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Project README

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

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

Johns Hopkins University. Retrieved from: ‘https://github.com/CSSEGISandData/COVID-19/’ [Online Resource]
OurWorldInData.org. Retrieved from: ‘https://ourworldindata.org/coronavirus’ [Online Resource]

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.

Open Source Agenda is not affiliated with "COVID19analyticsBak2109" Project. README Source: rOpenStats/COVID19analyticsBak2109

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