---
title: "2022"
output: 
  flexdashboard::flex_dashboard:
    css: style.css
    social: menu
    source_code: embed
    orientation: columns
    logo: INS1.png
    vertical_layout: fill
    mathjax: NULL
---

```{r setup, include=FALSE}
#------------------ Packages ------------------
library(flexdashboard)
library(rgdal)
library(leaflet)
library(kableExtra)
require(DT)
require(webr)
require(ggiraph)
require(ggiraphExtra)
require(ggplot2)
library(hpackedbubble)
require(dplyr)
library(leaflet.minicharts)
library(plotly)
library(rmapshaper)
library(rgeos)
library(treemap)
library(knitr)
library(packcircles)
library(ggplot2)
library(viridis)
library(ggiraph)


`%>%` <- magrittr::`%>%`
#------------------ Parameters ------------------
# Set colors
# https://www.w3.org/TR/css-color-3/#svg-color


confirmed_color <- "purple"
positive_color <- RColorBrewer::brewer.pal(9, "PuRd")[7]
active_color <- "#1f77b4"
recovered_color <- "forestgreen"
death_color <- "#E41317"
intensive_care_color <- "#9E0003"
gray_color <- "gray"
home_conf_color <- "#FDBBBC"
gold_color <- "gold"
orange_color <- "orange"
#------------------ Data ------------------

##Departamento
table_dep=read.csv("Mapa_tab.csv", sep=";", dec=",")
colnames(table_dep)=c("DPTO_CCDGO", "Departamento", "Alertas", "Bajo", "Moderado", "Alto")
table_dep$DPTO_CCDGO= ifelse(table_dep$DPTO_CCDGO==5, "05", table_dep$DPTO_CCDGO)
table_dep$DPTO_CCDGO= ifelse(table_dep$DPTO_CCDGO==8, "08", table_dep$DPTO_CCDGO)

table_dep$Bajo =  table_dep$Bajo*100
table_dep$Moderado =  table_dep$Moderado*100
table_dep$Alto =  table_dep$Alto*100

##Centroids
centroids= data.frame(Long= c( -75.56803, -74.96448,-75796387 ,-74.18080, -74.50609, -73.10743, -75.30508, -73.96229, -76.83276, -73.51648, -75.79623, -74.09766, -76.94353, -75.59676, -72.42792, -74.26044, -72.95420, -77.87499, -72.88281, -75.68956, -75.87724, -73.48842, -75.10955, -75.25275, -76.53567, -70.96862, -71.60362, -75.86060, -71.50694, -68.80753, -72.12416, -70.56132, -69.41472, -81.71769,-74.796387, -77.03116, -75.51444,  -74.19904),
Lat= c(6.9216279, 10.6787664, 10.963889 ,4.3141913,  8.7425958,  5.7748514,  5.3423204, 0.8001992,  2.3992567,  9.5498374,  8.3571365,  4.8222499,  5.9429284, 2.5572261, 11.4784290, 10.2457737,  3.3448880,  1.5764712,  8.0918374, 4.4550142,  5.1031966,  6.6985229,  9.0623682,  4.0415706,  3.8474144, 6.5701724,  5.4049869,  0.4592041, -1.5460253,  2.7204125,  1.9292007, 0.6456491,  4.7129771, 12.5426433, 10.963889, 3.8801, 10.39972, 11.24079),
DPTO_CCDGO = c("05","08", "08001", "11", "13", "15", "17", "18", "19", "20", "23", "25", "27", "41", 
          "44", "47", "50", "52", "54" , "63", "66", "68", "70", "73", "76", "81", "85" , "86", 
          "91", "94", "95", "97" , "99", "88", "08001", "76109", "13001", "47001"))

table_dep2=inner_join(table_dep, centroids)

ShapeDep_read <- readOGR("DeptosDistritoConSAI_2020.shp")
ShapeDep <- spTransform(ShapeDep_read, CRS("+proj=longlat +ellps=GRS80"))
head(ShapeDep@data)

shapesSimple <-
  gSimplify(ShapeDep, tol=0.1, topologyPreserve=TRUE) %>% 
  SpatialPolygonsDataFrame(., data = ShapeDep@data) %>% 
  ms_simplify()

ShapeMerge <- shapesSimple %>% merge(table_dep2,by='DPTO_CCDGO')
head(ShapeMerge@data)

bins <- c(0,1, 5, 10, 15, 20, 25, 30)
pal <- colorBin("YlOrBr", domain = ShapeMerge@data$Alertas, bins = bins, reverse = FALSE,na.color = "white" )

labels <- sprintf(
  "%s
  Alerta: %s",
  ShapeMerge@data$Departamento, ShapeMerge@data$Alertas) %>% lapply(htmltools::HTML)

colors <- c("orange", "gold", "forestgreen")

```


SATAES
=======================================================================
Column { data-width=210 }
-----------------------------------------------------------------------

### tested {.value-box}
```{r}
valueBox(value = 102, 
         caption = "Alertas", icon= "fas fa-exclamation-circle",
         color = active_color)
```



### Active {.value-box}
```{r}
valueBox(value = paste(100, "%"), 
         caption = "Riesgo BAJO (102/102)", 
         color = recovered_color)
```

### recovered {.value-box}
```{r}
valueBox(value = paste(0, "%"), 
         caption = "Riesgo MODERADO (0/102)", 
         color = gold_color)
```

### Intensive Care {.value-box}
```{r}
valueBox(value = paste(0, "%"), 
         caption = "Riesgo ALTO (0/102)", 
         color = orange_color)
```


### Muy alto {.value-box}
```{r}
valueBox(value = paste(0, "%"), 
         caption = "Riesgo MUY ALTO (0/102)", 
         color = death_color)
```


Column{ data-width=420 }
-----------------------------------------------------------------------

### **Alertas ambientales y riesgo por ET**  (I Trimestre, 2022)

```{r}
m=leaflet(ShapeMerge)  %>%  setView(-73, 5, zoom = 5) %>%  addProviderTiles("OpenStreetMap")  %>% 
  addEasyButton(easyButton(icon="fa-globe", title="Zoom Inicial", 
  onClick=JS("function(btn, map){ map.setZoom(4); }"))) %>% addPolygons(
  fillColor = ~pal(ShapeMerge@data$Alertas),
   weight = 1,   opacity = 0.9,   color = "black",  dashArray = "2", fillOpacity = 0.8, 
   highlightOptions = highlightOptions(color = "white", weight = 2, bringToFront = FALSE), 
  label= labels) %>%
  addMinicharts(ShapeMerge@data$Long, ShapeMerge@data$Lat,type = "pie",
    chartdata = ShapeMerge@data[, c("Alto", "Moderado", "Bajo")], colorPalette = colors, 
     transitionTime = 0, width=20, showLabels= FALSE)

m%>% addLegend(pal = pal, values = ShapeMerge@data$Alertas, opacity = 0.9,
               position = "bottomleft", title="Número de alertas",na.label = "sin alerta")
```


Column
-------------------------------------
### **Alertas registradas por el GFRA**

```{r}
Tendencia=read.csv("Tendencia.csv", sep=";")
Tendencia$Semana=as.factor(Tendencia$Semana)


plotly::plot_ly(data = Tendencia) %>%
  plotly::add_lines(x = ~ Semana, 
                    y = ~ Y2019,
                    line = list(color = "#00A5FF", width = 1),
                    name = "2019") %>%
  plotly::add_lines(x = ~ Semana, 
                    y = ~ Y2020,
                    line = list(color = "orange", width = 1),
                    name = "2020") %>%
  plotly::add_lines(x = ~ Semana, 
                    y = ~ Y2021,
                    line = list(color = "green", width = 1),
                    name = "2021") %>%
  plotly::add_lines(x = ~ Semana, 
                    y = ~ Y2022,
                    line = list(color = "red", width = 2),
                    name = "2022") %>%

  plotly::layout(title = "",
                 legend = list(x = 0.9, y = 0.9),
                 yaxis = list(title = "Alertas registradas"),
                 xaxis = list(title = "Semana"),
                 hovermode = "compare")

```


### **Riesgo ambiental por ET** (I Trimestre, 2022)

```{r }

# Create data
data <- data.frame(temporada= "Temporada de Lluvias",group=c("Sin Seguimientos en el Primer Trimestre 2022"),  value=1) 

# Add a column with the text you want to display for each bubble:
data$text <- paste("Departamento: ",data$group, "\n", "Temporada de lluvias")

# Generate the layout
packing <- circleProgressiveLayout(data$value, sizetype='area')
data <- cbind(data, packing)
dat.gg <- circleLayoutVertices(packing, npoints=50)

# Make the plot with a few differences compared to the static version:
p <- ggplot() + 
  geom_polygon_interactive(data = dat.gg, aes(x, y, group = id, fill=id, tooltip = data$text[id], data_id = id), colour = "black", alpha = 0.6, fill="gold") +
  geom_text(data = data, aes(x, y, label = gsub("Group_", "", group)), size=5, color="black") +
  theme_void() + 
  coord_equal() + ggtitle("Temporada de Lluvia \n Marzo")

# Turn it interactive
widg <- ggiraph(ggobj = p, width_svg = 7, height_svg = 7)
widg
```

Column { data-width=430}
-------------------------------------
### **Componente ambiental y fuente de afectación** (I Trimestre, 2022).

```{r}
Pie= read.csv("Pie.csv", sep=";", dec=",")
Pie=Pie[,c(1,2,6)]

colnames(Pie)=c("parents", "labels","values")
Pie= subset(Pie, Pie$values!=0)

datasun2=data.frame(labels = c("Componente 
 (%)", "Clima", "Aire", "Suelo", "Agua"),
                  parents = c("", "Componente 
 (%)", "Componente 
 (%)",  "Componente 
 (%)", "Componente 
 (%)"),
           values = c(100, 24.27, 20.39, 27.18, 28.16))

datasun=rbind(datasun2, Pie)


fig <- plot_ly(
  labels =  datasun$labels,
  parents = datasun$parents,
  values = datasun$values,
  type = 'sunburst',
  branchvalues = 'total')  %>%
  partial_bundle()

layout(fig, colorway = c("#3399FF", "#FFCC33", "#FF6600", "#99CC00"))
```


### **Número de alertas (Top 10)** (I Trimestre, 2022).
    
```{r alertas}
Departamento=c("Antioquia", "Santander","Tolima","Huila", "Valle del Cauca", "Cáldas", "Cauca", "Nariño", "Quindio", "Risaralda")
Alertas=c(17, 13, 11, 7, 4, 4, 4, 4, 4, 4)
Componente=c("Clima/Precip.", "Clima/Precip.", "Clima/Precip.", "Clima/Precip.", "Clima/Precip.", "Clima/Precip.", "Clima/Precip.", "Clima/Precip/Incend.", "Clima/Precip.", "Clima/Precip." )
Proporción=c("16.7%", "12.7%", "10.8%", "7.0%", "3.9%","3.9%", "3.9%", "3.9%", "3.9%", "3.9%")

table=data.frame(Departamento, Alertas, Componente, Proporción)

table %>% kbl() %>% kable_styling("condensed", full_width = F,  html_font = "cmu-serif")
```
   


MASSAES
=======================================================================
  
```{r, include=FALSE}

##Departamento
table_dep=read.csv("depto_MASSAES.csv", sep=";", dec=",")
names(table_dep)
table_dep=table_dep[,c(1,2,7, 12, 12, 20:24)]
colnames(table_dep)=c("DPTO_CCDGO", "Departamento", "Nivel Alerta S1", "Nivel Alerta S2", "Nivel Alerta S3", "Num. Alertas", "% Pob. Riesgo", "Nivel Alerta S4", "Semana", "Evento")
table_dep[is.na(table_dep)] <- 0

table_dep$Departamento=ifelse(table_dep$Departamento=="Archipielago de San Andres, Providencia y Santa Catalina", "San Andrés, Prov. y Sta.Cat.",table_dep$Departamento )
table_dep$DPTO_CCDGO=ifelse(table_dep$DPTO_CCDGO==5, "05", table_dep$DPTO_CCDGO)
table_dep$DPTO_CCDGO=ifelse(table_dep$DPTO_CCDGO==8, "08", table_dep$DPTO_CCDGO)

table_dep$`% Pob. Riesgo` = round(table_dep$`% Pob. Riesgo`,1)
table_dep$`Nivel Alerta S4` = round(table_dep$`Nivel Alerta S4`,1)
table_dep$`Nivel Alerta S3` = round(table_dep$`Nivel Alerta S3`,1)
table_dep$`Nivel Alerta S2` = round(table_dep$`Nivel Alerta S2`,1)
table_dep$`Nivel Alerta S1` = round(table_dep$`Nivel Alerta S1`,1)

ShapeDep_read <- readOGR("DeptosDistritoConSAI_2020.shp")
ShapeDep <- spTransform(ShapeDep_read, CRS("+proj=longlat +ellps=GRS80"))
head(ShapeDep@data)


shapesSimpleDep <-
  gSimplify(ShapeDep_read, tol=0.1, topologyPreserve=TRUE) %>% 
  SpatialPolygonsDataFrame(., data = ShapeDep_read@data) %>% 
  ms_simplify()

ShapeMerge <- shapesSimpleDep %>% 
  merge(table_dep,by='DPTO_CCDGO')


bins <- c(0, 25, 50, 75, 100)
pal <- colorBin("RdYlGn", domain = ShapeMerge@data$`Nivel Alerta S4`, bins = bins, reverse = TRUE)


## Municipio
table_muni=read.csv("muni_MASSAES.csv", sep=";", dec=",")
names(table_muni)
table_muni=table_muni[,c(1,2,3, 11, 16, 21, 22:26)]
colnames(table_muni)=c("MPIO_CCDGO",  "Municipio","Departamento", "Nivel Alerta S1", "Nivel Alerta S2", "Nivel Alerta S3", "Num. Alertas", "% Pob. Riesgo", "Semana", "Evento","Nivel Alerta S4")
head(table_muni)
table_muni[is.na(table_muni)] <- 0

table_muni$MPIO_CCDGO=ifelse(table_muni$Departamento=="ANTIOQUIA",paste("0",table_muni$MPIO_CCDGO,sep = ""),table_muni$MPIO_CCDGO)
table_muni$MPIO_CCDGO=ifelse(table_muni$Departamento=="ATLANTICO",paste("0",table_muni$MPIO_CCDGO,sep = ""),table_muni$MPIO_CCDGO)

table_muni$`% Pob. Riesgo`=round(table_muni$`% Pob. Riesgo`,1)
table_muni$`Nivel Alerta S1`=round(table_muni$`Nivel Alerta S1`,1)
table_muni$`Nivel Alerta S2`=round(table_muni$`Nivel Alerta S2`,1)
table_muni$`Nivel Alerta S3`=round(table_muni$`Nivel Alerta S3`,1)
table_muni$`Nivel Alerta S4`=round(table_muni$`Nivel Alerta S4`,1)


ShapeMuni_read <- readOGR("MGN_MPIO_POLITICO.shp")
ShapeMuni <- spTransform(ShapeMuni_read, CRS("+proj=longlat +ellps=GRS80"))
head(ShapeMuni@data)

shapesSimpleMuniMAS <-
  gSimplify(ShapeMuni_read, tol=0.01, topologyPreserve=TRUE) %>% 
  SpatialPolygonsDataFrame(., data = ShapeMuni_read@data) %>% 
  ms_simplify()

shapesSimpleMuniMAS@data$MPIO_CCDGO=shapesSimpleMuniMAS@data$MPIO_CCNCT
head(shapesSimpleMuniMAS@data)

ShapeMerge_Muni <- shapesSimpleMuniMAS %>% 
  merge(table_muni,by='MPIO_CCDGO')

ShapeMerge_Muni@data[is.na( ShapeMerge_Muni@data)] <- 0

bins <- c(0, 25, 50, 75, 100)
pal_Muni <- colorBin("RdYlGn", domain = ShapeMerge_Muni@data$`Nivel Alerta S4`, bins = bins, reverse = TRUE)


###CIRCLES
evento=read.csv("evento_dep.csv", sep=";")
colnames(evento) = c("DPTO_CCDGO", "Departamento", "Deslizamientos", "Incendios","Inundaciones", "Lluvias", "Crecientes")
evento$DPTO_CCDGO = as.character(evento$DPTO_CCDGO)

evento$DPTO_CCDGO=ifelse(evento$DPTO_CCDGO==5, "05", evento$DPTO_CCDGO)
evento$DPTO_CCDGO=ifelse(evento$DPTO_CCDGO==8, "08", evento$DPTO_CCDGO)
evento$Departamento=ifelse(evento$Departamento=="Archipielago de San Andres, Providencia y Santa Catalina", "San Andrés, Prov. y Sta.Cat.",evento$Departamento )

##Centroids
centroids= data.frame(Long= c( -75.56803, -74.96448,-75796387 ,-74.18080, -74.50609, -73.10743, -75.30508, -73.96229, -76.83276, -73.51648, -75.79623, -74.09766, -76.94353, -75.59676, -72.42792, -74.26044, -72.95420, -77.87499, -72.88281, -75.68956, -75.87724, -73.48842, -75.10955, -75.25275, -76.53567, -70.96862, -71.60362, -75.86060, -71.50694, -68.80753, -72.12416, -70.56132, -69.41472, -81.71769,-74.796387, -77.03116, -75.51444,  -74.19904),
                      Lat= c(6.9216279, 10.6787664, 10.963889 ,4.3141913,  8.7425958,  5.7748514,  5.3423204, 0.8001992,  2.3992567,  9.5498374,  8.3571365,  4.8222499,  5.9429284, 2.5572261, 11.4784290, 10.2457737,  3.3448880,  1.5764712,  8.0918374, 4.4550142,  5.1031966,  6.6985229,  9.0623682,  4.0415706,  3.8474144, 6.5701724,  5.4049869,  0.4592041, -1.5460253,  2.7204125,  1.9292007, 0.6456491,  4.7129771, 12.5426433, 10.963889, 3.8801, 10.39972, 11.24079),
                      DPTO_CCDGO = c("05","08", "08001", "11", "13", "15", "17", "18", "19", "20", "23", "25", "27", "41", 
                                     "44", "47", "50", "52", "54" , "63", "66", "68", "70", "73", "76", "81", "85" , "86", 
                                     "91", "94", "95", "97" , "99", "88", "08001", "76109", "13001", "47001"))

evento_centroide=left_join(evento, centroids)

```

Column { data-width=210 }
-----------------------------------------------------------------------
**Informacion alertas SE 13**: El nivel de riesgo es resultado de MASSAES, el cual utiliza información de IDEAM, DANE y DNP con la finalidad de priorizar efectos en salud por eventos ambientales.
 
### Precipitaciones {.value-box}

```{r}
valueBox(value = "Precipitaciones" , 
         caption = paste("Departamentos", nrow(subset(table_dep, table_dep$Evento=="TIEMPO LLUVIOSO")),"                                       ","Municipios",nrow(subset(table_muni, table_muni$Evento=="TIEMPO LLUVIOSO"))),
         color = "#1F78B4")
```


### Deslizamientos {.value-box}
  
```{r}

valueBox(value = "Deslizamientos", 
         caption =paste("Departamentos", nrow(subset(table_dep, table_dep$Evento=="DESLIZAMIENTOS")), "Municipios",nrow(subset(table_muni, table_muni$Evento=="DESLIZAMIENTOS"))),  
         icon = "fas fa-mountain", 
         color = "#A6CEE3")
```


### Incendios {.value-box}

```{r}
valueBox(value = "Incendios", 
         caption = paste("departamentos", nrow(subset(table_dep, table_dep$Evento=="INCENDIOS")),   
 "Municipios",nrow(subset(table_muni, table_muni$Evento=="INCENDIOS"))), 
         color = "#E6AB02")
```

### Crecientes {.value-box}

```{r}

valueBox(value = "Crecientes", 
         caption = paste("Departamentos", nrow(subset(table_dep, table_dep$Evento=="CRECIENTES")),
          "Municipios",nrow(subset(table_muni, table_muni$Evento=="CRECIENTES"))), 
         icon = "fas fa-water", 
         color = "#CAB2D6")
```


### Inundaciones {.value-box}

```{r}

valueBox(value = "Inundaciones", 
         caption = paste("Departamentos", nrow(subset(table_dep, table_dep$Evento=="INUNDACIONES")),
       "Municipios",nrow(subset(table_muni, table_muni$Evento=="INUNDACIONES"))),
         color = "#6A3D9A")
```

Column{ data-width=680 }
-------------------------------------
  
### **Exploración Geográfica**: Nivel de alerta para afectaciones en salud relacionado a eventos ambientales según la priorización de la herramienta MASSAES. 

  
```{r}

#####
labels <- sprintf(
  "%s
  Nivel alerta: %s 
  Evento: %s  
  Pob. Riesgo: %s",
  ShapeMerge_Muni@data$Municipio, ShapeMerge_Muni@data$`Nivel Alerta S4`,ShapeMerge_Muni@data$Evento, ShapeMerge_Muni@data$`% Pob. Riesgo`) %>% lapply(htmltools::HTML)

labelsdpt <- sprintf(
  "%s
  Nivel alerta: %s 
  Evento: %s  
  Pob. Riesgo: %s",
  ShapeMerge@data$Departamento, ShapeMerge@data$`Nivel Alerta S4`,ShapeMerge@data$Evento, ShapeMerge@data$`% Pob. Riesgo`) %>% lapply(htmltools::HTML)

munidep=leaflet()  %>%  setView(-73, 5, zoom = 5)  %>% addProviderTiles("OpenStreetMap") %>% 
  addEasyButton(easyButton(icon="fa-globe", title="Zoom Inicial",onClick=JS("function(btn, map){ map.setZoom(5); }"))) %>% 
  addPolygons(data=ShapeMerge_Muni,fillColor = ~pal(ShapeMerge_Muni@data$`Nivel Alerta S4`),
              weight = 1,   opacity = 1,   color = "black",  dashArray = "2", fillOpacity = 0.8, group="SE4-Municipios",
              highlightOptions = highlightOptions(color = "white", weight = 2, bringToFront = FALSE), 
              label= labels)    %>% 
addPolygons(data=ShapeMerge,fillColor = ~pal(ShapeMerge@data$`Nivel Alerta S1`),
              weight = 1,   opacity = 1,   color = "black",  dashArray = "2", fillOpacity = 0.8, group="Departamento-SE1",
              highlightOptions = highlightOptions(color = "white", weight = 2, bringToFront = FALSE), 
              label= labelsdpt)  %>% 
  addPolygons(data=ShapeMerge,fillColor = ~pal(ShapeMerge@data$`Nivel Alerta S2`),
              weight = 1,   opacity = 1,   color = "black",  dashArray = "2", fillOpacity = 0.8, group="Departamento-SE2",
              highlightOptions = highlightOptions(color = "white", weight = 2, bringToFront = FALSE), 
              label= labelsdpt) %>% 
  addPolygons(data=ShapeMerge,fillColor = ~pal(ShapeMerge@data$`Nivel Alerta S3`),
              weight = 1,   opacity = 1,   color = "black",  dashArray = "2", fillOpacity = 0.8, group="Departamento-SE3",
              highlightOptions = highlightOptions(color = "white", weight = 2, bringToFront = FALSE), 
              label= labelsdpt) %>% 
  addPolygons(data=ShapeMerge,fillColor = ~pal(ShapeMerge@data$`Nivel Alerta S4`),
              weight = 1,   opacity = 1,   color = "black",  dashArray = "2", fillOpacity = 0.8, group="Departamento",
              highlightOptions = highlightOptions(color = "white", weight = 2, bringToFront = FALSE), 
              label= labelsdpt)  %>%
  addCircles(lng = evento_centroide$Long, lat = evento_centroide$Lat, weight = evento_centroide$Deslizamientos, popup = paste0(evento_centroide$Departamento ,
      "
 Deslizamientos : " , evento_centroide$Deslizamientos), radius = evento_centroide$Deslizamientos+2 , color = "#A6CEE3", group="Deslizamientos")  %>%
  addCircles(lng = evento_centroide$Long, lat = evento_centroide$Lat, weight = evento_centroide$Incendios+2, popup = paste0(evento_centroide$Departamento ,
      "
 Incendios : " , evento_centroide$Incendios), radius = evento_centroide$Incendios , color = "#E6AB02", group="Incendios")  %>%
  addCircles(lng = evento_centroide$Long, lat = evento_centroide$Lat, weight = evento_centroide$Inundaciones+2, popup = paste0(evento_centroide$Departamento ,
   "
 Inundaciones : " , evento_centroide$Inundaciones), radius = evento_centroide$Inundaciones , color = "#6A3D9A", group="Inundaciones")  %>%
  addCircles(lng = evento_centroide$Long, lat = evento_centroide$Lat, weight = evento_centroide$Lluvias +2, popup = paste0(evento_centroide$Departamento ,
   "
 Lluvias : " , evento_centroide$Lluvias), radius = evento_centroide$Lluvias , color = "#1F78B4", group="Precipitaciones")  %>%
  addCircles(lng = evento_centroide$Long, lat = evento_centroide$Lat, weight = evento_centroide$Crecientes +2, popup = paste0(evento_centroide$Departamento ,
  "
 Crecientes : " , evento_centroide$Crecientes), radius = evento_centroide$Crecientes , color = "#CAB2D6", group="Crecientes")  %>% 
  addLayersControl(baseGroups =c( "Departamento", "Municipio","Departamento-SE3", "Departamento-SE2", "Departamento-SE1"), overlayGroups =c( "Deslizamientos", "Incendios", "Inundaciones", "Precipitaciones", "Crecientes"),
                                                   options = layersControlOptions(collapsed=TRUE)) 


munidep %>% addLegend(pal = pal, values = ShapeMerge@data$`Nivel Alerta S4`, opacity = 0.7, title = "Nivel de Alerta", labels= c("Bajo", "Moderado","Alto","Muy Alto"),position = "bottomleft") %>% hideGroup(c("SE3", "SE2", "SE1",  "Deslizamientos", "Incendios", "Inundaciones", "Crecientes")) 
```


Column  {.tabset}
-------------------------------------


### **Alertas** Departamentos
```{r}
table_dep4=read.csv("depto_MASSAES.csv", sep=";", dec=",")
table_dep4=table_dep4[,c(1,2,7, 12, 12, 20:24)]
colnames(table_dep4)=c("DPTO_CCDGO", "Departamento", "Nivel Alerta S1", "Nivel Alerta S2", "Nivel Alerta S3", "Num. Alertas", "% Pob. Riesgo", "Nivel Alerta S4", "Semana", "Evento")
table_dep4[is.na(table_dep4)] <- 0

table_dep4$Departamento=ifelse(table_dep4$Departamento=="Archipielago de San Andres, Providencia y Santa Catalina", "San Andrés, Prov. y Sta.Cat.",table_dep4$Departamento )
table_dep4$DPTO_CCDGO=ifelse(table_dep4$DPTO_CCDGO==5, "05", table_dep4$DPTO_CCDGO)
table_dep4$DPTO_CCDGO=ifelse(table_dep4$DPTO_CCDGO==8, "08", table_dep4$DPTO_CCDGO)

table_dep4$`% Pob. Riesgo` = round(table_dep4$`% Pob. Riesgo`,1)
table_dep4$`Nivel Alerta S4` = round(table_dep4$`Nivel Alerta S4`,1)
table_dep4$`Nivel Alerta S3` = round(table_dep4$`Nivel Alerta S3`,1)
table_dep4$`Nivel Alerta S2` = round(table_dep4$`Nivel Alerta S2`,1)
table_dep4$`Nivel Alerta S1` = round(table_dep4$`Nivel Alerta S1`,1)

table_dep_top10 <-table_dep4[order(-table_dep4$`Nivel Alerta S4`),-1]
rownames(table_dep_top10) <- NULL
table_dep_top10$Evento = ifelse(table_dep_top10$Evento == "DESLIZAMIENTOS", "Deslizamientos", table_dep_top10$Evento )
table_dep_top10$Evento = ifelse(table_dep_top10$Evento == "TIEMPO LLUVIOSO", "Lluvias", table_dep_top10$Evento )
table_dep_top10$Evento = ifelse(table_dep_top10$Evento == "INCENDIOS", "Incendios", table_dep_top10$Evento )
table_dep_top10$Evento = ifelse(table_dep_top10$Evento == "INUNDACIONES", "Inundaciones", table_dep_top10$Evento )

table_dep_top10[1:20,c(1:4,7,6,9)] %>% kbl() %>% kable_styling("condensed", full_width = F,  html_font = "cmu-serif")
```

###  Municipios

```{r}
table_muni_top10 <-table_muni[order(-table_muni$`Nivel Alerta S4`),-1]
rownames(table_muni_top10) <- NULL
table_muni_top10$Evento = ifelse(table_muni_top10$Evento == "DESLIZAMIENTOS", "Deslizamientos", table_muni_top10$Evento )
table_muni_top10$Evento = ifelse(table_muni_top10$Evento == "TIEMPO LLUVIOSO", "Lluvias", table_muni_top10$Evento )
table_muni_top10$Evento = ifelse(table_muni_top10$Evento == "INCENDIOS", "Incendios", table_muni_top10$Evento )
table_muni_top10$Evento = ifelse(table_muni_top10$Evento == "INUNDACIONES", "Inundaciones", table_muni_top10$Evento )

table_muni_top10[1:20,c(1:5,10, 7,9)] %>% kbl() %>% kable_styling("condensed", full_width = F,  html_font = "cmu-serif")
```


Column  {.tabset}
-------------------------------------

  
### **Población en riesgo** - Departamentos
  
```{r}
table_dep$`% Pob. Riesgo` = round(table_dep$`% Pob. Riesgo`,1)

plotly::plot_ly(
  data = table_dep,
  type= "treemap",
  values = table_dep$`% Pob. Riesgo`,
  labels= paste(table_dep$Departamento,"\n", table_dep$`% Pob. Riesgo`, "%", sep=""),
  parents=  NA,
  textinfo="Departamento",
  domain=list(column=1),
  hovertext= paste(table_dep$`Nivel Alerta`, "\n", table_dep$Evento),
  hovertemplate = "Población en riesgo: %{value}% 
 Nivel de alerta: %{hovertext} ") %>% 
  layout(
    margin = list(
      l = 0,
      r = 0,
      b = 0,
      t = 0,
      pad = 0
    ),
    yaxis = list(
      type = 'category',
      tickmode = 'linear',
      dtick = 0
    )
  )
```


  
### Municipios
  
```{r}
table_muni$`% Pob. Riesgo` = round(table_muni$`% Pob. Riesgo`,1)

table_muni= subset(table_muni, table_muni$`% Pob. Riesgo` > 7)

plotly::plot_ly(
  data = table_muni,
  type= "treemap",
  values = table_muni$`% Pob. Riesgo`,
  labels= paste(table_muni$Municipio,"\n", table_muni$`% Pob. Riesgo`, "%", sep=""),
  parents=  NA,
  textinfo="Departamento",
  domain=list(column=1),
  hovertext= paste(table_muni$`Nivel Alerta`, "\n", table_muni$Evento),
  hovertemplate = "Población en riesgo: %{value}% 
 Nivel de alerta: %{hovertext} ") %>% 
  layout(
    margin = list(
      l = 0,
      r = 0,
      b = 0,
      t = 0,
      pad = 0
    ),
    yaxis = list(
      type = 'category',
      tickmode = 'linear',
      dtick = 0
    )
  )

```


Efectos en Salud
=======================================================================


Column{ data-width=480}
-----------------------------------------------------------------------


### **DENGUE**  (Exploración Geográfica)

```{r}

###Dengue
table_dengue=read.csv("dengue_MASSAES.csv", sep=";", dec=",")
colnames(table_dengue)=c("MPIO_CCDGO",  "Municipio","Departamento", "Alerta Dengue")
table_dengue$`Alerta Dengue`=round(table_dengue$`Alerta Dengue`,1)

table_dengue$MPIO_CCDGO=ifelse(table_dengue$Departamento=="ANTIOQUIA",paste("0",table_dengue$MPIO_CCDGO,sep = ""),table_dengue$MPIO_CCDGO)
table_dengue$MPIO_CCDGO=ifelse(table_dengue$Departamento=="ATLANTICO",paste("0",table_dengue$MPIO_CCDGO,sep = ""),table_dengue$MPIO_CCDGO)


ShapeMerge_Muni_Dengue <- shapesSimpleMuniMAS %>% 
  merge(table_dengue,by='MPIO_CCDGO', duplicateGeoms = TRUE)



ShapeMerge_Muni_Dengue@data$`Alerta Dengue`[is.na( ShapeMerge_Muni_Dengue@data$`Alerta Dengue`)] <-6

bins <- c(0, 25, 50, 75, 100)
pal_Muni <- colorBin("RdYlGn", domain = ShapeMerge_Muni_Dengue@data$`Alerta Dengue`, bins = bins, reverse = TRUE)

labels <- sprintf(
  "%s
   Departamento: %s 
 Nivel alerta: %s 
 ",
  ShapeMerge_Muni_Dengue@data$Municipio, ShapeMerge_Muni_Dengue@data$Departamento, ShapeMerge_Muni_Dengue@data$`Alerta Dengue` 
) %>% lapply(htmltools::HTML)


mu_dengue=leaflet(ShapeMerge_Muni_Dengue)  %>% addProviderTiles("OpenStreetMap")   %>% setView(-73, 5, zoom = 6)  %>% addEasyButton(easyButton(icon="fa-globe", title="Zoom Inicial",
 onClick=JS("function(btn, map){ map.setZoom(6); }"))) %>% addPolygons(
 fillColor = ~pal(ShapeMerge_Muni_Dengue@data$`Alerta Dengue`),
    weight = 1,   opacity = 0.8,   color = "black",  dashArray = "2", fillOpacity = 0.8, 
    highlightOptions = highlightOptions(color = "white", weight = 2, bringToFront = TRUE), 
    label= labels) 

mu_dengue%>% addLegend(pal = pal, values=ShapeMerge_Muni_Dengue@data$`Alerta Dengue`, labels = c("Bajo", "Moderado", "Alto", "Muy Alto"), opacity = 0.7,  position = "bottomleft", title= "Nivel de Alerta")
```


Column
-----------------------------------------------------------------------
  
### **DENGUE** (Número de alertas)


```{r}
table_dengue_top10 <-table_dengue[order(-table_dengue$`Alerta Dengue`),-1]
rownames(table_dengue_top10) <- NULL

table_dengue_top10[1:50,] %>% kbl() %>% kable_styling("condensed", full_width = F,  html_font = "cmu-serif")

table_dengue$`Alerta Dengue`=round(table_dengue$`Alerta Dengue`,1)
```



Nosotros
=======================================================================
Row
-----------------------------------------------------------------------

### Grupo de Factores de Riesgo Ambiental 

Este tablero de control permite visualizar la información evaluada para el periodo epidemiológico (PE) actual, actualizado de manera semanal paras las herramientas SATATES y MASSAES.

**SATAES**

La vigilancia y evaluación de los factores de riesgo ambiental con implicación en salud, se lleva a cabo mediante el seguimiento de las situaciones captadas por medios de comunicación y otras fuentes de información para coordinar y orientar la respuesta inmediata a estos eventos.

Para llevar a cabo el análisis descriptivo de las situaciones de alerta y emergencia presentadas durante las semanas epidemiológicas 1-40 de 2020, se realizaron las siguientes actividades:

1. Revisión y lectura de las notificaciones diarias relacionadas con salud pública y factores de riesgo ambiental.

2. Resumen de cada notificación, ingresando los datos a la matriz de registro de situaciones ambientales, esto incluye fecha de notificación, periodo epidemiológico, semana epidemiológica, departamento, municipio, factor de riesgo ambiental, fuente de afectación/impacto, resumen, seguimiento y tipo de situación.

3. Valoración de las situaciones clasificadas como brote o alerta, usando la matriz de valoración del riesgo del Sistema de Alerta Temprana Ambiental y Efectos en Salud (SATAES). Así se obtiene el nivel de alerta: verde, amarilla, naranja y roja.

4. Seguimiento a las situaciones que implican un riesgo inmediato para la salud pública de los colombianos y de ser necesario se lleva a cabo una nueva valoración por SATAES.

5. Análisis descriptivo de los datos registrados en la matriz, teniendo en cuenta el nivel de alerta, el factor ambiental relacionado y la fuente de afectación.



**MASSAES**

El desarrollo de esta herramienta fue una propuesta del Grupo de Factores de Riesgo Ambiental (GFRA) del Instituto Nacional de Salud (INS) con el fin de generar alertas predictivas para posibles eventos ambientales y eventos o desenlaces en salud publica, desagregando el territorio hasta el nivel municipal, teniendo en cuenta factores sociales, ambientales, económicos, zoonóticos yepidemiológicos. 

**El Grupo**

El Grupo Factores de Riesgo Ambiental del Instituto Nacional de Salud tiene como principal objetivo el monitoreo, prevencion y control de afectacion en salud sobre la poblacion a causa de eventos ambientales o antropicos que se presenten en el territorio nacional. 

- Milena Borbón Ramos (Coordinadora)
- Alexandra Caiman Peñarete
- Ana Natalia Torres Labrador
- Andrés Zuluaga Quintero
- Germán Torres Rodríguez
- Gina Polo Infante
- Jorge Gamarra Cuellar
- Jose Corredor Martínez
- Juan Camilo Rojas Hernández
- Julián Chaves Guerrero
- Luisa Fernanda Soto
- Mónica Carreño Niño
- William León Quevedo


**Tablero de control**

El tablero de control fue construido  en  [Rmakrdown](https://rmarkdown.rstudio.com/) usando  [flexdashboard](https://rmarkdown.rstudio.com/flexdashboard/) a través del ambiente **R** y fue adaptado del [dashborard](https://ramikrispin.github.io/coronavirus_dashboard/){target="_blank"} previamente publicado por Rami Krispin.

Este tablero utiliza los [datos de proyecciones poblacionales para el año 2020](https://www.dane.gov.co/index.php/estadisticas-por-tema/demografia-y-poblacion/proyecciones-de-poblacion) del Departamento Administrativo Nacional de Estadística (DANE).


**Contacto**

Grupo de Factores de Riesgo Ambiental 

Dirección de Vigilancia y Análisis del Riesgo en Salud Pública 

gfrains@ins.gov.co 

Instituto Nacional de Salud 

Teléfono: (1) 2207700 Ext. 1432 

Bogotá, COLOMBIA 

www.ins.gov.co 

Línea gratuita nacional: 018000113400