R/england-covid-cases.R
In vfillis/automated-covid: What the Package Does (One Line, Title Case)
library(jsonlite)
library(httr)
library(dplyr)
library(zoo)
library(tidyr)
library(lubridate)
library(stringr)
# Covid cases in England
#' Extracts paginated data by requesting all of the pages
#' and combining the results.
#'
#' @param filters API filters. See the API documentations for
#' additional information.
#'
#' @param structure Structure parameter. See the API documentations
#' for additional information.
#'
#' @return list Comprehensive list of dictionaries containing all
#' the data for the given ``filter`` and ``structure`.`
get_paginated_data <- function (filters, structure) {
endpoint <- "https://api.coronavirus.data.gov.uk/v1/data"
results <- list()
current_page <- 1
repeat {
httr::GET(
url = endpoint,
query = list(
filters = paste(filters, collapse = ";"),
structure = jsonlite::toJSON(structure, auto_unbox = TRUE),
page = current_page
),
timeout(10)
) -> response
# Handle errors:
if ( response$status_code >= 400 ) {
err_msg = httr::http_status(response)
stop(err_msg)
} else if ( response$status_code == 204 ) {
break
}
# Convert response from binary to JSON:
json_text <- content(response, "text")
dt <- jsonlite::fromJSON(json_text)
results <- rbind(results, dt$data)
if ( is.null( dt$pagination$`next` ) ){
break
}
current_page <- current_page + 1;
}
return(results)
}
# Create filters:
query_filters <- c(
"areaType=nation"
)
# Create the structure as a list or a list of lists:
query_structure <- list(
date = "date",
name = "areaName",
code = "areaCode",
daily = "newCasesByPublishDate",
cumulative = "cumCasesByPublishDate"
)
cases <- get_paginated_data(query_filters, query_structure)
list(
"Shape" = dim(cases),
"Data (first 3 items)" = cases[0:3, 0:-1]
) -> report
print(report)
#Rename columns for better distinction
cases <- cases %>%
rename(
`Daily cases by date reported` = daily,
`Cumulative cases by date reported` = cumulative
)
cases.England <- subset(cases, cases$name == "England")
cases.England <- cases.England %>%
#dplyr::arrange(desc(date)) %>%
dplyr::mutate(`Seven-day average` = zoo::rollmean(`Daily cases by date reported`, k = 7, align="left", fill = NA))
write.csv(cases.England, file = "data/cases-England.csv")
# Covid deaths in England
#Deaths within 28 days of positive test by date reported
#' Extracts paginated data by requesting all of the pages
#' and combining the results.
#'
#' @param filters API filters. See the API documentations for
#' additional information.
#'
#' @param structure Structure parameter. See the API documentations
#' for additional information.
#'
#' @return list Comprehensive list of dictionaries containing all
#' the data for the given ``filter`` and ``structure`.`
get_paginated_data <- function (filters, structure) {
endpoint <- "https://api.coronavirus.data.gov.uk/v1/data"
results <- list()
current_page <- 1
repeat {
httr::GET(
url = endpoint,
query = list(
filters = paste(filters, collapse = ";"),
structure = jsonlite::toJSON(structure, auto_unbox = TRUE),
page = current_page
),
timeout(10)
) -> response
# Handle errors:
if ( response$status_code >= 400 ) {
err_msg = httr::http_status(response)
stop(err_msg)
} else if ( response$status_code == 204 ) {
break
}
# Convert response from binary to JSON:
json_text <- content(response, "text")
dt <- jsonlite::fromJSON(json_text)
results <- rbind(results, dt$data)
if ( is.null( dt$pagination$`next` ) ){
break
}
current_page <- current_page + 1;
}
return(results)
}
# Create filters:
query_filters <- c(
"areaType=nation",
"areaName=England"
)
# Create the structure as a list or a list of lists:
query_structure <- list(
date = "date",
name = "areaName",
code = "areaCode",
daily = "newDeaths28DaysByPublishDate",
cumulative = "cumDeaths28DaysByPublishDate"
)
deaths.England <- get_paginated_data(query_filters, query_structure)
list(
"Shape" = dim(deaths.England),
"Data (first 3 items)" = deaths.England[0:3, 0:-1]
) -> report
print(report)
#Rename columns for better distinction
deaths.England <- deaths.England %>%
rename(
`Daily deaths by date reported` = daily,
`Cumulative deaths by date reported` = cumulative
)
deaths.England <- deaths.England %>%
dplyr::mutate(`Seven-day average` = zoo::rollmean(`Daily deaths by date reported`, k = 7, align="left", fill = NA))
write.csv(deaths.England, file = "data/deaths-England.csv")
# Covid hospitalisations in England
# includes:
# - New admissions: Daily numbers of COVID-19 patients admitted to hospital
# - COVID-19 occupied beds with mechanical ventilators: Confirmed COVID-19 patients in mechanical ventilation beds
# - Hospital cases: Daily count of confirmed COVID-19 patients in hospital at midnight the preceding night
#' Extracts paginated data by requesting all of the pages
#' and combining the results.
#'
#' @param filters API filters. See the API documentations for
#' additional information.
#'
#' @param structure Structure parameter. See the API documentations
#' for additional information.
#'
#' @return list Comprehensive list of dictionaries containing all
#' the data for the given ``filter`` and ``structure`.`
get_paginated_data <- function (filters, structure) {
endpoint <- "https://api.coronavirus.data.gov.uk/v1/data"
results <- list()
current_page <- 1
repeat {
httr::GET(
url = endpoint,
query = list(
filters = paste(filters, collapse = ";"),
structure = jsonlite::toJSON(structure, auto_unbox = TRUE),
page = current_page
),
timeout(10)
) -> response
# Handle errors:
if ( response$status_code >= 400 ) {
err_msg = httr::http_status(response)
stop(err_msg)
} else if ( response$status_code == 204 ) {
break
}
# Convert response from binary to JSON:
json_text <- content(response, "text")
dt <- jsonlite::fromJSON(json_text)
results <- rbind(results, dt$data)
if ( is.null( dt$pagination$`next` ) ){
break
}
current_page <- current_page + 1;
}
return(results)
}
# Create filters:
query_filters <- c(
"areaType=nation",
"areaName=England"
)
# Create the structure as a list or a list of lists:
query_structure <- list(
date = "date",
name = "areaName",
code = "areaCode",
admissions = "newAdmissions",
mvbeds = "covidOccupiedMVBeds",
hospcases = "hospitalCases"
)
healthcare.England <- get_paginated_data(query_filters, query_structure)
list(
"Shape" = dim(healthcare.England),
"Data (first 3 items)" = healthcare.England[0:3, 0:-1]
) -> report
print(report)
#Rename columns for better distinction
healthcare.England <- healthcare.England %>%
rename(
`Daily new hospital admissions` = admissions,
`Daily people in MV beds` = mvbeds,
`Daily people in hospital with coronavirus` = hospcases
)
healthcare.England <- healthcare.England %>%
dplyr::mutate(`Seven-day average hospital admissions` = zoo::rollmean(`Daily new hospital admissions`, k = 7, align="left", fill = NA)) %>%
dplyr::mutate(`Seven-day average MV beds` = zoo::rollmean(`Daily people in MV beds`, k = 7, align="left", fill = NA)) %>%
dplyr::mutate(`Seven-day average hospital cases` = zoo::rollmean(`Daily people in hospital with coronavirus`, k = 7, align="left", fill = NA))
write.csv(healthcare.England, file = "data/healthcare-England.csv")
# Covid vaccinations in England: people vaccinated each day
#' Extracts paginated data by requesting all of the pages
#' and combining the results.
#'
#' @param filters API filters. See the API documentations for
#' additional information.
#'
#' @param structure Structure parameter. See the API documentations
#' for additional information.
#'
#' @return list Comprehensive list of dictionaries containing all
#' the data for the given ``filter`` and ``structure`.`
get_paginated_data <- function (filters, structure) {
endpoint <- "https://api.coronavirus.data.gov.uk/v1/data"
results <- list()
current_page <- 1
repeat {
httr::GET(
url = endpoint,
query = list(
filters = paste(filters, collapse = ";"),
structure = jsonlite::toJSON(structure, auto_unbox = TRUE),
page = current_page
),
timeout(10)
) -> response
# Handle errors:
if ( response$status_code >= 400 ) {
err_msg = httr::http_status(response)
stop(err_msg)
} else if ( response$status_code == 204 ) {
break
}
# Convert response from binary to JSON:
json_text <- content(response, "text")
dt <- jsonlite::fromJSON(json_text)
results <- rbind(results, dt$data)
if ( is.null( dt$pagination$`next` ) ){
break
}
current_page <- current_page + 1;
}
return(results)
}
# Create filters:
query_filters <- c(
"areaType=nation",
"areaName=England"
)
# Create the structure as a list or a list of lists:
query_structure <- list(
date = "date",
name = "areaName",
#code = "areaCode"
vaccination.first = "newPeopleVaccinatedFirstDoseByPublishDate",
vaccination.second = "newPeopleVaccinatedSecondDoseByPublishDate",
vaccination.third = "newPeopleVaccinatedThirdInjectionByPublishDate"
)
vaccinations.England <- get_paginated_data(query_filters, query_structure)
list(
"Shape" = dim(vaccinations.England),
"Data (first 3 items)" = vaccinations.England[0:3, 0:-1]
) -> report
print(report)
#Rename columns for better distinction
vaccinations.England <- vaccinations.England %>%
rename(
`First dose` = vaccination.first,
`Second dose` = vaccination.second,
`Third dose` = vaccination.third
)
write.csv(vaccinations.England, file = "data/vaccinations-England.csv")
# Covid vaccinations in England: percentage per dose per age group
#' Extracts paginated data by requesting all of the pages
#' and combining the results.
#'
#' @param filters API filters. See the API documentations for
#' additional information.
#'
#' @param structure Structure parameter. See the API documentations
#' for additional information.
#'
#' @return list Comprehensive list of dictionaries containing all
#' the data for the given ``filter`` and ``structure`.`
get_paginated_data <- function (filters, structure) {
endpoint <- "https://api.coronavirus.data.gov.uk/v1/data"
results <- list()
current_page <- 1
repeat {
httr::GET(
url = endpoint,
query = list(
filters = paste(filters, collapse = ";"),
structure = jsonlite::toJSON(structure, auto_unbox = TRUE),
page = current_page
),
timeout(10)
) -> response
# Handle errors:
if ( response$status_code >= 400 ) {
err_msg = httr::http_status(response)
stop(err_msg)
} else if ( response$status_code == 204 ) {
break
}
# Convert response from binary to JSON:
json_text <- content(response, "text")
dt <- jsonlite::fromJSON(json_text)
results <- rbind(results, dt$data)
if ( is.null( dt$pagination$`next` ) ){
break
}
current_page <- current_page + 1;
}
return(results)
}
# Create filters:
query_filters <- c(
"areaType=nation",
"areaName=England"
)
# Create the structure as a list or a list of lists:
query_structure <- list(
date = "date",
name = "areaName",
#code = "areaCode"
vaccination.ages = "vaccinationsAgeDemographics"
)
vaccinations.England.ages <- get_paginated_data(query_filters, query_structure)
list(
"Shape" = dim(vaccinations.England.ages),
"Data (first 3 items)" = vaccinations.England.ages[0:3, 0:-1]
) -> report
print(report)
## Unnest the data frame
vaccinations.England.ages <- vaccinations.England.ages %>%
unnest(vaccination.ages)
# Filter for latest data
latest.date = max(vaccinations.England.ages$date)
vaccinations.England.ages.latest <- subset(vaccinations.England.ages, vaccinations.England.ages$date == latest.date)
## Delete unnecessary columns
vaccinations.England.ages.latest <- vaccinations.England.ages.latest[-c(6,8,10,12,12:16)]
## First dose only
vaccinations.England.ages.latest$FirstDoseOnly <- vaccinations.England.ages.latest$cumPeopleVaccinatedFirstDoseByVaccinationDate - vaccinations.England.ages.latest$cumPeopleVaccinatedSecondDoseByVaccinationDate
# No vaccine
vaccinations.England.ages.latest$NoVaccine <- vaccinations.England.ages.latest$VaccineRegisterPopulationByVaccinationDate - vaccinations.England.ages.latest$cumPeopleVaccinatedFirstDoseByVaccinationDate
# Second dose only
vaccinations.England.ages.latest$SecondDoseOnly <- vaccinations.England.ages.latest$cumPeopleVaccinatedSecondDoseByVaccinationDate - vaccinations.England.ages.latest$cumPeopleVaccinatedThirdInjectionByVaccinationDate
# Combine age groups
# our age groups: 12-15, 16-17, 18-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80-89, 90+
# 12 to 15
vaccinations.12to15 <- subset(vaccinations.England.ages.latest, vaccinations.England.ages.latest$age == "12_15")
vaccinations.12to15 <- vaccinations.12to15 %>%
rename(
sum.vaccineRegister = VaccineRegisterPopulationByVaccinationDate,
sum.firstDoseOnly = FirstDoseOnly,
sum.secondDose = cumPeopleVaccinatedSecondDoseByVaccinationDate,
sum.secondDoseOnly = SecondDoseOnly,
sum.thirdDose = cumPeopleVaccinatedThirdInjectionByVaccinationDate,
sum.noVaccine = NoVaccine
) %>%
mutate_at("age", str_replace, "12_15", "12-15") %>%
select(-c(5,6))
# 16 to 17
vaccinations.16to17 <- subset(vaccinations.England.ages.latest, vaccinations.England.ages.latest$age == "16_17")
vaccinations.16to17 <- vaccinations.16to17 %>%
rename(
sum.vaccineRegister = VaccineRegisterPopulationByVaccinationDate,
sum.firstDoseOnly = FirstDoseOnly,
sum.secondDose = cumPeopleVaccinatedSecondDoseByVaccinationDate,
sum.secondDoseOnly = SecondDoseOnly,
sum.thirdDose = cumPeopleVaccinatedThirdInjectionByVaccinationDate,
sum.noVaccine = NoVaccine
) %>%
mutate_at("age", str_replace, "16_17", "16-17") %>%
select(-c(5,6))
# 18 to 29
vaccinations.18to29 <- subset(vaccinations.England.ages.latest, vaccinations.England.ages.latest$age == "18_24" | vaccinations.England.ages.latest$age == "25_29")
vaccinations.18to29 <- vaccinations.18to29 %>%
group_by(date) %>%
mutate(sum.vaccineRegister = sum(VaccineRegisterPopulationByVaccinationDate)) %>%
mutate(sum.secondDose = sum(cumPeopleVaccinatedSecondDoseByVaccinationDate)) %>%
mutate(sum.thirdDose = sum(cumPeopleVaccinatedThirdInjectionByVaccinationDate)) %>%
mutate(sum.firstDoseOnly = sum(FirstDoseOnly)) %>%
mutate(sum.noVaccine = sum(NoVaccine)) %>%
mutate(sum.secondDoseOnly = sum(SecondDoseOnly)) %>%
slice(-1) %>%
mutate_at("age", str_replace, "25_29", "18-29") %>%
select(-c(4,5,6,7,8,9,10,11))
# 30 to 39
vaccinations.30to39 <- subset(vaccinations.England.ages.latest, vaccinations.England.ages.latest$age == "30_34" | vaccinations.England.ages.latest$age == "35_39")
vaccinations.30to39 <- vaccinations.30to39 %>%
group_by(date) %>%
mutate(sum.vaccineRegister = sum(VaccineRegisterPopulationByVaccinationDate)) %>%
mutate(sum.secondDose = sum(cumPeopleVaccinatedSecondDoseByVaccinationDate)) %>%
mutate(sum.thirdDose = sum(cumPeopleVaccinatedThirdInjectionByVaccinationDate)) %>%
mutate(sum.firstDoseOnly = sum(FirstDoseOnly)) %>%
mutate(sum.noVaccine = sum(NoVaccine)) %>%
mutate(sum.secondDoseOnly = sum(SecondDoseOnly)) %>%
slice(-1) %>%
mutate_at("age", str_replace, "35_39", "30-39") %>%
select(-c(4,5,6,7,8,9,10,11))
# 40 to 49
vaccinations.40to49 <- subset(vaccinations.England.ages.latest, vaccinations.England.ages.latest$age == "40_44" | vaccinations.England.ages.latest$age == "45_49")
vaccinations.40to49 <- vaccinations.40to49 %>%
group_by(date) %>%
mutate(sum.vaccineRegister = sum(VaccineRegisterPopulationByVaccinationDate)) %>%
mutate(sum.secondDose = sum(cumPeopleVaccinatedSecondDoseByVaccinationDate)) %>%
mutate(sum.thirdDose = sum(cumPeopleVaccinatedThirdInjectionByVaccinationDate)) %>%
mutate(sum.firstDoseOnly = sum(FirstDoseOnly)) %>%
mutate(sum.noVaccine = sum(NoVaccine)) %>%
mutate(sum.secondDoseOnly = sum(SecondDoseOnly)) %>%
slice(-1) %>%
mutate_at("age", str_replace, "45_49", "40-49") %>%
select(-c(4,5,6,7,8,9,10,11))
# 50 to 59
vaccinations.50to59 <- subset(vaccinations.England.ages.latest, vaccinations.England.ages.latest$age == "50_54" | vaccinations.England.ages.latest$age == "55_59")
vaccinations.50to59 <- vaccinations.50to59 %>%
group_by(date) %>%
mutate(sum.vaccineRegister = sum(VaccineRegisterPopulationByVaccinationDate)) %>%
mutate(sum.secondDose = sum(cumPeopleVaccinatedSecondDoseByVaccinationDate)) %>%
mutate(sum.thirdDose = sum(cumPeopleVaccinatedThirdInjectionByVaccinationDate)) %>%
mutate(sum.firstDoseOnly = sum(FirstDoseOnly)) %>%
mutate(sum.noVaccine = sum(NoVaccine)) %>%
mutate(sum.secondDoseOnly = sum(SecondDoseOnly)) %>%
slice(-1) %>%
mutate_at("age", str_replace, "55_59", "50-59") %>%
select(-c(4,5,6,7,8,9,10,11))
# 60 to 69
vaccinations.60to69 <- subset(vaccinations.England.ages.latest, vaccinations.England.ages.latest$age == "60_64" | vaccinations.England.ages.latest$age == "65_69")
vaccinations.60to69 <- vaccinations.60to69 %>%
group_by(date) %>%
mutate(sum.vaccineRegister = sum(VaccineRegisterPopulationByVaccinationDate)) %>%
mutate(sum.secondDose = sum(cumPeopleVaccinatedSecondDoseByVaccinationDate)) %>%
mutate(sum.thirdDose = sum(cumPeopleVaccinatedThirdInjectionByVaccinationDate)) %>%
mutate(sum.firstDoseOnly = sum(FirstDoseOnly)) %>%
mutate(sum.noVaccine = sum(NoVaccine)) %>%
mutate(sum.secondDoseOnly = sum(SecondDoseOnly)) %>%
slice(-1) %>%
mutate_at("age", str_replace, "65_69", "60-69") %>%
select(-c(4,5,6,7,8,9,10,11))
# 70 to 79
vaccinations.70to79 <- subset(vaccinations.England.ages.latest, vaccinations.England.ages.latest$age == "70_74" | vaccinations.England.ages.latest$age == "75_79")
vaccinations.70to79 <- vaccinations.70to79 %>%
group_by(date) %>%
mutate(sum.vaccineRegister = sum(VaccineRegisterPopulationByVaccinationDate)) %>%
mutate(sum.secondDose = sum(cumPeopleVaccinatedSecondDoseByVaccinationDate)) %>%
mutate(sum.thirdDose = sum(cumPeopleVaccinatedThirdInjectionByVaccinationDate)) %>%
mutate(sum.firstDoseOnly = sum(FirstDoseOnly)) %>%
mutate(sum.noVaccine = sum(NoVaccine)) %>%
mutate(sum.secondDoseOnly = sum(SecondDoseOnly)) %>%
slice(-1) %>%
mutate_at("age", str_replace, "75_79", "70-79") %>%
select(-c(4,5,6,7,8,9,10,11))
# 80 to 89
vaccinations.80to89 <- subset(vaccinations.England.ages.latest, vaccinations.England.ages.latest$age == "80_84" | vaccinations.England.ages.latest$age == "85_89")
vaccinations.80to89 <- vaccinations.80to89 %>%
group_by(date) %>%
mutate(sum.vaccineRegister = sum(VaccineRegisterPopulationByVaccinationDate)) %>%
mutate(sum.secondDose = sum(cumPeopleVaccinatedSecondDoseByVaccinationDate)) %>%
mutate(sum.thirdDose = sum(cumPeopleVaccinatedThirdInjectionByVaccinationDate)) %>%
mutate(sum.firstDoseOnly = sum(FirstDoseOnly)) %>%
mutate(sum.noVaccine = sum(NoVaccine)) %>%
mutate(sum.secondDoseOnly = sum(SecondDoseOnly)) %>%
slice(-1) %>%
mutate_at("age", str_replace, "85_89", "80-89") %>%
select(-c(4,5,6,7,8,9,10,11))
# 90+
vaccinations.90 <- subset(vaccinations.England.ages.latest, vaccinations.England.ages.latest$age == "90+")
vaccinations.90 <- vaccinations.90 %>%
rename(
sum.vaccineRegister = VaccineRegisterPopulationByVaccinationDate,
sum.firstDoseOnly = FirstDoseOnly,
sum.secondDose = cumPeopleVaccinatedSecondDoseByVaccinationDate,
sum.secondDoseOnly = SecondDoseOnly,
sum.thirdDose = cumPeopleVaccinatedThirdInjectionByVaccinationDate,
sum.noVaccine = NoVaccine
) %>%
select(-c(5,6))
# Merge rows
vaccinations.England.grouped <- rbind(vaccinations.12to15, vaccinations.16to17, vaccinations.18to29, vaccinations.30to39, vaccinations.40to49, vaccinations.50to59, vaccinations.60to69, vaccinations.70to79, vaccinations.80to89, vaccinations.90)
# calculate %
vaccinations.England.grouped$`Three doses` <- vaccinations.England.grouped$sum.thirdDose / vaccinations.England.grouped$sum.vaccineRegister *100
vaccinations.England.grouped$`Two doses only` <- vaccinations.England.grouped$sum.secondDoseOnly / vaccinations.England.grouped$sum.vaccineRegister *100
vaccinations.England.grouped$`First dose only` <- vaccinations.England.grouped$sum.firstDoseOnly / vaccinations.England.grouped$sum.vaccineRegister *100
vaccinations.England.grouped$`No vaccine` <- vaccinations.England.grouped$sum.noVaccine / vaccinations.England.grouped$sum.vaccineRegister *100
vaccinations.England.grouped$`Two doses` <- vaccinations.England.grouped$sum.secondDose / vaccinations.England.grouped$sum.vaccineRegister *100
# export!!!!
write.csv(vaccinations.England.grouped, file = "data/vaccinations-England-ages.csv")
vfillis/automated-covid documentation built on July 1, 2022, 9:53 p.m.
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library(jsonlite)
library(httr)
library(dplyr)
library(zoo)
library(tidyr)
library(lubridate)
library(stringr)
# Covid cases in England
#' Extracts paginated data by requesting all of the pages
#' and combining the results.
#'
#' @param filters API filters. See the API documentations for
#' additional information.
#'
#' @param structure Structure parameter. See the API documentations
#' for additional information.
#'
#' @return list Comprehensive list of dictionaries containing all
#' the data for the given ``filter`` and ``structure`.`
get_paginated_data <- function (filters, structure) {
endpoint <- "https://api.coronavirus.data.gov.uk/v1/data"
results <- list()
current_page <- 1
repeat {
httr::GET(
url = endpoint,
query = list(
filters = paste(filters, collapse = ";"),
structure = jsonlite::toJSON(structure, auto_unbox = TRUE),
page = current_page
),
timeout(10)
) -> response
# Handle errors:
if ( response$status_code >= 400 ) {
err_msg = httr::http_status(response)
stop(err_msg)
} else if ( response$status_code == 204 ) {
break
}
# Convert response from binary to JSON:
json_text <- content(response, "text")
dt <- jsonlite::fromJSON(json_text)
results <- rbind(results, dt$data)
if ( is.null( dt$pagination$`next` ) ){
break
}
current_page <- current_page + 1;
}
return(results)
}
# Create filters:
query_filters <- c(
"areaType=nation"
)
# Create the structure as a list or a list of lists:
query_structure <- list(
date = "date",
name = "areaName",
code = "areaCode",
daily = "newCasesByPublishDate",
cumulative = "cumCasesByPublishDate"
)
cases <- get_paginated_data(query_filters, query_structure)
list(
"Shape" = dim(cases),
"Data (first 3 items)" = cases[0:3, 0:-1]
) -> report
print(report)
#Rename columns for better distinction
cases <- cases %>%
rename(
`Daily cases by date reported` = daily,
`Cumulative cases by date reported` = cumulative
)
cases.England <- subset(cases, cases$name == "England")
cases.England <- cases.England %>%
#dplyr::arrange(desc(date)) %>%
dplyr::mutate(`Seven-day average` = zoo::rollmean(`Daily cases by date reported`, k = 7, align="left", fill = NA))
write.csv(cases.England, file = "data/cases-England.csv")
# Covid deaths in England
#Deaths within 28 days of positive test by date reported
#' Extracts paginated data by requesting all of the pages
#' and combining the results.
#'
#' @param filters API filters. See the API documentations for
#' additional information.
#'
#' @param structure Structure parameter. See the API documentations
#' for additional information.
#'
#' @return list Comprehensive list of dictionaries containing all
#' the data for the given ``filter`` and ``structure`.`
get_paginated_data <- function (filters, structure) {
endpoint <- "https://api.coronavirus.data.gov.uk/v1/data"
results <- list()
current_page <- 1
repeat {
httr::GET(
url = endpoint,
query = list(
filters = paste(filters, collapse = ";"),
structure = jsonlite::toJSON(structure, auto_unbox = TRUE),
page = current_page
),
timeout(10)
) -> response
# Handle errors:
if ( response$status_code >= 400 ) {
err_msg = httr::http_status(response)
stop(err_msg)
} else if ( response$status_code == 204 ) {
break
}
# Convert response from binary to JSON:
json_text <- content(response, "text")
dt <- jsonlite::fromJSON(json_text)
results <- rbind(results, dt$data)
if ( is.null( dt$pagination$`next` ) ){
break
}
current_page <- current_page + 1;
}
return(results)
}
# Create filters:
query_filters <- c(
"areaType=nation",
"areaName=England"
)
# Create the structure as a list or a list of lists:
query_structure <- list(
date = "date",
name = "areaName",
code = "areaCode",
daily = "newDeaths28DaysByPublishDate",
cumulative = "cumDeaths28DaysByPublishDate"
)
deaths.England <- get_paginated_data(query_filters, query_structure)
list(
"Shape" = dim(deaths.England),
"Data (first 3 items)" = deaths.England[0:3, 0:-1]
) -> report
print(report)
#Rename columns for better distinction
deaths.England <- deaths.England %>%
rename(
`Daily deaths by date reported` = daily,
`Cumulative deaths by date reported` = cumulative
)
deaths.England <- deaths.England %>%
dplyr::mutate(`Seven-day average` = zoo::rollmean(`Daily deaths by date reported`, k = 7, align="left", fill = NA))
write.csv(deaths.England, file = "data/deaths-England.csv")
# Covid hospitalisations in England
# includes:
# - New admissions: Daily numbers of COVID-19 patients admitted to hospital
# - COVID-19 occupied beds with mechanical ventilators: Confirmed COVID-19 patients in mechanical ventilation beds
# - Hospital cases: Daily count of confirmed COVID-19 patients in hospital at midnight the preceding night
#' Extracts paginated data by requesting all of the pages
#' and combining the results.
#'
#' @param filters API filters. See the API documentations for
#' additional information.
#'
#' @param structure Structure parameter. See the API documentations
#' for additional information.
#'
#' @return list Comprehensive list of dictionaries containing all
#' the data for the given ``filter`` and ``structure`.`
get_paginated_data <- function (filters, structure) {
endpoint <- "https://api.coronavirus.data.gov.uk/v1/data"
results <- list()
current_page <- 1
repeat {
httr::GET(
url = endpoint,
query = list(
filters = paste(filters, collapse = ";"),
structure = jsonlite::toJSON(structure, auto_unbox = TRUE),
page = current_page
),
timeout(10)
) -> response
# Handle errors:
if ( response$status_code >= 400 ) {
err_msg = httr::http_status(response)
stop(err_msg)
} else if ( response$status_code == 204 ) {
break
}
# Convert response from binary to JSON:
json_text <- content(response, "text")
dt <- jsonlite::fromJSON(json_text)
results <- rbind(results, dt$data)
if ( is.null( dt$pagination$`next` ) ){
break
}
current_page <- current_page + 1;
}
return(results)
}
# Create filters:
query_filters <- c(
"areaType=nation",
"areaName=England"
)
# Create the structure as a list or a list of lists:
query_structure <- list(
date = "date",
name = "areaName",
code = "areaCode",
admissions = "newAdmissions",
mvbeds = "covidOccupiedMVBeds",
hospcases = "hospitalCases"
)
healthcare.England <- get_paginated_data(query_filters, query_structure)
list(
"Shape" = dim(healthcare.England),
"Data (first 3 items)" = healthcare.England[0:3, 0:-1]
) -> report
print(report)
#Rename columns for better distinction
healthcare.England <- healthcare.England %>%
rename(
`Daily new hospital admissions` = admissions,
`Daily people in MV beds` = mvbeds,
`Daily people in hospital with coronavirus` = hospcases
)
healthcare.England <- healthcare.England %>%
dplyr::mutate(`Seven-day average hospital admissions` = zoo::rollmean(`Daily new hospital admissions`, k = 7, align="left", fill = NA)) %>%
dplyr::mutate(`Seven-day average MV beds` = zoo::rollmean(`Daily people in MV beds`, k = 7, align="left", fill = NA)) %>%
dplyr::mutate(`Seven-day average hospital cases` = zoo::rollmean(`Daily people in hospital with coronavirus`, k = 7, align="left", fill = NA))
write.csv(healthcare.England, file = "data/healthcare-England.csv")
# Covid vaccinations in England: people vaccinated each day
#' Extracts paginated data by requesting all of the pages
#' and combining the results.
#'
#' @param filters API filters. See the API documentations for
#' additional information.
#'
#' @param structure Structure parameter. See the API documentations
#' for additional information.
#'
#' @return list Comprehensive list of dictionaries containing all
#' the data for the given ``filter`` and ``structure`.`
get_paginated_data <- function (filters, structure) {
endpoint <- "https://api.coronavirus.data.gov.uk/v1/data"
results <- list()
current_page <- 1
repeat {
httr::GET(
url = endpoint,
query = list(
filters = paste(filters, collapse = ";"),
structure = jsonlite::toJSON(structure, auto_unbox = TRUE),
page = current_page
),
timeout(10)
) -> response
# Handle errors:
if ( response$status_code >= 400 ) {
err_msg = httr::http_status(response)
stop(err_msg)
} else if ( response$status_code == 204 ) {
break
}
# Convert response from binary to JSON:
json_text <- content(response, "text")
dt <- jsonlite::fromJSON(json_text)
results <- rbind(results, dt$data)
if ( is.null( dt$pagination$`next` ) ){
break
}
current_page <- current_page + 1;
}
return(results)
}
# Create filters:
query_filters <- c(
"areaType=nation",
"areaName=England"
)
# Create the structure as a list or a list of lists:
query_structure <- list(
date = "date",
name = "areaName",
#code = "areaCode"
vaccination.first = "newPeopleVaccinatedFirstDoseByPublishDate",
vaccination.second = "newPeopleVaccinatedSecondDoseByPublishDate",
vaccination.third = "newPeopleVaccinatedThirdInjectionByPublishDate"
)
vaccinations.England <- get_paginated_data(query_filters, query_structure)
list(
"Shape" = dim(vaccinations.England),
"Data (first 3 items)" = vaccinations.England[0:3, 0:-1]
) -> report
print(report)
#Rename columns for better distinction
vaccinations.England <- vaccinations.England %>%
rename(
`First dose` = vaccination.first,
`Second dose` = vaccination.second,
`Third dose` = vaccination.third
)
write.csv(vaccinations.England, file = "data/vaccinations-England.csv")
# Covid vaccinations in England: percentage per dose per age group
#' Extracts paginated data by requesting all of the pages
#' and combining the results.
#'
#' @param filters API filters. See the API documentations for
#' additional information.
#'
#' @param structure Structure parameter. See the API documentations
#' for additional information.
#'
#' @return list Comprehensive list of dictionaries containing all
#' the data for the given ``filter`` and ``structure`.`
get_paginated_data <- function (filters, structure) {
endpoint <- "https://api.coronavirus.data.gov.uk/v1/data"
results <- list()
current_page <- 1
repeat {
httr::GET(
url = endpoint,
query = list(
filters = paste(filters, collapse = ";"),
structure = jsonlite::toJSON(structure, auto_unbox = TRUE),
page = current_page
),
timeout(10)
) -> response
# Handle errors:
if ( response$status_code >= 400 ) {
err_msg = httr::http_status(response)
stop(err_msg)
} else if ( response$status_code == 204 ) {
break
}
# Convert response from binary to JSON:
json_text <- content(response, "text")
dt <- jsonlite::fromJSON(json_text)
results <- rbind(results, dt$data)
if ( is.null( dt$pagination$`next` ) ){
break
}
current_page <- current_page + 1;
}
return(results)
}
# Create filters:
query_filters <- c(
"areaType=nation",
"areaName=England"
)
# Create the structure as a list or a list of lists:
query_structure <- list(
date = "date",
name = "areaName",
#code = "areaCode"
vaccination.ages = "vaccinationsAgeDemographics"
)
vaccinations.England.ages <- get_paginated_data(query_filters, query_structure)
list(
"Shape" = dim(vaccinations.England.ages),
"Data (first 3 items)" = vaccinations.England.ages[0:3, 0:-1]
) -> report
print(report)
## Unnest the data frame
vaccinations.England.ages <- vaccinations.England.ages %>%
unnest(vaccination.ages)
# Filter for latest data
latest.date = max(vaccinations.England.ages$date)
vaccinations.England.ages.latest <- subset(vaccinations.England.ages, vaccinations.England.ages$date == latest.date)
## Delete unnecessary columns
vaccinations.England.ages.latest <- vaccinations.England.ages.latest[-c(6,8,10,12,12:16)]
## First dose only
vaccinations.England.ages.latest$FirstDoseOnly <- vaccinations.England.ages.latest$cumPeopleVaccinatedFirstDoseByVaccinationDate - vaccinations.England.ages.latest$cumPeopleVaccinatedSecondDoseByVaccinationDate
# No vaccine
vaccinations.England.ages.latest$NoVaccine <- vaccinations.England.ages.latest$VaccineRegisterPopulationByVaccinationDate - vaccinations.England.ages.latest$cumPeopleVaccinatedFirstDoseByVaccinationDate
# Second dose only
vaccinations.England.ages.latest$SecondDoseOnly <- vaccinations.England.ages.latest$cumPeopleVaccinatedSecondDoseByVaccinationDate - vaccinations.England.ages.latest$cumPeopleVaccinatedThirdInjectionByVaccinationDate
# Combine age groups
# our age groups: 12-15, 16-17, 18-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80-89, 90+
# 12 to 15
vaccinations.12to15 <- subset(vaccinations.England.ages.latest, vaccinations.England.ages.latest$age == "12_15")
vaccinations.12to15 <- vaccinations.12to15 %>%
rename(
sum.vaccineRegister = VaccineRegisterPopulationByVaccinationDate,
sum.firstDoseOnly = FirstDoseOnly,
sum.secondDose = cumPeopleVaccinatedSecondDoseByVaccinationDate,
sum.secondDoseOnly = SecondDoseOnly,
sum.thirdDose = cumPeopleVaccinatedThirdInjectionByVaccinationDate,
sum.noVaccine = NoVaccine
) %>%
mutate_at("age", str_replace, "12_15", "12-15") %>%
select(-c(5,6))
# 16 to 17
vaccinations.16to17 <- subset(vaccinations.England.ages.latest, vaccinations.England.ages.latest$age == "16_17")
vaccinations.16to17 <- vaccinations.16to17 %>%
rename(
sum.vaccineRegister = VaccineRegisterPopulationByVaccinationDate,
sum.firstDoseOnly = FirstDoseOnly,
sum.secondDose = cumPeopleVaccinatedSecondDoseByVaccinationDate,
sum.secondDoseOnly = SecondDoseOnly,
sum.thirdDose = cumPeopleVaccinatedThirdInjectionByVaccinationDate,
sum.noVaccine = NoVaccine
) %>%
mutate_at("age", str_replace, "16_17", "16-17") %>%
select(-c(5,6))
# 18 to 29
vaccinations.18to29 <- subset(vaccinations.England.ages.latest, vaccinations.England.ages.latest$age == "18_24" | vaccinations.England.ages.latest$age == "25_29")
vaccinations.18to29 <- vaccinations.18to29 %>%
group_by(date) %>%
mutate(sum.vaccineRegister = sum(VaccineRegisterPopulationByVaccinationDate)) %>%
mutate(sum.secondDose = sum(cumPeopleVaccinatedSecondDoseByVaccinationDate)) %>%
mutate(sum.thirdDose = sum(cumPeopleVaccinatedThirdInjectionByVaccinationDate)) %>%
mutate(sum.firstDoseOnly = sum(FirstDoseOnly)) %>%
mutate(sum.noVaccine = sum(NoVaccine)) %>%
mutate(sum.secondDoseOnly = sum(SecondDoseOnly)) %>%
slice(-1) %>%
mutate_at("age", str_replace, "25_29", "18-29") %>%
select(-c(4,5,6,7,8,9,10,11))
# 30 to 39
vaccinations.30to39 <- subset(vaccinations.England.ages.latest, vaccinations.England.ages.latest$age == "30_34" | vaccinations.England.ages.latest$age == "35_39")
vaccinations.30to39 <- vaccinations.30to39 %>%
group_by(date) %>%
mutate(sum.vaccineRegister = sum(VaccineRegisterPopulationByVaccinationDate)) %>%
mutate(sum.secondDose = sum(cumPeopleVaccinatedSecondDoseByVaccinationDate)) %>%
mutate(sum.thirdDose = sum(cumPeopleVaccinatedThirdInjectionByVaccinationDate)) %>%
mutate(sum.firstDoseOnly = sum(FirstDoseOnly)) %>%
mutate(sum.noVaccine = sum(NoVaccine)) %>%
mutate(sum.secondDoseOnly = sum(SecondDoseOnly)) %>%
slice(-1) %>%
mutate_at("age", str_replace, "35_39", "30-39") %>%
select(-c(4,5,6,7,8,9,10,11))
# 40 to 49
vaccinations.40to49 <- subset(vaccinations.England.ages.latest, vaccinations.England.ages.latest$age == "40_44" | vaccinations.England.ages.latest$age == "45_49")
vaccinations.40to49 <- vaccinations.40to49 %>%
group_by(date) %>%
mutate(sum.vaccineRegister = sum(VaccineRegisterPopulationByVaccinationDate)) %>%
mutate(sum.secondDose = sum(cumPeopleVaccinatedSecondDoseByVaccinationDate)) %>%
mutate(sum.thirdDose = sum(cumPeopleVaccinatedThirdInjectionByVaccinationDate)) %>%
mutate(sum.firstDoseOnly = sum(FirstDoseOnly)) %>%
mutate(sum.noVaccine = sum(NoVaccine)) %>%
mutate(sum.secondDoseOnly = sum(SecondDoseOnly)) %>%
slice(-1) %>%
mutate_at("age", str_replace, "45_49", "40-49") %>%
select(-c(4,5,6,7,8,9,10,11))
# 50 to 59
vaccinations.50to59 <- subset(vaccinations.England.ages.latest, vaccinations.England.ages.latest$age == "50_54" | vaccinations.England.ages.latest$age == "55_59")
vaccinations.50to59 <- vaccinations.50to59 %>%
group_by(date) %>%
mutate(sum.vaccineRegister = sum(VaccineRegisterPopulationByVaccinationDate)) %>%
mutate(sum.secondDose = sum(cumPeopleVaccinatedSecondDoseByVaccinationDate)) %>%
mutate(sum.thirdDose = sum(cumPeopleVaccinatedThirdInjectionByVaccinationDate)) %>%
mutate(sum.firstDoseOnly = sum(FirstDoseOnly)) %>%
mutate(sum.noVaccine = sum(NoVaccine)) %>%
mutate(sum.secondDoseOnly = sum(SecondDoseOnly)) %>%
slice(-1) %>%
mutate_at("age", str_replace, "55_59", "50-59") %>%
select(-c(4,5,6,7,8,9,10,11))
# 60 to 69
vaccinations.60to69 <- subset(vaccinations.England.ages.latest, vaccinations.England.ages.latest$age == "60_64" | vaccinations.England.ages.latest$age == "65_69")
vaccinations.60to69 <- vaccinations.60to69 %>%
group_by(date) %>%
mutate(sum.vaccineRegister = sum(VaccineRegisterPopulationByVaccinationDate)) %>%
mutate(sum.secondDose = sum(cumPeopleVaccinatedSecondDoseByVaccinationDate)) %>%
mutate(sum.thirdDose = sum(cumPeopleVaccinatedThirdInjectionByVaccinationDate)) %>%
mutate(sum.firstDoseOnly = sum(FirstDoseOnly)) %>%
mutate(sum.noVaccine = sum(NoVaccine)) %>%
mutate(sum.secondDoseOnly = sum(SecondDoseOnly)) %>%
slice(-1) %>%
mutate_at("age", str_replace, "65_69", "60-69") %>%
select(-c(4,5,6,7,8,9,10,11))
# 70 to 79
vaccinations.70to79 <- subset(vaccinations.England.ages.latest, vaccinations.England.ages.latest$age == "70_74" | vaccinations.England.ages.latest$age == "75_79")
vaccinations.70to79 <- vaccinations.70to79 %>%
group_by(date) %>%
mutate(sum.vaccineRegister = sum(VaccineRegisterPopulationByVaccinationDate)) %>%
mutate(sum.secondDose = sum(cumPeopleVaccinatedSecondDoseByVaccinationDate)) %>%
mutate(sum.thirdDose = sum(cumPeopleVaccinatedThirdInjectionByVaccinationDate)) %>%
mutate(sum.firstDoseOnly = sum(FirstDoseOnly)) %>%
mutate(sum.noVaccine = sum(NoVaccine)) %>%
mutate(sum.secondDoseOnly = sum(SecondDoseOnly)) %>%
slice(-1) %>%
mutate_at("age", str_replace, "75_79", "70-79") %>%
select(-c(4,5,6,7,8,9,10,11))
# 80 to 89
vaccinations.80to89 <- subset(vaccinations.England.ages.latest, vaccinations.England.ages.latest$age == "80_84" | vaccinations.England.ages.latest$age == "85_89")
vaccinations.80to89 <- vaccinations.80to89 %>%
group_by(date) %>%
mutate(sum.vaccineRegister = sum(VaccineRegisterPopulationByVaccinationDate)) %>%
mutate(sum.secondDose = sum(cumPeopleVaccinatedSecondDoseByVaccinationDate)) %>%
mutate(sum.thirdDose = sum(cumPeopleVaccinatedThirdInjectionByVaccinationDate)) %>%
mutate(sum.firstDoseOnly = sum(FirstDoseOnly)) %>%
mutate(sum.noVaccine = sum(NoVaccine)) %>%
mutate(sum.secondDoseOnly = sum(SecondDoseOnly)) %>%
slice(-1) %>%
mutate_at("age", str_replace, "85_89", "80-89") %>%
select(-c(4,5,6,7,8,9,10,11))
# 90+
vaccinations.90 <- subset(vaccinations.England.ages.latest, vaccinations.England.ages.latest$age == "90+")
vaccinations.90 <- vaccinations.90 %>%
rename(
sum.vaccineRegister = VaccineRegisterPopulationByVaccinationDate,
sum.firstDoseOnly = FirstDoseOnly,
sum.secondDose = cumPeopleVaccinatedSecondDoseByVaccinationDate,
sum.secondDoseOnly = SecondDoseOnly,
sum.thirdDose = cumPeopleVaccinatedThirdInjectionByVaccinationDate,
sum.noVaccine = NoVaccine
) %>%
select(-c(5,6))
# Merge rows
vaccinations.England.grouped <- rbind(vaccinations.12to15, vaccinations.16to17, vaccinations.18to29, vaccinations.30to39, vaccinations.40to49, vaccinations.50to59, vaccinations.60to69, vaccinations.70to79, vaccinations.80to89, vaccinations.90)
# calculate %
vaccinations.England.grouped$`Three doses` <- vaccinations.England.grouped$sum.thirdDose / vaccinations.England.grouped$sum.vaccineRegister *100
vaccinations.England.grouped$`Two doses only` <- vaccinations.England.grouped$sum.secondDoseOnly / vaccinations.England.grouped$sum.vaccineRegister *100
vaccinations.England.grouped$`First dose only` <- vaccinations.England.grouped$sum.firstDoseOnly / vaccinations.England.grouped$sum.vaccineRegister *100
vaccinations.England.grouped$`No vaccine` <- vaccinations.England.grouped$sum.noVaccine / vaccinations.England.grouped$sum.vaccineRegister *100
vaccinations.England.grouped$`Two doses` <- vaccinations.England.grouped$sum.secondDose / vaccinations.England.grouped$sum.vaccineRegister *100
# export!!!!
write.csv(vaccinations.England.grouped, file = "data/vaccinations-England-ages.csv")
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