Corona Interactive Map

link1 <- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Recovered.csv"

link2 <- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Confirmed.csv"


link3 <-"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Deaths.csv"

link1 %>% 
  read_csv() %>% 
  rename(Province = `Province/State`, Country = `Country/Region`) %>% 
  gather(Date, Recovered, -Province, -Country, -Lat, -Long) %>% 
  mutate(Date = mdy(Date)) -> df_recovered
  
datatable(df_recovered)

link2 %>% 
  read_csv() %>% 
  rename(Province = `Province/State`, Country = `Country/Region`) %>% 
  gather(Date, Confirmed, -Province, -Country, -Lat, -Long) %>% 
  mutate(Date = mdy(Date)) -> df_confirmed

datatable(df_confirmed)

link3 %>% 
  read_csv() %>% 
  rename(Province = `Province/State`, Country = `Country/Region`) %>% 
  gather(Date, Death, -Province, -Country, -Lat, -Long) %>% 
  mutate(Date = mdy(Date)) -> df_Death 

datatable(df_Death)

end_date <- ymd("2020-03-05")

df_recovered %>% 
  filter(Date ==end_date) %>% 
  group_by(Country) %>% 
  mutate(Totalrecov = sum(Recovered)) %>% 
  ungroup() %>% 
  filter(!duplicated(Country)) %>% 
  select(Country, Totalrecov) -> recovered_0305


datatable(recovered_0305)

df_confirmed %>% 
  filter(Date == end_date) %>% 
  group_by(Country) %>% 
  mutate(Total_con = sum(Confirmed)) %>% 
  ungroup() %>% 
  filter(!duplicated(Country)) %>% 
  select(Country,Total_con) ->confirmed_0305


datatable(confirmed_0305)

df_Death %>% 
  filter(Date == end_date) %>% 
  group_by(Country) %>% 
  mutate(Total_death = sum(Death)) %>% 
  select(Country, Total_death) ->Death_0305

datatable(Death_0305)

## 데이터 합치기 

full_join(confirmed_0305, recovered_0305, by = "Country") %>% 
  mutate(Cases = Total_con - Totalrecov) -> df_type1


full_join(df_type1, Death_0305, by="Country") %>%
  mutate(death_rate= Total_death/ Total_con *100) %>% 
  mutate(death_rate = round(death_rate,1))-> df_final

datatable(df_final)

df_final %>% 
  mutate(Country = case_when(Country %in% c("Mainland China", "Macau", "Hong Kong") ~ "China", 
                             Country == "US" ~ "United States", 
                             Country == "UK" ~ "United Kingdom", 
                             TRUE ~ Country)) %>%
  group_by(Country) %>% 
  summarise(Case_Number  = sum(Cases)) %>% 
  ungroup() %>% 
  mutate(iso3 = countrycode(Country, origin = "country.name", destination = "iso3c")) -> df_final2


df_final2

## # A tibble: 147 x 3
##    Country             Case_Number iso3 
##    <chr>                     <dbl> <chr>
##  1 Afghanistan                   1 AFG  
##  2 Albania                       0 ALB  
##  3 Algeria                      12 DZA  
##  4 Andorra                       1 AND  
##  5 Antigua and Barbuda           0 ATG  
##  6 Argentina                     1 ARG  
##  7 Armenia                       1 ARM  
##  8 Aruba                         0 ABW  
##  9 Australia                   306 AUS  
## 10 Austria                      41 AUT  
## # ... with 137 more rows

highchart(type="map") %>% 
  hc_add_series_map(map = worldgeojson, df = df_final2, value = "Case_Number", joinBy = "iso3") %>% 
  hc_colorAxis(stops = color_stops()) %>% 
  hc_title(text = "Corona Virus Prevalence by Country as of 05-03-2020", 
           style = list(fontSize = "24px")) %>% 
  hc_credits(enabled = TRUE, style = list(fontSize = "12px"),  
             text = "Source: Johns Hopkins University Center for Systems Science and Engineering") %>% 
  hc_tooltip(useHTML = TRUE, headerFormat = "",
             pointFormat = "Country: {point.Country}, Number of Corona Cases: {point.N_cases}") %>% 
  hc_add_theme(hc_theme_flatdark())

Corna Interactive Graph

# 1월 30일을 기점으로 하는 데이터만 뽑음 
start_date <- ymd("2020-01-30")

df_recovered %>% 
  filter(Date >= start_date) %>% 
  group_by(Date, Country) %>% 
  mutate(Total_recov = sum(Recovered)) %>% 
  select(-Province, -Lat, -Long, - Recovered) -> Recov


df_confirmed %>% 
  filter(Date >= start_date) %>% 
  group_by(Date, Country) %>% 
  mutate(Total_conf = sum(Confirmed)) %>% 
  select(-Province, -Lat, -Long, - Confirmed) -> Conf

df_Death %>%  
  filter(Date >= start_date) %>% 
  group_by(Date, Country) %>% 
  mutate(Total_death = sum(Death)) %>% 
  select(-Province, -Lat, -Long, - Death) -> Death


Conf %>% 
  group_by(Date, Country) %>% 
  filter(Country == "Korea, South") -> Korea
  
Korea

## # A tibble: 46 x 3
## # Groups:   Date, Country [46]
##    Country      Date       Total_conf
##    <chr>        <date>          <dbl>
##  1 Korea, South 2020-01-30          4
##  2 Korea, South 2020-01-31         11
##  3 Korea, South 2020-02-01         12
##  4 Korea, South 2020-02-02         15
##  5 Korea, South 2020-02-03         15
##  6 Korea, South 2020-02-04         16
##  7 Korea, South 2020-02-05         19
##  8 Korea, South 2020-02-06         23
##  9 Korea, South 2020-02-07         24
## 10 Korea, South 2020-02-08         24
## # ... with 36 more rows

Korea %>% 
  hchart('line', hcaes(x = 'Date', y = 'Total_conf'), name="The Number of Cases") %>% 
  hc_title(text ="Accumulative Corona Confirmed Cases of South Korea",
           style = list(color = "white",useHTML = TRUE)) %>% 
  hc_subtitle(text="Source : refer to the links", color="white") %>%   hc_add_theme(hc_theme_flatdark())

Conf %>% 
  group_by(Date, Country) %>% 
  filter(Country %in% c("Korea, South", "Japan")) ->Asia_Table

Asia_Table

## # A tibble: 92 x 3
## # Groups:   Date, Country [92]
##    Country      Date       Total_conf
##    <chr>        <date>          <dbl>
##  1 Japan        2020-01-30         11
##  2 Korea, South 2020-01-30          4
##  3 Japan        2020-01-31         15
##  4 Korea, South 2020-01-31         11
##  5 Japan        2020-02-01         20
##  6 Korea, South 2020-02-01         12
##  7 Japan        2020-02-02         20
##  8 Korea, South 2020-02-02         15
##  9 Japan        2020-02-03         20
## 10 Korea, South 2020-02-03         15
## # ... with 82 more rows

Asia_Table %>% 
  hchart('line', hcaes(x="Date", y="Total_conf", group= "Country")) %>% 
  hc_tooltip(crosshairs = TRUE, borderWidth = 1.5, 
             headerFormat = "", pointFormat = paste("Year: <b>{point.x}</b> <br>","Gender: <b>{point.sex}</b><br>",
                                                    "Suicides: <b>{point.y}</b>")) %>%
    hc_title(text = "Accumulative Corona Confirmed Cases of South Korea & Japan") %>% 
    hc_subtitle(text = "From 2020-01-30") %>%
    hc_xAxis(title = list(text = "Year")) %>% 
    hc_yAxis(titla = list(text= "Number of Cases"))

## 1월 30일 이후 사망자가 가장 많이 나온 나라 

Death[order(Death$Total_death, decreasing = T),] %>% 
  group_by(Country) %>% 
  filter(!duplicated(Country))%>% 
  filter(Total_death  >= 10) -> dd 

  
highchart() %>% 
  hc_add_series(dd, hcaes(x= factor(Country), y=Total_death, color=Total_death), type="bar") %>%  
  hc_tooltip(borderWidth = 1.8, pointFormat = paste("The Number of Death :<b>{point.y}</b>")) %>%
   hc_legend(enabled = FALSE) %>%
   hc_title(text = "Death Rate of countries") %>% 
   hc_subtitle(text = "Since 2020-01-30") %>%
   hc_xAxis(categories = dd$Country, scrollbar = list(enabled = TRUE)) %>%
   hc_yAxis(title = list(text = "Death Rate per 100K people")) %>%
   hc_plotOptions(bar = list(stacking = "normal", 
                              pointPadding = 0, groupPadding = 0, borderWidth = 0.5))