Lockdowns

rm(list = ls())
require(dplyr)
require(ggplot2)
require(ISOweek)
require(scales)
library(readr)
library(timetk)
rivm.data <- read_csv("COVID-19_casus_landelijk_2020-07-01.csv")
rivm.death <- rivm.data %>%
  dplyr::filter(Deceased == "Yes") ## Extract deaths data only
rivm.hospital <- rivm.data %>%
  dplyr::filter(Hospital_admission == "Yes") ## Extract hospital data only
rivm.hospital$Date_statistics <- as.Date(rivm.hospital$Date_statistics)
hospital <- rivm.hospital %>%
  filter(Date_statistics_type == "DOO") %>% 
  group_by(Date_statistics, Province) %>% 
  summarise(aantal_opnames = n())
hospital$aantal_opnames<-as.numeric(hospital$aantal_opnames)
str(hospital)
hospital<-as.data.frame(hospital)
str(hospital)
interventie <- data.frame(Province = unique(hospital$Province), 
                          int = c(rep(as.Date("2020-03-16"), 
                                      times = length((unique(hospital$Province))))))
ggplot(hospital, aes(x=Date_statistics, y=aantal_opnames)) + 
  geom_line() + 
  facet_wrap(~ Province, scales="free") +
  geom_vline(aes(xintercept = int), interventie, col="red", lty=2) +
  theme(axis.text.x = element_text(angle = 90)) + 
  labs(x="Date", y="Hopsital Admision")+
  theme_bw()
hospital%>%
  group_by(Province)%>%
  summarise_by_time(
  .date_var = Date_statistics,
  .by       = "week", 
  value  = sum(aantal_opnames))%>%
  mutate(deriv = value-lag(value), 
         deriv2 = deriv - lag(deriv))%>%
  ggplot(., aes(x=Date_statistics)) + 
  geom_line(aes(y=value, colour="Hospital Admission")) +
  geom_line(aes(y=deriv, colour="First derivative"))+
  geom_line(aes(y=deriv2, colour="Second derivative"))+
  facet_wrap(~ Province, scales="free_y") + 
  theme(axis.text.x = element_text(angle = 90)) + 
  scale_colour_manual(name="Legend",
                      values=c('seagreen','grey','darkred'), 
                      labels = c("Hospital Admission", 
                                 "First derivative", 
                                 "Second derivative"))+
  geom_point(aes(y=value), color="black", size=1.5)+
  geom_point(aes(y=deriv), color="green", size=1.5)+
  geom_point(aes(y=deriv2), color="red", size=1.5)+
  geom_vline(aes(xintercept = int), interventie, col="purple", lty=2) +
  labs(x="Date", 
       y="Hopsital Admision", 
       title="Hospital Admisions prior and following first lockdown")+
  theme_bw()+
  theme(legend.position="bottom")
hospital%>%
  group_by(Province)%>%
  summarise_by_time(
    .date_var = Date_statistics,
    .by       = "week", 
    value  = sum(aantal_opnames))%>%
  mutate(deriv = value-lag(value), 
         deriv2 = deriv - lag(deriv))%>%
  ggplot(., aes(x=Date_statistics)) + 
  geom_line(aes(y=value, colour=Province)) +
  geom_point(aes(y=value, colour=Province), size=1.5) +
  theme(axis.text.x = element_text(angle = 90)) + 
  geom_vline(aes(xintercept = int), interventie, col="black", lty=2) +
  labs(x="Date", 
       y="Infections", 
       title="Infections prior and following first lockdown")+
  theme_bw()+
  theme(legend.position="bottom")