---
title: "Dashboard Covid19"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: fill
    social: ["twitter", "facebook", "menu"]
    source_code: embed
    theme: cerulean
---  

```{r setup, include=FALSE}
source("dash_setup.R")

```


```{r definition functions}

source("dash_country_plot.R")
source("dash_motion_tracking.R")
source("dash_motionAnalysis.R")

```


```{r cleaning data from ECDC }




```


```{r  clearing data from JHU Data}
source("dash_data_cleaning_JHU.R")

```

Ueberblick
======================================================


Row
-----------------------------------------------------


### Worldwide

```{r first frame}

valueBox(paste("Covid 19" ),ytd,
         color = "warning")


```


### COVID19 Infection worldwide

```{r second frame}

x1 <-format( world_total_sum_cases  , decimal.mark=",", big.mark="'")
valueBox(x1)
      
```


### COVID19 Deaths worldwide


```{r third frame}

x1 <-format( world_total_sum_deaths , decimal.mark=",", big.mark="'")
valueBox(x1, color = "red")

      

```


### COVID19 Infection CH


```{r forte frame}

x1 <-format( ch_sum_cases  , decimal.mark=",", big.mark="'")
valueBox(x1)

```


### COVID19 Deaths CH


```{r six frame}

x1 <-format( ch_sum_deaths  , decimal.mark=",", big.mark="'")
valueBox(x1, color ="red")
```


### COVID19 Infection last 7 days CH

```{r}

x1 <-format( last_week_conf_sum  , decimal.mark=",", big.mark="'")
valueBox(x1)

```



### COVID19 Deaths last 7 days CH

```{r}
x1 <-format( last_week_death_sum  , decimal.mark=",", big.mark="'")
valueBox(x1 , color = "red")
```



### COVID19 7 Tages Inzidenz / 100'000 E


```{r}


x1 <-format( inzidenz_7day  , decimal.mark=",", big.mark="'")

valueBox(x1)
```




Row
-----------------------------------------------------------------------

### 7 Tage Infektionen im Vergleich

```{r}
country <- c("CHE", "SWE", "FIN", "IRL", "PRT","DEU", "AUT")
lcountry <- length(country)

country_plot(country)

```


### 7 Tages Todesfaelle im Vergleich

```{r}
country <- c("CHE", "SWE", "FIN", "IRL", "PRT","DEU", "AUT")
lcountry <- length(country)

country_plot(country, indicat = "death")



```




Row
---------------------------------------------------------------------

### Rapportierte Infectionen

```{r}
source("dash_merged_data_countries.R")

df.x21 <- merged_data_countries()


p <- ggplot(df.x21, mapping = aes(x = confirmend_cases , y = deaths )) +
  geom_point() +
  ggrepel::geom_label_repel(ggplot2::aes(label = iso3c, color = iso3c), show.legend = FALSE) +
  ggplot2::scale_x_continuous(trans = "log10", labels = scales::comma) +
  ggplot2::scale_y_continuous(labels = scales::comma) +  
  theme_minimal() +
  labs(
    x = "Reported Confirmend Cases  ",
    y = "Number of Deaths "
  )
plot(p)




```





### Rapportierte INfektionen pro 100`000 Einwohner

```{r}
source("dash_merged_data_countries.R")

df.x21 <- merged_data_countries()

p <- ggplot(df.x21, mapping = aes(x = (confirmend_cases / pop)*100000, y = (deaths/ pop)*100000)) +
  geom_point() +
  ggrepel::geom_label_repel(ggplot2::aes(label = iso3c, color = iso3c), show.legend = FALSE) +
  ggplot2::scale_x_continuous(trans = "log10", labels = scales::comma) +
  ggplot2::scale_y_continuous(labels = scales::comma) +  
  theme_minimal() +
  labs(
    x = "Reported Confirmend Cases / 100'000 ",
    y = "Number of Deaths / 100'000"
  )
plot(p)


```










Uebersicht Schweiz
====================================================================


Row
---------------------------------------------------------------------

### 7 Tage Infektionen Schweiz

```{r}

df <- download_merged_data(cached = TRUE, silent = TRUE)
df$date <- as.Date(df$date, format = "%Y/%m/%d")
df_ch <- filter(df, iso3c == "CHE")
df_ch$confirmed_daily <- df_ch$confirmed - lag(df_ch$confirmed)
df_ch$rolling7 = rollmean(df_ch$confirmed_daily, k =7, fill = NA)

p <- ggplot(df_ch, aes(x = date, y = rolling7 )) +
  geom_line() +
  geom_bar(aes(y = confirmed_daily), stat = "identity", fill = "lightblue") +
  theme_minimal()+
  xlab("Datum") +
  ylab("7 Tages Infektionen")

fig <- ggplotly(p)
fig



```


### 7 Tage Todesfaelle

```{r}
df <- download_merged_data(cached = TRUE, silent = TRUE)

df_ch <- filter(df, iso3c == "CHE")

for (i in 274:279) {
  df_ch[i, 5] = 2145    #Korrigieren der Daten in Col "deaths"
  
}

df_ch$death_daily <- df_ch$deaths - lag(df_ch$deaths)
df_ch$death_rolling7 = rollmean(df_ch$death_daily, k = 7, fill = NA)

p <- ggplot(df_ch, aes(x = date, y = death_rolling7)) +
  geom_line() +
  geom_bar(aes(y = death_daily), stat = "identity", fill = "lightblue") +
  theme_minimal()+
  xlab("Datum")+
  ylab("7 Tages Todesfaelle")

fig <- ggplotly(p)
fig

```


### Test Situation


```{r}

merged <- download_merged_data(cached = TRUE, silent = TRUE)

ch_merged <- filter(merged, iso3c == "CHE")


fig <- plot_ly(ch_merged, x = ~date, y = ~ total_tests - lag(total_tests), type = "scatter", mode = "lines", name = "Daily Test") %>%
  add_trace(x = ~date, y = ~positive_rate, mode = "lines", yaxis = "y2", name = "Positiv Rate") %>%
  layout(yaxis2 = list(overlaying = "y", side = "right"))

fig


```




Row
----------------------------------------------------------------


### Chart D






### Chart E





Bewegungsanalyse
=====================================================================



Row
-------------------------------------------------------------------

### Chart A

```{r   7 day Rolling average Switzerland}

df <- download_merged_data(cached = TRUE, silent = TRUE)

df_ch <- filter(df, iso3c == "CHE")
df_ch$confirmed_daily <- df_ch$confirmed - lag(df_ch$confirmed)
df_ch$rolling7 = rollmean(df_ch$confirmed_daily, k =7, fill = NA)

p <- ggplot(df_ch, aes(x = date, y = rolling7 )) +
  geom_line() +
  geom_bar(aes(y = confirmed_daily), stat = "identity", fill = "lightblue") +
  theme_minimal()

fig <- ggplotly(p)
fig



```




### Chart B


Row
---------------------------------------------------------

### Chart C

```{r Motion Tracking Switzerland}

motion_tracking("CHE", "Switzerland")

```



### Chart D



Uebersicht Kantone
============================================================


Row
----------------------------------------------------------

### Chart A


```{r}
source("dash_canton_all.R")

p <- ggplot(canton_all, aes(x = date, y = ncumul_hosp, color = canton)) +
  geom_line( aes( group = 2, linetype = canton)) +
  facet_wrap( ~canton) +
  theme_minimal() +
  xlab("Datum") +
  ylab("Hospitalisirungen")


fig <- ggplotly(p)
fig


```


### Chart B


```{r}
source("dash_canton_all.R")

p <- ggplot(canton_all, aes(x = date, y = ncumul_ICU, color = canton )) +
  geom_line(  aes(  group = 2, linetype = canton) )    +
  facet_wrap(~canton) +
  theme_minimal() +
  xlab("Datum") +
  ylab("Belegung der Intensivbetten")

fig <- ggplotly(p)
fig

```