README-africa.md
In rOpenStats/Covid19Analytics: 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 '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
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$setupData()
#> INFO [07:51:31.629] {stage: `processor-setup`}
#> INFO [07:51:31.865] Checking required downloaded {downloaded.max.date: `2023-01-09`, daily.update.time: `21:00:00`, current.datetime: `2023-01-11 07:51:31`, download.flag: `TRUE`}
#> INFO [07:51:34.888] Checking required downloaded {downloaded.max.date: `2023-01-09`, daily.update.time: `21:00:00`, current.datetime: `2023-01-11 07:51:34`, download.flag: `TRUE`}
#> INFO [07:51:36.562] Checking required downloaded {downloaded.max.date: `2023-01-09`, daily.update.time: `21:00:00`, current.datetime: `2023-01-11 07:51:36`, download.flag: `TRUE`}
#> INFO [07:51:38.103] {stage: `data loaded`}
#> INFO [07:51:38.105] {stage: `data-setup`}
dummy <- data.processor$transform()
#> INFO [07:51:38.108] Executing transform
#> INFO [07:51:38.109] Executing consolidate
#> INFO [07:52:13.160] {stage: `consolidated`}
#> INFO [07:52:13.161] Executing standarize
#> INFO [07:52:17.230] gathering DataModel
#> INFO [07:52:17.231] {stage: `datamodel-setup`}
# Curate is the process made by missing values method
dummy <- data.processor$curate()
#> INFO [07:52:17.241] {stage: `loading-aggregated-data-model`}
#> Warning in countrycode_convert(sourcevar = sourcevar, origin = origin, destination = dest, : Some values were not matched unambiguously: Antarctica
#> Warning in countrycode_convert(sourcevar = sourcevar, origin = origin, destination = dest, : Some values were not matched unambiguously: Micronesia
#> Warning in countrycode_convert(sourcevar = sourcevar, origin = origin, destination = dest, : Some values were not matched unambiguously: MS Zaandam
#> Warning in countrycode_convert(sourcevar = sourcevar, origin = origin, destination = dest, : Some values were not matched unambiguously: Summer Olympics 2020
#> Warning in countrycode_convert(sourcevar = sourcevar, origin = origin, destination = dest, : Some values were not matched unambiguously: Winter Olympics 2022
#> INFO [07:52:21.891] {stage: `calculating-rates`}
#> INFO [07:52:22.236] {stage: `making-data-comparison`}
#> INFO [07:52:38.136] {stage: `applying-missing-values-method`}
#> INFO [07:52:38.138] {stage: `Starting first imputation`}
#> INFO [07:52:38.192] {stage: `calculating-rates`}
#> INFO [07:52:38.567] {stage: `making-data-comparison-2`}
#> INFO [07:52:54.635] {stage: `calculating-top-countries`}
#> INFO [07:52:54.672] {stage: `curated`}
current.date <- max(data.processor$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 × 7
#> # Groups: country [10]
#> country date rate.inc.daily confirmed.inc confirmed deaths death…¹
#> <chr> <date> <dbl> <int> <int> <int> <int>
#> 1 Japan 2023-01-10 0.0026 78982 30670001 6.04e4 253
#> 2 Brazil 2023-01-10 0.0021 75218 36552432 6.95e5 206
#> 3 US 2023-01-10 0.0006 59695 101345042 1.10e6 864
#> 4 Korea, South 2023-01-10 0.0018 54343 29654090 3.27e4 76
#> 5 Thailand 2023-01-10 0.0062 29164 4752782 3.47e4 1018
#> 6 Taiwan* 2023-01-10 0.0028 25049 9097554 1.56e4 26
#> 7 Germany 2023-01-10 0.0006 22119 37562191 1.63e5 269
#> 8 France 2023-01-10 0.0003 11773 39625664 1.64e5 115
#> 9 China 2023-01-10 0.002 9379 4804906 1.78e4 75
#> 10 Austria 2023-01-10 0.0005 3019 5731160 2.15e4 27
#> # … with abbreviated variable name ¹deaths.inc
# Top 10 daily deaths increment
(data.processor$getData() %>%
filter(date == current.date) %>%
select(country, date, rate.inc.daily, confirmed.inc, confirmed, deaths, deaths.inc) %>%
arrange(desc(deaths.inc)))[1:10,]
#> # A tibble: 10 × 7
#> # Groups: country [10]
#> country date rate.inc.daily confirmed.inc confirmed deaths death…¹
#> <chr> <date> <dbl> <int> <int> <int> <int>
#> 1 Thailand 2023-01-10 0.0062 29164 4752782 3.47e4 1018
#> 2 US 2023-01-10 0.0006 59695 101345042 1.10e6 864
#> 3 Germany 2023-01-10 0.0006 22119 37562191 1.63e5 269
#> 4 Japan 2023-01-10 0.0026 78982 30670001 6.04e4 253
#> 5 Brazil 2023-01-10 0.0021 75218 36552432 6.95e5 206
#> 6 France 2023-01-10 0.0003 11773 39625664 1.64e5 115
#> 7 Korea, South 2023-01-10 0.0018 54343 29654090 3.27e4 76
#> 8 China 2023-01-10 0.002 9379 4804906 1.78e4 75
#> 9 Russia 2023-01-10 0.0001 3010 21524480 3.86e5 46
#> 10 Australia 2023-01-10 0.0001 1150 11211305 1.74e4 31
#> # … with abbreviated variable name ¹deaths.inc
rg$ggplotTopCountriesStackedBarDailyInc(included.countries = africa.countries,
countries.text = "Africa")
#> Warning: Removed 318 rows containing missing values (`position_stack()`).
rc$ggplotComparisonExponentialGrowth(included.countries = africa.countries, min.cases = 20)
#> Warning: ggrepel: 41 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps
rg$ggplotCountriesLines(included.countries = africa.countries, countries.text = "Africa countries",
field = "confirmed.inc", log.scale = TRUE)
#> Warning: Removed 318 rows containing missing values (`geom_line()`).
#> Warning: ggrepel: 24 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps
rc$ggplotComparisonExponentialGrowth(included.countries = africa.countries,
field = "deaths", y.label = "deaths", min.cases = 1)
#> Warning: ggrepel: 37 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps
rg$ggplotTopCountriesStackedBarDailyInc(top.countries)
#> Warning: Removed 69 rows containing missing values (`position_stack()`).
rc$ggplotComparisonExponentialGrowth(included.countries = international.countries,
min.cases = 100)
#> Warning: Removed 2 rows containing missing values (`geom_line()`).
#> Warning: ggrepel: 5 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps
rg$ggplotCountriesLines(field = "confirmed.inc", log.scale = TRUE)
#> Warning: Removed 66 rows containing missing values (`geom_line()`).
rg$ggplotCountriesLines(field = "rate.inc.daily", log.scale = TRUE)
#> Warning: Transformation introduced infinite values in continuous y-axis
#> Warning in self$trans$transform(x): NaNs produced
#> Warning: Transformation introduced infinite values in continuous y-axis
#> Warning in self$trans$transform(x): NaNs produced
#> Warning: Transformation introduced infinite values in continuous y-axis
#> Warning: Removed 119 rows containing missing values (`geom_line()`).
#> Warning: Removed 1 rows containing missing values (`geom_text_repel()`).
rg$ggplotTopCountriesPie()
rg$ggplotTopCountriesBarPlots()
rg$ggplotCountriesBarGraphs(selected.country = "Ethiopia")
rOpenStats/Covid19Analytics documentation built on Dec. 7, 2023, 9:28 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 '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
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$setupData()
#> INFO [07:51:31.629] {stage: `processor-setup`}
#> INFO [07:51:31.865] Checking required downloaded {downloaded.max.date: `2023-01-09`, daily.update.time: `21:00:00`, current.datetime: `2023-01-11 07:51:31`, download.flag: `TRUE`}
#> INFO [07:51:34.888] Checking required downloaded {downloaded.max.date: `2023-01-09`, daily.update.time: `21:00:00`, current.datetime: `2023-01-11 07:51:34`, download.flag: `TRUE`}
#> INFO [07:51:36.562] Checking required downloaded {downloaded.max.date: `2023-01-09`, daily.update.time: `21:00:00`, current.datetime: `2023-01-11 07:51:36`, download.flag: `TRUE`}
#> INFO [07:51:38.103] {stage: `data loaded`}
#> INFO [07:51:38.105] {stage: `data-setup`}
dummy <- data.processor$transform()
#> INFO [07:51:38.108] Executing transform
#> INFO [07:51:38.109] Executing consolidate
#> INFO [07:52:13.160] {stage: `consolidated`}
#> INFO [07:52:13.161] Executing standarize
#> INFO [07:52:17.230] gathering DataModel
#> INFO [07:52:17.231] {stage: `datamodel-setup`}
# Curate is the process made by missing values method
dummy <- data.processor$curate()
#> INFO [07:52:17.241] {stage: `loading-aggregated-data-model`}
#> Warning in countrycode_convert(sourcevar = sourcevar, origin = origin, destination = dest, : Some values were not matched unambiguously: Antarctica
#> Warning in countrycode_convert(sourcevar = sourcevar, origin = origin, destination = dest, : Some values were not matched unambiguously: Micronesia
#> Warning in countrycode_convert(sourcevar = sourcevar, origin = origin, destination = dest, : Some values were not matched unambiguously: MS Zaandam
#> Warning in countrycode_convert(sourcevar = sourcevar, origin = origin, destination = dest, : Some values were not matched unambiguously: Summer Olympics 2020
#> Warning in countrycode_convert(sourcevar = sourcevar, origin = origin, destination = dest, : Some values were not matched unambiguously: Winter Olympics 2022
#> INFO [07:52:21.891] {stage: `calculating-rates`}
#> INFO [07:52:22.236] {stage: `making-data-comparison`}
#> INFO [07:52:38.136] {stage: `applying-missing-values-method`}
#> INFO [07:52:38.138] {stage: `Starting first imputation`}
#> INFO [07:52:38.192] {stage: `calculating-rates`}
#> INFO [07:52:38.567] {stage: `making-data-comparison-2`}
#> INFO [07:52:54.635] {stage: `calculating-top-countries`}
#> INFO [07:52:54.672] {stage: `curated`}
current.date <- max(data.processor$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 × 7
#> # Groups: country [10]
#> country date rate.inc.daily confirmed.inc confirmed deaths death…¹
#> <chr> <date> <dbl> <int> <int> <int> <int>
#> 1 Japan 2023-01-10 0.0026 78982 30670001 6.04e4 253
#> 2 Brazil 2023-01-10 0.0021 75218 36552432 6.95e5 206
#> 3 US 2023-01-10 0.0006 59695 101345042 1.10e6 864
#> 4 Korea, South 2023-01-10 0.0018 54343 29654090 3.27e4 76
#> 5 Thailand 2023-01-10 0.0062 29164 4752782 3.47e4 1018
#> 6 Taiwan* 2023-01-10 0.0028 25049 9097554 1.56e4 26
#> 7 Germany 2023-01-10 0.0006 22119 37562191 1.63e5 269
#> 8 France 2023-01-10 0.0003 11773 39625664 1.64e5 115
#> 9 China 2023-01-10 0.002 9379 4804906 1.78e4 75
#> 10 Austria 2023-01-10 0.0005 3019 5731160 2.15e4 27
#> # … with abbreviated variable name ¹deaths.inc
# Top 10 daily deaths increment
(data.processor$getData() %>%
filter(date == current.date) %>%
select(country, date, rate.inc.daily, confirmed.inc, confirmed, deaths, deaths.inc) %>%
arrange(desc(deaths.inc)))[1:10,]
#> # A tibble: 10 × 7
#> # Groups: country [10]
#> country date rate.inc.daily confirmed.inc confirmed deaths death…¹
#> <chr> <date> <dbl> <int> <int> <int> <int>
#> 1 Thailand 2023-01-10 0.0062 29164 4752782 3.47e4 1018
#> 2 US 2023-01-10 0.0006 59695 101345042 1.10e6 864
#> 3 Germany 2023-01-10 0.0006 22119 37562191 1.63e5 269
#> 4 Japan 2023-01-10 0.0026 78982 30670001 6.04e4 253
#> 5 Brazil 2023-01-10 0.0021 75218 36552432 6.95e5 206
#> 6 France 2023-01-10 0.0003 11773 39625664 1.64e5 115
#> 7 Korea, South 2023-01-10 0.0018 54343 29654090 3.27e4 76
#> 8 China 2023-01-10 0.002 9379 4804906 1.78e4 75
#> 9 Russia 2023-01-10 0.0001 3010 21524480 3.86e5 46
#> 10 Australia 2023-01-10 0.0001 1150 11211305 1.74e4 31
#> # … with abbreviated variable name ¹deaths.inc
rg$ggplotTopCountriesStackedBarDailyInc(included.countries = africa.countries,
countries.text = "Africa")
#> Warning: Removed 318 rows containing missing values (`position_stack()`).
rc$ggplotComparisonExponentialGrowth(included.countries = africa.countries, min.cases = 20)
#> Warning: ggrepel: 41 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps
rg$ggplotCountriesLines(included.countries = africa.countries, countries.text = "Africa countries",
field = "confirmed.inc", log.scale = TRUE)
#> Warning: Removed 318 rows containing missing values (`geom_line()`).
#> Warning: ggrepel: 24 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps
rc$ggplotComparisonExponentialGrowth(included.countries = africa.countries,
field = "deaths", y.label = "deaths", min.cases = 1)
#> Warning: ggrepel: 37 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps
rg$ggplotTopCountriesStackedBarDailyInc(top.countries)
#> Warning: Removed 69 rows containing missing values (`position_stack()`).
rc$ggplotComparisonExponentialGrowth(included.countries = international.countries,
min.cases = 100)
#> Warning: Removed 2 rows containing missing values (`geom_line()`).
#> Warning: ggrepel: 5 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps
rg$ggplotCountriesLines(field = "confirmed.inc", log.scale = TRUE)
#> Warning: Removed 66 rows containing missing values (`geom_line()`).
rg$ggplotCountriesLines(field = "rate.inc.daily", log.scale = TRUE)
#> Warning: Transformation introduced infinite values in continuous y-axis
#> Warning in self$trans$transform(x): NaNs produced
#> Warning: Transformation introduced infinite values in continuous y-axis
#> Warning in self$trans$transform(x): NaNs produced
#> Warning: Transformation introduced infinite values in continuous y-axis
#> Warning: Removed 119 rows containing missing values (`geom_line()`).
#> Warning: Removed 1 rows containing missing values (`geom_text_repel()`).
rg$ggplotTopCountriesPie()
rg$ggplotTopCountriesBarPlots()
rg$ggplotCountriesBarGraphs(selected.country = "Ethiopia")
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