/corona19

Data Package of Coronavirus Disease 2019 in South Korea

Primary LanguageROtherNOASSERTION

corona19

R package for import datasets of Coronavirus disease 2019 in South Korea from Data-Science-for-COVID-19.

Installation

# install.packages("remotes")
remotes::install_github("youngwoos/corona19")

Example

library(corona19)

# time
time <- getdata("time")
time
#> # A tibble: 57 x 7
#>    date        time   test negative confirmed released deceased
#>    <date>     <int>  <int>    <int>     <int>    <int>    <int>
#>  1 2020-03-16     0 274504   251297      8236     1137       75
#>  2 2020-03-15     0 268212   243778      8126      834       75
#>  3 2020-03-14     0 261335   235615      8086      714       72
#>  4 2020-03-13     0 248647   222728      7979      510       67
#>  5 2020-03-12     0 234998   209402      7869      333       66
#>  6 2020-03-11     0 222395   196100      7755      288       60
#>  7 2020-03-10     0 210144   184179      7513      247       54
#>  8 2020-03-09     0 196618   171778      7382      166       51
#>  9 2020-03-08     0 188518   162008      7134      130       50
#> 10 2020-03-07     0 178189   151802      6767      118       44
#> # ... with 47 more rows

library(ggplot2) 
ggplot(data = time, aes(x = date, y = confirmed)) + 
  geom_area(color="darkblue", fill="lightblue") + 
  scale_x_date(date_breaks = "weeks" , date_labels = "%m-%d")

# patient
patient <- getdata("patient") 
patient
#> # A tibble: 2,119 x 18
#>    patient_id global_num sex   birth_year age   country province city  disease
#>         <dbl>      <int> <chr>      <int> <chr> <chr>   <chr>    <chr> <lgl>  
#>  1 1000000001          2 male        1964 50s   Korea   Seoul    Gang~ NA     
#>  2 1000000002          5 male        1987 30s   Korea   Seoul    Jung~ NA     
#>  3 1000000003          6 male        1964 50s   Korea   Seoul    Jong~ NA     
#>  4 1000000004          7 male        1991 20s   Korea   Seoul    Mapo~ NA     
#>  5 1000000005          9 fema~       1992 20s   Korea   Seoul    Seon~ NA     
#>  6 1000000006         10 fema~       1966 50s   Korea   Seoul    Jong~ NA     
#>  7 1000000007         11 male        1995 20s   Korea   Seoul    Jong~ NA     
#>  8 1000000008         13 male        1992 20s   Korea   Seoul    etc   NA     
#>  9 1000000009         19 male        1983 30s   Korea   Seoul    Song~ NA     
#> 10 1000000010         21 fema~       1960 60s   Korea   Seoul    Seon~ NA     
#> # ... with 2,109 more rows, and 9 more variables: infection_case <chr>,
#> #   infection_order <int>, infected_by <chr>, contact_number <int>,
#> #   symptom_onset_date <date>, confirmed_date <date>, released_date <date>,
#> #   deceased_date <date>, state <chr>

ggplot(data = patient, aes(x = 2020-birth_year, fill = sex, colour = sex)) + 
  geom_density(alpha = 0.3) +
  xlab("age")

# route
route <- getdata("route") 

library(dplyr)
cnt_province <- route %>% 
  count(province, sort = T) %>% 
  head(10)

cnt_province
#> # A tibble: 8 x 2
#>   province         n
#>   <chr>        <int>
#> 1 Seoul           76
#> 2 Gyeonggi-do     55
#> 3 Incheon         17
#> 4 Daegu           11
#> 5 Gangwon-do       5
#> 6 Gwangju          5
#> 7 Jeollabuk-do     4
#> 8 Jeollanam-do     2

ggplot(cnt_province, aes(x = reorder(province, n), y = n)) + 
  geom_col() + 
  coord_flip() +
  xlab("province")

Dataset infomation

dataset-detailed-description.ipynb

getdata() import 3 of these datasets.