global.R

# load libraries ----------------------------------------------------------

library(shiny)
library(shinydashboard)
library(rvest)
library(tidyverse)
library(gganimate)
library(shinythemes)
library(highcharter)
library(formattable)
library(lubridate)
library(shinyWidgets)
library(shinyjs)
library(DT)
library(sever)
library(jsonlite)
library(httr)
library(textrank)
library(shinyalert)
library(tidytext)
library(shinycssloaders)
library(googlesheets)

options(scipen = 999)


mapdata <- read_csv("mapdata.csv")


# get data ----------------------------------------------------------------


gs_auth(token = "token/googlesheets_token.RDS")

dat <-
  gs_key(x = "1XYprNQTpX3n4YPFpq3Rq5l77RUfv6MnFRgDxysSAlUc") %>%
  gs_read()

selected <- top_n(dat, n = 5, wt = Confirmed) %>% pull(Country)


dat_map <- dat %>%
  mutate(
    Country = case_when(
      Country == "US" ~ "United States of America",
      Country == "Korea, South" ~ "South Korea",
      Country == "Czechia" ~ "Czech Republic",
      Country == "Serbia" ~ "Republic of Serbia",
      Country == "Taiwan*" ~ "Taiwan",
      TRUE ~ Country
    )
  ) %>%
  left_join(mapdata, by = c("Country" = "name"))


# confirmed cases ---------------------------------------------------------


today_confirmed <- dat %>%
  select(Country, Confirmed) %>%
  mutate(datetime = Sys.Date() %>% as.character(),
         Country = as.factor(Country))

colnames(today_confirmed) <-
  colnames(today_confirmed) %>% str_to_lower()

ts_confirmed <-
  read.csv(
    url(
      "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv"
    )
  )

date_seq <- seq(
  from = ymd("2020-01-22"),
  length.out = ncol(ts_confirmed) - 4,
  by = "day"
)

colnames(ts_confirmed) <-
  c("province_state",
    "country",
    "lat",
    "long",
    as.character(date_seq))



ts_confirmed_long <- pivot_longer(
  ts_confirmed,
  cols = -c(province_state, country, lat, long),
  names_to = "datetime",
  values_to = "confirmed"
) %>%
  select(country, confirmed, datetime) %>%
  bind_rows(today_confirmed) %>%
  arrange(country)




# recovered ---------------------------------------------------------------


today_recovered <- dat %>%
  select(Country, Recovered) %>%
  mutate(datetime = Sys.Date() %>% as.character(),
         Country = as.factor(Country))

colnames(today_recovered) <-
  colnames(today_recovered) %>% str_to_lower()

ts_recovered <-
  read.csv(
    url(
      "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv"
    )
  )

date_seq_recov <- seq(
  from = ymd("2020-01-22"),
  length.out = ncol(ts_recovered) - 4,
  by = "day"
)

colnames(ts_recovered) <-
  c("province_state",
    "country",
    "lat",
    "long",
    as.character(date_seq_recov))



ts_recovered_long <- pivot_longer(
  ts_recovered,
  cols = -c(province_state, country, lat, long),
  names_to = "datetime",
  values_to = "recovered"
) %>%
  select(country, recovered, datetime) %>%
  bind_rows(today_recovered) %>%
  arrange(country)



# deaths ------------------------------------------------------------------


today_deaths <- dat %>%
  select(Country, Deaths) %>%
  mutate(datetime = Sys.Date() %>% as.character(),
         Country = as.factor(Country))

colnames(today_deaths) <- colnames(today_deaths) %>% str_to_lower()

ts_deaths <-
  read.csv(
    url(
      "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv"
    )
  )


date_seq_deaths <- seq(
  from = ymd("2020-01-22"),
  length.out = ncol(ts_deaths) - 4,
  by = "day"
)


colnames(ts_deaths) <-
  c("province_state",
    "country",
    "lat",
    "long",
    as.character(date_seq_deaths))



ts_deaths_long <- pivot_longer(
  ts_deaths,
  cols = -c(province_state, country, lat, long),
  names_to = "datetime",
  values_to = "deaths"
) %>%
  select(country, deaths, datetime) %>%
  bind_rows(today_deaths) %>%
  arrange(country)


# Indonesia cases ---------------------------------------------------------


dat_indo <-
  gs_key(x = "1-EW7SNgJx4oBOUTl_E8hCV9tt2zOKBq_d-osQXVZLnA") %>%
  gs_read()



map_indo <- read.csv("map-indo.csv") %>%
  left_join(dat_indo, by = "Provinsi") %>%
  select(woe_name, kasus = Kasus_Posi)

dat_indo <- dat_indo %>%
  rename(
    Province = Provinsi,
    Confirmed = Kasus_Posi,
    Recovered = Kasus_Semb,
    Death = Kasus_Meni
  ) %>%
  arrange(desc(Confirmed)) %>%
  select(Province, Confirmed, Recovered, Death)

ts_confirmed_indo <- ts_confirmed_long %>%
  filter(country == "Indonesia")

ts_recovered_indo <- ts_recovered_long %>%
  filter(country == "Indonesia")

ts_deaths_indo <- ts_deaths_long %>%
  filter(country == "Indonesia")

ts_indo <- ts_confirmed_indo %>%
  left_join(ts_recovered_indo, by = c("datetime", "country")) %>%
  left_join(ts_deaths_indo, by = c("datetime", "country")) %>%
  select(datetime, confirmed, recovered, deaths)

ts_indo_longer <- pivot_longer(
  ts_indo,
  cols = -c(datetime),
  names_to = "variable",
  values_to = "count"
)

infobox_ts_indo <- ts_indo %>% slice(nrow(.))