dashboard.Rmd

---
title: "India against COVID19"
output: html_document
knit: (function(input_file, encoding) {
  out_dir <- 'docs';
  rmarkdown::render(
    input_file,
    encoding=encoding,
    output_file=file.path(
      dirname(input_file), 
      out_dir, 
      'index.html'  
      )
    )
  })
---


```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_chunk$set(fig.width=8, fig.height=5) 
```

```{r setup libraries, include=FALSE}
  library(tidyverse)
  library(lubridate)
  library(viridis)
  library(readr)
  library(janitor)
  library(kableExtra)
  library(gganimate)
  library(animation)
  library(gifski)
  library(jsonlite)
  # library(plotly)
  # library(plotly)
  source('./source_crowd_data_v2.r')
  source('./source_official_data.r')
  source('./source_world_data.R')
```

```{r calculate data, include = FALSE}

  
  dc <- build_crowd_data()
  dc_i <- dc$dc_i
  dc_raw_i <- dc$dc_raw_i  
  dc_age_i <- dc_raw_i %>% 
    mutate(AgeBracket = as.numeric(AgeBracket)) %>% 
    filter(!is.na(AgeBracket)) %>% 
    ungroup()
  
  do_i <- build_official_data()
  d_i_wide <- rbind(do_i, dc_i)
  d_i_long <- d_i_wide %>% pivot_longer(
    -c(StateUt, Date, Source), 
    values_to = 'Cumulative', 
    names_to = 'Status')
  ## Statewise Data
  
  data_panindia_total<- d_i_wide %>% 
    group_by(Date, Source) %>% 
    summarize(Total = sum(Total)) %>%
    mutate(StateUt = "India Total") %>% 
    ungroup()

  data_states_total <- d_i_wide %>% 
    select(StateUt, Source, Date, Total) %>% 
    ungroup()
  
  data_i_total <- rbind(data_panindia_total, data_states_total)
  
  data_i_aggregated_long <- d_i_long %>%
    group_by(Date, Source, Status) %>% 
    summarise(Cumulative = sum(Cumulative)) %>% 
    filter( Date > dmy('03-03-2020')) %>%
    ungroup() 
    
  
  ## World Data
  from <- dmy('1-02-20')

  d_w_wide <- source_world_data()
  
  data_w_long <- d_w_wide %>% select(-c(Lat, Long)) %>% filter(Date > from) %>%
    pivot_longer(-c(StateUt, Country,  Date), values_to = 'Total', names_to = 'Status')
  
  data_total_w <- data_w_long %>%
    group_by(Date, Status) %>% 
    summarize( Total = sum(Total) )  %>% ungroup() 
  
  data_rate_w <- data_total_w %>% 
    group_by(Status) %>%
    mutate(TotalPrev = lag(Total, default = 0, order_by = Date )) %>% 
    mutate(DelTotal = Total - TotalPrev) %>%
    mutate(DatePrev = lag(Date, order_by = Date )) %>%
    filter(!is.na(DatePrev)) %>% mutate(DelDay = as.integer(Date-DatePrev)) %>%
    mutate( Rate1 = DelTotal*100/(DelDay*TotalPrev) ) %>%
    ungroup() %>%
    filter(!(Status %in% c('Hospitalized', 'Deaths')))

```

<!-- ## Current Status -->

```{r echo=F, results='asis', layout="l-body-outset"}

  data_today <- data_i_aggregated_long %>%
    filter(Date == Sys.Date() ) %>%
    pivot_wider(values_from = Cumulative, names_from = Source) %>%
    select(-c(Date)) %>% filter(!(Status %in% c('NA', NA)))
  
  kable(data_today) %>% 
    kable_styling(
      bootstrap_options = c("striped", "hover"), 
      full_width = F, 
      position = 'float_right')
  
```

**Crowd Sourced Data: ** https://www.covid19india.org/

**Official Data: ** Ministry of Health and Family Welfare Data hosted on this repository
https://github.com/Impactech/covid19_india_data

**World Data: ** Repository hosted by Johns Hopkins CSSE
https://github.com/CSSEGISandData/COVID-19

Last update at **`r Sys.time()`**

## Different cross sections of COVID19 data {.tabset .tabset-fade}

&nbsp;
&nbsp;

### **Pan India**

&nbsp;

####  Daily growth rate 
The solid lines are smoothed conditional means, which indicated the overall trend. 
The dots and the soft lines represent actual data. 

Growth rate is calculated as:

$$G_{current} = (Total_{previous}- Total_{current})/Total_{previous}$$

```{r echo=FALSE, fig.cap="Daily Growth rate of COVID19 cases in India", message=FALSE, warning=FALSE}

  data_i_rate <- d_i_wide  %>% select(StateUt, Date, Total, Source) %>% 
    group_by(Date, Source) %>%
    summarise(Total = sum(Total)) %>% group_by(Source) %>%
    mutate( TotalPrev = lag(Total, default = 0, order_by = Date)) %>%
    mutate(  DatePrev = lag(Date, order_by = Date) ) %>% 
    mutate(DateCurPrev = as.integer(Date - DatePrev) ) %>%
    filter( Date > ymd('2020-03-03')	) %>%
    mutate( DailyRate = round((Total - TotalPrev)*100/(DateCurPrev*TotalPrev) ) ) %>%
    ungroup()

  ggplot(
    data = data_i_rate,  
    aes(x = Date, y = DailyRate, group = Source, color = Source, fill=Source)
    ) + 
  geom_vline(xintercept = dmy('24-03-2020'), linetype = '11' ) + 
  annotate(
  "text", x = dmy('25-03-2020'), y = 34, 
  size = 5, label = "Lockdown", hjust = 0, fontface = 1 ) +
  theme_minimal() +
  geom_line(linetype = 1, alpha = 0.3, size = 1) + 
  geom_point(alpha = 0.3, size = 3, stroke = 1, shape = 21, fill = 'white') +  
  geom_smooth(alpha = 0.05, size=1, se = F) +
  labs(y = "Growth rate in Percentage")  + 
  ylim(c(-10, 50)) +
  theme(legend.position = c(0.2, 0.8) ) +
  scale_fill_viridis_d(end = 0.6) + scale_color_viridis_d(end=0.6)
# 
#   ggplotly(p) %>% layout(legend = list(
#       orientation = "h"
#     )
#   ) %>% config(displayModeBar = F)
```

Code: https://github.com/Impactech/COVID19_India/blob/master/IndiaGrowthRate_v2.R


&nbsp;

#### New cases per day

```{r include=F}
  data_i_new_cases <- d_i_wide  %>% select(StateUt, Date, Total, Source) %>% 
    group_by(Date, Source) %>%
    summarise(Total = sum(Total)) %>% group_by(Source) %>%
    mutate( TotalPrev = lag(Total, default = 0, order_by = Date)) %>%
    filter( Date > ymd('2020-03-03')	) %>%
    mutate( NewCases = (Total - TotalPrev) ) %>%
    ungroup()
```


```{r new_cases, echo=FALSE, message=FALSE, warning=FALSE, fig.height=3, fig.width=8}
  ggplot(
    data = data_i_new_cases,  
    aes(x = Date, y = NewCases, group = Source, color = Source, fill=Source)
    ) + 
    geom_vline(xintercept = dmy('24-03-2020'), linetype = '11' ) + 
    annotate(
      "text", x = dmy('25-03-2020'), y = 250, 
      size = 5, label = "Lockdown", hjust = 0, fontface = 1 ) +
    theme_minimal() +
    geom_line(linetype = 1, alpha = 0.8, size = 1) + 
    geom_point(alpha = 0.8, size = 3, stroke = 1, shape = 21, fill = 'white') + 
    # geom_smooth(alpha = 0.05, size=0.6, se = F) +
    labs(y = "Number of new cases reported")  + 
    # ylim(c(-10, 50)) +
    theme(
      legend.position = c(0.4, 0.6),
      panel.grid.minor = element_blank()
      ) +
    scale_fill_viridis_d(end = 0.6) + scale_color_viridis_d(end=0.6)

```

Code: https://github.com/Impactech/COVID19_India/blob/master/DailyNewCasesIndia.R

&nbsp;

#### Cumulative

```{r include = FALSE}
  data_label <- data_i_aggregated_long %>% filter(Status == 'Total') %>% 
    filter( Date > Sys.Date()-5 )
  data_split_status <- data_i_aggregated_long %>% 
    filter(!(Status %in% c('Total', 'NA', NA)))
```


```{r echo=FALSE, fig.cap="Total number of COVID19 cases in India by Date", message=FALSE, warning=FALSE, fig.height = 8, fig.width = 8}

  ggplot(
      data = data_split_status,
      aes(x = Date, y = Cumulative, fill=Status)
    ) +      
    theme_minimal() +
    geom_bar(stat = 'identity') + facet_wrap(~Source, ncol = 1) + 
    geom_text(
      data = data_label, 
      aes(label = Cumulative), show.legend = F,
      angle = 90, vjust = 0.5, hjust = 0, nudge_y = 50, nudge_x = 0) + 
    geom_vline(xintercept = dmy('24-03-2020'), linetype = '11' ) + 
    annotate(
    "text", x = dmy('25-03-2020'), y = 2000, 
    size = 5, label = "Lockdown", hjust = 0, fontface =1 ) +
    theme(
      legend.position = c(0.2, 0.8),
      plot.margin = margin(0, 0, 0, 0)
      ) + coord_cartesian(clip = 'off') +
    scale_fill_viridis_d(begin = 0.1, end = 1) + 
    scale_color_viridis_d(begin = 0.1, end=1) 
  

```

Code: https://github.com/Impactech/COVID19_India/blob/master/IndiaCumulativeCases_v2.R

### **Statewise** 
&nbsp;

```{r 'Statewise Data Calculation', include = F }

  data_statewise <- data_i_total %>% group_by(Source) %>%
    mutate(Day = as.integer(strftime(Date, format = "%j")) ) %>%
    ungroup() %>%
    ###
    group_by(StateUt, Source) %>% 
    mutate( DaysSince0 = Day - min(Day)  ) %>% 
    mutate( MaxDays = max(DaysSince0) ) %>%
    mutate( MaxCases = max(Total) ) %>%
    ungroup() %>%
    ###
    mutate(label =  paste(MaxCases, StateUt, sep=" | ")) %>%
    filter(Date > dmy('01-03-2020'))


d_i_long_total <- d_i_long %>% 
  filter(Status == 'Total') %>% 
  group_by(Source) %>%
  filter(Date == max(Date)) %>%
  ungroup() %>%
  filter(Cumulative > 5) %>%
  filter(!(StateUt == 'unassigned'))

d_i_long_total$StateUt[d_i_long_total$StateUt == "Telengana"] <- "Telangana"


```

```{r 'statewise growth', include=F}

data_i_statewise_growth <- rbind(do_i, dc_i) %>% 
    ungroup() %>%
    mutate(
      StateUt = ifelse( 
        (StateUt %in% c('Telengana')), 'Telangana', StateUt ) 
      ) %>%
    group_by(StateUt, Source) %>% 
    mutate(MaxCases = max(Total)) %>% 
    filter(Date >= dmy('25-03-2020')) %>%
    filter(Source == 'Ministry of Health and Family Welfare') %>%
    ungroup() 

threshold <- min(tail(sort(unique(data_i_statewise_growth$MaxCases)), 6))

data_i_statewise_growth <- data_i_statewise_growth %>% 
  filter(MaxCases >= threshold) %>%
  select(StateUt, Date, Total, Source, MaxCases) %>% 
  group_by(Date, Source, StateUt, MaxCases) %>%
  summarise( Total = sum(Total)) %>% 
  ungroup() %>%
  group_by(Source, StateUt, MaxCases) %>%
  mutate( TotalPrev = lag(Total, default = 0, order_by = Date)) %>%
  mutate( DatePrev = lag(Date, order_by = Date) ) %>% 
  mutate( DateCurPrev = as.integer(Date - DatePrev) ) %>%
  mutate( 
    DailyRate = round((Total - TotalPrev)*100/(DateCurPrev*TotalPrev) ) 
    ) %>%
  mutate( NewCases = (Total - TotalPrev) ) %>%
  ungroup() %>%
  select(-c(Total, TotalPrev, DatePrev, DateCurPrev)) %>%
  pivot_longer(-c(Date, Source, StateUt, MaxCases), names_to = 'Metric') %>%
  mutate(Series = paste(StateUt, MaxCases, sep = ' | '))
```  

#### Statewise Growth Rate of the worst affected states
&nbsp;

The following plot is the Smoothed conditional mean of the cumulative daily growth rate. The actual data is noisy. Smoothed mean gives a better picture of the overall trend.  



```{r 'statwise growth plot', echo = F, message=F, warning=F, fig.height = 6, fig.width = 8}
ggplot(
    data = data_i_statewise_growth,  
    aes(x = Date, y = value, color=reorder(Series, -MaxCases))
    ) + 
    facet_wrap(~Metric, ncol=1, scales = 'free_y') +
    geom_smooth(size=1.2, alpha = 0.2 , se = F) + 
    theme_minimal() +
    theme(
      legend.title = element_blank(), 
      axis.title = element_blank(),
      axis.text.x = element_text(angle = 30),
      strip.text = element_text(hjust = 0),
      legend.position = 'top'
    )

```

Code: https://github.com/Impactech/COVID19_India/blob/master/IndiaGrowthRateStatewise.R

Data: Ministry of Health and Family Welfare

&nbsp;

#### State wise split of cumulative number of cases as per last update.
&nbsp;

```{r echo=FALSE, fig.cap="Statewise Split", message=FALSE, warning=FALSE, fig.height = 5, fig.width = 8}
 ggplot(
      data = d_i_long_total, 
      aes(x = reorder( StateUt, Cumulative ), y = Cumulative, fill = Source)
      ) + 
    geom_bar(position = 'dodge', stat = 'identity') +
    theme(
      legend.position = c(0.4, 0.8),
      panel.grid.major = element_blank(),
      panel.background = element_blank(),
      axis.title.x = element_blank(),
      axis.text.x = element_text(angle = 45, hjust = 1)
      ) + scale_fill_viridis_d(option = 'cividis', end = 0.9)
```

Code: https://github.com/Impactech/COVID19_India/blob/master/IndiaStateWise.R

&nbsp;

#### Statewise growth in cumulative number of cases. 
&nbsp;


```{r echo=FALSE, fig.cap="Statewise Split", message=FALSE, warning=FALSE, fig.height = 8, fig.width = 8}

  ggplot(
      data = data_statewise %>% filter(Source == 'Crowd Source'), 
      aes(x=Date, y=reorder(label, MaxCases), 
          fill = StateUt, size = Total^(0.3))
      ) +
    geom_point(shape = 21, color = 'white') + 
    geom_vline(xintercept = dmy('24-03-2020'), linetype = '11' ) + 
    annotate(
    "text", x = dmy('25-03-2020'), y = 2, 
    size = 4, label = "Lockdown", hjust = 0.5, vjust = -1, fontface =1  ) +
    theme_minimal() + 
    theme(
      legend.position = 'off',
      axis.title.x = element_blank(),
      axis.title.y = element_blank(),
      # panel.grid.major = element_blank(), 
      panel.grid.minor = element_blank(),
      axis.text.y = element_text(size=10,face="bold"),
      axis.text.x = element_text(angle = 30, hjust=0)
    ) + 
    scale_color_viridis_d(option="inferno", end = 0.9) + 
    scale_fill_viridis_d(option="inferno", end = 0.9) + 
    scale_y_discrete(position = "right") +
    scale_x_date(position = "top", date_breaks = '7 days') +
    scale_radius(
      range = c(1, 12),
      trans = "identity",
      guide = "legend"
    ) + 
    coord_cartesian(clip = 'off') + 
    theme(legend.position='none')

```

Data: Crowd sourced

Code: https://github.com/Impactech/COVID19_India/blob/master/IndiaTimeseries_v3.R

&nbsp;


### **Animation**

&nbsp;

Statewise progression of the cumulative number of cases against the number of days since first report

```{r include=F}

animate <- T

###

  data_anim_2 <-  do_i %>% select(StateUt, Date, Total) %>% ungroup() %>%
    mutate(Day = as.integer(strftime(Date, format = "%j")) ) %>%
    group_by(StateUt) %>%
    mutate( DaysSince0 = Day - min(Day)  ) %>%
    mutate( MaxDays = max(DaysSince0) ) %>%
    ungroup() %>%
    filter(Total > 2) %>%
    mutate(label =  paste(Total, StateUt, sep=" | "))

  y_max <- max(data_anim_2$Total)
  x_max <- max(data_anim_2$DaysSince0)
  x_label <- x_max + 2

  labels_anim_2 <- data_anim_2 %>%
    filter(Total > 50) %>%
    filter(Date == Sys.Date()-1 ) %>%
    arrange( Total ) %>%
    mutate(yend = (y_max/n())*row_number()) %>%
    select(StateUt, yend)

  data_anim_2 <- left_join(data_anim_2, labels_anim_2, by = c("StateUt") )

###
  # data_anim <- data_i_total %>% 
  #   filter(Source == 'Crowd Source') %>%
  #   mutate(Day = as.integer(strftime(Date, format = "%j")) ) %>%
  #   group_by(StateUt) %>% 
  #   mutate( DaysSince0 = Day - min(Day)  ) %>% 
  #   mutate( MaxDays = max(DaysSince0) ) %>%
  #   mutate( MaxCases = max(round(Total))) %>%
  #   ungroup() %>%
  #   mutate(label =  paste(Total, StateUt, sep=" | "))
###

```

```{r animation_2, echo=FALSE, message=F, warning=F, fig.height = 6, fig.width = 10}

  if (animate) {
    p2 <- ggplot(
          data = data_anim_2,
          aes(x=DaysSince0, y=Total, fill = StateUt, size = Total^(0.5), color=StateUt )
          )  +
        geom_point(shape = 21, color = 'black', alpha=0.7) +
        geom_line(size = 0.3, alpha=0.5)  +
        geom_label(
          aes(x=x_label, y = yend, label = label, fill=StateUt),
          color='white',
          hjust = 0,
          size=3) +
        geom_segment(
          aes(xend = x_label, yend = yend, color = StateUt),
          linetype = "11",
          size=0.2,
          alpha=0.5) +
        theme_minimal() +
        theme(
          axis.text=element_text(size=10, color = "darkgrey"),
          axis.title=element_text(size=12),
          legend.position = 'off',
          plot.title = element_text(size = 12, hjust = 0.5),
          plot.margin = margin(15, 100, 15, 10),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank()
          ) +
        coord_cartesian(clip = 'off')  +
        scale_color_viridis_d(option="inferno", end = 0.9) +
        scale_fill_viridis_d(option="inferno", end = 0.9) +
        xlab("Number of day since first report") +
        ylab("Number of cases (Cumulative)")  +
        scale_radius(
          range = c(1, 7),
          trans = "identity",
          guide = "legend"
        ) +
        labs(title = 'Date {frame_along}', fontface = "bold") +
        transition_time(Date) + ease_aes('cubic-in-out') +
        transition_reveal(Date)

    animate(
      p2,
      renderer=gifski_renderer( loop = T ),
      end_pause = 40
    )
  }
```

Code: https://github.com/Impactech/COVID19_India/blob/master/IndiaTimeseries_v2.R


<!-- ```{r animation_1, echo=FALSE, message=F, warning=F, fig.height = 6, fig.width = 10} -->
<!--   ani.options(end_pause = 30 ) -->
<!--   options( -->
<!--     gganimate.nframes = 120,  -->
<!--     gganimate.fps=8 -->
<!--   ) -->


<!--   if (animate) { -->
<!--     p <- ggplot( -->
<!--       data = data_anim, -->
<!--       aes( -->
<!--         x=reorder(StateUt, -MaxCases),  -->
<!--         y=DaysSince0,  -->
<!--         fill = Total^(0.3),  -->
<!--         size = Total^(0.3), -->
<!--         color = Total^(0.3) ) -->
<!--       )  + -->
<!--     geom_line(alpha=0.3) + -->
<!--     geom_label(aes(label = as.character(Total) ), size=3, color='white', nudge_x = 0) + -->
<!--     theme_minimal() + -->
<!--     theme( -->
<!--       legend.position = 'off', -->
<!--       axis.text.x = element_text(angle = 45, hjust = 1, size=8), -->
<!--       panel.grid.major = element_blank(), -->
<!--       panel.grid.minor = element_blank(), -->
<!--       axis.title.x = element_blank(), -->
<!--       plot.title = element_text(hjust = 0.5, size=13) -->
<!--       ) + -->
<!--     coord_cartesian(clip = 'off')  + -->
<!--     scale_color_viridis_c(option="inferno", direction = -1) + -->
<!--     scale_fill_viridis_c(option="inferno", direction = -1) + -->
<!--     ylab("Days since first case reported")  + -->
<!--     scale_radius( -->
<!--       range = c(1, 12), -->
<!--       trans = "identity", -->
<!--       guide = "legend" -->
<!--     ) + labs( title = '{frame_along}', fontface = "bold" ) + -->
<!--     transition_time(Date) + ease_aes('cubic-in-out') + -->
<!--     transition_reveal(Date) -->

<!--     animate( -->
<!--       p, -->
<!--       renderer=gifski_renderer( loop = T ), -->
<!--       end_pause = 35 -->
<!--     ) -->
<!--   } -->
<!-- ``` -->

<!-- Data: Crowd sourced -->

<!-- Code: https://github.com/Impactech/COVID19_India/blob/master/IndiaTimeseriesAnimation.R -->
&nbsp;


### **Yesterday**

&nbsp;

#### Cases reported since yesterday (including today)

```{r include=F}

dc_i_yesterday <- dc_raw_i %>% 
  filter(!(is.na(Date))) %>% 
  filter(Date == Sys.Date()-1) %>%
  group_by(StateUt, Status) %>% 
  summarise(Total = n())
```


```{r echo=FALSE, message=FALSE, warning=FALSE}
    ggplot(
      data = dc_i_yesterday, 
      aes(x = reorder( StateUt, Total ), 
          y = Total, 
          fill = reorder( StateUt, -Total )
          )) + 
    geom_bar(position = 'dodge', stat = 'identity') +
    geom_text(aes(label = Total), hjust = 0, vjust = 0.5, nudge_y = 10) +
    theme(
      legend.position = "off",
      panel.grid.major = element_blank(),
      panel.background = element_blank(),
      axis.title.x = element_blank(),
      axis.text.y = element_text(angle = 0, hjust = 1, size = 10, face = 2),
      axis.title.y = element_blank(),
      axis.text.x = element_blank(),
      axis.ticks = element_blank()
    ) + 
    scale_fill_viridis_d(option = 'cividis', end = 0.9) +
    coord_cartesian(clip = "off") + 
    coord_flip()

```


Data: Crowd sourced

Code: https://github.com/Impactech/COVID19_India/blob/master/IndiaYesterday.R


### **Weekly**

&nbsp;

Since the daily growth rate can be noisy, it may be helpful to see the weekly mean of daily growth rate to identify the tapering. 

The Weekly mean of daily growth rate is calculated as follows: 

$$ G_{WeeklyMean} = NewCases_{CurrentWeek}*100/(Sunday_{Previous} - Sunday_{Latest})*Cumulative_{PreviousWeek} $$


```{r include=F}
  
  d <-  Sys.Date()
  prev.days <- seq(d-6,d,by='day')
  PrevSunday <- prev.days[weekdays(prev.days)=='Sunday']
  
  dweek <- d_i_wide  %>% select(StateUt, Date, Total, Source) %>% 
    group_by(Date, Source) %>%
    summarise(Total = sum(Total)) %>% group_by(Source) %>%
    mutate( TotalPrev = lag(Total, default = 0, order_by = Date)) %>%
    filter( Date > ymd('2020-03-01')	) %>%
    mutate( NewCases = (Total - TotalPrev) ) %>%
    ungroup() %>%
    mutate(week = lubridate::isoweek(Date)) %>% 
    group_by(week) %>%
    mutate(WeekEnd = max(Date)) %>% 
    ungroup() %>%
    group_by(WeekEnd, Source) %>%
    summarise(NewCases = sum(NewCases), TotalCases = max(Total)) %>% 
    group_by(Source) %>%
    mutate(TotalCasesPrev = lag(TotalCases, order_by = WeekEnd)) %>%
    mutate(WeeklyMeanRate = NewCases*100/(7*TotalCasesPrev)) %>%
    filter(!is.na(WeeklyMeanRate), WeekEnd <= PrevSunday) %>%
    select(WeekEnd, Source, NewCases, WeeklyMeanRate) %>%
    pivot_longer(-c(WeekEnd, Source), names_to = 'Metric', values_to = 'Value')

  
```

```{r echo=FALSE, message=FALSE, warning=FALSE}

  ggplot(
    data = dweek,  
    aes(x = WeekEnd, y = Value, group = Source, color = Source, fill=Source)
    ) + facet_wrap(~Metric, ncol = 1, scales = "free_y") +
    geom_vline(xintercept = dmy('24-03-2020'), linetype = '11' ) + 
    theme_minimal() +
    geom_line(aes(linetype = Metric), alpha = 0.8, size = 1) + 
    geom_point(alpha = 0.8, size = 3, stroke = 1, shape = 21, fill = 'white') + 
    labs(y = "Number of new cases reported")  + 
    scale_fill_viridis_d(end = 0.6) + scale_color_viridis_d(end=0.6) + 
    coord_cartesian(clip = F) + 
    scale_x_date(
        breaks = as.Date( unique(dweek$WeekEnd) ), 
        date_labels = "%A, %b %d"
        ) + 
    theme(
      axis.text.x = element_text(face = 'bold', angle = 30, hjust = 1),
      panel.grid.minor = element_blank(),
      axis.title.y = element_blank(),
      strip.text.x = element_text(angle = 0, hjust = 0, size = 10, face = 2),
      legend.position = 'top',
      plot.margin = margin(20, 10, 10, 10)
    )

```


### **Agewise**

&nbsp;

#### Agewise split of cases in India

Data available for **`r nrow(dc_age_i)`** cases

```{r echo=FALSE,  message=FALSE, warning=FALSE, fig.height = 3, fig.width = 8}

  ggplot(data = dc_age_i, aes(x = AgeBracket, fill=Status)) +
    geom_bar(stat = "bin", position = "stack", binwidth = 5, color = 'white') +
    scale_fill_viridis_d(option = 'magma', end = 0.9) + 
    theme_minimal() + 
    theme(
      axis.text=element_text(size=8, color = "darkgrey"), 
      axis.title=element_text(size=10),
      panel.grid.major = element_blank(),
      plot.title = element_text(size = 12, hjust = 0),
      legend.title = element_blank(), 
      legend.position = c(0.8, 0.8)
    )

  # ggplotly(p) %>% layout(legend = list(
  #     orientation = "h"
  #   )
  # ) %>% config(displayModeBar = F)

```

Code: https://github.com/Impactech/COVID19_India/blob/master/IndiaAgewise.R

### **World**

&nbsp;

#### Progression

![](https://github.com/rahulnyk/covid19_timeseries_animation/blob/master/output.gif?raw=true)

#### Global Growth rate

```{r echo=FALSE, message=FALSE, warning=FALSE, fig.height = 4, fig.width = 8}
  pal <- "viridis"

  plot_theme <- theme_minimal() + theme(
    axis.text=element_text(size=8, color = "darkgrey"), 
    axis.title=element_text(size=10),
    axis.title.x = element_blank(),
    # panel.grid.minor = element_blank(),
    panel.grid.major = element_blank(),
    plot.title = element_text(size = 12, hjust = 0),
    legend.title = element_blank(), 
    legend.position = c(0.2, 0.8)
  )
  ggplot(data = data_rate_w,  aes(x = Date, y = Rate1, color = Status, fill = Status)) + 
    geom_smooth(aes(group = Status), se = F, alpha = 1, size = 1, linetype=1) + 
    geom_line(linetype = '11', alpha = 0.8, size = 1) + 
    geom_point(alpha = 0.6, size = 3, stroke = 1, shape = 21, fill = 'white') + 
    labs(y = "Growth rate in Percentage") + # labs(title = "Groth rate by date") + 
    scale_fill_viridis_d(option=pal, end = 0.6) + 
    scale_color_viridis_d(option=pal, end = 0.6) + 
    ylim(c(-10, 50)) +
    scale_x_date(date_breaks = '7 days') +
    plot_theme 

```


&nbsp;

#### Global Daily New Cases

```{r echo=FALSE, message=FALSE, warning=FALSE, fig.height = 4, fig.width = 8}
  pal <- "viridis"

  plot_theme <- theme_minimal() + theme(
    axis.text=element_text(size=8, color = "darkgrey"), 
    axis.title=element_text(size=10),
    axis.title.x = element_blank(),
    # panel.grid.minor = element_blank(),
    panel.grid.major = element_blank(),
    plot.title = element_text(size = 12, hjust = 0),
    legend.title = element_blank(), 
    legend.position = c(0.2, 0.8)
  )
  ggplot(data = data_rate_w,  aes(x = Date, y = DelTotal, color = Status, fill = Status)) + 
    geom_smooth(aes(group = Status), se = F, alpha = 0.1, size = 1, linetype=1) + 
    geom_line(linetype = '11', alpha = 0.8, size = 1) + 
    geom_point(alpha = 0.8, size = 3, stroke = 1, shape = 21, fill = 'white') + 
    labs(y = "Number of New Cases (Log)") + # labs(title = "Groth rate by date") + 
    scale_fill_viridis_d(option=pal, end = 0.6) + 
    scale_color_viridis_d(option=pal, end = 0.6) + 
    # ylim(c(-10, 50)) +
    scale_x_date(date_breaks = '7 days') +
    scale_y_continuous(trans = 'log2' ) +
    plot_theme 

```


&nbsp;

#### Global Cumulative cases

``` {r echo=FALSE,  message=FALSE, warning=FALSE, fig.height = 4, fig.width = 8}
  ggplot(
      data = data_total_w %>% filter(!(Status %in% c('Total'))), 
      aes(x = Date, y = Total/1000, fill = Status)
      ) + 
    geom_area(position = "stack", stat = "identity") +
    # scale_y_continuous(trans = 'log2') +
    labs(y = "Cumulative Number of cases (thousands)") + 
    # labs(title = "Reported number of cases by date") + 
    scale_fill_viridis_d(option=pal, begin = 0, end = 0.9) + 
    scale_color_viridis_d(option=pal, begin = 0, end = 0.9) +
    plot_theme
```


##

---

This Dashboard is <a href="https://github.com/Impactech/COVID19_India" target="_blank">**Hosted On this Repo**</a>

by [Rahul Nayak](https://www.linkedin.com/in/rahulnyk/)

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**Impactech!**
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