TwitterVariants

rm(list = ls())
library(ggplot)
library(dplyr)
library(readr)
library(ggthemes)
library(lubridate)
library(zoo)
library(gganimate)
library(cowplot)
library(gridExtra)
library(ggExtra)
library(grid)

enddate = Sys.Date()

getwd()
data_folder <- file.path("C:/Users/marcj/Nutreco/StatisticsPlatform/Website/ELearning/Workshops/CausalAnalysis/Covid/")
url <- "https://covid.ourworldindata.org/data/owid-covid-data.csv"
name <- "owid-covid-data.csv"
download.file(url = url, destfile = paste0(data_folder,name))
setwd(data_folder)
Covidowid_covid_data <- read_csv(paste0(data_folder,name))

data_folder <- file.path("C:/Users/marcj/Nutreco/StatisticsPlatform/Website/ELearning/Workshops/CausalAnalysis/Covid/")
url <- "https://data.rivm.nl/covid-19/COVID-19_varianten.csv"
name <- "COVID-19_varianten.csv"
download.file(url = url, destfile = paste0(data_folder,name))
setwd(data_folder)
variants  <- read_delim(paste0(data_folder,name),
                        delim = ";", escape_double = FALSE, trim_ws = TRUE)

df<-Covidowid_covid_data
countries <- c(unique(df_owid$iso_code))
dfNLD <- df%>%
  dplyr::filter(iso_code == "NLD")%>%
  dplyr::select(date,iso_code,date,new_cases_per_million, new_deaths_per_million)%>%
  dplyr::mutate(cases_07da       = zoo::rollmean(new_cases_per_million, k = 7, fill = NA),
                deaths_07da      = zoo::rollmean(new_deaths_per_million, k = 7, fill = NA),
                deathdate_21plus = date - 21)
head(dfNLD)
ggplot(dfNLD)+
  geom_line(aes(x=date, y=log(cases_07da), colour="New Cases"))+
  geom_line(aes(x=deathdate_21plus, y=log(deaths_07da), colour="New Deaths"))+
  scale_colour_manual(name="",
                      values=c('red', 'grey'), 
                      labels = c("Cases", "Deaths 21 days later"))+
  theme_bw()+
  theme(legend.position="bottom")+
  labs(x="Date", 
        y="New Cases and New Deaths (log Scale)", 
        title="New Cases vs New Deaths on a 7-day moving average log scale")

dfNLD <- df%>%
  dplyr::filter(iso_code == "NLD")%>%
  dplyr::select(date,iso_code,date,new_cases_per_million, icu_patients_per_million)%>%
  dplyr::mutate(cases_07da       = zoo::rollmean(new_cases_per_million, k = 7, fill = NA),
                ICU_07da      = zoo::rollmean(icu_patients_per_million, k = 7, fill = NA),
                ICUdate_14plus = date - 14)
ggplot(dfNLD)+
  geom_line(aes(x=date, y=log(cases_07da), colour="New Cases"))+
  geom_line(aes(x=ICUdate_14plus , y=log(ICU_07da), colour="New ICU"))+
  scale_colour_manual(name="",
                      values=c('red', 'grey'), 
                      labels = c("Cases", "ICU 14 days later"))+
  theme_bw()+
  theme(legend.position="bottom")+
  labs(x="Date", 
       y="New Cases and ICU (log Scale)", 
       title="New Cases vs ICU on a 7-day moving average log scale")

dfNLD <- df%>%
  dplyr::filter(iso_code == "NLD")%>%
  dplyr::select(date,iso_code,date,new_cases_per_million, hosp_patients_per_million)%>%
  dplyr::mutate(cases_07da       = zoo::rollmean(new_cases_per_million, k = 7, fill = NA),
                hosp_07da      = zoo::rollmean(hosp_patients_per_million, k = 7, fill = NA),
                HOSPdate_14plus = date - 14)
ggplot(dfNLD)+
  geom_line(aes(x=date, y=log(cases_07da), colour="New Cases"))+
  geom_line(aes(x=HOSPdate_14plus , y=log(hosp_07da), colour="New Hospital"))+
  scale_colour_manual(name="",
                      values=c('red', 'grey'), 
                      labels = c("Cases", "Hospital 14 days later"))+
  theme_bw()+
  theme(legend.position="bottom")+
  labs(x="Date", 
       y="New Cases and Hospital (log Scale)", 
       title="New Cases vs Hospital on a 7-day moving average log scale")

dfNLD <- df%>%
  dplyr::filter(iso_code == "NLD")%>%
  dplyr::select(date,iso_code,date,new_cases_per_million, hosp_patients_per_million,icu_patients_per_million,new_deaths_per_million)%>%
  dplyr::mutate(cases_07da       = zoo::rollmean(new_cases_per_million, k = 7, fill = NA),
                hosp_07da        = zoo::rollmean(hosp_patients_per_million, k = 7, fill = NA),
                HOSPdate_14plus  = date - 14,
                deaths_07da      = zoo::rollmean(new_deaths_per_million, k = 7, fill = NA),
                deathdate_21plus = date - 21,
                ICU_07da         = zoo::rollmean(icu_patients_per_million, k = 7, fill = NA),
                ICUdate_14plus   = date - 14)
ggplot(dfNLD)+
  geom_line(aes(x=date, y=log(cases_07da), colour="New Cases"))+
  geom_line(aes(x=HOSPdate_14plus , y=log(hosp_07da), colour="New Hosp"))+
  geom_line(aes(x=ICUdate_14plus , y=log(ICU_07da), colour="New ICU"))+
  geom_line(aes(x=deathdate_21plus, y=log(deaths_07da), colour="New Deaths"))+
  scale_colour_manual(name="",
                      values=c('grey', 'red','green','blue'), 
                      labels = c("Cases", "Hospital 14 days later", "ICU 14 days later","Deaths 21 days later"))+
  theme_bw()+
  theme(legend.position="bottom")+
  labs(x="Date", 
       y="New Cases, Hospital, ICU and Deaths (log Scale)", 
       title="New Cases vs Hospital, ICU, and Deaths on a 7-day moving average log scale")

knitr::opts_chunk$set(fig.width=unit(25,"cm"), fig.height=unit(11,"cm"))
dfNLD<-df%>%
  dplyr::filter(iso_code == "NLD")%>%
  dplyr::select(date,iso_code,date,new_cases_per_million, hosp_patients_per_million,icu_patients_per_million,new_deaths_per_million)%>%
  dplyr::mutate(cases_07da       = zoo::rollmean(new_cases_per_million, k = 7, fill = NA),
                hosp_07da        = zoo::rollmean(hosp_patients_per_million, k = 7, fill = NA),
                HOSPdate_14plus  = date - 14,
                deaths_07da      = zoo::rollmean(new_deaths_per_million, k = 7, fill = NA),
                deathdate_21plus = date - 21,
                ICU_07da         = zoo::rollmean(icu_patients_per_million, k = 7, fill = NA),
                ICUdate_14plus   = date - 14)
my.animation<-ggplot(dfNLD)+
  geom_line(aes(x=date, y=log(cases_07da), colour="New Cases"))+
  geom_line(aes(x=HOSPdate_14plus , y=log(hosp_07da), colour="New Hosp"))+
  geom_line(aes(x=ICUdate_14plus , y=log(ICU_07da), colour="New ICU"))+
  geom_line(aes(x=deathdate_21plus, y=log(deaths_07da), colour="New Deaths"))+
  scale_colour_manual(name="",
                      values=c('grey', 'red','green','blue'), 
                      labels = c("Cases", "Hospital 14 days later", "ICU 14 days later","Deaths 21 days later"))+
  theme_bw()+
  theme(legend.position="bottom")+
  labs(x="Date", 
       y="New Cases, Hospital, ICU and Deaths (log Scale)", 
       title="New Cases vs Hospital, ICU, and Deaths on a 7-day moving average log scale")+
  transition_reveal(date)
animate(my.animation, width=2000, height=1000, 
        res=150,
        end_pause = 60,
        nframes=300);anim_save("Covid2.gif")


variants_sum<-variants%>%
  group_by(Date_of_statistics_week_start,Variant_name)%>%
  summarize(ss=sum(Sample_size),
            vc=sum(Variant_cases), 
            perc=(vc/ss)*100)
variants_sum$date<-variants_sum$Date_of_statistics_week_start
combined<-merge(dfNLD,variants_sum, by=c("date"))


g1<-ggplot(combined)+
  geom_line(aes(x=date, y=log(cases_07da), colour="New Cases"))+
  geom_line(aes(x=HOSPdate_14plus , y=log(hosp_07da), colour="New Hosp"))+
  geom_line(aes(x=ICUdate_14plus , y=log(ICU_07da), colour="New ICU"))+
  geom_line(aes(x=deathdate_21plus, y=log(deaths_07da), colour="New Deaths"))+
  scale_colour_manual(name="",
                      values=c('black', 'red','green','blue'), 
                      labels = c("Cases", "Hospital 14 days later", "ICU 14 days later","Deaths 21 days later"))+
  theme_bw()+
  theme(legend.position="bottom")+
  labs(x="Date", 
       y="New Cases, Hospital, ICU and Deaths (log Scale)", 
       title="New Cases vs Hospital, ICU, and Deaths on a 7-day moving average log scale")+
  scale_x_date(limits = as.Date(c("2021-01-01", enddate)))
g2<-ggplot(combined, 
         aes(x=date,
                     fill=Variant_name))+
         geom_area(aes(y=perc), alpha=0.5)+
  theme_bw()+
  labs(x="Date", 
       y="Number of Variants / Sample Size ", 
       fill="Variant name",
       title="Variant Progression over Time in the NL ")+
  theme(legend.position = "bottom")+
  scale_x_date(limits = as.Date(c("2021-01-01", enddate)))
grid.arrange(g1,g2, ncol=1)       
plot_grid(g1, g2, 
          align = "v", 
          nrow = 2, 
          rel_heights = c(2/3, 1/3))