index.Rmd

---
title: "Home"
output:
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    social: menu
    source_code: embed
    includes:
      before_body: _header.html
      after_body: _footer.html
      background_image: ".//assets/img/mod_petral.jpg"
#runtime: shiny
# params:
#     lup: "unknown"
editor_options: 
  chunk_output_type: console
---

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```{r setup, include=TRUE}
library(tidyverse)
library(ggmap)
library(leaflet)
library(plotly)
library(flexdashboard)
library(DT)
library(rgdal)
library(crosstalk)
# devtools::install_github("r-spatial/leafem")
# library(leafem)
#latest dataset
tab3 <- read.csv("https://raw.githubusercontent.com/green-striped-gecko/covid_canberra/main/data/last.csv")
# Aggregate method

cols <- c( "red", "yellow","blue")

addBuses <- FALSE

tab3$Contact <- factor(tab3$Contact,levels=c(  "Close"  , "Casual", "Monitor") )
cc <- as.numeric( tab3$Contact)
ncols <- c("black","cyan")
nn <- as.numeric(factor(tab3$Status))
nn2 <- ifelse(nn==1,nn, 3)



tab3$Date <- substr(tab3$Date,1,10) #ditch day of the week
labs <- paste(tab3$Contact, tab3$Status,tab3$Exposure.Location, tab3$Street, tab3$Suburb, tab3$Date,tab3$Arrival.Time, tab3$Departure.Time, tab3$doubles, sep="<br/>") 

###############################################
##plot the map
df_shared <- crosstalk::SharedData$new(tab3[,c(1,8,2:7)])


m <- leaflet(df_shared) %>% addTiles()


if (addBuses) {
#read from shape file
busses <- readOGR(dsn = "./bus", layer = "geo_export_69c76e06-1d3f-4619-be3b-b4e5789be8ca", verbose = FALSE)

#search all bus lines that are mentioned

bindex <- grep("Bus Route", tab3$Exposure.Location)
buslanes <- tab3$Exposure.Location[bindex]

busnumbers <- gsub("Bus Route ([0-9,A-Z]+) Transport.*","\\1", buslanes)
blineindex <- which(busses$short_name %in% busnumbers)
blabs <- paste(paste0("Bus route: ", busses$short_name[blineindex]),"<strong> For bus stops and ","exposure times, please"," search the table." , sep="<br/>")
bb <- (busses[blineindex,])
coo <- coordinates(bb)
bcols <- colorRampPalette(c("purple", "green"))( length(coo))

for (ib in 1:length(coo))
{
  cood <- data.frame(coo[[ib]])
  m <- m %>% addPolylines(lng=cood[,1], lat=cood[,2], color = bcols[ib], weight   = 5, opacity = 0.7, popup  = blabs[ib])
}

}


m <- m %>% addCircleMarkers(lat=tab3$lat, lng=tab3$lon,popup = labs, weight=nn2, fillColor = cols[cc],color=ncols[nn], opacity =0.8, radius = 5 , fillOpacity = 0.8)
  #                           , clusterOptions =markerClusterOptions(spiderfyDistanceMultiplier=1.5,
  # iconCreateFunction=JS("function (cluster) {    
  # 
  #   var childCount = cluster.getChildCount();  
  # 
  #   if (childCount < 100) {  
  #     c = 'rgba(64, 64, 64, 0.5);'
  #   } else if (childCount < 1000) {  
  #     c = 'rgba(64, 64, 64, 0.5);'  
  #   } else { 
  #     c = 'rgba(64, 64, 64, 0.5);'  
  #   }    
  #    return new L.DivIcon({ html: '<div style=\"background-color:'+c+'\"><span>' + childCount + '</span></div>', className: 'marker-cluster', iconSize: new L.Point(40, 40) });
  # }"))                                                                                                                           #) 
m <- m %>%  addLegend("bottomright", labels = levels(tab3$Contact), colors = cols, opacity = 0.8)

####################################################
### add bus lines mentioned

# m + addLogo("./assets/img/Capture1.PNG", alpha = 1, src = c("remote", "local"), url,
#   position = c("topleft", "topright", "bottomleft", "bottomright"),
#   offset.x = 50, offset.y = 13, width = 60, height = 60)
# 

```

Current {data-orientation=columns}
=====================================

<style>
.crosstalk-input-checkboxgroup{
  color: blue;
  font-size: 12px;
}

.navbar {
  background-color:grey;
  hover-color:yellow;
}
</style> 


Inputs {.sidebar}
-----------------------------------------------------------------------

### Information

<sup>Disclaimer: This map shows the covid exposure locations in the ACT and is an **unofficial website** based on [official sources](https://www.covid19.act.gov.au/act-status-and-response/act-covid-19-exposure-locations). So if in doubt, refer to the [offical website](https://www.covid19.act.gov.au/act-status-and-response/act-covid-19-exposure-locations), which has now also an [**official map**](https://www.covid19.act.gov.au/act-status-and-response/act-covid-19-exposure-locations/map#Map-of-ACT-exposure-locations) included. Supported by Volunteers from the University of Canberra. Contacts: Bernd Gruber [bernd.gruber@canberra.edu.au] & Anthony Davidson [anthony.davidson@canberra.edu.au]</sup>

<br>
<br>

### Filter

```{r}

bscols(
  filter_checkbox(
    id = "contact",
    label = "Contact",
    sharedData = df_shared,
    group = ~Contact,
    columns = 1
  ))

bscols(
  filter_checkbox(
    id = "status",
    label = "Status",
    sharedData = df_shared,
    group = ~Status,
    columns = 1
  ))

bscols(  filter_select(id="dates", label="Date",sharedData = df_shared, group=~Date))
bscols(  filter_select(id="suburb", label="Suburb",sharedData = df_shared, group=~Suburb))

```

```{r}


```

Column {data-width=500}{.tabset}
------------------------------------------------------------

### Map of Exposure sites and exposed bus routes

```{r}
m  %>%
  addProviderTiles("Stamen.TonerLite")

# %>%
  # addPolygons(
  #   # fill
  #   fillColor   = ~pal(water_km2),
  #   fillOpacity = 0.7,
  #   # line
  #   dashArray   = "3",
  #   weight      = 2,
  #   color       = "white",
  #   opacity     = 1,
  #   # interaction
  #   highlight = highlightOptions(
  #     weight = 5,
  #     color = "#666",
  #     dashArray = "",
  #     fillOpacity = 0.7,
  #     bringToFront = TRUE))
```

### New Cases today

```{r eval = TRUE}
##current dataset.. 
#import now
library(tidyverse)
library(ggmap)
library(leaflet)
library(plotly)
library(flexdashboard)
library(DT)
library(rgdal)
library(crosstalk)
library(absmapsdata)
library(sf)



############very ugly function But should work.....
##ARD sep2021
last_five_days_new <- function(dataList){

    #no new sites test
  tab3 <- read.csv(dataList)
  
  
  
  cols <- c( "red", "yellow","blue")

  tab3$Contact <- factor(tab3$Contact,levels=c(  "Close"  , "Casual", "Monitor") )
  cc <- as.numeric( tab3$Contact)
  ncols <- c("black","cyan")
  nn <- as.numeric(factor(tab3$Status))
  nn2 <- ifelse(nn==1,nn, 3)
  
  # glimpse(tab3)
  
  tab3$Date <- substr(tab3$Date,1,10) #ditch day of the week
  labs <- paste(tab3$Contact, tab3$Status,tab3$Exposure.Location, tab3$Street, tab3$Suburb, tab3$Date,tab3$Arrival.Time, tab3$Departure.Time, tab3$doubles, sep="<br/>") 

  dataJoin <- absmapsdata::sa12016 %>%
    filter(gcc_name_2016 == "Australian Capital Territory") %>%
    mutate(Suburb = sa2_name_2016) %>%
    left_join(tab3, by = c("Suburb")) %>%
    select(Suburb, lat, lon, Exposure.Location, geometry,Status, Contact) %>%
    na.omit() %>%
    mutate(Status = ifelse(Status == "New", "New", "Previous")) %>%
    st_as_sf
  
  glimpse(dataJoin)
  # <NA> and NA
  
  # remove_missing(vars = c("Exposure.Location"))%>%  # let's just look Melbourne
  plot1 <-  ggplot(dataJoin,aes(y=lat, x=lon)) +
    ggspatial::annotation_map_tile() +
    geom_sf(aes(geometry = geometry, fill = Status), 
            col = c("red"), 
            lty = 1, alpha =0.1)+ 
    scale_fill_manual(values = c("red", "yellow","blue")) + 
    geom_point(data = dataJoin, aes(y=lat, x=lon, fill = Status,alpha = 0.7), size = 2 ) #+ 
  # scale_fill_manual(values = c("red", "yellow","blue","black", "white")) +
  # scale_color_manual(values = c("red", "yellow","blue","black", "white")) 
  
  return(plot1)  
  
}  #function end


#lapply
p1 <- last_five_days_new(dataList = "./data/last.csv")


# dataList = "./data/allfiles/September/table_01_Sep_2021_136pm.csv"
# lapply(dataList, last_five_days_new)

p1

```


Column {data-width=350}
------------------------------------------------------------

### Table of new cases

```{r}
# bscols(
#   filter_checkbox(
#     id = "contact",
#     label = "",
#     sharedData = df_shared,
#     group = ~Contact,
#     inline=TRUE
#   ))
#bscols(  filter_select(id="dates", label="Date",sharedData = df_shared, group=~Date))

dtt <-DT::datatable(df_shared,
     caption = 'Search for entries, (shift)click to select, this highlights the locations in the map.',selection = "multiple", rownames = FALSE, 
  extensions = c("Buttons", "Select", "Responsive"), options = list(autoHideNavigation=FALSE, pageLength=50,
   dom = 'Bfrtip',
            select = TRUE, 
            buttons = list(
              "copy",
              list(
                extend = "selectNone",
                text = "Clear"
              )
            ),                                   
  initComplete = JS(
        "function(settings, json) {",
        "$(this.api().table().header()).css({'font-size': '50%'});",

        "}")))  %>% DT::formatStyle(columns = 1:8, fontSize='70%') 

dtt
```


Cases, contacts and hotspots {data-orientation=columns}
=====================================  

<!-- This data is collected using code that accesses twitter, act govt site, nsw govt site, vic govt site...? -->

<sup>Disclaimer: This work This map shows the covid exposure locations in the ACT and is an **unofficial website** based on [official sources](https://www.covid19.act.gov.au/act-status-and-response/act-covid-19-exposure-locations). So if in doubt, refer to the [offical website](https://www.covid19.act.gov.au/act-status-and-response/act-covid-19-exposure-locations), which has now also an [**official map**](https://www.covid19.act.gov.au/act-status-and-response/act-covid-19-exposure-locations/map#Map-of-ACT-exposure-locations) included. Supported by Volunteers from the University of Canberra. Contacts: Bernd Gruber [bernd.gruber@canberra.edu.au] & Anthony Davidson [anthony.davidson@canberra.edu.au]</sup>


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

```{r eval = TRUE}
# source("R/plotly_source_script.R")
source("R/plotly_source_script.R")
```

### Map of Exposure sites

```{r}
# source("R/plotly_source_script.R")
library(plotly)
library(ggplotlyExtra)
library(leaflet)

leaflet(plotsumms) %>% addTiles() %>%
  addCircleMarkers(lat=plotsumms$lat,
                            lng=plotsumms$lon,
                   weight = 0.2,
    radius = log(plotsumms$contactcount)*20,
                            popup = paste0(" COUNT:", plotsumms$contactcount),
                            fillOpacity = 0.1,
    # color = plotsumms$colsN,
   color = "grey50"
                            ) %>%
  addCircles(lat=tab4$lat,lng=tab4$lon,
             popup = paste0(plotsumms$Exposure.Location," ", plotsumms$Date),
                            color = plotsumms$colsN,
                            stroke = TRUE,
                            fill = rep("black", length(plotsumms$colsN))) %>%
  addLegend("bottomright",
            labels = levels(plotsumms$Contact),
            colors = levels(plotsumms$colsN),
            opacity = 0.8)
```

This website tracks COVID-19 data and mobility information for the ACT and Canberra using code and data developed in the open source community. This information is only a guide. Please refer to official reports and press releases from government certified resources.

### Yesterdays case numbers

```{r eval = TRUE}
##current dataset.. 
#import now
library(tidyverse)
library(ggmap)
library(leaflet)
library(plotly)
library(flexdashboard)
library(DT)
library(rgdal)
library(crosstalk)
library(absmapsdata)
library(sf)



############very ugly function But should work.....

#dataList is a list...
##ARD sep2021
last_five_days_new <- function(dataList){

    #no new sites test
  tab3 <- read.csv(dataList)
  
  cols <- c( "red", "yellow","blue")

  tab3$Contact <- factor(tab3$Contact,levels=c(  "Close"  , "Casual", "Monitor") )
  cc <- as.numeric( tab3$Contact)
  ncols <- c("black","cyan")
  nn <- as.numeric(factor(tab3$Status))
  nn2 <- ifelse(nn==1,nn, 3)
  
  # glimpse(tab3)
  
  tab3$Date <- substr(tab3$Date,1,10) #ditch day of the week
  labs <- paste(tab3$Contact, tab3$Status,tab3$Exposure.Location, tab3$Street, tab3$Suburb, tab3$Date,tab3$Arrival.Time, tab3$Departure.Time, tab3$doubles, sep="<br/>") 

  dataJoin <- absmapsdata::sa12016 %>%
    filter(gcc_name_2016 == "Australian Capital Territory") %>%
    mutate(Suburb = sa2_name_2016) %>%
    left_join(tab3, by = c("Suburb")) %>%
    select(Suburb, lat, lon, Exposure.Location, geometry,Status, Contact) %>%
    na.omit() %>%
    mutate(Status = ifelse(Status == "New", "New", "Previous")) %>%
    st_as_sf
  
  glimpse(dataJoin)
  # <NA> and NA
  
  # remove_missing(vars = c("Exposure.Location"))%>%  # let's just look Melbourne
  plot1 <-  ggplot(dataJoin,aes(y=lat, x=lon)) +
    ggspatial::annotation_map_tile() +
    geom_sf(aes(geometry = geometry, fill = Status), 
            col = c("red"), 
            lty = 1, alpha =0.5)+ 
    scale_fill_manual(values = c("red", "yellow","blue")) + 
    geom_point(data = dataJoin, aes(y=lat, x=lon), size = 4, alpha = 0.3) #+ 
  # scale_fill_manual(values = c("red", "yellow","blue","black", "white")) +
  # scale_color_manual(values = c("red", "yellow","blue","black", "white")) 
  
  return(plot1)  
  
}  #function end


#lapply
# p1 <- last_five_days_new(dataList = "./data/allfiles/September/table_01_Sep_2021_136pm.csv")


# dataList = "./data/allfiles/September/table_01_Sep_2021_136pm.csv"
# lapply(dataList, last_five_days_new)

p1

```


### TODAY 

```{r eval = FALSE}
#lapply
p1 <- last_five_days_new(dataList = "./data/allfiles/September/table_02_Sep_2021_704pm.csv")


# dataList = "./data/allfiles/September/table_01_Sep_2021_136pm.csv"
# lapply(dataList, last_five_days_new)

p1
```

```{r child = "about.Rmd"}

```

Previous draft {data-orientation=columns}
=====================================

```{r child = "Covid_Exposure_ACT.rmd"}

```