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hospitalizations.R
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hospitalizations.R
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library(RSocrata)
library(tidyverse)
library(lubridate)
library(hrbrthemes)
library(runner)
library(geofacet)
hospitalizations = read.socrata("https://healthdata.gov/resource/g62h-syeh.csv")
hospitalizations$date = as.Date(hospitalizations$date)
hospitalizations <-tibble(hospitalizations)
unique(hospitalizations$state)
hospitalizations_wa = subset(hospitalizations,state == 'WA')
ggplot(hospitalizations_wa)+
#geom_line(aes(x=date,y=total_adult_patients_hospitalized_confirmed_and_suspected_covid))+
geom_line(aes(x=date,y=inpatient_beds))
## Hospitalizations for Covid are on the rise
ggplot(hospitalizations_wa)+
geom_line(aes(x=date,y=inpatient_beds_utilization))
ggplot(hospitalizations_wa)+
#geom_line(aes(x=date,y=total_adult_patients_hospitalized_confirmed_and_suspected_covid))+
geom_line(aes(x=date,y=inpatient_bed_covid_utilization))
ggplot(hospitalizations_wa)+
geom_line(aes(x=date,y=inpatient_bed_covid_utilization)) +
geom_line(aes(x=date,y=inpatient_beds_utilization))
hospitalizations_wa %>%
filter(date == max(date)) %>%
select(date,
inpatient_beds,
inpatient_beds_used,
inpatient_beds_utilization,
inpatient_bed_covid_utilization,
percent_of_inpatients_with_covid,
staffed_adult_icu_bed_occupancy,
staffed_icu_adult_patients_confirmed_and_suspected_covid,
total_adult_patients_hospitalized_confirmed_and_suspected_covid)
## Deaths
ggplot(hospitalizations_wa)+
#geom_line(aes(x=date,y=total_adult_patients_hospitalized_confirmed_and_suspected_covid))+
geom_line(aes(x=date,y=deaths_covid))
last_7_days_reported_deaths = sum(subset(hospitalizations_wa,date >= Sys.Date()-7,select=c(date,deaths_covid))$deaths_covid)
## Hospitalizations by age group
hospitalizations_wa %>%
# filter(date>='2021-05-01') %>%
select(c("state","date",starts_with("previous_day_admission_adult_covid"))) %>%
select(-c(ends_with("coverage"),"previous_day_admission_adult_covid_confirmed","previous_day_admission_adult_covid_suspected")) %>%
pivot_longer(cols=starts_with("previous_day_admission_adult_covid"),
names_to="age_range",
values_to = "admissions",
names_prefix = "previous_day_admission_adult_covid_") %>%
separate(age_range,c("type","age_range"),"ed_") %>%
# filter(type != "suspect") %>%
mutate(week_start=floor_date(date,'week')) %>%
group_by(week_start, age_range) %>%
summarise(weekly_admissions = sum(admissions)) %>%
ggplot(aes(x=week_start,y=weekly_admissions,fill=age_range,color=age_range)) +
geom_area() +
ylab("Weekly Admissions (confirmed and suspected)")
## Currently Hospitalized
hospitalizations_wa %>%
filter(date == max(date)) %>%
select(state,
date,
inpatient_beds,
inpatient_beds_used,
inpatient_beds_utilization, ## This should be a KPI card
inpatient_bed_covid_utilization, ## This should be a KPI card
percent_of_inpatients_with_covid,
total_adult_patients_hospitalized_confirmed_and_suspected_covid,
total_pediatric_patients_hospitalized_confirmed_and_suspected_covid,
total_staffed_adult_icu_beds,
staffed_adult_icu_bed_occupancy, ## This should be a KPI card
staffed_icu_adult_patients_confirmed_and_suspected_covid,
adult_icu_bed_covid_utilization) ## This should be a KPI card
### Hospitalized
hospitalizations_wa %>%
filter(date>='2021-08-01') %>%
select(state,
date,
inpatient_beds,
inpatient_beds_used,
inpatient_beds_used_covid) %>%
pivot_longer(cols=starts_with("inpatient"),
names_to="measure",
values_to = "beds",
names_prefix = "inpatient_") %>%
ggplot(aes(x=date,y=beds,color=measure)) +
geom_line()
hospitalizations_wa %>%
filter(date>='2021-01-01' & inpatient_beds_coverage > 98) %>%
select(state,
date,
inpatient_beds_utilization,
inpatient_bed_covid_utilization) %>%
pivot_longer(cols=any_of(c("inpatient_beds_utilization","inpatient_bed_covid_utilization","inpatient_beds_coverage")),
names_to="measure",
values_to = "utilization") %>%
ggplot(aes(x=date,y=utilization,color=measure)) +
geom_line()
hospitalizations_wa %>%
filter(date>='2021-01-01' & adult_icu_bed_utilization_coverage > 98) %>%
select(state,
date,
adult_icu_bed_utilization,
adult_icu_bed_covid_utilization) %>%
pivot_longer(cols=any_of(c("adult_icu_bed_utilization","adult_icu_bed_covid_utilization")),
names_to="measure",
values_to = "utilization") %>%
ggplot(aes(x=date,y=utilization,color=measure)) +
geom_line()
hospitalizations_wa %>%
filter(date>='2021-01-01' & inpatient_beds_coverage > 98) %>%
select(state,
date,
`Inpatient_beds utilized` = inpatient_beds_utilization,
`Inpatient_beds used for covid` = inpatient_bed_covid_utilization,
`Adult ICU_beds utilized` = adult_icu_bed_utilization,
`Adult ICU_beds used for covid` = adult_icu_bed_covid_utilization) %>%
pivot_longer(cols=any_of(c("Inpatient_beds utilized", "Inpatient_beds used for covid", "Adult ICU_beds utilized", "Adult ICU_beds used for covid")),
names_to="measure",
values_to = "utilization") %>%
separate(measure,into= c("bed_type","measure"),sep ="_") %>%
ggplot(aes(x=date,y=utilization,color=measure)) +
geom_line() +
scale_y_continuous(labels = scales::percent) +
scale_x_date(date_labels = "%b-%y",date_breaks = "1 month") +
ylab('') + xlab('') +
facet_wrap(~bed_type) +
theme_ipsum_rc() + theme(legend.title = element_blank())
### US Pediatric Hospitalizations by Percent
hospitalizations %>%
filter(date>='2021-01-01' & inpatient_beds_coverage > 98) %>%
select(state,
date,
total_adult_hospitalizations = total_adult_patients_hospitalized_confirmed_and_suspected_covid,
total_pediatric_hospitalizatons = total_pediatric_patients_hospitalized_confirmed_and_suspected_covid) %>%
mutate(pediatric_hospitalizations_percent = total_pediatric_hospitalizatons/(total_pediatric_hospitalizatons + total_adult_hospitalizations)) %>%
pivot_longer(cols=any_of(c("total_adult_hospitalizations", "total_pediatric_hospitalizatons","pediatric_hospitalizations_percent")),
names_to="measure",
values_to = "hospitalized") %>%
filter(measure == "pediatric_hospitalizations_percent") %>%
mutate(hospitalized = mean_run(hospitalized,k=7)) %>%
ggplot(aes(x=date,y=hospitalized,color=measure,fill=measure)) +
geom_area(alpha=0.3) +
# geom_bar(position="fill", stat="identity") +
scale_y_continuous(labels = scales::percent) +
scale_x_date(date_labels = "%b-%y",date_breaks = "1 month") +
ylab('') + xlab('') +
geofacet::facet_geo(~state)+
theme_ipsum_rc() + theme(legend.title = element_blank())
## Hospital Bed Utilization By State
hospitalizations %>%
filter(date>='2021-07-01') %>%
select(state,
date,
`Inpatient_beds utilized` = inpatient_beds_utilization,
`Inpatient_beds used for covid` = inpatient_bed_covid_utilization,
`Adult ICU_beds utilized` = adult_icu_bed_utilization,
`Adult ICU_beds used for covid` = adult_icu_bed_covid_utilization) %>%
pivot_longer(cols=any_of(c("Inpatient_beds utilized", "Adult ICU_beds utilized")),
names_to="measure",
values_to = "utilization") %>%
separate(measure,into= c("bed_type","measure"),sep ="_") %>%
ggplot(aes(x=date,y=utilization,color=bed_type)) +
geom_line() +
scale_y_continuous(labels = scales::percent, limits=c(NA,1)) +
scale_x_date(date_labels = "%b-%y",date_breaks = "1 month") +
ylab('') + xlab('') +
geofacet::facet_geo(~ state) +
theme_ft_rc() + theme(legend.title = element_blank())
## US Daily Hospitalizations
#confirmed only
hospitalizations %>%
filter(date >= '2020-06-01') %>%
select(date,previous_day_admission_adult_covid_confirmed,previous_day_admission_pediatric_covid_confirmed)%>%
group_by(date) %>%
summarise(previous_day_addmissions = sum(previous_day_admission_adult_covid_confirmed + previous_day_admission_pediatric_covid_confirmed)) %>%
ggplot(aes(x=date,y=previous_day_addmissions))+
geom_line() +
scale_y_continuous(labels = scales::comma) +
scale_x_date(date_labels = "%b-%y",date_breaks = "2 month") +
ylab('') + xlab('') +
labs(title="Previous Days Hospitalizations",caption="Includes both adult and pediatric admissions. Data from COVID-19 Reported Patient Impact and Hospital Capacity by State Timeseries https://healthdata.gov/resource/g62h-syeh") +
theme_ft_rc()
### Currently Hospitalized by State
hospitalizations %>%
filter(date == max(date)) %>%
select(state,
date,
total_adult_patients_hospitalized_confirmed_and_suspected_covid,
staffed_icu_adult_patients_confirmed_and_suspected_covid,
total_pediatric_patients_hospitalized_confirmed_and_suspected_covid) %>%
mutate(hospitalized = total_adult_patients_hospitalized_confirmed_and_suspected_covid + total_pediatric_patients_hospitalized_confirmed_and_suspected_covid + staffed_icu_adult_patients_confirmed_and_suspected_covid)
### Currently Hospitalized US
hospitalizations %>%
filter(date == max(date)) %>%
select(state,
date,
total_adult_patients_hospitalized_confirmed_and_suspected_covid,
staffed_icu_adult_patients_confirmed_and_suspected_covid,
total_pediatric_patients_hospitalized_confirmed_and_suspected_covid) %>%
mutate(hospitalized = +total_adult_patients_hospitalized_confirmed_and_suspected_covid + total_pediatric_patients_hospitalized_confirmed_and_suspected_covid + staffed_icu_adult_patients_confirmed_and_suspected_covid) %>%
summarise(hospitalized = sum(hospitalized))