In databrew/covid19: COVID-19 App

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = NA, 
               echo = TRUE, 
               warning = FALSE, 
               message = FALSE, 
               error = TRUE, 
               cache = FALSE,
  fig.width = 10,
  fig.height = 10,
  fig.path = "figures/",
  out.width = "100%"
)

cat('Date-time: ', Sys.time())
## Load libraries
library(covid19)
library(ggplot2)
library(lubridate)
library(dplyr)
library(readr)
options(scipen = '999')

# "Congo, Dem. Rep." "Congo, Rep."      "Egypt, Arab Rep." "Gambia, The"
pd <- df_country %>% ungroup %>%
  mutate(country = 
           ifelse(country == 'Congo (Kinshasa)', 'Congo, Dem. Rep.',
                  ifelse(country == 'Congro (Brazzaville)',
                         'Congro, Rep.',
                         ifelse(country == 'Egypt', 'Egypt, Arab Rep.',
                                ifelse(country == 'Gambia', 'Gambia, The',
                                       country)))))
pd <- pd %>% left_join(world_pop) %>%
  filter(region %in% c('Africa')) %>%
    # filter(region %in% c('Asia') &
    #          country != 'China') %>%
  # filter(sub_region %in% 'Latin America and the Caribbean') %>%
  filter(cases > 0) %>%
  group_by(date) %>%
  summarise(cases = sum(cases),
            deaths = sum(deaths),
            countries = length(unique(country)))
pd %>% tail
length(unique(world_pop$country[world_pop$region == 'Africa']))

pd <- df_country  %>% ungroup %>%
  mutate(country = 
           ifelse(country == 'Congo (Kinshasa)', 'Congo, Dem. Rep.',
                  ifelse(country == 'Congro (Brazzaville)',
                         'Congro, Rep.',
                         ifelse(country == 'Egypt', 'Egypt, Arab Rep.',
                                ifelse(country == 'Gambia', 'Gambia, The',
                                       country)))))
pd <- pd %>% left_join(world_pop) %>%
  filter(region %in% c('Africa')) %>%
    # filter(region %in% c('Asia') &
    #          country != 'China') %>%
  # filter(sub_region %in% 'Latin America and the Caribbean') %>%
  filter(cases > 0) %>%
  filter(date == '2020-04-21') %>%
  arrange(desc(cases))
# Keep only the top 30 cases
pd <- pd[1:30,]
pd %>% ungroup %>%
  summarise(cases = sum(cases),
            deaths = sum(deaths),
            countries = length(unique(country)))

pd <- df_country
pd <- pd %>% left_join(world_pop) %>%
  filter(region %in% c('Oceania')) %>%
    # filter(region %in% c('Asia') &
    #          country != 'China') %>%
  # filter(sub_region %in% 'Latin America and the Caribbean') %>%
  filter(cases > 0) %>%
  filter(date == '2020-04-21') %>% # 1!!!
  arrange(desc(cases))

pd %>% group_by(country) %>%
  summarise(cases = sum(cases),
            deaths = sum(deaths),
            countries = length(unique(country)))