Covid_Exposure_ACT_backup.Rmd

---
title: "Covid19 exposure locations in the ACT"
resource_files:
- data/last.csv
#runtime: shiny
output:
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
params:
    lup: "unknown"
---

```{r setup, include=TRUE}
library(tidyverse)
library(ggmap)
library(leaflet)
library(plotly)
library(flexdashboard)
library(DT)
#latest dataset
tab3 <- read.csv("data/last.csv")
# Aggregate method
cols <-c("yellow", "red", "blue")



cc <- as.numeric(factor(tab3$Contact))

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



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

m <- m %>% addCircleMarkers(lat=tab3$lat, lng=tab3$lon,popup = labs, weight=0.5, color = cols[cc], radius = 5 , fillOpacity = 0.8) 
m <- m %>% addLegend("bottomright", labels = levels(factor(tab3$Contact)), colors = cols)
```

```{r title, results='asis'} 

cat(paste0("# ", params$lup))

```

<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>

Column {data-width=450}
-----------------------------------------------------------------------

### Map of Exposure sites

```{r}

#checks 
m


```

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

### Table

```{r}
dtt <-DT::datatable(tab3[,c(1,8,2:7)],
  rownames = FALSE, options = list(pageLength = 50, ausoHideNavigation=TRUE, 
  initComplete = JS(
        "function(settings, json) {",
        "$(this.api().table().header()).css({'font-size': '50%'});",

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

dtt

```



```{r}

### Chart C
# names(tab3)
# glimpse(tab3)

# countSub <- as.data.frame(table(as.factor(tab3$Suburb)))
# names(countSub) <- c("Suburb", "Total Count")
# 
# 
# # ggplot(countSub) + 
# #   geom_point(aes(x = Var1, y = Freq, col = Status))
# 
# 
# locs <- select(tab3, lat, lon, Suburb)
# 
# plotdat1 <- left_join(countSub, tab3, by = "Suburb")
# 
# # length(unique(as.factor(tab3$Suburb)))
# tab4 <- tab3 %>%
#   group_by(Suburb) %>%
#     summarise(count = length(as.factor(Suburb)),
#               lat = mean(lat),
#               lon = mean(lon)) 
# 
# tab5 <- tab4 %>%
# group_by(count)%>%
#       arrange(desc = TRUE, .by_group = TRUE)
#               
#               # ,
# 
# # glimpse(tab4)
# # glimpse(plotdat1)
# #filter on location
# 
# ggplot(tab5) + 
#   geom_point(aes(x = Suburb, y = count), size = 3) + 
#   theme(axis.text.x = element_text(angle = 90))
```