---
title: "Covid19 Chile"
output: 
  flexdashboard::flex_dashboard:
    source_code: embed
    orientation: column
    vertical_layout: fill
    theme: flatly
---

```{r setup, include=FALSE}
library(flexdashboard)
library(R0)
library(tidyverse)
library(highcharter)
library(DT)
library(reshape)
library(data.table)
library(ggplot2)

corona_cases<-read.csv("https://covid.ourworldindata.org/data/owid-covid-data.csv", header = TRUE, sep = ",")
population<-read.csv("https://covid.ourworldindata.org/data/ecdc/locations.csv", header = TRUE, sep = ",")
corona_total<-merge(population, corona_cases, by = "location")
corona_total<-filter(corona_total, corona_total$total_cases >= 1|corona_total$total_deaths >=1)
corona_total<-mutate(corona_total, 
                     incidenciaX1000 = corona_total$new_cases/corona_total$population.x * 1000, 
                     mortalidadX1000 = corona_total$new_deaths/corona_total$population.x * 1000,
                     incidenciaX1000000 = corona_total$total_cases/corona_total$population.x * 1000000,
                     mortalidadX1000000 = corona_total$total_deaths/corona_total$population.x * 1000000,
                     letalidad = corona_total$new_deaths/corona_total$new_cases * 100, 
                     letalidad_acumulada = corona_total$total_deaths/corona_total$total_cases * 100,
                     total_cases_log = log10(corona_total$total_cases)
                     )
target<-c("United States",
          "Italy",
          "Argentina",
          "Brazil",
          "Bolivia",
          "Colombia",
          "Ecuador",
          "Paraguay",
          "Peru",
          "Uruguay",
          "Venezuela",
          "Germany",
          "France",
          "Spain",
          "China",
          "South Korea",
          "Japan"
          )
target2<-c("Chile")
target3<-c("United States",
           "Italy",
           "Argentina",
           "Brazil",
           "Bolivia",
           "Colombia",
           "Ecuador",
           "Paraguay",
           "Peru",
           "Uruguay",
           "Venezuela",
           "Germany",
           "France",
           "Spain",
           "China",
           "South Korea",
           "Japan",
           "Chile")
corona_dinamic_graph<-filter(corona_total, corona_total$location %in% target)
corona_dinamic_graph2<-filter(corona_total, corona_total$location %in% target2)
corona_table<-filter(corona_total, corona_total$location %in% target3)
corona_dinamic_graph<-arrange(corona_dinamic_graph, corona_dinamic_graph$date)
corona_dinamic_graph2<-arrange(corona_dinamic_graph2, corona_dinamic_graph2$date)
corona_dinamic_graph<-mutate(corona_dinamic_graph, new_date = as.Date(corona_dinamic_graph$date))
corona_dinamic_graph2<-mutate(corona_dinamic_graph2, new_date = as.Date(corona_dinamic_graph2$date))
total_casos_chile<-max(corona_dinamic_graph2$total_cases, na.rm = TRUE)
total_muertes_chile<-max(corona_dinamic_graph2$total_deaths, na.rm = TRUE)
total_test_chile<-max(corona_dinamic_graph2$total_tests, na.rm = TRUE)
incidencia_chile<-round(last(corona_dinamic_graph2$incidenciaX1000000), 1)
mortalidad_chile<-round(last(corona_dinamic_graph2$mortalidadX1000000), 1)
letalidad_chile<-round(last(corona_dinamic_graph2$letalidad_acumulada), 1)
chile_totales_nacionales<-read.csv("https://raw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto5/TotalesNacionales.csv", header = TRUE, sep = ",", check.names = TRUE)
casos_nuevos_sintomaticos<-chile_totales_nacionales[1,ncol(chile_totales_nacionales)]
casos_totales<-chile_totales_nacionales[2,ncol(chile_totales_nacionales)]
casos_recuperados<-chile_totales_nacionales[3,ncol(chile_totales_nacionales)]
casos_fallecidos<-chile_totales_nacionales[4,ncol(chile_totales_nacionales)]
casos_activos<-chile_totales_nacionales[5,ncol(chile_totales_nacionales)]
casos_nuevos_asintomaticos<-chile_totales_nacionales[6,ncol(chile_totales_nacionales)]
casos_nuevos_totales<-chile_totales_nacionales[7,ncol(chile_totales_nacionales)]
regiones_nuevo<-c("Arica y Parinacota", "Tarapaca", "Antofagasta", "Atacama", "Coquimbo", "Valparaiso", 
              "Metropolitana", "OHiggins", "Maule", "Nuble", "Biobio", "Araucania", "Los Rios", "Los Lagos", "Aysen", "Magallanes", "Total")
corona_cases_chile<-read.csv("https://raw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto3/CasosTotalesCumulativo.csv", header = TRUE, sep = ",")
corona_cases_chile<-cbind.data.frame(corona_cases_chile, regiones_nuevo)
col_idx <- grep("regiones_nuevo", names(corona_cases_chile))
corona_cases_chile <-corona_cases_chile[, c(col_idx, (1:ncol(corona_cases_chile))[-col_idx])]
names(corona_cases_chile)<-sub("^X", "", names(corona_cases_chile))
mapdata<-get_data_from_map(download_map_data("countries/cl/cl-all"))
mapdata<-mutate(mapdata, regiones_codigo = mapdata$`hc-a2`)
regiones_code<-data.frame("regiones_codigo"=c("AY", "TA", "AN", "AT", "CO", "VS", "RM", "LI", "ML", "ML","BI", "LA", "AR", "LL", "AI", "MA", "TO"))
corona_cases_chile<-cbind(corona_cases_chile, regiones_code)
col_idx <- grep("regiones_codigo", names(corona_cases_chile))
corona_cases_chile <-corona_cases_chile[, c(col_idx, (1:ncol(corona_cases_chile))[-col_idx])]
corona_cases_chile <- corona_cases_chile[-nrow(corona_cases_chile),]
mapdata<-merge(mapdata, corona_cases_chile, by = "regiones_codigo")
data_fake <- mapdata %>%
  select(code = regiones_codigo) %>% 
  mutate(value = mapdata[,ncol(mapdata)])
comunas_region_totales<-read.csv("https://raw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto1/Covid-19.csv", header = TRUE, sep = ",")
names(comunas_region_totales)<-sub("^X", "", names(comunas_region_totales))
comunas_region_totales<-select(comunas_region_totales, -Tasa)
casos_por_comuna<-comunas_region_totales[,ncol(comunas_region_totales)]
nombre_comunas<-comunas_region_totales$Comuna
top_comunas<-cbind.data.frame(nombre_comunas, casos_por_comuna)
top_comunas<-arrange(top_comunas, -top_comunas$casos_por_comuna)
names(top_comunas)[names(top_comunas) == "casos_por_comuna"] <- "Casos"
names(top_comunas)[names(top_comunas) == "nombre_comunas"] <- "Comuna"
region_new<-c("Arica y Parinacota", "Tarapaca", "Antofagasta", "Atacama", "Coquimbo", "Valparaiso", 
              "Metropolitana", "OHiggins", "Maule", "Nuble", "Biobio", "Araucania", "Los Rios", "Los Lagos", "Aysen", "Magallanes", "Total")
region_totales<-read.csv("https://raw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto3/CasosTotalesCumulativo.csv", header = TRUE, sep = ",")
region_totales<-cbind.data.frame(region_totales, region_new)
col_idx <- grep("region_new", names(region_totales))
region_totales <- region_totales[, c(col_idx, (1:ncol(region_totales))[-col_idx])]
names(region_totales)<-sub("^X", "", names(region_totales))
casos_por_region<-region_totales[,ncol(region_totales)]
nombre_region<-region_totales$region_new
top_regiones<-cbind.data.frame(nombre_region, casos_por_region)
top_regiones<-arrange(top_regiones, -top_regiones$casos_por_region)
top_regiones<-top_regiones %>% slice(-1)
names(top_regiones)[names(top_regiones) == "casos_por_region"] <- "Casos"
names(top_regiones)[names(top_regiones) == "nombre_region"] <- "Región"
corona_cases<-read.csv("https://covid.ourworldindata.org/data/owid-covid-data.csv", header = TRUE, sep = ",")
iso_pais<-read.csv("https://gist.githubusercontent.com/brenes/1095110/raw/0d30463c80403c19384323d8267f48e8ef5e8e8d/paises.csv", header = TRUE, sep = ",",encoding = "UTF-8", stringsAsFactors = FALSE)
iso_pais = rename(iso_pais, c(iso3 = "iso_code"))
corona_cases<-mutate(corona_cases, new_date = as.Date(corona_cases$date))
corona_cases<-subset(corona_cases, corona_cases$new_date == Sys.Date())
corona_cases<-arrange(corona_cases, -corona_cases$total_cases)
corona_cases<-corona_cases %>% slice(-1)
corona_cases<-merge(iso_pais, corona_cases, by = "iso_code")
#corona_cases<-as.data.frame(corona_cases)
top_pais<-select(corona_cases, nombre, total_cases)
top_pais<-arrange(top_pais, -total_cases)
names(top_pais)[names(top_pais) == "total_cases"] <- "Casos"
names(top_pais)[names(top_pais) == "nombre"] <- "País"
chile_g4<-read.csv("https://raw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto5/TotalesNacionales.csv", header = TRUE, sep = ",", check.names = TRUE)
names(chile_g4)<-sub("^X", "", names(chile_g4))
chile_g4t<-transpose(chile_g4)
fecha<-c(colnames(chile_g4))
chile_g4t<-cbind.data.frame(fecha, chile_g4t)
chile_g4t<-chile_g4t %>% slice(-1)
fecha2<-chile_g4t$fecha
fecha2<-chartr(".", "-", fecha2)
fecha2<-as.Date(fecha2)
chile_g4t<-cbind.data.frame(fecha2, chile_g4t)
chile_g4t$V1<-as.numeric(as.character(chile_g4t$V1))#casos nuevos con sintomas
chile_g4t$V2<-as.numeric(as.character(chile_g4t$V2))#casos totales
chile_g4t$V3<-as.numeric(as.character(chile_g4t$V3))#casos recuperados
chile_g4t$V4<-as.numeric(as.character(chile_g4t$V4))#fallecidos
chile_g4t$V5<-as.numeric(as.character(chile_g4t$V5))#casos activos
chile_g4t$V6<-as.numeric(as.character(chile_g4t$V6))#casos nuevos sin sintomas
chile_g4t$V7<-as.numeric(as.character(chile_g4t$V7))#casos nuevos totales
datos_genero_edad<-read.csv("https://raw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto16/CasosGeneroEtario.csv", header = TRUE, sep = ",", check.names = FALSE)
grupo_edad<-sub(" años| y más años", "", datos_genero_edad$`Grupo de edad`)
grupo_edad<-sub("80", "80 y mas", grupo_edad)
datos_genero_edad<-cbind.data.frame(grupo_edad, datos_genero_edad)
datos_genero_edad<-select(datos_genero_edad, -'Grupo de edad')
f<-filter(datos_genero_edad, datos_genero_edad$Sexo == "F")
m<-filter(datos_genero_edad, datos_genero_edad$Sexo == "M")
fm<-select(f, +ncol(f)) + select(m, +ncol(m))
colnames(fm)<-c("Total")
grupo<-c(datos_genero_edad[1:17,1])
fm<-cbind(grupo, fm)

#pacientes UCI por grupo de edad

datos_uci_edad<-read.csv("https://raw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto9/HospitalizadosUCIEtario.csv", header = TRUE, sep = ",", check.names = FALSE)

#pacientes fallecidos por grupo de edad#

datos_fallecidos_edad<-read.csv("https://raw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto10/FallecidosEtario.csv", header = TRUE, sep = ",", check.names = FALSE)

datos_comuna_sem_epi<-read.csv("https://raw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto15/FechaInicioSintomasHistorico_std.csv", header = TRUE, sep = ",", check.names = FALSE)
#target_comunas<-c("Estacion Central", "Santiago", "Las Condes", "Ancud", "Dalcahue", "Castro", "Puente Alto")
target_comunas<-c(head(top_comunas$Comuna, n = 20L))
semana_epi<-read.csv("https://raw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto15/SemanasEpidemiologicas.csv", header =  TRUE, sep = ",", check.names = FALSE)
datos_comuna_sem_epi<-filter(datos_comuna_sem_epi, datos_comuna_sem_epi$Comuna %in% target_comunas)
datos_comuna_sem_epi<-mutate(datos_comuna_sem_epi, fecha_std = as.Date(datos_comuna_sem_epi$Publicacion))
semana_epi_t<-transpose(semana_epi)
names(semana_epi_t)<-c("Inicio", "Fin")
nombre_semanas<-colnames(semana_epi)
semana_epi_t<-cbind(nombre_semanas, semana_epi_t)
names(semana_epi_t)<-c("Semana Epidemiologica", "Fecha de Inicio", "Fecha de Fin")
datos_comuna_sem_epi<-merge(datos_comuna_sem_epi, semana_epi_t, by = "Semana Epidemiologica")
datos_comuna_sem_epi<-mutate(datos_comuna_sem_epi, f_inicio = as.Date.character(datos_comuna_sem_epi$`Fecha de Inicio`), f_termino = as.Date.character(datos_comuna_sem_epi$`Fecha de Fin`))
datos_comuna_sem_epi<-unite(datos_comuna_sem_epi, "se_fechas", f_inicio:f_termino ,sep = " ", remove = FALSE, na.rm = FALSE)

#vector de incidencias para estimacion r0#

incidencias_michi<-chile_totales_nacionales[7,3:ncol(chile_totales_nacionales)]
incidencias_michi<-as_vector(incidencias_michi)

#estimacion R0 ultimos 14 días#

mGT<-generation.time("gamma", c(5, 2))
estR0<-estimate.R(incidencias_michi, mGT, begin = as.numeric(length(incidencias_michi) - 14), end = as.numeric(length(incidencias_michi)), methods=c("EG", "ML", "TD", "AR", "SB"),pop.size=19116209, nsim=1000)

#EG#

ro_eg_rvalue<-round(estR0$estimates$EG$R, 2)
ro_eg_conf_int<-round(estR0$estimates$EG$conf.int, 2)

ro_eg_conf_int[1]
ro_eg_conf_int[2]

ro_eg<-paste0(ro_eg_rvalue, " ", "(", ro_eg_conf_int[1], "-", ro_eg_conf_int[2], ")")

#ML#

ro_ml_rvalue<-round(estR0$estimates$ML$R, 3)
ro_ml_conf_int<-round(estR0$estimates$ML$conf.int, 3)

ro_ml_conf_int[1]
ro_ml_conf_int[2]

ro_ml<-paste0(ro_ml_rvalue, " ", "(", ro_ml_conf_int[1], "-", ro_ml_conf_int[2], ")")

#AR#

ro_ar_rvalue<-round(estR0$estimates$AR$R, 5)
ro_ar_conf_int<-round(estR0$estimates$AR$conf.int, 5)

ro_ar_conf_int[1]
ro_ar_conf_int[2]

ro_ar<-paste0(ro_ar_rvalue, " ", "(", ro_ar_conf_int[1], "-", ro_ar_conf_int[2], ")")

#TD#

ro_ad_rvalue<-estR0$estimates$TD$R
ro_ad_rvalue<-unname(ro_ad_rvalue, force = FALSE)
ro_ad_rvalue<-round(ro_ad_rvalue[14],3)

ro_ad_conf_int<-estR0$estimates$TD$conf.int

ro_ad<-paste0(ro_ad_rvalue, " ", "(", round(ro_ad_conf_int[14,1], 3), "-", round(ro_ad_conf_int[14,2], 3), ")")

#SB#

ro_sb<-paste0(
  round(last(estR0$estimates$SB$R), 3), 
  " ", 
  "(",
  round(estR0$estimates$SB$conf.int[14,1],3),
  "-",
  round(estR0$estimates$SB$conf.int[14,2], 3),
  ")"
)

#dataframes#

chile_totales_nacionales<-read.csv("https://raw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto5/TotalesNacionales.csv", 
                                   header = TRUE, 
                                   sep = ",", 
                                   check.names = FALSE)

#vector de incidencias para estimacion r0#

incidencias<-chile_totales_nacionales[7,3:ncol(chile_totales_nacionales)]
incidencias<-as_vector(incidencias)

#ro toda la pandemia#

mGT2<-generation.time("gamma", c(5, 2))
estR02<-estimate.R(incidencias, mGT2, begin = 1, end = as.numeric(length(incidencias)), methods=c("SB"),
                   pop.size=19116209, nsim=1000)

#ro ultimos 14 dias#

mGT3<-generation.time("gamma", c(5, 2))
estR03<-estimate.R(incidencias, mGT3, begin = as.numeric(length(incidencias) - 14), end = as.numeric(length(incidencias)), methods=c("SB"),
                   pop.size=19116209, nsim=1000)

#graficos#

#data preparation#

ro_sb_graph1<-cbind(estR02$estimates$SB$R, estR02$estimates$SB$conf.int)
ro_sb_graph2<-cbind(estR03$estimates$SB$R, estR03$estimates$SB$conf.int)

colnames(ro_sb_graph1)<-c("ro", "ci_low", "ci_upp")
colnames(ro_sb_graph2)<-c("ro", "ci_low", "ci_upp")

```


Home
=====================================

Column
-------------------------------------
   
### Fecha de la Actualización

```{r}
articles <- Sys.Date()
valueBox(articles, icon = "body")
``` 

### Casos Nuevos Totales

```{r}
articles <- casos_nuevos_totales
valueBox(articles, icon = "body")
``` 
 
### Casos Activos

```{r}
spam <-casos_activos
valueBox(spam, 
         icon = "flask-sharp")
```

### Incidencia por Millón de Habitantes
    
```{r}
comments <- incidencia_chile
valueBox(comments, icon = "fas fa-skull-crossbones")
```
   
### Región con Mayor Casos en Chile

```{r}
comments <- top_regiones[1,1]
valueBox(comments, icon = "fas fa-skull-crossbones")
```


Column
-------------------------------------

### Casos Totales en Chile

```{r}
comments <- casos_totales
valueBox(comments, icon = "fas fa-skull-crossbones")
``` 

### Casos Nuevos Sintomáticos

```{r}
comments <- casos_nuevos_sintomaticos
valueBox(comments, icon = "fas fa-skull-crossbones")
``` 
 
### Casos Recuperados
 
```{r}
activ <- casos_recuperados
valueBox(activ, icon = "body")
```

### Mortalidad por Millón de Habitantes
    
```{r}
spam <- mortalidad_chile
valueBox(spam, 
         icon = "flask-sharp",
         color = ifelse(spam > 10, "warning", "primary"))
```

### Comuna con Mayor Casos en Chile

```{r}
comments <- top_comunas[1,1]
valueBox(comments, icon = "fas fa-skull-crossbones")
```

Column
-------------------------------------

### Fallecidos Totales en Chile

```{r}
spam <-casos_fallecidos
valueBox(spam, 
         icon = "flask-sharp")
```
 
### Casos Nuevos Asintomáticos

```{r}
recu <- casos_nuevos_asintomaticos
valueBox(recu, icon = "body")
```

### Letalidad %

```{r}
spam <-letalidad_chile
valueBox(spam, 
         icon = "flask-sharp", 
         color = ifelse(spam > 5, "warning", "primary"))
```

### Cantidad de Test Diarios

```{r}
comments <- total_test_chile
valueBox(comments, icon = "fas fa-skull-crossbones")
```

### País con Mayor Casos

```{r}
comments <- top_pais[1,1]
valueBox(comments, icon = "fas fa-skull-crossbones")
```

Comunas
=====================================

Column
-------------------------------------
``` {r}
highchart() %>%
  hc_add_series(datos_comuna_sem_epi,
                "column",
                hcaes(x = datos_comuna_sem_epi$f_inicio,
                      y = datos_comuna_sem_epi$`Casos confirmados`,
                      group = datos_comuna_sem_epi$Comuna,
                      visible = FALSE)) %>%
  hc_add_theme(hc_theme_google()) %>% 
  hc_title(text = "Casos Confirmados de COVID19 por Semana Epidemiológica de las 20 comunas de Chile más afectadas") %>% 
  hc_xAxis(title = list(text = "Fecha de Inicio de la Semana Epidemiológica"),
           opposite = FALSE, 
           type = "datetime",
           tickmarkPlacement = "on", 
           tickLength = 0,
           plotLines = list(
             list(label = list(text = "Nueva Normalidad"),
                  color = "#FE0202",
                  width = 20,
                  value = 2020-04-20))) %>% 
  hc_yAxis(title = list(text = "Casos Confirmados"),opposite = FALSE) %>% 
  hc_legend(title = list(text = "Comuna"), align = "left", vertilAlign = "top", layout = "vertical", x = 0, y = 0)
```

Comparativo
=====================================

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

### Lineal

```{r}
highchart() %>%
  hc_add_series(chile_g4t, 
                "spline", 
                hcaes(x = chile_g4t$fecha2, 
                      y = chile_g4t$V2, 
                ), visible = TRUE, name = "Casos Totales") %>%
  hc_add_series(chile_g4t, 
                "spline", 
                hcaes(x = chile_g4t$fecha2, 
                      y = chile_g4t$V1,
                ), visible = TRUE, name = "Casos Nuevos SINTOMÁTICOS") %>%
  hc_add_series(chile_g4t, 
                "spline", 
                hcaes(x = chile_g4t$fecha2, 
                      y = chile_g4t$V3, 
                ), visible = TRUE, name = "Casos Recuperados") %>%
  hc_add_series(chile_g4t, 
                "spline", 
                hcaes(x = chile_g4t$fecha2, 
                      y = chile_g4t$V4, 
                ), visible = TRUE, name = "Casos Fallecidos") %>%
  hc_add_series(chile_g4t, 
                "spline", 
                hcaes(x = chile_g4t$fecha2, 
                      y = chile_g4t$V5, 
                ), visible = TRUE, name = "Casos Activos") %>%
  hc_add_series(chile_g4t, 
                "spline", 
                hcaes(x = chile_g4t$fecha2, 
                      y = chile_g4t$V6, 
                ), visible = TRUE, name = "Casos Nuevos ASINTOMÁTICOS") %>%
  hc_add_series(chile_g4t, 
                "spline", 
                hcaes(x = chile_g4t$fecha2, 
                      y = chile_g4t$V7, 
                ), visible = TRUE, name = "Casos Nuevos Totales (SINTOMÁTICO Y ASINTOMÁTICOS") %>%
  hc_add_theme(hc_theme_google()) %>% 
  hc_title(text = "Comparativa de Casos Totales, Nuevos, Recuperados, Fallecidos y Activos de COVID19 en CHile") %>% 
  hc_xAxis(title = list(text = "Día de la Infección"),opposite = FALSE, type = "datetime") %>% 
  hc_yAxis(title = list(text = "casos"),opposite = FALSE) %>% 
  hc_legend(title = list(text = "País"), align = "left", vertilAlign = "top", layout = "vertical", x = 0, y = 0)
```

### Logarítmico

```{r}
highchart() %>%
  hc_add_series(chile_g4t, 
                "spline", 
                hcaes(x = chile_g4t$fecha2, 
                      y = chile_g4t$V2, 
                ), visible = TRUE, name = "Casos Totales") %>%
  hc_add_series(chile_g4t, 
                "spline", 
                hcaes(x = chile_g4t$fecha2, 
                      y = chile_g4t$V1,
                ), visible = TRUE, name = "Casos Nuevos SINTOMÁTICOS") %>%
  hc_add_series(chile_g4t, 
                "spline", 
                hcaes(x = chile_g4t$fecha2, 
                      y = chile_g4t$V3, 
                ), visible = TRUE, name = "Casos Recuperados") %>%
  hc_add_series(chile_g4t, 
                "spline", 
                hcaes(x = chile_g4t$fecha2, 
                      y = chile_g4t$V4, 
                ), visible = TRUE, name = "Casos Fallecidos") %>%
  hc_add_series(chile_g4t, 
                "spline", 
                hcaes(x = chile_g4t$fecha2, 
                      y = chile_g4t$V5, 
                ), visible = TRUE, name = "Casos Activos") %>%
  hc_add_series(chile_g4t, 
                "spline", 
                hcaes(x = chile_g4t$fecha2, 
                      y = chile_g4t$V6, 
                ), visible = TRUE, name = "Casos Nuevos ASINTOMÁTICOS") %>%
  hc_add_series(chile_g4t, 
                "spline", 
                hcaes(x = chile_g4t$fecha2, 
                      y = chile_g4t$V7, 
                ), visible = TRUE, name = "Casos Nuevos Totales (SINTOMÁTICO Y ASINTOMÁTICOS") %>%
  hc_add_theme(hc_theme_google()) %>% 
  hc_title(text = "Comparativa de Casos Totales, Nuevos, Recuperados, Fallecidos y Activos de COVID19 en CHile") %>% 
  hc_xAxis(title = list(text = "Día de la Infección"),opposite = FALSE, type = "datetime") %>% 
  hc_yAxis(title = list(text = "casos"),opposite = FALSE, type = "logarithmic") %>% 
  hc_legend(title = list(text = "País"), align = "left", vertilAlign = "top", layout = "vertical", x = 0, y = 0)
```


Casos {data-navmenu="Diarios"}
=====================================

```{r}
highchart() %>%
  hc_add_series(corona_dinamic_graph2, 
                "column", 
                hcaes(x = corona_dinamic_graph2$new_date, 
                      y = corona_dinamic_graph2$new_cases,
                      group = corona_dinamic_graph2$location), visible = TRUE) %>%
  hc_add_series(corona_dinamic_graph, 
                "column", 
                hcaes(x = corona_dinamic_graph$new_date, 
                      y = corona_dinamic_graph$new_cases, 
                      group = corona_dinamic_graph$location), visible = FALSE) %>%
  hc_add_theme(hc_theme_google()) %>% 
  hc_title(text = "Casos Nuevos de COVID19 por día de infección") %>% 
  hc_xAxis(title = list(text = "Día de la Infección"),opposite = FALSE, type = "datetime") %>% 
  hc_yAxis(title = list(text = "Nuevos casos"),opposite = FALSE) %>% 
  hc_legend(title = list(text = "País"), align = "left", vertilAlign = "top", layout = "vertical", x = 0, y = 0)
```

Muertes {data-navmenu="Diarios"}
=====================================
```{r}
highchart() %>%
  hc_add_series(corona_dinamic_graph2, 
                "column", 
                hcaes(x = corona_dinamic_graph2$new_date, 
                      y = corona_dinamic_graph2$new_deaths,
                      group = corona_dinamic_graph2$location), visible = TRUE) %>%
  hc_add_series(corona_dinamic_graph, 
                "column", 
                hcaes(x = corona_dinamic_graph$new_date, 
                      y = corona_dinamic_graph$new_deaths, 
                      group = corona_dinamic_graph$location), visible = FALSE) %>%
  hc_add_theme(hc_theme_google()) %>% 
  hc_title(text = "Nuevas Muertes de COVID19 por día de infección") %>% 
  hc_xAxis(title = list(text = "Día de la Infección"),opposite = FALSE, type = "datetime") %>% 
  hc_yAxis(title = list(text = "Nuevos casos"),opposite = FALSE) %>% 
  hc_legend(title = list(text = "País"), align = "left", vertilAlign = "top", layout = "vertical", x = 0, y = 0)
```

Casos {data-navmenu="Totales"}
=====================================

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

### Lineal

```{r}
highchart() %>%
  hc_add_series(corona_dinamic_graph2, 
                "spline", 
                hcaes(x = corona_dinamic_graph2$new_date, 
                      y = corona_dinamic_graph2$total_cases,
                      group = corona_dinamic_graph2$location), visible = TRUE) %>%
  hc_add_series(corona_dinamic_graph, 
                "spline", 
                hcaes(x = corona_dinamic_graph$new_date, 
                      y = corona_dinamic_graph$total_cases, 
                      group = corona_dinamic_graph$location), visible = FALSE) %>%
  hc_add_theme(hc_theme_google()) %>% 
  hc_title(text = "Casos Totales de COVID19 por día de infección") %>% 
  hc_xAxis(title = list(text = "Día de la Infección"),opposite = FALSE, type = "datetime") %>% 
  hc_yAxis(title = list(text = "Casos Totales"),opposite = FALSE) %>% 
  hc_legend(title = list(text = "País"), align = "left", vertilAlign = "top", layout = "vertical", x = 0, y = 0)
```

### Logarítmico

```{r}
highchart() %>%
  hc_add_series(corona_dinamic_graph2, 
                "spline", 
                hcaes(x = corona_dinamic_graph2$new_date, 
                      y = corona_dinamic_graph2$total_cases,
                      group = corona_dinamic_graph2$location), visible = TRUE) %>%
  hc_add_series(corona_dinamic_graph, 
                "spline", 
                hcaes(x = corona_dinamic_graph$new_date, 
                      y = corona_dinamic_graph$total_cases, 
                      group = corona_dinamic_graph$location), visible = FALSE) %>%
  hc_add_theme(hc_theme_google()) %>% 
  hc_title(text = "Casos Totales de COVID19 por día de infección (escala logarítmica") %>% 
  hc_xAxis(title = list(text = "Día de la Infección"),opposite = FALSE, type = "datetime") %>% 
  hc_yAxis(title = list(text = "Casos Totales"),opposite = FALSE, type = "logarithmic") %>% 
  hc_legend(title = list(text = "País"), align = "left", vertilAlign = "top", layout = "vertical", x = 0, y = 0)
```

Muertes {data-navmenu="Totales"}
=====================================

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

### Lineal

```{r}
highchart() %>%
  hc_add_series(corona_dinamic_graph2, 
                "spline", 
                hcaes(x = corona_dinamic_graph2$new_date, 
                      y = corona_dinamic_graph2$total_deaths,
                      group = corona_dinamic_graph2$location), visible = TRUE) %>%
  hc_add_series(corona_dinamic_graph, 
                "spline", 
                hcaes(x = corona_dinamic_graph$new_date, 
                      y = corona_dinamic_graph$total_deaths, 
                      group = corona_dinamic_graph$location), visible = FALSE) %>%
  hc_add_theme(hc_theme_google()) %>% 
  hc_title(text = "Muertes Totales de COVID19 por día de infección") %>% 
  hc_xAxis(title = list(text = "Día de la Infección"),opposite = FALSE, type = "datetime") %>% 
  hc_yAxis(title = list(text = "Muertes Totales"),opposite = FALSE) %>% 
  hc_legend(title = list(text = "País"), align = "left", vertilAlign = "top", layout = "vertical", x = 0, y = 0)
```

### Logarítmico

```{r}
highchart() %>%
  hc_add_series(corona_dinamic_graph2, 
                "spline", 
                hcaes(x = corona_dinamic_graph2$new_date, 
                      y = corona_dinamic_graph2$total_deaths,
                      group = corona_dinamic_graph2$location), visible = TRUE) %>%
  hc_add_series(corona_dinamic_graph, 
                "spline", 
                hcaes(x = corona_dinamic_graph$new_date, 
                      y = corona_dinamic_graph$total_deaths, 
                      group = corona_dinamic_graph$location), visible = FALSE) %>%
  hc_add_theme(hc_theme_google()) %>% 
  hc_title(text = "Muertes Totales de COVID19 por día de infección") %>% 
  hc_xAxis(title = list(text = "Día de la Infección"),opposite = FALSE, type = "datetime") %>% 
  hc_yAxis(title = list(text = "Muertes totales"),opposite = FALSE, type = "logarithmic") %>% 
  hc_legend(title = list(text = "País"), align = "left", vertilAlign = "top", layout = "vertical", x = 0, y = 0)
```

Casos Totales {data-navmenu="Grupos"}
=====================================

```{r}
highchart() %>%
  hc_add_series(datos_genero_edad, 
                "column", 
                hcaes(x = datos_genero_edad$grupo_edad, 
                      y = datos_genero_edad[,ncol(datos_genero_edad)],
                      group = datos_genero_edad$Sexo), visible = TRUE) %>%
  hc_add_series(fm,
                "column",
                hcaes(x = fm$grupo,
                      y = fm$Total),
                visible = FALSE, name = "Total") %>%
  hc_add_theme(hc_theme_google()) %>% 
  hc_title(text = "Casos Totales por Grupo de Edad") %>% 
  hc_xAxis(title = list(text = "Grupo de Edad"),opposite = FALSE, categories = datos_genero_edad$grupo_edad) %>% 
  hc_yAxis(title = list(text = "Casos Totales"),opposite = FALSE) %>% 
  hc_legend(title = list(text = "Genero"), align = "left", vertilAlign = "top", layout = "vertical", x = 0, y = 0)
```

Casos UCI {data-navmenu="Grupos"}
=====================================

``` {r}
highchart() %>%
  hc_add_series(datos_uci_edad,
                "column",
                hcaes(x = datos_uci_edad$`Grupo de edad`,
                      y = datos_uci_edad[,ncol(datos_uci_edad)], 
                      visible = TRUE)) %>%
  hc_add_theme(hc_theme_google()) %>% 
  hc_title(text = "Casos UCI por Grupo de Edad") %>% 
  hc_xAxis(title = list(text = "Grupo de Edad"),opposite = FALSE, categories = datos_uci_edad$`Grupo de edad`) %>% 
  hc_yAxis(title = list(text = "Tasos UCI"),opposite = FALSE) %>% 
  hc_legend(title = list(text = "Género"), align = "left", vertilAlign = "top", layout = "vertical", x = 0, y = 0)
```

Fallecidos Totales {data-navmenu="Grupos"}
=====================================

``` {r}
highchart() %>%
  hc_add_series(datos_fallecidos_edad,
                "column",
                hcaes(x = datos_fallecidos_edad$`Grupo de edad`,
                      y = datos_fallecidos_edad[,ncol(datos_fallecidos_edad)], 
                      visible = TRUE)) %>%
  hc_add_theme(hc_theme_google()) %>% 
  hc_title(text = "Fallecidos Totales por Grupo de Edad") %>% 
  hc_xAxis(title = list(text = "Grupo de Edad"),opposite = FALSE, categories = datos_uci_edad$`Grupo de edad`) %>% 
  hc_yAxis(title = list(text = "Fallecidos Totales"),opposite = FALSE) %>% 
  hc_legend(title = list(text = "Fallecidos"), align = "left", vertilAlign = "top", layout = "vertical", x = 0, y = 0)
```



Gráfico {data-navmenu="Incidencia"}
=====================================
```{r}
highchart() %>%
  hc_add_series(corona_dinamic_graph2, 
                "spline", 
                hcaes(x = corona_dinamic_graph2$new_date, 
                      y = corona_dinamic_graph2$incidenciaX1000000,
                      group = corona_dinamic_graph2$location), visible = TRUE) %>%
  hc_add_series(corona_dinamic_graph, 
                "spline", 
                hcaes(x = corona_dinamic_graph$new_date, 
                      y = corona_dinamic_graph$incidenciaX1000, 
                      group = corona_dinamic_graph$location), visible = FALSE) %>%
  hc_add_theme(hc_theme_google()) %>% 
  hc_title(text = "Incidencia de COVID19 por día de infección por millón de habitantes") %>% 
  hc_xAxis(title = list(text = "Día de la Infección"),opposite = FALSE, type = "datetime") %>% 
  hc_yAxis(title = list(text = "Incidencia"),opposite = FALSE) %>% 
  hc_legend(title = list(text = "País"), align = "left", vertilAlign = "top", layout = "vertical", x = 0, y = 0)
```

Gráfico {data-navmenu="Mortalidad"}
=====================================
```{r}
highchart() %>%
  hc_add_series(corona_dinamic_graph2, 
                "spline", 
                hcaes(x = corona_dinamic_graph2$new_date, 
                      y = corona_dinamic_graph2$mortalidadX1000000,
                      group = corona_dinamic_graph2$location), visible = TRUE) %>%
  hc_add_series(corona_dinamic_graph, 
                "spline", 
                hcaes(x = corona_dinamic_graph$new_date, 
                      y = corona_dinamic_graph$mortalidadX1000, 
                      group = corona_dinamic_graph$location), visible = FALSE) %>%
  hc_add_theme(hc_theme_google()) %>% 
  hc_title(text = "Mortalidad de COVID19 por día de infección por millón de habitantes") %>% 
  hc_xAxis(title = list(text = "Día de la Infección"),opposite = FALSE, type = "datetime") %>% 
  hc_yAxis(title = list(text = "Mortalidad"),opposite = FALSE) %>% 
  hc_legend(title = list(text = "País"), align = "left", vertilAlign = "top", layout = "vertical", x = 0, y = 0)
```

Puntual {data-navmenu="Letalidad"}
=====================================  
```{r}
highchart() %>%
  hc_add_series(corona_dinamic_graph2, 
                "column", 
                hcaes(x = corona_dinamic_graph2$new_date, 
                      y = corona_dinamic_graph2$letalidad,
                      group = corona_dinamic_graph2$location), visible = TRUE) %>%
  hc_add_series(corona_dinamic_graph, 
                "column", 
                hcaes(x = corona_dinamic_graph$new_date, 
                      y = corona_dinamic_graph$letalidad, 
                      group = corona_dinamic_graph$location), visible = FALSE) %>%
  hc_add_theme(hc_theme_google()) %>% 
  hc_title(text = "Letalidad (%) de COVID19 por día de infección") %>% 
  hc_xAxis(title = list(text = "Día de la Infección"),opposite = FALSE, type = "datetime") %>% 
  hc_yAxis(title = list(text = "Letalidad (%)"),opposite = FALSE) %>% 
  hc_legend(title = list(text = "País"), align = "left", vertilAlign = "top", layout = "vertical", x = 0, y = 0)
```

Acumulada {data-navmenu="Letalidad"}
=====================================
```{r}
highchart() %>%
  hc_add_series(corona_dinamic_graph2, 
                "spline", 
                hcaes(x = corona_dinamic_graph2$new_date, 
                      y = corona_dinamic_graph2$letalidad_acumulada,
                      group = corona_dinamic_graph2$location), visible = TRUE) %>%
  hc_add_series(corona_dinamic_graph, 
                "spline", 
                hcaes(x = corona_dinamic_graph$new_date, 
                      y = corona_dinamic_graph$letalidad_acumulada, 
                      group = corona_dinamic_graph$location), visible = FALSE) %>%
  hc_add_theme(hc_theme_google()) %>% 
  hc_title(text = "Letalidad Acumulada (%) de COVID19 por día de infección") %>% 
  hc_xAxis(title = list(text = "Día de la Infección"),opposite = FALSE, type = "datetime") %>% 
  hc_yAxis(title = list(text = "Letalidad Acumulada (%)"),opposite = FALSE) %>% 
  hc_legend(title = list(text = "País"), align = "left", vertilAlign = "top", layout = "vertical", x = 0, y = 0)
```

Diarios {data-navmenu="Test"}
=====================================
```{r}
highchart() %>%
  hc_add_series(corona_dinamic_graph2, 
                "column", 
                hcaes(x = corona_dinamic_graph2$new_date, 
                      y = corona_dinamic_graph2$new_tests,
                      group = corona_dinamic_graph2$location), visible = TRUE) %>%
  hc_add_series(corona_dinamic_graph, 
                "column", 
                hcaes(x = corona_dinamic_graph$new_date, 
                      y = corona_dinamic_graph$new_tests, 
                      group = corona_dinamic_graph$location), visible = FALSE) %>%
  hc_add_theme(hc_theme_google()) %>% 
  hc_title(text = "Nuevos Tests Realizados de COVID19 por día de infección") %>% 
  hc_xAxis(title = list(text = "Día de la Infección"),opposite = FALSE, type = "datetime") %>% 
  hc_yAxis(title = list(text = "Nuevos Tests"),opposite = FALSE) %>% 
  hc_legend(title = list(text = "País"), align = "left", vertilAlign = "top", layout = "vertical", x = 0, y = 0)
```

Totales {data-navmenu="Test"}
=====================================
```{r}
highchart() %>%
  hc_add_series(corona_dinamic_graph2, 
                "spline", 
                hcaes(x = corona_dinamic_graph2$new_date, 
                      y = corona_dinamic_graph2$total_tests,
                      group = corona_dinamic_graph2$location), visible = TRUE) %>%
  hc_add_series(corona_dinamic_graph, 
                "spline", 
                hcaes(x = corona_dinamic_graph$new_date, 
                      y = corona_dinamic_graph$total_tests, 
                      group = corona_dinamic_graph$location), visible = FALSE) %>%
  hc_add_theme(hc_theme_google()) %>% 
  hc_title(text = "Tests Acumulados de COVID19 por día de infección") %>% 
  hc_xAxis(title = list(text = "Día de la Infección"),opposite = FALSE, type = "datetime") %>% 
  hc_yAxis(title = list(text = "Tests Acumulados"),opposite = FALSE) %>% 
  hc_legend(title = list(text = "País"), align = "left", vertilAlign = "top", layout = "vertical", x = 0, y = 0)
```

Mapas
=====================================

Column
-------------------------------------
    
### Chile
    
```{r plots, echo=FALSE}
hcmap("countries/cl/cl-all", data = data_fake, value = "value",
      joinBy = c("hc-a2", "code"), name = "Casos por Región",
      dataLabels = list(enabled = TRUE, format = '{point.name}'),
      borderColor = "#0048BA", borderWidth = 1, tooltip = list(valueDecimals = 0, valuePrefix = "Casos Totales = "))
```

Tablas
=====================================

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

### Top Casos Confirmados por Comuna

```{r}
DT::datatable(top_comunas, options = list(
  bPaginate = FALSE
))
```

### Top Casos Confirmados por Región

```{r}
DT::datatable(top_regiones, options = list(
  bPaginate = FALSE
))
```

### Top Casos Confirmados por País

```{r}
DT::datatable(top_pais, options = list(
  bPaginate = FALSE
))
```

R0 Estimado
=====================================

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

### Método Bayesiano

```{r}
comments <- ro_sb
valueBox(comments, icon = "fas fa-skull-crossbones")
``` 

### Método de Crecimiento Exponencial

```{r}
comments <- ro_eg
valueBox(comments, icon = "fas fa-skull-crossbones")
``` 

### Método de Máxima Verosimilitud

```{r}
comments <- ro_ml
valueBox(comments, icon = "fas fa-skull-crossbones")
``` 

### Método de Tasa de Ataque

```{r}
comments <- ro_ar
valueBox(comments, icon = "fas fa-skull-crossbones")
``` 

### Método Tiempo Dependiente

```{r}
comments <- ro_ad
valueBox(comments, icon = "fas fa-skull-crossbones")
```

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

``` {r}
p1<-ggplot(ro_sb_graph1, aes(x=rownames(ro_sb_graph1), y=ro, color = "blue")) + geom_point(aes(x = rownames(ro_sb_graph1), y = ro)) + geom_line(aes(y = ro, x = rownames(ro_sb_graph1), color = "blue"))
p1<-p1+geom_ribbon(aes(x = 1:length(rownames(ro_sb_graph1)), ymin=ci_low, ymax=ci_upp, color = "red"), linetype=2, alpha=0.2)
p1
```

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

``` {r}
p2<-ggplot(ro_sb_graph2, aes(x=rownames(ro_sb_graph2), y=ro, color = "blue")) + geom_point(aes(x = rownames(ro_sb_graph2), y = ro)) + geom_line(aes(y = ro, x = rownames(ro_sb_graph2), color = "blue"))
p2<-p2+geom_ribbon(aes(x = 1:length(rownames(ro_sb_graph2)), ymin=ci_low, ymax=ci_upp, color = "red"), linetype=2, alpha=0.2)
p2
```