regionalplot.Rmd

---
title: "Reagional Model Plots"
output: html_notebook
---
```{r}

library(tidyverse)
library(tidyverse)
library(gghighlight)
library(purrr)
library(plotly)
library(cowplot)
library(scales)

```


```{r}
wardlookup<-read.csv('~/GitHub/MetaWards/data/2011/WardsProcessing/Ward_Lookup.csv') 
lad2region<-read.csv('~/GitHub/MetaWards/data/2011/WardsProcessing/Output_Area_2011_to_Builtup_Area_Subdivision_to_Builtup_Area_to_Local_Authority_District_to_Region_December_2011_Lookup_in_England_and_Wales.csv') %>% 
  group_by(LAD11CD) %>% 
  summarise(Region=unique(RGN11NM))

wardlookupregion<-wardlookup %>% 
  inner_join(.,lad2region, by=("LAD11CD"))


allinfections=read.table(file='Testing/BrigthonNewOutput/ForMattData.dat',sep = ' ')


allinfections %>% # 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_long # rename name to Ward integers for easier matching


wardlookupregion %>% 
  inner_join(.,inf_long, by=c('FID'='Ward'),all.y=T,all.x=F) %>%
  group_by(Region,Time)%>% 
  summarise(Cases=sum(value)) -> region_inf

region_inf%>%
  group_by(Region) %>%
  summarise(PeakTime=which.max(Cases),PeakCases=max(Cases))


```
# Plot region epidemics in ggplot
```{r}


region_inf %>% 
  filter(Time<250) %>% 
  ggplot(aes(x=Time,y=Cases,colour=Region)) +
  geom_line(size=2,alpha=0.5)+
  theme_minimal_grid()



```