Index.Rmd

---
title: "Index"
author: "GNtem2"
date: "01/10/2021"
output: html_document
---

```{r setup, warning=F, message=F, echo=F,include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

### Thematic Maps of Children Hospitals in Melbourne


```{r MCH, warning=F,echo=F, message=F}
rm(list=ls())

library(DT)
library(leaflet)
library(stringr)
library(tidyverse)
#
library(readxl)
library(sf)
library(mapview)
library(DT)
```

This is a thematic map of the common childhood presentation to emergency departments (ED). The map is constrained to local government areas within 80 km of the two major children hospitals in Melbourne: Royal Children's Hospital and Monash Children's Hospital. The data comes from Torrens University Australia and can be accessed directly from their website. Data on Australian Early Development Census (AEDC) can also be downloaded from the AEDC website. The shapefiles for the local government areas are available from Australian Bureau of Statistics. 

```{r Data, warning=F,message=F, echo=F,results='hide'}

#Pop 2020
X<-read_xls("./Data/phidu_child_and_youth_data_lga_aust.xls",sheet = "Age_distribution_Persons", skip = 4) %>% mutate(Total4=as.numeric(Number...3),Total5_9=as.numeric(Number...7),Total10_14=as.numeric(Number...11),Total0_14=Total4+Total5_9+Total10_14) %>% select(Code, Total4,Total5_9,Total10_14,Total0_14)

#subset Victoria
X<-X[c(135:215),]

#AEDC 2018
#https://www.aedc.gov.au/researchers
X1<-read_xls("./Data/phidu_child_and_youth_data_lga_aust.xls",sheet = "Early_childhood_development", skip=4) %>% mutate(Vuln=round(as.numeric(`Children developmentally vulnerable on one or more domains`),1),PerVuln=as.numeric(`% Children developmentally vulnerable on one or more domains`)) %>% select(Code, Vuln,PerVuln)

X1=X1[c(135:215),]

#merge
X1X<-right_join(X1,X,by="Code")

#Socioeconomic disadvantage
XIRSD<-read_xls("./Data/phidu_child_and_youth_data_lga_aust.xls",sheet = "IRSD", skip=4) %>% mutate(IRSD=round(as.numeric(`Index score (based on Australian score of 1000)`))) %>% select (Code, IRSD)

XIRSD<-XIRSD[c(135:215),]

#merge
XIRSD<-right_join(XIRSD,X1X, by="Code")

#Disability
XDis<-read_xls("./Data/phidu_child_and_youth_data_lga_aust.xls",sheet = "Disability", skip=4) %>% mutate(Dis_4=round(as.numeric(`Has need for asssistance with core activities, \n0 to 4 years`),1), Dis_5_9=round(as.numeric(`Has need for asssistance with core activities, \n5 to 9 years`),1),Dis_10_14=round(as.numeric(`Has need for asssistance with core activities, \n10 to 14 years`),1),Dis_14=Dis_4+Dis_5_9+Dis_10_14) %>% select(Code, Dis_4, Dis_5_9, Dis_10_14, Dis_14)
  
XDis<-XDis[c(135:215),]

#merge
XDis<-right_join(XDis,XIRSD, by="Code")

#ED
XED<-read_xls("./Data/phidu_child_and_youth_data_lga_aust.xls",sheet = "ED_total", skip=4) %>% mutate(ED_Mental=round(as.numeric(`Number...14`),1),ED_Circ=round(as.numeric(`Number...24`),1),ED_Resp=round(as.numeric(`Number...34`),1), ED_Injury=round(as.numeric(`Number...79`),1)) %>% select(Code, ED_Mental, ED_Circ,ED_Resp, ED_Injury) 

XED<-XED[c(135:215),]

#merge
XED<-right_join(XED,XDis, by="Code")

#Preventable admission
XPD<-read_xls("./Data/phidu_child_and_youth_data_lga_aust.xls",sheet = "Admissions_prevent_diag_total", skip=4) %>% mutate(PreDiag=round(as.numeric(`Number...4`)))%>% select(Code,PreDiag)

XPD<-XPD[c(135:215),]

#merge
XED<-right_join(XED, XPD, by="Code")


#LGA
LGAsh<-st_read("./Data/LGA_2016_AUST.shp")
  
#merge
LGAshX<-right_join(LGAsh, XED, by=c("LGA_CODE16"="Code")) 

 
```  


```{r Map, warning=F, echo=F,message=F}

#Expected AEDC
LGAshX$Vuln<-round(LGAshX$Vuln,1)

#check for NA
#which(is.na(LGAshX$Vuln))
#which(is.na(LGAshX$ED_Mental))
#which(is.na(LGAshX$ED_Circ))
#which(is.na(LGAshX$ED_Resp))

#replace NA
LGAshX$Vuln<-ifelse(is.na(LGAshX$PerVuln),0,LGAshX$PerVuln)
LGAshX$ED_Mental<-ifelse(is.na(LGAshX$ED_Mental),0,LGAshX$ED_Mental)
LGAshX$ED_Circ<-ifelse(is.na(LGAshX$ED_Circ),0,LGAshX$ED_Circ)
LGAshX$ED_Resp<-ifelse(is.na(LGAshX$ED_Resp),0,LGAshX$ED_Resp)
LGAshX$IRSD<-ifelse(is.na(LGAshX$IRSD),0,LGAshX$IRSD)
LGAshX$Vuln<-ifelse(is.na(LGAshX$Vuln),0,LGAshX$Vuln)
LGAshX$Dis_14<-ifelse(is.na(LGAshX$Dis_14),0,LGAshX$Dis_14)
LGAshX$PreDiag<-ifelse(is.na(LGAshX$PreDiag),0,LGAshX$PreDiag)

#map Victoria
#m<-mapview(LGAshX["ED_Mental"],layer.name="ED Attendance Mental Health")+
#mapview(LGAshX["ED_Circ"],layer.name="ED Attendance Circulatory Disorders")+
#mapview(LGAshX["ED_Resp"],layer.name="ED Attendance Respiratory Disorders")+
#mapview(LGAshX["ED_Injury"],layer.name="ED Attendance Injury")+
#mapview(LGAshX["Dis_14"],layer.name="Disability 0-14 years")+ 
#mapview(LGAshX["IRSD"],layer.name="Socioeconomic Disadvantage")+
#mapview(LGAshX["Vuln"],layer.name="Vulnerable Children AEDC")
#m

#make html
#mapshot(m, url = paste0(getwd(), "/Victoria_Children.html"))

```
The catchment was created by determining which local government areas are within 80 km of the two major children hospitals. The zoom button (+/-) is in the upper left hand corner. The viewer can also choose the base tile map by clicking on the layer control box, located below the zoom button. The data can also be downloaded directly from the screen by clicking the icons (Copy, CSV, Excel, Print, PDF).

```{r Distance, warning=F,echo=F, message=F}
#Children Hospital
Children_Addresses<-c(Hosp=c("MCH","RCH"),Long=c(145.12070,144.94991), Lat=c(-37.92093,-37.79347))
Children_Addresses<-c(MCH="Monash Children's Hospital, Clayton VIC 3168, Australia", RCH="Royal Children's Hospital, Flemington VIC 3031, Australia")
ChildrenHosp <- tmaptools::geocode_OSM(Children_Addresses, as.sf=TRUE)
ChildrenHosp<-sf::st_transform(ChildrenHosp, sf::st_crs(LGAshX))
#mapview::mapview(ChildrenHosp, map.type="OpenStreetMap.HOT", color='red', col.regions='red', cex=10)

#Distance to Hosp
dist_to_loc <- function (geometry, location){
    units::set_units(st_distance(st_centroid (geometry), location)[,1], km)}

#set range at 80 km
dist_range <- units::set_units(80, km)

#LGAshX <- mutate(LGAshX,
#       DirectDistanceToMCH= dist_to_loc(geometry,ChildrenHosp["MCH", ]),
#       DirectDistanceToRCH= dist_to_loc(geometry,ChildrenHosp["RCH", ]),
#       DirectDistanceToNearest   = pmin(DirectDistanceToMCH,                                        DirectDistanceToRCH))

#error with function due to NA in row 81
a<-dist_to_loc(LGAshX$geometry,ChildrenHosp["MCH", ])
b<-dist_to_loc(LGAshX$geometry,ChildrenHosp["RCH", ])
c<-pmin(a,b)

LGAshX$DirectDistanceToMCH<-a
LGAshX$DirectDistanceToRCH<-b
LGAshX$DirectDistanceToNearest<-c

LGAshX$MCHcatchment<-ifelse(LGAshX$DirectDistanceToMCH<LGAshX$DirectDistanceToRCH,1,0)
LGAshX$RCHcatchment<-ifelse(LGAshX$DirectDistanceToRCH<LGAshX$DirectDistanceToMCH,1,0)

#apply distance range
LGAChildren <- filter(LGAshX,
                        DirectDistanceToNearest < dist_range) %>%
        mutate(LGA = as.numeric(LGA_CODE16)) %>%
        select(-starts_with("LGA_"))

L<-mapview::mapview (LGAChildren["MCHcatchment"], layer.name="Hospital Catchment",zoom=12)+mapview(ChildrenHosp["query"],layer.name="Hospital", legend=F)
mapshot(L,url = paste0(getwd(), "/ChildrenHospital.html"),file="ChildrenHospital.png")


#Vulnerable children
Mental_MCH<-round(sum(LGAChildren$ED_Mental*LGAChildren$MCHcatchment, na.rm = T),0)
Mental_RCH<-round(sum(LGAChildren$ED_Mental*LGAChildren$RCHcatchment, na.rm = T),0)
Circ_MCH<-round(sum(LGAChildren$ED_Circ*LGAChildren$MCHcatchment, na.rm = T),0)
Circ_RCH<-round(sum(LGAChildren$ED_Circ*LGAChildren$RCHcatchment, na.rm = T),0)
Resp_MCH<-round(sum(LGAChildren$ED_Resp*LGAChildren$MCHcatchment, na.rm = T),0)
Resp_RCH<-round(sum(LGAChildren$ED_Resp*LGAChildren$RCHcatchment, na.rm = T),0)
Injury_MCH<-round(sum(LGAChildren$ED_Injury*LGAChildren$MCHcatchment, na.rm = T),0)
Injury_RCH<-round(sum(LGAChildren$ED_Injury*LGAChildren$RCHcatchment, na.rm = T),0)
Dis_MCH<-round(sum(LGAChildren$Dis_14*LGAChildren$MCHcatchment, na.rm = T),0)
Dis_RCH<-round(sum(LGAChildren$Dis_14*LGAChildren$RCHcatchment, na.rm = T),0)
IRSD_MCH<-round(sum(LGAChildren$IRSD*LGAChildren$MCHcatchment, na.rm = T),0)
IRSD_RCH<-round(sum(LGAChildren$IRSD*LGAChildren$RCHcatchment, na.rm = T),0)
Vuln_MCH<-round(sum(LGAChildren$Vuln*LGAChildren$MCHcatchment, na.rm=T ),0)
Vuln_RCH<-round(sum(LGAChildren$Vuln*LGAChildren$RCHcatchment, na.rm = T),0)
PD_MCH<-round(sum(LGAChildren$PreDiag*LGAChildren$MCHcatchment, na.rm=T ),0)
PD_RCH<-round(sum(LGAChildren$PreDiag*LGAChildren$RCHcatchment, na.rm = T),0)
MCH_0_14<-round(sum(LGAChildren$Total0_14*LGAChildren$MCHcatchment, na.rm = T),0)
RCH_0_14<-round(sum(LGAChildren$Total0_14*LGAChildren$RCHcatchment, na.rm = T),0)

library(DT)
DF<-data.frame(Disorder=c("ED Attendance Mental Health","ED Attendance Circulatory Disorders", "ED Attendance Respiratory Disorders","ED Attendance Injury/Poisoning", "Admissions for preventable conditions", "Disability", "Socioeconomic Disadvantage", "developmentally vulnerable on one or more domains - AEDC"),MCH=c(Mental_MCH,Circ_MCH,Resp_MCH,Injury_MCH, PD_MCH, Dis_MCH, IRSD_MCH,Vuln_MCH),`MCH Children 0-14` = c(MCH_0_14,MCH_0_14,MCH_0_14,MCH_0_14,MCH_0_14,MCH_0_14,NA, MCH_0_14),RCH=c(Mental_RCH,Circ_RCH, PD_RCH, Resp_RCH, Injury_RCH, Dis_RCH, IRSD_RCH, Vuln_RCH), `RCH Children 0-14` = c(RCH_0_14,RCH_0_14,RCH_0_14,RCH_0_14,RCH_0_14,RCH_0_14,NA, RCH_0_14))

#enable extensions in data.table
DT_DF<-datatable(DF,extensions = c('Buttons','KeyTable','Responsive'),
options = list(dom='Bfrtip', keys=T, button=c('copy', 'csv', 'excel', 'print', 'pdf'))) 
saveWidget(DT_DF, "DT_DF.html")
webshot::webshot("DT_DF.html",file="DT_DF.png")

DT_DF
#map Victoria
#n<-mapview(LGAChildren["ED_Mental"],layer.name="ED Attendance Mental Health")+
#mapview(LGAChildren["ED_Circ"],layer.name="ED Attendance Circulatory Disorders")+
#mapview(LGAChildren["ED_Resp"],layer.name="ED Attendance Respiratory Disorders")+
#mapview(LGAChildren["ED_Injury"],layer.name="ED Attendance Injury")+
#mapview(LGAChildren["Dis_14"],layer.name="Disability 0-14 year")+
#mapview(LGAChildren["IRSD"],layer.name="Socioeconomic Disadvantage")+
#mapview(LGAChildren["Vuln"],layer.name="Vulnerable Children AEDC")
#n
```

ED attendance for mental health issues in children (age 0-14).

```{r Mental, warning=F,echo=F,message=F}
n1<-mapview(LGAChildren["ED_Mental"],layer.name="ED Attendance Mental Health",col.regions=c("blue","green","red"))+mapview(ChildrenHosp["query"],layer.name="Hospital",legend=F)
#mapshot(n1, url = paste0(getwd(), "/MCH_Mental.html"),file="MCH_Mental.png")
#n1
```

ED attendance for injury/poisoning issues in children (age 0-14).

```{r Injury, warning=F,echo=F,message=F}
n2<-mapview(LGAChildren["ED_Injury"],layer.name="ED Attendance Injury/Poisoning",col.regions=c("blue","green","red"))+mapview(ChildrenHosp["query"],layer.name="Hospital",legend=F)
mapshot(n2, url = paste0(getwd(), "/MCH_injury.html"),file="MCH_injury.png")
#n1
```

ED attendance for Respiratory disorders in children (age 0-14).

```{r Resp, warning=F,echo=F,message=F}
n3<-mapview(LGAChildren["ED_Resp"],layer.name="ED Attendance Respiratory Disorders",col.regions=c("blue","green","red"))+mapview(ChildrenHosp["query"],layer.name="Hospital",legend=F)
mapshot(n3, url = paste0(getwd(), "/MCH_Resp.html"),file="MCH_Resp.png")

```

ED attendance for Circulatory disorders in children (age 0-14).

```{r, Circ, warning=F,echo=F,message=F}
n4<-mapview(LGAChildren["ED_Circ"],layer.name="ED Attendance Circulatory Disorders",col.regions=c("blue","green","red"))+mapview(ChildrenHosp["query"],layer.name="Hospital",legend=F)
mapshot(n4, url = paste0(getwd(), "/MCH_Circ.html"),file="MCH_Circ.png")

```

Preventable admissions in children (age 0-14).

```{r, PD, warning=F,echo=F,message=F}
n5<-mapview(LGAChildren["PreDiag"],layer.name="Preventable admission conditions",col.regions=c("blue","green","red"))+mapview(ChildrenHosp["query"],layer.name="Hospital",legend=F)
mapshot(n5, url = paste0(getwd(), "/MCH_PD.html"),file="MCH_PD.png")

```

Prevalence of disability in children (age 0-14).

```{r Disability, warning=F,echo=F,message=F}
p<-mapview(LGAChildren["Dis_14"],layer.name="Disability 0-14 year",col.regions=c("blue","green","red"))+mapview(ChildrenHosp["query"],layer.name="Hospital",legend=F)

mapshot(p, url = paste0(getwd(), "/MCH_disability.html"),file="MCH_disability.png")
```


Prevalence of Vulnerable children (age 0-14). The term vulnerable refers to Children developmentally vulnerable on one or more domains. These children were assessed in their Australian Early Development Census (AEDC) first year at school.

```{r Vulnerable, warning=F,echo=F,message=F}
q<-mapview(LGAChildren["Vuln"],layer.name="Vulnerable Children AEDC",col.regions=c("blue","green","red"))+mapview(ChildrenHosp["query"],layer.name="Hospital",legend=F)
mapshot(q, url = paste0(getwd(), "/MCH_AEDC.html"),file = "MCH_AEDC.png")
```

```{r resize image, echo=F, warning=F, message=F}
library(magick)
a<-image_read("MCH_Mental.png")
a1<-image_resize(a,"x400")
image_write(a1,path = "MCH_Mental2.png",format="png")

b<-image_read("MCH_Resp.png")
b1<-image_resize(b,"x400")
image_write(a1,path = "MCH_Resp2.png",format="png")

d<-image_read("MCH_Circ.png")
d1<-image_resize(d,"x400")
image_write(d1,path = "MCH_Circ2.png",format="png")

e<-image_read("MCH_PD.png")
e1<-image_resize(e,"x400")
image_write(e1,path = "MCH_PD2.png",format="png")

f<-image_read("MCH_disability.png")
f1<-image_resize(f,"x400")
image_write(f1,path = "MCH_disability2.png",format="png")

g<-image_read("MCH_injury.png")
g1<-image_resize(g,"x400")
image_write(g1,path = "MCH_injury2.png",format="png")

h<-image_read("MCH_AEDC.png")
h1<-image_resize(h,"x400")
image_write(h1,path = "MCH_AEDC2.png",format="png")

i<-image_read("ChildrenHospital.png")
i1<-image_resize(i,"x400")
image_write(i1,path = "ChildrenHospital2.png",format="png")
```




Note that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot.