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COVID19analytics

This package curate (downloads, clean, consolidate, smooth) data from Johns Hopkins and Our world in data for analysing international outbreak of COVID-19.

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

  • 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).

Package

Release Usage Development
minimal R version Travis
CRAN codecov
Project Status: Active – The project has reached a stable, usable state and is being actively developed.

How to get started (Development version)

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

# install.packages("devtools")
devtools::install_github("rOpenStats/COVID19analytics", build_opts = NULL)

First configurate environment variables with your preferred configurations in ~/.Renviron. COVID19analytics_data_dir is mandatory while COVID19analytics_credits can be configured if you want to publish your own research with space separated alias. Mention previous authors where corresponding

COVID19analytics_data_dir = "~/.R/COVID19analytics"
# If you want to generate your own reports
COVID19analytics_credits = "@alias1 @alias2 @aliasn"

How to use it

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 'readr::edition_get' by
#> 'testthat::edition_get' 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 'readr::local_edition' by
#> 'testthat::local_edition' 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, union
library(knitr)
library(lgr)

log.dir <- file.path(getEnv("data_dir"), "logs")
dir.create(log.dir, recursive = TRUE, showWarnings = FALSE)
log.file <- file.path(log.dir, "covid19analytics.log")
lgr::get_logger("root")$add_appender(AppenderFile$new(log.file))
lgr::threshold("info", lgr::get_logger("root"))
lgr::threshold("info", lgr::get_logger("COVID19ARCurator"))

data.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  [09:12:34.000]  {stage: `processor-setup`}
#> INFO  [09:12:34.116] Checking required downloaded  {downloaded.max.date: `2021-09-26`, daily.update.time: `21:00:00`, current.datetime: `2021-09-28 09:12:34`, download.flag: `TRUE`}
#> INFO  [09:12:35.748] Checking required downloaded  {downloaded.max.date: `2021-09-26`, daily.update.time: `21:00:00`, current.datetime: `2021-09-28 09:12:35`, download.flag: `TRUE`}
#> INFO  [09:12:36.912] Checking required downloaded  {downloaded.max.date: `2021-09-26`, daily.update.time: `21:00:00`, current.datetime: `2021-09-28 09:12:36`, download.flag: `TRUE`}
#> INFO  [09:12:37.996]  {stage: `data loaded`}
#> INFO  [09:12:37.998]  {stage: `data-setup`}
dummy <- data.processor$transform()
#> INFO  [09:12:38.001] Executing transform 
#> INFO  [09:12:38.003] Executing consolidate 
#> INFO  [09:12:59.462]  {stage: `consolidated`}
#> INFO  [09:12:59.466] Executing standarize 
#> INFO  [09:13:02.569] gathering DataModel 
#> INFO  [09:13:02.571]  {stage: `datamodel-setup`}
# Curate is the process made by missing values method
dummy <- data.processor$curate()
#> INFO  [09:13:02.580]  {stage: `loading-aggregated-data-model`}
#> Warning in countrycode_convert(sourcevar = sourcevar, origin = origin, destination = dest, : Some values were not matched unambiguously: Micronesia
#> Warning in countrycode_convert(sourcevar = sourcevar, origin = origin, destination = dest, : Some values were not matched unambiguously: MS Zaandam
#> Warning in countrycode_convert(sourcevar = sourcevar, origin = origin, destination = dest, : Some values were not matched unambiguously: Summer Olympics 2020
#> INFO  [09:13:10.117]  {stage: `calculating-rates`}
#> INFO  [09:13:10.346]  {stage: `making-data-comparison`}
#> INFO  [09:13:19.438]  {stage: `applying-missing-values-method`}
#> INFO  [09:13:19.440]  {stage: `Starting first imputation`}
#> INFO  [09:13:19.451]  {stage: `calculating-rates`}
#> INFO  [09:13:19.776]  {stage: `making-data-comparison-2`}
#> INFO  [09:13:29.486]  {stage: `calculating-top-countries`}
#> INFO  [09:13:29.509]  {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"))
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")))

# Top 10 daily cases confirmed increment
kable((data.processor$getData() %>%
  filter(date == current.date) %>%
  select(country, date, rate.inc.daily, confirmed.inc, confirmed, deaths, deaths.inc) %>%
  arrange(desc(confirmed.inc)) %>%
  filter(confirmed >=10))[1:10,])
country date rate.inc.daily confirmed.inc confirmed deaths deaths.inc
US 2021-09-27 0.0043 185088 43116442 690426 2394
United Kingdom 2021-09-27 0.0049 37583 7737941 136569 40
Turkey 2021-09-27 0.0039 27188 7066658 63372 206
Russia 2021-09-27 0.0030 21731 7334843 201015 770
India 2021-09-27 0.0006 18795 33697581 447373 179
Iran 2021-09-27 0.0026 14470 5547990 119649 289
Brazil 2021-09-27 0.0007 14423 21366395 594653 210
Malaysia 2021-09-27 0.0050 10959 2209194 25695 258
Thailand 2021-09-27 0.0066 10288 1571926 16369 101
Vietnam 2021-09-27 0.0124 9362 766051 18758 174 
# Top 10 daily deaths increment
kable((data.processor$getData() %>%
  filter(date == current.date) %>%
  select(country, date, rate.inc.daily, confirmed.inc, confirmed, deaths, deaths.inc) %>%
  arrange(desc(deaths.inc)))[1:10,])
country date rate.inc.daily confirmed.inc confirmed deaths deaths.inc
US 2021-09-27 0.0043 185088 43116442 690426 2394
Russia 2021-09-27 0.0030 21731 7334843 201015 770
France 2021-09-27 0.0002 1503 7087110 117581 399
Kazakhstan 2021-09-27 0.0018 1730 956025 15865 362
Iran 2021-09-27 0.0026 14470 5547990 119649 289
Malaysia 2021-09-27 0.0050 10959 2209194 25695 258
Mexico 2021-09-27 0.0008 3007 3635807 275676 226
Brazil 2021-09-27 0.0007 14423 21366395 594653 210
Turkey 2021-09-27 0.0039 27188 7066658 63372 206
India 2021-09-27 0.0006 18795 33697581 447373 179 
rg$ggplotTopCountriesStackedBarDailyInc(included.countries = latam.countries, countries.text = "Latam countries")
#> Warning: Removed 144 rows containing missing values (position_stack).

rc$ggplotComparisonExponentialGrowth(included.countries = latam.countries, countries.text = "Latam countries",   
                                     field = "confirmed", y.label = "Confirmed", min.cases = 100)
#> Warning: ggrepel: 6 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps

rc$ggplotComparisonExponentialGrowth(included.countries = latam.countries, countries.text = "Latam countries",   
                                     field = "remaining.confirmed", y.label = "Active cases", min.cases = 100)
#> Warning in self$trans$transform(x): NaNs produced
#> Warning: Transformation introduced infinite values in continuous y-axis
#> Warning: ggrepel: 4 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps

rc$ggplotComparisonExponentialGrowth(included.countries = latam.countries, field = "deaths", y.label = "Deaths", min.cases = 1)

rg$ggplotCrossSection(included.countries = latam.countries,
                       field.x = "confirmed",
                       field.y = "fatality.rate.max",
                       plot.description  = "Cross section Confirmed vs  Death rate min",
                       log.scale.x = TRUE,
                       log.scale.y = FALSE)
#> Warning: Removed 144 row(s) containing missing values (geom_path).

rg$ggplotCountriesLines(included.countries = latam.countries, countries.text = "Latam countries",
                        field = "confirmed.inc", log.scale = TRUE)
#> Warning: Removed 144 row(s) containing missing values (geom_path).

rg$ggplotCountriesLines(included.countries = latam.countries, countries.text = "Latam countries",
                        field = "deaths.inc", log.scale = TRUE)
#> Warning in self$trans$transform(x): NaNs produced
#> Warning: Transformation introduced infinite values in continuous y-axis
#> Warning in self$trans$transform(x): NaNs produced
#> Warning: Transformation introduced infinite values in continuous y-axis
#> Warning: Removed 5 rows containing missing values (geom_point).
#> Warning: Removed 144 row(s) containing missing values (geom_path).
#> Warning: ggrepel: 7 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps

rg$ggplotCountriesLines(included.countries = latam.countries, countries.text = "Latam countries",
                        field = "rate.inc.daily", log.scale = TRUE)
#> Warning: Transformation introduced infinite values in continuous y-axis
#> Warning: Removed 144 row(s) containing missing values (geom_path).

rg$ggplotTopCountriesStackedBarDailyInc(top.countries)
#> Warning: Removed 67 rows containing missing values (position_stack).

rc$ggplotComparisonExponentialGrowth(included.countries = international.countries, 
                                     field = "confirmed", y.label = "Confirmed", min.cases = 100)
#> Warning: Removed 2 row(s) containing missing values (geom_path).
#> Warning: ggrepel: 4 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps

rc$ggplotComparisonExponentialGrowth(included.countries = international.countries, 
                                     field = "remaining.confirmed", y.label = "Active cases", min.cases = 100)
#> Warning: Removed 2 row(s) containing missing values (geom_path).
#> Warning: ggrepel: 3 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps

rc$ggplotComparisonExponentialGrowth(included.countries = international.countries, field = "deaths", 
                                     y.label = "Deaths", min.cases = 1)
#> Warning: Removed 2 row(s) containing missing values (geom_path).
#> Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps

rg$ggplotCrossSection(included.countries = international.countries,
                       field.x = "confirmed",
                       field.y = "fatality.rate.max",
                       plot.description  = "Cross section Confirmed vs Death rate min",
                       log.scale.x = TRUE,
                       log.scale.y = FALSE)
#> Warning: Removed 90 row(s) containing missing values (geom_path).

rg$ggplotCountriesLines(field = "confirmed.inc", log.scale = TRUE)
#> Warning: Removed 66 row(s) containing missing values (geom_path).

rg$ggplotCountriesLines(field = "deaths.inc", log.scale = TRUE)
#> Warning in self$trans$transform(x): NaNs produced
#> Warning: Transformation introduced infinite values in continuous y-axis

#> Warning: Transformation introduced infinite values in continuous y-axis
#> Warning: Removed 7 rows containing missing values (geom_point).
#> 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 = "Argentina")

References

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.

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