covid19

Calling libs

library(tidyverse)
library(tidylog)
library(plotly)

Reading data

d <- read.csv("processesd.csv")

Gulf countries

g_countries <- c("Saudi Arabia", "United Arab Emirates", "Bahrain", "Qatar", "Kuwait", "Oman")


g_gulf <- 
  d %>% 
  filter(country_region %in% g_countries,
         date == '2020-04-03')

Saudi Arabia has the highest number of death cases but the number is reasonable given the number of people living in the kingdom.

 g_gulf %>% 
  ggplot(aes(x = reorder(country_region, -deaths_num), y = deaths_num)) + 
  geom_col() +
  labs(
   x = "",
   y = "number of death cases",
   title = "Saudi Arabia has the highest death cases among gulf countries"
  )

Highest confirmed and deaths countries

highest_confirmed_countries <- 
  d %>% 
  filter(date == '2020-04-03') %>% 
  pivot_longer(-c(date, country_region), names_to = "case_type") %>% 
  filter(case_type == "confirmed_num") %>% 
  arrange(desc(value)) %>% 
  head(10) %>% 
  pull(country_region)




highest_deaths_countries <- 
   d %>% 
  filter(date == '2020-04-03') %>% 
  pivot_longer(-c(date, country_region), names_to = "case_type") %>% 
  filter(case_type == "deaths_num") %>% 
  arrange(desc(value)) %>% 
  head(10) %>% 
  pull(country_region)



highest_recovered_countries <- 
   d %>% 
  filter(date == '2020-04-03') %>% 
  pivot_longer(-c(date, country_region), names_to = "case_type") %>% 
  filter(case_type == "recovered_num") %>% 
  arrange(desc(value)) %>% 
  head(10) %>% 
  pull(country_region)


  
d_top_countries <- 
  c(highest_recovered_countries, highest_deaths_countries, highest_confirmed_countries) %>% 
  unique() 

d %>%
  filter(date == '2020-04-03',country_region %in% d_top_countries) %>% 
 pivot_longer(-c(date, country_region), names_to = "case_type") %>% 
  ggplot(aes(x = reorder(country_region, value), y = value, fill= case_type))+
  geom_col(position = "dodge")+
  coord_flip()+
  labs(
    x = "",
    y = "",
    title = "China has the highest recovery numbers")