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}


wardlookupregionCity <-   read_csv('~/GitHub/MetaWards/data/2011/WardsProcessing/WardLookupRegionCity.csv')




read.table(file='Runs/30_02/UV0.0/1/ForMattData.dat',sep =' ') %>% # 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

```


```{r}

wardlookupregionCity %>% 
  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) %>% 
  mutate(Date=as.Date(Time,origin="2020-3-01")) %>% 
  ggplot(aes(x=Date,y=Cases,colour=Region)) +
  geom_line(size=2,alpha=0.5)+
  theme_minimal_grid()+
  theme(legend.position = 'top')+
  scale_x_date(breaks=date_breaks("3 months"),
               labels = date_format("%b"))->p


p

```


```{r}

wardlookupregionCity %>% 
  inner_join(.,inf_long, by=c('FID'='Ward'),all.y=T,all.x=F) %>%
  group_by(City,Time)%>% 
  summarise(Cases=sum(value)) -> city_inf
```

```{r}

#citiestoplot=c('London','Liverpool','Birmingham','Sheffield','Bristol','Coventry','Cardiff','Leeds','Bradford','Manchester')

citiestoplot=c('London','Liverpool','Birmingham','Sheffield','Bristol','Cardiff','Manchester','Newcastle upon Tyne')

city_inf %>% 
  filter(City%in%citiestoplot) %>% 
  mutate(Date=as.Date(Time,origin="2020-3-01")) %>% 

#  filter(Time<250) %>% 
  group_by(City) %>% 
  mutate(PropCases=Cases/sum(Cases))%>% 
  ungroup() %>% 
  group_by(Week=floor(Time/7),City)%>% 
  summarise(WeekPropCases=sum(PropCases))%>% 
  ggplot(aes(x=Week,y=WeekPropCases,colour=City)) +
  geom_line(size=2,alpha=0.5)+
  theme_minimal_grid()+
  theme(legend.position = 'top')+
```

```{r}
city_inf %>% 
  filter(City%in%citiestoplot) %>% 
  mutate(Date=as.Date(Time,origin="2020-3-01")) %>% 

#  filter(Time<250) %>% 
  group_by(City) %>% 
  mutate(PropCases=Cases/sum(Cases))%>% 
  ungroup() %>% 
#  group_by(Week=floor(Time/7),City)%>% 
 # summarise(WeekPropCases=sum(PropCases))%>% 
  ggplot(aes(x=Date,y=Cases,colour=City)) +
  geom_line(size=2,alpha=0.5)+
  stat_summary(fun.y = sum, na.rm = TRUE, color = 'black', geom ='line')+
  theme_minimal_grid()+
  theme(legend.position = 'top')+
  scale_x_date(breaks=date_breaks("3 months"),
               labels = date_format("%b"))
```

```{r}


city_inf %>% 
  filter(City%in%citiestoplot) %>% 
  mutate(Date=as.Date(Time,origin="2020-3-01")) %>% 

#  filter(Time<250) %>% 
  group_by(City) %>% 
  mutate(PropCases=Cases/sum(Cases))%>% 
  ungroup() %>% 
#  group_by(Week=floor(Time/7),City)%>% 
 # summarise(WeekPropCases=sum(PropCases))%>% 
  ggplot(aes(x=Date,y=PropCases,colour=City)) +
  geom_line(size=1.5,alpha=0.5)+
  stat_summary(fun.y = mean, na.rm = TRUE, color = 'black', geom ='line')+
  theme_minimal_grid()+
  theme(legend.position = 'top')+
  scale_x_date(breaks=date_breaks("3 months"),
               labels = date_format("%b"))

```


```{r}

city_inf %>% 
  filter(City%in%citiestoplot) %>% 
  filter(Time<250) %>% 
  group_by(City) %>% 
  summarise(TimeToPeak=which.max(Cases))

```



```{r}
city_inf %>% 
  filter(City%in%citiestoplot) %>% 
  group_by(Time) %>% 
  mutate(WholeEpidemic = sum(Cases)) %>% 
  ungroup() %>% 
  mutate(TimeToPeak=which.max(WholeEpidemic))%>% 
  mutate(TimeFromPeak=(Time-TimeToPeak))%>% 
  group_by(City) %>% 
  mutate(PropCases=Cases/sum(Cases))%>% 
  ungroup() %>% 
  group_by(City,Week = floor(TimeFromPeak/7)) %>% 
  summarise(WeeklyCases = sum(Cases)) %>% 
  mutate(PropWeeklyCases=WeeklyCases/sum(WeeklyCases)) %>% 
#  filter(Week) %>% 
  ggplot(aes(x=Week,y=PropWeeklyCases,colour=City)) +
  geom_line(size=2,alpha=0.5)+
  stat_summary(fun.y = mean, na.rm = TRUE, color = 'black', geom ='line')+

  theme_minimal_grid()+
  theme(legend.position = 'top')

```