README-africa.md
In kenarab/COVID19: COVID-19 data processing
COVID19analytics
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).
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 |
|:---------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| 
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| 
| | 
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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)
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 '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, union
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$setupData()
#> INFO [19:05:20.317] {stage: processor-setup}
#> INFO [19:05:20.394] Checking required downloaded {downloaded.max.date: 2021-01-20, daily.update.time: 21:00:00, current.datetime: 2021-01-21 19:05:20, download.flag: FALSE}
#> INFO [19:05:20.526] Checking required downloaded {downloaded.max.date: 2021-01-20, daily.update.time: 21:00:00, current.datetime: 2021-01-21 19:05:20, download.flag: FALSE}
#> INFO [19:05:20.679] Checking required downloaded {downloaded.max.date: 2021-01-20, daily.update.time: 21:00:00, current.datetime: 2021-01-21 19:05:20, download.flag: FALSE}
#> INFO [19:05:20.829] {stage: data loaded}
#> INFO [19:05:20.830] {stage: data-setup}
dummy <- data.processor$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$getData() %>%
filter(date == current.date) %>%
select(country, date, rate.inc.daily, confirmed.inc, confirmed, deaths, deaths.inc) %>%
arrange(desc(deaths.inc)))[1:10,]
#> # A tibble: 10 x 7
#> # Groups: country [10]
#> country date rate.inc.daily confirmed.inc confirmed deaths deaths.inc
#> <chr> <date> <dbl> <int> <int> <int> <int>
#> 1 US 2021-01-20 0.0075 182695 24438723 406147 4375
#> 2 United K… 2021-01-20 0.0112 38992 3515796 93469 1826
#> 3 Mexico 2021-01-20 0.0123 20548 1688944 144371 1539
#> 4 Brazil 2021-01-20 0.0075 64385 8638249 212831 1340
#> 5 Germany 2021-01-20 0.014 29003 2100618 50010 1013
#> 6 Russia 2021-01-20 0.00580 20806 3595136 66214 582
#> 7 South Af… 2021-01-20 0.0094 12710 1369426 38854 566
#> 8 Italy 2021-01-20 0.0057 13568 2414166 83681 524
#> 9 Spain 2021-01-20 0.0175 41576 2412318 54637 464
#> 10 Poland 2021-01-20 0.00480 6943 1450747 34141 443
rg$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")
kenarab/COVID19 documentation built on Oct. 1, 2021, 11:37 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
COVID19analytics
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).
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("rOpenStats/COVID19analytics", build_opts = NULL)
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 '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, union
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$setupData()
#> INFO [19:05:20.317] {stage: processor-setup}
#> INFO [19:05:20.394] Checking required downloaded {downloaded.max.date: 2021-01-20, daily.update.time: 21:00:00, current.datetime: 2021-01-21 19:05:20, download.flag: FALSE}
#> INFO [19:05:20.526] Checking required downloaded {downloaded.max.date: 2021-01-20, daily.update.time: 21:00:00, current.datetime: 2021-01-21 19:05:20, download.flag: FALSE}
#> INFO [19:05:20.679] Checking required downloaded {downloaded.max.date: 2021-01-20, daily.update.time: 21:00:00, current.datetime: 2021-01-21 19:05:20, download.flag: FALSE}
#> INFO [19:05:20.829] {stage: data loaded}
#> INFO [19:05:20.830] {stage: data-setup}
dummy <- data.processor$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$getData() %>%
filter(date == current.date) %>%
select(country, date, rate.inc.daily, confirmed.inc, confirmed, deaths, deaths.inc) %>%
arrange(desc(deaths.inc)))[1:10,]
#> # A tibble: 10 x 7
#> # Groups: country [10]
#> country date rate.inc.daily confirmed.inc confirmed deaths deaths.inc
#> <chr> <date> <dbl> <int> <int> <int> <int>
#> 1 US 2021-01-20 0.0075 182695 24438723 406147 4375
#> 2 United K… 2021-01-20 0.0112 38992 3515796 93469 1826
#> 3 Mexico 2021-01-20 0.0123 20548 1688944 144371 1539
#> 4 Brazil 2021-01-20 0.0075 64385 8638249 212831 1340
#> 5 Germany 2021-01-20 0.014 29003 2100618 50010 1013
#> 6 Russia 2021-01-20 0.00580 20806 3595136 66214 582
#> 7 South Af… 2021-01-20 0.0094 12710 1369426 38854 566
#> 8 Italy 2021-01-20 0.0057 13568 2414166 83681 524
#> 9 Spain 2021-01-20 0.0175 41576 2412318 54637 464
#> 10 Poland 2021-01-20 0.00480 6943 1450747 34141 443
rg$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")
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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