Introducción a Shiny app de R

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# Introducción a Shiny app de R
## Mayo 2021
---
# Índice

### 1. Descripción y usos de shiny.
  ### 2. Referencias.
  ### 3. Shinyapps.io iniciar sesión vincular con shiny.
  ### 4. Creación de la aplicación.
---
#Cualquier duda y/o comentario es bienvenida :)

<img src="https://cdn.verbub.com/images/cualquier-duda-me-hacen-pspsps-34618.jpg"
width="450px" align:"center"/>}
---

# R Shiny app

### Descripción:

.Justify[
- Shiny es un paquete que permite crear aplicaciones web interactivas, directamente desde Rstudio, este paquete viene instalado por defecto con R. Y tiene como finalidad la interacción gráfica con los datos.

- R Shiny App es un paquete destinado al desarrollo de aplicaciones interactivas de visualización de datos. Permite la instalación de una aplicación web corriendo en R en un servidor, y que podremos personalizar de forma sencilla con sintaxis HTML, CSS o Javascript.

### Componentes:
Una app de shiny consta (al menos) de dos archivos:
- Un script para la interfaz del usuario (ui.R), que recibe los inputs y muestra los outputs.
- Un script para los cálculos (server.R) que realiza los cálculos necesarios.
]

---

# Referencias de shiny app 
.Justify[
En el [sitio oficial de Shiny](https://shiny.rstudio.com/) donde hay una variedad de plataformas, artículos, foros y demás elaboradas con shiny de R. Shiny app tiene una gran comunidad de entusiasta desarrolladores que comparten lo elaborado con esta paquetería a los demás usuarios.

Aquí encontrarás un manual tambien llamada [hoja de referencia](https://shiny.rstudio.com/images/shiny-cheatsheet.pdf) en la cual se presenta las funciones que integra una shiny dividida por UI y Server los cuales en su conjunto forman una shiny.

El paquete Shiny tiene once ejemplos incorporados que muestran a grandes razgos como funciona:

```r
library(shiny)
#runExample ("01_hello")      # histograma
#runExample ("02_text")       # resumen y tabla
#runExample ("03_reactivity") # desplegables reactivos
#runExample ("04_mpg")        # boxplot reactivos
#runExample ("05_sliders")    # deslizadores reactivos
#runExample ("06_tabsets")    # varias pestanas
#runExample ("07_widgets")    # textos de ayuda y botones
#runExample ("08_html")       # Shiny desde HTML
#runExample ("09_upload")     # carga de archivos
#runExample ("10_download")   # descarga de archivos
#runExample ("11_timer")      # dia y hora
```

]

---
###Libros y material electrónico
- [Learn ggplot2 Using Shiny App - Keon Woong Moon](https://maximinshop.com/producto/learn-ggplot2-using-shiny-app/?fbclid=IwAR1nVVtDevqHXP_t_gVpvKhrjdKti4j7alwItUN8cFi-EBKYqH8DEKcgHMg)
- [Interactive web-based data visualization with R, plotly, and shiny- Carson Sievert](https://xiscapericas.shinyapps.io/xiscape/)

###Más ejemplo de shiny con sus código al público
- [Portafolio de presentación de Xiscapericas](https://xiscapericas.shinyapps.io/xiscape/)
- [Plataforma interactiva de Diego López](https://diego-lopezt.shinyapps.io/Poblacion_Economia/)
---

##Crear un proyecto nuevo

Antes de crear la aplicación es necesario tener un orden de los elementos que integrarán la shiny, es por eso que una ventaja de los proyectos es que cualquier fichero que creemos o elemento que utilizaremos se guardará en la carpeta del proyecto que acabamos de crear.

- Hacer clic en el menú Archivo, luego en Nuevo proyecto.
- Hacer clic en Nuevo directorio .
- Hacer clic en Nuevo proyecto.
- Introducir el nombre del directorio para guardar tu proyecto, por ejemplo: shiny_cucea.

##Elementos que utilizaremos:
El material de esta sesion se encuentra en:
- [Repositorio](https://github.com/paomrom/taller_cucea)

Basado en el trabajo de:
- [GitHub de Campos J](https://github.com/JuveCampos/30DayMapChallenge2019/tree/master/18.%20Globe)

---
# Deployment de shiny app 
Antes de construir la shiny, debemos configurar.

Para esto, nos vamos a hacer una cuenta en [Shinyapps.io](https://www.shinyapps.io). Hay varios tipos de cuenta, con distintos objetivos y con sus precios correspondientes. Como siempre, la gente de RStudio ofrece una opcion gratuita con las caracteristicas suficientes para comenzar a probar.

El plan gratuito incluye 5 aplicaciones alojadas y hasta 25 horas de actividad mensuales (la app permanece idle hasta algun acceso). Esto ultimo es muy importante a la hora de subir la version final de nuestra aplicacion, ya que las horas se contabilizan por ciclo mensual y cuando se alcanza el limite en la cuenta gratuita deja de estar disponible para el acceso publico hasta el proximo ciclo.
---

##Vincular cuenta en RStudio
- En el menú principal clic en Tools.
- Clic en Global options.
- Clic en Publishing.
- Después clic en connect.
- Se abrirá un cuadro de diálogo de Connect Account, dar clic en Shinyapps.io.
- Dar clic en your accout on Shinyapps la cual te lleva al sitio oficial. Deberás iniciar sesión (sing up) ya sea que creas tu cuenta o que ingreses con tu email.
- Una vez inscrito en la página, ir al menú de Account y posterior a Tokens 
- Clic en Show secret y después en Copy to clipboard deberás asegurarte que lo has copiado correctamente.
- Una vez copiado, lo pegas en el recuadro blanco, le das clic Connect...
- Dado el último paso verás que ya se intrego tu cuenta, es posible que algunas paqueterias se descarguen en automatico ya que R se debe enlazar a la web.
- A continuación clic en Apply, y después en OK.

Nuestra cuenta ya se encuentra vinculada al IDE. Esto nos va a permitir publicar la app directamente desde Rstudio con suma facilidad y con bastante control.

[Procedimiento ilustrativo](https://rpubs.com/santiago_lator/publicar-shiny-101)
---
#Ejemplo de vincular cuenta

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background-image: url(image2.jpeg)
background-size: cover

# Creación shiny app de R

---
# Creación de shiny 
Una aplicación shiny tiene 2 funciones o archivos, uno llamado ui en donde se describe la interfaz gráfica de la aplicación, y otro llamado server en donde se escribe la lógica de la aplicación.

###Para crear una nueva aplicación shiny:

- Haga click en la pestaña Archivo.
- Seleccione la opción Nuevo archivo.
- Seleccione Shiny Web App.
- Ponga nombre a su aplicación (Sugerencia: shiny_example).
- En la opcion Application type, seleccione Single File (app.R).
- Guarde su aplicación en una carpeta de fácil acceso

---
### Importar bases de datos y manipulación que integrarán la shiny

Librerias necesarias:

```r
library(shiny)  # permite la generación de app
library(shinythemes) # personalizar theme en shiny

library(readxl) # leer archivos xlxs
library(plotly) # genera gráficos interactivos
library(dplyr)  # manipulación de dataframes

library(readxl) # importar archivos xlxs
```

Base de datos (df) con los registro de la deuda interna por país:

```r
df <- read_excel("/Users/paolamanzor/Desktop/Taller/shiny_cucea/shiny_cucea/inflacion.xlsx")

head(df, n=7)
```

```
## # A tibble: 7 x 6
## pais inflacion aÒo codigo region continente
## <chr> <dbl> <dbl> <chr> <chr> <chr> 
## 1 Afganistan 0.0224 2019 AFG Asia meridional   Asia   
## 2 Albania 0.00382 2019 ALB Europa meridional   Europa   
## 3 Alemania 0.0214 2019 DEU Europa occidental   Europa   
## 4 Algeria -0.00651 2019 ALGR África meridional   África   
## 5 Andorra 0.0151 2019 AND Europa meridional   Europa   
## 6 Angola 0.359 2019 AGO Europa meridional   Europa   
## 7 Antigua y Barbuda 0.0251 2019 ATG Caribe   América  
```
---
###Manipulación de datos, renombrar variables

```r
df %>% names()
```

```
## [1] "pais"       "inflacion"  "aÒo"        "codigo"     "region"    
## [6] "continente"
```

```r
names(df)[names(df) == "aÒo"] <- "año"
names(df)[names(df) == "codigo"] <- "abreviatura"

#Renombrados
df %>% names()
```

```
## [1] "pais"        "inflacion"   "año"         "abreviatura" "region"     
## [6] "continente"
```

```r
#Creación de un conteo o ranking
df %>%
    select(pais, inflacion, abreviatura, continente, region) %>%
    filter(inflacion!=0) %>% 
    arrange( -as.numeric(inflacion)) %>%
    mutate(rank = 1:n())->  ranked_by_inf
```

---
#Generando ranking para la visualización

```r
# Primeros 10 paises con mayor inflación

#dput(ranked_by_inf_less$pais) 
ranked_by_inf_top10<-ranked_by_inf %>%
 filter(rank<=10)%>% 
 mutate(pais=factor(pais,
 levels=c("Argentina", "Angola", "Uzbekistan", "Sudan", 
 "Surinam", "Haiti", "Turquia", "Egipto", 
 "Etiopia", "Nigeria")))

ranked_by_inf_top10 %>% head(n=2)
```

```
## # A tibble: 2 x 6
## pais inflacion abreviatura continente region rank
## <fct> <dbl> <chr> <chr> <chr> <int>
## 1 Argentina 0.515 ARG América   América del Sur   1
## 2 Angola 0.359 AGO Europa   Europa meridional   2
```

```r
# Últimos 10 paises con menor inflación
ranked_by_inf_less<-ranked_by_inf %>%
 filter(rank>=165) %>% 
 mutate(pais=factor(pais,
 levels=c("Emiratos Arabes Unidos", "Burkina Faso", 
 "Brunei", "Oman", 
 "Liberia", "Catar", 
 "Zimbabue", "Kuwait", 
 "Guinea Ecuatorial", "Guinea Bissau")))
```
---
#Construcción de UI

```r
ui <- navbarPage("inflacion mundial",
 theme = shinytheme("united"),
 tabPanel("Inicio", icon = icon("home"),
 "Inflación",
 plotlyOutput("rank")),
 
 tabPanel("Visualizaciones", icon = icon("bar-chart-o"),
 h2(aling= "center", "Mapa mundial de inflacion"),
 h4(align = "justify","El mapa interactivo muestra la inflacion por países de acuerdo a las estimaciones de gapminder"),
 sliderInput(inputId = "rango", 
 label = "Ranking",
 min = 1, 
 max = 174, 
 value = c(1,174),
 sep = ""),
 submitButton(text = "Create my plot!"),
 plotOutput(outputId = "lineal"),
 plotlyOutput("plot", width = "1200px"),
 plotlyOutput("top10"),
 plotlyOutput("less10"),
 
 ),
 tabPanel("Descarga",icon = icon("fas fa-cloud-download-alt"),
 downloadButton('downloadData', 'Download'),
 DT::dataTableOutput("table1"))
)
```

---
#Construcción de Server

```r
#Gráfico lineal
    ranked_by_inf %>% 
        ggplot() +
        geom_line(aes(x = rank, y = inflacion)) +
        theme_minimal()
```

![](ppt_shiny_files/figure-html/unnamed-chunk-6-1.png)
---

```r
#Mapa mundial
 #Set country boundaries as light grey
 l <- list(color = toRGB("#d9b779"), width = 0.5)
 #Specify map projection and options
 g <- list(
 showframe = FALSE,
 showcoastlines = FALSE,
 projection = list(type = 'orthographic'),
 resolution = '100',
 showcountries = TRUE,
 countrycolor = '#d1a547',
 showocean = TRUE,
 oceancolor = '#3fa8d4',
 showlakes = TRUE,
 lakecolor = '#1fa8e0',
 showrivers = F,
 rivercolor = '#24AAEC')
 
 p <- plot_geo(df) %>%
 add_trace(z = ~inflacion, color = ~inflacion, colors = 'Oranges',
 text = ~pais, locations = ~abreviatura, marker = list(line = l)) %>%
 colorbar(title = 'Inflacion') %>%
 layout(title = '', geo = g)
```

---

```r
 p <- plot_geo(df) %>%
 add_trace(z = ~inflacion, color = ~inflacion, colors = 'Oranges',
 text = ~pais, locations = ~abreviatura, marker = list(line = l)) %>%
 colorbar(title = 'Inflacion') %>%
 layout(title = '', geo = g)

p
```

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---

```r
rank<-ggplot(ranked_by_inf, aes(rank, group = continente,
 fill = as.factor(continente), color = as.factor(continente))) +
 geom_tile(aes(y = inflacion/2,
 height = inflacion,
 width = 0.9), alpha = 0.8, color = NA) +
 scale_fill_manual(values=c("#d03f26", "#d06126", "#d08326",	"#d0a526","#d0c726"))+
 coord_flip(clip = "off", expand = FALSE) +
 scale_y_continuous(labels = scales::comma) +
 guides(color = FALSE, fill = FALSE) +
 labs(title='', x = "", y = " ") +
 scale_x_reverse()+
 theme_minimal()+
 theme(axis.ticks.y= element_blank(),
 axis.text.y = element_blank())
 
 ggplotly(rank, dynamicTicks = TRUE)
```

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---
#Tabla

```r
downloadHandler(
        filename = 'Download.csv',
        content = function(file) {
            write.csv(Data[input[["table1_rows_all"]],], file, row.names = FALSE)}
    )
```

<a id="out97f299d7d9a7779c" class="btn btn-default shiny-download-link " href="" target="_blank" download>

Download
</a>

```r
ranked_by_inf
```

```
## # A tibble: 174 x 6
## pais inflacion abreviatura continente region rank
## <chr> <dbl> <chr> <chr> <chr> <int>
## 1 Argentina 0.515 ARG América   América del Sur   1
## 2 Angola 0.359 AGO Europa   Europa meridional   2
## 3 Uzbekistan 0.192 UZB Asia   Asia central   3
## 4 Sudan 0.178 SDN África   África septentrional   4
## 5 Surinam 0.161 SUR América   América del Sur   5
## 6 Haiti 0.142 HTI América   Caribe   6
## 7 Turquia 0.139 TUR Asia   Asia occidental   7
## 8 Egipto 0.136 EGY África   África septentrional   8
## 9 Etiopia 0.132 ETH África   África oriental   9
## 10 Nigeria 0.104 NGA África   África occidental   10
## # … with 164 more rows
```
---

class: middle, inverse, center
.pull-center[
#¡Gracias por tu atención!

En este momento puedes realizar más preguntas que tengas y si me quieren contactar, en esta diapositiva incluí mi información.
]