OutputMaps.Rmd

---
title: "OutpuMaps"
author: "Leon Danon"
date: "07/02/2020"
output: html_document
---

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

# Spatial Analysis

```{r}

library(tidyverse)
library(sf)
library(rgdal)
library(viridis)
library(mapview)

WardsEW <- read_sf(dsn='~/GitHub/MetaWards/data/2011/shapefile/Wards_December_2011_Boundaries_EW_BFC',layer = 'Wards_December_2011_Boundaries_EW_BFC')

st_centroid(WardsEW)->wardCentroids

wardlookup<-read.csv('~/GitHub/MetaWards/data/2011/WardsProcessing/Ward_Lookup.csv') 

wardCentroids %>% 
  inner_join(.,wardlookup,by=c('wd11cd'='WD11CD'))->wardslookup2 

#mapview(wardslookup2,zcol='FID')


```


```{r}

allinfectionsShef=read.table(file='ForMattData.dat',sep = ' ')

allinfectionsShef %>% # make long data frame
  mutate(Time=row_number()) %>% # count up time, as row number
  pivot_longer(-Time) %>% 
  mutate(Ward=as.integer(str_remove(name,'V'))) %>% 
  select(-name)->inf_longShef # rename name to Ward integers for easier matching



inf_longShef %>% 
  group_by(Ward) %>%               # by ward
  filter(cumsum(value) < 1) %>%    # cumulative sum > 1 means it's just arrived/first infection
  mutate(Arrival=max(Time)) %>%    # find the maximum before that
  filter(row_number()==1) %>%      # take the first row of group_by
  select(Ward,Arrival) %>%    # # select columns and rename and save in a data frame
  ungroup() -> arrivals
      
wardslookup2 %>% inner_join(.,arrivals, by=c('FID'='Ward')) %>% 
  mutate(AltArrival=log(1/Arrival)*100)->wardslookuparrivalShef

mapview(wardslookuparrivalShef,zcol='AltArrival',cex = 'AltArrival')


# plotme %>% 
#   ggplot(aes(x=Time,y=value,colour=name))+
#   geom_line()

```

```{r}

allinfectionsLon=read.table(file='OldRuns/London/0/ForMattData.dat',sep = ' ')

allinfectionsLon %>% # make long data frame
  mutate(Time=row_number()) %>% # count up time, as row number
  pivot_longer(-Time,values_to = 'Infected') %>% 
  mutate(Ward=as.integer(str_remove(name,'V'))) %>% 
  select(-name)->inf_longLon # rename name to Ward integers for easier matching

inf_longLon %>% 
  group_by(Ward) %>%  # by ward
  filter(cumsum(Infected) < 1) %>%    # cumulative sum > 1 means it's just arrived/first infection
  mutate(Arrival=max(Time)) %>%    # find the maximum before that
  filter(row_number()==1) %>%      # take the first row of group_by
  select(Ward,Arrival) %>%    # # select columns and rename and save in a data frame
  ungroup() -> arrivalsLon

inf_longLon %>% 
  group_by(Ward) %>% 
  slice(which.max(Infected))->TimeToPeak
```


```{r}

TimeToPeak %>% 
  ggplot(aes(x=Time))+
  geom_histogram()

arrivalsLon %>% 
  ggplot(aes(x=Arrival))+
  geom_histogram()

```

```{r}
      
wardslookup2 %>% 
  inner_join(.,arrivalsLon, by=c('FID'='Ward')) %>% 
  mutate(AltArrival=log(1/Arrival)*100)->wardslookuparrivalLon

wardslookuparrivalLon %>% 
  inner_join(.,TimeToPeak, by=c('FID'='Ward'))%>% 
  mutate(AltTime=Arrival/1000)->wardslookuparrivaltimetopeakL

mapview(wardslookuparrivaltimetopeakL,zcol='Arrival',cex = 'AltTime')


# TODO:
# Time to peak. DONE 
# Time to 10 cases. 
# Lookup from Postcode to Ward/OA.
```


```{r ccg map}
CCGEW <- read_sf(dsn='~/GitHub/MetaWards/data/2011/shapefile/Clinical_Commissioning_Groups_April_2018_Full_Clipped_Boundaries_in_England',layer = 'Clinical_Commissioning_Groups_April_2018_Full_Clipped_Boundaries_in_England')

mapview(CCGEW)+mapview(wardslookuparrivaltimetopeakL,zcol='Time',cex = 'AltTime')


```