ATR

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# ATR
## A todo R en RH
### Sergio Garcia Mora | Yanel Paulette
### R4HR Club de R para RRHH

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# ¿Qué es R?

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# ¿Qué es R?

**R** es un lenguaje de código abierto, que se lo conoce inicialmente como un lenguaje de análisis estadístico.

Hoy en día, y gracias a la comunidad de desarrolladores quienes expandieron sus capacidades, se puede usar R para muchas cosas más.

En R podés trabajar con cualquier tipo de datos, y hacer todo tipo de análisis que se te ocurra.

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# ¿Por qué R en RRHH?

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<img src="https://c.tenor.com/bD9vHNiR1rQAAAAd/boom-mind-blown.gif" />
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.pull-right[Una de las grandes ventajas de R es que permite trabajar de punta a punta en todo el ciclo de vida del dato, esto significa que podemos conectar a una base de datos, a varias hojas de cálculo, y en el mismo script hacer los procesamientos de datos, los gráficos, reportes, tableros y presentaciones por ejemplo.]

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# ¿Qué cosas se pueden hacer en R?

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## Análisis Predictivos

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🔮 Un caso de uso bien conocido es para poder realizar *análisis predictivos*, por ejemplo para predecir las renuncias de los empleados.

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.pull-right[
<img src="index_files/figure-html/pred2-1.png" width="504" />

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## Cluster Analysis

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<img src="index_files/figure-html/clus1-1.png" width="504" />
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.pull-right[
<img src="index_files/figure-html/clus2-1.png" width="504" />

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## Organizational Network Analysis

.pull-left[
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]

.pull-right[

Se pueden hacer análisis de grafos para desarrollar proyectos de Organizational Network Analysis.

En este sencillo ejemplo, estamos analizando las conecciones de LinkedIn de 3 profesores de People Analytics, para detectar los Data Scientists que tenemos en común. Este análisis se puede usar para desarrollar un programa de referidos. 🤯

]

---
## Text Mining

Se puede analizar el texto de encuestas, curriculum vitaes, y opiniones de sitios como Glassdoor. Este es un ejemplo de una encuesta sobre Home Office del 2020.

.pull-left[
<img src="index_files/figure-html/tm1-1.png" width="70%" />
]

.pull-right[
<div id="htmlwidget-8d8c79c5753d56391835" style="width:75%;height:504px;" class="wordcloud2 html-widget"></div>
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]

---
## Aplicaciones Interactivas con Shiny

.left-column[
<img src="https://www.shinyapps.dev/package/shiny/featured.png" />
]

.right-column[
**Shiny** te permite construir aplicaciones interactivas que otorgan a los usuarios finales de los datos la posibilidad de interactuar, navegar y explorar los datos, permitiéndoles hallar sus propios insights por sus propios medios.

Pueden chequear este ejemplo desarrollado por [Edward Parker](https://www.lshtm.ac.uk/aboutus/people/parker.edward) y su [Covid-19 Tracker](https://vac-lshtm.shinyapps.io/ncov_tracker/?_ga=2.199884654.193509739.1617140093-220020266.1617140093).

<img src="Archivos/shiny_app_example.png" width="70%" />
]

---
## Gráficos

En R podés hacer cualquier tipo de gráfico.

---
## Plots

En R podés hacer cualquier tipo de gráfico. Visita el repositorio de GitHub de [Ariadna Angulo Brunet](https://github.com/AnguloB/datosdemiercoles) y revisa su trabajo del desafío `#30DiasDeGráficos`.
.center[

]
---
class: inverse, center, middle
# Literalmente,

### En R podés hacer

### cualquier tipo de gráfico

---
## En serio... cualquier tipo de gráfico

Créditos: Ashten Anthony: [Guy checking out a girl meme](https://github.com/ashten28/my_ggplots/tree/master/guy_checking_out_a_girl_meme)

---
background-color: #f2f5f7

.pull-left[
<img src="https://www.yucatanalamano.com/wp-content/uploads/2021/03/D0XyhF-WoAIW8vj.jpg" />
]

.pull-right[
# Codear en R para RH: <br> Una Breve Guía para Aprender a Programar y Vivir Para Contarlo
]

---
## El perfil de un Data Scientist

.center[
<img src="https://i.ytimg.com/vi/r2I3IDKwyMw/maxresdefault.jpg" width="80%"/>
]

---
## Codear puede ser duro

.pull-left[
Aprender a programar es como practicar un nuevo deporte cuando tenés 40 años y han pasado décadas desde la última vez que hacías alguna actividad física.

Tienes que saber que va a ser doloroso, confuso, feo y frustrante.

Pero, también puede ser divertido. Tenes que encontrar la forma de lograr victorias rápidas que te hagan sentir que estás cosechando logros.

Y cuando tu primer código funcione, te vas a sentir así: 
]

.pull-right[
<img src="https://movie-fanatic-res.cloudinary.com/iu/s--jWnmUCCb--/t_full/cs_srgb,f_auto,fl_strip_profile.lossy,q_auto:420/v1364991045/run-fatboy-run.jpg" />
]

---
background-color: #696969
class: center, middle

---

# Sergio García Mora

.left-column[
<img src="Archivos/eu.jpg" />
]

.right-column[
* ### 🤓 HR Nerd
* 💪 Licenciado en Relaciones del Trabajo con formación en Data Mining
* 👨‍💻 Diplomado en Ciencias Sociales Computacionales de UNSAM
* 💹 SME People Analytics at [Data IQ](https://dataiq.com.ar/)
* 👨‍🏫 Profesor de People Analytics en ITBA
* ✈️ Fundador de [Data 4HR](https://data-4hr.com/)
* 🍷 Fundador de [R4HR Club de R para RRHH](https://r4hr.club)
* 🔨 Intructor certificado Data Carpentry por [The Carpentries](https://carpentries.org/).

]

---
# R es mucho más que Estadística

R fue concebido como un lenguaje para análisis estadístico... pero hoy en día es mucho más que eso. R tiene muchos paquetes y sus capacidades se han expandido tanto que puede hacer nuestro trabajo cotidiano mucho más fácil.

En R podés:

* Unir datos de múltiples fuentes 🤹

* Automatizar reportes con `R Markdown` 🆒

* Hacer presentaciones, como la que estás viendo, con el paquete `xaringan` 👍

* 💹 Hacer tableros de comando con el paquete `flexdashboard`.

* 👨‍🎤 Compartir tu trabajo y hacerlo reproducible.

* Escribir libros 📖, crear blogs 💬... y mucho más

---
class: inverse, center, middle
# Usos de R en la comunidad de RRHH

---
## Informes salariales en el ámbito privado

.pull-left[
<img src="Archivos/flexdashboard.png" />
]

.pull-right[
Una de nuestras integrantes utilizó el paquete `flexdashboard` para crear un tablero para mostrar la composición de los salarios, aportes y contribuciones, y movimientos de personal.

Pudo consolidar información de diversas fuentes, adaptarla en función de las solicitudes de sus clientes internos y despertar interés en los análisis de parte de los stakeholders.
]
---
## Informes salariales en el ámbito público

.pull-left[
En el **Ministerio de Educación de la Provincia de Corrientes** utilizan R para cruzar muchas planillas de cálculo con información de todo el personal de educación de la provincia, y consolidar los informes salariales.

Lo que ganaron usando R fue velocidad, menor cantidad de errores, reutilización del código y además mayor capacidad de respuesta a las nuevas inquietudes que surgieron gracias a los primeros análisis que hicieron.   
]

.pull-right[
<iframe width="560" height="315" src="https://www.youtube.com/embed/p-csppcvYn4" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
]

---
## Devolución de resultados individualizados

.pull-left[
<iframe width="560" height="315" src="https://www.youtube.com/embed/EScgUrq1pGE" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
]

.pull-right[
El grupo **International Cognitive Research Consortium** de Buenos Aires, realiza investigaciones con técnicas psicométricas relacionados con psicologogía cognitiva, comportamientos y educación.

En una investigación realizaron una serie de test psicométricos a varias personas que se presentaron voluntariamente. Y a cada una de los participantes le realizaron un informe personalizado.

Además de los resultados y análisis que realizaron internamente, utilizaron R para generar un informe personalizado para cada una de las más de 300 personas que participaron de los estudios.
]

---
## ¿En qué lo uso yo?

* Preparación de datos para cargarlos en un tablero
* Análisis predictivos
* Tutoriales interactivos
* Devolución de informes de encuestas incluyendo:
  * los análisis de resultados, gráficos y sus conclusiones.
  * análisis estadísticos como clusters y matrices de correlaciones.
  * text mining

---
# 4 tips para aprender a codear

No hay recetas mágicas para aprender a codear: vas a necesitar dedicar tiempo, y requiere mucha práctica, paciencia y persistencia. Sin embargo considero que estos consejos te pueden ser muy útiles:

--
* 🤡 Haz proyectos divertidos.

--
* 🏋 Busca apoyo en las comunidades de R.

--
* 📆 Fija fechas de entrega.

--
* 🐶 Se amable contigo.

---

.left-column[

### **Haz proyectos divertidos**

### Busca apoyo en las comunidades de R

### Fija fechas de entrega

### Se amable contigo

]

.right-column[
[Ryan Timpe](https://www.linkedin.com/in/ryantimpe/) en [su presentación](https://www.rstudio.com/resources/rstudioconf-2020/learning-r-with-humorous-side-projects/) en la `RStudio::conf` nos muestra distintas... estrategias para aprender a programar y divertirnos al mismo tiempo.

Además, hay mucho paquetes y fuentes de datos para jugar, que son entretenidos y estimulantes, y que te permitirán aprender algo de Ciencia de Datos, sin la sensación de estar haciendo algo extremadamente difícil.
]

---
### Algunos ejemplos

.pull-left[Usaremos una encuesta de Sueldos de RH de Argentina:

```r
library(tidyverse)

# Carga de datos
rh <- read_delim("data/rh_ar.csv", delim = ";")

# Limpiamos los datos y calculamos el sueldo promedio por posición
d1 <- rh %>% 
  filter(between(sueldo_bruto,
                 20000,
                 200000), # Filtramos salarios
         puesto %in% c("Analista", "HRBP", "Jefe", "Gerente")) %>% # Filtramos puestos
  mutate(puesto = factor(puesto, 
                         levels = c("Analista", "HRBP", "Jefe", "Gerente"))) %>% # Ordena puestos
  group_by(puesto) %>% 
  summarise(sueldo_promedio = mean(sueldo_bruto))

# Just a regular plot
p1 <- ggplot(d1, aes(x = puesto, y = sueldo_promedio, fill = puesto)) +
  geom_col() +
  theme_minimal()

p1 + ggtitle("Sueldo Promedio por Posición")
```

]

.pull-right[
<img src="index_files/figure-html/unnamed-chunk-1-1.png" width="504" />

]

---
## Juguemos con algunas paletas de colores

.pull-left[

```r
library(metallicaRt) # Cargar paquete

# Elige paleta de colores
*fuel <- metalli_palette("fuel")

# Plot
p1 +
* scale_fill_manual(values = fuel) +
  labs(title = "Presentando: paquete metallicaRt por John MacKintosh",
       caption = "GitHub: https://github.com/johnmackintosh/metallicaRt")
```

]

.pull-right[
<img src="index_files/figure-html/unnamed-chunk-2-1.png" width="504" />
]

---
## Juguemos con algunas paletas de colores

.pull-left[

```r
library(gameofthrones) # Cargar paquete

# Plot
p1 +
* scale_fill_got(discrete = T, option = "Stark2") +
  labs(title = "Presentando: paquete gameofthrones por Alejandro Jiménez",
       caption = "GitHub: https://github.com/aljrico/gameofthrones")
```
]

.pull-right[
<img src="index_files/figure-html/unnamed-chunk-3-1.png" width="504" />

]

---
## Hagamos algo más divertido

.pull-left[
<img src="index_files/figure-html/unnamed-chunk-4-1.png" width="504" />

]

```r
library(ggbernie) # Cargar paquete

# Plot
ggplot(d1) +
* geom_bernie(aes(x = puesto, y = sueldo_promedio),
*             bernie = "sitting") +
  labs(title = "Presentando: ggbernie por R-CoderDotCom",
       caption = "GitHub: https://github.com/R-CoderDotCom/ggbernie")
```

---
## Clones

.pull-left[
Un día estaba explicando el método de clustering **de vecinos más cercanos** o **k-nn** por su nombre en inglés y usé este dibujo hecho en Paint.

.center[<img src="clones/knn.jpg" width="40%" />]

Y después de hacerlo pensé *"esto lo podría hacer con datos"*.

Y salió esto
]

.pull-right[
<img src="clones/clones.png" />
]
---
.left-column[

### Haz proyectos divertidos

### **Busca apoyo en las comunidades de R**

### Fija fechas de entrega

### Se amable contigo

]

.right-column[
Algo **impresionante** de R son los Grupos de Usuarios de R (RUG por sus siglas en inglés) y las comunidades que hay en todas partes del mundo.

Hay decenas de RUGs y de grupos de R-Ladies que organizan talleres, brindan apoyo (con frecuencia más allá de R), escriben libros y los comparten de manera abierta y gratuita. Traducen cheatsheets, se preocupan por ser inclusivos, organizan meetups y conferencias, comparten contenido, y lo más importante, te hacen saber que no estás solo o sola en este viaje.

En la misma línea hemos creado para los profesionales de RRHH de habla hispana nuestra propia comunidad:  **[R4HR Club de R para RRHH](https://linktr.ee/r4hrclub)**.

También puedes encontrar recursos gratuitos dirigidos a los profesionales de People Analytics y RRHH, por ejemplo los libros (en inglés)  de [Hendrik Feddersen](https://www.linkedin.com/in/hendrikfeddersen/)'s *["HR Analytics in R"](https://hranalyticslive.netlify.app/index.html)*, y el de [Keith McNulty](https://www.linkedin.com/in/keith-mcnulty/)'s *["Handbook of Regression Modeling in People Analytics"](http://peopleanalytics-regression-book.org/)*.

Podés encontrar a todos los Grupos de Usuarios de R de todo el mundo en [este link](https://benubah.github.io/r-community-explorer/rugs.html) 🎉
]

---
### #TidyTuesday

*Tidy Tuesday* es una iniciativa organizada por la comunidad global de R, en la cual cada martes, comparten un dataset para que cualquiera pueda practicar y compartir su trabajo. Busca el hashtag `#TidyTuesday` en Twitter para saber más al respecto.

Las comunidades de Latinoamérica y España desarrollaron un proyecto similar que pueden encontrar con el hashtag `#DatosDeMiercoles`.

Estas iniciativas son muy útiles porque te permiten ver como alguien hace un análisis y luego replicarlo con tus propios datos.

Veamos un ejemplo.

---

### Vacaciones y Vino

Imagina que viajas de vacaciones hacia Argentina y que quieres visitar bodegas de vino en Mendoza, la provincia productora de vino por excelencia del país, para probar el mejor vino Malbec.

Así que exploraremos el archivo [Wine Ratings Dataset](https://github.com/rfordatascience/tidytuesday/tree/master/data/2019/2019-05-28) para encontrar dónde se producen los mejores Malbecs de la provincia.

¡Salud! 🍷

.center[
<img src="https://news.agrofystatic.com/vino_malbec_agrofy_news.jpg?d=620x375", width="40%" />
]
---

### Vacaciones y Vino

.pull-left[ Carguemos los datos, y filtremos por `Mendoza Province`.

```r
library(tidyverse)

# Carga de datos
wine_ratings <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-05-28/winemag-data-130k-v2.csv")

wine_ar <- wine_ratings %>% 
  filter(province == "Mendoza Province")

dim(wine_ar) # Explore dataset
```

```
## [1] 3264   14
```

]

.pull-right[
¡Hay muchos productores de vino! 😨

```r
wine_ar %>% 
  summarise(n_winery = unique(winery))
```

```
## # A tibble: 455 x 1
##    n_winery          
##    <chr>             
##  1 Gaucho Andino     
##  2 Mendel            
##  3 Andean Sky        
##  4 Cueva de las Manos
##  5 Algodon           
##  6 Kaiken            
##  7 O. Fournier       
##  8 Sottano           
##  9 Viña Cobos        
## 10 Finca Sophenia    
## # ... with 445 more rows
```

]

---
### Vacaciones y Vino

.pull-left[
Filtremos los datos por `Malbec` y veamos las regiones donde podemos encontrar los vinos con los puntajes más alto.

```r
wine_ar %>% 
  filter(variety == "Malbec") %>% 
  group_by(region_1) %>% 
  summarise(avg_points = mean(points)) %>% 
  arrange(-avg_points)
```

]

.pull-right[

```
## # A tibble: 14 x 2
##    region_1         avg_points
##    <chr>                 <dbl>
##  1 Perdriel               90.5
##  2 La Consulta            90.1
##  3 Valle de Uco           89.5
##  4 Agrelo                 89.3
##  5 Luján de Cuyo          88.8
##  6 Uco Valley             88.4
##  7 San Carlos             88.3
##  8 Tupungato              88.2
##  9 Medrano                88  
## 10 Vista Flores           87.8
## 11 <NA>                   87.5
## 12 Mendoza                87.1
## 13 Maipú                  87  
## 14 Altos de Mendoza       86
```

]

---
### Vacaciones y Vino

.pull-left[
Ahora, podemos tomar una decisión:

```r
wine_ar %>% 
  filter(variety == "Malbec",
         region_1 == "Perdriel") %>% 
  ggplot(aes(x = points, y = price, color = winery)) +
  geom_point(size = 6, alpha = 0.6, position = "jitter") +
  ggthemes::scale_color_colorblind() +
  theme_minimal()
```

]

.pull-right[
<img src="index_files/figure-html/unnamed-chunk-6-1.png" width="504" />

]

---
### Ahora úsalo con tus propios datos

Con un enfoque similar, podemos analizar los puntajes de los programas de capacitación de una empresa, y realizar un ránking de los mejores proveedores que más alineados están con los objetivos del área.

Básicamente, reproduciremos el proyecto *Vacaciones y Vino* pero con nuestros propios datos.

Usaré un divertido paquete llamado `noah`, desarrollado por [Tobias Busch](https://teebusch.github.io/noah/), para anonimizar los nombres de los proveedores.

.right[
<img src="https://teebusch.github.io/noah/reference/figures/logo.png" />
]

---
### Ranking de Proveedores de Capacitación

.pull-left[En este paso, cargamos los datos, anonimizamos los datos de los proveedores, y calculamos el puntaje promedio de alineación para cada proveedor.

```r
library(noah)

# Carga de datos
training_ratings <- read_delim("data/training_ratings.csv", delim = ";")

# Anonimizamos datos y calculamos la alineación promedio
(training_analysis <- training_ratings %>% 
    mutate(pseudo_supplier = pseudonymize(supplier)) %>% 
    group_by(pseudo_supplier) %>% 
    summarise(alignment = mean(area_goals_alignment))) %>% 
  arrange(-alignment)
```
]

.pull-right[

```
## # A tibble: 16 x 2
##    pseudo_supplier   alignment
##    <chr>                 <dbl>
##  1 Kaput Shrew           0.833
##  2 Detailed Kangaroo     0.7  
##  3 Happy Sailfish        0.7  
##  4 Tasteful Iguana       0.633
##  5 Happy Snail           0.614
##  6 Giant Lapwing         0.6  
##  7 Gullible Ermine       0.6  
##  8 First Carp            0.583
##  9 Utter Haddock         0.575
## 10 Far-Flung Bass        0.562
## 11 Highfalutin Stoat     0.5  
## 12 Aboard Landfowl       0.457
## 13 Tested Vixen          0.433
## 14 Right Serval          0.4  
## 15 Dirty Aardwolf        0.375
## 16 Shrill Mallard        0.3
```
]
---
### Ranking de Proveedores de Capacitación

.pull-left[
<img src="index_files/figure-html/unnamed-chunk-8-1.png" width="504" />

]

.pull-right[
Let's plot the ranking.

```r
ggplot(training_analysis, aes(x = alignment, 
                              y = reorder(pseudo_supplier, alignment))) + 
  geom_col(fill = "#d6dbdf") +
  labs(title = "Ranking de Proveedores de Capacitación \n por Promedio de Alineación con Objetivos",
       x = "% Alineación",
       y = "Proveedor",
       caption = "Puntajes generados aleatoriamente") +
  theme_minimal() +
  geom_vline(xintercept = 0.6,
             color = "red",
             linetype = 2)
```

]

---
.left-column[

### Haz proyectos divertidos

### Busca apoyo en las comunidades de R

### **Fija fechas de entrega**

### **Se amable contigo**

]

.right-column[
Algo que me sirve mucho es **fijar fechas de entrega**. Saber que tengo que entregar un análisis en una fecha específica, hace que siempre encuentre la forma de que las cosas funcionen.
]

.right-column[Finalmente, si estás empezando a codear y nada parece funcionar, tienes que saber que todos pasamos por lo mismo. Aprender a programar y usar R en tu trabajo requiere de tiempo, paciencia y de consistencia.
]
--
.right-column[
Y si sientes que nada funciona, pide ayuda en una comunidad de R: busca una comunidad o simplemente crea una. No necesitas ser un usuario experto para hacerlo.
]

--
.right-column[
No tengas miedo de abandonar un curso si no entiendes nada. Busca profesores, cursos y fuentes que mejor se adapten a tus necesidades y conocimientos.]

--
.right-column[
Y recuerda que no necesitas ser un genio o una genia para usar R. Podés usarlo para tareas simples (o no tan simples) como unir datos de múltiples archivos de Excel, o hacer una presentación para mostrar resultados de una manera mucho más precisa y rápida.

]

---
class: inverse middle center
# Conclusiones

---
# Conclusiones

* R es un viaje de ida. Te puede llevar tan lejos como imagines.

* Busca comunidades afines a tus intereses que impulsen tus habilidades y que te levanten cada vez que necesites ayuda.

* Comparte lo que sabes hacer. Vas a obtener feedback para mejorar tu trabajo, y puedes empoderar a alguien para que aprenda de vos.

---
# Conclusiones

.pull-left[
<img src="Archivos/principio.png" width="70%"/>

.bottom[Ilustración por Allison Horst.]
]

.pull-right[

> *"Aprender es adquirir poder (y por eso da mucho trabajo), pero es un poder que no se gasta, y que una vez adquirido nadie puede quitármelo.*

> *Porque así es el conocimiento: no se gasta con el uso y no es expropiable.*

> *Lo que significa que cuando enseñamos, cuando compartimos nuestros conocimientos, cuando publicamos nuestros materiales de forma libre, reusable y en nuestro idioma, **estamos compartiendo poder**."*

> [Yanina Bellini Saibene](https://twitter.com/yabellini). Researcher at INTA. Global Chair of LatinR_Conf.

]

---
# Let's connect!

.pull-left[
<svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg">  <path d="M326.612 185.391c59.747 59.809 58.927 155.698.36 214.59-.11.12-.24.25-.36.37l-67.2 67.2c-59.27 59.27-155.699 59.262-214.96 0-59.27-59.26-59.27-155.7 0-214.96l37.106-37.106c9.84-9.84 26.786-3.3 27.294 10.606.648 17.722 3.826 35.527 9.69 52.721 1.986 5.822.567 12.262-3.783 16.612l-13.087 13.087c-28.026 28.026-28.905 73.66-1.155 101.96 28.024 28.579 74.086 28.749 102.325.51l67.2-67.19c28.191-28.191 28.073-73.757 0-101.83-3.701-3.694-7.429-6.564-10.341-8.569a16.037 16.037 0 0 1-6.947-12.606c-.396-10.567 3.348-21.456 11.698-29.806l21.054-21.055c5.521-5.521 14.182-6.199 20.584-1.731a152.482 152.482 0 0 1 20.522 17.197zM467.547 44.449c-59.261-59.262-155.69-59.27-214.96 0l-67.2 67.2c-.12.12-.25.25-.36.37-58.566 58.892-59.387 154.781.36 214.59a152.454 152.454 0 0 0 20.521 17.196c6.402 4.468 15.064 3.789 20.584-1.731l21.054-21.055c8.35-8.35 12.094-19.239 11.698-29.806a16.037 16.037 0 0 0-6.947-12.606c-2.912-2.005-6.64-4.875-10.341-8.569-28.073-28.073-28.191-73.639 0-101.83l67.2-67.19c28.239-28.239 74.3-28.069 102.325.51 27.75 28.3 26.872 73.934-1.155 101.96l-13.087 13.087c-4.35 4.35-5.769 10.79-3.783 16.612 5.864 17.194 9.042 34.999 9.69 52.721.509 13.906 17.454 20.446 27.294 10.606l37.106-37.106c59.271-59.259 59.271-155.699.001-214.959z"></path></svg> Presentación: [https://bit.ly/paw-la-r4hr-2021](https://bit.ly/paw-la-r4hr-2021)

]

.pull-right[
<img src="https://media.giphy.com/media/fWfowxJtHySJ0SGCgN/giphy.gif" />
]

---
class: inverse, middle, center
# Agradecimientos, fuentes y créditos