Plot confirmed COVID-19 cases in SK

South_Korea_COVID-19.R

library(tidyverse)
library(lubridate)
library(ggthemes)
library(forecast)
library(xts)
library(timetk)

future <- 7
confirmed <- read_csv("https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Confirmed.csv")
South_Korea <- list()

# Bind into single dataframe ----------------------------------------------

South_Korea$Confirmed <- confirmed %>% 
  filter(`Country/Region`=="Korea, South") %>%
  select(-(`Province/State`:Long),`Country/Region`) %>%
  summarise_if(is.numeric, sum)

# Future ------------------------------------------------------------------

South_Korea$melted <- South_Korea$Confirmed %>%
  gather %>% 
  mutate(date = key %>% mdy) %>% 
  rename(counts = value)
South_Korea$xts <- xts(South_Korea$melted %>% select(counts),South_Korea$melted$date)
d.arima <- auto.arima(South_Korea$xts)
d.forecast <- forecast(d.arima, level = c(95), h = future)

# Build that model out ----------------------------------------------------

ts_future <- cbind(y = d.forecast$mean, 
                   y.lo = d.forecast$lower, 
                   y.hi = d.forecast$upper) %>% 
  xts(South_Korea$melted$date %>% tk_make_future_timeseries(future))

# Format original xts object
ts_final <- cbind(y = South_Korea$xts,
                  y.lo = NA,
                  y.hi = NA) %>% rbind(ts_future) %>% 
  tk_tbl %>% mutate(counts = round(counts))


# Plot forecast - Note ggplot South_Korea data frames, tk_tbl() converts to df
ts_final$StatSouth_Korea <- "Confirmed Case Count"
ts_final$StatSouth_Korea[which(!is.na(ts_final$y.lo))] <- "Predicted Case Count"

# +(x)% -------------------------------------------------------------------

ts_final$diff <- 1:nrow(ts_final) %>% sapply(function(index){
  if(index==1) { return(0) }
  (((ts_final$counts[index] - ts_final$counts[index-1]) / ts_final$counts[index-1]) * 100) %>% 
    round(digits = 1) %>% return
})
ts_final$label <- paste0(scales::comma(ts_final$counts,big.mark=","), '\n(+',ts_final$diff,'%)')
ts_final$label[-seq(1,length(ts_final$label),3)] <- NA

ts_final %>% filter(index >= ymd("2020-02-01")) %>% 
  ggplot(aes(x = index, y = counts, fill = StatSouth_Korea, label = label)) +
  geom_bar(stat="identity") +
  geom_label(color = "#ffffff", nudge_y = 500) +
  theme_fivethirtyeight() +
  #theme_blue() +
  scale_fill_manual(values = c("#E16036","#D6A99A")) +
  theme(axis.title = element_text()) +
  scale_y_continuous(expand = c(0, 0),limits = c(0,max(ts_final$counts)*1.2)) +
  scale_x_date(date_breaks = "1 days", date_labels = "%b %d") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        legend.direction = "horizontal",
        legend.title = element_blank()) +
  labs(title = "Confirmed Cases of COVID-19 in South Korea",
       subtitle = "ARIMA model South Korea for future predictions. Percentages are based on increase from previous day.",
       caption = "\n@appupio") +
  ylab("Number of Confirmed Cases\n") + xlab("\n\nDate")
ggsave("last.png",width = 16,height = 8,units = "in")