---
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
---
```{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$doubles, sep="
")
###############################################
##plot the map
df_shared <- crosstalk::SharedData$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])," For bus stops and ","exposure times, please"," search the table." , sep="
")
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: '' + childCount + '
', 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}
=====================================
Inputs {.sidebar}
-----------------------------------------------------------------------
### Information
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]
### 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="
")
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)
# 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) {",
"$(this.api().table().header()).css({'font-size': '50%'});",
"}"))) %>% DT::formatStyle(columns = 1:8, fontSize='70%')
dtt
```
Cases, contacts and hotspots {data-orientation=columns}
=====================================
Disclaimer: This work This map shows the covid exposure locations in the ACT and is an **unofficial website** based on [official sources](https://www.covid19.act.gov.au/act-status-and-response/act-covid-19-exposure-locations). So if in doubt, refer to the [offical website](https://www.covid19.act.gov.au/act-status-and-response/act-covid-19-exposure-locations), which has now also an [**official map**](https://www.covid19.act.gov.au/act-status-and-response/act-covid-19-exposure-locations/map#Map-of-ACT-exposure-locations) included. Supported by Volunteers from the University of Canberra. Contacts: Bernd Gruber [bernd.gruber@canberra.edu.au] & Anthony Davidson [anthony.davidson@canberra.edu.au]
Column {data-width=400}{.tabset}
------------------------------------------------------------
```{r eval = TRUE}
# source("R/plotly_source_script.R")
source("R/plotly_source_script.R")
```
### Map of Exposure sites
```{r}
# source("R/plotly_source_script.R")
library(plotly)
library(ggplotlyExtra)
library(leaflet)
leaflet(plotsumms) %>% addTiles() %>%
addCircleMarkers(lat=plotsumms$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="
")
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)
# 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"}
```
