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.
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
- visualizations by Yanchang Zhao are included in ReportGenerator R6 object
- More visualizations included int ReportGeneratorEnhanced R6 object
- Visualizations ReportGeneratorDataComparison compares all countries counting epidemy day 0 when confirmed cases > n (i.e. n = 100).
Data is still noisy because there are missing data from some regions in some days. We are working on in it.
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Install the R package using the following commands on the R console:
# install.packages("devtools")
devtools::install_github("rOpenStats/COVID19analytics", build_opts = NULL)library(COVID19analytics)
#> Warning: replacing previous import 'ggplot2::Layout' by 'lgr::Layout' when
#> loading 'COVID19analytics'
#> Warning: replacing previous import 'readr::col_factor' by 'scales::col_factor'
#> when loading 'COVID19analytics'
#> Warning: replacing previous import 'magrittr::not' by 'testthat::not' when
#> loading 'COVID19analytics'
#> Warning: replacing previous import 'dplyr::matches' by 'testthat::matches' when
#> loading 'COVID19analytics'
#> Warning: replacing previous import 'magrittr::equals' by 'testthat::equals' when
#> loading 'COVID19analytics'
#> Warning: replacing previous import 'magrittr::is_less_than' by
#> 'testthat::is_less_than' when loading 'COVID19analytics'
#> Warning: replacing previous import 'testthat::matches' by 'tidyr::matches' when
#> loading 'COVID19analytics'
#> Warning: replacing previous import 'magrittr::extract' by 'tidyr::extract' when
#> loading 'COVID19analytics'
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, uniondata.processor <- COVID19DataProcessor$new(provider = "JohnsHopkingsUniversity", missing.values = "imputation")
#dummy <- data.processor$preprocess() is setupData + transform is the preprocess made by data provider
dummy <- data.processor$setupData()
#> INFO [19:05:20.317] {stage: processor-setup}
#> INFO [19:05:20.394] Checking required downloaded {downloaded.max.date: 2021-01-20, daily.update.time: 21:00:00, current.datetime: 2021-01-21 19:05:20, download.flag: FALSE}
#> INFO [19:05:20.526] Checking required downloaded {downloaded.max.date: 2021-01-20, daily.update.time: 21:00:00, current.datetime: 2021-01-21 19:05:20, download.flag: FALSE}
#> INFO [19:05:20.679] Checking required downloaded {downloaded.max.date: 2021-01-20, daily.update.time: 21:00:00, current.datetime: 2021-01-21 19:05:20, download.flag: FALSE}
#> INFO [19:05:20.829] {stage: data loaded}
#> INFO [19:05:20.830] {stage: data-setup}
dummy <- data.processor$transform()
#> INFO [19:05:20.832] Executing transform
#> INFO [19:05:20.833] Executing consolidate
#> INFO [19:05:30.671] {stage: consolidated}
#> INFO [19:05:30.672] Executing standarize
#> INFO [19:05:32.129] gathering DataModel
#> INFO [19:05:32.130] {stage: datamodel-setup}
# Curate is the process made by missing values method
dummy <- data.processor$curate()
#> INFO [19:05:32.135] {stage: loading-aggregated-data-model}
#> Warning in countrycode(x, origin = "country.name", destination = "continent"): Some values were not matched unambiguously: MS Zaandam
#> INFO [19:05:34.350] {stage: calculating-rates}
#> INFO [19:05:34.537] {stage: making-data-comparison}
#> INFO [19:05:41.545] {stage: applying-missing-values-method}
#> INFO [19:05:41.546] {stage: Starting first imputation}
#> INFO [19:05:41.552] {stage: calculating-rates}
#> INFO [19:05:41.738] {stage: making-data-comparison-2}
#> INFO [19:05:49.822] {stage: calculating-top-countries}
#> INFO [19:05:49.842] {stage: curated}
current.date <- max(data.processor$getData()$date)
rg <- ReportGeneratorEnhanced$new(data.processor)
rc <- ReportGeneratorDataComparison$new(data.processor = data.processor)
top.countries <- data.processor$top.countries
international.countries <- unique(c(data.processor$top.countries,
"China", "Japan", "Singapore", "Korea, South"))
africa.countries <- sort(data.processor$countries$getCountries(division = "continent", name = "Africa"))# Top 10 daily cases confirmed increment
(data.processor$getData() %>%
filter(date == current.date) %>%
select(country, date, rate.inc.daily, confirmed.inc, confirmed, deaths, deaths.inc) %>%
arrange(desc(confirmed.inc)) %>%
filter(confirmed >=10))[1:10,]
#> # A tibble: 10 x 7
#> # Groups: country [10]
#> country date rate.inc.daily confirmed.inc confirmed deaths deaths.inc
#> <chr> <date> <dbl> <int> <int> <int> <int>
#> 1 US 2021-01-20 0.0075 182695 24438723 406147 4375
#> 2 Brazil 2021-01-20 0.0075 64385 8638249 212831 1340
#> 3 Spain 2021-01-20 0.0175 41576 2412318 54637 464
#> 4 United K… 2021-01-20 0.0112 38992 3515796 93469 1826
#> 5 Germany 2021-01-20 0.014 29003 2100618 50010 1013
#> 6 France 2021-01-20 0.009 26877 3023661 71792 310
#> 7 Russia 2021-01-20 0.00580 20806 3595136 66214 582
#> 8 Mexico 2021-01-20 0.0123 20548 1688944 144371 1539
#> 9 Colombia 2021-01-20 0.0092 17908 1956979 49792 390
#> 10 India 2021-01-20 0.0014 15244 10610883 152869 151# Top 10 daily deaths increment
(data.processor$getData() %>%
filter(date == current.date) %>%
select(country, date, rate.inc.daily, confirmed.inc, confirmed, deaths, deaths.inc) %>%
arrange(desc(deaths.inc)))[1:10,]
#> # A tibble: 10 x 7
#> # Groups: country [10]
#> country date rate.inc.daily confirmed.inc confirmed deaths deaths.inc
#> <chr> <date> <dbl> <int> <int> <int> <int>
#> 1 US 2021-01-20 0.0075 182695 24438723 406147 4375
#> 2 United K… 2021-01-20 0.0112 38992 3515796 93469 1826
#> 3 Mexico 2021-01-20 0.0123 20548 1688944 144371 1539
#> 4 Brazil 2021-01-20 0.0075 64385 8638249 212831 1340
#> 5 Germany 2021-01-20 0.014 29003 2100618 50010 1013
#> 6 Russia 2021-01-20 0.00580 20806 3595136 66214 582
#> 7 South Af… 2021-01-20 0.0094 12710 1369426 38854 566
#> 8 Italy 2021-01-20 0.0057 13568 2414166 83681 524
#> 9 Spain 2021-01-20 0.0175 41576 2412318 54637 464
#> 10 Poland 2021-01-20 0.00480 6943 1450747 34141 443rg$ggplotTopCountriesStackedBarDailyInc(included.countries = africa.countries,
countries.text = "Africa")
#> Warning: Removed 318 rows containing missing values (position_stack).
rc$ggplotComparisonExponentialGrowth(included.countries = africa.countries, min.cases = 20)
#> Warning: ggrepel: 19 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps
rg$ggplotCountriesLines(included.countries = africa.countries, countries.text = "Africa countries",
field = "confirmed.inc", log.scale = TRUE)
#> Warning: Removed 318 row(s) containing missing values (geom_path).
#> Warning: ggrepel: 3 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps
rc$ggplotComparisonExponentialGrowth(included.countries = africa.countries,
field = "deaths", y.label = "deaths", min.cases = 1)
#> Warning: ggrepel: 17 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps
rg$ggplotTopCountriesStackedBarDailyInc(top.countries)
#> Warning: Removed 67 rows containing missing values (position_stack).
rc$ggplotComparisonExponentialGrowth(included.countries = international.countries,
min.cases = 100)
#> Warning: Removed 2 row(s) containing missing values (geom_path).
#> Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps
rg$ggplotCountriesLines(field = "confirmed.inc", log.scale = TRUE)
#> Warning: Removed 66 row(s) containing missing values (geom_path).
rg$ggplotCountriesLines(field = "rate.inc.daily", log.scale = TRUE)
#> Warning: Transformation introduced infinite values in continuous y-axis
#> Warning: Removed 66 row(s) containing missing values (geom_path).
rg$ggplotTopCountriesPie()
rg$ggplotTopCountriesBarPlots()
rg$ggplotCountriesBarGraphs(selected.country = "Ethiopia")