Description_CCCSL_v2.Rmd

---
title: '**Description of the CCCSL dataset - Suppporting Information**'
author: "Amélie Desvars-Larrive, David Garcia"
output:
  pdf_document: default
date: "12/07/2020"
---

```{r, echo=FALSE, message=FALSE, results='hide', warning = FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(dplyr)
library(knitr)
library(ggplot2)
library(tidyr)
library(lubridate)
library(kableExtra)
```

```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4, results='hide'}

# For an updated version of the dataset, use the dataset from the CCCSL Github repository: https://github.com/amel-github/covid19-interventionmeasures
# See http://covid19-interventions.com/

# Import static version of the data on governmental NPIs from Github
measures1 <- read.csv("https://raw.githubusercontent.com/amel-github/CCCSL-Codes/master/COVID19_non-pharmaceutical-interventions_version2_utf8_static_2020-07-12.csv", colClasses=c("Country"="character"))

# If you download the dataset, the .Rmd file and the dataset need to be in the same folder (change file name if another version is used)
#measures1 <- read.csv("COVID19_non-pharmaceutical-interventions_version2_utf8_static_2020-07-12.csv", colClasses=c("Country"="character"))

# For using live data
#measures1 <- read.csv("https://raw.githubusercontent.com/amel-github/covid19-interventionmeasures/master/COVID19_non-pharmaceutical-interventions_version2_utf8.csv", colClasses=c("Country"="character"))

# Date into class date data
measures1$Date <- as.Date (measures1$Date , format = "%Y-%m-%d")

# change country names to be the same as the in Johns Hopkins University CSSE (for data usage)
measures1$Country[measures1$Country=="Taiwan"]<- "Taiwan*"
measures1$Country[measures1$Country=="South Korea"]<- "Korea, South"
measures1$Country[measures1$Country=="Czech Republic"]<- "Czechia"
measures1$Country[measures1$Country=="Republic of Ireland"]<- "Ireland"
measures1$Country[measures1$Country=="United States of America"]<- "US"

# Number of measures in the Diamond Princess cruise ship
DP <- nrow(measures1 %>% filter(Country ==  "Diamond Princess"))

# Remove the Diamond Princess from graphs and tables
measures <- measures1 %>% filter (Country !=  "Diamond Princess")

# Subsets for each L1-code category
L1_1 <- measures %>% filter(Measure_L1 == "Case identification, contact tracing and related measures")
L1_2 <- measures %>% filter(Measure_L1 == "Environmental measures")
L1_3 <- measures %>% filter(Measure_L1 == "Healthcare and public health capacity")
L1_4 <- measures %>% filter(Measure_L1 == "Resource allocation")
L1_5 <- measures %>% filter(Measure_L1 == "Returning to normal life")
L1_6 <- measures %>% filter(Measure_L1 == "Risk communication")
L1_7 <- measures %>% filter(Measure_L1 == "Social distancing")
L1_8 <- measures %>% filter(Measure_L1 == "Travel restriction")
```

```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4, results='hide'}

# number of records per region of  the world

asia <- c("China" , "Indonesia", "Kuwait", "Malaysia","Singapore","Korea, South","Syria","Taiwan*","Thailand","Japan" ,"Kazakhstan","India")
europe <- c("Albania" ,"Austria","Belgium", "Italy" , "Croatia","Czechia","Denmark","Estonia","Finland","France","Germany","Iceland",
            "Liechtenstein" ,"Lithuania", "Montenegro","Netherlands","Norway", "Poland","Portugal","Ireland","Romania", "Serbia","Slovakia","Spain", "Sweden", "Switzerland","Greece","Kosovo","Hungary","Bosnia and Herzegovina", "United Kingdom" ,"North Macedonia", "Slovenia")
north.america <- c("Canada", "US")
south.america <- c("Ecuador","Mexico","Honduras" ,"Brazil","El Salvador")
oceania <- c("New Zealand" )
africa <- c("Ghana", "Mauritius", "Senegal")

# number of records per country
count.country <- aggregate(measures, by=list(measures$Country), FUN=length) 
```

The CCCSL dataset includes information for `r nrow(measures1)` NPIs implemented between `r min(measures1$Date)` and `r max(measures1$Date)`. 
The CCCSL dataset presents data for `r length(unique(measures$Country))` countries, including `r length(europe)` European countries, `r length(asia)` Asian countries, `r length(south.america)` South American, `r length(north.america)` North American countries, `r length((oceania))` Oceanian country, `r length((africa))` African countries, and the Diamond Princess cruise ship. 

The median number of NPIs implemented by the governments to mitigate the burden of COVID-19 is `r median(count.country$Country)` (min. = `r min(count.country$Country)`; max. = `r max(count.country$Country)`). 

*Note: Version 2 of the CCCSL dataset presents a consolidated coding scheme. Data on Poland, Senegal, and Ghana have been added as well as data for 24 states of the USA.*
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}

## plot of the  number of the NPIs implemented worldwide over calendar time

 plot1 <- ggplot(measures, aes(x = Date, fill = Measure_L1)) + 
  geom_bar(position = "stack") + theme_minimal() +
  labs(title = "Implementation of interventions over calendar time",
       x = "Date",
       y = "Number of interventions")+ 
  scale_x_date(name = 'Date', date_breaks = '7 days',
               date_labels = '%d-%m-%y')+
  theme(axis.text.x = element_text(size=8, angle=45, hjust = 1))+
  theme(axis.ticks.x = element_line(colour = "black", size = 1))+
  guides(fill=guide_legend(title=""))+
  theme(legend.title = element_blank()) +
  theme(legend.position = "bottom")+ 
  theme(legend.key.size = unit(0.5, "cm"),
        legend.text = element_text(size = 10)) + 
  guides(fill=guide_legend(nrow=4, byrow=TRUE))
```


```{r plot1, echo=FALSE}
plot(plot1, caption = "Histogram of the NPIs recorded in the CCCSL at level 1 (themes) of the coding scheme and time of implementation over calendar time (Diamond Princess cruise ship excluded).")
```

```{r echo=FALSE, fig.height=4, fig.width=8, message=FALSE}

# Summary of the number of measures for each L1-, L2-, and L3-code category of NPI for each country

prop.table.L1 <- prop.table(table(measures$Measure_L1))
value.L1 <- as.vector(round(prop.table.L1, 2))
value.L2  <- as.vector(table(measures$Measure_L1))
table0 <- cbind.data.frame(names(prop.table.L1 ), value.L2, value.L1 )
rownames(table0) <- NULL

# number of unique measures for each code category
uniqueL1 <- nrow(unique(L1_1[,c("Measure_L1","Measure_L2")]))
uniqueL2 <- nrow(unique(L1_2[,c("Measure_L1","Measure_L2")]))
uniqueL3 <- nrow(unique(L1_3[,c("Measure_L1","Measure_L2")]))
uniqueL4 <- nrow(unique(L1_4[,c("Measure_L1","Measure_L2")]))
uniqueL5 <- nrow(unique(L1_5[,c("Measure_L1","Measure_L2")]))
uniqueL6 <- nrow(unique(L1_6[,c("Measure_L1","Measure_L2")]))
uniqueL7 <- nrow(unique(L1_7[,c("Measure_L1","Measure_L2")]))
uniqueL8 <- nrow(unique(L1_8[,c("Measure_L1","Measure_L2")]))

colnames (table0) <- c("Theme (L1)", "Number of records", "Frequency")

kable(table0, booktabs = T,
             caption = "Summary of the NPIs recorded in the CCCSL at level 1 (themes) of the coding scheme (Diamond Princess cruise ship excluded).", format="latex") %>% 
  kable_styling(latex_options="scale_down")

```

```{r echo=FALSE, fig.height=4, fig.width=8, message=FALSE}

# Top 20 most frequent L2-code categories in the CCCSL

rank.l2 <- aggregate( Country~ Measure_L2, data = measures, FUN = length)
rank.l2.2 <- rank.l2 %>%
  arrange(Country) 
prop.L2 <- (rank.l2.2[,2])/sum(rank.l2.2[,2])
prop.L2.1 <- cbind.data.frame(rank.l2.2, round(prop.L2,2))
table2 <- prop.L2.1 [order(-prop.L2.1$Country ),]
table2.f <- table2[c(1:20),]
colnames(table2.f) <- c("Category (L2)", "Number of record", "Frequency")
rownames(table2.f) <- NULL

kable(table2.f, booktabs = T,
             caption = "Top 20 most frequent NPIs recorded in the CCCSL at level 2 (categories) of the coding scheme (Diamond Princess cruise ship excluded).")
```

\pagebreak

# Country-based summaries of NPIs recorded in the CCCSL at level 1 (themes) of the coding scheme.
```{r echo=FALSE, fig.height=4, fig.width=8, message=FALSE}

# summary of ALL measures per country 

m_L1 <- sort(unique(measures1$Measure_L1))
measures1 %>%
  group_by(Country) %>%
  summarise(case.id.cnt= sum(Measure_L1==m_L1[1]) ,  envir.id.cnt= sum(Measure_L1==m_L1[2])  , healthcare.capacity.cnt= sum(Measure_L1==m_L1[3]),resource.allocation.cnt= sum(Measure_L1==m_L1[4]) ,returen.id =sum(Measure_L1==m_L1[5])   ,risk.communication.cnt= sum(Measure_L1==m_L1[6]),social.distancing.cnt= sum(Measure_L1==m_L1[7]) , travel.restriction.cnt= sum(Measure_L1==m_L1[8]) )   ->L1count
 
L1count$total <- rowSums(L1count[,2:9]) 
colnames(L1count) <- c("Country", "Case identification, contact tracing and related measures", "Environmental measures","Healthcare and public health capacity", "Resource allocation", "Returning to normal life","Risk communication", "Social distancing","Travel restriction", "Total")

kable(L1count, booktabs = T,
             caption = "Summary per country of the NPIs recorded in the CCCSL at level 1 (themes) of the coding scheme.", format="latex") %>% 
  kable_styling(latex_options="scale_down")


```

## Overview of the NPIs recorded in the CCCSL at level 1 (themes) of the coding scheme over the epidemic age (t0 = 10 confirmed cases)
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4, results='hide'}
# Import data on number of cases, recovered, and deaths
# We use the case data from the Johns Hopkins repository: https://github.com/CSSEGISandData/COVID-19
# See https://linkinghub.elsevier.com/retrieve/pii/S1473309920301201

confirmeddf <- read.csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv", stringsAsFactors = F, colClasses=c("Country.Region"="character"))
deathsdf <- read.csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv", stringsAsFactors = F, colClasses=c("Country.Region"="character"))
recovereddf <- read.csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv", stringsAsFactors = F, colClasses=c("Country.Region"="character"))

# List of countries to iterate
countries <-  unique(c(confirmeddf$Country.Region, deathsdf$Country.Region, recovereddf$Country.Region))

# We convert the data frame from wide to long format

ldf <- data.frame(country=NULL, date=NULL, confirmed=NULL, deaths=NULL, recovered=NULL)

for (country in countries)
{
  # The first four columns contain the name of the country, location, etc. The time series win wide format starts from the fifth.
  confirmeddf %>% 
    filter(Country.Region == country) %>% 
    summarise_at(5:ncol(confirmeddf), sum) -> confirmedcounts
  dates <- names(confirmeddf)[5:ncol(confirmeddf)] 
  
  # Dates are in a weird format, we convert them for R
  dates <- as.Date(gsub("\\.", "-", sub(".", "", dates)), format = "%m-%d-%y")
  confirmeddfsel <- data.frame(confirmed = as.numeric(confirmedcounts), date=dates)
  
  # We repeat the above process for deaths and recovered time series
  deathsdf %>% 
    filter(Country.Region == country) %>% 
    summarise_at(5:ncol(deathsdf), sum) -> deathscounts
  dates <- names(deathsdf)[5:ncol(deathsdf)] 
  dates <- as.Date(gsub("\\.", "-", sub(".", "", dates)), format = "%m-%d-%y")
  deathsdfsel <- data.frame(deaths = as.numeric(deathscounts), date=dates)
  
  recovereddf %>% 
    filter(Country.Region == country) %>% 
    summarise_at(5:ncol(recovereddf), sum) -> recoveredcounts
  dates <- names(recovereddf)[5:ncol(recovereddf)] 
  dates <- as.Date(gsub("\\.", "-", sub(".", "", dates)), format = "%m-%d-%y")
  recovereddfsel <- data.frame(recovered = as.numeric(recoveredcounts), date=dates)
  
  # inner_joins map all data by date so the long format has a column for confirmed, another for deaths, and a third one for recovered
  newdf <- inner_join(confirmeddfsel, deathsdfsel)
  newdf <- inner_join(newdf, recovereddfsel)
  
  # We add the country name and include this country in the full data frame
  newdf$Country = country
  ldf <- rbind(ldf, newdf)
}
```

```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}

# create t0 of epidemic age for each country t0 = day with 10 cases

ldf %>%
  group_by(Country) %>%
  filter(confirmed >=10) %>%
  summarise(edate = min(date)) -> ctry_edate

ldf %>%
  select(Country) %>%
  unique() -> ctry_names

measures %>%
  left_join(ctry_edate, by = "Country") %>%
  filter(!is.na(edate)) %>%
  mutate(Date = as.numeric(Date - edate)) %>%
  left_join(ctry_names, by = "Country") %>%
  select(Country, Date, edate,  Measure_L1) -> edates

```

```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}

# plot number of measures function of epidemic age

lab_x <- "Epidemic age (t0 = 10 confirmed cases)"

plot2 <- ggplot(edates, aes(x = Date, fill = Measure_L1)) +
  geom_bar(position = "stack") + theme_minimal() +
  labs(title = "Implementation of interventions over epidemic age",
       x = lab_x,
       y = "Number of interventions")+
  theme(legend.title = element_blank()) +
  theme(axis.text.x = element_text(size=10)) +
  theme(axis.title.y = element_text( size=10))+
  theme(legend.position = "bottom")+ 
  theme(legend.key.size = unit(0.5, "cm"),
        legend.text = element_text(size = 10)) + 
  guides(fill=guide_legend(nrow=4, byrow=TRUE))
```


```{r plot2, echo=FALSE}
plot(plot2, caption = "NPIs recorded in the CCCSL at level 1 (themes) of the coding scheme and time of implementation over epidemic age and all countries (t0 = day when 10 cases were reported)")
```

```{r echo=FALSE, fig.height=4, fig.width=8, message=FALSE}

#identify range of dates for each L1-code category: timeline and responsiveness

test <- edates %>%
  group_by(Date, Measure_L1) %>%  
  summarise(
    count = n()
  ) %>%
  ungroup() %>%
  arrange(Measure_L1, Date) %>% 
  group_by(Measure_L1) %>%  
  mutate(count =  cumsum(count)) %>%
  complete(Date = min(edates$Date):max(edates$Date)) %>%
  fill(count) %>%
  replace_na(list(count = 0))

test1 <- aggregate(count ~ Date + Measure_L1, data = test, FUN = sum)

test1 %>%
  filter (Measure_L1 == "Case identification, contact tracing and related measures") %>%
  filter (count != 0) -> case.id

test1 %>%
  filter (Measure_L1 == "Environmental measures") %>%
  filter (count != 0) -> envir.id

test1 %>%
  filter (Measure_L1 == "Healthcare and public health capacity") %>%
  filter (count != 0) ->health.id

test1 %>%
  filter (Measure_L1 == "Resource allocation") %>%
  filter (count != 0) -> ress.id

test1 %>%
  filter (Measure_L1 == "Returning to normal life") %>%
  filter (count != 0) -> return.id

test1 %>%
  filter (Measure_L1 == "Risk communication") %>%
  filter (count != 0) -> risk.id

test1 %>%
  filter (Measure_L1 == "Social distancing") %>%
  filter (count != 0) -> socd.id

test1 %>%
  filter (Measure_L1 == "Travel restriction") %>%
  filter (count != 0) -> trav.id

table01 <- rbind.data.frame(summary(case.id$Date), summary(envir.id$Date),summary(health.id$Date),summary(ress.id$Date),  summary(return.id$Date), summary(risk.id$Date), summary(socd.id$Date),summary(trav.id$Date) )
table1 <- cbind (unique(test1$Measure_L1),  table01)
colnames(table1) <- c("L1-code category","Min. epi-date", "1st Qu. epi-date" ,"Median epi-date","Mean epi-date","3rd Qu. epi-date","Max. epi-date")

kable(table1, booktabs = T,
             caption = "Characterization of the NPIs recorded in the CCCSL at level 1 (themes) of the coding scheme: summary of the timeline of implementation over all countries.", format="latex") %>% 
  kable_styling(latex_options="scale_down")
```
 
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}

# Stacked area chart of the L1-code categories 

plot3 <- edates %>%
   group_by(Date, Measure_L1) %>%
   summarise(
     count = n()
   ) %>%
   ungroup() %>%
   arrange(Measure_L1, Date) %>%
   group_by(Measure_L1) %>%
   mutate(count =  cumsum(count)) %>%
   complete(Date = min(edates$Date):max(edates$Date)) %>%
   fill(count) %>%
   replace_na(list(count = 0)) %>%
   ggplot(aes(x = Date, fill = Measure_L1, y = count)) +
   theme_minimal() + labs(
     x = lab_x,
     y = "Percentage share of all interventions at event date",
     fill = "Measure_L1"
   ) +
   geom_area(position = "fill") +
  theme(axis.text.x = element_text(size=10)) +
  theme(axis.title.y = element_text( size=10))+ 
  scale_y_continuous(labels = scales::percent)+
  theme(legend.title = element_blank())+
  theme(legend.position = "bottom")+ 
  theme(legend.key.size = unit(0.5, "cm"),
        legend.text = element_text(size = 10)) + 
  guides(fill=guide_legend(nrow=4, byrow=TRUE))
```


```{r plot3, echo=FALSE}
plot(plot3, caption = "Stacked area chart of the NPIs recorded in the CCCSL at level 1 (themes) of the coding scheme. Time is in epidemic age (t0 = day when 10 cases were reported).")
```


## Country-based overview of the NPIs recorded in the CCCSL at level 1 (themes) of the coding scheme over the epidemic age (t0 = 10 confirmed cases) - All countries
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}

lab_x <- "Epidemic age (t0 = 10 confirmed cases)"

plot.country <- function (name.country)
{
edates [edates$Country==name.country,] %>%
  group_by(Date, Measure_L1) %>%
  summarise(
    count = n()
  ) %>%
  ungroup() %>%
  arrange(Measure_L1, Date) %>%
  group_by(Measure_L1) %>%
  mutate(count =  cumsum(count)) %>%
  complete(Date = min(edates$Date):max(edates$Date)) %>%
  fill(count) %>% 
  replace_na(list(count = 0)) %>%
  ggplot(aes(x = Date, fill = Measure_L1, y = count)) +
  theme_minimal() + labs(
    x = lab_x,
    y = "Percentage share of all interventions at event date",
    fill = ""
  ) + 
  geom_area(position = "fill") + 
  scale_y_continuous(labels = scales::percent)+
  labs(title=name.country)+
  theme(legend.title = element_blank())+
  theme(legend.position = "bottom")+ 
  theme(legend.key.size = unit(0.5, "cm"),
        legend.text = element_text(size = 10)) + 
  guides(fill=guide_legend(nrow=4, byrow=TRUE))
}
```

## Asia

### Hong Kong, China
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4, warning=FALSE}
## need to create HK data set for cases
regions <-  unique(c(confirmeddf$Province.State, deathsdf$Province.State, recovereddf$Province.State))

ldf2 <- data.frame(region=NULL, date=NULL, confirmed=NULL, deaths=NULL, recovered=NULL)

for (region in regions)
{
  # The first four columns contain the name of the country, location, etc. The time series win wide format starts from the fifth.
  confirmeddf %>% 
    filter(Province.State == region) %>% 
    summarise_at(5:ncol(confirmeddf), sum) -> confirmedcounts
  dates <- names(confirmeddf)[5:ncol(confirmeddf)] 
  
  # Dates are in a weird format, we convert them for R
  dates <- as.Date(gsub("\\.", "-", sub(".", "", dates)), format = "%m-%d-%y")
  confirmeddfsel <- data.frame(confirmed = as.numeric(confirmedcounts), date=dates)
  
  # We repeat the above process for deaths and recovered time series
  deathsdf %>% 
    filter(Province.State == region) %>% 
    summarise_at(5:ncol(deathsdf), sum) -> deathscounts
  dates <- names(deathsdf)[5:ncol(deathsdf)] 
  dates <- as.Date(gsub("\\.", "-", sub(".", "", dates)), format = "%m-%d-%y")
  deathsdfsel <- data.frame(deaths = as.numeric(deathscounts), date=dates)
  
  recovereddf %>% 
    filter(Province.State == region) %>% 
    summarise_at(5:ncol(recovereddf), sum) -> recoveredcounts
  dates <- names(recovereddf)[5:ncol(recovereddf)] 
  dates <- as.Date(gsub("\\.", "-", sub(".", "", dates)), format = "%m-%d-%y")
  recovereddfsel <- data.frame(recovered = as.numeric(recoveredcounts), date=dates)
  
  # inner_joins map all data by date so the long format has a column for confirmed, another for deaths, and a third one for recovered
  newdf <- inner_join(confirmeddfsel, deathsdfsel)
  newdf <- inner_join(newdf, recovereddfsel)
  
  # We add the country name and include this country in the full data frame
  newdf$Country = region
  ldf2 <- rbind(ldf2, newdf)
}

ldf2 %>%
  group_by(Country) %>%
  filter(confirmed >= 10) %>%
  summarise(edate = min(date)) -> ctry_edate2

ldf2 %>%
  select(Country) %>%
  unique() -> ctry_names2

#  measures
 measures %>%
   left_join(ctry_edate2, by = c("Region" = "Country")) %>%
   filter(!is.na(edate)) %>%
   mutate(Date = as.numeric(Date - edate)) %>%
   left_join(ctry_names, by = "Country") %>%
   select(Region, Country, Date, edate,  Measure_L1) -> edates2


edates2 [edates2$Region=="Hong Kong",] %>%
  group_by(Date, Measure_L1) %>%
  summarise(
    count = n()
  ) %>%
  ungroup() %>%
  arrange(Measure_L1, Date) %>%
  group_by(Measure_L1) %>%
  mutate(count =  cumsum(count)) %>%
  complete(Date = min(edates2$Date):max(edates2$Date)) %>%
  fill(count) %>% 
  replace_na(list(count = 0)) %>%
  ggplot(aes(x = Date, fill = Measure_L1, y = count)) +
  theme_minimal() + labs(
    x = lab_x,
    y = "Percentage share of all interventions at event date",
    fill = ""
  ) + 
  geom_area(position = "fill") + 
  scale_y_continuous(labels = scales::percent)+
  labs(title="Hong Kong")+
  theme(legend.title = element_blank())+
  theme(legend.position = "bottom")+ 
  theme(legend.key.size = unit(0.5, "cm"),
        legend.text = element_text(size = 10)) + 
  guides(fill=guide_legend(nrow=4, byrow=TRUE))

```

### India
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "India")
```

### Indonesia
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Indonesia")
```

### Japan
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Japan")
```

### Kazakhstan
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Kazakhstan")
```

### Kuwait
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Kuwait")
```

### Malaysia
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Malaysia")
```

### Singapore
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Singapore")
```

### South Korea
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Korea, South")
```

### Taiwan
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Taiwan*")
```

### Thailand
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Thailand")
```

### Syria
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Syria")
```

## Europe
### Albania
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Albania")
```

### Austria
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Austria")
```

### Belgium
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Belgium")
```

### Bosnia and Herzegovina
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Bosnia and Herzegovina")
```

### Croatia
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Croatia")
```

### Czech Republic
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Czechia")
```

### Denmark
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Denmark")
```

### Estonia
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Estonia")
```

### Finland
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Finland")
```

### France (metropole)
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "France")
```

### Germany
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Germany")
```

### Greece
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Greece")
```

### Hungary
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Hungary")
```

### Iceland
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Iceland")
```

### Ireland
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Ireland")
```

### Italy
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Italy")
```

### Kosovo
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Kosovo")
```

### Liechtenstein
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Liechtenstein")
```

### Lithuania
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Lithuania")
```

### Montenegro
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Montenegro")
```

### Netherlands
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Netherlands")
```

### North Macedonia
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "North Macedonia")
```

### Norway
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Norway")
```

### Poland
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Poland")
```

### Portugal
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Portugal")
```

### Romania
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Romania")
```

### Serbia
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Serbia")
```

### Slovakia
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Slovakia")
```

### Slovenia
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Slovenia")
```

### Spain
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Spain")
```

### Sweden
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Sweden")
```

### Switzerland
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Switzerland")
```

### United Kingdom
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "United Kingdom")
```

## North America
### Canada
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Canada")
```

### United States of America
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ("US")
```

## South America
### Brazil
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Brazil")
```

### Ecuador
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Ecuador")
```

### El Salvador
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "El Salvador")
```

### Honduras
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Honduras")
```

### Mexico
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Mexico")
```

## Oceania
### New Zealand
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "New Zealand")
```

## Africa
### Ghana
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Ghana")
```

### Mauritius
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Mauritius")
```

### Senegal
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
plot.country ( "Senegal")
```

## Diamond Princess cruise ship
```{r, echo=FALSE, message=FALSE, fig.width=8, fig.height=4}
#  measures
 measures1 %>%
  left_join(ctry_edate, by = "Country") %>%
  filter(!is.na(edate)) %>%
  mutate(Date = as.numeric(Date - edate)) %>%
  left_join(ctry_names, by = "Country") %>%
  select(Country, Date, edate,  Measure_L1) -> edates3

edates3 [edates3$Country== "Diamond Princess",] %>%
  group_by(Date, Measure_L1) %>%
  summarise(
    count = n()
  ) %>%
  ungroup() %>%
  arrange(Measure_L1, Date) %>%
  group_by(Measure_L1) %>%
  mutate(count =  cumsum(count)) %>%
  complete(Date = min(edates3$Date):max(edates3$Date)) %>%
  fill(count) %>% 
  replace_na(list(count = 0)) %>%
  ggplot(aes(x = Date, fill = Measure_L1, y = count)) +
  theme_minimal() + labs(
    x = lab_x,
    y = "Percentage share of all interventions at event date",
    fill = ""
  ) + 
  geom_area(position = "fill") + 
  scale_y_continuous(labels = scales::percent)+
  labs(title="Diamond Princess")+
  theme(legend.title = element_blank())+
  theme(legend.position = "bottom")+ 
  theme(legend.key.size = unit(0.5, "cm"),
        legend.text = element_text(size = 10)) + 
  guides(fill=guide_legend(nrow=4, byrow=TRUE))

```