---
title: "Ukraine.COVID-19"
author: ""
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    # social: ["facebook", "twitter", "linkedin"]
    source_code: embed
    vertical_layout: fill
---
```{r setup1, include=FALSE}
#------------------ Libaries ------------------
source('D:/R/Work/functions_Covid.R')
source('D:/R/Work/Library_Constants.R')
library(flexdashboard)
library(data.table)

library(plotly)
library(maps)
library(rjson)
library(earlyR)
library(rvest)
library(deSolve)
library("wbstats")
library(covdata)


pop<-data.table(wb(indicator = c("SP.POP.TOTL"),mrv = 1))
pop[country=="United States"]$country<-'US'
pop[country=="Iran, Islamic Rep."]$country<-'Iran'
pop[country=="Korea, Rep."]$country<-"Korea, South"
pop[country=="Russian Federation"]$country<-"Russia"
pop[country=="Syrian Arab Republic"]$country <- "Syria"
pop[country=="Bahamas, The"]$country<-"Bahamas"
pop[country=="Brunei Darussalam"]$country<-"Brunei"
pop[country=="Congo, Dem. Rep."]$country<-"DR Congo"
pop[country=="Congo, Rep."]$country<-"Congo (Kinshasa)"
pop[country=="Czech Republic"]$country<-"Czechia"
pop[country=="Egypt, Arab Rep."]$country<-"Egypt"
#pop[country=="Kyrgyz Republic"]$country<-"Kyrgyzstan"
pop[country=="Slovak Republic"]$country<-"Slovakia"
 url<-"https://covid19.com.ua/"
 webpage <- read_html(url)
 number_html <- html_nodes(webpage, "div")
 number_html <-html_nodes(number_html[[22]],"div")
 number_html <-html_nodes(number_html[[4]],"div")
 s<-html_text(number_html)
 s<-trimws(s)
 s<-as.numeric(gsub(" ", "", s, fixed = TRUE))

 test<-readRDS("D:/R/Work/test.rdf")
 #test[13]$date<-as.Date("2020-04-25")
 if (Sys.Date()%in%test$date){
   test[date==Sys.Date()]$test=s
 } else
   {
   test<-rbind(test,data.table(date=Sys.Date(),test=s))
 }
saveRDS(test,"D:/R/Work/test.rdf")



#------------------ Parameters ------------------
# Set colors
# https://www.w3.org/TR/css-color-3/#svg-color
confirmed_color <- "purple"
active_color <- "#1f77b4"
recovered_color <- "forestgreen"
death_color <- "red"
tested_color <- "black"
#country_list<-c("US","Italy","Spain","Ukraine","France", "Canada","China")


jsonUP <- fromJSON(file ="https://api.covidnow.com/v1/global/countries",simplify=T)
x<-jsonUP[[1]][[1]]
countries<-attr(jsonUP[[1]],"names")
r<-lapply(jsonUP[[1]],function(x){
  return(data.table(x$nationwide$update,x$nationwide$recovered,x$nationwide$confirmed,x$nationwide$deaths))
})
r<-rbindlist(r)
countries[countries=="UK"]<-"United Kingdom"
countries[countries=="South Korea"]<-"Korea, South"
countries[countries=="UAE"]<-"United Arab Emirates"

r$Counrty<-countries
names(r)<-c("ts","recovered","confirmed","death","Country/Region")
r[,date:=as.Date(as.POSIXlt(as.numeric(ts)/1000,origin="1970-01-01",tz="UTC"))]
r[is.na(date)]$date<-as.Date(as.POSIXlt(as.numeric(jsonUP[[2]])/1000,origin="1970-01-01",tz="UTC"))



#------------------ Data ------------------
ids<-c("Province/State","Country/Region","Lat","Long")
conf<-data.table(read.csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv",check.names = F))
conf<-melt(conf,id.vars = ids,measure.vars = names(conf)[!names(conf)%in%ids])
names(conf)<-c(ids,"date","confirmed")
conf$date<-as.Date(conf$date,"%m/%d/%y")
conf[confirmed<0,confirmed:=0]


dead<-data.table(read.csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv",check.names = F))
dead<-melt(dead,id.vars = ids,measure.vars = names(dead)[!names(dead)%in%ids])
names(dead)<-c(ids,"date","death")
dead$date<-as.Date(dead$date,"%m/%d/%y")
dead[death<0,death:=0]

recovered<-data.table(read.csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv",check.names = F))
names(recovered)[1]<-names(dead)[1]
recovered<-melt(recovered,id.vars = ids,measure.vars = names(recovered)[!names(recovered)%in%ids])
names(recovered)<-c(ids,"date","recovered")
recovered$date<-as.Date(recovered$date,"%m/%d/%y")
recovered[recovered<0,recovered:=0]

cor<-merge(conf,dead,by=c(ids,"date"),all.x = T,)
cor<-merge(cor,recovered,by=c(ids,"date"),all.x = T)

cor[is.na(recovered)]$recovered<-0
cor[is.na(death)]$death<-0
cor$`Country/Region`<-as.character(cor$`Country/Region`)
cor_ll<-cor
cor<-cor[,.(confirmed=sum(confirmed),recovered=sum(recovered),death=sum(death)),by=.(`Country/Region`,date) ]
if (max(cor[`Country/Region`=="Ukraine"]$confirmed)1000000]
ccountry_list[,conf:=round(conf/(value/1000000),2)][,death:=round(death/(value/1000000),2)]
setorder(ccountry_list,-conf)
ccountry_list_pm<-rbind(ccountry_list[1:10],ccountry_list[`Country/Region`=="Ukraine"])$`Country/Region`

 
df<-cor_U[nrow(cor_U)]
df$recovered<-max(cor_U$recovered)
df[,unrecovered:=confirmed-death-recovered]
df_daily<-cor_U[,.(date,confirmed,death,recovered)]
df_daily[,active_cum:=confirmed-death-recovered]
names(df_daily)<-c("date","confirmed_cum","death_cum","recovered_cum","active_cum")

cor_W<-cor[,.(confirmed=sum(confirmed),death=sum(death),recovered=sum(recovered)),by=.(date)]
df_W<-cor_W[nrow(cor_U)]
df_W$recovered<-max(cor_W$recovered)
df_W[,unrecovered:=confirmed-death-recovered]
df_daily_W<-cor_W[,.(date,confirmed,death,recovered)]
df_daily_W[,active_cum:=confirmed-death-recovered]
names(df_daily_W)<-c("date","confirmed_cum","death_cum","recovered_cum","active_cum")
#ggplot(cor_W,aes(date,recovered))+geom_point()


cor_U[,active:=confirmed-death-recovered]
cor<-merge(cor,pop[,.(country,value)],by.x = "Country/Region", by.y='country',all.x=T)
cor[,confirmed_pm:=round(confirmed/(value/1000000),2)]
cor[,death_pm:=round(death/(value/1000000),2)]
cor[,recovered_pm:=round(recovered/(value/1000000),2)]
mobility<-data.table(apple_mobility)
mobility[region=="Ukraine"]
#sort(unique(google_mobility$country_region))
```



Основные показатели.Украина
=======================================================================

Row {data-width=400}
-----------------------------------------------------------------------

### confirmed {.value-box}

```{r}

valueBox(
  value = paste(format(sum(df$confirmed), big.mark = ","), "", sep = " "),
  caption = "Подтвержденные носители",
  icon = "fas fa-user-md",
  color = confirmed_color
)
```


### active {.value-box}

```{r}
valueBox(
  value = paste(format(sum(df$unrecovered, na.rm = TRUE), big.mark = ","), " (",
    round(100 * sum(df$unrecovered, na.rm = TRUE) / sum(df$confirmed), 1),
    "%)",
    sep = ""
  ),
  caption = "Активные носители (% of всех)", icon = "fas fa-ambulance",
  color = active_color
)
```

### death {.value-box}

```{r}

valueBox(
  value = paste(format(sum(df$death, na.rm = TRUE), big.mark = ","), " (",
    round(100 * sum(df$death, na.rm = TRUE) / sum(df$confirmed), 1),
    "%)",
    sep = ""
  ),
  caption = "Летальный исход (% от носителей)",
  icon = "fas fa-heart-broken",
  color = death_color
)
```


### recovered {.value-box}

```{r}
valueBox(
  value = paste(format(sum(df$recovered), big.mark = ","), " (",
    round(100 * sum(df$recovered)/sum(df$confirmed), 1),
    "%)",
    sep = ""
  ),
  caption = "Вылечились (% от носителей)",
  icon = "fas fa-heart-broken",
  color = recovered_color
)
```

 
### tested {.value-box}

```{r}
valueBox(
  value = paste(format(s, big.mark = ","), " (",
    round(100 * sum(df$confirmed)/s, 1),
    "%)",
    sep = ""
  ),
  caption = "Количество тестов (% носителей)",
  icon = "fas fa-heart-broken",
  color = death_color
)
```


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

### **Динамика активных и летальных случаев** (Украина)
    
```{r}
plotly::plot_ly(data = df_daily[confirmed_cum>0]) %>%
  plotly::add_trace(
    x = ~date,
    y = ~active_cum,
    type = "scatter",
    mode = "lines",
    name = "Инфицированы",
    fillcolor  = active_color,
    stackgroup = 'one'
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~recovered_cum,
    type = "scatter",
    mode = "lines",
    name = "Вылечились",
    fillcolor =recovered_color,
    stackgroup = 'one'
  ) %>%
   plotly::add_trace(
    x = ~date,
    y = ~death_cum,
    type = "scatter",
    mode = "lines",
    name = "Летальные",
    fillcolor =death_color,
    stackgroup = 'one'
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~test,
    type = "scatter",
    mode = "lines+markers",
    name = "Протестировано",
    line = list(color = tested_color),
    marker = list(color = tested_color),
    data=test,
    yaxis = "y2"
  ) %>%
  
  plotly::add_annotations(
    x = as.Date("2020-03-03"),
    y = 1,
    text = paste("Первый случай заражения"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -10,
    ay = -90
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-03-13"),
    y = 1,
    text = paste("Первый летальный"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -90,
    ay = +10
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-03-25"),
    y = 140,
    text = paste("100 носителей"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
   ax = -90,
    ay = -10
  ) %>%
  
  plotly::layout(
    title = "",
    yaxis = list(title = "Кол-во инцидентов"),
    xaxis = list(title = "Date"),
    yaxis2 = list(overlaying = "y", side = "right",title="Количество тестов"),
    legend = list(x = 0.1, y = 0.9),
    hovermode = "compare"
  )
```

Основные показатели.Мир
=======================================================================

Row {data-width=400}
-----------------------------------------------------------------------

### confirmed {.value-box}

```{r}

valueBox(
  value = paste(format(sum(df_W$confirmed), big.mark = ","), "", sep = " "),
  caption = "Подтвержденные носители",
  icon = "fas fa-user-md",
  color = confirmed_color
)
```


### active {.value-box}

```{r}
valueBox(
  value = paste(format(sum(df_W$unrecovered, na.rm = TRUE), big.mark = ","), " (",
    round(100 * sum(df_W$unrecovered, na.rm = TRUE) / sum(df_W$confirmed), 1),
    "%)",
    sep = ""
  ),
  caption = "Активные носители (% of всех)", icon = "fas fa-ambulance",
  color = active_color
)
```

### death {.value-box}

```{r}

valueBox(
  value = paste(format(sum(df_W$death, na.rm = TRUE), big.mark = ","), " (",
    round(100 * sum(df_W$death, na.rm = TRUE) / sum(df_W$confirmed), 1),
    "%)",
    sep = ""
  ),
  caption = "Летальный исход (% от носителей)",
  icon = "fas fa-heart-broken",
  color = death_color
)
```


### recovered {.value-box}

```{r}
valueBox(
  value = paste(format(sum(df_W$recovered), big.mark = ","), " (",
    round(100 * sum(df_W$recovered)/sum(df_W$confirmed), 1),
    "%)",
    sep = ""
  ),
  caption = "Вылечились (% от носителей)",
  icon = "fas fa-heart-broken",
  color = recovered_color
)
```

 
### tested {.value-box}

```{r}
valueBox(
  value = paste(format(s, big.mark = ","), " (",
    round(100 * sum(df$confirmed)/s, 1),
    "%)",
    sep = ""
  ),
  caption = "Количество тестов (% носителей)",
  icon = "fas fa-heart-broken",
  color = death_color
)
```


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

### **Динамика активных и летальных случаев** (Украина)
    
```{r}
plotly::plot_ly(data = df_daily_W[confirmed_cum>0]) %>%
  plotly::add_trace(
    x = ~date,
    y = ~active_cum,
    type = "scatter",
    mode = "lines+markers",
    name = "Активные",
    line = list(color = active_color),
    marker = list(color = active_color)
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~death_cum,
    type = "scatter",
    mode = "lines+markers",
    name = "Летальные",
    line = list(color = death_color),
    marker = list(color = death_color)
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~recovered_cum,
    type = "scatter",
    mode = "lines+markers",
    name = "Вылечились",
    line = list(color = recovered_color),
    marker = list(color = recovered_color)
  ) %>%

  plotly::layout(
    title = "",
    yaxis = list(title = "Кол-во инцидентов"),
    xaxis = list(title = "Date"),
    yaxis2 = list(overlaying = "y", side = "right",title="Количество тестов"),
    legend = list(x = 0.1, y = 0.9),
    hovermode = "compare"
  )
```


Сравнение по странам
=======================================================================


Column {data-width=400, .tabset}
------

### **Инциденты на 1 млн**
    
```{r}

daily_confirmed<-cor
setorder(daily_confirmed,`Country/Region`,date)



#----------------------------------------
# Plotting the data

Pr<- plot_ly(daily_confirmed) 
Pr<-add_trace(Pr,
    x = ~daily_confirmed[`Country/Region`=="Ukraine"]$date,
    y = ~daily_confirmed[`Country/Region`=='Ukraine']$confirmed_pm,
    type = "scatter",
    mode = "lines",
    name = 'Ukraine',
    line = list( width = 4)
  )
for (i in country_list){
  pdata<-daily_confirmed[`Country/Region`%in%i]
    
  Pr<-add_trace(Pr,
    x = ~date,
    y = ~confirmed_pm,
    type = "scatter",
    mode = "lines",
    name = i,
    data=pdata
    
  )
}
 Pr<-layout(Pr, title = "",legend = list(x = 0.1, y = 0.9),
    yaxis = list(title = "Количество инцидентов"),
    xaxis = list(title = "Date"),
    # paper_bgcolor = "black",
    # plot_bgcolor = "black",
    # font = list(color = 'white'),
    hovermode = "compare",
    margin = list(
       l = 60,
       r = 40,
      b = 10,
      t = 10,
      pad = 2
      )
  )
 Pr
```

### **Скороcть появления новых инцидентов**
    
```{r}
daily_confirmed<-cor
setorder(daily_confirmed,`Country/Region`,date)
daily_confirmed<-daily_confirmed[,confirmed_Speed:=confirmed-shift(confirmed, 1L, type="lag"),by=.(`Country/Region`)][is.na(confirmed_Speed)| confirmed_Speed<0,confirmed_Speed:=0]
daily_confirmed_W<-df_daily_W[,confirmed_Speed:=confirmed_cum-shift(confirmed_cum
, 1L, type="lag"),][is.na(confirmed_Speed)|confirmed_Speed<0,confirmed_Speed:=0]



#----------------------------------------
# Plotting the data

Pr<- plot_ly(daily_confirmed) 
Pr <- add_trace(Pr,
                x=~date,
                y=~confirmed_Speed,
                mode = "lines",
                name = 'World',
                line = list(width = 4,color="black"),
                data=daily_confirmed_W,
                yaxis = "y2")
Pr<-add_trace(Pr,
    x = ~daily_confirmed[`Country/Region`=="Ukraine"]$date,
    y = ~daily_confirmed[`Country/Region`=='Ukraine']$confirmed_Speed,
    type = "scatter",
    mode = "lines",
    name = 'Ukraine',
    line = list( width = 4)
  )
for (i in country_list){
  pdata<-daily_confirmed[`Country/Region`%in%i]
    
  Pr<-add_trace(Pr,
    x = ~date,
    y = ~confirmed_Speed,
    type = "scatter",
    mode = "lines+markers",
    name = i,
    data=pdata
    
  )
}
 Pr<-layout(Pr, title = "",legend = list(x = 0.1, y = 0.9),
    yaxis = list(title = "Количество инцидентов"),
    xaxis = list(title = "Date"),
    yaxis2 = list(overlaying = "y", side = "right",title="Количество инцидентов. Мир"),
    # paper_bgcolor = "black",
    # plot_bgcolor = "black",
    # font = list(color = 'white'),
    hovermode = "compare",
    margin = list(
       l = 60,
       r = 40,
      b = 10,
      t = 10,
      pad = 2
      )
  )
 Pr
```
 
 
### **Относительная скороcть появления новых инцидентов**
```{r}
daily_confirmed<-cor
setorder(daily_confirmed,`Country/Region`,date)
daily_confirmed<-daily_confirmed[confirmed>100,confirmed_Speed:=confirmed/shift(confirmed, 1L, type="lag")-1,by=.(`Country/Region`)][is.na(confirmed),confirmed:=0]



#----------------------------------------
# Plotting the data

Pr<- plot_ly(daily_confirmed) 
Pr<-add_trace(Pr,
    x = ~daily_confirmed[`Country/Region`=="Ukraine"]$date,
    y = ~daily_confirmed[`Country/Region`=='Ukraine']$confirmed_Speed,
    type = "scatter",
    mode = "lines",
    name = 'Ukraine',
    line = list( width = 4)
  )
for (i in country_list){
  pdata<-daily_confirmed[`Country/Region`%in%i]
    
  Pr<-add_trace(Pr,
    x = ~date,
    y = ~confirmed_Speed,
    type = "scatter",
    mode = "lines+markers",
    name = i,
    data=pdata
    
  )
}
 Pr<-layout(Pr, title = "",legend = list(x = 0.1, y = 0.9),
    yaxis = list(title = "Количество инцидентов"),
    xaxis = list(title = "Date"),
    # paper_bgcolor = "black",
    # plot_bgcolor = "black",
    # font = list(color = 'white'),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
      )
  )
 Pr
```

Column {data-width=400, .tabset}
------
 
### **Количество инцидентов по странам на текущий момент**

```{r daily_summary}
  df_world_last<-cor[,.(confirmed=max(confirmed),death=max(death),unrecovered=max(confirmed)-max(death)-max(recovered),
                recovered=max(recovered),country=`Country/Region`),by=.(`Country/Region`)]
  
df_EU<-df_world_last[country%in%c(country_list,"Ukraine")]
df_EU$country<-as.character(df_EU$country)
df_EU<-df_EU[,.(unrecovered=sum(unrecovered),death=sum(death),recovered=sum(recovered),sorter=sum(recovered)+sum(unrecovered)+sum(death)),by=.(country)]
df_EU$country <- factor(df_EU$country, levels = unique(df_EU$country)[order(df_EU$sorter, decreasing = TRUE)])

plotly::plot_ly(
  data = df_EU,
  x = ~country,
  y = ~unrecovered,
  # text =  ~ confirmed,
  # textposition = 'auto',
  type = "bar",
  name = "Активные",
  marker = list(color = active_color)
) %>%
  plotly::add_trace(
    y = ~death,
    # text =  ~ death,
    # textposition = 'auto',
    name = "Летальные",
    marker = list(color = death_color)
  ) %>%
  plotly::add_trace(
    y = ~recovered,
    # text =  ~ death,
    # textposition = 'auto',
    name = "Выле",
    marker = list(color = recovered_color)
  ) %>%
  plotly::layout(
    barmode = "stack",
    yaxis = list(title = "Всего"),
    xaxis = list(title = ""),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )
```

Тренды
=======================================================================

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


### **Подтвержденные. Логарифмические координаты**

```{r}
cummul<-cor
setorder(cummul,`Country/Region`,date)
cummul[,confirmed1:=confirmed-shift(confirmed, 1L, type="lag"),by=.(`Country/Region`)][is.na(confirmed1),confirmed1:=0]
cummul[,confirmed7:=Reduce(`+`, shift(confirmed1, 0:6, type="lag")),by=.(`Country/Region`)]
Pr<-plot_ly(cummul)
tdata<-cummul[`Country/Region`=='Ukraine'&confirmed>0]
tdata$day<-seq(1:nrow(tdata))
Pr<-add_trace(Pr,
    x = ~day,
    y = ~confirmed,
    mode = "lines",
    line = list(color = 'rgb(255, 0, 0)', width = 4),
    name = "Ukraine",
    data=tdata
  )
for (i in c(country_list,"China")){
  tdata<-cummul[`Country/Region`==i&confirmed>0]
  tdata$day<-seq(1:nrow(tdata))
  Pr<-add_trace(Pr,
    x = ~day,
    y = ~confirmed,
    mode = "lines",
    line = list(color = 'rgb(100, 100, 100)', width = 1),
    name = i,
    data=tdata
  )
}
  Pr<-layout(Pr,
    title = "",
    #legend = list(x = 0.1, y = 0.9),
    yaxis = list(title = "Количество инцидентов",type = "log"),
    xaxis = list(title = "Дни"),
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
      )
  )
Pr
```


### **Заражение (отн. скорость)**

```{r}
cummul<-cor
setorder(cummul,`Country/Region`,date)
cummul[,confirmed1:=confirmed-shift(confirmed, 1L, type="lag"),by=.(`Country/Region`)][is.na(confirmed1),confirmed1:=0]
cummul[,confirmed7:=Reduce(`+`, shift(confirmed1, 0:6, type="lag")),by=.(`Country/Region`)]
Pr<-plot_ly(cummul)
tdata<-cummul[`Country/Region`=='Ukraine']
Pr<-add_trace(Pr,
    x = ~confirmed,
    y = ~confirmed7,
    mode = "lines",
    line = list(color = 'rgb(255, 0, 0)', width = 4),
    name = "Ukraine",
    data=tdata
  )
for (i in c(country_list,"China")){
  tdata<-cummul[`Country/Region`==i]
  Pr<-add_trace(Pr,
    x = ~confirmed,
    y = ~confirmed7,
    mode = "lines",
    line = list(color = 'rgb(100, 100, 100)', width = 1),
    name = i,
    data=tdata
  )
}
  Pr<-layout(Pr,
    title = "",
    legend = list(x = 0.1, y = 0.9),
    yaxis = list(title = "Количество инцидентов за последнюю неделю",type = "log"),
    xaxis = list(title = "Количество инцидентов",type = "log"),
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
      )
  )
Pr

```


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

### **Летальные. Логарифмические координаты**

```{r}
cummul<-cor
setorder(cummul,`Country/Region`,date)
cummul[,confirmed1:=confirmed-shift(confirmed, 1L, type="lag"),by=.(`Country/Region`)][is.na(confirmed1),confirmed1:=0]
cummul[,confirmed7:=Reduce(`+`, shift(confirmed1, 0:6, type="lag")),by=.(`Country/Region`)]
Pr<-plot_ly(cummul)
tdata<-cummul[`Country/Region`=='Ukraine'&death>0]
tdata$day<-seq(1:nrow(tdata))
Pr<-add_trace(Pr,
    x = ~day,
    y = ~death,
    mode = "lines",
    line = list(color = 'rgb(255, 0, 0)', width = 4),
    name = "Ukraine",
    data=tdata
  )
for (i in c(country_list,"China")){
  tdata<-cummul[`Country/Region`==i&death>0]
  tdata$day<-seq(1:nrow(tdata))
  Pr<-add_trace(Pr,
    x = ~day,
    y = ~death,
    mode = "lines",
    line = list(color = 'rgb(100, 100, 100)', width = 1),
    name = i,
    data=tdata
  )
}
  Pr<-layout(Pr,
    title = "",
    #legend = list(x = 0.1, y = 0.9),
    yaxis = list(title = "Количество инцидентов",type = "log"),
    xaxis = list(title = "Дни"),
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
      )
  )
Pr
```


### **Смертность. (отн. скорость)**

```{r}
cummul<-cor
setorder(cummul,`Country/Region`,date)
cummul[,death1:=death-shift(death, 1L, type="lag"),by=.(`Country/Region`)][is.na(death),death:=0]
cummul[,death7:=Reduce(`+`, shift(death1, 0:6, type="lag")),by=.(`Country/Region`)]
Pr<-plot_ly(cummul)
tdata<-cummul[`Country/Region`=='Ukraine']
Pr<-add_trace(Pr,
    x = ~death,
    y = ~death7,
    mode = "lines",
    line = list(color = 'rgb(255, 0, 0)', width = 4),
    name = "Ukraine",
    data=tdata
  )
for (i in c(country_list,"China")){
  tdata<-cummul[`Country/Region`==i]
  Pr<-add_trace(Pr,
    x = ~death,
    y = ~death7,
    mode = "lines",
    line = list(color = 'rgb(100, 100, 100)', width = 1),
    name = i,
    data=tdata
  )
}
  Pr<-layout(Pr,
    title = "",
    legend = list(x = 0.1, y = 0.9),
    yaxis = list(title = "Количество инцидентов за последнюю неделю",type = "log"),
    xaxis = list(title = "Количество инцидентов",type = "log"),
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
      )
  )
Pr

```


Карта
=======================================================================


### **Количество инцидентов по странам** (*use + and - icons to zoom in/out*)

```{r}
# map tab added by Art Steinmetz
library(leaflet)
library(leafpop)
library(purrr)
 df_world_last<-cor_ll[,.(confirmed=max(confirmed),death=max(death),unrecovered=max(confirmed)-max(death)-max(recovered),
                recovered=max(recovered),country=`Country/Region`),by=.(`Country/Region`,`Province/State`,Lat,Long)]
cv_data_for_plot<-melt(df_world_last,c("Country/Region","Province/State","Lat",'Long'),measure.vars =  c("confirmed", "death", "recovered"),variable.name = "type",value.name = "cases")[cases>1][,log_cases := 2 * log(cases)]
ge <- list(
  showland = TRUE,
  landcolor = toRGB("gray85"),
  watercolor= toRGB("blue"),
  countrywidth = 1,
  countrycolor = toRGB("white")
)

plot_geo(cv_data_for_plot[`Province/State`==""]) %>%
add_markers(x = ~Long, 
            y = ~Lat, 
            color = ~type,
            size=~log_cases,
            colors = c(confirmed_color,death_color,recovered_color), 
            text=~paste(cases, type, `Country/Region`,sep=" "),
            )%>%
  layout(geo=ge)
```

Прогноз
=======================================================================

Row {data-width=400}
-----------------------------------------------------------------------

### U_estimate {.value-box}
```{r}
ues<-ExpDeadRate(cor_U$confirmed,cor_U$death)
valueBox(
  value = paste(format(round(ues),  big.mark = " "),"вместо", format(round(cor_U$confirmed[length(cor_U$confirmed)]),  big.mark = " "), "", sep = " "),
  caption = "Оценка количества зараженных на основании летальных исходов. Украина",
  icon = "fas fa-user-md",
  color = confirmed_color
)


```


### W_estimate {.value-box}
```{r}
wes<-ExpDeadRate(cor_W$confirmed,cor_W$death)

valueBox(
  value = paste(format(round(wes),  big.mark = " "),"вместо", format(round(cor_W$confirmed[length(cor_W$confirmed)]),  big.mark = " "), "", sep = " "),
  caption = "Оценка количества зараженных на основании летальных исходов. Мир",
  icon = "fas fa-user-md",
  color = confirmed_color
)

```

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

### **Прогноз на 10 дней (экспоненциальная экстраполяция). Украина**
```{r}
source('D:/R/Work/functions_Covid.R')
#plot(cfi(df_daily$active[df_daily$active!=0]))
#plot(growthRate(df_daily$active_cum[df_daily$active_cum!=0]))
t<-projSimple(cor_U[date!=Sys.Date()]$confirmed,cor_U[date!=Sys.Date()]$date)
#t %>%
#  t
  plotly::plot_ly() %>%
  plotly::add_trace(
    x = ~t$x,
    y = ~t$y[,1],
    type = "scatter",
    mode = "markers",
    name = "Прогноз"
  ) %>%
  plotly::add_trace(
    x = ~cor_U$date,
    y = ~cor_U$confirmed,
    type = "scatter",
    mode = "lines",
    name = "Факт"
  ) %>%
  plotly::add_trace(
    x = ~t$x,
    y = ~t$y[,2],
    type = "scatter",
    mode = "markers",
    name = "Нижний прогноз"
  ) %>%
  plotly::add_trace(
    x = ~t$x,
    y = ~t$y[,3],
    type = "scatter",
    mode = "markers",
    name = "Верхний прогноз"
  ) %>%
  
    
  plotly::layout(
    title = "",
    legend = list(x = 0.1, y = 0.9),
    yaxis = list(title = "Number of new cases"),
    xaxis = list(title = "Date"),
    # paper_bgcolor = "black",
    # plot_bgcolor = "black",
    # font = list(color = 'white'),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )


```

### **Прогноз на 10 дней (экспоненциальная экстраполяция). Мир**
```{r}
tw<-projSimple(cor_W[date!=Sys.Date()]$confirmed,cor_W[date!=Sys.Date()]$date)
  plotly::plot_ly() %>%
  plotly::add_trace(
    x = ~tw$x,
    y = ~tw$y[,1],
    type = "scatter",
    mode = "markers",
    name = "Прогноз"
  ) %>%
  plotly::add_trace(
    x = ~cor_W$date,
    y = ~cor_W$confirmed,
    type = "scatter",
    mode = "lines",
    name = "Факт"
  ) %>%
  plotly::add_trace(
    x = ~tw$x,
    y = ~tw$y[,2],
    type = "scatter",
    mode = "markers",
    name = "Нижний прогноз"
  ) %>%
  plotly::add_trace(
    x = ~tw$x,
    y = ~tw$y[,3],
    type = "scatter",
    mode = "markers",
    name = "Верхний прогноз"
  ) %>%
  
    
  plotly::layout(
    title = "",
    legend = list(x = 0.1, y = 0.9),
    yaxis = list(title = "Number of new cases"),
    xaxis = list(title = "Date"),
    # paper_bgcolor = "black",
    # plot_bgcolor = "black",
    # font = list(color = 'white'),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )

ues1<-cor_U$confirmed/detRate(cor_U$confirmed,cor_U$death)

```

Модель Украина
===================

```{r}
get_SIR_max <- function(Active,dates,population) {
  Infected<-Active
  N <- population
  init <- c(
    S = N - Infected[1],
    I = Infected[1],
    R = 0
  )
  Day <- 1:(length(Infected))
  RSS <- function(parameters) {
    names(parameters) <- c("beta", "gamma")
    out <- ode(y = init, times = Day, func = SIR, parms = parameters)
    fit <- out[, 3]
    sum((Infected - fit)^2)
  }
  
  
  SIR <- function(time, state, parameters) {
    par <- as.list(c(state, parameters))
    with(par, {
      dS <- -beta * I * S / N
      dI <- beta * I * S / N - gamma * I
      dR <- gamma * I
      list(c(dS, dI, dR))
    })
  }
  
  Opt <- optim(c(0.5, 0.5),
    RSS,
    method = "L-BFGS-B",
    lower = c(0, 0),
    upper = c(1, 1)
  )

  sir_start_date <- min(dates)
  Opt_par <- setNames(Opt$par, c("beta", "gamma"))
  t <- 1:as.integer(Sys.Date() - ymd(sir_start_date))
  
  fitted_cumulative_incidence <- data.frame(ode(
    y = init, times = t,
    func = SIR, parms = Opt_par
  ))
  
  fitted_cumulative_incidence <- fitted_cumulative_incidence %>%
    mutate(
      Date = ymd(sir_start_date) + days(t - 1),
      Country = "Ukraine",
      cumulative_incident_cases = Infected
    )
  
    fittedmin<-fitted_cumulative_incidence
  
  
  
  
  t <- 1:120
  
  # get the fitted values from our SIR model
  fitted_cumulative_incidence <- data.frame(ode(
    y = init, times = t,
    func = SIR, parms = Opt_par
  ))
  R0<-Opt_par[1] / Opt_par[2]
  # add a Date column and join the observed incidence data
  fitted_cumulative_incidence <- fitted_cumulative_incidence %>%
    mutate(
      Date = ymd(sir_start_date) + days(t - 1),
      Country = "Belgium",
      cumulative_incident_cases = I
    )
  MaxI<-max(fitted_cumulative_incidence$I)
  setNames(Opt$par, c("beta", "gamma"))
  MaxT<-(sir_start_date+fitted_cumulative_incidence$time[fitted_cumulative_incidence$I==MaxI])
  return(list(MaxI,MaxT,Opt$message, setNames(Opt$par, c("beta", "gamma")),R0 , fittedmin, fitted_cumulative_incidence))
}
CorA<-cor[date!=Sys.Date()]
CorA$Active<-CorA$confirmed-CorA$recovered-CorA$death
CorA$`Country/Region`<-as.character(CorA$`Country/Region`)
setorder(CorA,`Country/Region`,date)

x<-"Spain"
d<-as.Date("2020-02-26")
l<-lapply(unique(c(long_country_list[!long_country_list%in%c("Korea, South","Spain")],"Ukraine","Russia")), function(x){
#print(x)
resin<-get_SIR_max(CorA[date>d&`Country/Region`==x&Active>0]$Active,
                 CorA[date>d&`Country/Region`==x&Active>0]$date ,pop[country==x]$value) 

#print(resin[[4]][1]/resin[[4]][2])
return(data.table(R_null=resin[[4]][1]/resin[[4]][2],Inf_max=resin[[1]],max_date=resin[[2]],country=x,model=resin[[3]]))
})
l<-rbindlist(l)
x<-"Ukraine"
ukr<-resin<-get_SIR_max(CorA[`Country/Region`==x&Active>0]$Active,
                 CorA[`Country/Region`==x&Active>0]$date ,pop[country==x]$value) 

```



Row {data-width=400}
-----------------------------------------------------------------------

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

valueBox(
  value = paste(format(ukr[[4]][1]/ukr[[4]][2], big.mark = ","),"", sep = " "),
  caption = "Оценка R0. R0 — количество зараженных одним инфицированным.",
  icon = "fas fa-user-md",
  color = confirmed_color
)
```

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

valueBox(
  value = paste(format(l[country=="Ukraine"]$max_date[1], big.mark = ","),"", sep = " "),caption = "Дата максимального количества зараженных.",
  icon = "fas fa-user-md",
  color = confirmed_color
)
```

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


valueBox(
  value = paste(format(l[country=="Ukraine"]$Inf_max[1], big.mark = ","),"", sep = " "),caption = "Максимальное количество зараженных одновременно.",
  icon = "fas fa-user-md",
  color = confirmed_color
)
```

Column {data-width=400}
-----------------------------------------------------------------------
### Chart 1
```{r}
fittedmin<-ukr[[6]]
  
  plotly::plot_ly() %>%
  plotly::add_trace(
    x = ~date,
    y = ~active,
    type = "scatter",
    mode = "markers",
    name = "Факт",
    data=cor_U
  ) %>%
  plotly::add_trace(
    x = ~Date,
    y = ~I,
    type = "scatter",
    mode = "lines",
    name = "Прогноз",
    data=fittedmin
  ) %>%
  plotly::layout(
    title = "",
    legend = list(x = 0.1, y = 0.9),
    yaxis = list(title = "Number of new cases"),
    xaxis = list(title = "Date"),
    # paper_bgcolor = "black",
    # plot_bgcolor = "black",
    # font = list(color = 'white'),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )


```

### Chart 1
```{r}
fitted_max<-ukr[[7]]
inf<-data.table(Infect=cor_U[date!=Sys.Date()]$confirmed,date=fitted_max$Date[1:length(cor_U[date!=Sys.Date()]$confirmed)])
  fitted_max%>%
  plotly::plot_ly() %>%
  plotly::add_trace(
    x = ~date,
    y = ~active,
    type = "scatter",
    mode = "markers",
    name = "Факт",
    data=cor_U
  ) %>%
  plotly::add_trace(
    x = ~Date,
    y = ~I,
    type = "scatter",
    mode = "lines",
    name = "Прогноз Инфицированные",
    data=fitted_max
  ) %>%
   plotly::add_trace(
    x = ~Date,
    y = ~R,
    type = "scatter",
    mode = "lines",
    name = "Прогноз Восстановленные",
    data=fitted_max
  ) %>%
  plotly::add_trace(
    x = ~Date,
    y = ~S,
    type = "scatter",
    mode = "lines",
    name = "Прогноз Не болели",
    data=fitted_max
  ) %>%
  plotly::layout(
    title = "",
    legend = list(x = 0.1, y = 0.9),
    yaxis = list(title = "Number of new cases"),
    xaxis = list(title = "Date"),
    # paper_bgcolor = "black",
    # plot_bgcolor = "black",
    # font = list(color = 'white'),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )
```



Модель Мир
=======

```{r}

library(DT)
datatable(l,options = list(pageLength = 100))
```


Логистическая
=======
Row {data-width=400}
-----------------------------------------------------------------------


```{r}
y2<-cor_U[confirmed>0]$confirmed
x2<-1:nrow(cor_U[confirmed>0])
data<-data.table(x2,y2)
logik<-function(par,x1){
  a <- par[1]
  b <- par[2]
  c <- par[3]
  p <-a/(1+exp(-b*(x1-c)))
  return(p)
}
RSS <- function(par,data) {
  return(sum((data$y-logik(par,data$x))^2))
}
Opt<-optim(par=c(00,00,00),method = "L-BFGS-B",
    lower = c(-0, 0,0),
    upper = c(1000000, 1000000,1000000), fn=RSS,data=data)
par<-Opt$par
sir_start_date <- 1
near<-data.table(t= 1:length(cor_U[confirmed>0]$date),date=cor_U[confirmed>0]$date)
near[,y1:=logik(Opt$par,t)]
near$y2<-y2
#ggplot(near,aes(t,y1))+geom_point()+geom_point(aes(t,y2))

long<-data.table(t= 1:1000, date=seq(from=cor_U[confirmed>0]$date[1],by=1,len=1000))
long[,y1:=logik(Opt$par,t)]
long[,dy1:=y1-shift(y1,1L,"lag")]
long<-long[!is.na(dy1)&dy1>1]
#ggplot(long,aes(date,y1))+geom_point()

```

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

valueBox(
  value = paste(format(long$date[nrow(long)], big.mark = ","),"", sep = " "),caption = "Дата максимального количества зараженных.",
  icon = "fas fa-user-md",
  color = confirmed_color
)
```

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


valueBox(
  value = paste(format(round(long$y1[nrow(long)],0), big.mark = ","),"", sep = " "),caption = "Максимальное количество зараженных одновременно.",
  icon = "fas fa-user-md",
  color = confirmed_color
)
```

Column {data-width=400}
-----------------------------------------------------------------------
### Chart 1
```{r}

  
  Pr<-plot_ly(near)
  Pr<-add_trace(Pr,
    x = ~date,
    y = ~y2,
    type = "scatter",
    mode = "markers",
    name = "Факт",
    data=near
  ) 
  Pr<-add_trace(Pr,
    x = ~date,
    y = ~y1,
    type = "scatter",
    mode = "lines",
    name = "Прогноз",
    data=long
  ) 
  Pr<-layout(Pr,
    title = "",
    legend = list(x = 0.1, y = 0.9),
    yaxis = list(title = "Number of new cases"),
    xaxis = list(title = "Date"),
    # paper_bgcolor = "black",
    # plot_bgcolor = "black",
    # font = list(color = 'white'),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )
Pr

```

About

Условия Кабмина
=======
```{r}
daily_confirmed<-cor
setorder(daily_confirmed,`Country/Region`,date)
daily_confirmed<-daily_confirmed[,confirmed_Speed:=confirmed-shift(confirmed, 1L, type="lag"),by=.(`Country/Region`)][is.na(confirmed_Speed),confirmed_Speed:=0]
daily_confirmed<-daily_confirmed[,rec_Speed:=recovered-shift(recovered, 1L, type="lag"),by=.(`Country/Region`)][is.na(rec_Speed),rec_Speed:=0]
daily_confirmed<-merge(daily_confirmed[`Country/Region`=="Ukraine"],test,by="date",all.x=T)[!is.na(test)]
daily_confirmed<-daily_confirmed[,test_Speed:=test-shift(test, 1L, type="lag"),by=.(`Country/Region`)][is.na(test_Speed),test_Speed:=0]
daily_confirmed[test_Speed>0,test_perc:=confirmed_Speed/test_Speed*100]

```
Row {data-width=400}
-----------------------------------------------------------------------

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

valueBox(
  value = paste(format(sum(tail(daily_confirmed,1)$confirmed_Speed)/sum(tail(daily_confirmed,1)$test_Speed)*100, big.mark = ","),"", sep = " "),
  caption = "Процент положительніх тестов за последний день.",
  icon = "fas fa-user-md",
  color = confirmed_color
)
```

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

valueBox(
  value = paste(format(sum(tail(daily_confirmed,10)$confirmed_Speed)/sum(tail(daily_confirmed,10)$test_Speed)*100, big.mark = ","),"", sep = " "),
  caption = "Средний процент положительных тестов за 10 дней.",
  icon = "fas fa-user-md",
  color = confirmed_color
)
```

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

valueBox(
  value = paste(format(sum(daily_confirmed$confirmed_Speed)/sum(daily_confirmed$test_Speed)*100, big.mark = ","),"", sep = " "),
  caption = "Средний процент положительніх тестов за весь период.",
  icon = "fas fa-user-md",
  color = confirmed_color
)
```


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

### **Процент выявленных от тестов**
```{r}

plotly::plot_ly(
  data = daily_confirmed,
  x = ~date,
  y = ~test_perc,
  text =  ~ test_perc,
  textposition = 'auto',
  type = "bar",
  name = "Процент положительных тестов",
  color = active_color
) %>%
  plotly::layout(
    barmode = "stack",
    yaxis = list(title = "%"),
    xaxis = list(title = ""),
    hovermode = "group",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )



```


### **Рост заболевших/выздоровевших**
```{r}
plot_ly(data=daily_confirmed)%>%
  add_trace(
  x = ~date,
  y = ~-confirmed_Speed,
  type = "bar",
  name = "Инфицировались",
  marker = list(color = death_color,width=1)
) %>%
  add_trace(
  x = ~date,
  y = ~rec_Speed,
  type = "bar",
  name = "Вылечились",
  marker = list(color = recovered_color,width=1)
  )%>%
  add_trace(
  x = ~date,
  y = ~rec_Speed-confirmed_Speed,
  type = "scatter",
   mode = "lines",
  name = "Баланс",
  marker = list(color = confirmed_color,width=2)
  )%>%
  plotly::layout(
    barmode = "group",
    yaxis = list(title = "%"),
    xaxis = list(title = ""),
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )
```

Мобильность
=======

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

### **Пешеходы**
```{r}
mob_walk<-mobility[transportation_type=="walking"]
setorder(mob_walk,region,date)
Pr<-plot_ly(mob_walk)
tdata<-mob_walk[`region`=='Ukraine']
Pr<-add_trace(Pr,
    x = ~date,
    y = ~index,
    mode = "lines",
    line = list( width = 4),
    name = "Ukraine",
    data=tdata
  )
for (i in c(country_list)){
  tdata<-mob_walk[`region`==i]
  Pr<-add_trace(Pr,
    x = ~date,
    y = ~index,
    mode = "lines",
    line = list( width = 1),
    name = i,
    data=tdata
  )
}
  Pr<-layout(Pr,
    title = "",
    legend = list(x = 0.1, y = 0.9),
    yaxis = list(title = "Index, Apple"),
    xaxis = list(title = "Дата"),
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
      )
  )
Pr

```


### **Водители**
```{r}
mob_walk<-mobility[transportation_type=="driving"]
setorder(mob_walk,region,date)
Pr<-plot_ly(mob_walk)
tdata<-mob_walk[`region`=='Ukraine']
Pr<-add_trace(Pr,
    x = ~date,
    y = ~index,
    mode = "lines",
    line = list( width = 4),
    name = "Ukraine",
    data=tdata
  )
for (i in c(country_list)){
  tdata<-mob_walk[`region`==i]
  Pr<-add_trace(Pr,
    x = ~date,
    y = ~index,
    mode = "lines",
    line = list( width = 1),
    name = i,
    data=tdata
  )
}
  Pr<-layout(Pr,
    title = "",
    legend = list(x = 0.1, y = 0.9),
    yaxis = list(title = "Index, Apple"),
    xaxis = list(title = "Дата"),
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
      )
  )
Pr
```


=======================================================================


**The Coronavirus Dashboard: the case of Ukraine**

This Coronavirus dashboard: the case of Belgium provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic for Ukraine This dashboard is built with R using the R Makrdown framework and was adapted from this [dashboard](https://ramikrispin.github.io/coronavirus_dashboard/){target="_blank"}.

**Code**

The code behind this dashboard is available

**Data**

John Hopkins Institute
```

```

The data and dashboard are refreshed on a daily basis.

The raw data is pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus [repository](https://github.com/CSSEGISandData/COVID-19){target="_blank"}.

**Update**

The data is as of `r format(max(cor$date), "%A %B %d, %Y")` and the dashboard has been updated on `r format(Sys.time(), "%A %B %d, %Y")`, las day update time `r format(as.POSIXlt(as.numeric(jsonUP[[2]])/1000,origin="1970-01-01",tz="UTC"), "%H:%M %A %B %d, %Y")`.