In averissimo/r-analysis-covid19: COVID-19 Data Analysis

Other plots (and tables)

Confirmed cases / Deaths {.tabset .tabset-fade .tabset-pills}

Confirmed

ommit.start(my.corona, 'confirmed', params$confirmed.start, filter.states = countries)

Deaths

ommit.start(my.corona, 'death', params$confirmed.start, filter.states = countries)

Confirmed (log2 scale)

ommit.start(my.corona, 'confirmed', params$confirmed.start, filter.states = countries, log2.flag = TRUE)

Deaths (log2 scale)

ommit.start(my.corona, 'death', params$confirmed.start, filter.states = countries, log2.flag = TRUE)

Data for Confirmed

ommit.data(my.corona, 'confirmed') %>% datatable(rownames = FALSE)

Data for Deaths

ommit.data(my.corona, 'confirmed') %>% datatable(rownames = FALSE)

Confirmed cases / Deaths (ommiting early start of epidemic)

Starting point after exceeding r params$confirmed.start cases or r params$death.start deaths {.tabset .tabset-fade .tabset-pills}

The visualizations presented below have been time-shifted to the day when each r region(1, TRUE) exceeded r params$confirmed.start cases or r params$death.start deaths (depending on the visualization).

It is an arbitrary number, but allows to have a more accurate view of the progression as the first cases are quite sporadic and make the plots more complex.

Important note: All plots are in the logarithm scale (base 2) as it allows to better visualize when cases duplicate.

It is used because covid-19 cases grow very fast, and this way it becomes easier to read over time the number of cases. Always look at the y axis.

If you do not understand logarythm scale, I recommend this video.

Cases

ommit.start(after.100.dat$all, 'confirmed', params$confirmed.start, filter.states = countries)

Deaths

ommit.start(after.100.dat$all, 'death', params$death.start, filter.states = countries)

Cases (log2 scale)

ommit.start(after.100.dat$all, 'confirmed', params$confirmer.start, filter.states = countries, log2.flag = TRUE)

Deaths (log2 scale)

ommit.start(after.100.dat$all, 'death', params$death.start, filter.states = countries, log2.flag = TRUE)

Data for Cases

after.100.dat$confirmed %>% datatable(rownames = FALSE)

Data for Deaths

after.100.dat$death %>% datatable(rownames = FALSE)

Starting point after exceeding r params$confirmed.start cases or r params$death.start deaths (per 100k population) {.tabset .tabset-fade .tabset-pills}

Showing population for plotted r region(2, TRUE) This becomes relevant for the next plots.

Cases

ommit.start(after.100.dat$all, 'confirmed', params$confirmed.start, filter.states = countries, log2.flag = FALSE, per.100k.flag = TRUE)

Deaths

ommit.start(after.100.dat$all, 'death', params$confirmer.start, filter.states = countries, log2.flag = FALSE, per.100k.flag = TRUE)

Cases (log2 scale)

ommit.start(after.100.dat$all, 'confirmed', params$confirmer.start, filter.states = countries, log2.flag = TRUE, per.100k.flag = TRUE)

Deaths (log2 scale)

ommit.start(after.100.dat$all, 'death', params$confirmer.start, filter.states = countries, log2.flag = TRUE, per.100k.flag = TRUE)

Data for Cases

after.100.dat$confirmed %>% datatable(rownames = FALSE)

Data for Deaths

after.100.dat$death %>% datatable(rownames = FALSE)

Last r params$last.days days

Cases/Deaths by day in Last r params$last.days days {.tabset .tabset-fade .tabset-pills}

Instead of showing the evolution of the cases/deaths since a r region(1, TRUE) exceeded r params$confirmed.start/r params$death.start cases, it insteads shows the last 12 days.

The first data point is the number of new cases that day.

Cases

last.days(last.days.dat, 'confirmed', params$last.days, countries, log2.flag = FALSE, per.100k.flag = FALSE, new.flag = TRUE)

Deaths

last.days(last.days.dat, 'death', params$last.days, countries, log2.flag = FALSE, per.100k.flag = FALSE, new.flag = TRUE)

Cases (log2 scale)

last.days(last.days.dat, 'confirmed', params$last.days, countries, log2.flag = TRUE, per.100k.flag = FALSE, new.flag = TRUE)

Deaths

last.days(last.days.dat, 'death', params$last.days, countries, log2.flag = TRUE, per.100k.flag = FALSE, new.flag = TRUE)

Data for Cases

warning: sub-total is only for last 12 days

last.days.data(last.days.dat, 'confirmed') %>% datatable(rownames = FALSE)

Data for Deaths

warning: sub-total is only for last 12 days

last.days.data(last.days.dat, 'death') %>% datatable(rownames = FALSE)

Cases/Deaths by day in Last r params$last.days days (per 100k population) {.tabset .tabset-fade .tabset-pills}

Instead of showing the evolution of the cases/deaths since a r region(1, TRUE) exceeded r params$confirmed.start/r params$death.start cases, it insteads shows the last 12 days.

The first data point is the number of new cases that day.

Cases

last.days(last.days.dat, 'confirmed', params$last.days, countries, log2.flag = FALSE, per.100k.flag = TRUE, new.flag = TRUE)

Deaths

last.days(last.days.dat, 'death', params$last.days, countries, log2.flag = FALSE, per.100k.flag = TRUE, new.flag = TRUE)

Cases (log2 scale)

last.days(last.days.dat, 'confirmed', params$last.days, countries, log2.flag = TRUE, per.100k.flag = TRUE, new.flag = TRUE)

Deaths

last.days(last.days.dat, 'death', params$last.days, countries, log2.flag = TRUE, per.100k.flag = TRUE, new.flag = TRUE)

Data for Cases

warning: sub-total is only for last 12 days

last.days.data(last.days.dat, 'confirmed') %>% datatable(rownames = FALSE)

Data for Deaths

warning: sub-total is only for last 12 days

last.days.data(last.days.dat, 'death') %>% datatable(rownames = FALSE)

Total Cases/Deaths in the last r params$last.days days {.tabset .tabset-fade .tabset-pills}

Instead of showing the evolution of the cases/deaths since a r region(1, TRUE) exceeded r params$confirmed.start/r params$death.start cases, it insteads shows the last 12 days.

The first data point is the number of new cases that day.

Cases

last.days(last.days.dat, 'confirmed', params$last.days, countries, log2.flag = FALSE, per.100k.flag = FALSE)

Deaths

last.days(last.days.dat, 'death', params$last.days, countries, log2.flag = FALSE, per.100k.flag = FALSE)

Cases (log2 scale)

last.days(last.days.dat, 'confirmed', params$last.days, countries, log2.flag = TRUE, per.100k.flag = FALSE)

Deaths

last.days(last.days.dat, 'death', params$last.days, countries, log2.flag = TRUE, per.100k.flag = FALSE)

Data for Cases

warning: sub-total is only for last 12 days

last.days.data(last.days.dat, 'confirmed', countries) %>% datatable(rownames = FALSE)

Data for Deaths

warning: sub-total is only for last 12 days

last.days.data(last.days.dat, 'death', countries) %>% datatable(rownames = FALSE)

Total Cases/Deaths in the last r params$last.days days (per 100k population) {.tabset .tabset-fade .tabset-pills}

Instead of showing the evolution of the cases/deaths since a r region(1, TRUE) exceeded r params$confirmed.start/r params$death.start cases, it insteads shows the last 12 days.

The first data point is the number of new cases that day.

Cases

last.days(last.days.dat, 'confirmed', params$last.days, countries, log2.flag = FALSE, per.100k.flag = TRUE)

Deaths

last.days(last.days.dat, 'death', params$last.days, countries, log2.flag = FALSE, per.100k.flag = TRUE)

Cases (log2 scale)

last.days(last.days.dat, 'confirmed', params$last.days, countries, log2.flag = TRUE, per.100k.flag = TRUE)

Deaths (log2 scale)

last.days(last.days.dat, 'death', params$last.days, countries, log2.flag = TRUE, per.100k.flag = TRUE)

Data for Cases

warning: sub-total is only for last 12 days

last.days.data(last.days.dat, 'confirmed') %>% datatable(rownames = FALSE)

Data for Deaths

warning: sub-total is only for last 12 days

last.days.data(last.days.dat, 'death') %>% datatable(rownames = FALSE)

Rolling average of previous 4 days (tracking over time) {.tabset .tabset-fade .tabset-pills}

Warning: This visualization is not very intuitive, use it to see the trend over the last days.

Cases

last.week.cumulative(last.week.dat, 'confirmed', 4, countries, log2.flag = FALSE, per.100k.flag = FALSE)

Deaths

last.week.cumulative(last.week.dat, 'death', 4, countries, log2.flag = FALSE, per.100k.flag = FALSE)

Cases (per 100k population)

last.week.cumulative(last.week.dat, 'confirmed', 4, countries, log2.flag = FALSE, per.100k.flag = TRUE)

Deaths (per 100k population)

last.week.cumulative(last.week.dat, 'death', 4, countries, log2.flag = FALSE, per.100k.flag = TRUE)

Data for Cases

cumulative.last.days.data(last.week.dat, 'confirmed', countries, 4) %>% datatable(rownames = FALSE)

Data for Deaths

cumulative.last.days.data(last.week.dat, 'death', countries, 4) %>% datatable(rownames = FALSE)

averissimo/r-analysis-covid19 documentation built on April 24, 2021, 11:01 a.m.