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

```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(highcharter)
library(DT)
library(reshape)
library(data.table)
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 * 1000, 
                     mortalidadX1000 = corona_total$new_deaths/corona_total$population * 1000,
                     incidenciaX1000000 = corona_total$total_cases/corona_total$population * 1000000,
                     mortalidadX1000000 = corona_total$total_deaths/corona_total$population * 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)
recuperados<-chile_totales_nacionales[3,ncol(chile_totales_nacionales)]
activos<-chile_totales_nacionales[5,ncol(chile_totales_nacionales)]
casos_totales<-chile_totales_nacionales[2,ncol(chile_totales_nacionales)]
muertes_totales<-chile_totales_nacionales[4,ncol(chile_totales_nacionales)]
casos_nuevos<-chile_totales_nacionales[1,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$V1))
chile_g4t$V2<-as.numeric(as.character(chile_g4t$V2))
chile_g4t$V3<-as.numeric(as.character(chile_g4t$V3))
chile_g4t$V4<-as.numeric(as.character(chile_g4t$V4))
chile_g4t$V5<-as.numeric(as.character(chile_g4t$V5))
```


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


Row {data-width=350}
-------------------------------------
   
### Casos Totales en Chile

```{r}
articles <- casos_totales
valueBox(articles, icon = "body")
``` 
 
### Casos Recuperados a la Fecha

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

### 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")
```


Row {data-width=350}
-------------------------------------

### Muertes Totales en Chile

```{r}
comments <- muertes_totales
valueBox(comments, icon = "fas fa-skull-crossbones")
``` 
 
### Casos Activos a la Fecha 
 
```{r}
activ <- activos
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")
```

Row {data-width=350}
-------------------------------------

### Test Totales en Chile

```{r}
spam <-total_test_chile
valueBox(spam, 
         icon = "flask-sharp")
```
 
### Casos Nuevos en Chile

```{r}
spam <-casos_nuevos
valueBox(spam, 
         icon = "flask-sharp")
```
 
### Letalidad %

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

### País con Mayor Casos

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

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") %>%
  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_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") %>%
  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_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)
```

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$incidenciaX1000000, 
                      group = corona_dinamic_graph$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$mortalidadX1000000, 
                      group = corona_dinamic_graph$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
-------------------------------------

### Top Casos Confirmados por Comuna

```{r}
knitr::kable(top_comunas)
```

### Top Casos Confirmados por Región

```{r}
knitr::kable(top_regiones)
```

### Top Casos Confirmados por País

```{r}
knitr::kable(top_pais)
```