global.R

# ---- Loading libraries ----
library("shiny")
library("shinydashboard")
library("tidyverse")
library("leaflet")
library("plotly")
library("DT")
library("fs")
library("wbstats")

# COMMENTS THESE 3 LINES BELOW FOR LOCAL DEPLOYMENTS
# ATTENTION: IF SHINY DEPLOY FAIL RUN LOCALLY COMMANDS BELOW
# install.packages("remotes")
# remotes::install_github("rstudio/reticulate")
reticulate::virtualenv_create("python35_env", python = "python3")
reticulate::virtualenv_install("python35_env", packages = c("setuptools","pandas", "requests", "numpy", "lxml", "unicodecsv"), ignore_installed = TRUE)
reticulate::use_virtualenv('python35_env', required = TRUE)

reticulate::source_python("backend.py")

source("utils.R", local = T)

updateData <- function() {
    # Call backend update function
    backend_update_data()
}

# Update with start of app
backend_update_data()
# Get last update data day and time
changed_date <- backend_global_status_getter('timestamp')
global_changed_date <- backend_global_status_getter('timestamp')
# Get time series from backend
global_time_series <- backend_global_status_getter('time_series')
# Get information for each location from backend
global_geoinfo <- backend_global_status_getter('geoinfo')
# Get time series melted from backend
global_time_series_melt <- backend_global_status_getter('time_series_melt') %>%
                           mutate(date = as.Date(date))
# Get data from backend
data_evolution <- backend_global_status_getter('soon_deprecated') %>%
                  mutate(date = as.Date(date))
# Get latest day
current_date <- as.Date(max(data_evolution$date), format = "%d/%m/%y")

data_atDate <- function(inputDate) {
  x <- data_evolution[which(data_evolution$date == inputDate),] %>%
       distinct() %>%
       pivot_wider(id_cols = c("Province/State", "Country/Region", "date", "Lat", "Long", "population"), names_from = var, values_from = value) %>%
       filter(confirmed > 0 |
              recovered > 0 |
              deceased > 0 |
              active > 0);
  x

}

data_latest <- data_atDate(max(data_evolution$date))

top5_countries <- data_evolution %>%
  filter(var == "active", date == current_date) %>%
  group_by(`Country/Region`) %>%
  summarise(value = sum(value, na.rm = T)) %>%
  arrange(desc(value)) %>%
  top_n(5) %>%
  select(`Country/Region`) %>%
  pull()