index.Rmd

---
title: "Crna Gora COVID-19"
featuredImage: "Users/Dovla/Desktop/img.png"
author: "W"
output: 
  flexdashboard::flex_dashboard:
    theme: cosmo
    orientation: rows
    social: menu
    source_code: embed
---

```{r setup, include=FALSE}
library(ggplot2)
library(plotly)
library(plyr)
library(flexdashboard)
library(tidyverse)
library(leaflet)
library(stringr)
library(sf)
library(here)
library(widgetframe)
library(tidyr)
library(DT)
mne <- read.csv("/Users/Dovla/Desktop/covidMNE2703.csv")
```

Mapa oboljelih
=======================================================================

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

### Samoizolacija

```{r}
articles <- 7398
valueBox(articles, icon = "fa-home", color = "success")
```

### COVID-19 slucajevi

```{r}
comments <- 262
valueBox(comments, icon = "fa-notes-medical", color = "warning")
```

### Umrli

```{r}
spam <- 2
valueBox(spam, 
         icon = "fa-battery-empty",
         color = "danger")
```

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

### Mapa

```{r,fig.width = 12, echo=FALSE, warning=FALSE}
#gra <- data.frame("gr"=c("Podgorica","Tuzi","Ulcinj","Andrijevica","Bar","Budva","Herceg Novi","Danilovgrad","Tivat","Bijelo Polje","Niksic"),"nr" = c(49,33,8,7,10,4,4,1,2,1,1))
gra <- read.csv('/Users/Dovla/Desktop/covid19-mne/tweetStatus.csv')

b <-c()
for(i in 1:nrow(gra)) {
  b <- c(b,rep(paste0(gra$gr[i]),gra$nr[i]))
}
gra2 <- data.frame(unlist(b))
colnames(gra2) <- "gr"
grad <- read.csv("/Users/Dovla/Desktop/grad.csv")
grad1 <- unique(grad)
mapa <- left_join(gra2, grad1, by = c("gr" = "Grad"))


#mapa <- na.omit(read.csv("/Users/Dovla/Desktop/grad.csv"))

m <- leaflet() %>%
  addTiles(urlTemplate = 'https://{s}.tile.thunderforest.com/pioneer/{z}/{x}/{y}.png') 
#m %>%
#  frameWidget()
leaflet(data = mapa) %>%
  addTiles() %>%
  addMarkers(clusterOptions = markerClusterOptions())  #%>%  frameWidget()
```

### Novi COVID-19 slucajevi

```{r}
mne22 <- subset(mne[mne$InfectedNew>0,])
p3 <- ggplot(data=mne22, aes(x=Day, y=InfectedNew)) + geom_bar(stat="identity",color="red", fill="white") +
  labs(x="Dana od 9/3 tj epidemije", y = "Novih slucajeva") + coord_flip()
ggplotly(p3) 
```

Podaci JIZCG
=======================================================================

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

### Evolucija COVID-19 slucajeva 

```{r}

mne <- read.csv("/Users/Dovla/Desktop/covidMNE2703.csv")
mne$InfectionRate1 <- as.numeric(levels(mne$InfectionRate))[mne$InfectionRate]
myvars <- c("Day","Infected", "Tested")#, "UnderWatch")
mne1 <- mne[myvars]
#mne1$UnderWatch <- mne1$UnderWatch/10
names(mne1) <- c("Day","covid-19", "Testirano")#, "Izolacija / 10")
mne2 <- mne1 %>%
  pivot_longer(-Day, names_to = "Legenda", values_to = "count")

p11 <- ggplot(mne2, aes(x=Day, y=count,group = Legenda)) + geom_line(aes(linetype=Legenda)) + geom_point() +
    labs(x = "Proslo dana od 09/03/2020 tj prvog dana izvjestavanja o CG", y = "") 
#p11 <- ggplot(mne2, aes(x=Day, y=count, col=series)) + geom_line() + geom_point()
ggplotly(p11)

```

### Indeksni/Kontakti slucajevi

```{r,echo=FALSE, warning=FALSE}
library(igraph)
library(sna)
library(GGally)

gra <- read.csv("/Users/Dovla/Desktop/gra.csv")
graC <- read.csv("/Users/Dovla/Desktop/nodCol.csv")
g1 <- graph.data.frame(gra, directed=FALSE)
V(g1)$color <- "red"
V(g1)[gra$e]$color = "green"


#plot(g1, vertex.size = 10, edge.size=0.1, edge.color = "black", lty = 0, arrow.mode=3, arrow.size = 3, main="MNE covid19 clusters 29/03/2020")

#library(visNetwork)
#visIgraph(g1)

g2 <- ggnet2(g1,label = TRUE, label.size = 3, label.color="black",layout.par = list(cell.jitter = 0.75), 
             shape = as.character(graC$s),
             node.color = c(rep("red",22),rep("darkgreen",62)),
             legend.size = 5)

ggplotly(g2)
```

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

### Stopa rasta novih COVID-19 slucajeva

```{r}
p2 <- ggplot(data=mne, aes(x=Day, y=InfectionRate1*100, group=1)) + geom_line(linetype = "dashed", colour="red") +
  geom_point() +
  labs(x = "Proslo dana od 09/03/2020 tj prvog dana izvjestavanja o CG", y = "Stopa u %") 
ggplotly(p2)

```


### Novi COVID-19 slucajevi

```{r}
p3 <- ggplot(data=mne, aes(x=Day, y=InfectedNew)) + geom_bar(stat="identity",color="red", fill="white") +
  labs(x = "Proslo dana od 09/03/2020 tj prvog dana izvjestavanja o CG", y = "Novih slucajeva") 
ggplotly(p3)
```

Mapa svijeta
=======================================================================

**Map**

```{r}
# map tab added by Art Steinmetz
library(leaflet)
library(leafpop)
library(purrr)
library(coronavirus)
load("/Users/Dovla/Downloads/coronavirus.rda")

cv_data_for_plot <- coronavirus %>% 
  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) 
  )
```

SIR Model - Test
=======================================================================

<iframe style="position: absolute; width :100%; height: 100%; border: none" src="https://praeconium.shinyapps.io/myapp/"></iframe>

About
=======================================================================

I study data, risk and quantiative approaches in various domains including bioinformatics and digital epidemiology. Given current situation it felt natural to visualize and track global/Montenegro statistics, implement basic SIR models, do tweeter analysis and little bit more. Hopefully its not too bad :)

**The Coronavirus Dashboard**

This Coronavirus dashboard provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic. This dashboard is built with R using the Rmakrdown framework and can easily reproduce by others. The code behind the dashboard available [here](https://github.com/0x3w/covid19-mne)

**Data**

The input data for this dashboard is the [Public Health Institute of Montenegro (IJZCG)](https://www.ijzcg.me/me/novosti/azurirani-podaci-o-novom-koronavirusu-2019-ncov) official data.. The data and dashboard is refreshed on a daily bases. The raw data is available [here](https://github.com/0x3W/covid19-me)


**Packages**

* Dashboard interface - the [flexdashboard](https://rmarkdown.rstudio.com/flexdashboard/) package. 
* Visualization - the [plotly](https://plot.ly/r/) package for the plots and [leaflet](https://rstudio.github.io/leaflet/) for the map
* Data manipulation - [dplyr](https://dplyr.tidyverse.org/), and [tidyr](https://tidyr.tidyverse.org/)
* Tables - the [DT](https://rstudio.github.io/DT/) package

**Deployment and reproducibly**

The dashboard was deployed to Github docs. If you wish to deploy and/or modify the dashboard on your Github account, you can apply the following steps:

* Fork the dashboard [repository](https://github.com/0x3w/covid19-mne), or
* Clone it and push it to your Github package
* Here some general guidance about deployment of flexdashboard on Github page - [link](https://github.com/pbatey/flexdashboard-example)

For any question or feedback, you can either open an [issue](https://github.com/0x3W/covid19-mne/issues) or contact me on [Twitter](https://twitter.com/praeconium).

**Contribution** 

The **Map** tab was contributed by [Art Steinmetz](@adababbage) on this [pull request](https://github.com/RamiKrispin/coronavirus_dashboard/pull/1). Thanks Art!