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covid19analytics's Introduction

COVID19analytics

This package curate (downloads, clean, consolidate, smooth) data from Johns Hokpins 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::equals' by 'testthat::equals' when
#> loading 'COVID19analytics'
#> Warning: replacing previous import 'magrittr::not' by 'testthat::not' when
#> loading 'COVID19analytics'
#> Warning: replacing previous import 'magrittr::is_less_than' by
#> 'testthat::is_less_than' when loading 'COVID19analytics'
#> Warning: replacing previous import 'dplyr::matches' by 'testthat::matches' 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  [10:14:12.965]  {stage: processor-setup}
#> INFO  [10:14:13.025] Checking required downloaded  {downloaded.max.date: 2020-07-17, daily.update.time: 21:00:00, current.datetime: 2020-07-19 1.., download.flag: TRUE}
#> INFO  [10:14:14.114] Checking required downloaded  {downloaded.max.date: 2020-07-17, daily.update.time: 21:00:00, current.datetime: 2020-07-19 1.., download.flag: TRUE}
#> INFO  [10:14:14.886] Checking required downloaded  {downloaded.max.date: 2020-07-17, daily.update.time: 21:00:00, current.datetime: 2020-07-19 1.., download.flag: TRUE}
#> INFO  [10:14:15.981]  {stage: data loaded}
#> INFO  [10:14:15.984]  {stage: data-setup}
dummy <- data.processor$transform()
#> INFO  [10:14:15.988] Executing transform 
#> INFO  [10:14:15.990] Executing consolidate 
#> INFO  [10:14:18.362]  {stage: consolidated}
#> INFO  [10:14:18.364] Executing standarize 
#> INFO  [10:14:18.924] gathering DataModel 
#> INFO  [10:14:18.926]  {stage: datamodel-setup}
# Curate is the process made by missing values method
dummy <- data.processor$curate()
#> INFO  [10:14:18.930]  {stage: loading-aggregated-data-model}
#> Warning in countrycode(x, origin = "country.name", destination = "continent"): Some values were not matched unambiguously: MS Zaandam
#> INFO  [10:14:20.710]  {stage: calculating-rates}
#> INFO  [10:14:20.908]  {stage: making-data-comparison}
#> INFO  [10:14:26.456]  {stage: applying-missing-values-method}
#> INFO  [10:14:26.459]  {stage: Starting first imputation}
#> INFO  [10:14:26.465]  {stage: calculating-rates}
#> INFO  [10:14:26.680]  {stage: making-data-comparison-2}
#> INFO  [10:14:32.335]  {stage: calculating-top-countries}
#> INFO  [10:14:32.354]  {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$getData() %>%
  filter(date == current.date) %>%
  select(country, date, rate.inc.daily, confirmed.inc, confirmed, deaths, deaths.inc) %>%
  arrange(desc(confirmed.inc)) %>%
  filter(confirmed >=10))[1:10,])
country date rate.inc.daily confirmed.inc confirmed deaths deaths.inc
US 2020-07-18 0.0175 63698 3711413 140119 853
India 2020-07-18 0.0372 38697 1077781 26816 543
Brazil 2020-07-18 0.0139 28532 2074860 78772 921
South Africa 2020-07-18 0.0394 13285 350879 4948 144
Kyrgyzstan 2020-07-18 0.8782 11505 24606 900 727
Colombia 2020-07-18 0.0470 8560 190700 6516 228
Mexico 2020-07-18 0.0230 7615 338913 38888 578
Russia 2020-07-18 0.0082 6214 764215 12228 122
Peru 2020-07-18 0.0115 3963 349500 12998 199
Argentina 2020-07-18 0.0270 3223 122524 2220 42 
# Top 10 daily deaths increment
kable((data.processor$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
Brazil 2020-07-18 0.0139 28532 2074860 78772 921
US 2020-07-18 0.0175 63698 3711413 140119 853
Kyrgyzstan 2020-07-18 0.8782 11505 24606 900 727
Mexico 2020-07-18 0.0230 7615 338913 38888 578
India 2020-07-18 0.0372 38697 1077781 26816 543
Colombia 2020-07-18 0.0470 8560 190700 6516 228
Peru 2020-07-18 0.0115 3963 349500 12998 199
Iran 2020-07-18 0.0080 2166 271606 13979 188
South Africa 2020-07-18 0.0394 13285 350879 4948 144
Russia 2020-07-18 0.0082 6214 764215 12228 122 
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)

rc$ggplotComparisonExponentialGrowth(included.countries = latam.countries, countries.text = "Latam countries",   
                                     field = "remaining.confirmed", y.label = "Active cases", min.cases = 100)

rg$ggplotCountriesLines(included.countries = latam.countries, countries.text = "Latam countries",
                        field = "confirmed.inc", log.scale = TRUE)
#> Warning: Removed 126 row(s) containing missing values (geom_path).

rg$ggplotCountriesLines(included.countries = latam.countries, countries.text = "Latam countries",
                        field = "rate.inc.daily", log.scale = TRUE)
#> Warning: Removed 126 row(s) containing missing values (geom_path).

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 126 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).

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).

rc$ggplotComparisonExponentialGrowth(included.countries = international.countries, field = "deaths", 
                                     y.label = "Deaths", min.cases = 1)
#> Warning: Removed 2 row(s) containing missing values (geom_path).

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 = "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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