---
title: "COVID-19 no Brasil"
author: "Carlos Eduardo Veras Neves (adaptado de https://github.com/AntoineSoetewey/coronavirus_dashboard)"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    # social: ["facebook", "twitter", "linkedin"]
    source_code: embed
    vertical_layout: fill
---

```{r message=FALSE, warning=FALSE, include=FALSE}
#----------------------------------------------------
# Pulling the coronvirus data from John Hopkins repo
# https://github.com/CSSEGISandData/COVID-19
#----------------------------------------------------
# Setting functions
`%>%` <- magrittr::`%>%`
#----------------------------------------------------
#------------ Pulling confirmed cases------------
conf_url <- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv"
raw_conf <- read.csv(file = conf_url,
                     stringsAsFactors = FALSE)

lapply(1:ncol(raw_conf), function(i){
  if(all(is.na(raw_conf[, i]))){
    raw_conf <<- raw_conf[, -i]
    return(print(paste("Column", names(raw_conf)[i], "is missing", sep = " ")))
  } else {
    return(NULL)
  }
})

# Transforming the data from wide to long
# Creating new data frame
df_conf <- raw_conf[, 1:4]

for(i in 5:ncol(raw_conf)){
  
  raw_conf[,i] <- as.integer(raw_conf[,i])
  # raw_conf[,i] <- ifelse(is.na(raw_conf[, i]), 0 , raw_conf[, i])
  print(names(raw_conf)[i])
  
  if(i == 5){
    df_conf[[names(raw_conf)[i]]] <- raw_conf[, i]
  } else {
    df_conf[[names(raw_conf)[i]]] <- raw_conf[, i] - raw_conf[, i - 1]
  }
  
  
}


df_conf1 <-  df_conf %>% tidyr::pivot_longer(cols = dplyr::starts_with("X"),
                                             names_to = "date_temp",
                                             values_to = "cases_temp")

# Parsing the date
df_conf1$month <- sub("X", "",
                      strsplit(df_conf1$date_temp, split = "\\.") %>%
                        purrr::map_chr(~.x[1]) )

df_conf1$day <- strsplit(df_conf1$date_temp, split = "\\.") %>%
  purrr::map_chr(~.x[2])


df_conf1$date <- as.Date(paste("2020", df_conf1$month, df_conf1$day, sep = "-"))

# Aggregate the data to daily
df_conf2 <- df_conf1 %>%
  dplyr::group_by(Province.State, Country.Region, Lat, Long, date) %>%
  dplyr::summarise(cases = sum(cases_temp)) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(type = "confirmed",
                Country.Region = trimws(Country.Region),
                Province.State = trimws(Province.State))


#----------------------------------------------------
# Pulling death cases

death_url <- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv"
raw_death <- read.csv(file =death_url,
                      stringsAsFactors = FALSE,
                      fill =FALSE)

lapply(1:ncol(raw_death), function(i){
  if(all(is.na(raw_death[, i]))){
    raw_death <<- raw_death[, -i]
    return(print(paste("Column", names(raw_death)[i], "is missing", sep = " ")))
  } else {
    return(NULL)
  }
})

# Transforming the data from wide to long
# Creating new data frame
df_death <- raw_death[, 1:4]

for(i in 5:ncol(raw_death)){
  print(i)
  raw_death[,i] <- as.integer(raw_death[,i])
  raw_death[,i] <- ifelse(is.na(raw_death[, i]), 0 , raw_death[, i])
  
  if(i == 5){
    df_death[[names(raw_death)[i]]] <- raw_death[, i]
  } else {
    df_death[[names(raw_death)[i]]] <- raw_death[, i] - raw_death[, i - 1]
  }
}


df_death1 <-  df_death %>% tidyr::pivot_longer(cols = dplyr::starts_with("X"),
                                               names_to = "date_temp",
                                               values_to = "cases_temp")

# Parsing the date
df_death1$month <- sub("X", "",
                       strsplit(df_death1$date_temp, split = "\\.") %>%
                         purrr::map_chr(~.x[1]) )

df_death1$day <- strsplit(df_death1$date_temp, split = "\\.") %>%
  purrr::map_chr(~.x[2])


df_death1$date <- as.Date(paste("2020", df_death1$month, df_death1$day, sep = "-"))

# Aggregate the data to daily
df_death2 <- df_death1 %>%
  dplyr::group_by(Province.State, Country.Region, Lat, Long, date) %>%
  dplyr::summarise(cases = sum(cases_temp)) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(type = "death",
                Country.Region = trimws(Country.Region),
                Province.State = trimws(Province.State))
# 
#---------------- Aggregate all cases ----------------

#
coronavirus <- dplyr::bind_rows(df_conf2, df_death2) %>%
  as.data.frame()




```

```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(lubridate)
library(rvest)
library(stringdist)


setwd("C:/GoogleDrive/Colab Notebooks/COVID-19/covid19-Brazil-dashboard")

df_saudegovbr <- read.csv(file ='COVID19_20200421.csv', header = TRUE, sep = ";",
                      stringsAsFactors = FALSE,
                      fill =FALSE)

`%>%` <- magrittr::`%>%`
#------------------ 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"
#------------------ Data ------------------


df_saudegovbr = df_saudegovbr %>% 
                mutate(data = as.Date(data, "%Y-%m-%d")) %>% 
                arrange(data) 

df_saudegovbr = df_saudegovbr %>% 
                mutate(estado = estado)



df_regiao = df_saudegovbr %>% 
            group_by(regiao) %>%
            summarise(confirmados = sum(casosNovos), 
                      obitos = sum(obitosNovos)) %>% 
            mutate(ativos = confirmados - obitos)
           

df_estado = df_saudegovbr %>% 
            group_by(estado) %>%
            summarise(confirmados = sum(casosNovos), 
                      obitos = sum(obitosNovos))%>%
            mutate(ativos = confirmados - obitos)

df_brasil = df_saudegovbr %>% 
            summarise(confirmados = sum(casosNovos), 
                      obitos = sum(obitosNovos)) %>%
            mutate(ativos = confirmados - obitos)
            

df_daily_br = df_saudegovbr %>% group_by(data) %>% 
              summarise(confirmados = sum(casosNovos), obitos = sum(obitosNovos)) %>% 
              mutate(ativos = confirmados - obitos) %>%
              ungroup() %>%
              mutate( confirmado_cum = cumsum(confirmados),
                      obito_cum = cumsum(obitos),
                      ativo_cum = cumsum(ativos))
              
df_daily_estados = df_saudegovbr %>% 
                    group_by(estado, data) %>%
                    summarise(confirmados = sum(casosNovos), 
                              obitos = sum(obitosNovos)) %>%
                    mutate(ativos = confirmados - obitos) %>%
                    mutate( confirmado_cum = cumsum(confirmados),
                      obito_cum = cumsum(obitos),
                      ativo_cum = cumsum(ativos)) %>%
                    tidyr::pivot_wider(names_from = estado, values_from = confirmado_cum)


```


Sumário
=======================================================================

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

### confirmed {.value-box}

```{r}

mort = df_brasil$obitos / (df_brasil$confirmados)

casos_reais = round((df_brasil$confirmados)*mort/.02,0)

valueBox(
  value = paste(format(sum(df_brasil$confirmados),  big.mark = ".", decimal.mark =  ","), sep = " "),
  caption = "Número total de casos confirmados",
  icon = "fas fa-user-md",
  color = confirmed_color
)
```

### eff_confirmed {.value-box}

```{r}


mort = df_brasil$obitos / (df_brasil$confirmados)

casos_reais = round((df_brasil$confirmados)*mort/.02,0)

valueBox(
  value = paste(format(casos_reais,  big.mark = ".", decimal.mark =  ","), "", sep = " "),
  caption = "Número real de casos (estimado com 2% de tx. de mortalidade)",
  icon = "fas fa-user-md",
  color = confirmed_color
)
```

### active {.value-box}

```{r}
valueBox(
  value = paste(format(df_brasil$ativos, big.mark = ".", decimal.mark =  ","), " (",
    round(100 * df_brasil$ativos/ df_brasil$confirmados, 1),
    "%)",
    sep = ""
  ),
  caption = "Casos ativos (% do total de casos)", icon = "fas fa-ambulance",
  color = active_color
)
```

### death {.value-box}

```{r}
valueBox(
  value = paste(format(df_brasil$obitos, big.mark = ".", decimal.mark =  ","), " (",
    round(100 * df_brasil$obitos / df_brasil$confirmados, 1),
    "%)",
    sep = ""
  ),
  caption = "Mortes (taxa de mortalidade)",
  icon = "fas fa-heart-broken",
  color = death_color
)
```


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

### **Número de casos acumulados por tipo - Brasil ** 
    
```{r}
plotly::plot_ly(data = df_daily_br) %>%
  plotly::add_trace(
    x = ~data,
    y = ~ativo_cum,
    type = "scatter",
    mode = "lines+markers",
    name = "Ativos",
    line = list(color = active_color),
    marker = list(color = active_color)
  ) %>%
    plotly::add_trace(
    x = ~data,
    y = ~confirmado_cum,
    type = "scatter",
    mode = "lines+markers",
    name = "Confirmados",
    line = list(color = confirmed_color),
    marker = list(color = confirmed_color)
  ) %>%
  plotly::add_trace(
    x = ~data,
    y = ~obito_cum,
    type = "scatter",
    mode = "lines+markers",
    name = "Óbitos",
    line = list(color = death_color),
    marker = list(color = death_color)
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-02-26"),
    y = 1,
    text = paste("Primeiro caso"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -10,
    ay = -90
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-03-17"),
    y = 3,
    text = paste("Primeira morte"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = 90,
    ay = -90
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-03-16"),
    y = 14,
    text = paste(
      "Medidas de",
      "
",
      "contenção"
    ),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -20,
    ay = -90
  ) %>%
  plotly::layout(
    title = "",
    yaxis = list(title = "Número acumulado de casos"),
    xaxis = list(title = "Data"),
    legend = list(x = 0.1, y = 0.9),
    hovermode = "compare"
  )
```


Números do Brasil
=======================================================================

Column {data-width=400}
-------------------------------------
### **Distribuição dos casos por tipo entre as regiões**

```{r br_porregiao}
plotly::plot_ly(
  data = df_regiao,
  x = ~regiao,
  y = ~ativos,
  # text =  ~ confirmed,
  # textposition = 'auto',
  type = "bar",
  name = "Casos Ativos",
  marker = list(color = active_color)
) %>%
  plotly::add_trace(
    y = ~obitos,
    # text =  ~ death,
    # textposition = 'auto',
    name = "Mortes",
    marker = list(color = death_color)
  ) %>%
  plotly::layout(
    barmode = "stack",
    yaxis = list(title = "Total de casos"),
    xaxis = list(title = ""),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )
```

### **Distribuição dos casos por tipo entre as unidades da federação**

```{r br_porestado}
plotly::plot_ly(
  data = df_estado,
  x = ~estado,
  y = ~ativos,
  # text =  ~ confirmed,
  # textposition = 'auto',
  type = "bar",
  name = "Casos Ativos",
  marker = list(color = active_color)
) %>%
  plotly::add_trace(
    y = ~obitos,
    # text =  ~ death,
    # textposition = 'auto',
    name = "Mortes",
    marker = list(color = death_color)
  ) %>%
  plotly::layout(
    barmode = "stack",
    yaxis = list(title = "Total de casos"),
    xaxis = list(title = ""),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )
```



Column {data-width=400}
-------------------------------------


### **Número de casos confirmados acumulados - Estados Selecionados** 
    
```{r}
df_daily_estados = df_saudegovbr %>% 
                    group_by(estado, data) %>%
                    summarise(confirmados = sum(casosNovos), 
                              obitos = sum(obitosNovos)) %>%
                    mutate(ativos = confirmados - obitos) %>%
                    mutate( confirmado_cum = cumsum(confirmados),
                      obito_cum = cumsum(obitos),
                      ativo_cum = cumsum(ativos)) %>%
                    tidyr::pivot_wider(names_from = estado, values_from = confirmado_cum)


plotly::plot_ly(data = df_daily_estados) %>%
  plotly::add_trace(
    x = ~data,
    y = ~DF,
    type = "scatter",
    mode = "lines+markers",
    name = "DF") %>%
    plotly::add_trace(
    x = ~data,
    y = ~SP,
    type = "scatter",
    mode = "lines+markers",
    name = "SP") %>%
  plotly::add_trace(
    x = ~data,
    y = ~RJ,
    type = "scatter",
    mode = "lines+markers",
    name = "RJ") %>%
  plotly::add_trace(
    x = ~data,
    y = ~CE,
    type = "scatter",
    mode = "lines+markers",
    name = "CE") %>%
  plotly::add_trace(
    x = ~data,
    y = ~MG,
    type = "scatter",
    mode = "lines+markers",
    name = "MG") %>%
  plotly::add_trace(
    x = ~data,
    y = ~AM,
    type = "scatter",
    mode = "lines+markers",
    name = "AM") %>%
  plotly::layout(
    title = "",
    yaxis = list(title = "Número acumulado de casos"),
    xaxis = list(title = "Data", range=c('2020-02-25','2020-04-25')),
    legend = list(x = 0.1, y = 0.9),
    hovermode = "compare"
  )
```

### **Número de novos casos confirmados por dia - Estados Selecionados** 
    
```{r}
df_daily_estados = df_saudegovbr %>% 
                    group_by(estado, data) %>%
                    summarise(confirmados = sum(casosNovos), 
                              obitos = sum(obitosNovos)) %>%
                    mutate(ativos = confirmados - obitos) %>%
                    mutate( confirmado_cum = cumsum(confirmados),
                      obito_cum = cumsum(obitos),
                      ativo_cum = cumsum(ativos)) %>%
                    tidyr::pivot_wider(names_from = estado, values_from = confirmados)


plotly::plot_ly(data = df_daily_estados) %>% 
    #plotly::layout(yaxis = list(type = "log")) %>%
    plotly::add_trace(
    x = ~data,
    y = ~DF,
    type = "scatter",
    mode = "lines+markers",
    name = "DF") %>%
    plotly::add_trace(
    x = ~data,
    y = ~SP,
    type = "scatter",
    mode = "lines+markers",
    name = "SP") %>%
  plotly::add_trace(
    x = ~data,
    y = ~RJ,
    type = "scatter",
    mode = "lines+markers",
    name = "RJ") %>%
    plotly::add_trace(
    x = ~data,
    y = ~CE,
    type = "scatter",
    mode = "lines+markers",
    name = "CE") %>%
  plotly::add_trace(
    x = ~data,
    y = ~MG,
    type = "scatter",
    mode = "lines+markers",
    name = "MG") %>%
  plotly::add_trace(
    x = ~data,
    y = ~AM,
    type = "scatter",
    mode = "lines+markers",
    name = "AM") %>%
  plotly::layout(
    title = "",
    yaxis = list(title = "Número de novos casos confirmados por dia"),
    xaxis = list(title = "Data",range=c('2020-02-25','2020-04-25')),
    legend = list(x = 0.1, y = 0.9),
    hovermode = "compare"
  )
```

### **Número de novos casos confirmados por dia (Regressão Linear)- Estados Selecionados** 
    
```{r}

fit_DF = lm(log(DF) ~ data, data = df_daily_estados, subset=DF>0)
fit_SP = lm(log(SP) ~ data, data = df_daily_estados, subset=SP>0)
fit_RJ = lm(log(RJ) ~ data, data = df_daily_estados, subset=RJ>0)

#----------------------------------------
# Plotting the data
df_daily_estados %>%
  plotly::plot_ly() %>%
  #plotly::layout(yaxis = list(type = "log")) %>%
  plotly::add_trace(
    x = ~data,
    y = ~log(DF),
    type = "scatter",
    mode = "markers",
    name = "DF",
    color=I('blue')
  ) %>%
  plotly::add_lines(
    x = ~data[as.numeric(names(fit_DF$fitted.values))],
    y = fitted(fit_DF),
    name="DF reg.linear",
    color=I('blue')
    ) %>%
    plotly::add_trace(
    x = ~data,
    y = ~log(SP),
    type = "scatter",
    mode = "markers",
    name = "SP",
    color=I('green')
  ) %>%
  plotly::add_lines(
    x = ~data[as.numeric(names(fit_SP$fitted.values))],
    y = fitted(fit_SP),
    name="SP reg.linear",
    color=I('green')
    ) %>%
  plotly::add_trace(
    x = ~data,
    y = ~log(RJ),
    type = "scatter",
    mode = "markers",
    name = "SP",
    color=I('red')
  ) %>%
  plotly::add_lines(
    x = ~data[as.numeric(names(fit_RJ$fitted.values))],
    y = fitted(fit_RJ),
    name="RJ reg.linear",
    color=I('red')
    ) %>%
  plotly::layout(
    title = "",
    legend = list(x = .1, y = 0.8),
    yaxis = list(title = "Número de novos casos confirmados (por dia) - Log"),
    xaxis = list(title = "Data", range=c('2020-02-25','2020-04-21')),
    # paper_bgcolor = "black",
    # plot_bgcolor = "black",
    # font = list(color = 'white'),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )




```


### **Número de novos óbitos por dia - Estados Selecionados** 
    
```{r}
df_daily_estados = df_saudegovbr %>% 
                    group_by(estado, data) %>%
                    summarise(confirmados = sum(casosNovos), 
                              obitos = sum(obitosNovos)) %>%
                    mutate(ativos = confirmados - obitos) %>%
                    mutate( confirmado_cum = cumsum(confirmados),
                      obito_cum = cumsum(obitos),
                      ativo_cum = cumsum(ativos)) %>%
                    tidyr::pivot_wider(names_from = estado, values_from = obitos)


plotly::plot_ly(data = df_daily_estados) %>% 
    #plotly::layout(yaxis = list(type = "log")) %>%
    plotly::add_trace(
    x = ~data,
    y = ~DF,
    type = "scatter",
    mode = "lines+markers",
    name = "DF") %>%
    plotly::add_trace(
    x = ~data,
    y = ~SP,
    type = "scatter",
    mode = "lines+markers",
    name = "SP") %>%
  plotly::add_trace(
    x = ~data,
    y = ~RJ,
    type = "scatter",
    mode = "lines+markers",
    name = "RJ") %>%
    plotly::add_trace(
    x = ~data,
    y = ~CE,
    type = "scatter",
    mode = "lines+markers",
    name = "CE") %>%
  plotly::add_trace(
    x = ~data,
    y = ~MG,
    type = "scatter",
    mode = "lines+markers",
    name = "MG") %>%
  plotly::add_trace(
    x = ~data,
    y = ~AM,
    type = "scatter",
    mode = "lines+markers",
    name = "AM") %>%
  plotly::layout(
    title = "",
    yaxis = list(title = "Número de novos óbitos por dia"),
    xaxis = list(title = "Data",range=c('2020-02-25','2020-04-25')),
    legend = list(x = 0.1, y = 0.9),
    hovermode = "compare"
  )
```


Números no mundo
=======================================================================


Column {data-width=400}
-------------------------------------


### **Novos casos diários**
    
```{r}

daily_confirmed <- coronavirus %>%
  dplyr::filter(type == "confirmed") %>%
  dplyr::filter(date >= "2020-02-26") %>%
  dplyr::mutate(country = Country.Region) %>%
  dplyr::group_by(date, country) %>%
  dplyr::summarise(total = sum(cases)) %>%
  dplyr::ungroup() %>%
  tidyr::pivot_wider(names_from = country, values_from = total)

elements = 28:(length(df_daily_br$confirmados))

#----------------------------------------
# Plotting the data
daily_confirmed %>%
  plotly::plot_ly() %>%
  plotly::add_trace(
    x = ~date,
    y = ~df_daily_br$confirmados[elements],
    type = "scatter",
    mode = "lines+markers",
    name = "Brasil"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~France,
    type = "scatter",
    mode = "lines+markers",
    name = "França"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Spain,
    type = "scatter",
    mode = "lines+markers",
    name = "Espanha"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Italy,
    type = "scatter",
    mode = "lines+markers",
    name = "Itália"
  ) %>%
    plotly::add_trace(
    x = ~date,
    y = ~Germany,
    type = "scatter",
    mode = "lines+markers",
    name = "Alemanha"
  ) %>%
    plotly::add_trace(
    x = ~date,
    y = ~Sweden,
    type = "scatter",
    mode = "lines+markers",
    name = "Suécia"
  ) %>%
  plotly::layout(
    title = "",
    legend = list(x = 0.1, y = 0.9),
    yaxis = list(title = "Número de novos casos confirmados (por dia)"),
    xaxis = list(title = "Data"),
    # paper_bgcolor = "black",
    # plot_bgcolor = "black",
    # font = list(color = 'white'),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )
```

### **Número de novos casos diários a partir de 150 novos casos confirmados (Log) e os dias decorridos desde o registro**


```{r , echo=FALSE}
x <- coronavirus[order(coronavirus$date, coronavirus$Country.Region, decreasing = TRUE), ]

onset = x %>% 
          group_by(Country.Region) %>% 
          #filter(Country.Region == c('Brazil', 'Italy', 'France')) %>% 
          filter(type=='confirmed')%>% filter(cases >= 150) %>% 
          mutate(days_since_case_onset := as.integer(date - min(date)))

# corrigindo a base para os dados do Ministério da Saúde
onset_br = rev(df_daily_br$confirmados[df_daily_br$confirmados > 150])

onset %>%
  plotly::plot_ly()%>%
  plotly::layout(yaxis = list(type = "log")) %>%
  group_by(Country.Region) %>% 
  plotly::add_trace(
    x = ~subset(onset$days_since_case_onset, Country.Region=='Brazil'),
    y = ~onset_br[1:(length(onset_br))],
    type = "scatter",
    mode = "lines+markers",
    name = "Brasil"
  ) %>%
  plotly::add_trace(
    x = ~subset(onset$days_since_case_onset, Country.Region=='France'),
    y = ~subset(onset$cases, Country.Region=='France'),
    type = "scatter",
    mode = "lines+markers",
    name = "França"
  ) %>%
  plotly::add_trace(
    x = ~subset(onset$days_since_case_onset, Country.Region=='Spain'),
    y = ~subset(onset$cases, Country.Region=='Spain'),
    type = "scatter",
    mode = "lines+markers",
    name = "Espanha"
  ) %>%
  plotly::add_trace(
    x = ~subset(onset$days_since_case_onset, Country.Region=='Italy'),
    y = ~subset(onset$cases, Country.Region=='Italy'),
    type = "scatter",
    mode = "lines+markers",
    name = "Itália"
  ) %>%
    plotly::add_trace(
    x = ~subset(onset$days_since_case_onset, Country.Region=='Germany'),
    y = ~subset(onset$cases, Country.Region=='Germany'),
    type = "scatter",
    mode = "lines+markers",
    name = "Alemanha"
  ) %>%
    plotly::add_trace(
    x = ~subset(onset$days_since_case_onset, Country.Region=='Sweden'),
    y = ~subset(onset$cases, Country.Region=='Sweden'),
    type = "scatter",
    mode = "lines+markers",
    name = "Suécia"
  ) %>%
  plotly::layout(
    title = "",
    legend = list(x = 0.1, y = 0.9),
    yaxis = list(title = "Número de novos casos conf
                 irmados (por dia)"),
    xaxis = list(title = "Dias decorridos desde o caso de número 150"),
    hovermode = "compare",
    margin = list(
    b = 10,
    t = 10,
    pad = 2
    )
  )
```


### **Distribuição dos casos por tipo**

```{r daily_summary}
df_EU <- coronavirus %>%
  # dplyr::filter(date == max(date)) %>%
  dplyr::filter(Country.Region == "Brazil" |
    Country.Region == "France" |
    Country.Region == "Italy" |
    Country.Region == "Spain" |
    Country.Region == "Germany" |
    Country.Region == "Sweden")%>%
  dplyr::group_by(Country.Region, type) %>%
  dplyr::summarise(total = sum(cases)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
  dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
  dplyr::arrange(confirmed) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(country = dplyr::if_else(Country.Region == "United Arab Emirates", "UAE", Country.Region)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
  dplyr::mutate(country = trimws(country)) %>%
  dplyr::mutate(country = factor(country, levels = country))
plotly::plot_ly(
  data = df_EU,
  x = ~country,
  y = ~unrecovered,
  # text =  ~ confirmed,
  # textposition = 'auto',
  type = "bar",
  name = "Casos Ativos",
  marker = list(color = active_color)
) %>%
  plotly::add_trace(
    y = ~death,
    # text =  ~ death,
    # textposition = 'auto',
    name = "Mortes",
    marker = list(color = death_color)
  ) %>%
  plotly::layout(
    barmode = "stack",
    yaxis = list(title = "Total de casos"),
    xaxis = list(title = ""),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )
```


Modelo de Previsão para o Brasil
=======================================================================


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


### R0 {.value-box}

```{r}
library(deSolve)

Infected = df_daily_br$confirmados[df_daily_br$confirmados >= 0]
N = 211000000 # população Brasileira
Day <- 1:(length(Infected))

init <- c(S = N-Infected[match(1,Infected)], I = Infected[match(1,Infected)], R = 0)

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/N * I * S
    dI <- beta/N * I * S - 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)) # optimize with some sensible conditions
#Opt$message
## [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH"

Opt_par <- setNames(Opt$par, c("beta", "gamma"))

## Warning in xy.coords(x, y, xlabel, ylabel, log = log): 1 y value <= 0
## omitted from logarithmic plot

t <- 1:240 # time in days
fit <- data.frame(ode(y = init, times = t, func = SIR, parms = Opt_par))
col <- 1:3 # colour

R0 <- setNames(Opt_par["beta"] / Opt_par["gamma"], "R0")

##       R0 
## 2.073224

pico = fit[fit$I == max(fit$I), "I", drop = FALSE] # height of pandemic
##            I
## 50 232001865

max_mortes = max(fit$I) * 0.02 # max deaths with supposed 2% fatality rate
## [1] 4640037



valueBox(
  value = paste(format(round(R0,2), decimal.mark =  ",")
  ),
  caption = "Taxa Básica de Reprodução (R0)", icon = "fas fa-user-md",
  color = active_color
)



```

### infectados_pico {.value-box}

```{r}


valueBox(
  value = paste(format(pico, big.mark = ".", decimal.mark =  ",")," (",
    round(100 * pico / N, 1),
    "%)",
    sep = ""
  ),
  caption = "Número máximo de infectados", icon = "fas fa-ambulance",
  color = active_color
)



```

### infectados_early5days {.value-box}

```{r}
peakday = match(max(fit$I), fit$I)
today = length(Infected)


valueBox(
  value = paste(format(round(peakday,0), big.mark = ".", decimal.mark =  ",")," ( daqui a ",
    round(peakday - today, 1),
    "dias)",
    sep = ""
  ),
  caption = "Dia da epidemia em que ocorre o pico", icon = "fas fa-ambulance",
  color = active_color
)



```

### infectados_5days {.value-box}

```{r}


fivedaysahead = length(Infected) + 5
valueBox(
  value = paste(format(round(sum(fit$I[1:fivedaysahead]),0), big.mark = ".", decimal.mark =  ","),
    sep = ""
  ),
  caption = "Número total de infectados nos próximos 5 dias", icon = "fas fa-ambulance",
  color = active_color
)



```

### infectados_early5days {.value-box}

```{r}


today = length(Infected)
valueBox(
  value = paste(format(round(sum(fit$I[1:today]),0), big.mark = ".", decimal.mark =  ","),
    sep = ""
  ),
  caption = "Número total de infectados hoje", icon = "fas fa-ambulance",
  color = active_color
)



```

### mortes_pico {.value-box}

```{r}


valueBox(
  value = paste(format(max_mortes, big.mark = ".", decimal.mark =  ",")," (",
    round(100 * max_mortes / N, 2),
    "%)",
    sep = ""
  ),
  caption = "Número de mortos", icon = "fas fa-heart-broken",
  color = active_color
)



```


Column {data-width=400}
-------------------------------------



### **Modelo de regressão linear para os novos casos diários (escala logarítmica)  **
    
```{r}


#----------------------------------------
# linear fit for data in log scale
fit_br = lm(log(confirmados) ~ data, data = df_daily_br, subset=confirmados>0)
fit_sp = lm(log(Spain) ~ date, data = daily_confirmed, subset=Spain>0)
fit_it = lm(log(Italy) ~ date, data = daily_confirmed, subset=Italy>0)
fit_fr = lm(log(France) ~ date, data = daily_confirmed, subset=France>0)

names(fit_br$fitted.values) = as.character(seq(1:length(fit_br$fitted.values)))

p <- ggplot(df_daily_br, aes(x=data, y=log(confirmados))) +
    geom_point(shape=1) +    # Use hollow circles
    geom_smooth(method=lm) +  # Add linear regression line
    xlab('Data') + ylab('log. casos confirmados')



fig <- plotly::ggplotly(p)
fig






```


### **Modelo de Previsão(SIR:susceptible-infected-removed) para o Brasil (população de 211 milhões)** 


```{r }
#----------------------------------------
# Plotting the data
fit %>%
  plotly::plot_ly() %>%
  plotly::layout(yaxis = list(type = "log")) %>%
  plotly::add_trace(
    x = ~time,
    y = ~S,
    type = "scatter",
    mode = "lines+markers",
    name = "Suscetíveis"
  ) %>%
  plotly::add_trace(
    x = ~time,
    y = ~I,
    type = "scatter",
    mode = "lines+markers",
    name = "Infectados"
  ) %>%
  plotly::add_trace(
    x = ~time,
    y = ~R,
    type = "scatter",
    mode = "lines+markers",
    name = "Recuperados"
  ) %>%
  plotly::add_trace(
    x = ~Day,
    y = ~Infected,
    type = "scatter",
    mode = "markers",
    name = "Confirmados"
  ) %>%
  plotly::layout(
    title = "",
    legend = list(x = 0.1, y = 0.9),
    yaxis = list(title = "Número de pessoas"),
    xaxis = list(title = "Data"),
    # paper_bgcolor = "black",
    # plot_bgcolor = "black",
    # font = list(color = 'white'),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )




```

### **Curva exponencial de infecção prevista para o Brasil ** 


```{r }
#----------------------------------------
# Plotting the data
fit %>%
  plotly::plot_ly() %>%
  plotly::add_trace(
    x = ~time,
    y = ~I,
    type = "scatter",
    mode = "lines+markers",
    name = "Infectados"
  ) %>%
  plotly::add_trace(
    x = ~Day,
    y = ~Infected,
    type = "scatter",
    mode = "markers",
    name = "Confirmados"
  ) %>%
  plotly::layout(
    title = "",
    legend = list(x = 0.1, y = 0.9),
    yaxis = list(title = "Número de pessoas", range=c(1,4.5e3)),
    xaxis = list(title = "Data", range=c(1,120)),
    # paper_bgcolor = "black",
    # plot_bgcolor = "black",
    # font = list(color = 'white'),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )

```

Mapa
=======================================================================

### **Mapa mundial dos casos** (*use + e - para dar zoom in/out*)

```{r}
# map tab added by Art Steinmetz
library(leaflet)
library(leafpop)
library(purrr)
cv_data_for_plot <- coronavirus %>%
  # dplyr::filter(Country.Region == "Belgium") %>%
  dplyr::filter(cases > 0) %>%
  dplyr::group_by(Country.Region, Province.State, Lat, Long, type) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::mutate(log_cases = 2 * log(cases)) %>%
  dplyr::ungroup()
cv_data_for_plot.split <- cv_data_for_plot %>% split(cv_data_for_plot$type)
pal <- colorFactor(c("orange", "red", "green"), domain = c("confirmed", "death", "recovered"))
map_object <- leaflet() %>% addProviderTiles(providers$Stamen.Toner)
names(cv_data_for_plot.split) %>%
  purrr::walk(function(df) {
    map_object <<- map_object %>%
      addCircleMarkers(
        data = cv_data_for_plot.split[[df]],
        lng = ~Long, lat = ~Lat,
        #                 label=~as.character(cases),
        color = ~ pal(type),
        stroke = FALSE,
        fillOpacity = 0.8,
        radius = ~log_cases,
        popup = leafpop::popupTable(cv_data_for_plot.split[[df]],
          feature.id = FALSE,
          row.numbers = FALSE,
          zcol = c("type", "cases", "Country.Region", "Province.State")
        ),
        group = df,
        #                 clusterOptions = markerClusterOptions(removeOutsideVisibleBounds = F),
        labelOptions = labelOptions(
          noHide = F,
          direction = "auto"
        )
      )
  })
map_object %>%
  addLayersControl(
    overlayGroups = names(cv_data_for_plot.split),
    options = layersControlOptions(collapsed = FALSE)
  )
```