In averissimo/covid19-de_rki-data: COVID-19 Germany data by state/district
rmarkdown::render('_README.Rmd', output_format = 'github_document', output_file = 'README.md')
if (file.exists(file.path('README.html')) { unlink('README.html') }
if (file.exists(file.path('..', 'README.md')) { unlink('../README.md') }
if (dir.exists(file.path('..', 'README_files')) { unlink('../README.md', recursive = TRUE, force = TRUE) }
file.rename('README.md', '../README.md')
file.rename('README_files', '../README_files')
rmarkdown::render_site(output_format = 'html_document')
knitr::opts_chunk$set(echo = FALSE, collapse = TRUE, fig.height=8, fig.width=10)
library(tidyverse)
library(rjson)
library(glue)
library(DT)
library(anytime)
library(futile.logger)
library(httr)
library(ggrepel)
library(ggthemes)
library(viridis)
library(reshape2)
flog.threshold('info')
flog.layout(layout.format('~m'))
Sys.setlocale('LC_TIME', 'en_GB.UTF-8')
devtools::load_all()
COVID-19 District level data from Robert Koch Institute in Germany
The data is updated daily and is downloaded from a ARCGIS REST API using the RKI_COVID19 feature server.
Data from previous dates can be changed over time and update the data files accordingly, therefore object.id for any given row will change daily.
Source code available at averissimo/covid19-rki_de-data.
Other covid-19 confirmed/deaths analysis
Age group analysis
- Germany (by state and district)
Install / Usage
The data is available inside the data/ folder in .csv format.
It can also be used as an R package by installing this repository directly:
BiocManager::install_github('averissimo/covid19.de.data')
# or
devtools::install_github('averissimo/covid19.de.data')
Update data
To retrieve the lastest yourself use the following function of the R package.
covid19.de.data::update_dataset()
covid19.de.data::update_dataset.no.age()
Note that, as of now, the data is updated by the Robert Koch Institute once a day.
rki.covid19.tmp <- update_dataset()
rki.covid19.tmp2 <- update_dataset.no.age(force.all = TRUE)
data('rki.covid19.no.age')
if (!exists('rki.covid19.no.age') || (!all(rki.covid19.tmp2$object.id %in% rki.covid19.no.age$object.id))) {
rki.covid19.no.age <- rki.covid19.tmp2
usethis::use_data(rki.covid19.no.age, overwrite = TRUE)
write_csv2(rki.covid19.no.age, path = '../data/rki.covid19_no_age.csv')
}
data('rki.covid19')
if (!exists('rki.covid19') || (!all(rki.covid19.tmp$object.id %in% rki.covid19$object.id))) {
rki.covid19 <- rki.covid19.tmp
usethis::use_data(rki.covid19, overwrite = TRUE)
write_csv2(rki.covid19, path = '../data/rki.covid19.csv')
}
my.rki <- rki.covid19 %>%
group_by(date, id.state, state, NUTS_3, NUTS_3.code, age.group, gender) %>%
summarise(cases = sum(cases) ,deaths = sum(deaths)) %>%
ungroup() %>%
select(date, id.state, state, district = NUTS_3, id.district = NUTS_3.code, age.group, gender, cases, deaths) %>%
add.factors %>%
#group_by(id.state, state, id.district, district, gender, age.group) %>%
arrange(date)
top.states <- my.rki %>% group_by(state) %>% summarise(cases = sum(cases)) %>% arrange(-cases) %>% pull(state)
top.districts <- my.rki %>% group_by(district) %>% summarise(cases = sum(cases)) %>% arrange(-cases) %>% pull(district)
last.date.string <- 'Latest data from {my.rki %>% pull(date) %>% max}' %>% glue
Data visualization
Cases by age groups in Germany {.tabset .tabset-fade .tabset-pills}
my.rki %>%
mutate(state = 'Germany') %>%
select(-district, -id.district, -id.state, -date) %>%
group_by(state, age.group, gender) %>%
summarise(cases = sum(cases), deaths = sum(deaths)) %>%
age.plot.state('cases', c(), 'Cases in Germany', FALSE, '')
my.rki %>%
mutate(state = 'Germany') %>%
select(-district, -id.district, -id.state, -date) %>%
group_by(state, age.group, gender) %>%
summarise(cases = sum(cases), deaths = sum(deaths)) %>%
age.plot.state('deaths', c(), 'Deaths in Germany', FALSE, '')
Cases by day {.tabset .tabset-fade .tabset-pills}
(showing only 3 latest days)
age.by.day <- my.rki %>%
mutate(state = 'Germany') %>%
select(-district, -id.district, -id.state) %>%
group_by(state, age.group, gender, date) %>%
filter(gender != 'unbekannt') %>%
summarise(cases = sum(cases), deaths = sum(deaths)) %>%
mutate(cases = if_else(gender == 'M', cases * -1, cases),
deaths = if_else(gender == 'M', deaths * -1, deaths))
age.ix <- age.by.day$date %>% unique %>% sort(decreasing = TRUE)
age.ix <- age.ix[1:3]
confirmed.max <- age.by.day %>% pull(cases) %>% max
death.max <- age.by.day %>% pull(deaths) %>% max
for (ix.aux in seq_along(age.ix)) {
ix <- age.ix[ix.aux]
cat('\n')
cat('\n')
cat('#### {ix}' %>% glue)
cat('\n')
cat('\n')
cat('\n')
print(
age.by.day %>%
filter(date == ix) %>%
ggplot(aes(x = cases, y = age.group, fill = gender)) +
geom_bar(stat = 'identity') +
expand_limits(x =c(-1 * confirmed.max, confirmed.max)) +
scale_x_continuous('', labels = function(ix) { return(abs(ix)) }) +
scale_y_discrete(limits = age.by.day$age.group %>% unique %>% sort) +
scale_fill_viridis_d(end = .8) +
labs(title = "Confirmed cases from {ix}" %>% glue,
y = '',
x = 'Confirmed Cases') +
theme_minimal() +
theme(legend.position = 'bottom')
)
print(
age.by.day %>%
filter(date == ix) %>%
ggplot(aes(x = deaths, y = age.group, fill = gender)) +
geom_bar(stat = 'identity') +
expand_limits(x =c(-1 * death.max, death.max)) +
scale_x_continuous('', labels = function(ix) { return(abs(ix)) }) +
scale_y_discrete(limits = age.by.day$age.group %>% unique %>% sort) +
scale_fill_viridis_d(end = .8) +
labs(title = "Deaths from {ix}" %>% glue,
y = '',
x = 'Deaths') +
theme_minimal() +
theme(legend.position = 'bottom')
)
cat('\n')
cat('\n')
cat('\n')
cat('\n')
}
Cases by age groups {.tabset .tabset-fade .tabset-pills}
Cases in states
age.plot.state(my.rki, 'cases', top.states %>% head(8), 'Cases in States')
Deaths in states
age.plot.state(my.rki, 'deaths', top.states %>% head(8), 'Deaths in States')
Cases in districts
age.plot.district(my.rki, 'cases', top.districts %>% head(8), 'Number of Cases')
Deaths in districts
age.plot.district(my.rki, 'deaths', top.districts %>% head(8), 'Number of Deaths')
Cases by Federal State {.tabset .tabset-fade .tabset-pills}
Confirmed cases
sum.rki <- my.rki %>%
group_by(state) %>%
summarise(cases = sum(cases), deaths = sum(deaths)) %>%
melt(id.vars = 'state', variable.name = 'type', value.name = 'cases') %>%
mutate(type = if_else(type == 'cases', 'confirmed', 'death'))
top30(sum.rki, 'confirmed', 'Federal State')
Deaths
top30(sum.rki, 'death', 'Federal State')
Cases by Districts (Showing only 50 districts with most cases/deaths) {.tabset .tabset-fade .tabset-pills}
Confirmed cases
Showing only 50
sum.rki.dist <- my.rki %>%
group_by(district) %>%
summarise(cases = sum(cases), deaths = sum(deaths)) %>%
melt(id.vars = 'district', variable.name = 'type', value.name = 'cases') %>%
mutate(type = if_else(type == 'cases', 'confirmed', 'death')) %>%
select(state = district, type, cases)
top30(sum.rki.dist, 'confirmed', 'District', 50)
Deaths
Showing only 50
top30(sum.rki.dist, 'death', 'District', 50)
New cases/deaths per day in most affected states/districts {.tabset .tabset-fade .tabset-pills}
New Cases in states
my.rki %>%
filter(state %in% (top.states %>% head(8))) %>%
filter(anydate(base::date()) - date <= 12) %>%
arrange(desc(date)) %>%
group_by(state, date) %>%
summarize(cases = sum(cases)) %>%
arrange(date) %>%
mutate(label = if_else(date == last(date), paste0(state, ' (', format(cases, big.mark = ','), ')'),'')) %>%
ggplot(aes(x = date, y = cases, color = state)) +
geom_line(size = 1.2) +
geom_point(size = 2) +
geom_label_repel(aes(label = label, fill = state),
na.rm = TRUE, alpha = .8, color = 'white', size = 3, segment.alpha = .4, segment.colour = 'black', force = 5) +
scale_color_viridis_d(end = .8) +
scale_fill_viridis_d(end = .8) +
theme_minimal() +
theme(legend.position = 'none') +
labs(title = 'New cases per day in last 12 days', y = 'Cases', x = 'Day',
subtitle = 'Showing only 8 most affected states')
New Deaths in states
my.rki %>%
filter(state %in% (top.states %>% head(8))) %>%
filter(anydate(base::date()) - date <= 12) %>%
arrange(desc(date)) %>%
group_by(state, date) %>%
summarize(cases = sum(deaths)) %>%
arrange(date) %>%
mutate(label = if_else(date == last(date), paste0(state, ' (', format(cases, big.mark = ','), ')'),'')) %>%
ggplot(aes(x = date, y = cases, color = state)) +
geom_line(size = 1.2) +
geom_point(size = 2) +
geom_label_repel(aes(label = label, fill = state),
na.rm = TRUE, alpha = .8, color = 'white', size = 3, segment.alpha = .4, segment.colour = 'black', force = 5) +
scale_color_viridis_d(end = .8) +
scale_fill_viridis_d(end = .8) +
theme_minimal() +
theme(legend.position = 'none') +
labs(title = 'New deaths per day in last 12 days', y = 'Deaths', x = 'Day',
subtitle = 'Showing only 8 most affected states')
New cases in districts
my.rki %>%
filter(district %in% (top.districts %>% head(8))) %>%
filter(anydate(base::date()) - date <= 12) %>%
arrange(desc(date)) %>%
group_by(district, date) %>%
summarize(cases = sum(cases)) %>%
arrange(date) %>%
mutate(label = if_else(date == last(date), paste0(district, ' (', format(cases, big.mark = ','), ')'),'')) %>%
ggplot(aes(x = date, y = cases, color = district)) +
geom_line(size = 1.2) +
geom_point(size = 2) +
geom_label_repel(aes(label = label, fill = district),
na.rm = TRUE, alpha = .8, color = 'white', size = 3, segment.alpha = .4, segment.colour = 'black', force = 4) +
scale_color_viridis_d(end = .9) +
scale_fill_viridis_d(end = .9) +
theme_minimal() +
theme(legend.position = 'none') +
labs(title = 'New cases per day in last 12 days', y = 'Cases', x = 'Day',
subtitle = 'Showing only 8 most affected states')
New deaths in districts
my.rki %>%
filter(district %in% (top.districts %>% head(8))) %>%
filter(anydate(base::date()) - date <= 12) %>%
arrange(desc(date)) %>%
group_by(district, date) %>%
summarize(cases = sum(deaths)) %>%
arrange(date) %>%
mutate(label = if_else(date == last(date), paste0(district, ' (', format(cases, big.mark = ','), ')'),'')) %>%
ggplot(aes(x = date, y = cases, color = district)) +
geom_line(size = 1.2) +
geom_point(size = 2) +
geom_label_repel(aes(label = label, fill = district),
na.rm = TRUE, alpha = .8, color = 'white', size = 3, segment.alpha = .4, segment.colour = 'black', force = 4) +
scale_color_viridis_d(end = .9) +
scale_fill_viridis_d(end = .9) +
theme_minimal() +
theme(legend.position = 'none') +
labs(title = 'New cases per day in last 12 days', y = 'Cases', x = 'Day',
subtitle = 'Showing only 8 most affected districts')
Total cases in last 12 days in most affected states/districts {.tabset .tabset-fade .tabset-pills}
Total cases in states
my.rki %>%
filter(state %in% (top.states %>% head(8))) %>%
filter(anydate(base::date()) - date <= 12) %>%
arrange(date) %>%
group_by(state, date) %>%
summarize(cases = sum(cases)) %>%
mutate(cumul.cases = cumsum(cases),
label = if_else(date == last(date), paste0(state, '(', format(cumul.cases, big.mark=','), ')'), '')) %>%
ggplot(aes(x = date, y = cumul.cases, color = state)) +
geom_line(size = 1.2) +
geom_point(size = 2) +
geom_label_repel(aes(label = label, fill = state),
na.rm = TRUE, alpha = .8, color = 'white', size = 3, segment.alpha = .4, segment.colour = 'black', force = 2) +
scale_color_viridis(discrete = TRUE, end = .5) +
scale_fill_viridis(discrete = TRUE, end = .5) +
theme_minimal() +
theme(legend.position = 'none') +
labs(title = 'Total cases per day in last 12 days', y = 'Total Cases', x = 'Day',
subtitle = 'Showing only 8 most affected states')
Total deaths in states
Showing only 6 states most affected
my.rki %>%
filter(state %in% (top.states %>% head(8))) %>%
filter(anydate(base::date()) - date <= 12) %>%
arrange(date) %>%
group_by(state, date) %>%
summarize(cases = sum(deaths)) %>%
mutate(cumul.cases = cumsum(cases),
label = if_else(date == last(date), paste0(state, '(', format(cumul.cases, big.mark=','), ')'), '')) %>%
ggplot(aes(x = date, y = cumul.cases, color = state)) +
geom_line(size = 1.2) +
geom_point(size = 2) +
geom_label_repel(aes(label = label, fill = state),
na.rm = TRUE, alpha = .8, color = 'white', size = 3, segment.alpha = .4, segment.colour = 'black', force = 2) +
scale_color_viridis(discrete = TRUE, end = .5) +
scale_fill_viridis(discrete = TRUE, end = .5) +
theme_minimal() +
theme(legend.position = 'none') +
labs(title = 'Total deaths per day in last 12 days', y = 'Total Deaths', x = 'Day',
subtitle = 'Showing only 8 most affected states')
Total cases in districs
Showing only 6 districs most affected
my.rki %>%
filter(district %in% (top.districts %>% head(8))) %>%
filter(anydate(base::date()) - date <= 12) %>%
arrange(date) %>%
group_by(district, date) %>%
summarize(cases = sum(cases)) %>%
mutate(cumul.cases = cumsum(cases),
label = if_else(date == last(date), paste0(district, '(', format(cumul.cases, big.mark=','), ')'), '')) %>%
ggplot(aes(x = date, y = cumul.cases, color = district)) +
geom_line(size = 1.2) +
geom_point(size = 2) +
geom_label_repel(aes(label = label, fill = district),
na.rm = TRUE, alpha = .8, color = 'white', size = 3, segment.alpha = .4, segment.colour = 'black', force = 2) +
scale_color_viridis(discrete = TRUE, end = .5) +
scale_fill_viridis(discrete = TRUE, end = .5) +
theme_minimal() +
theme(legend.position = 'none') +
labs(title = 'Total cases per day in last 12 days', y = 'Total Cases', x = 'Day',
subtitle = 'Showing only 8 most affected districts')
Total deaths in districs
Showing only 6 districs most affected
my.rki %>%
filter(district %in% (top.districts %>% head(8))) %>%
filter(anydate(base::date()) - date <= 12) %>%
arrange(date) %>%
group_by(district, date) %>%
summarize(cases = sum(deaths)) %>%
mutate(cumul.cases = cumsum(cases),
label = if_else(date == last(date), paste0(district, '(', format(cumul.cases, big.mark=','), ')'), '')) %>%
ggplot(aes(x = date, y = cumul.cases, color = district)) +
geom_line(size = 1.2) +
geom_point(size = 2) +
geom_label_repel(aes(label = label, fill = district),
na.rm = TRUE, alpha = .8, color = 'white', size = 3, segment.alpha = .4, segment.colour = 'black', force = 2) +
scale_color_viridis(discrete = TRUE, end = .5) +
scale_fill_viridis(discrete = TRUE, end = .5) +
theme_minimal() +
theme(legend.position = 'none') +
labs(title = 'Total deaths per day in last 12 days', y = 'Total Deaths', x = 'Day',
subtitle = 'Showing only 8 most affected districts')
averissimo/covid19-de_rki-data documentation built on Nov. 14, 2020, 1:43 p.m.
R Package Documentation
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knitr::opts_chunk$set(echo = FALSE, collapse = TRUE, fig.height=8, fig.width=10) library(tidyverse) library(rjson) library(glue) library(DT) library(anytime) library(futile.logger) library(httr) library(ggrepel) library(ggthemes) library(viridis) library(reshape2) flog.threshold('info') flog.layout(layout.format('~m')) Sys.setlocale('LC_TIME', 'en_GB.UTF-8') devtools::load_all()
COVID-19 District level data from Robert Koch Institute in Germany
The data is updated daily and is downloaded from a ARCGIS REST API using the RKI_COVID19 feature server.
Data from previous dates can be changed over time and update the data files accordingly, therefore object.id for any given row will change daily.
Source code available at averissimo/covid19-rki_de-data.
Other covid-19 confirmed/deaths analysis
Age group analysis
- Germany (by state and district)
Install / Usage
The data is available inside the data/ folder in .csv format.
It can also be used as an R package by installing this repository directly:
BiocManager::install_github('averissimo/covid19.de.data') # or devtools::install_github('averissimo/covid19.de.data')
Update data
To retrieve the lastest yourself use the following function of the R package.
covid19.de.data::update_dataset() covid19.de.data::update_dataset.no.age()
Note that, as of now, the data is updated by the Robert Koch Institute once a day.
rki.covid19.tmp <- update_dataset() rki.covid19.tmp2 <- update_dataset.no.age(force.all = TRUE)
data('rki.covid19.no.age') if (!exists('rki.covid19.no.age') || (!all(rki.covid19.tmp2$object.id %in% rki.covid19.no.age$object.id))) { rki.covid19.no.age <- rki.covid19.tmp2 usethis::use_data(rki.covid19.no.age, overwrite = TRUE) write_csv2(rki.covid19.no.age, path = '../data/rki.covid19_no_age.csv') }
data('rki.covid19') if (!exists('rki.covid19') || (!all(rki.covid19.tmp$object.id %in% rki.covid19$object.id))) { rki.covid19 <- rki.covid19.tmp usethis::use_data(rki.covid19, overwrite = TRUE) write_csv2(rki.covid19, path = '../data/rki.covid19.csv') }
my.rki <- rki.covid19 %>% group_by(date, id.state, state, NUTS_3, NUTS_3.code, age.group, gender) %>% summarise(cases = sum(cases) ,deaths = sum(deaths)) %>% ungroup() %>% select(date, id.state, state, district = NUTS_3, id.district = NUTS_3.code, age.group, gender, cases, deaths) %>% add.factors %>% #group_by(id.state, state, id.district, district, gender, age.group) %>% arrange(date) top.states <- my.rki %>% group_by(state) %>% summarise(cases = sum(cases)) %>% arrange(-cases) %>% pull(state) top.districts <- my.rki %>% group_by(district) %>% summarise(cases = sum(cases)) %>% arrange(-cases) %>% pull(district) last.date.string <- 'Latest data from {my.rki %>% pull(date) %>% max}' %>% glue
Data visualization
Cases by age groups in Germany {.tabset .tabset-fade .tabset-pills}
my.rki %>% mutate(state = 'Germany') %>% select(-district, -id.district, -id.state, -date) %>% group_by(state, age.group, gender) %>% summarise(cases = sum(cases), deaths = sum(deaths)) %>% age.plot.state('cases', c(), 'Cases in Germany', FALSE, '')
my.rki %>% mutate(state = 'Germany') %>% select(-district, -id.district, -id.state, -date) %>% group_by(state, age.group, gender) %>% summarise(cases = sum(cases), deaths = sum(deaths)) %>% age.plot.state('deaths', c(), 'Deaths in Germany', FALSE, '')
Cases by day {.tabset .tabset-fade .tabset-pills}
(showing only 3 latest days)
age.by.day <- my.rki %>% mutate(state = 'Germany') %>% select(-district, -id.district, -id.state) %>% group_by(state, age.group, gender, date) %>% filter(gender != 'unbekannt') %>% summarise(cases = sum(cases), deaths = sum(deaths)) %>% mutate(cases = if_else(gender == 'M', cases * -1, cases), deaths = if_else(gender == 'M', deaths * -1, deaths)) age.ix <- age.by.day$date %>% unique %>% sort(decreasing = TRUE) age.ix <- age.ix[1:3] confirmed.max <- age.by.day %>% pull(cases) %>% max death.max <- age.by.day %>% pull(deaths) %>% max for (ix.aux in seq_along(age.ix)) { ix <- age.ix[ix.aux] cat('\n') cat('\n') cat('#### {ix}' %>% glue) cat('\n') cat('\n') cat('\n') print( age.by.day %>% filter(date == ix) %>% ggplot(aes(x = cases, y = age.group, fill = gender)) + geom_bar(stat = 'identity') + expand_limits(x =c(-1 * confirmed.max, confirmed.max)) + scale_x_continuous('', labels = function(ix) { return(abs(ix)) }) + scale_y_discrete(limits = age.by.day$age.group %>% unique %>% sort) + scale_fill_viridis_d(end = .8) + labs(title = "Confirmed cases from {ix}" %>% glue, y = '', x = 'Confirmed Cases') + theme_minimal() + theme(legend.position = 'bottom') ) print( age.by.day %>% filter(date == ix) %>% ggplot(aes(x = deaths, y = age.group, fill = gender)) + geom_bar(stat = 'identity') + expand_limits(x =c(-1 * death.max, death.max)) + scale_x_continuous('', labels = function(ix) { return(abs(ix)) }) + scale_y_discrete(limits = age.by.day$age.group %>% unique %>% sort) + scale_fill_viridis_d(end = .8) + labs(title = "Deaths from {ix}" %>% glue, y = '', x = 'Deaths') + theme_minimal() + theme(legend.position = 'bottom') ) cat('\n') cat('\n') cat('\n') cat('\n') }
Cases by age groups {.tabset .tabset-fade .tabset-pills}
Cases in states
age.plot.state(my.rki, 'cases', top.states %>% head(8), 'Cases in States')
Deaths in states
age.plot.state(my.rki, 'deaths', top.states %>% head(8), 'Deaths in States')
Cases in districts
age.plot.district(my.rki, 'cases', top.districts %>% head(8), 'Number of Cases')
Deaths in districts
age.plot.district(my.rki, 'deaths', top.districts %>% head(8), 'Number of Deaths')
Cases by Federal State {.tabset .tabset-fade .tabset-pills}
Confirmed cases
sum.rki <- my.rki %>% group_by(state) %>% summarise(cases = sum(cases), deaths = sum(deaths)) %>% melt(id.vars = 'state', variable.name = 'type', value.name = 'cases') %>% mutate(type = if_else(type == 'cases', 'confirmed', 'death')) top30(sum.rki, 'confirmed', 'Federal State')
Deaths
top30(sum.rki, 'death', 'Federal State')
Cases by Districts (Showing only 50 districts with most cases/deaths) {.tabset .tabset-fade .tabset-pills}
Confirmed cases
Showing only 50
sum.rki.dist <- my.rki %>% group_by(district) %>% summarise(cases = sum(cases), deaths = sum(deaths)) %>% melt(id.vars = 'district', variable.name = 'type', value.name = 'cases') %>% mutate(type = if_else(type == 'cases', 'confirmed', 'death')) %>% select(state = district, type, cases) top30(sum.rki.dist, 'confirmed', 'District', 50)
Deaths
Showing only 50
top30(sum.rki.dist, 'death', 'District', 50)
New cases/deaths per day in most affected states/districts {.tabset .tabset-fade .tabset-pills}
New Cases in states
my.rki %>% filter(state %in% (top.states %>% head(8))) %>% filter(anydate(base::date()) - date <= 12) %>% arrange(desc(date)) %>% group_by(state, date) %>% summarize(cases = sum(cases)) %>% arrange(date) %>% mutate(label = if_else(date == last(date), paste0(state, ' (', format(cases, big.mark = ','), ')'),'')) %>% ggplot(aes(x = date, y = cases, color = state)) + geom_line(size = 1.2) + geom_point(size = 2) + geom_label_repel(aes(label = label, fill = state), na.rm = TRUE, alpha = .8, color = 'white', size = 3, segment.alpha = .4, segment.colour = 'black', force = 5) + scale_color_viridis_d(end = .8) + scale_fill_viridis_d(end = .8) + theme_minimal() + theme(legend.position = 'none') + labs(title = 'New cases per day in last 12 days', y = 'Cases', x = 'Day', subtitle = 'Showing only 8 most affected states')
New Deaths in states
my.rki %>% filter(state %in% (top.states %>% head(8))) %>% filter(anydate(base::date()) - date <= 12) %>% arrange(desc(date)) %>% group_by(state, date) %>% summarize(cases = sum(deaths)) %>% arrange(date) %>% mutate(label = if_else(date == last(date), paste0(state, ' (', format(cases, big.mark = ','), ')'),'')) %>% ggplot(aes(x = date, y = cases, color = state)) + geom_line(size = 1.2) + geom_point(size = 2) + geom_label_repel(aes(label = label, fill = state), na.rm = TRUE, alpha = .8, color = 'white', size = 3, segment.alpha = .4, segment.colour = 'black', force = 5) + scale_color_viridis_d(end = .8) + scale_fill_viridis_d(end = .8) + theme_minimal() + theme(legend.position = 'none') + labs(title = 'New deaths per day in last 12 days', y = 'Deaths', x = 'Day', subtitle = 'Showing only 8 most affected states')
New cases in districts
my.rki %>% filter(district %in% (top.districts %>% head(8))) %>% filter(anydate(base::date()) - date <= 12) %>% arrange(desc(date)) %>% group_by(district, date) %>% summarize(cases = sum(cases)) %>% arrange(date) %>% mutate(label = if_else(date == last(date), paste0(district, ' (', format(cases, big.mark = ','), ')'),'')) %>% ggplot(aes(x = date, y = cases, color = district)) + geom_line(size = 1.2) + geom_point(size = 2) + geom_label_repel(aes(label = label, fill = district), na.rm = TRUE, alpha = .8, color = 'white', size = 3, segment.alpha = .4, segment.colour = 'black', force = 4) + scale_color_viridis_d(end = .9) + scale_fill_viridis_d(end = .9) + theme_minimal() + theme(legend.position = 'none') + labs(title = 'New cases per day in last 12 days', y = 'Cases', x = 'Day', subtitle = 'Showing only 8 most affected states')
New deaths in districts
my.rki %>% filter(district %in% (top.districts %>% head(8))) %>% filter(anydate(base::date()) - date <= 12) %>% arrange(desc(date)) %>% group_by(district, date) %>% summarize(cases = sum(deaths)) %>% arrange(date) %>% mutate(label = if_else(date == last(date), paste0(district, ' (', format(cases, big.mark = ','), ')'),'')) %>% ggplot(aes(x = date, y = cases, color = district)) + geom_line(size = 1.2) + geom_point(size = 2) + geom_label_repel(aes(label = label, fill = district), na.rm = TRUE, alpha = .8, color = 'white', size = 3, segment.alpha = .4, segment.colour = 'black', force = 4) + scale_color_viridis_d(end = .9) + scale_fill_viridis_d(end = .9) + theme_minimal() + theme(legend.position = 'none') + labs(title = 'New cases per day in last 12 days', y = 'Cases', x = 'Day', subtitle = 'Showing only 8 most affected districts')
Total cases in last 12 days in most affected states/districts {.tabset .tabset-fade .tabset-pills}
Total cases in states
my.rki %>% filter(state %in% (top.states %>% head(8))) %>% filter(anydate(base::date()) - date <= 12) %>% arrange(date) %>% group_by(state, date) %>% summarize(cases = sum(cases)) %>% mutate(cumul.cases = cumsum(cases), label = if_else(date == last(date), paste0(state, '(', format(cumul.cases, big.mark=','), ')'), '')) %>% ggplot(aes(x = date, y = cumul.cases, color = state)) + geom_line(size = 1.2) + geom_point(size = 2) + geom_label_repel(aes(label = label, fill = state), na.rm = TRUE, alpha = .8, color = 'white', size = 3, segment.alpha = .4, segment.colour = 'black', force = 2) + scale_color_viridis(discrete = TRUE, end = .5) + scale_fill_viridis(discrete = TRUE, end = .5) + theme_minimal() + theme(legend.position = 'none') + labs(title = 'Total cases per day in last 12 days', y = 'Total Cases', x = 'Day', subtitle = 'Showing only 8 most affected states')
Total deaths in states
Showing only 6 states most affected
my.rki %>% filter(state %in% (top.states %>% head(8))) %>% filter(anydate(base::date()) - date <= 12) %>% arrange(date) %>% group_by(state, date) %>% summarize(cases = sum(deaths)) %>% mutate(cumul.cases = cumsum(cases), label = if_else(date == last(date), paste0(state, '(', format(cumul.cases, big.mark=','), ')'), '')) %>% ggplot(aes(x = date, y = cumul.cases, color = state)) + geom_line(size = 1.2) + geom_point(size = 2) + geom_label_repel(aes(label = label, fill = state), na.rm = TRUE, alpha = .8, color = 'white', size = 3, segment.alpha = .4, segment.colour = 'black', force = 2) + scale_color_viridis(discrete = TRUE, end = .5) + scale_fill_viridis(discrete = TRUE, end = .5) + theme_minimal() + theme(legend.position = 'none') + labs(title = 'Total deaths per day in last 12 days', y = 'Total Deaths', x = 'Day', subtitle = 'Showing only 8 most affected states')
Total cases in districs
Showing only 6 districs most affected
my.rki %>% filter(district %in% (top.districts %>% head(8))) %>% filter(anydate(base::date()) - date <= 12) %>% arrange(date) %>% group_by(district, date) %>% summarize(cases = sum(cases)) %>% mutate(cumul.cases = cumsum(cases), label = if_else(date == last(date), paste0(district, '(', format(cumul.cases, big.mark=','), ')'), '')) %>% ggplot(aes(x = date, y = cumul.cases, color = district)) + geom_line(size = 1.2) + geom_point(size = 2) + geom_label_repel(aes(label = label, fill = district), na.rm = TRUE, alpha = .8, color = 'white', size = 3, segment.alpha = .4, segment.colour = 'black', force = 2) + scale_color_viridis(discrete = TRUE, end = .5) + scale_fill_viridis(discrete = TRUE, end = .5) + theme_minimal() + theme(legend.position = 'none') + labs(title = 'Total cases per day in last 12 days', y = 'Total Cases', x = 'Day', subtitle = 'Showing only 8 most affected districts')
Total deaths in districs
Showing only 6 districs most affected
my.rki %>% filter(district %in% (top.districts %>% head(8))) %>% filter(anydate(base::date()) - date <= 12) %>% arrange(date) %>% group_by(district, date) %>% summarize(cases = sum(deaths)) %>% mutate(cumul.cases = cumsum(cases), label = if_else(date == last(date), paste0(district, '(', format(cumul.cases, big.mark=','), ')'), '')) %>% ggplot(aes(x = date, y = cumul.cases, color = district)) + geom_line(size = 1.2) + geom_point(size = 2) + geom_label_repel(aes(label = label, fill = district), na.rm = TRUE, alpha = .8, color = 'white', size = 3, segment.alpha = .4, segment.colour = 'black', force = 2) + scale_color_viridis(discrete = TRUE, end = .5) + scale_fill_viridis(discrete = TRUE, end = .5) + theme_minimal() + theme(legend.position = 'none') + labs(title = 'Total deaths per day in last 12 days', y = 'Total Deaths', x = 'Day', subtitle = 'Showing only 8 most affected districts')
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