 4.00","total: 17.31 propina: 3.50","total: 10.65 propina: 1.50","total: 12.43 propina: 1.80","total: 24.08 propina: 2.92","total: 13.42 propina: 1.68","total: 12.48 propina: 2.52","total: 29.80 propina: 4.20","total: 14.52 propina: 2.00","total: 11.38 propina: 2.00","total: 20.27 propina: 2.83","total: 11.17 propina: 1.50","total: 12.26 propina: 2.00","total: 18.26 propina: 3.25","total: 8.51 propina: 1.25","total: 10.33 propina: 2.00","total: 14.15 propina: 2.00","total: 13.16 propina: 2.75","total: 17.47 propina: 3.50","total: 27.05 propina: 5.00","total: 16.43 propina: 2.30","total: 8.35 propina: 1.50","total: 18.64 propina: 1.36","total: 11.87 propina: 1.63","total: 29.85 propina: 5.14","total: 25.00 propina: 3.75","total: 13.39 propina: 2.61","total: 16.21 propina: 2.00","total: 17.51 propina: 3.00","total: 10.59 propina: 1.61","total: 10.63 propina: 2.00","total: 9.60 propina: 4.00","total: 20.90 propina: 3.50","total: 18.15 propina: 3.50","total: 19.81 propina: 4.19","total: 43.11 propina: 5.00","total: 13.00 propina: 2.00","total: 12.74 propina: 2.01","total: 13.00 propina: 2.00","total: 16.40 propina: 2.50","total: 16.47 propina: 3.23","total: 12.76 propina: 2.23","total: 13.27 propina: 2.50","total: 28.17 propina: 6.50","total: 12.90 propina: 1.10","total: 30.14 propina: 3.09","total: 13.42 propina: 3.48","total: 15.98 propina: 3.00","total: 16.27 propina: 2.50","total: 10.09 propina: 2.00","total: 22.12 propina: 2.88","total: 35.83 propina: 4.67","total: 27.18 propina: 2.00","total: 18.78 propina: 3.00"],"type":"scatter","mode":"markers","marker":{"autocolorscale":false,"color":"rgba(248,118,109,1)","opacity":1,"size":5.66929133858268,"symbol":"circle","line":{"width":1.88976377952756,"color":"rgba(248,118,109,1)"}},"hoveron":"points","name":"F","legendgroup":"F","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","frame":null},{"x":[10.34,21.01,23.68,25.29,8.77,26.88,15.04,14.78,10.27,15.42,18.43,21.58,16.29,20.65,17.92,39.42,19.82,17.81,13.37,12.69,21.7,9.55,18.35,17.78,24.06,16.31,18.69,31.27,16.04,17.46,13.94,9.68,30.4,18.29,22.23,32.4,28.55,18.04,12.54,9.94,25.56,19.49,38.01,11.24,48.27,20.29,13.81,11.02,18.29,17.59,20.08,20.23,15.01,12.02,10.51,17.92,27.2,22.76,17.29,19.44,16.66,32.68,15.98,13.03,18.28,24.71,21.16,28.97,22.49,40.17,27.28,12.03,21.01,12.46,15.36,20.49,25.21,18.24,14,38.07,23.95,29.93,11.69,14.26,15.95,8.52,22.82,19.08,16,34.3,41.19,9.78,7.51,14.07,13.13,17.26,24.55,19.77,48.17,16.49,21.5,12.66,13.81,24.52,20.76,31.71,50.81,15.81,7.25,31.85,16.82,32.9,17.89,14.48,34.63,34.65,23.33,45.35,23.17,40.55,20.69,30.46,23.1,15.69,28.44,15.48,16.58,7.56,10.34,13.51,18.71,20.53,26.59,38.73,24.27,30.06,25.89,48.33,28.15,11.59,7.74,12.16,8.58,13.42,20.45,13.28,24.01,15.69,11.61,10.77,15.53,10.07,12.6,32.83,29.03,22.67,17.82],"y":[1.66,3.5,3.31,4.71,2,3.12,1.96,3.23,1.71,1.57,3,3.92,3.71,3.35,4.08,7.58,3.18,2.34,2,2,4.3,1.45,2.5,3.27,3.6,2,2.31,5,2.24,2.54,3.06,1.32,5.6,3,5,6,2.05,3,2.5,1.56,4.34,3.51,3,1.76,6.73,3.21,2,1.98,3.76,2.64,3.15,2.01,2.09,1.97,1.25,3.08,4,3,2.71,3,3.4,5,2.03,2,4,5.85,3,3,3.5,4.73,4,1.5,3,1.5,1.64,4.06,4.29,3.76,3,4,2.55,5.07,2.31,2.5,2,1.48,2.18,1.5,2,6.7,5,1.73,2,2.5,2,2.74,2,2,5,2,3.5,2.5,2,3.48,2.24,4.5,10,3.16,5.15,3.18,4,3.11,2,2,3.55,3.68,5.65,3.5,6.5,3,5,2,4,1.5,2.56,2.02,4,1.44,2,2,4,4,3.41,3,2.03,2,5.16,9,3,1.5,1.44,2.2,1.92,1.58,3,2.72,2,3,3.39,1.47,3,1.25,1,1.17,5.92,2,1.75],"text":["total: 10.34 propina: 1.66","total: 21.01 propina: 3.50","total: 23.68 propina: 3.31","total: 25.29 propina: 4.71","total: 8.77 propina: 2.00","total: 26.88 propina: 3.12","total: 15.04 propina: 1.96","total: 14.78 propina: 3.23","total: 10.27 propina: 1.71","total: 15.42 propina: 1.57","total: 18.43 propina: 3.00","total: 21.58 propina: 3.92","total: 16.29 propina: 3.71","total: 20.65 propina: 3.35","total: 17.92 propina: 4.08","total: 39.42 propina: 7.58","total: 19.82 propina: 3.18","total: 17.81 propina: 2.34","total: 13.37 propina: 2.00","total: 12.69 propina: 2.00","total: 21.70 propina: 4.30","total: 9.55 propina: 1.45","total: 18.35 propina: 2.50","total: 17.78 propina: 3.27","total: 24.06 propina: 3.60","total: 16.31 propina: 2.00","total: 18.69 propina: 2.31","total: 31.27 propina: 5.00","total: 16.04 propina: 2.24","total: 17.46 propina: 2.54","total: 13.94 propina: 3.06","total: 9.68 propina: 1.32","total: 30.40 propina: 5.60","total: 18.29 propina: 3.00","total: 22.23 propina: 5.00","total: 32.40 propina: 6.00","total: 28.55 propina: 2.05","total: 18.04 propina: 3.00","total: 12.54 propina: 2.50","total: 9.94 propina: 1.56","total: 25.56 propina: 4.34","total: 19.49 propina: 3.51","total: 38.01 propina: 3.00","total: 11.24 propina: 1.76","total: 48.27 propina: 6.73","total: 20.29 propina: 3.21","total: 13.81 propina: 2.00","total: 11.02 propina: 1.98","total: 18.29 propina: 3.76","total: 17.59 propina: 2.64","total: 20.08 propina: 3.15","total: 20.23 propina: 2.01","total: 15.01 propina: 2.09","total: 12.02 propina: 1.97","total: 10.51 propina: 1.25","total: 17.92 propina: 3.08","total: 27.20 propina: 4.00","total: 22.76 propina: 3.00","total: 17.29 propina: 2.71","total: 19.44 propina: 3.00","total: 16.66 propina: 3.40","total: 32.68 propina: 5.00","total: 15.98 propina: 2.03","total: 13.03 propina: 2.00","total: 18.28 propina: 4.00","total: 24.71 propina: 5.85","total: 21.16 propina: 3.00","total: 28.97 propina: 3.00","total: 22.49 propina: 3.50","total: 40.17 propina: 4.73","total: 27.28 propina: 4.00","total: 12.03 propina: 1.50","total: 21.01 propina: 3.00","total: 12.46 propina: 1.50","total: 15.36 propina: 1.64","total: 20.49 propina: 4.06","total: 25.21 propina: 4.29","total: 18.24 propina: 3.76","total: 14.00 propina: 3.00","total: 38.07 propina: 4.00","total: 23.95 propina: 2.55","total: 29.93 propina: 5.07","total: 11.69 propina: 2.31","total: 14.26 propina: 2.50","total: 15.95 propina: 2.00","total: 8.52 propina: 1.48","total: 22.82 propina: 2.18","total: 19.08 propina: 1.50","total: 16.00 propina: 2.00","total: 34.30 propina: 6.70","total: 41.19 propina: 5.00","total: 9.78 propina: 1.73","total: 7.51 propina: 2.00","total: 14.07 propina: 2.50","total: 13.13 propina: 2.00","total: 17.26 propina: 2.74","total: 24.55 propina: 2.00","total: 19.77 propina: 2.00","total: 48.17 propina: 5.00","total: 16.49 propina: 2.00","total: 21.50 propina: 3.50","total: 12.66 propina: 2.50","total: 13.81 propina: 2.00","total: 24.52 propina: 3.48","total: 20.76 propina: 2.24","total: 31.71 propina: 4.50","total: 50.81 propina: 10.00","total: 15.81 propina: 3.16","total: 7.25 propina: 5.15","total: 31.85 propina: 3.18","total: 16.82 propina: 4.00","total: 32.90 propina: 3.11","total: 17.89 propina: 2.00","total: 14.48 propina: 2.00","total: 34.63 propina: 3.55","total: 34.65 propina: 3.68","total: 23.33 propina: 5.65","total: 45.35 propina: 3.50","total: 23.17 propina: 6.50","total: 40.55 propina: 3.00","total: 20.69 propina: 5.00","total: 30.46 propina: 2.00","total: 23.10 propina: 4.00","total: 15.69 propina: 1.50","total: 28.44 propina: 2.56","total: 15.48 propina: 2.02","total: 16.58 propina: 4.00","total: 7.56 propina: 1.44","total: 10.34 propina: 2.00","total: 13.51 propina: 2.00","total: 18.71 propina: 4.00","total: 20.53 propina: 4.00","total: 26.59 propina: 3.41","total: 38.73 propina: 3.00","total: 24.27 propina: 2.03","total: 30.06 propina: 2.00","total: 25.89 propina: 5.16","total: 48.33 propina: 9.00","total: 28.15 propina: 3.00","total: 11.59 propina: 1.50","total: 7.74 propina: 1.44","total: 12.16 propina: 2.20","total: 8.58 propina: 1.92","total: 13.42 propina: 1.58","total: 20.45 propina: 3.00","total: 13.28 propina: 2.72","total: 24.01 propina: 2.00","total: 15.69 propina: 3.00","total: 11.61 propina: 3.39","total: 10.77 propina: 1.47","total: 15.53 propina: 3.00","total: 10.07 propina: 1.25","total: 12.60 propina: 1.00","total: 32.83 propina: 1.17","total: 29.03 propina: 5.92","total: 22.67 propina: 2.00","total: 17.82 propina: 1.75"],"type":"scatter","mode":"markers","marker":{"autocolorscale":false,"color":"rgba(0,191,196,1)","opacity":1,"size":5.66929133858268,"symbol":"circle","line":{"width":1.88976377952756,"color":"rgba(0,191,196,1)"}},"hoveron":"points","name":"M","legendgroup":"M","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","frame":null}],"layout":{"margin":{"t":24.7671232876712,"r":7.30593607305936,"b":38.7214611872146,"l":48.9497716894977},"plot_bgcolor":"rgba(235,235,235,1)","paper_bgcolor":"rgba(255,255,255,1)","font":{"color":"rgba(0,0,0,1)","family":"","size":14.6118721461187},"xaxis":{"domain":[0,1],"automargin":true,"type":"linear","autorange":false,"range":[0.683,53.197],"tickmode":"array","ticktext":["10","20","30","40","50"],"tickvals":[10,20,30,40,50],"categoryorder":"array","categoryarray":["10","20","30","40","50"],"nticks":null,"ticks":"outside","tickcolor":"rgba(51,51,51,1)","ticklen":3.65296803652968,"tickwidth":0.66417600664176,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"","size":11.689497716895},"tickangle":-0,"showline":false,"linecolor":null,"linewidth":0,"showgrid":true,"gridcolor":"rgba(255,255,255,1)","gridwidth":0.66417600664176,"zeroline":false,"anchor":"y","title":"total","titlefont":{"color":"rgba(0,0,0,1)","family":"","size":14.6118721461187},"hoverformat":".2f"},"yaxis":{"domain":[0,1],"automargin":true,"type":"linear","autorange":false,"range":[0.55,10.45],"tickmode":"array","ticktext":["2.5","5.0","7.5","10.0"],"tickvals":[2.5,5,7.5,10],"categoryorder":"array","categoryarray":["2.5","5.0","7.5","10.0"],"nticks":null,"ticks":"outside","tickcolor":"rgba(51,51,51,1)","ticklen":3.65296803652968,"tickwidth":0.66417600664176,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"","size":11.689497716895},"tickangle":-0,"showline":false,"linecolor":null,"linewidth":0,"showgrid":true,"gridcolor":"rgba(255,255,255,1)","gridwidth":0.66417600664176,"zeroline":false,"anchor":"x","title":"propina","titlefont":{"color":"rgba(0,0,0,1)","family":"","size":14.6118721461187},"hoverformat":".2f"},"shapes":[{"type":"rect","fillcolor":null,"line":{"color":null,"width":0,"linetype":[]},"yref":"paper","xref":"paper","x0":0,"x1":1,"y0":0,"y1":1}],"showlegend":true,"legend":{"bgcolor":"rgba(255,255,255,1)","bordercolor":"transparent","borderwidth":1.88976377952756,"font":{"color":"rgba(0,0,0,1)","family":"","size":11.689497716895},"y":0.913385826771654},"annotations":[{"text":"sexo","x":1.02,"y":1,"showarrow":false,"ax":0,"ay":0,"font":{"color":"rgba(0,0,0,1)","family":"","size":14.6118721461187},"xref":"paper","yref":"paper","textangle":-0,"xanchor":"left","yanchor":"bottom","legendTitle":true}],"hovermode":"closest","barmode":"relative"},"config":{"doubleClick":"reset","modeBarButtonsToAdd":[{"name":"Collaborate","icon":{"width":1000,"ascent":500,"descent":-50,"path":"M487 375c7-10 9-23 5-36l-79-259c-3-12-11-23-22-31-11-8-22-12-35-12l-263 0c-15 0-29 5-43 15-13 10-23 23-28 37-5 13-5 25-1 37 0 0 0 3 1 7 1 5 1 8 1 11 0 2 0 4-1 6 0 3-1 5-1 6 1 2 2 4 3 6 1 2 2 4 4 6 2 3 4 5 5 7 5 7 9 16 13 26 4 10 7 19 9 26 0 2 0 5 0 9-1 4-1 6 0 8 0 2 2 5 4 8 3 3 5 5 5 7 4 6 8 15 12 26 4 11 7 19 7 26 1 1 0 4 0 9-1 4-1 7 0 8 1 2 3 5 6 8 4 4 6 6 6 7 4 5 8 13 13 24 4 11 7 20 7 28 1 1 0 4 0 7-1 3-1 6-1 7 0 2 1 4 3 6 1 1 3 4 5 6 2 3 3 5 5 6 1 2 3 5 4 9 2 3 3 7 5 10 1 3 2 6 4 10 2 4 4 7 6 9 2 3 4 5 7 7 3 2 7 3 11 3 3 0 8 0 13-1l0-1c7 2 12 2 14 2l218 0c14 0 25-5 32-16 8-10 10-23 6-37l-79-259c-7-22-13-37-20-43-7-7-19-10-37-10l-248 0c-5 0-9-2-11-5-2-3-2-7 0-12 4-13 18-20 41-20l264 0c5 0 10 2 16 5 5 3 8 6 10 11l85 282c2 5 2 10 2 17 7-3 13-7 17-13z m-304 0c-1-3-1-5 0-7 1-1 3-2 6-2l174 0c2 0 4 1 7 2 2 2 4 4 5 7l6 18c0 3 0 5-1 7-1 1-3 2-6 2l-173 0c-3 0-5-1-8-2-2-2-4-4-4-7z m-24-73c-1-3-1-5 0-7 2-2 3-2 6-2l174 0c2 0 5 0 7 2 3 2 4 4 5 7l6 18c1 2 0 5-1 6-1 2-3 3-5 3l-174 0c-3 0-5-1-7-3-3-1-4-4-5-6z"},"click":"function(gd) { \n // is this being viewed in RStudio?\n if (location.search == '?viewer_pane=1') {\n alert('To learn about plotly for collaboration, visit:\\n https://cpsievert.github.io/plotly_book/plot-ly-for-collaboration.html');\n } else {\n window.open('https://cpsievert.github.io/plotly_book/plot-ly-for-collaboration.html', '_blank');\n }\n }"}],"cloud":false},"source":"A","attrs":{"71f677f187d3":{"x":{},"y":{},"colour":{},"type":"scatter"}},"cur_data":"71f677f187d3","visdat":{"71f677f187d3":["function (y) ","x"]},"highlight":{"on":"plotly_click","persistent":false,"dynamic":false,"selectize":false,"opacityDim":0.2,"selected":{"opacity":1},"debounce":0},"base_url":"https://plot.ly"},"evals":["config.modeBarButtonsToAdd.0.click"],"jsHooks":[]}</script>
---
## `ggplotly`: personalizar `tooltip`

- Argumento `tooltip` para restringir `aes`

```r
library(plotly)
pl <- ggplot(propinas, aes(total, propina, colour = sexo,
 text = paste('Sexo', sexo))) +
 geom_point()

ggplotly(pl, tooltip = "text")
```
---
## `ggplotly`: personalizar `tooltip`

- Argumento `tooltip` para restringir `aes`
<div id="htmlwidget-31eaccaf9a46cced2152" style="width:432px;height:360px;" class="plotly html-widget"></div>
<script type="application/json" data-for="htmlwidget-31eaccaf9a46cced2152">{"x":{"data":[{"x":[16.99,24.59,35.26,14.83,10.33,16.97,20.29,15.77,19.65,15.06,20.69,16.93,10.29,34.81,26.41,16.45,3.07,17.07,26.86,25.28,14.73,10.07,34.83,5.75,16.32,22.75,11.35,15.38,44.3,22.42,20.92,14.31,7.25,25.71,17.31,10.65,12.43,24.08,13.42,12.48,29.8,14.52,11.38,20.27,11.17,12.26,18.26,8.51,10.33,14.15,13.16,17.47,27.05,16.43,8.35,18.64,11.87,29.85,25,13.39,16.21,17.51,10.59,10.63,9.6,20.9,18.15,19.81,43.11,13,12.74,13,16.4,16.47,12.76,13.27,28.17,12.9,30.14,13.42,15.98,16.27,10.09,22.12,35.83,27.18,18.78],"y":[1.01,3.61,5,3.02,1.67,3.5,2.75,2.23,3,3,2.45,3.07,2.6,5.2,1.5,2.47,1,3,3.14,5,2.2,1.83,5.17,1,4.3,3.25,2.5,3,2.5,3.48,4.08,4,1,4,3.5,1.5,1.8,2.92,1.68,2.52,4.2,2,2,2.83,1.5,2,3.25,1.25,2,2,2.75,3.5,5,2.3,1.5,1.36,1.63,5.14,3.75,2.61,2,3,1.61,2,4,3.5,3.5,4.19,5,2,2.01,2,2.5,3.23,2.23,2.5,6.5,1.1,3.09,3.48,3,2.5,2,2.88,4.67,2,3],"text":"Sexo F","type":"scatter","mode":"markers","marker":{"autocolorscale":false,"color":"rgba(248,118,109,1)","opacity":1,"size":5.66929133858268,"symbol":"circle","line":{"width":1.88976377952756,"color":"rgba(248,118,109,1)"}},"hoveron":"points","name":"F","legendgroup":"F","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","frame":null},{"x":[10.34,21.01,23.68,25.29,8.77,26.88,15.04,14.78,10.27,15.42,18.43,21.58,16.29,20.65,17.92,39.42,19.82,17.81,13.37,12.69,21.7,9.55,18.35,17.78,24.06,16.31,18.69,31.27,16.04,17.46,13.94,9.68,30.4,18.29,22.23,32.4,28.55,18.04,12.54,9.94,25.56,19.49,38.01,11.24,48.27,20.29,13.81,11.02,18.29,17.59,20.08,20.23,15.01,12.02,10.51,17.92,27.2,22.76,17.29,19.44,16.66,32.68,15.98,13.03,18.28,24.71,21.16,28.97,22.49,40.17,27.28,12.03,21.01,12.46,15.36,20.49,25.21,18.24,14,38.07,23.95,29.93,11.69,14.26,15.95,8.52,22.82,19.08,16,34.3,41.19,9.78,7.51,14.07,13.13,17.26,24.55,19.77,48.17,16.49,21.5,12.66,13.81,24.52,20.76,31.71,50.81,15.81,7.25,31.85,16.82,32.9,17.89,14.48,34.63,34.65,23.33,45.35,23.17,40.55,20.69,30.46,23.1,15.69,28.44,15.48,16.58,7.56,10.34,13.51,18.71,20.53,26.59,38.73,24.27,30.06,25.89,48.33,28.15,11.59,7.74,12.16,8.58,13.42,20.45,13.28,24.01,15.69,11.61,10.77,15.53,10.07,12.6,32.83,29.03,22.67,17.82],"y":[1.66,3.5,3.31,4.71,2,3.12,1.96,3.23,1.71,1.57,3,3.92,3.71,3.35,4.08,7.58,3.18,2.34,2,2,4.3,1.45,2.5,3.27,3.6,2,2.31,5,2.24,2.54,3.06,1.32,5.6,3,5,6,2.05,3,2.5,1.56,4.34,3.51,3,1.76,6.73,3.21,2,1.98,3.76,2.64,3.15,2.01,2.09,1.97,1.25,3.08,4,3,2.71,3,3.4,5,2.03,2,4,5.85,3,3,3.5,4.73,4,1.5,3,1.5,1.64,4.06,4.29,3.76,3,4,2.55,5.07,2.31,2.5,2,1.48,2.18,1.5,2,6.7,5,1.73,2,2.5,2,2.74,2,2,5,2,3.5,2.5,2,3.48,2.24,4.5,10,3.16,5.15,3.18,4,3.11,2,2,3.55,3.68,5.65,3.5,6.5,3,5,2,4,1.5,2.56,2.02,4,1.44,2,2,4,4,3.41,3,2.03,2,5.16,9,3,1.5,1.44,2.2,1.92,1.58,3,2.72,2,3,3.39,1.47,3,1.25,1,1.17,5.92,2,1.75],"text":"Sexo M","type":"scatter","mode":"markers","marker":{"autocolorscale":false,"color":"rgba(0,191,196,1)","opacity":1,"size":5.66929133858268,"symbol":"circle","line":{"width":1.88976377952756,"color":"rgba(0,191,196,1)"}},"hoveron":"points","name":"M","legendgroup":"M","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","frame":null}],"layout":{"margin":{"t":24.7671232876712,"r":7.30593607305936,"b":38.7214611872146,"l":48.9497716894977},"plot_bgcolor":"rgba(235,235,235,1)","paper_bgcolor":"rgba(255,255,255,1)","font":{"color":"rgba(0,0,0,1)","family":"","size":14.6118721461187},"xaxis":{"domain":[0,1],"automargin":true,"type":"linear","autorange":false,"range":[0.683,53.197],"tickmode":"array","ticktext":["10","20","30","40","50"],"tickvals":[10,20,30,40,50],"categoryorder":"array","categoryarray":["10","20","30","40","50"],"nticks":null,"ticks":"outside","tickcolor":"rgba(51,51,51,1)","ticklen":3.65296803652968,"tickwidth":0.66417600664176,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"","size":11.689497716895},"tickangle":-0,"showline":false,"linecolor":null,"linewidth":0,"showgrid":true,"gridcolor":"rgba(255,255,255,1)","gridwidth":0.66417600664176,"zeroline":false,"anchor":"y","title":"total","titlefont":{"color":"rgba(0,0,0,1)","family":"","size":14.6118721461187},"hoverformat":".2f"},"yaxis":{"domain":[0,1],"automargin":true,"type":"linear","autorange":false,"range":[0.55,10.45],"tickmode":"array","ticktext":["2.5","5.0","7.5","10.0"],"tickvals":[2.5,5,7.5,10],"categoryorder":"array","categoryarray":["2.5","5.0","7.5","10.0"],"nticks":null,"ticks":"outside","tickcolor":"rgba(51,51,51,1)","ticklen":3.65296803652968,"tickwidth":0.66417600664176,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"","size":11.689497716895},"tickangle":-0,"showline":false,"linecolor":null,"linewidth":0,"showgrid":true,"gridcolor":"rgba(255,255,255,1)","gridwidth":0.66417600664176,"zeroline":false,"anchor":"x","title":"propina","titlefont":{"color":"rgba(0,0,0,1)","family":"","size":14.6118721461187},"hoverformat":".2f"},"shapes":[{"type":"rect","fillcolor":null,"line":{"color":null,"width":0,"linetype":[]},"yref":"paper","xref":"paper","x0":0,"x1":1,"y0":0,"y1":1}],"showlegend":true,"legend":{"bgcolor":"rgba(255,255,255,1)","bordercolor":"transparent","borderwidth":1.88976377952756,"font":{"color":"rgba(0,0,0,1)","family":"","size":11.689497716895},"y":0.913385826771654},"annotations":[{"text":"sexo","x":1.02,"y":1,"showarrow":false,"ax":0,"ay":0,"font":{"color":"rgba(0,0,0,1)","family":"","size":14.6118721461187},"xref":"paper","yref":"paper","textangle":-0,"xanchor":"left","yanchor":"bottom","legendTitle":true}],"hovermode":"closest","barmode":"relative"},"config":{"doubleClick":"reset","modeBarButtonsToAdd":[{"name":"Collaborate","icon":{"width":1000,"ascent":500,"descent":-50,"path":"M487 375c7-10 9-23 5-36l-79-259c-3-12-11-23-22-31-11-8-22-12-35-12l-263 0c-15 0-29 5-43 15-13 10-23 23-28 37-5 13-5 25-1 37 0 0 0 3 1 7 1 5 1 8 1 11 0 2 0 4-1 6 0 3-1 5-1 6 1 2 2 4 3 6 1 2 2 4 4 6 2 3 4 5 5 7 5 7 9 16 13 26 4 10 7 19 9 26 0 2 0 5 0 9-1 4-1 6 0 8 0 2 2 5 4 8 3 3 5 5 5 7 4 6 8 15 12 26 4 11 7 19 7 26 1 1 0 4 0 9-1 4-1 7 0 8 1 2 3 5 6 8 4 4 6 6 6 7 4 5 8 13 13 24 4 11 7 20 7 28 1 1 0 4 0 7-1 3-1 6-1 7 0 2 1 4 3 6 1 1 3 4 5 6 2 3 3 5 5 6 1 2 3 5 4 9 2 3 3 7 5 10 1 3 2 6 4 10 2 4 4 7 6 9 2 3 4 5 7 7 3 2 7 3 11 3 3 0 8 0 13-1l0-1c7 2 12 2 14 2l218 0c14 0 25-5 32-16 8-10 10-23 6-37l-79-259c-7-22-13-37-20-43-7-7-19-10-37-10l-248 0c-5 0-9-2-11-5-2-3-2-7 0-12 4-13 18-20 41-20l264 0c5 0 10 2 16 5 5 3 8 6 10 11l85 282c2 5 2 10 2 17 7-3 13-7 17-13z m-304 0c-1-3-1-5 0-7 1-1 3-2 6-2l174 0c2 0 4 1 7 2 2 2 4 4 5 7l6 18c0 3 0 5-1 7-1 1-3 2-6 2l-173 0c-3 0-5-1-8-2-2-2-4-4-4-7z m-24-73c-1-3-1-5 0-7 2-2 3-2 6-2l174 0c2 0 5 0 7 2 3 2 4 4 5 7l6 18c1 2 0 5-1 6-1 2-3 3-5 3l-174 0c-3 0-5-1-7-3-3-1-4-4-5-6z"},"click":"function(gd) { \n // is this being viewed in RStudio?\n if (location.search == '?viewer_pane=1') {\n alert('To learn about plotly for collaboration, visit:\\n https://cpsievert.github.io/plotly_book/plot-ly-for-collaboration.html');\n } else {\n window.open('https://cpsievert.github.io/plotly_book/plot-ly-for-collaboration.html', '_blank');\n }\n }"}],"cloud":false},"source":"A","attrs":{"71f632971516":{"x":{},"y":{},"colour":{},"text":{},"type":"scatter"}},"cur_data":"71f632971516","visdat":{"71f632971516":["function (y) ","x"]},"highlight":{"on":"plotly_click","persistent":false,"dynamic":false,"selectize":false,"opacityDim":0.2,"selected":{"opacity":1},"debounce":0},"base_url":"https://plot.ly"},"evals":["config.modeBarButtonsToAdd.0.click"],"jsHooks":[]}</script>
---

## `ggplotly` seleccionar una clase

```r
propi <- highlight_key(propinas, ~sexo)

pl <- ggplot(propi, aes(total, propina, colour = sexo,
 text = paste('Sexo', sexo)), group = sexo) +
 geom_point()

gg <- ggplotly(pl, tooltip = "text")

highlight(gg, on = "plotly_click")
```
---

## `ggplotly` seleccionar una clase

<div id="htmlwidget-f66d063c00bbf67fa555" style="width:432px;height:360px;" class="plotly html-widget"></div>
<script type="application/json" data-for="htmlwidget-f66d063c00bbf67fa555">{"x":{"data":[{"x":[16.99,24.59,35.26,14.83,10.33,16.97,20.29,15.77,19.65,15.06,20.69,16.93,10.29,34.81,26.41,16.45,3.07,17.07,26.86,25.28,14.73,10.07,34.83,5.75,16.32,22.75,11.35,15.38,44.3,22.42,20.92,14.31,7.25,25.71,17.31,10.65,12.43,24.08,13.42,12.48,29.8,14.52,11.38,20.27,11.17,12.26,18.26,8.51,10.33,14.15,13.16,17.47,27.05,16.43,8.35,18.64,11.87,29.85,25,13.39,16.21,17.51,10.59,10.63,9.6,20.9,18.15,19.81,43.11,13,12.74,13,16.4,16.47,12.76,13.27,28.17,12.9,30.14,13.42,15.98,16.27,10.09,22.12,35.83,27.18,18.78],"y":[1.01,3.61,5,3.02,1.67,3.5,2.75,2.23,3,3,2.45,3.07,2.6,5.2,1.5,2.47,1,3,3.14,5,2.2,1.83,5.17,1,4.3,3.25,2.5,3,2.5,3.48,4.08,4,1,4,3.5,1.5,1.8,2.92,1.68,2.52,4.2,2,2,2.83,1.5,2,3.25,1.25,2,2,2.75,3.5,5,2.3,1.5,1.36,1.63,5.14,3.75,2.61,2,3,1.61,2,4,3.5,3.5,4.19,5,2,2.01,2,2.5,3.23,2.23,2.5,6.5,1.1,3.09,3.48,3,2.5,2,2.88,4.67,2,3],"text":"Sexo F","key":["F"],"type":"scatter","mode":"markers","marker":{"autocolorscale":false,"color":"rgba(248,118,109,1)","opacity":1,"size":5.66929133858268,"symbol":"circle","line":{"width":1.88976377952756,"color":"rgba(248,118,109,1)"}},"hoveron":"points","set":"SharedDatacbaaad6f","name":"F","legendgroup":"F","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","_isSimpleKey":true,"_isNestedKey":false,"frame":null},{"x":[10.34,21.01,23.68,25.29,8.77,26.88,15.04,14.78,10.27,15.42,18.43,21.58,16.29,20.65,17.92,39.42,19.82,17.81,13.37,12.69,21.7,9.55,18.35,17.78,24.06,16.31,18.69,31.27,16.04,17.46,13.94,9.68,30.4,18.29,22.23,32.4,28.55,18.04,12.54,9.94,25.56,19.49,38.01,11.24,48.27,20.29,13.81,11.02,18.29,17.59,20.08,20.23,15.01,12.02,10.51,17.92,27.2,22.76,17.29,19.44,16.66,32.68,15.98,13.03,18.28,24.71,21.16,28.97,22.49,40.17,27.28,12.03,21.01,12.46,15.36,20.49,25.21,18.24,14,38.07,23.95,29.93,11.69,14.26,15.95,8.52,22.82,19.08,16,34.3,41.19,9.78,7.51,14.07,13.13,17.26,24.55,19.77,48.17,16.49,21.5,12.66,13.81,24.52,20.76,31.71,50.81,15.81,7.25,31.85,16.82,32.9,17.89,14.48,34.63,34.65,23.33,45.35,23.17,40.55,20.69,30.46,23.1,15.69,28.44,15.48,16.58,7.56,10.34,13.51,18.71,20.53,26.59,38.73,24.27,30.06,25.89,48.33,28.15,11.59,7.74,12.16,8.58,13.42,20.45,13.28,24.01,15.69,11.61,10.77,15.53,10.07,12.6,32.83,29.03,22.67,17.82],"y":[1.66,3.5,3.31,4.71,2,3.12,1.96,3.23,1.71,1.57,3,3.92,3.71,3.35,4.08,7.58,3.18,2.34,2,2,4.3,1.45,2.5,3.27,3.6,2,2.31,5,2.24,2.54,3.06,1.32,5.6,3,5,6,2.05,3,2.5,1.56,4.34,3.51,3,1.76,6.73,3.21,2,1.98,3.76,2.64,3.15,2.01,2.09,1.97,1.25,3.08,4,3,2.71,3,3.4,5,2.03,2,4,5.85,3,3,3.5,4.73,4,1.5,3,1.5,1.64,4.06,4.29,3.76,3,4,2.55,5.07,2.31,2.5,2,1.48,2.18,1.5,2,6.7,5,1.73,2,2.5,2,2.74,2,2,5,2,3.5,2.5,2,3.48,2.24,4.5,10,3.16,5.15,3.18,4,3.11,2,2,3.55,3.68,5.65,3.5,6.5,3,5,2,4,1.5,2.56,2.02,4,1.44,2,2,4,4,3.41,3,2.03,2,5.16,9,3,1.5,1.44,2.2,1.92,1.58,3,2.72,2,3,3.39,1.47,3,1.25,1,1.17,5.92,2,1.75],"text":"Sexo M","key":["M"],"type":"scatter","mode":"markers","marker":{"autocolorscale":false,"color":"rgba(0,191,196,1)","opacity":1,"size":5.66929133858268,"symbol":"circle","line":{"width":1.88976377952756,"color":"rgba(0,191,196,1)"}},"hoveron":"points","set":"SharedDatacbaaad6f","name":"M","legendgroup":"M","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","_isSimpleKey":true,"_isNestedKey":false,"frame":null}],"layout":{"margin":{"t":24.7671232876712,"r":7.30593607305936,"b":38.7214611872146,"l":48.9497716894977},"plot_bgcolor":"rgba(235,235,235,1)","paper_bgcolor":"rgba(255,255,255,1)","font":{"color":"rgba(0,0,0,1)","family":"","size":14.6118721461187},"xaxis":{"domain":[0,1],"automargin":true,"type":"linear","autorange":false,"range":[0.683,53.197],"tickmode":"array","ticktext":["10","20","30","40","50"],"tickvals":[10,20,30,40,50],"categoryorder":"array","categoryarray":["10","20","30","40","50"],"nticks":null,"ticks":"outside","tickcolor":"rgba(51,51,51,1)","ticklen":3.65296803652968,"tickwidth":0.66417600664176,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"","size":11.689497716895},"tickangle":-0,"showline":false,"linecolor":null,"linewidth":0,"showgrid":true,"gridcolor":"rgba(255,255,255,1)","gridwidth":0.66417600664176,"zeroline":false,"anchor":"y","title":"total","titlefont":{"color":"rgba(0,0,0,1)","family":"","size":14.6118721461187},"hoverformat":".2f"},"yaxis":{"domain":[0,1],"automargin":true,"type":"linear","autorange":false,"range":[0.55,10.45],"tickmode":"array","ticktext":["2.5","5.0","7.5","10.0"],"tickvals":[2.5,5,7.5,10],"categoryorder":"array","categoryarray":["2.5","5.0","7.5","10.0"],"nticks":null,"ticks":"outside","tickcolor":"rgba(51,51,51,1)","ticklen":3.65296803652968,"tickwidth":0.66417600664176,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"","size":11.689497716895},"tickangle":-0,"showline":false,"linecolor":null,"linewidth":0,"showgrid":true,"gridcolor":"rgba(255,255,255,1)","gridwidth":0.66417600664176,"zeroline":false,"anchor":"x","title":"propina","titlefont":{"color":"rgba(0,0,0,1)","family":"","size":14.6118721461187},"hoverformat":".2f"},"shapes":[{"type":"rect","fillcolor":null,"line":{"color":null,"width":0,"linetype":[]},"yref":"paper","xref":"paper","x0":0,"x1":1,"y0":0,"y1":1}],"showlegend":true,"legend":{"bgcolor":"rgba(255,255,255,1)","bordercolor":"transparent","borderwidth":1.88976377952756,"font":{"color":"rgba(0,0,0,1)","family":"","size":11.689497716895},"y":0.913385826771654},"annotations":[{"text":"sexo","x":1.02,"y":1,"showarrow":false,"ax":0,"ay":0,"font":{"color":"rgba(0,0,0,1)","family":"","size":14.6118721461187},"xref":"paper","yref":"paper","textangle":-0,"xanchor":"left","yanchor":"bottom","legendTitle":true}],"hovermode":"closest","barmode":"relative","dragmode":"zoom"},"config":{"doubleClick":"reset","modeBarButtonsToAdd":[{"name":"Collaborate","icon":{"width":1000,"ascent":500,"descent":-50,"path":"M487 375c7-10 9-23 5-36l-79-259c-3-12-11-23-22-31-11-8-22-12-35-12l-263 0c-15 0-29 5-43 15-13 10-23 23-28 37-5 13-5 25-1 37 0 0 0 3 1 7 1 5 1 8 1 11 0 2 0 4-1 6 0 3-1 5-1 6 1 2 2 4 3 6 1 2 2 4 4 6 2 3 4 5 5 7 5 7 9 16 13 26 4 10 7 19 9 26 0 2 0 5 0 9-1 4-1 6 0 8 0 2 2 5 4 8 3 3 5 5 5 7 4 6 8 15 12 26 4 11 7 19 7 26 1 1 0 4 0 9-1 4-1 7 0 8 1 2 3 5 6 8 4 4 6 6 6 7 4 5 8 13 13 24 4 11 7 20 7 28 1 1 0 4 0 7-1 3-1 6-1 7 0 2 1 4 3 6 1 1 3 4 5 6 2 3 3 5 5 6 1 2 3 5 4 9 2 3 3 7 5 10 1 3 2 6 4 10 2 4 4 7 6 9 2 3 4 5 7 7 3 2 7 3 11 3 3 0 8 0 13-1l0-1c7 2 12 2 14 2l218 0c14 0 25-5 32-16 8-10 10-23 6-37l-79-259c-7-22-13-37-20-43-7-7-19-10-37-10l-248 0c-5 0-9-2-11-5-2-3-2-7 0-12 4-13 18-20 41-20l264 0c5 0 10 2 16 5 5 3 8 6 10 11l85 282c2 5 2 10 2 17 7-3 13-7 17-13z m-304 0c-1-3-1-5 0-7 1-1 3-2 6-2l174 0c2 0 4 1 7 2 2 2 4 4 5 7l6 18c0 3 0 5-1 7-1 1-3 2-6 2l-173 0c-3 0-5-1-8-2-2-2-4-4-4-7z m-24-73c-1-3-1-5 0-7 2-2 3-2 6-2l174 0c2 0 5 0 7 2 3 2 4 4 5 7l6 18c1 2 0 5-1 6-1 2-3 3-5 3l-174 0c-3 0-5-1-7-3-3-1-4-4-5-6z"},"click":"function(gd) { \n // is this being viewed in RStudio?\n if (location.search == '?viewer_pane=1') {\n alert('To learn about plotly for collaboration, visit:\\n https://cpsievert.github.io/plotly_book/plot-ly-for-collaboration.html');\n } else {\n window.open('https://cpsievert.github.io/plotly_book/plot-ly-for-collaboration.html', '_blank');\n }\n }"}],"cloud":false},"source":"A","attrs":{"71f664aaba3d":{"key":{},"x":{},"y":{},"colour":{},"text":{},"type":"scatter"}},"cur_data":"71f664aaba3d","visdat":{"71f664aaba3d":["function (y) ","x"]},"highlight":{"on":"plotly_click","off":"plotly_doubleclick","persistent":false,"dynamic":false,"color":null,"selectize":false,"defaultValues":null,"opacityDim":0.2,"selected":{"opacity":1},"debounce":0,"ctGroups":["SharedDatacbaaad6f"]},"base_url":"https://plot.ly"},"evals":["config.modeBarButtonsToAdd.0.click"],"jsHooks":[]}</script>
---
## `ggplotly` seleccionar una clase

```r
propi <- highlight_key(propinas)

pl <- ggplot(propi, aes(total, propina, colour = sexo,
 text = paste('Sexo', sexo)), group = sexo) +
 geom_point()

gg <- ggplotly(pl, tooltip = "text")

highlight(gg, on = "plotly_selected")
```

---

## `ggplotly` seleccionar una clase
<div id="htmlwidget-9f680b5627026f64f3ad" style="width:432px;height:360px;" class="plotly html-widget"></div>
<script type="application/json" data-for="htmlwidget-9f680b5627026f64f3ad">{"x":{"data":[{"x":[16.99,24.59,35.26,14.83,10.33,16.97,20.29,15.77,19.65,15.06,20.69,16.93,10.29,34.81,26.41,16.45,3.07,17.07,26.86,25.28,14.73,10.07,34.83,5.75,16.32,22.75,11.35,15.38,44.3,22.42,20.92,14.31,7.25,25.71,17.31,10.65,12.43,24.08,13.42,12.48,29.8,14.52,11.38,20.27,11.17,12.26,18.26,8.51,10.33,14.15,13.16,17.47,27.05,16.43,8.35,18.64,11.87,29.85,25,13.39,16.21,17.51,10.59,10.63,9.6,20.9,18.15,19.81,43.11,13,12.74,13,16.4,16.47,12.76,13.27,28.17,12.9,30.14,13.42,15.98,16.27,10.09,22.12,35.83,27.18,18.78],"y":[1.01,3.61,5,3.02,1.67,3.5,2.75,2.23,3,3,2.45,3.07,2.6,5.2,1.5,2.47,1,3,3.14,5,2.2,1.83,5.17,1,4.3,3.25,2.5,3,2.5,3.48,4.08,4,1,4,3.5,1.5,1.8,2.92,1.68,2.52,4.2,2,2,2.83,1.5,2,3.25,1.25,2,2,2.75,3.5,5,2.3,1.5,1.36,1.63,5.14,3.75,2.61,2,3,1.61,2,4,3.5,3.5,4.19,5,2,2.01,2,2.5,3.23,2.23,2.5,6.5,1.1,3.09,3.48,3,2.5,2,2.88,4.67,2,3],"text":"Sexo F","key":["1","5","12","15","17","19","22","23","30","33","34","38","52","53","58","67","68","72","73","74","75","83","86","93","94","95","101","102","103","104","105","110","112","115","116","118","119","120","122","125","126","128","129","132","133","134","135","136","137","138","140","141","144","145","146","147","148","156","158","159","163","165","169","170","179","187","189","192","198","199","202","203","204","206","210","214","215","216","220","222","224","226","227","230","239","241","244"],"type":"scatter","mode":"markers","marker":{"autocolorscale":false,"color":"rgba(248,118,109,1)","opacity":1,"size":5.66929133858268,"symbol":"circle","line":{"width":1.88976377952756,"color":"rgba(248,118,109,1)"}},"hoveron":"points","set":"SharedData7921bb1b","name":"F","legendgroup":"F","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","_isNestedKey":false,"frame":null},{"x":[10.34,21.01,23.68,25.29,8.77,26.88,15.04,14.78,10.27,15.42,18.43,21.58,16.29,20.65,17.92,39.42,19.82,17.81,13.37,12.69,21.7,9.55,18.35,17.78,24.06,16.31,18.69,31.27,16.04,17.46,13.94,9.68,30.4,18.29,22.23,32.4,28.55,18.04,12.54,9.94,25.56,19.49,38.01,11.24,48.27,20.29,13.81,11.02,18.29,17.59,20.08,20.23,15.01,12.02,10.51,17.92,27.2,22.76,17.29,19.44,16.66,32.68,15.98,13.03,18.28,24.71,21.16,28.97,22.49,40.17,27.28,12.03,21.01,12.46,15.36,20.49,25.21,18.24,14,38.07,23.95,29.93,11.69,14.26,15.95,8.52,22.82,19.08,16,34.3,41.19,9.78,7.51,14.07,13.13,17.26,24.55,19.77,48.17,16.49,21.5,12.66,13.81,24.52,20.76,31.71,50.81,15.81,7.25,31.85,16.82,32.9,17.89,14.48,34.63,34.65,23.33,45.35,23.17,40.55,20.69,30.46,23.1,15.69,28.44,15.48,16.58,7.56,10.34,13.51,18.71,20.53,26.59,38.73,24.27,30.06,25.89,48.33,28.15,11.59,7.74,12.16,8.58,13.42,20.45,13.28,24.01,15.69,11.61,10.77,15.53,10.07,12.6,32.83,29.03,22.67,17.82],"y":[1.66,3.5,3.31,4.71,2,3.12,1.96,3.23,1.71,1.57,3,3.92,3.71,3.35,4.08,7.58,3.18,2.34,2,2,4.3,1.45,2.5,3.27,3.6,2,2.31,5,2.24,2.54,3.06,1.32,5.6,3,5,6,2.05,3,2.5,1.56,4.34,3.51,3,1.76,6.73,3.21,2,1.98,3.76,2.64,3.15,2.01,2.09,1.97,1.25,3.08,4,3,2.71,3,3.4,5,2.03,2,4,5.85,3,3,3.5,4.73,4,1.5,3,1.5,1.64,4.06,4.29,3.76,3,4,2.55,5.07,2.31,2.5,2,1.48,2.18,1.5,2,6.7,5,1.73,2,2.5,2,2.74,2,2,5,2,3.5,2.5,2,3.48,2.24,4.5,10,3.16,5.15,3.18,4,3.11,2,2,3.55,3.68,5.65,3.5,6.5,3,5,2,4,1.5,2.56,2.02,4,1.44,2,2,4,4,3.41,3,2.03,2,5.16,9,3,1.5,1.44,2.2,1.92,1.58,3,2.72,2,3,3.39,1.47,3,1.25,1,1.17,5.92,2,1.75],"text":"Sexo M","key":["2","3","4","6","7","8","9","10","11","13","14","16","18","20","21","24","25","26","27","28","29","31","32","35","36","37","39","40","41","42","43","44","45","46","47","48","49","50","51","54","55","56","57","59","60","61","62","63","64","65","66","69","70","71","76","77","78","79","80","81","82","84","85","87","88","89","90","91","92","96","97","98","99","100","106","107","108","109","111","113","114","117","121","123","124","127","130","131","139","142","143","149","150","151","152","153","154","155","157","160","161","162","164","166","167","168","171","172","173","174","175","176","177","178","180","181","182","183","184","185","186","188","190","191","193","194","195","196","197","200","201","205","207","208","209","211","212","213","217","218","219","221","223","225","228","229","231","232","233","234","235","236","237","238","240","242","243"],"type":"scatter","mode":"markers","marker":{"autocolorscale":false,"color":"rgba(0,191,196,1)","opacity":1,"size":5.66929133858268,"symbol":"circle","line":{"width":1.88976377952756,"color":"rgba(0,191,196,1)"}},"hoveron":"points","set":"SharedData7921bb1b","name":"M","legendgroup":"M","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","_isNestedKey":false,"frame":null}],"layout":{"margin":{"t":24.7671232876712,"r":7.30593607305936,"b":38.7214611872146,"l":48.9497716894977},"plot_bgcolor":"rgba(235,235,235,1)","paper_bgcolor":"rgba(255,255,255,1)","font":{"color":"rgba(0,0,0,1)","family":"","size":14.6118721461187},"xaxis":{"domain":[0,1],"automargin":true,"type":"linear","autorange":false,"range":[0.683,53.197],"tickmode":"array","ticktext":["10","20","30","40","50"],"tickvals":[10,20,30,40,50],"categoryorder":"array","categoryarray":["10","20","30","40","50"],"nticks":null,"ticks":"outside","tickcolor":"rgba(51,51,51,1)","ticklen":3.65296803652968,"tickwidth":0.66417600664176,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"","size":11.689497716895},"tickangle":-0,"showline":false,"linecolor":null,"linewidth":0,"showgrid":true,"gridcolor":"rgba(255,255,255,1)","gridwidth":0.66417600664176,"zeroline":false,"anchor":"y","title":"total","titlefont":{"color":"rgba(0,0,0,1)","family":"","size":14.6118721461187},"hoverformat":".2f"},"yaxis":{"domain":[0,1],"automargin":true,"type":"linear","autorange":false,"range":[0.55,10.45],"tickmode":"array","ticktext":["2.5","5.0","7.5","10.0"],"tickvals":[2.5,5,7.5,10],"categoryorder":"array","categoryarray":["2.5","5.0","7.5","10.0"],"nticks":null,"ticks":"outside","tickcolor":"rgba(51,51,51,1)","ticklen":3.65296803652968,"tickwidth":0.66417600664176,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"","size":11.689497716895},"tickangle":-0,"showline":false,"linecolor":null,"linewidth":0,"showgrid":true,"gridcolor":"rgba(255,255,255,1)","gridwidth":0.66417600664176,"zeroline":false,"anchor":"x","title":"propina","titlefont":{"color":"rgba(0,0,0,1)","family":"","size":14.6118721461187},"hoverformat":".2f"},"shapes":[{"type":"rect","fillcolor":null,"line":{"color":null,"width":0,"linetype":[]},"yref":"paper","xref":"paper","x0":0,"x1":1,"y0":0,"y1":1}],"showlegend":true,"legend":{"bgcolor":"rgba(255,255,255,1)","bordercolor":"transparent","borderwidth":1.88976377952756,"font":{"color":"rgba(0,0,0,1)","family":"","size":11.689497716895},"y":0.913385826771654},"annotations":[{"text":"sexo","x":1.02,"y":1,"showarrow":false,"ax":0,"ay":0,"font":{"color":"rgba(0,0,0,1)","family":"","size":14.6118721461187},"xref":"paper","yref":"paper","textangle":-0,"xanchor":"left","yanchor":"bottom","legendTitle":true}],"hovermode":"closest","barmode":"relative","dragmode":"select"},"config":{"doubleClick":"reset","modeBarButtonsToAdd":[{"name":"Collaborate","icon":{"width":1000,"ascent":500,"descent":-50,"path":"M487 375c7-10 9-23 5-36l-79-259c-3-12-11-23-22-31-11-8-22-12-35-12l-263 0c-15 0-29 5-43 15-13 10-23 23-28 37-5 13-5 25-1 37 0 0 0 3 1 7 1 5 1 8 1 11 0 2 0 4-1 6 0 3-1 5-1 6 1 2 2 4 3 6 1 2 2 4 4 6 2 3 4 5 5 7 5 7 9 16 13 26 4 10 7 19 9 26 0 2 0 5 0 9-1 4-1 6 0 8 0 2 2 5 4 8 3 3 5 5 5 7 4 6 8 15 12 26 4 11 7 19 7 26 1 1 0 4 0 9-1 4-1 7 0 8 1 2 3 5 6 8 4 4 6 6 6 7 4 5 8 13 13 24 4 11 7 20 7 28 1 1 0 4 0 7-1 3-1 6-1 7 0 2 1 4 3 6 1 1 3 4 5 6 2 3 3 5 5 6 1 2 3 5 4 9 2 3 3 7 5 10 1 3 2 6 4 10 2 4 4 7 6 9 2 3 4 5 7 7 3 2 7 3 11 3 3 0 8 0 13-1l0-1c7 2 12 2 14 2l218 0c14 0 25-5 32-16 8-10 10-23 6-37l-79-259c-7-22-13-37-20-43-7-7-19-10-37-10l-248 0c-5 0-9-2-11-5-2-3-2-7 0-12 4-13 18-20 41-20l264 0c5 0 10 2 16 5 5 3 8 6 10 11l85 282c2 5 2 10 2 17 7-3 13-7 17-13z m-304 0c-1-3-1-5 0-7 1-1 3-2 6-2l174 0c2 0 4 1 7 2 2 2 4 4 5 7l6 18c0 3 0 5-1 7-1 1-3 2-6 2l-173 0c-3 0-5-1-8-2-2-2-4-4-4-7z m-24-73c-1-3-1-5 0-7 2-2 3-2 6-2l174 0c2 0 5 0 7 2 3 2 4 4 5 7l6 18c1 2 0 5-1 6-1 2-3 3-5 3l-174 0c-3 0-5-1-7-3-3-1-4-4-5-6z"},"click":"function(gd) { \n // is this being viewed in RStudio?\n if (location.search == '?viewer_pane=1') {\n alert('To learn about plotly for collaboration, visit:\\n https://cpsievert.github.io/plotly_book/plot-ly-for-collaboration.html');\n } else {\n window.open('https://cpsievert.github.io/plotly_book/plot-ly-for-collaboration.html', '_blank');\n }\n }"}],"cloud":false},"source":"A","attrs":{"71f63cd8c3f4":{"key":{},"x":{},"y":{},"colour":{},"text":{},"type":"scatter"}},"cur_data":"71f63cd8c3f4","visdat":{"71f63cd8c3f4":["function (y) ","x"]},"highlight":{"on":"plotly_selected","off":"plotly_deselect","persistent":false,"dynamic":false,"color":null,"selectize":false,"defaultValues":null,"opacityDim":0.2,"selected":{"opacity":1},"debounce":0,"ctGroups":["SharedData7921bb1b"]},"base_url":"https://plot.ly"},"evals":["config.modeBarButtonsToAdd.0.click"],"jsHooks":[]}</script>

---

## `highlight` otros
 
<div id="htmlwidget-53e38e39c42ba9f40ad8" style="width:432px;height:360px;" class="plotly html-widget"></div>
<script type="application/json" data-for="htmlwidget-53e38e39c42ba9f40ad8">{"x":{"data":[{"x":[16.99,24.59,35.26,14.83,10.33,16.97,20.29,15.77,19.65,15.06,20.69,16.93,10.29,34.81,26.41,16.45,3.07,17.07,26.86,25.28,14.73,10.07,34.83,5.75,16.32,22.75,11.35,15.38,44.3,22.42,20.92,14.31,7.25,25.71,17.31,10.65,12.43,24.08,13.42,12.48,29.8,14.52,11.38,20.27,11.17,12.26,18.26,8.51,10.33,14.15,13.16,17.47,27.05,16.43,8.35,18.64,11.87,29.85,25,13.39,16.21,17.51,10.59,10.63,9.6,20.9,18.15,19.81,43.11,13,12.74,13,16.4,16.47,12.76,13.27,28.17,12.9,30.14,13.42,15.98,16.27,10.09,22.12,35.83,27.18,18.78],"y":[1.01,3.61,5,3.02,1.67,3.5,2.75,2.23,3,3,2.45,3.07,2.6,5.2,1.5,2.47,1,3,3.14,5,2.2,1.83,5.17,1,4.3,3.25,2.5,3,2.5,3.48,4.08,4,1,4,3.5,1.5,1.8,2.92,1.68,2.52,4.2,2,2,2.83,1.5,2,3.25,1.25,2,2,2.75,3.5,5,2.3,1.5,1.36,1.63,5.14,3.75,2.61,2,3,1.61,2,4,3.5,3.5,4.19,5,2,2.01,2,2.5,3.23,2.23,2.5,6.5,1.1,3.09,3.48,3,2.5,2,2.88,4.67,2,3],"text":"Sexo F","key":["F"],"type":"scatter","mode":"markers","marker":{"autocolorscale":false,"color":"rgba(248,118,109,1)","opacity":1,"size":5.66929133858268,"symbol":"circle","line":{"width":1.88976377952756,"color":"rgba(248,118,109,1)"}},"hoveron":"points","set":"SharedDatab67f09ce","name":"F","legendgroup":"F","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","_isSimpleKey":true,"_isNestedKey":false,"frame":null},{"x":[10.34,21.01,23.68,25.29,8.77,26.88,15.04,14.78,10.27,15.42,18.43,21.58,16.29,20.65,17.92,39.42,19.82,17.81,13.37,12.69,21.7,9.55,18.35,17.78,24.06,16.31,18.69,31.27,16.04,17.46,13.94,9.68,30.4,18.29,22.23,32.4,28.55,18.04,12.54,9.94,25.56,19.49,38.01,11.24,48.27,20.29,13.81,11.02,18.29,17.59,20.08,20.23,15.01,12.02,10.51,17.92,27.2,22.76,17.29,19.44,16.66,32.68,15.98,13.03,18.28,24.71,21.16,28.97,22.49,40.17,27.28,12.03,21.01,12.46,15.36,20.49,25.21,18.24,14,38.07,23.95,29.93,11.69,14.26,15.95,8.52,22.82,19.08,16,34.3,41.19,9.78,7.51,14.07,13.13,17.26,24.55,19.77,48.17,16.49,21.5,12.66,13.81,24.52,20.76,31.71,50.81,15.81,7.25,31.85,16.82,32.9,17.89,14.48,34.63,34.65,23.33,45.35,23.17,40.55,20.69,30.46,23.1,15.69,28.44,15.48,16.58,7.56,10.34,13.51,18.71,20.53,26.59,38.73,24.27,30.06,25.89,48.33,28.15,11.59,7.74,12.16,8.58,13.42,20.45,13.28,24.01,15.69,11.61,10.77,15.53,10.07,12.6,32.83,29.03,22.67,17.82],"y":[1.66,3.5,3.31,4.71,2,3.12,1.96,3.23,1.71,1.57,3,3.92,3.71,3.35,4.08,7.58,3.18,2.34,2,2,4.3,1.45,2.5,3.27,3.6,2,2.31,5,2.24,2.54,3.06,1.32,5.6,3,5,6,2.05,3,2.5,1.56,4.34,3.51,3,1.76,6.73,3.21,2,1.98,3.76,2.64,3.15,2.01,2.09,1.97,1.25,3.08,4,3,2.71,3,3.4,5,2.03,2,4,5.85,3,3,3.5,4.73,4,1.5,3,1.5,1.64,4.06,4.29,3.76,3,4,2.55,5.07,2.31,2.5,2,1.48,2.18,1.5,2,6.7,5,1.73,2,2.5,2,2.74,2,2,5,2,3.5,2.5,2,3.48,2.24,4.5,10,3.16,5.15,3.18,4,3.11,2,2,3.55,3.68,5.65,3.5,6.5,3,5,2,4,1.5,2.56,2.02,4,1.44,2,2,4,4,3.41,3,2.03,2,5.16,9,3,1.5,1.44,2.2,1.92,1.58,3,2.72,2,3,3.39,1.47,3,1.25,1,1.17,5.92,2,1.75],"text":"Sexo M","key":["M"],"type":"scatter","mode":"markers","marker":{"autocolorscale":false,"color":"rgba(0,191,196,1)","opacity":1,"size":5.66929133858268,"symbol":"circle","line":{"width":1.88976377952756,"color":"rgba(0,191,196,1)"}},"hoveron":"points","set":"SharedDatab67f09ce","name":"M","legendgroup":"M","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","_isSimpleKey":true,"_isNestedKey":false,"frame":null}],"layout":{"margin":{"t":24.7671232876712,"r":7.30593607305936,"b":38.7214611872146,"l":48.9497716894977},"plot_bgcolor":"rgba(235,235,235,1)","paper_bgcolor":"rgba(255,255,255,1)","font":{"color":"rgba(0,0,0,1)","family":"","size":14.6118721461187},"xaxis":{"domain":[0,1],"automargin":true,"type":"linear","autorange":false,"range":[0.683,53.197],"tickmode":"array","ticktext":["10","20","30","40","50"],"tickvals":[10,20,30,40,50],"categoryorder":"array","categoryarray":["10","20","30","40","50"],"nticks":null,"ticks":"outside","tickcolor":"rgba(51,51,51,1)","ticklen":3.65296803652968,"tickwidth":0.66417600664176,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"","size":11.689497716895},"tickangle":-0,"showline":false,"linecolor":null,"linewidth":0,"showgrid":true,"gridcolor":"rgba(255,255,255,1)","gridwidth":0.66417600664176,"zeroline":false,"anchor":"y","title":"total","titlefont":{"color":"rgba(0,0,0,1)","family":"","size":14.6118721461187},"hoverformat":".2f"},"yaxis":{"domain":[0,1],"automargin":true,"type":"linear","autorange":false,"range":[0.55,10.45],"tickmode":"array","ticktext":["2.5","5.0","7.5","10.0"],"tickvals":[2.5,5,7.5,10],"categoryorder":"array","categoryarray":["2.5","5.0","7.5","10.0"],"nticks":null,"ticks":"outside","tickcolor":"rgba(51,51,51,1)","ticklen":3.65296803652968,"tickwidth":0.66417600664176,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"","size":11.689497716895},"tickangle":-0,"showline":false,"linecolor":null,"linewidth":0,"showgrid":true,"gridcolor":"rgba(255,255,255,1)","gridwidth":0.66417600664176,"zeroline":false,"anchor":"x","title":"propina","titlefont":{"color":"rgba(0,0,0,1)","family":"","size":14.6118721461187},"hoverformat":".2f"},"shapes":[{"type":"rect","fillcolor":null,"line":{"color":null,"width":0,"linetype":[]},"yref":"paper","xref":"paper","x0":0,"x1":1,"y0":0,"y1":1}],"showlegend":true,"legend":{"bgcolor":"rgba(255,255,255,1)","bordercolor":"transparent","borderwidth":1.88976377952756,"font":{"color":"rgba(0,0,0,1)","family":"","size":11.689497716895},"y":0.913385826771654},"annotations":[{"text":"sexo","x":1.02,"y":1,"showarrow":false,"ax":0,"ay":0,"font":{"color":"rgba(0,0,0,1)","family":"","size":14.6118721461187},"xref":"paper","yref":"paper","textangle":-0,"xanchor":"left","yanchor":"bottom","legendTitle":true}],"hovermode":"closest","barmode":"relative","dragmode":"zoom"},"config":{"doubleClick":"reset","modeBarButtonsToAdd":[{"name":"Collaborate","icon":{"width":1000,"ascent":500,"descent":-50,"path":"M487 375c7-10 9-23 5-36l-79-259c-3-12-11-23-22-31-11-8-22-12-35-12l-263 0c-15 0-29 5-43 15-13 10-23 23-28 37-5 13-5 25-1 37 0 0 0 3 1 7 1 5 1 8 1 11 0 2 0 4-1 6 0 3-1 5-1 6 1 2 2 4 3 6 1 2 2 4 4 6 2 3 4 5 5 7 5 7 9 16 13 26 4 10 7 19 9 26 0 2 0 5 0 9-1 4-1 6 0 8 0 2 2 5 4 8 3 3 5 5 5 7 4 6 8 15 12 26 4 11 7 19 7 26 1 1 0 4 0 9-1 4-1 7 0 8 1 2 3 5 6 8 4 4 6 6 6 7 4 5 8 13 13 24 4 11 7 20 7 28 1 1 0 4 0 7-1 3-1 6-1 7 0 2 1 4 3 6 1 1 3 4 5 6 2 3 3 5 5 6 1 2 3 5 4 9 2 3 3 7 5 10 1 3 2 6 4 10 2 4 4 7 6 9 2 3 4 5 7 7 3 2 7 3 11 3 3 0 8 0 13-1l0-1c7 2 12 2 14 2l218 0c14 0 25-5 32-16 8-10 10-23 6-37l-79-259c-7-22-13-37-20-43-7-7-19-10-37-10l-248 0c-5 0-9-2-11-5-2-3-2-7 0-12 4-13 18-20 41-20l264 0c5 0 10 2 16 5 5 3 8 6 10 11l85 282c2 5 2 10 2 17 7-3 13-7 17-13z m-304 0c-1-3-1-5 0-7 1-1 3-2 6-2l174 0c2 0 4 1 7 2 2 2 4 4 5 7l6 18c0 3 0 5-1 7-1 1-3 2-6 2l-173 0c-3 0-5-1-8-2-2-2-4-4-4-7z m-24-73c-1-3-1-5 0-7 2-2 3-2 6-2l174 0c2 0 5 0 7 2 3 2 4 4 5 7l6 18c1 2 0 5-1 6-1 2-3 3-5 3l-174 0c-3 0-5-1-7-3-3-1-4-4-5-6z"},"click":"function(gd) { \n // is this being viewed in RStudio?\n if (location.search == '?viewer_pane=1') {\n alert('To learn about plotly for collaboration, visit:\\n https://cpsievert.github.io/plotly_book/plot-ly-for-collaboration.html');\n } else {\n window.open('https://cpsievert.github.io/plotly_book/plot-ly-for-collaboration.html', '_blank');\n }\n }"}],"cloud":false},"source":"A","attrs":{"71f622377b6e":{"key":{},"x":{},"y":{},"colour":{},"text":{},"type":"scatter"}},"cur_data":"71f622377b6e","visdat":{"71f622377b6e":["function (y) ","x"]},"highlight":{"on":"plotly_click","off":"plotly_doubleclick","persistent":false,"dynamic":false,"color":null,"selectize":false,"defaultValues":null,"opacityDim":0.2,"selected":{"opacity":1},"debounce":0,"ctGroups":["SharedDatab67f09ce"]},"base_url":"https://plot.ly"},"evals":["config.modeBarButtonsToAdd.0.click"],"jsHooks":[]}</script>
---
 
## `highlight` otros

```r
highlight(gg,  on = "plotly_hover", selectize = TRUE, dynamic = TRUE)
```

<div id="htmlwidget-321629f10e3ace5cd08e" style="width:432px;height:360px;" class="plotly html-widget"></div>
<script type="application/json" data-for="htmlwidget-321629f10e3ace5cd08e">{"x":{"data":[{"x":[16.99,24.59,35.26,14.83,10.33,16.97,20.29,15.77,19.65,15.06,20.69,16.93,10.29,34.81,26.41,16.45,3.07,17.07,26.86,25.28,14.73,10.07,34.83,5.75,16.32,22.75,11.35,15.38,44.3,22.42,20.92,14.31,7.25,25.71,17.31,10.65,12.43,24.08,13.42,12.48,29.8,14.52,11.38,20.27,11.17,12.26,18.26,8.51,10.33,14.15,13.16,17.47,27.05,16.43,8.35,18.64,11.87,29.85,25,13.39,16.21,17.51,10.59,10.63,9.6,20.9,18.15,19.81,43.11,13,12.74,13,16.4,16.47,12.76,13.27,28.17,12.9,30.14,13.42,15.98,16.27,10.09,22.12,35.83,27.18,18.78],"y":[1.01,3.61,5,3.02,1.67,3.5,2.75,2.23,3,3,2.45,3.07,2.6,5.2,1.5,2.47,1,3,3.14,5,2.2,1.83,5.17,1,4.3,3.25,2.5,3,2.5,3.48,4.08,4,1,4,3.5,1.5,1.8,2.92,1.68,2.52,4.2,2,2,2.83,1.5,2,3.25,1.25,2,2,2.75,3.5,5,2.3,1.5,1.36,1.63,5.14,3.75,2.61,2,3,1.61,2,4,3.5,3.5,4.19,5,2,2.01,2,2.5,3.23,2.23,2.5,6.5,1.1,3.09,3.48,3,2.5,2,2.88,4.67,2,3],"text":"Sexo F","key":["F"],"type":"scatter","mode":"markers","marker":{"autocolorscale":false,"color":"rgba(248,118,109,1)","opacity":1,"size":5.66929133858268,"symbol":"circle","line":{"width":1.88976377952756,"color":"rgba(248,118,109,1)"}},"hoveron":"points","set":"SharedDatab67f09ce","name":"F","legendgroup":"F","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","_isSimpleKey":true,"_isNestedKey":false,"frame":null},{"x":[10.34,21.01,23.68,25.29,8.77,26.88,15.04,14.78,10.27,15.42,18.43,21.58,16.29,20.65,17.92,39.42,19.82,17.81,13.37,12.69,21.7,9.55,18.35,17.78,24.06,16.31,18.69,31.27,16.04,17.46,13.94,9.68,30.4,18.29,22.23,32.4,28.55,18.04,12.54,9.94,25.56,19.49,38.01,11.24,48.27,20.29,13.81,11.02,18.29,17.59,20.08,20.23,15.01,12.02,10.51,17.92,27.2,22.76,17.29,19.44,16.66,32.68,15.98,13.03,18.28,24.71,21.16,28.97,22.49,40.17,27.28,12.03,21.01,12.46,15.36,20.49,25.21,18.24,14,38.07,23.95,29.93,11.69,14.26,15.95,8.52,22.82,19.08,16,34.3,41.19,9.78,7.51,14.07,13.13,17.26,24.55,19.77,48.17,16.49,21.5,12.66,13.81,24.52,20.76,31.71,50.81,15.81,7.25,31.85,16.82,32.9,17.89,14.48,34.63,34.65,23.33,45.35,23.17,40.55,20.69,30.46,23.1,15.69,28.44,15.48,16.58,7.56,10.34,13.51,18.71,20.53,26.59,38.73,24.27,30.06,25.89,48.33,28.15,11.59,7.74,12.16,8.58,13.42,20.45,13.28,24.01,15.69,11.61,10.77,15.53,10.07,12.6,32.83,29.03,22.67,17.82],"y":[1.66,3.5,3.31,4.71,2,3.12,1.96,3.23,1.71,1.57,3,3.92,3.71,3.35,4.08,7.58,3.18,2.34,2,2,4.3,1.45,2.5,3.27,3.6,2,2.31,5,2.24,2.54,3.06,1.32,5.6,3,5,6,2.05,3,2.5,1.56,4.34,3.51,3,1.76,6.73,3.21,2,1.98,3.76,2.64,3.15,2.01,2.09,1.97,1.25,3.08,4,3,2.71,3,3.4,5,2.03,2,4,5.85,3,3,3.5,4.73,4,1.5,3,1.5,1.64,4.06,4.29,3.76,3,4,2.55,5.07,2.31,2.5,2,1.48,2.18,1.5,2,6.7,5,1.73,2,2.5,2,2.74,2,2,5,2,3.5,2.5,2,3.48,2.24,4.5,10,3.16,5.15,3.18,4,3.11,2,2,3.55,3.68,5.65,3.5,6.5,3,5,2,4,1.5,2.56,2.02,4,1.44,2,2,4,4,3.41,3,2.03,2,5.16,9,3,1.5,1.44,2.2,1.92,1.58,3,2.72,2,3,3.39,1.47,3,1.25,1,1.17,5.92,2,1.75],"text":"Sexo M","key":["M"],"type":"scatter","mode":"markers","marker":{"autocolorscale":false,"color":"rgba(0,191,196,1)","opacity":1,"size":5.66929133858268,"symbol":"circle","line":{"width":1.88976377952756,"color":"rgba(0,191,196,1)"}},"hoveron":"points","set":"SharedDatab67f09ce","name":"M","legendgroup":"M","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","_isSimpleKey":true,"_isNestedKey":false,"frame":null}],"layout":{"margin":{"t":24.7671232876712,"r":7.30593607305936,"b":38.7214611872146,"l":48.9497716894977},"plot_bgcolor":"rgba(235,235,235,1)","paper_bgcolor":"rgba(255,255,255,1)","font":{"color":"rgba(0,0,0,1)","family":"","size":14.6118721461187},"xaxis":{"domain":[0,1],"automargin":true,"type":"linear","autorange":false,"range":[0.683,53.197],"tickmode":"array","ticktext":["10","20","30","40","50"],"tickvals":[10,20,30,40,50],"categoryorder":"array","categoryarray":["10","20","30","40","50"],"nticks":null,"ticks":"outside","tickcolor":"rgba(51,51,51,1)","ticklen":3.65296803652968,"tickwidth":0.66417600664176,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"","size":11.689497716895},"tickangle":-0,"showline":false,"linecolor":null,"linewidth":0,"showgrid":true,"gridcolor":"rgba(255,255,255,1)","gridwidth":0.66417600664176,"zeroline":false,"anchor":"y","title":"total","titlefont":{"color":"rgba(0,0,0,1)","family":"","size":14.6118721461187},"hoverformat":".2f"},"yaxis":{"domain":[0,1],"automargin":true,"type":"linear","autorange":false,"range":[0.55,10.45],"tickmode":"array","ticktext":["2.5","5.0","7.5","10.0"],"tickvals":[2.5,5,7.5,10],"categoryorder":"array","categoryarray":["2.5","5.0","7.5","10.0"],"nticks":null,"ticks":"outside","tickcolor":"rgba(51,51,51,1)","ticklen":3.65296803652968,"tickwidth":0.66417600664176,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"","size":11.689497716895},"tickangle":-0,"showline":false,"linecolor":null,"linewidth":0,"showgrid":true,"gridcolor":"rgba(255,255,255,1)","gridwidth":0.66417600664176,"zeroline":false,"anchor":"x","title":"propina","titlefont":{"color":"rgba(0,0,0,1)","family":"","size":14.6118721461187},"hoverformat":".2f"},"shapes":[{"type":"rect","fillcolor":null,"line":{"color":null,"width":0,"linetype":[]},"yref":"paper","xref":"paper","x0":0,"x1":1,"y0":0,"y1":1}],"showlegend":true,"legend":{"bgcolor":"rgba(255,255,255,1)","bordercolor":"transparent","borderwidth":1.88976377952756,"font":{"color":"rgba(0,0,0,1)","family":"","size":11.689497716895},"y":0.913385826771654},"annotations":[{"text":"sexo","x":1.02,"y":1,"showarrow":false,"ax":0,"ay":0,"font":{"color":"rgba(0,0,0,1)","family":"","size":14.6118721461187},"xref":"paper","yref":"paper","textangle":-0,"xanchor":"left","yanchor":"bottom","legendTitle":true}],"hovermode":"closest","barmode":"relative","dragmode":"zoom"},"config":{"doubleClick":"reset","modeBarButtonsToAdd":[{"name":"Collaborate","icon":{"width":1000,"ascent":500,"descent":-50,"path":"M487 375c7-10 9-23 5-36l-79-259c-3-12-11-23-22-31-11-8-22-12-35-12l-263 0c-15 0-29 5-43 15-13 10-23 23-28 37-5 13-5 25-1 37 0 0 0 3 1 7 1 5 1 8 1 11 0 2 0 4-1 6 0 3-1 5-1 6 1 2 2 4 3 6 1 2 2 4 4 6 2 3 4 5 5 7 5 7 9 16 13 26 4 10 7 19 9 26 0 2 0 5 0 9-1 4-1 6 0 8 0 2 2 5 4 8 3 3 5 5 5 7 4 6 8 15 12 26 4 11 7 19 7 26 1 1 0 4 0 9-1 4-1 7 0 8 1 2 3 5 6 8 4 4 6 6 6 7 4 5 8 13 13 24 4 11 7 20 7 28 1 1 0 4 0 7-1 3-1 6-1 7 0 2 1 4 3 6 1 1 3 4 5 6 2 3 3 5 5 6 1 2 3 5 4 9 2 3 3 7 5 10 1 3 2 6 4 10 2 4 4 7 6 9 2 3 4 5 7 7 3 2 7 3 11 3 3 0 8 0 13-1l0-1c7 2 12 2 14 2l218 0c14 0 25-5 32-16 8-10 10-23 6-37l-79-259c-7-22-13-37-20-43-7-7-19-10-37-10l-248 0c-5 0-9-2-11-5-2-3-2-7 0-12 4-13 18-20 41-20l264 0c5 0 10 2 16 5 5 3 8 6 10 11l85 282c2 5 2 10 2 17 7-3 13-7 17-13z m-304 0c-1-3-1-5 0-7 1-1 3-2 6-2l174 0c2 0 4 1 7 2 2 2 4 4 5 7l6 18c0 3 0 5-1 7-1 1-3 2-6 2l-173 0c-3 0-5-1-8-2-2-2-4-4-4-7z m-24-73c-1-3-1-5 0-7 2-2 3-2 6-2l174 0c2 0 5 0 7 2 3 2 4 4 5 7l6 18c1 2 0 5-1 6-1 2-3 3-5 3l-174 0c-3 0-5-1-7-3-3-1-4-4-5-6z"},"click":"function(gd) { \n // is this being viewed in RStudio?\n if (location.search == '?viewer_pane=1') {\n alert('To learn about plotly for collaboration, visit:\\n https://cpsievert.github.io/plotly_book/plot-ly-for-collaboration.html');\n } else {\n window.open('https://cpsievert.github.io/plotly_book/plot-ly-for-collaboration.html', '_blank');\n }\n }"}],"cloud":false},"source":"A","attrs":{"71f622377b6e":{"key":{},"x":{},"y":{},"colour":{},"text":{},"type":"scatter"}},"cur_data":"71f622377b6e","visdat":{"71f622377b6e":["function (y) ","x"]},"highlight":{"on":"plotly_hover","off":"plotly_doubleclick","persistent":false,"dynamic":true,"color":["rgba(228,26,28,1)","rgba(55,126,184,1)","rgba(77,175,74,1)","rgba(152,78,163,1)"],"selectize":true,"defaultValues":null,"opacityDim":0.2,"selected":{"opacity":1},"debounce":0,"ctGroups":["SharedDatab67f09ce"]},"selectize":{"71f673b113b9":{"items":[{"value":"F","label":"F"},{"value":"M","label":"M"}],"group":"SharedDatab67f09ce"}},"base_url":"https://plot.ly"},"evals":["config.modeBarButtonsToAdd.0.click"],"jsHooks":[]}</script>
---

## Iniciar plotly con `plot_ly`

```r
pl2 <- plot_ly(data = propinas, x = ~total, y = ~propina, color = ~sexo)
```
---
 
## Iniciar plotly con `plot_ly` 
 
 
<div id="htmlwidget-02554d8979a27d6c0d46" style="width:432px;height:360px;" class="plotly html-widget"></div>
<script type="application/json" data-for="htmlwidget-02554d8979a27d6c0d46">{"x":{"visdat":{"71f678cde0e3":["function () ","plotlyVisDat"]},"cur_data":"71f678cde0e3","attrs":{"71f678cde0e3":{"x":{},"y":{},"color":{},"alpha_stroke":1,"sizes":[10,100],"spans":[1,20]}},"layout":{"margin":{"b":40,"l":60,"t":25,"r":10},"xaxis":{"domain":[0,1],"automargin":true,"title":"total"},"yaxis":{"domain":[0,1],"automargin":true,"title":"propina"},"hovermode":"closest","showlegend":true},"source":"A","config":{"modeBarButtonsToAdd":[{"name":"Collaborate","icon":{"width":1000,"ascent":500,"descent":-50,"path":"M487 375c7-10 9-23 5-36l-79-259c-3-12-11-23-22-31-11-8-22-12-35-12l-263 0c-15 0-29 5-43 15-13 10-23 23-28 37-5 13-5 25-1 37 0 0 0 3 1 7 1 5 1 8 1 11 0 2 0 4-1 6 0 3-1 5-1 6 1 2 2 4 3 6 1 2 2 4 4 6 2 3 4 5 5 7 5 7 9 16 13 26 4 10 7 19 9 26 0 2 0 5 0 9-1 4-1 6 0 8 0 2 2 5 4 8 3 3 5 5 5 7 4 6 8 15 12 26 4 11 7 19 7 26 1 1 0 4 0 9-1 4-1 7 0 8 1 2 3 5 6 8 4 4 6 6 6 7 4 5 8 13 13 24 4 11 7 20 7 28 1 1 0 4 0 7-1 3-1 6-1 7 0 2 1 4 3 6 1 1 3 4 5 6 2 3 3 5 5 6 1 2 3 5 4 9 2 3 3 7 5 10 1 3 2 6 4 10 2 4 4 7 6 9 2 3 4 5 7 7 3 2 7 3 11 3 3 0 8 0 13-1l0-1c7 2 12 2 14 2l218 0c14 0 25-5 32-16 8-10 10-23 6-37l-79-259c-7-22-13-37-20-43-7-7-19-10-37-10l-248 0c-5 0-9-2-11-5-2-3-2-7 0-12 4-13 18-20 41-20l264 0c5 0 10 2 16 5 5 3 8 6 10 11l85 282c2 5 2 10 2 17 7-3 13-7 17-13z m-304 0c-1-3-1-5 0-7 1-1 3-2 6-2l174 0c2 0 4 1 7 2 2 2 4 4 5 7l6 18c0 3 0 5-1 7-1 1-3 2-6 2l-173 0c-3 0-5-1-8-2-2-2-4-4-4-7z m-24-73c-1-3-1-5 0-7 2-2 3-2 6-2l174 0c2 0 5 0 7 2 3 2 4 4 5 7l6 18c1 2 0 5-1 6-1 2-3 3-5 3l-174 0c-3 0-5-1-7-3-3-1-4-4-5-6z"},"click":"function(gd) { \n // is this being viewed in RStudio?\n if (location.search == '?viewer_pane=1') {\n alert('To learn about plotly for collaboration, visit:\\n https://cpsievert.github.io/plotly_book/plot-ly-for-collaboration.html');\n } else {\n window.open('https://cpsievert.github.io/plotly_book/plot-ly-for-collaboration.html', '_blank');\n }\n }"}],"cloud":false},"data":[{"x":[16.99,24.59,35.26,14.83,10.33,16.97,20.29,15.77,19.65,15.06,20.69,16.93,10.29,34.81,26.41,16.45,3.07,17.07,26.86,25.28,14.73,10.07,34.83,5.75,16.32,22.75,11.35,15.38,44.3,22.42,20.92,14.31,7.25,25.71,17.31,10.65,12.43,24.08,13.42,12.48,29.8,14.52,11.38,20.27,11.17,12.26,18.26,8.51,10.33,14.15,13.16,17.47,27.05,16.43,8.35,18.64,11.87,29.85,25,13.39,16.21,17.51,10.59,10.63,9.6,20.9,18.15,19.81,43.11,13,12.74,13,16.4,16.47,12.76,13.27,28.17,12.9,30.14,13.42,15.98,16.27,10.09,22.12,35.83,27.18,18.78],"y":[1.01,3.61,5,3.02,1.67,3.5,2.75,2.23,3,3,2.45,3.07,2.6,5.2,1.5,2.47,1,3,3.14,5,2.2,1.83,5.17,1,4.3,3.25,2.5,3,2.5,3.48,4.08,4,1,4,3.5,1.5,1.8,2.92,1.68,2.52,4.2,2,2,2.83,1.5,2,3.25,1.25,2,2,2.75,3.5,5,2.3,1.5,1.36,1.63,5.14,3.75,2.61,2,3,1.61,2,4,3.5,3.5,4.19,5,2,2.01,2,2.5,3.23,2.23,2.5,6.5,1.1,3.09,3.48,3,2.5,2,2.88,4.67,2,3],"type":"scatter","mode":"markers","name":"F","marker":{"color":"rgba(102,194,165,1)","line":{"color":"rgba(102,194,165,1)"}},"textfont":{"color":"rgba(102,194,165,1)"},"error_y":{"color":"rgba(102,194,165,1)"},"error_x":{"color":"rgba(102,194,165,1)"},"line":{"color":"rgba(102,194,165,1)"},"xaxis":"x","yaxis":"y","frame":null},{"x":[10.34,21.01,23.68,25.29,8.77,26.88,15.04,14.78,10.27,15.42,18.43,21.58,16.29,20.65,17.92,39.42,19.82,17.81,13.37,12.69,21.7,9.55,18.35,17.78,24.06,16.31,18.69,31.27,16.04,17.46,13.94,9.68,30.4,18.29,22.23,32.4,28.55,18.04,12.54,9.94,25.56,19.49,38.01,11.24,48.27,20.29,13.81,11.02,18.29,17.59,20.08,20.23,15.01,12.02,10.51,17.92,27.2,22.76,17.29,19.44,16.66,32.68,15.98,13.03,18.28,24.71,21.16,28.97,22.49,40.17,27.28,12.03,21.01,12.46,15.36,20.49,25.21,18.24,14,38.07,23.95,29.93,11.69,14.26,15.95,8.52,22.82,19.08,16,34.3,41.19,9.78,7.51,14.07,13.13,17.26,24.55,19.77,48.17,16.49,21.5,12.66,13.81,24.52,20.76,31.71,50.81,15.81,7.25,31.85,16.82,32.9,17.89,14.48,34.63,34.65,23.33,45.35,23.17,40.55,20.69,30.46,23.1,15.69,28.44,15.48,16.58,7.56,10.34,13.51,18.71,20.53,26.59,38.73,24.27,30.06,25.89,48.33,28.15,11.59,7.74,12.16,8.58,13.42,20.45,13.28,24.01,15.69,11.61,10.77,15.53,10.07,12.6,32.83,29.03,22.67,17.82],"y":[1.66,3.5,3.31,4.71,2,3.12,1.96,3.23,1.71,1.57,3,3.92,3.71,3.35,4.08,7.58,3.18,2.34,2,2,4.3,1.45,2.5,3.27,3.6,2,2.31,5,2.24,2.54,3.06,1.32,5.6,3,5,6,2.05,3,2.5,1.56,4.34,3.51,3,1.76,6.73,3.21,2,1.98,3.76,2.64,3.15,2.01,2.09,1.97,1.25,3.08,4,3,2.71,3,3.4,5,2.03,2,4,5.85,3,3,3.5,4.73,4,1.5,3,1.5,1.64,4.06,4.29,3.76,3,4,2.55,5.07,2.31,2.5,2,1.48,2.18,1.5,2,6.7,5,1.73,2,2.5,2,2.74,2,2,5,2,3.5,2.5,2,3.48,2.24,4.5,10,3.16,5.15,3.18,4,3.11,2,2,3.55,3.68,5.65,3.5,6.5,3,5,2,4,1.5,2.56,2.02,4,1.44,2,2,4,4,3.41,3,2.03,2,5.16,9,3,1.5,1.44,2.2,1.92,1.58,3,2.72,2,3,3.39,1.47,3,1.25,1,1.17,5.92,2,1.75],"type":"scatter","mode":"markers","name":"M","marker":{"color":"rgba(141,160,203,1)","line":{"color":"rgba(141,160,203,1)"}},"textfont":{"color":"rgba(141,160,203,1)"},"error_y":{"color":"rgba(141,160,203,1)"},"error_x":{"color":"rgba(141,160,203,1)"},"line":{"color":"rgba(141,160,203,1)"},"xaxis":"x","yaxis":"y","frame":null}],"highlight":{"on":"plotly_click","persistent":false,"dynamic":false,"selectize":false,"opacityDim":0.2,"selected":{"opacity":1},"debounce":0},"base_url":"https://plot.ly"},"evals":["config.modeBarButtonsToAdd.0.click"],"jsHooks":[]}</script>
---

## Más plotly 
Combinar varios gráficos

<div id="htmlwidget-ab39318eb4667c3428a5" style="width:504px;height:504px;" class="plotly html-widget"></div>
<script type="application/json" data-for="htmlwidget-ab39318eb4667c3428a5">{"x":{"data":[{"x":[5.7,5.7,6.2,6.2,7],"y":[8,8,8,8,8],"key":["24","25","26","27","28"],"set":"SharedDataebec251e","type":"scatter","mode":"markers","name":"2seater","marker":{"color":"rgba(102,194,165,1)","line":{"color":"rgba(102,194,165,1)"}},"textfont":{"color":"rgba(102,194,165,1)"},"error_y":{"color":"rgba(102,194,165,1)"},"error_x":{"color":"rgba(102,194,165,1)"},"line":{"color":"rgba(102,194,165,1)"},"xaxis":"x","yaxis":"y2","_isNestedKey":false,"frame":null},{"x":[1.8,1.8,2,2,2.8,2.8,3.1,1.8,1.8,2,2,2.8,2.8,3.1,3.1,2.4,2.4,2.5,2.5,2.5,2.5,2.2,2.2,2.4,2.4,3,3,3.3,1.8,1.8,1.8,1.8,1.8,2,2,2,2,2.8,1.9,2,2,2,2,2.5,2.5,2.8,2.8],"y":[4,4,4,4,6,6,6,4,4,4,4,6,6,6,6,4,4,4,4,4,4,4,4,4,4,6,6,6,4,4,4,4,4,4,4,4,4,6,4,4,4,4,4,5,5,6,6],"key":["1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","142","143","170","171","172","173","187","188","189","190","191","192","193","194","195","196","197","198","208","209","210","211","212","213","214","215","216","217","218","219","220","221"],"set":"SharedDataebec251e","type":"scatter","mode":"markers","name":"compact","marker":{"color":"rgba(252,141,98,1)","line":{"color":"rgba(252,141,98,1)"}},"textfont":{"color":"rgba(252,141,98,1)"},"error_y":{"color":"rgba(252,141,98,1)"},"error_x":{"color":"rgba(252,141,98,1)"},"line":{"color":"rgba(252,141,98,1)"},"xaxis":"x","yaxis":"y2","_isNestedKey":false,"frame":null},{"x":[2.8,3.1,4.2,2.4,2.4,3.1,3.5,3.6,2.4,2.4,2.4,2.4,2.5,2.5,3.3,2.5,2.5,3.5,3.5,3,3,3.5,3.1,3.8,3.8,3.8,5.3,2.2,2.2,2.4,2.4,3,3,3.5,1.8,1.8,2,2,2.8,2.8,3.6],"y":[6,6,8,4,4,6,6,6,4,4,4,4,6,6,6,4,4,6,6,6,6,6,6,6,6,6,8,4,4,4,4,6,6,6,4,4,4,4,6,6,6],"key":["16","17","18","33","34","35","36","37","109","110","111","112","113","114","115","144","145","146","147","148","149","150","155","156","157","158","159","180","181","182","183","184","185","186","228","229","230","231","232","233","234"],"set":"SharedDataebec251e","type":"scatter","mode":"markers","name":"midsize","marker":{"color":"rgba(141,160,203,1)","line":{"color":"rgba(141,160,203,1)"}},"textfont":{"color":"rgba(141,160,203,1)"},"error_y":{"color":"rgba(141,160,203,1)"},"error_x":{"color":"rgba(141,160,203,1)"},"line":{"color":"rgba(141,160,203,1)"},"xaxis":"x","yaxis":"y2","_isNestedKey":false,"frame":null},{"x":[2.4,3,3.3,3.3,3.3,3.3,3.3,3.8,3.8,3.8,4],"y":[4,6,6,6,6,6,6,6,6,6,6],"key":["38","39","40","41","42","43","44","45","46","47","48"],"set":"SharedDataebec251e","type":"scatter","mode":"markers","name":"minivan","marker":{"color":"rgba(231,138,195,1)","line":{"color":"rgba(231,138,195,1)"}},"textfont":{"color":"rgba(231,138,195,1)"},"error_y":{"color":"rgba(231,138,195,1)"},"error_x":{"color":"rgba(231,138,195,1)"},"line":{"color":"rgba(231,138,195,1)"},"xaxis":"x","yaxis":"y2","_isNestedKey":false,"frame":null},{"x":[3.7,3.7,3.9,3.9,4.7,4.7,4.7,5.2,5.2,4.7,4.7,4.7,4.7,4.7,4.7,5.2,5.2,5.7,5.9,4.2,4.2,4.6,4.6,4.6,5.4,5.4,2.7,2.7,2.7,3.4,3.4,4,4],"y":[6,6,6,6,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,6,6,8,8,8,8,8,4,4,4,6,6,6,6],"key":["49","50","51","52","53","54","55","56","57","65","66","67","68","69","70","71","72","73","74","84","85","86","87","88","89","90","201","202","203","204","205","206","207"],"set":"SharedDataebec251e","type":"scatter","mode":"markers","name":"pickup","marker":{"color":"rgba(166,216,84,1)","line":{"color":"rgba(166,216,84,1)"}},"textfont":{"color":"rgba(166,216,84,1)"},"error_y":{"color":"rgba(166,216,84,1)"},"error_x":{"color":"rgba(166,216,84,1)"},"line":{"color":"rgba(166,216,84,1)"},"xaxis":"x","yaxis":"y2","_isNestedKey":false,"frame":null},{"x":[3.8,3.8,4,4,4.6,4.6,4.6,4.6,5.4,1.6,1.6,1.6,1.6,1.6,1.8,1.8,1.8,2,2,2,2,2,2.7,2.7,2.7,2.2,2.2,2.5,2.5,1.9,1.9,2,2,2.5,2.5],"y":[6,6,6,6,8,8,8,8,8,4,4,4,4,4,4,4,4,4,4,4,4,4,6,6,6,4,4,4,4,4,4,4,4,5,5],"key":["91","92","93","94","95","96","97","98","99","100","101","102","103","104","105","106","107","108","116","117","118","119","120","121","122","166","167","168","169","222","223","224","225","226","227"],"set":"SharedDataebec251e","type":"scatter","mode":"markers","name":"subcompact","marker":{"color":"rgba(255,217,47,1)","line":{"color":"rgba(255,217,47,1)"}},"textfont":{"color":"rgba(255,217,47,1)"},"error_y":{"color":"rgba(255,217,47,1)"},"error_x":{"color":"rgba(255,217,47,1)"},"line":{"color":"rgba(255,217,47,1)"},"xaxis":"x","yaxis":"y2","_isNestedKey":false,"frame":null},{"x":[5.3,5.3,5.3,5.7,6,5.3,5.3,5.7,6.5,3.9,4.7,4.7,4.7,5.2,5.7,5.9,4.6,5.4,5.4,4,4,4,4,4.6,5,3,3.7,4,4.7,4.7,4.7,5.7,6.1,4,4.2,4.4,4.6,5.4,5.4,5.4,4,4,4.6,5,3.3,3.3,4,5.6,2.5,2.5,2.5,2.5,2.5,2.5,2.7,2.7,3.4,3.4,4,4.7,4.7,5.7],"y":[8,8,8,8,8,8,8,8,8,6,8,8,8,8,8,8,8,8,8,6,6,6,6,8,8,6,6,6,8,8,8,8,8,8,8,8,8,8,8,8,6,6,8,8,6,6,6,8,4,4,4,4,4,4,4,4,6,6,6,8,8,8],"key":["19","20","21","22","23","29","30","31","32","58","59","60","61","62","63","64","75","76","77","78","79","80","81","82","83","123","124","125","126","127","128","129","130","131","132","133","134","135","136","137","138","139","140","141","151","152","153","154","160","161","162","163","164","165","174","175","176","177","178","179","199","200"],"set":"SharedDataebec251e","type":"scatter","mode":"markers","name":"suv","marker":{"color":"rgba(229,196,148,1)","line":{"color":"rgba(229,196,148,1)"}},"textfont":{"color":"rgba(229,196,148,1)"},"error_y":{"color":"rgba(229,196,148,1)"},"error_x":{"color":"rgba(229,196,148,1)"},"line":{"color":"rgba(229,196,148,1)"},"xaxis":"x","yaxis":"y2","_isNestedKey":false,"frame":null},{"x":["2seater","2seater","2seater","2seater","2seater"],"y":[16,15,16,15,15],"type":"box","key":["24","25","26","27","28"],"set":"SharedDataebec251e","name":"2seater","marker":{"color":"rgba(102,194,165,1)","line":{"color":"rgba(102,194,165,1)"}},"line":{"color":"rgba(102,194,165,1)"},"xaxis":"x2","yaxis":"y","_isNestedKey":false,"frame":null},{"x":["compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact"],"y":[18,21,20,21,16,18,18,18,16,20,19,15,17,17,15,21,19,20,20,19,20,21,21,21,22,18,18,18,24,24,26,28,26,21,19,21,22,17,33,21,19,22,21,21,21,16,17],"type":"box","key":["1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","142","143","170","171","172","173","187","188","189","190","191","192","193","194","195","196","197","198","208","209","210","211","212","213","214","215","216","217","218","219","220","221"],"set":"SharedDataebec251e","name":"compact","marker":{"color":"rgba(252,141,98,1)","line":{"color":"rgba(252,141,98,1)"}},"line":{"color":"rgba(252,141,98,1)"},"xaxis":"x2","yaxis":"y","_isNestedKey":false,"frame":null},{"x":["midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize"],"y":[15,17,16,19,22,18,18,17,18,18,21,21,18,18,19,23,23,19,19,18,19,19,18,16,17,18,16,21,21,21,21,18,18,19,21,18,19,21,16,18,17],"type":"box","key":["16","17","18","33","34","35","36","37","109","110","111","112","113","114","115","144","145","146","147","148","149","150","155","156","157","158","159","180","181","182","183","184","185","186","228","229","230","231","232","233","234"],"set":"SharedDataebec251e","name":"midsize","marker":{"color":"rgba(141,160,203,1)","line":{"color":"rgba(141,160,203,1)"}},"line":{"color":"rgba(141,160,203,1)"},"xaxis":"x2","yaxis":"y","_isNestedKey":false,"frame":null},{"x":["minivan","minivan","minivan","minivan","minivan","minivan","minivan","minivan","minivan","minivan","minivan"],"y":[18,17,16,16,17,17,11,15,15,16,16],"type":"box","key":["38","39","40","41","42","43","44","45","46","47","48"],"set":"SharedDataebec251e","name":"minivan","marker":{"color":"rgba(231,138,195,1)","line":{"color":"rgba(231,138,195,1)"}},"line":{"color":"rgba(231,138,195,1)"},"xaxis":"x2","yaxis":"y","_isNestedKey":false,"frame":null},{"x":["pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup"],"y":[15,14,13,14,14,14,9,11,11,12,9,13,13,12,9,11,11,13,11,14,14,13,13,13,11,13,15,16,17,15,15,15,16],"type":"box","key":["49","50","51","52","53","54","55","56","57","65","66","67","68","69","70","71","72","73","74","84","85","86","87","88","89","90","201","202","203","204","205","206","207"],"set":"SharedDataebec251e","name":"pickup","marker":{"color":"rgba(166,216,84,1)","line":{"color":"rgba(166,216,84,1)"}},"line":{"color":"rgba(166,216,84,1)"},"xaxis":"x2","yaxis":"y","_isNestedKey":false,"frame":null},{"x":["subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact"],"y":[18,18,17,16,15,15,15,15,14,28,24,25,23,24,26,25,24,21,19,19,20,20,17,16,17,21,19,19,19,35,29,21,19,20,20],"type":"box","key":["91","92","93","94","95","96","97","98","99","100","101","102","103","104","105","106","107","108","116","117","118","119","120","121","122","166","167","168","169","222","223","224","225","226","227"],"set":"SharedDataebec251e","name":"subcompact","marker":{"color":"rgba(255,217,47,1)","line":{"color":"rgba(255,217,47,1)"}},"line":{"color":"rgba(255,217,47,1)"},"xaxis":"x2","yaxis":"y","_isNestedKey":false,"frame":null},{"x":["suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv"],"y":[14,11,14,13,12,14,11,11,14,13,13,9,13,11,13,11,11,11,12,14,15,14,13,13,13,17,15,15,14,9,14,13,11,11,12,12,11,11,11,12,14,13,13,13,14,15,14,12,18,18,20,19,20,18,15,16,15,15,16,14,11,13],"type":"box","key":["19","20","21","22","23","29","30","31","32","58","59","60","61","62","63","64","75","76","77","78","79","80","81","82","83","123","124","125","126","127","128","129","130","131","132","133","134","135","136","137","138","139","140","141","151","152","153","154","160","161","162","163","164","165","174","175","176","177","178","179","199","200"],"set":"SharedDataebec251e","name":"suv","marker":{"color":"rgba(229,196,148,1)","line":{"color":"rgba(229,196,148,1)"}},"line":{"color":"rgba(229,196,148,1)"},"xaxis":"x2","yaxis":"y","_isNestedKey":false,"frame":null},{"x":["2seater","2seater","2seater","2seater","2seater"],"key":["24","25","26","27","28"],"set":"SharedDataebec251e","type":"histogram","name":"2seater","marker":{"color":"rgba(228,26,28,1)","line":{"color":"rgba(228,26,28,1)"}},"error_y":{"color":"rgba(228,26,28,1)"},"error_x":{"color":"rgba(228,26,28,1)"},"xaxis":"x3","yaxis":"y3","_isNestedKey":false,"frame":null},{"x":["compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact","compact"],"key":["1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","142","143","170","171","172","173","187","188","189","190","191","192","193","194","195","196","197","198","208","209","210","211","212","213","214","215","216","217","218","219","220","221"],"set":"SharedDataebec251e","type":"histogram","name":"compact","marker":{"color":"rgba(78,142,150,1)","line":{"color":"rgba(78,142,150,1)"}},"error_y":{"color":"rgba(78,142,150,1)"},"error_x":{"color":"rgba(78,142,150,1)"},"xaxis":"x3","yaxis":"y3","_isNestedKey":false,"frame":null},{"x":["midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize","midsize"],"key":["16","17","18","33","34","35","36","37","109","110","111","112","113","114","115","144","145","146","147","148","149","150","155","156","157","158","159","180","181","182","183","184","185","186","228","229","230","231","232","233","234"],"set":"SharedDataebec251e","type":"histogram","name":"midsize","marker":{"color":"rgba(139,116,136,1)","line":{"color":"rgba(139,116,136,1)"}},"error_y":{"color":"rgba(139,116,136,1)"},"error_x":{"color":"rgba(139,116,136,1)"},"xaxis":"x3","yaxis":"y3","_isNestedKey":false,"frame":null},{"x":["minivan","minivan","minivan","minivan","minivan","minivan","minivan","minivan","minivan","minivan","minivan"],"key":["38","39","40","41","42","43","44","45","46","47","48"],"set":"SharedDataebec251e","type":"histogram","name":"minivan","marker":{"color":"rgba(255,127,0,1)","line":{"color":"rgba(255,127,0,1)"}},"error_y":{"color":"rgba(255,127,0,1)"},"error_x":{"color":"rgba(255,127,0,1)"},"xaxis":"x3","yaxis":"y3","_isNestedKey":false,"frame":null},{"x":["pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup","pickup"],"key":["49","50","51","52","53","54","55","56","57","65","66","67","68","69","70","71","72","73","74","84","85","86","87","88","89","90","201","202","203","204","205","206","207"],"set":"SharedDataebec251e","type":"histogram","name":"pickup","marker":{"color":"rgba(227,197,50,1)","line":{"color":"rgba(227,197,50,1)"}},"error_y":{"color":"rgba(227,197,50,1)"},"error_x":{"color":"rgba(227,197,50,1)"},"xaxis":"x3","yaxis":"y3","_isNestedKey":false,"frame":null},{"x":["subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact","subcompact"],"key":["91","92","93","94","95","96","97","98","99","100","101","102","103","104","105","106","107","108","116","117","118","119","120","121","122","166","167","168","169","222","223","224","225","226","227"],"set":"SharedDataebec251e","type":"histogram","name":"subcompact","marker":{"color":"rgba(222,114,140,1)","line":{"color":"rgba(222,114,140,1)"}},"error_y":{"color":"rgba(222,114,140,1)"},"error_x":{"color":"rgba(222,114,140,1)"},"xaxis":"x3","yaxis":"y3","_isNestedKey":false,"frame":null},{"x":["suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv","suv"],"key":["19","20","21","22","23","29","30","31","32","58","59","60","61","62","63","64","75","76","77","78","79","80","81","82","83","123","124","125","126","127","128","129","130","131","132","133","134","135","136","137","138","139","140","141","151","152","153","154","160","161","162","163","164","165","174","175","176","177","178","179","199","200"],"set":"SharedDataebec251e","type":"histogram","name":"suv","marker":{"color":"rgba(153,153,153,1)","line":{"color":"rgba(153,153,153,1)"}},"error_y":{"color":"rgba(153,153,153,1)"},"error_x":{"color":"rgba(153,153,153,1)"},"xaxis":"x3","yaxis":"y3","_isNestedKey":false,"frame":null}],"layout":{"xaxis":{"domain":[0,0.48],"automargin":true,"title":"displ","anchor":"y2"},"xaxis2":{"domain":[0.52,1],"automargin":true,"title":"class","type":"category","categoryorder":"array","categoryarray":["2seater","compact","midsize","minivan","pickup","subcompact","suv"],"anchor":"y"},"xaxis3":{"domain":[0,1],"automargin":true,"title":"class","type":"category","categoryorder":"array","categoryarray":["2seater","compact","midsize","minivan","pickup","subcompact","suv"],"anchor":"y3"},"yaxis3":{"domain":[0,0.48],"automargin":true,"anchor":"x3"},"yaxis":{"domain":[0.52,1],"automargin":true,"title":"cty","anchor":"x2"},"yaxis2":{"domain":[0.52,1],"automargin":true,"title":"cyl","anchor":"x"},"margin":{"b":40,"l":60,"t":25,"r":10},"dragmode":"select","hovermode":"closest","showlegend":false,"barmode":"overlay"},"attrs":{"71f6661e80ab":{"x":{},"y":{},"color":{},"alpha_stroke":1,"sizes":[10,100],"spans":[1,20],"type":"box","inherit":true},"71f65c918eed":{"x":{},"color":{},"colors":"Set1","alpha_stroke":1,"sizes":[10,100],"spans":[1,20]}},"source":"A","config":{"modeBarButtonsToAdd":[{"name":"Collaborate","icon":{"width":1000,"ascent":500,"descent":-50,"path":"M487 375c7-10 9-23 5-36l-79-259c-3-12-11-23-22-31-11-8-22-12-35-12l-263 0c-15 0-29 5-43 15-13 10-23 23-28 37-5 13-5 25-1 37 0 0 0 3 1 7 1 5 1 8 1 11 0 2 0 4-1 6 0 3-1 5-1 6 1 2 2 4 3 6 1 2 2 4 4 6 2 3 4 5 5 7 5 7 9 16 13 26 4 10 7 19 9 26 0 2 0 5 0 9-1 4-1 6 0 8 0 2 2 5 4 8 3 3 5 5 5 7 4 6 8 15 12 26 4 11 7 19 7 26 1 1 0 4 0 9-1 4-1 7 0 8 1 2 3 5 6 8 4 4 6 6 6 7 4 5 8 13 13 24 4 11 7 20 7 28 1 1 0 4 0 7-1 3-1 6-1 7 0 2 1 4 3 6 1 1 3 4 5 6 2 3 3 5 5 6 1 2 3 5 4 9 2 3 3 7 5 10 1 3 2 6 4 10 2 4 4 7 6 9 2 3 4 5 7 7 3 2 7 3 11 3 3 0 8 0 13-1l0-1c7 2 12 2 14 2l218 0c14 0 25-5 32-16 8-10 10-23 6-37l-79-259c-7-22-13-37-20-43-7-7-19-10-37-10l-248 0c-5 0-9-2-11-5-2-3-2-7 0-12 4-13 18-20 41-20l264 0c5 0 10 2 16 5 5 3 8 6 10 11l85 282c2 5 2 10 2 17 7-3 13-7 17-13z m-304 0c-1-3-1-5 0-7 1-1 3-2 6-2l174 0c2 0 4 1 7 2 2 2 4 4 5 7l6 18c0 3 0 5-1 7-1 1-3 2-6 2l-173 0c-3 0-5-1-8-2-2-2-4-4-4-7z m-24-73c-1-3-1-5 0-7 2-2 3-2 6-2l174 0c2 0 5 0 7 2 3 2 4 4 5 7l6 18c1 2 0 5-1 6-1 2-3 3-5 3l-174 0c-3 0-5-1-7-3-3-1-4-4-5-6z"},"click":"function(gd) { \n // is this being viewed in RStudio?\n if (location.search == '?viewer_pane=1') {\n alert('To learn about plotly for collaboration, visit:\\n https://cpsievert.github.io/plotly_book/plot-ly-for-collaboration.html');\n } else {\n window.open('https://cpsievert.github.io/plotly_book/plot-ly-for-collaboration.html', '_blank');\n }\n }"}],"cloud":false},"highlight":{"on":"plotly_selected","off":"plotly_deselect","persistent":false,"dynamic":false,"color":null,"selectize":false,"defaultValues":null,"opacityDim":0.2,"selected":{"opacity":1},"debounce":0,"ctGroups":["SharedDataebec251e"]},"subplot":true,"base_url":"https://plot.ly"},"evals":["config.modeBarButtonsToAdd.0.click"],"jsHooks":[]}</script>
---

## Más plotly

```r
d <- highlight_key(mpg)
dots <- plot_ly(d, color = ~class, x = ~displ, y = ~cyl)
boxs <- plot_ly(d, color = ~class, x = ~class, y = ~cty) %>% 
 add_boxplot()
bars <- plot_ly(d, colors = "Set1", x = ~class, color = ~class)
subplot(dots, boxs, titleX = TRUE, titleY = TRUE) %>%
 subplot(bars, nrows = 2, titleX = TRUE, titleY = TRUE) %>%
 layout(
 barmode = "overlay",
 showlegend = FALSE
 ) %>%
 highlight("plotly_selected")
```
---

## Material plotly

https://plotly-book.cpsievert.me

---

## FIN

GRACIAS POR LA ATENCIÓN
  
 - natalia@iesta.edu.uy
 
 - @pacocuak
 
 - http://natydasilva.com
 
 
---

##

---

# References

<a name=bib-anscombe></a>[Anscombe F, J.](#cite-anscombe) (1973).
"Graphs in statistical analysis". In: _The American Statistician_.

<a name=bib-cleveland></a>[Cleveland William, S. and R.
McGuill](#cite-cleveland) (1985). " Graphical perception and
graphical methods for analyzing scientific data ".

<a name=bib-gelman2002let></a>[Gelman, A, C. Pasarica and R.
Dodhia](#cite-gelman2002let) (2002). "Let's practice what we
preach: turning tables into graphs". In: _The American
Statistician_ 56.2, pp. 121-130.

<a name=bib-plotly></a>[Inc., P. T.](#cite-plotly) (2015).
_Collaborative data science_. URL:
[https://plot.ly](https://plot.ly).

<a name=bib-matejka2017same></a>[Matejka, J. and G.
Fitzmaurice](#cite-matejka2017same) (2017). "Same stats, different
graphs: generating datasets with varied appearance and identical
statistics through simulated annealing". In: _Proceedings of the
2017 CHI Conference on Human Factors in Computing Systems_. ACM. ,
pp. 1290-1294.

<a name=bib-sievert2016plotly></a>[Sievert, C, C. Parmer, T.
Hocking, et al.](#cite-sievert2016plotly) (2016). "plotly: Create
Interactive Web Graphics via ‘plotly. js’". In: _R package
version_ 3.0.

<a name=bib-tukey77></a>[Tukey, J. W.](#cite-tukey77) (1977).
"Exploratory Data Analysis".

<a name=bib-wickham2016ggplot2></a>[Wickham,
H.](#cite-wickham2016ggplot2) (2016). _ggplot2: elegant graphics
for data analysis_. Springer.

<a name=bib-wilkinson2006grammar></a>[Wilkinson,
L.](#cite-wilkinson2006grammar) (2006). _The grammar of graphics_.
Springer Science & Business Media.