This repo provides hourly updates from the World Helath Organization Coronavirus disease (COVID-2019) situation reports in CSV and pins format.
From R, you can query the list of available datasets as follows:
library(pins)
board_register_datatxt("https://raw.githubusercontent.com/javierluraschi/covid/datasets/data.txt", "covid")
pin_find(board = "covid")## # A tibble: 94 x 4
## name description type board
## <chr> <chr> <chr> <chr>
## 1 covid-20200228-1 "" table covid
## 2 covid-20200228-2 "" table covid
## 3 covid-20200228-3 "" table covid
## 4 covid-20200228-4 "" table covid
## 5 covid-20200227-1 "" table covid
## 6 covid-20200227-2 "" table covid
## 7 covid-20200227-3 "" table covid
## 8 covid-20200226-1 "" table covid
## 9 covid-20200226-2 "" table covid
## 10 covid-20200225-1 "" table covid
## # … with 84 more rows
You can then retrieve specific tables from the daily reports as follows:
pin_get("covid-20200227-1", board = "covid")## # A tibble: 41 x 6
## V1 V2 V3 V4 V5 V6
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Province/ "" "" Daily "" Cumulative
## 2 "" Population "" "" "" ""
## 3 Region/ "" "" "" "" ""
## 4 "" (10,000s) Confirmed Suspected "" Confirmed
## 5 City "" "" "" Deaths Deaths
## 6 "" "" cas es cas es "" cases
## 7 Hubei 5917 409 403 26 65596 2641
## 8 Guangdong 11346 0 0 0 1347 7
## 9 Henan 9605 1 2 1 1272 20
## 10 Zhejiang 5737 0 0 0 1205 1
## # … with 31 more rows
Do notice that this dataset come straight from the PDF files, while the data is kept up-to-date by parsing it as soon as it’s released, the headers still need to be cleaned up and some tables joined together.
The CSV files can be found under covid/tree/datasets, a cleaned up dataset can be found in other repos like ramikrispin/coronavirus-csv and downloaded with pins as follows:
covid <- pin("https://raw.githubusercontent.com/RamiKrispin/coronavirus-csv/master/coronavirus_dataset.csv") %>%
read.csv() %>% tibble::as_tibble() %>% print()## # A tibble: 2,309 x 7
## Province.State Country.Region Lat Long date cases type
## <fct> <fct> <dbl> <dbl> <fct> <int> <fct>
## 1 "" Japan 36 138 2020-01-22 2 confirmed
## 2 "" South Korea 36 128 2020-01-22 1 confirmed
## 3 "" Thailand 15 101 2020-01-22 2 confirmed
## 4 Anhui Mainland China 31.8 117. 2020-01-22 1 confirmed
## 5 Beijing Mainland China 40.2 116. 2020-01-22 14 confirmed
## 6 Chongqing Mainland China 30.1 108. 2020-01-22 6 confirmed
## 7 Fujian Mainland China 26.1 118. 2020-01-22 1 confirmed
## 8 Guangdong Mainland China 23.3 113. 2020-01-22 26 confirmed
## 9 Guangxi Mainland China 23.8 109. 2020-01-22 2 confirmed
## 10 Guizhou Mainland China 26.8 107. 2020-01-22 1 confirmed
## # … with 2,299 more rows
library(dplyr, warn.conflicts = FALSE)
library(ggplot2)
filter(covid, type == "confirmed") %>%
group_by(date) %>%
summarise(confirmed = sum(cases)) %>%
arrange(date) %>%
mutate(date = as.Date(date), confirmed = cumsum(confirmed)) %>%
ggplot(aes(x=date, y=confirmed)) + geom_line() + theme_light()