JHU_COVID-19_Global.Rmd

---
title: "R Notebook"
output:
  word_document: default
  html_notebook: default
---
```{r setup, include=FALSE}
library(tidyverse)
library(magrittr)
library(ggplot2)
library(ggpubr)
library(flextable)
library(lubridate)
library(DescTools)
library(effsize)
library(pwr)
library(sjPlot)
library(webshot)
library(scales)

library(maps)
library(plotly)
library(kableExtra)
library(knitr)
```

```{r}
yd <- today()  - ddays(1)
td <- today()

td
day <- yd

exclude = c("")
# date for daily data release
a <- as.character(ymd(day))
b <- str_split(a, "-")
c1 <- as.character(b[[1]][2])
c2 <- as.character(b[[1]][3]) 
c3 <- as.character(b[[1]][1])
d_lookup_str <- str_c(c1, c2, c3, sep = "-")

sinceMarch16 <- seq(date("2020-03-16"), day, by=1)
#sinceMarch16
last30 <- seq(td-30, td, by=1)
last21 <- seq(td-21, td, by=1)
last14 <- seq(td-14, td, by=1)
last7 <- seq(td-7, td, by=1)

bold.14.text <- element_text(face = "bold", size = 12)

red.fill.5 <- c("#bf080e","#bf080e","#bf080e","#bf080e","#bf080e")
blue.fill.5 <- c("#0016bd","#0016bd","#0016bd","#0016bd","#0016bd")

red.fill.10 <- c("#bf080e","#bf080e","#bf080e","#bf080e","#bf080e","#bf080e","#bf080e","#bf080e","#bf080e","#bf080e", "#bf080e")
blue.fill.10 <- c("#0016bd","#0016bd","#0016bd","#0016bd","#0016bd","#0016bd","#0016bd","#0016bd","#0016bd","#0016bd", "#0016bd")

x.limit.march15 <- seq(ymd("2020-03-15"), day, by=1)

#annotation.moving.average <- annotate(geom = "text", x = xrange[1], y = yrange[2], label = paste(strwrap("Note: 5 Day Moving Average", 40), collapse = "\n"), hjust = 0, vjust = 1, size = 4)

pq <- theme(text = bold.14.text) +
  #theme(axis.ticks = element_blank()) +
  theme(panel.border = element_rect(colour = "black", fill=NA, size=2),
        panel.background=element_rect(colour = NA, fill = "white"))
jhu_caption <- labs(caption="Data Source: Johns Hopkins University CSSE")
```
List of Countries by Continent
```{r}
EU_countries <- c(
  "Albania", 
  "Andora", 
  "Armenia", 
  "Austria", 
  "Azerbaijan", 
  "Belarus", 
  "Belgium", 
  "Bosnia and Herzegovina", 
  "Bulgaria", 
  "Croatia", 
  "Cyprus", 
  "Czechia", 
  "Denmark",
  "Estonia",
  "Finland",
  "France",
  "Georgia",
  "Germany",
  "Greece",
  "Hungary",
  "Iceland",
  "Ireland",
  "Italy",
  "Kazakhstan",
  "Kyrgyzstan",
  "Latvia",
  "Lithuania",
  "Luxembourg",
  "Malta",
  "Monaco",
  "Montenegro",
  "Netherlands",
  "North Macedonia",
  "Norway",
  "Poland",
  "Portugal",
  "Moldova",
  "Romania",
  "Russia",
  "San Marino",
  "Serbia",
  "Slovakia",
  "Slovenia",
  "Spain",
  "Sweden",
  "Switzerland",
  "Tajikistan",
  "Turkey",
  "Turkmenistan",
  "Ukraine",
  "United Kingdom",
  "Uzbekistan"
)
NA_countries <- c("Canada", "Mexico", "US")



```

Functions
```{r}
ma <- function(arr, n=5){
  res = arr
  for(i in n:length(arr)){
    res[i] = mean(arr[(i-n+1):i])
  }
  res
}

movingAverage <- function(x, n=7, centered=FALSE) {
    
    if (centered) {
        before <- floor  ((n-1)/2)
        after  <- ceiling((n-1)/2)
    } else {
        before <- n-1
        after  <- 0
    }

    # Track the sum and count of number of non-NA items
    s     <- rep(0, length(x))
    count <- rep(0, length(x))
    
    # Add the centered data 
    new <- x
    # Add to count list wherever there isn't a 
    count <- count + !is.na(new)
    # Now replace NA_s with 0_s and add to total
    new[is.na(new)] <- 0
    s <- s + new
    
    # Add the data from before
    i <- 1
    while (i <= before) {
        # This is the vector with offset values to add
        new   <- c(rep(NA, i), x[1:(length(x)-i)])

        count <- count + !is.na(new)
        new[is.na(new)] <- 0
        s <- s + new
        
        i <- i+1
    }

    # Add the data from after
    i <- 1
    while (i <= after) {
        # This is the vector with offset values to add
        new   <- c(x[(i+1):length(x)], rep(NA, i))
       
        count <- count + !is.na(new)
        new[is.na(new)] <- 0
        s <- s + new
        
        i <- i+1
    }
    
    # return sum divided by count
    s/count
}

bar_chart_ts <- function(df, y, custom.fill = blue.fill.10, lab.title, y.lim = max(y)) {
  plot <- ggplot(df, aes(x = date, y = y, fill = "")) + 
    geom_col() +
    scale_fill_manual(values = custom.fill) +
    labs(y = "", title = lab.title, x = "Date") +
    theme(legend.position = "none") +
    ylim(0,y.lim) +
    pq
  plot
}

line_chart_ts <- function(df, y, custom.fill = blue.fill.10, lab.title, y.lim = max(y)) {
  plot <- ggplot(df, aes(x = date, y = y, color = "", fill = "")) + 
    geom_line(size = 1.2) +
    scale_fill_manual(values = custom.fill) +
    scale_color_manual(values = custom.fill)+
    labs(y = "", title = lab.title, x = "Date") +
    theme(legend.position = "none") +
    ylim(0,y.lim) +
    pq
  plot
}

line_chart_ma_ts <- function(df, y, custom.fill = blue.fill.10, lab.title, y.lim = max(y)) {
  plot <- ggplot(df, aes(x = date, y = y, color = "", fill = "")) + 
    geom_smooth(size = 1.2) +
    scale_fill_manual(values = custom.fill) +
    scale_color_manual(values = custom.fill)+
    labs(y = "", title = lab.title, x = "Date") +
    theme(legend.position = "none") +
    ylim(0,y.lim) +
    pq
  plot
}

```

Read Raw Data
```{r}
csse_confirmed_global_ts <- read_csv("../COVID-19/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv")
csse_deaths_global_ts <- read_csv("../COVID-19/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv")
uid <- read_csv("../COVID-19/csse_covid_19_data/UID_ISO_FIPS_LookUp_Table.csv")

```

Process Cases
```{r}
cases_stretched <- csse_confirmed_global_ts %>%
  rename(state=`Province/State`, country=`Country/Region`) %>% 
  mutate(state = factor(state), country = factor(country))

cases_pivot <- cases_stretched %>% pivot_longer(`1/22/20`:last_col(), names_to = "date", values_to = "confirmed") %>% select(-state) %>% mutate(date = mdy(date)) %>% group_by(country,date) %>% summarise(confirmed = sum(confirmed)) %>% ungroup() %>% arrange(country, date)
cases_pivot
```

Process Deaths
```{r}
deaths_stretched <- csse_deaths_global_ts %>% 
  rename(state=`Province/State`, country=`Country/Region`) %>% 
  mutate(state = factor(state), country = factor(country)) 

deaths_pivot <- deaths_stretched %>% pivot_longer(`1/22/20`:last_col(), names_to = "date", values_to = "deaths") %>% select(-state) %>% mutate(date = mdy(date)) %>% group_by(country, date) %>% summarise(deaths = sum(deaths)) %>% ungroup() %>% arrange(country, date)
deaths_pivot
```

Combine Deaths and Cases
```{r}
global <- inner_join(cases_pivot, deaths_pivot, key = "country") %>% mutate(mortality_rate = 100 * deaths / confirmed)
global
```
Combine with UID info
```{r}
global_comb <- uid %>%
  rename(population = Population, combined_key = Combined_Key, state=Province_State, country=Country_Region) %>% 
  mutate(state = factor(state), country = factor(country)) %>%
  filter(is.na(state)) %>%
  filter(country %in% c(NA_countries, EU_countries)) %>% 
  select(UID, -state, country, population) %>% 
  inner_join(global, key = "country")# Join with global stats
tail(unique(global_comb$country))

global_comb_deltas <- global_comb %>% 
  group_by(country) %>% 
  arrange(date) %>% 
  mutate(
    confirmed_delta = confirmed - lag(confirmed), 
    deaths_delta = deaths - lag(deaths)
  ) %>% 
  mutate(confirmed_delta = case_when(
      is.na(confirmed_delta) ~ as.double(confirmed),
      TRUE ~ as.double(confirmed) - as.double(lag(confirmed))
    ), 
    deaths_delta = case_when(
      is.na(deaths_delta) ~ as.double(deaths),
      TRUE ~ as.double(deaths) - as.double(lag(deaths))
    )
  ) %>% 
  ungroup() %>% 
  arrange(country, date) %>% 
  select(-confirmed, -deaths)
global_comb_deltas
```
Top 5 Country
```{r}
top_cases <- global_comb %>% filter(date == max(date)) %>% top_n(7,confirmed) %>% pull(var=country)
top_deaths <- global_comb %>% filter(date == max(date)) %>% top_n(7, deaths) %>% pull(var=country)
top_mortality <- global_comb %>% filter(date == max(date)) %>% top_n(7, mortality_rate) %>% pull(var=country)
head(top_cases, 10)
head(top_deaths, 10)
head(top_mortality)

```
Top 5 Country (Deltas)
```{r}
top_new_cases <- global_comb_deltas %>% filter(date == max(date)) %>% top_n(10, confirmed_delta) %>% pull(var=country)
top_new_deaths <- global_comb_deltas %>% filter(date == max(date)) %>% top_n(10, deaths_delta) %>% pull(var=country)
head(top_new_cases)
head(top_new_deaths)
```



```{r}
p1_data <- global_comb %>%
  filter(country %in% top_cases, date > td-60) %>% 
  select(country, date, confirmed, deaths, population, mortality_rate) %>% 
  group_by(country) %>% 
  mutate(case_rate = 1e5*confirmed/population, death_rate = 1e5*deaths/population) %>% 
  ungroup() 
p1_data

p1 <- p1_data %>% ggline(x = "date",
                         y = "death_rate",
                         group = "country",
                         color = "country",
                         #fill = "country",
                         font.xtickslab = c(8,"bold", "black"),
                         x.text.angle = 60,
                         lab.size = 3,
                         plot_type="l",
                         size = 1.5,
                         #label = TRUE,
                         show.line.label = TRUE,
                         repel=TRUE,
                         legend = "none",
                         lab.nb.digits = 0,
                         #sort.by.groups = FALSE,
                         #sort.val = "desc"
                         #ylim=c(1,3000),
                         yscale="none"
                         )
p1
```

```{r}

```