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covid_calcs_oct5_week.R
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covid_calcs_oct5_week.R
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# Use Government API to download COVID case data
remotes::install_github("publichealthengland/coronavirus-dashboard-api-R-sdk")
# Use https://coronavirus.data.gov.uk/developers-guide#sdks
library(tidyverse)
library(ukcovid19)
cases_and_deaths = list(
date = "date",
areaName = "areaName",
areaCode = "areaCode",
newCasesBySpecimenDate = "newCasesBySpecimenDate",
cumCasesBySpecimenDate = "cumCasesBySpecimenDate",
cumCasesBySpecimenDateRate = "cumCasesBySpecimenDateRate",
newDeaths28DaysByPublishDate = "newDeaths28DaysByPublishDate",
cumDeaths28DaysByPublishDate = "cumDeaths28DaysByPublishDate",
cumDeaths28DaysByPublishDateRate = "cumDeaths28DaysByPublishDateRate"
)
cov_filters_oct2 <- c("areaType=ltla", "date=2020-10-02")
uk_covid_oct2 <- get_data(
filters = cov_filters_oct2,
structure = cases_and_deaths
)
cov_filters_oct3 <- c("areaType=ltla", "date=2020-10-03")
uk_covid_oct3 <- get_data(
filters = cov_filters_oct3,
structure = cases_and_deaths
)
cov_filters_oct4 <- c("areaType=ltla", "date=2020-10-04")
uk_covid_oct4 <- get_data(
filters = cov_filters_oct4,
structure = cases_and_deaths
)
cov_filters_oct5 <- c("areaType=ltla", "date=2020-10-05")
uk_covid_oct5 <- get_data(
filters = cov_filters_oct5,
structure = cases_and_deaths
)
cov_filters_sep24 <- c("areaType=ltla", "date=2020-09-24")
uk_covid_sep24 <- get_data(
filters = cov_filters_sep24,
structure = cases_and_deaths
)
cov_filters_sep25 <- c("areaType=ltla", "date=2020-09-25")
uk_covid_sep25 <- get_data(
filters = cov_filters_sep25,
structure = cases_and_deaths
)
cov_filters_sep26 <- c("areaType=ltla", "date=2020-09-26")
uk_covid_sep26 <- get_data(
filters = cov_filters_sep26,
structure = cases_and_deaths
)
cov_filters_sep27 <- c("areaType=ltla", "date=2020-09-27")
uk_covid_sep27 <- get_data(
filters = cov_filters_sep27,
structure = cases_and_deaths
)
cov_filters_sep28 <- c("areaType=ltla", "date=2020-09-28")
uk_covid_sep28 <- get_data(
filters = cov_filters_sep28,
structure = cases_and_deaths
)
cov_filters_sep29 <- c("areaType=ltla", "date=2020-09-29")
uk_covid_sep29 <- get_data(
filters = cov_filters_sep29,
structure = cases_and_deaths
)
cov_filters_sep30 <- c("areaType=ltla", "date=2020-09-30")
uk_covid_sep30 <- get_data(
filters = cov_filters_sep30,
structure = cases_and_deaths
)
cov_filters_oct1 <- c("areaType=ltla", "date=2020-10-01")
uk_covid_oct1 <- get_data(
filters = cov_filters_oct1,
structure = cases_and_deaths
)
uk_covid_data <- rbind( uk_covid_sep29, uk_covid_sep30, uk_covid_oct1, uk_covid_oct2, uk_covid_oct3, uk_covid_oct4, uk_covid_oct5)
uk_covid_data %>%
arrange(areaName, date) %>%
group_by(areaName) %>% View()
uk_covid_data <- uk_covid_data %>%
group_by(areaName) %>%
mutate(week_cases = sum(newCasesBySpecimenDate),
week_cases_rate = week_cases/(cumCasesBySpecimenDate/cumCasesBySpecimenDateRate),
week_deaths = sum(newDeaths28DaysByPublishDate) ) %>%
filter(date == "2020-10-05") %>%
select(-c(newCasesBySpecimenDate, newDeaths28DaysByPublishDate))
uk_covid_data %>%
filter(str_sub(areaCode, 1, 1)!="N") %>%
mutate(russell = if_else(areaName %in% c("Nottingham", "Newcastle upon Tyne", "Birmingham", "County Durham", "Manchester",
"Leeds", "Sheffield", "Southampton", "Exeter", "Liverpool", "Cardiff",
"City of Edinburgh", "Glasgow City", "Bristol, City of", "Coventry", "York"), "black", "grey")) %>%
ggplot(aes(x = cumCasesBySpecimenDateRate, y = week_cases_rate))+
geom_text(aes(label=areaName, color = russell))+
scale_color_manual(values = c("black", "grey"))+
xlab("Cumulative Case Rate per 100,000")+ylab("Weekly Case Rate per 100,000 (Oct 5)")+
theme_classic()+
theme(legend.position = "none")