COVID19

This package curate (downloads, clean, consolidate, smooth) data from Johns Hokpins for analysing international outbreak of COVID-19.

It includes several visualizations of the COVID-19 international outbreak.

The package was inspired by this blogpost from Yanchang Zhao

Yanchang Zhao, COVID-19 Data Analysis with Tidyverse and Ggplot2 - China. RDataMining.com, 2020.

URL: http://www.rdatamining.com/docs/Coronavirus-data-analysis-china.pdf.

• COVID19DataProcessor generates curated series
• The original process and visualizations are included in ReportGenerator R6 object
• More process and visualization included int ReportGeneratorEnhanced R6 object

Consideration

Data is still noisy because there are missing data from some regions in some days. We are working on in it.

Package

Release	Usage	Development

How to get started (Development version)

Install the R package using the following commands on the R console:

# install.packages("devtools")
devtools::install_github("kenarab/COVID19", build_opts = NULL)

How to use it

library(COVID19)
library(dplyr)
library(dplyr)
library(tidyverse)
library(kableExtra)
library(lubridate)
library(knitr)
library(ggplot2)
library(gridExtra)
library(magrittr)


data.processor <- COVID19DataProcessor$new(force.download = FALSE)
data.processor$curate()

rg <- ReportGeneratorEnhanced$new(data.processor)


rc <- ReportGeneratorDataComparison$new(data.processor = data.processor)

top.countries <- data.processor$top.countries
international.countries <- unique(c(data.processor$top.countries,
                                    "Japan", "Singapur", "Hong Kong"))

latam.countries <- sort(c("Mexico",
                     data.processor$countries$getCountries(division = "sub.continent", name = "Caribbean"),
                     data.processor$countries$getCountries(division = "sub.continent", name = "Central America"),
                     data.processor$countries$getCountries(division = "sub.continent", name = "South America")))

ggplot <- rg$ggplotTopCountriesStackedBarDailyInc(included.countries = latam.countries,
                                                  map.region = "Latam")
ggsave(file.path(data.dir, paste("latam-daily-increment-", dataviz.date, ".png", sep ="")), ggplot,
       width = 7, height = 5, dpi = 300)

ggplot <- rg$ggplotTopCountriesStackedBarDailyInc(included.countries = latam.countries,
                                                  map.region = "Latam")
ggsave(file.path(data.dir, paste("latam-daily-increment-", dataviz.date, ".png", sep ="")), ggplot,
       width = 7, height = 5, dpi = 300)

# Comparation by epidemy day
countries.plot <- unique(c(data.processor$top.countries,
                           "Japan", "Singapur", "Hong Kong",
                           data.processor$countries$getCountries(division = "sub.continent", name = "South America")))

ggplot <- rc$ggplotComparisonExponentialGrowth(included.countries = countries.plot, min.cases = 20)
ggsave(file.path(data.dir, paste("exponential-growth-", Sys.Date(), ".png", sep ="")), ggplot,
       width = 7, height = 5, dpi = 300)

latam.countries <- c("Mexico",
                     data.processor$countries$getCountries(division = "sub.continent", name = "Caribbean"),
                     data.processor$countries$getCountries(division = "sub.continent", name = "Central America"),
                     data.processor$countries$getCountries(division = "sub.continent", name = "South America"))

ggplot <- rc$ggplotComparisonExponentialGrowth(included.countries = latam.countries, min.cases = 20)
ggsave(file.path(data.dir, paste("covid-19-exponential-latam-growth-", Sys.Date(), ".png", sep ="")), ggplot,
       width = 7, height = 5, dpi = 300)

ggplot <- rg$ggplotTopCountriesLines(field = "confirmed.inc", log.scale = TRUE)
ggsave(file.path(data.dir, paste("top-countries-daily-conf-inc-log-", Sys.Date(), ".png", sep ="")), ggplot,
       width = 7, height = 5, dpi = 300)

ggplot <- rg$ggplotTopCountriesLines(field = "rate.inc.daily", log.scale = FALSE)
ggsave(file.path(data.dir, paste("top-countries-lines-rate-daily-inc-", Sys.Date(), ".png", sep ="")), ggplot,
       width = 7, height = 5, dpi = 300)

Selected Yanchang Zhao visualizations

ggplot <- rg$ggplotTopCountriesPie()
ggsave(file.path(data.dir, paste("top-countries-pie-", Sys.Date(), ".png", sep ="")), ggplot,
       width = 20, height = 15, dpi = 300)

ggplot <- rg$ggplotTopCountriesBarPlots()
ggsave(file.path(data.dir, paste("top-countries-bar-plot-", Sys.Date(), ".png", sep ="")), ggplot,
       width = 20, height = 15, dpi = 300)

ggplot <- rg$ggplotTopCountriesPie(excluded.countries = c("World", "China"))
ggsave(file.path(data.dir, paste("top-countries-pie-wo-china-", Sys.Date(), ".png", sep ="")), ggplot,
       width = 20, height = 15, dpi = 300)

ggplot <- rg$ggplotTopCountriesBarPlots(excluded.countries = c("World", "China"))
ggsave(file.path(data.dir, paste("top-countries-bar-plot-wo-china-", Sys.Date(), ".png", sep ="")), ggplot,
       width = 20, height = 15, dpi = 300)

# Including Australia
ggplot <- rg$ggplotCountriesBarGraphs(selected.country = "Australia")
ggsave(file.path(data.dir, paste("countries-bar-plot-australia-", Sys.Date(), ".png", sep ="")), ggplot,
       width = 20, height = 15, dpi = 300)

# Including Argentina
ggplot <- rg$ggplotCountriesBarGraphs(selected.country = "Argentina")
ggsave(file.path(data.dir, paste("countries-bar-plot-argentina-", Sys.Date(), ".png", sep ="")), ggplot,
       width = 20, height = 15, dpi = 300)

ggplot <- rg$ggplotConfirmedCases()
ggsave(file.path(data.dir, paste("confirmed-cases-", Sys.Date(), ".png", sep ="")), ggplot,
       width = 20, height = 15, dpi = 300)

Make a latex graph

rg$data.confirmed.original[, 1:10] %>% sample_n(10) %>%
  kable("latex", booktabs=T, caption="Raw Data (Confirmed, First 10 Columns only)") %>%
  kable_styling(font_size=6, latex_options = c("striped", "hold_position", "repeat_header"))

Sources of code, data and information

• https://www.r-bloggers.com/coronavirus-data-analysis-with-r-tidyverse-and-ggplot2/ Original blogpost which inspired this package

• http://www.rdatamining.com/docs/Coronavirus-data-analysis-world.pdf Original blogpost dialy updated report

• https://github.com/CSSEGISandData/COVID-19/ Johns Hopkins data source updated almost daily

• https://www.repidemicsconsortium.org/ RECON page with R sources for epidemic analysis

• https://rviews.rstudio.com/2020/03/05/covid-19-epidemiology-with-r/ COVID-19 research using R tools from Rstudio

Source Repository Status

Updated up to 2020-03-13

last.update	n	total.confirmed	countries (confirmed)
2020-03-13	63	143808	China(80945), Italy(17660), Iran(11364), Korea, South(7979), Spain(5232), Germany(3675), France(3667), US(2179), Switzerland(1139), Norway(996), Sweden(814), Denmark(804), Netherlands(804), United Kingdom(801), Japan(701), Belgium(559), Austria(504), Qatar(320), Australia(200), Singapore(200), Malaysia(197), Canada(193), Greece(190), Israel(161), Finland(155), Brazil(151), Czechia(141), Slovenia(141), Iceland(134), Portugal(112), Iraq(101), Ireland(90), Romania(89), Saudi Arabia(86), India(82), Egypt(80), San Marino(80), Estonia(79), Lebanon(77), Thailand(75), Indonesia(69), Poland(68), Philippines(64), Taiwan*(50), Vietnam(47), Russia(45), Chile(43), Brunei(37), Serbia(35), Luxembourg(34), Albania(33), Croatia(32), Slovakia(32), Argentina(31), Pakistan(28), Peru(28), Belarus(27), Panama(27), Algeria(26), Georgia(25), South Africa(24), Bulgaria(23), Costa Rica(23)
2020-03-12	2	165	United Arab Emirates(85), Kuwait(80)
2020-03-11	1	195	Bahrain(195)
2020-03-03	1	706	Cruise Ship(706)