R/downloadData.R
In MARC-KC/Covid19MARCData:
Defines functions
getBaseCovidData
downloadAllCovidAPIData
downloadMARCCovidData
Documented in
downloadAllCovidAPIData
downloadMARCCovidData
getBaseCovidData
#' @title Download MARC's COVID-19 Data
#'
#' @description Allows data to be downloaded from the MARC Data API with a
#' simple function.
#'
#' @param dataset What dataset should be downloaded? Select one of 'CDT',
#' 'CDT_NewlyReported' 'Hospital', 'Vaccination', or 'VaccinationCDC'.
#'
#' @return A tibble with the selected COVID-19 data
#'
#'
#' @examples
#' \dontrun{
#'
#' cdtData <- downloadBaseData(dataset = "CDT")
#'
#' cdtNRData <- downloadBaseData(dataset = "CDT_NewlyReported")
#'
#'
#' }
#' @export
downloadMARCCovidData <- function(dataset = c("CDT", "CDT_NewlyReported", "Hospital", "Vaccination", "VaccinationCDC")) {
#Match Arguments
dataset <- match.arg(dataset)
#Download Case, Death, Test Data
if (dataset == "CDT") {
message(crayon::yellow("Downloading Case, Death, and Test data from the MARC Data API: https://gis2.marc2.org/MARCDataAPI/api/covidcasedeathtest"))
out <- marcR::MARCDataAPI_read('https://gis2.marc2.org/MARCDataAPI/api/covidcasedeathtest') %>%
dplyr::mutate(Date = as.Date(Date),
LastUpdated = lubridate::as_datetime(LastUpdated),
LastUpdated = lubridate::with_tz(LastUpdated, "America/Chicago"))
}
#Download the Newly Reported Case, Death, Test Data
if (dataset == "CDT_NewlyReported") {
message(crayon::yellow("Downloading Newly Reported Case, Death, and Test data from the MARC Data API: https://gis2.marc2.org/MARCDataAPI/api/covidcasedeathtestnewlyreported"))
out <- marcR::MARCDataAPI_read('https://gis2.marc2.org/MARCDataAPI/api/covidcasedeathtestnewlyreported') %>%
dplyr::mutate(Date = as.Date(Date),
LastUpdated = lubridate::as_datetime(LastUpdated),
LastUpdated = lubridate::with_tz(LastUpdated, "America/Chicago"))
}
#Download the Hospital Data
if (dataset == "Hospital") {
message(crayon::yellow("Downloading Hospital data from the MARC Data API: https://gis2.marc2.org/MARCDataAPI/api/covidhospital"))
out <- marcR::MARCDataAPI_read('https://gis2.marc2.org/MARCDataAPI/api/covidhospital') %>%
dplyr::mutate(Date = as.Date(Date))
}
#Download the Vaccination Data
if (dataset == "Vaccination") {
message(crayon::yellow("Downloading Vaccination data from the MARC Data API: https://gis2.marc2.org/marcdataapi/api/covidvaccination"))
out <- marcR::MARCDataAPI_read('https://gis2.marc2.org/marcdataapi/api/covidvaccination') %>%
dplyr::mutate(Date = as.Date(Date),
LastUpdated = lubridate::as_datetime(LastUpdated),
LastUpdated = lubridate::with_tz(LastUpdated, "America/Chicago"))
}
#Download the Vaccination Data
if (dataset == "VaccinationCDC") {
message(crayon::yellow("Downloading CDC Vaccination data from the MARC Data API: https://gis2.marc2.org/marcdataapi/api/covidvaccinationcdc"))
out <- marcR::MARCDataAPI_read('https://gis2.marc2.org/marcdataapi/api/covidvaccinationcdc') %>%
dplyr::mutate(Date = as.Date(Date),
LastUpdated = lubridate::as_datetime(LastUpdated),
LastUpdated = lubridate::with_tz(LastUpdated, "America/Chicago"))
}
out
}
#' @title Download All of MARC's COVID-19 Data
#'
#' @description Allows all the COVID-19 data to be downloaded from the MARC Data
#' API with a simple function. This is a helper function for the data pipeline
#' so that for testing purposes, data can be ingested directly from MARC
#' internal SQL servers and not hard coded to the API data.
#'
#'
#' @return A list of tibbles containing the COVID-19 data
#'
#'
#' @examples
#' \dontrun{
#'
#' downloadAllCovidAPIData()
#'
#' }
#' @export
downloadAllCovidAPIData <- function() {
cdtData <- downloadMARCCovidData(dataset = "CDT") %>%
dplyr::filter(GeoID %in% Covid19MARCData::GeoIDs[['base']])
cdtNRData <- downloadMARCCovidData(dataset = "CDT_NewlyReported") %>%
dplyr::filter(GeoID %in% Covid19MARCData::GeoIDs[['base']])
hospData <- downloadMARCCovidData(dataset = "Hospital") %>%
dplyr::filter(GeoID %in% Covid19MARCData::GeoIDs[['base']])
vaccData <- downloadMARCCovidData(dataset = "Vaccination") #don't cut to base GeoID because it filters out Missouri
vaccCDCData <- downloadMARCCovidData(dataset = "VaccinationCDC")
out <- list('cdtData' = cdtData, 'cdtNRData' = cdtNRData, 'hospData' = hospData,
'vaccData' = vaccData, "vaccCDCData" = vaccCDCData)
out
}
#' @title Create Base Calculated Datasets from Raw Data
#'
#' @description Create the Base Datasets that all of the COVID-19 Data Hub is ran from
#'
#' @param baseDataList A named list of data.frames containing the base data. See
#' details for more information. Defaults to the return from
#' \code{downloadAllCovidAPIData()}
#' @details \code{baseDataList} should contain a named list of the base data.frames. These
#' are available through the MARC data API through the helpful functions
#' \code{downloadMARCCovidData()} and \code{downloadAllCovidAPIData()} This should be 3
#' data.frames with the following names:
#' \describe{
#' \item{cdtData}{Case, Death, and Test Data}
#' \item{cdtNRData}{Newly Reported Case, Death, and Test Data}
#' \item{hospData}{Hospital Data}
#' \item{vaccData}{Vaccination Data}
#' }
#'
#' @return A list of data.frames that are used by MARC's COVID Data Hub.
#'
#' @export
getBaseCovidData <- function(baseDataList = downloadAllCovidAPIData()) {
list2env(baseDataList, env = rlang::current_env())
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# Combine Hospital and CDT Base Data ####
message(crayon::blue("Combining CDT and Hospital Time Series"))
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
cdtHospData <- dplyr::full_join(cdtData, hospData, by = c("GeoID", "Date")) %>%
marcR::coalesceJoin(showMessage = FALSE) %>%
dplyr::mutate(CovidNew = CovidNew24HConfirmed + CovidNew24HSuspected) %>%
dplyr::mutate(
BedsInpatientUsedCovid = CovidConfirmedAdult + CovidConfirmedPediatric
# BedsInpatientUsedCovidProportion = BedsInpatientUsedCovid/dplyr::if_else(BedsInpatientTotal == 0, NA_integer_, BedsInpatientTotal)
) %>%
dplyr::select(
Jurisdiction, State, GeoID, Region, Date,
CasesNew, CasesTotal,
DeathsNew, DeathsTotal,
TestsNew, TestsTotal,
Population,
HospitalsReporting, HospitalsTotal,
BedsTotal, BedsUsed, BedsAvailable,
BedsICUTotal, BedsICUUsed, BedsICUAvailable,
CovidTotal,
CovidNew,
CovidICUTotal, CovidICUConfirmed, CovidICUSuspected,
VentilatorsTotal, VentilatorsUsed, VentilatorsAvailable,
CovidVentilatorsUsed,
BedsInpatientUsedCovid, BedsInpatientTotal)
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# Full Hospital Data With Calculations And Most Recent ####
message(crayon::blue("Formatting base hospital data."))
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
hospData <- hospData %>%
dplyr::mutate(CovidNew = CovidNew24HConfirmed + CovidNew24HSuspected) %>%
dplyr::mutate(
#Calculate Inpatient Beds used by COVID
BedsInpatientUsedCovid = CovidConfirmedAdult + CovidConfirmedPediatric,
#Calculate Used by Other Columns
BedsUsedOther = (BedsUsed - CovidTotal),
BedsICUUsedOther = (BedsICUUsed - CovidICUTotal),
VentilatorsUsedOther = (VentilatorsUsed - CovidVentilatorsUsed),
#Force Non-Negative
BedsUsedOther = dplyr::if_else(BedsUsedOther < 0, as.integer(0), BedsUsedOther),
BedsICUUsedOther = dplyr::if_else(BedsICUUsedOther < 0, as.integer(0), BedsICUUsedOther),
VentilatorsUsedOther = dplyr::if_else(VentilatorsUsedOther < 0, as.integer(0), VentilatorsUsedOther),
#Explicitly Calculate Totals using used and available
BedsTotal = (BedsAvailable + BedsUsedOther + CovidTotal),
BedsICUTotal = (BedsICUAvailable + BedsICUUsedOther + CovidICUTotal),
VentilatorsTotal = (VentilatorsAvailable + VentilatorsUsedOther + CovidVentilatorsUsed),
#Calculate Proportions of Use
BedsAvailableProportion = BedsAvailable/dplyr::if_else(BedsTotal == 0, NA_integer_, BedsTotal),
BedsUsedOtherProportion = BedsUsedOther/dplyr::if_else(BedsTotal == 0, NA_integer_, BedsTotal),
CovidTotalProportion = CovidTotal/dplyr::if_else(BedsTotal == 0, NA_integer_, BedsTotal),
BedsICUAvailableProportion = BedsICUAvailable/dplyr::if_else(BedsICUTotal == 0, NA_integer_, BedsICUTotal),
BedsICUUsedOtherProportion = BedsICUUsedOther/dplyr::if_else(BedsICUTotal == 0, NA_integer_, BedsICUTotal),
CovidICUTotalProportion = CovidICUTotal/dplyr::if_else(BedsICUTotal == 0, NA_integer_, BedsICUTotal),
VentilatorsAvailableProportion = VentilatorsAvailable/dplyr::if_else(VentilatorsTotal == 0, NA_integer_, VentilatorsTotal),
VentilatorsUsedOtherProportion = VentilatorsUsedOther/dplyr::if_else(VentilatorsTotal == 0, NA_integer_, VentilatorsTotal),
CovidVentilatorsUsedProportion = CovidVentilatorsUsed/dplyr::if_else(VentilatorsTotal == 0, NA_integer_, VentilatorsTotal),
BedsInpatientUsedCovidProportion = BedsInpatientUsedCovid/dplyr::if_else(BedsInpatientTotal == 0, NA_integer_, BedsInpatientTotal)
)
#HospitalTotal based on a 3 week window so that it can adapt to reporting over time
hospData_dt = data.table::as.data.table(hospData)
hospData <- hospData %>%
dplyr::mutate(
HospitalsTotal = purrr::map2_int(GeoID, Date, ~hospData_dt[(hospData_dt[['GeoID']] == .x) & (hospData_dt[['Date']] >= .y - 10) & (hospData_dt[['Date']] <= .y + 10),][['HospitalsReporting']] %>% max(., na.rm = TRUE) %>% as.integer())
)
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# Calculate Vaccinations by Age Range Columns ####
message(crayon::blue("Calculating Vaccinations by Age Range Columns."))
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
vaccCDCData <- vaccCDCData %>%
dplyr::mutate(
# Calculate Vaccines Completed 5-11
RegimenCompleted_Total5to11 = RegimenCompleted_TotalGTE5 - RegimenCompleted_TotalGTE12,
Population_5to11 = PopulationGTE5 - PopulationGTE12,
RegimenCompleted_Total5to11Prop = (RegimenCompleted_Total5to11)/Population_5to11,
NotFullyVaccinated_5to11 = Population_5to11 - RegimenCompleted_Total5to11, #Either initiated not completed, or not initiated
NotFullyVaccinated_5to11Prop = NotFullyVaccinated_5to11/Population_5to11,
# Calculate Vaccines Completed 12-17
RegimenCompleted_Total12to17 = RegimenCompleted_TotalGTE12 - RegimenCompleted_TotalGTE18,
Population_12to17 = PopulationGTE12 - PopulationGTE18,
RegimenCompleted_Total12to17Prop = (RegimenCompleted_Total12to17)/Population_12to17,
NotFullyVaccinated_12to17 = Population_12to17 - RegimenCompleted_Total12to17, #Either initiated not completed, or not initiated
NotFullyVaccinated_12to17Prop = NotFullyVaccinated_12to17/Population_12to17,
# Calculate Vaccines Completed 18 - 65
RegimenCompleted_Total18to65 = RegimenCompleted_TotalGTE18 - RegimenCompleted_TotalGTE65,
Population_18to65 = PopulationGTE18 - PopulationGTE65,
RegimenCompleted_Total18to65Prop = (RegimenCompleted_Total18to65)/Population_18to65,
NotFullyVaccinated_18to65 = Population_18to65 - RegimenCompleted_Total18to65,
NotFullyVaccinated_18to65Prop = NotFullyVaccinated_18to65 / Population_18to65,
# Calculate Vaccines Initiated 5-11
RegimenInitiated_Total5to11 = RegimenInitiated_TotalGTE5 - RegimenInitiated_TotalGTE12,
RegimenInitiated_Total5to11Prop = (RegimenInitiated_Total5to11)/Population_5to11,
Initiated_NotCompleted_Total5to11 = RegimenInitiated_Total5to11 - RegimenCompleted_Total5to11,
Initiated_NotCompleted_Total5to11Prop = Initiated_NotCompleted_Total5to11 / Population_5to11,
Neither_Initiated_Nor_Completed_Total5to11 = Population_5to11 - RegimenInitiated_Total5to11,
Neither_Initiated_Nor_Completed_Total5to11Prop = Neither_Initiated_Nor_Completed_Total5to11 / Population_5to11,
# Calculate Vaccines Initiated 12-17
RegimenInitiated_Total12to17 = RegimenInitiated_TotalGTE12 - RegimenInitiated_TotalGTE18,
RegimenInitiated_Total12to17Prop = (RegimenInitiated_Total12to17)/Population_12to17,
Initiated_NotCompleted_Total12to17 = RegimenInitiated_Total12to17 - RegimenCompleted_Total12to17,
Initiated_NotCompleted_Total12to17Prop = Initiated_NotCompleted_Total12to17 / Population_12to17,
Neither_Initiated_Nor_Completed_Total12to17 = Population_12to17 - RegimenInitiated_Total12to17,
Neither_Initiated_Nor_Completed_Total12to17Prop = Neither_Initiated_Nor_Completed_Total12to17 / Population_12to17,
# Calculate Vaccines Initiated 18 - 65
RegimenInitiated_Total18to65 = RegimenInitiated_TotalGTE18 - RegimenInitiated_TotalGTE65,
RegimenInitiated_Total18to65Prop = (RegimenInitiated_Total18to65)/Population_18to65,
Initiated_NotCompleted_Total18to65 = RegimenInitiated_Total18to65 - RegimenCompleted_Total18to65,
Initiated_NotCompleted_Total18to65Prop = Initiated_NotCompleted_Total18to65 / Population_18to65,
#Calculate Pop Neither Initiated Nor Completed (ie. unvaccinated)
Unvaccinated_Total18to65 = Population_18to65 - RegimenInitiated_Total18to65,
Unvaccinated_Total18to65Prop = Unvaccinated_Total18to65 / Population_18to65,
#Calculate 65+ Vaccination Columns
Initiated_NotCompleted_TotalGTE65 = PopulationGTE65 - RegimenCompleted_TotalGTE65,
Initiated_NotCompleted_TotalGTE65Prop = Initiated_NotCompleted_TotalGTE65 / PopulationGTE65,
Unvaccinated_TotalGTE65 = PopulationGTE65 - RegimenInitiated_TotalGTE65,
Unvaccinated_Total18to65Prop = Unvaccinated_TotalGTE65 / PopulationGTE65,
NotFullyVaccinated_GTE65 = PopulationGTE65 - RegimenCompleted_TotalGTE65,
NotFullyVaccinated_GTE65Prop = NotFullyVaccinated_GTE65 / PopulationGTE65,
# Calculate Boosters 12-17
Boosted_Total12to17 = Boosted_TotalGTE12 - Boosted_TotalGTE18,
Boosted_Total12to17Prop = (Boosted_Total12to17)/Population_12to17,
# Calculate Boosters 18 - 65
Boosted_Total18to65 = Boosted_TotalGTE18 - Boosted_TotalGTE65,
Boosted_Total18to65Prop = (Boosted_Total18to65)/Population_18to65,
# Calculate Boosters GTE65
Boosted_TotalGTE65Prop = (Boosted_TotalGTE65)/PopulationGTE65
)
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# Calculate Rolling Average Tables ####
message(crayon::blue("Calculating 7 and 14 day rolling averages."))
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
varTable <- tibble::tribble(
~variable, ~Avg, ~Total, ~CalcString,
"CasesNew", TRUE, TRUE, NA,
"DeathsNew", TRUE, TRUE, NA,
"TestsNew", TRUE, TRUE, NA,
"CovidNew", TRUE, TRUE, NA,
"CovidTotal", TRUE, TRUE, NA,
"BedsUsedOther", TRUE, TRUE, "BedsUsed - CovidTotal",
"BedsAvailable", TRUE, TRUE, NA,
"CovidICUTotal", TRUE, FALSE, NA,
"BedsICUUsedOther", TRUE, FALSE, "BedsICUUsed - CovidICUTotal",
"BedsICUAvailable", TRUE, FALSE, NA,
"CovidVentilatorsUsed", TRUE, FALSE, NA,
"VentilatorsUsedOther", TRUE, FALSE, "VentilatorsUsed - CovidVentilatorsUsed",
"VentilatorsAvailable", TRUE, FALSE, NA,
"HospitalsReporting", TRUE, TRUE, NA,
"HospitalsTotal", TRUE, TRUE, NA,
"BedsInpatientUsedCovid", TRUE, FALSE, NA,
"BedsInpatientTotal", TRUE, FALSE, NA
)
cdtHosp7DayRollingData <- rollSummaryXDays(df = cdtHospData, numDays = 7, varTable = varTable)
cdtHosp14DayRollingData <- rollSummaryXDays(df = cdtHospData, numDays = 14, varTable = varTable)
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
out <- list(
'cdtData' = cdtData, 'cdtNRData' = cdtNRData, 'hospData' = hospData, 'vaccData' = vaccData, "vaccCDCData" = vaccCDCData,
'cdtHospData' = cdtHospData,
'cdtHosp7DayRollingData' = cdtHosp7DayRollingData, 'cdtHosp14DayRollingData' = cdtHosp14DayRollingData
)
return(out)
}
MARC-KC/Covid19MARCData documentation built on April 7, 2022, 11:43 a.m.
R Package Documentation
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Defines functions getBaseCovidData downloadAllCovidAPIData downloadMARCCovidData
Documented in downloadAllCovidAPIData downloadMARCCovidData getBaseCovidData
#' @title Download MARC's COVID-19 Data
#'
#' @description Allows data to be downloaded from the MARC Data API with a
#' simple function.
#'
#' @param dataset What dataset should be downloaded? Select one of 'CDT',
#' 'CDT_NewlyReported' 'Hospital', 'Vaccination', or 'VaccinationCDC'.
#'
#' @return A tibble with the selected COVID-19 data
#'
#'
#' @examples
#' \dontrun{
#'
#' cdtData <- downloadBaseData(dataset = "CDT")
#'
#' cdtNRData <- downloadBaseData(dataset = "CDT_NewlyReported")
#'
#'
#' }
#' @export
downloadMARCCovidData <- function(dataset = c("CDT", "CDT_NewlyReported", "Hospital", "Vaccination", "VaccinationCDC")) {
#Match Arguments
dataset <- match.arg(dataset)
#Download Case, Death, Test Data
if (dataset == "CDT") {
message(crayon::yellow("Downloading Case, Death, and Test data from the MARC Data API: https://gis2.marc2.org/MARCDataAPI/api/covidcasedeathtest"))
out <- marcR::MARCDataAPI_read('https://gis2.marc2.org/MARCDataAPI/api/covidcasedeathtest') %>%
dplyr::mutate(Date = as.Date(Date),
LastUpdated = lubridate::as_datetime(LastUpdated),
LastUpdated = lubridate::with_tz(LastUpdated, "America/Chicago"))
}
#Download the Newly Reported Case, Death, Test Data
if (dataset == "CDT_NewlyReported") {
message(crayon::yellow("Downloading Newly Reported Case, Death, and Test data from the MARC Data API: https://gis2.marc2.org/MARCDataAPI/api/covidcasedeathtestnewlyreported"))
out <- marcR::MARCDataAPI_read('https://gis2.marc2.org/MARCDataAPI/api/covidcasedeathtestnewlyreported') %>%
dplyr::mutate(Date = as.Date(Date),
LastUpdated = lubridate::as_datetime(LastUpdated),
LastUpdated = lubridate::with_tz(LastUpdated, "America/Chicago"))
}
#Download the Hospital Data
if (dataset == "Hospital") {
message(crayon::yellow("Downloading Hospital data from the MARC Data API: https://gis2.marc2.org/MARCDataAPI/api/covidhospital"))
out <- marcR::MARCDataAPI_read('https://gis2.marc2.org/MARCDataAPI/api/covidhospital') %>%
dplyr::mutate(Date = as.Date(Date))
}
#Download the Vaccination Data
if (dataset == "Vaccination") {
message(crayon::yellow("Downloading Vaccination data from the MARC Data API: https://gis2.marc2.org/marcdataapi/api/covidvaccination"))
out <- marcR::MARCDataAPI_read('https://gis2.marc2.org/marcdataapi/api/covidvaccination') %>%
dplyr::mutate(Date = as.Date(Date),
LastUpdated = lubridate::as_datetime(LastUpdated),
LastUpdated = lubridate::with_tz(LastUpdated, "America/Chicago"))
}
#Download the Vaccination Data
if (dataset == "VaccinationCDC") {
message(crayon::yellow("Downloading CDC Vaccination data from the MARC Data API: https://gis2.marc2.org/marcdataapi/api/covidvaccinationcdc"))
out <- marcR::MARCDataAPI_read('https://gis2.marc2.org/marcdataapi/api/covidvaccinationcdc') %>%
dplyr::mutate(Date = as.Date(Date),
LastUpdated = lubridate::as_datetime(LastUpdated),
LastUpdated = lubridate::with_tz(LastUpdated, "America/Chicago"))
}
out
}
#' @title Download All of MARC's COVID-19 Data
#'
#' @description Allows all the COVID-19 data to be downloaded from the MARC Data
#' API with a simple function. This is a helper function for the data pipeline
#' so that for testing purposes, data can be ingested directly from MARC
#' internal SQL servers and not hard coded to the API data.
#'
#'
#' @return A list of tibbles containing the COVID-19 data
#'
#'
#' @examples
#' \dontrun{
#'
#' downloadAllCovidAPIData()
#'
#' }
#' @export
downloadAllCovidAPIData <- function() {
cdtData <- downloadMARCCovidData(dataset = "CDT") %>%
dplyr::filter(GeoID %in% Covid19MARCData::GeoIDs[['base']])
cdtNRData <- downloadMARCCovidData(dataset = "CDT_NewlyReported") %>%
dplyr::filter(GeoID %in% Covid19MARCData::GeoIDs[['base']])
hospData <- downloadMARCCovidData(dataset = "Hospital") %>%
dplyr::filter(GeoID %in% Covid19MARCData::GeoIDs[['base']])
vaccData <- downloadMARCCovidData(dataset = "Vaccination") #don't cut to base GeoID because it filters out Missouri
vaccCDCData <- downloadMARCCovidData(dataset = "VaccinationCDC")
out <- list('cdtData' = cdtData, 'cdtNRData' = cdtNRData, 'hospData' = hospData,
'vaccData' = vaccData, "vaccCDCData" = vaccCDCData)
out
}
#' @title Create Base Calculated Datasets from Raw Data
#'
#' @description Create the Base Datasets that all of the COVID-19 Data Hub is ran from
#'
#' @param baseDataList A named list of data.frames containing the base data. See
#' details for more information. Defaults to the return from
#' \code{downloadAllCovidAPIData()}
#' @details \code{baseDataList} should contain a named list of the base data.frames. These
#' are available through the MARC data API through the helpful functions
#' \code{downloadMARCCovidData()} and \code{downloadAllCovidAPIData()} This should be 3
#' data.frames with the following names:
#' \describe{
#' \item{cdtData}{Case, Death, and Test Data}
#' \item{cdtNRData}{Newly Reported Case, Death, and Test Data}
#' \item{hospData}{Hospital Data}
#' \item{vaccData}{Vaccination Data}
#' }
#'
#' @return A list of data.frames that are used by MARC's COVID Data Hub.
#'
#' @export
getBaseCovidData <- function(baseDataList = downloadAllCovidAPIData()) {
list2env(baseDataList, env = rlang::current_env())
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# Combine Hospital and CDT Base Data ####
message(crayon::blue("Combining CDT and Hospital Time Series"))
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
cdtHospData <- dplyr::full_join(cdtData, hospData, by = c("GeoID", "Date")) %>%
marcR::coalesceJoin(showMessage = FALSE) %>%
dplyr::mutate(CovidNew = CovidNew24HConfirmed + CovidNew24HSuspected) %>%
dplyr::mutate(
BedsInpatientUsedCovid = CovidConfirmedAdult + CovidConfirmedPediatric
# BedsInpatientUsedCovidProportion = BedsInpatientUsedCovid/dplyr::if_else(BedsInpatientTotal == 0, NA_integer_, BedsInpatientTotal)
) %>%
dplyr::select(
Jurisdiction, State, GeoID, Region, Date,
CasesNew, CasesTotal,
DeathsNew, DeathsTotal,
TestsNew, TestsTotal,
Population,
HospitalsReporting, HospitalsTotal,
BedsTotal, BedsUsed, BedsAvailable,
BedsICUTotal, BedsICUUsed, BedsICUAvailable,
CovidTotal,
CovidNew,
CovidICUTotal, CovidICUConfirmed, CovidICUSuspected,
VentilatorsTotal, VentilatorsUsed, VentilatorsAvailable,
CovidVentilatorsUsed,
BedsInpatientUsedCovid, BedsInpatientTotal)
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# Full Hospital Data With Calculations And Most Recent ####
message(crayon::blue("Formatting base hospital data."))
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
hospData <- hospData %>%
dplyr::mutate(CovidNew = CovidNew24HConfirmed + CovidNew24HSuspected) %>%
dplyr::mutate(
#Calculate Inpatient Beds used by COVID
BedsInpatientUsedCovid = CovidConfirmedAdult + CovidConfirmedPediatric,
#Calculate Used by Other Columns
BedsUsedOther = (BedsUsed - CovidTotal),
BedsICUUsedOther = (BedsICUUsed - CovidICUTotal),
VentilatorsUsedOther = (VentilatorsUsed - CovidVentilatorsUsed),
#Force Non-Negative
BedsUsedOther = dplyr::if_else(BedsUsedOther < 0, as.integer(0), BedsUsedOther),
BedsICUUsedOther = dplyr::if_else(BedsICUUsedOther < 0, as.integer(0), BedsICUUsedOther),
VentilatorsUsedOther = dplyr::if_else(VentilatorsUsedOther < 0, as.integer(0), VentilatorsUsedOther),
#Explicitly Calculate Totals using used and available
BedsTotal = (BedsAvailable + BedsUsedOther + CovidTotal),
BedsICUTotal = (BedsICUAvailable + BedsICUUsedOther + CovidICUTotal),
VentilatorsTotal = (VentilatorsAvailable + VentilatorsUsedOther + CovidVentilatorsUsed),
#Calculate Proportions of Use
BedsAvailableProportion = BedsAvailable/dplyr::if_else(BedsTotal == 0, NA_integer_, BedsTotal),
BedsUsedOtherProportion = BedsUsedOther/dplyr::if_else(BedsTotal == 0, NA_integer_, BedsTotal),
CovidTotalProportion = CovidTotal/dplyr::if_else(BedsTotal == 0, NA_integer_, BedsTotal),
BedsICUAvailableProportion = BedsICUAvailable/dplyr::if_else(BedsICUTotal == 0, NA_integer_, BedsICUTotal),
BedsICUUsedOtherProportion = BedsICUUsedOther/dplyr::if_else(BedsICUTotal == 0, NA_integer_, BedsICUTotal),
CovidICUTotalProportion = CovidICUTotal/dplyr::if_else(BedsICUTotal == 0, NA_integer_, BedsICUTotal),
VentilatorsAvailableProportion = VentilatorsAvailable/dplyr::if_else(VentilatorsTotal == 0, NA_integer_, VentilatorsTotal),
VentilatorsUsedOtherProportion = VentilatorsUsedOther/dplyr::if_else(VentilatorsTotal == 0, NA_integer_, VentilatorsTotal),
CovidVentilatorsUsedProportion = CovidVentilatorsUsed/dplyr::if_else(VentilatorsTotal == 0, NA_integer_, VentilatorsTotal),
BedsInpatientUsedCovidProportion = BedsInpatientUsedCovid/dplyr::if_else(BedsInpatientTotal == 0, NA_integer_, BedsInpatientTotal)
)
#HospitalTotal based on a 3 week window so that it can adapt to reporting over time
hospData_dt = data.table::as.data.table(hospData)
hospData <- hospData %>%
dplyr::mutate(
HospitalsTotal = purrr::map2_int(GeoID, Date, ~hospData_dt[(hospData_dt[['GeoID']] == .x) & (hospData_dt[['Date']] >= .y - 10) & (hospData_dt[['Date']] <= .y + 10),][['HospitalsReporting']] %>% max(., na.rm = TRUE) %>% as.integer())
)
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# Calculate Vaccinations by Age Range Columns ####
message(crayon::blue("Calculating Vaccinations by Age Range Columns."))
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
vaccCDCData <- vaccCDCData %>%
dplyr::mutate(
# Calculate Vaccines Completed 5-11
RegimenCompleted_Total5to11 = RegimenCompleted_TotalGTE5 - RegimenCompleted_TotalGTE12,
Population_5to11 = PopulationGTE5 - PopulationGTE12,
RegimenCompleted_Total5to11Prop = (RegimenCompleted_Total5to11)/Population_5to11,
NotFullyVaccinated_5to11 = Population_5to11 - RegimenCompleted_Total5to11, #Either initiated not completed, or not initiated
NotFullyVaccinated_5to11Prop = NotFullyVaccinated_5to11/Population_5to11,
# Calculate Vaccines Completed 12-17
RegimenCompleted_Total12to17 = RegimenCompleted_TotalGTE12 - RegimenCompleted_TotalGTE18,
Population_12to17 = PopulationGTE12 - PopulationGTE18,
RegimenCompleted_Total12to17Prop = (RegimenCompleted_Total12to17)/Population_12to17,
NotFullyVaccinated_12to17 = Population_12to17 - RegimenCompleted_Total12to17, #Either initiated not completed, or not initiated
NotFullyVaccinated_12to17Prop = NotFullyVaccinated_12to17/Population_12to17,
# Calculate Vaccines Completed 18 - 65
RegimenCompleted_Total18to65 = RegimenCompleted_TotalGTE18 - RegimenCompleted_TotalGTE65,
Population_18to65 = PopulationGTE18 - PopulationGTE65,
RegimenCompleted_Total18to65Prop = (RegimenCompleted_Total18to65)/Population_18to65,
NotFullyVaccinated_18to65 = Population_18to65 - RegimenCompleted_Total18to65,
NotFullyVaccinated_18to65Prop = NotFullyVaccinated_18to65 / Population_18to65,
# Calculate Vaccines Initiated 5-11
RegimenInitiated_Total5to11 = RegimenInitiated_TotalGTE5 - RegimenInitiated_TotalGTE12,
RegimenInitiated_Total5to11Prop = (RegimenInitiated_Total5to11)/Population_5to11,
Initiated_NotCompleted_Total5to11 = RegimenInitiated_Total5to11 - RegimenCompleted_Total5to11,
Initiated_NotCompleted_Total5to11Prop = Initiated_NotCompleted_Total5to11 / Population_5to11,
Neither_Initiated_Nor_Completed_Total5to11 = Population_5to11 - RegimenInitiated_Total5to11,
Neither_Initiated_Nor_Completed_Total5to11Prop = Neither_Initiated_Nor_Completed_Total5to11 / Population_5to11,
# Calculate Vaccines Initiated 12-17
RegimenInitiated_Total12to17 = RegimenInitiated_TotalGTE12 - RegimenInitiated_TotalGTE18,
RegimenInitiated_Total12to17Prop = (RegimenInitiated_Total12to17)/Population_12to17,
Initiated_NotCompleted_Total12to17 = RegimenInitiated_Total12to17 - RegimenCompleted_Total12to17,
Initiated_NotCompleted_Total12to17Prop = Initiated_NotCompleted_Total12to17 / Population_12to17,
Neither_Initiated_Nor_Completed_Total12to17 = Population_12to17 - RegimenInitiated_Total12to17,
Neither_Initiated_Nor_Completed_Total12to17Prop = Neither_Initiated_Nor_Completed_Total12to17 / Population_12to17,
# Calculate Vaccines Initiated 18 - 65
RegimenInitiated_Total18to65 = RegimenInitiated_TotalGTE18 - RegimenInitiated_TotalGTE65,
RegimenInitiated_Total18to65Prop = (RegimenInitiated_Total18to65)/Population_18to65,
Initiated_NotCompleted_Total18to65 = RegimenInitiated_Total18to65 - RegimenCompleted_Total18to65,
Initiated_NotCompleted_Total18to65Prop = Initiated_NotCompleted_Total18to65 / Population_18to65,
#Calculate Pop Neither Initiated Nor Completed (ie. unvaccinated)
Unvaccinated_Total18to65 = Population_18to65 - RegimenInitiated_Total18to65,
Unvaccinated_Total18to65Prop = Unvaccinated_Total18to65 / Population_18to65,
#Calculate 65+ Vaccination Columns
Initiated_NotCompleted_TotalGTE65 = PopulationGTE65 - RegimenCompleted_TotalGTE65,
Initiated_NotCompleted_TotalGTE65Prop = Initiated_NotCompleted_TotalGTE65 / PopulationGTE65,
Unvaccinated_TotalGTE65 = PopulationGTE65 - RegimenInitiated_TotalGTE65,
Unvaccinated_Total18to65Prop = Unvaccinated_TotalGTE65 / PopulationGTE65,
NotFullyVaccinated_GTE65 = PopulationGTE65 - RegimenCompleted_TotalGTE65,
NotFullyVaccinated_GTE65Prop = NotFullyVaccinated_GTE65 / PopulationGTE65,
# Calculate Boosters 12-17
Boosted_Total12to17 = Boosted_TotalGTE12 - Boosted_TotalGTE18,
Boosted_Total12to17Prop = (Boosted_Total12to17)/Population_12to17,
# Calculate Boosters 18 - 65
Boosted_Total18to65 = Boosted_TotalGTE18 - Boosted_TotalGTE65,
Boosted_Total18to65Prop = (Boosted_Total18to65)/Population_18to65,
# Calculate Boosters GTE65
Boosted_TotalGTE65Prop = (Boosted_TotalGTE65)/PopulationGTE65
)
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# Calculate Rolling Average Tables ####
message(crayon::blue("Calculating 7 and 14 day rolling averages."))
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
varTable <- tibble::tribble(
~variable, ~Avg, ~Total, ~CalcString,
"CasesNew", TRUE, TRUE, NA,
"DeathsNew", TRUE, TRUE, NA,
"TestsNew", TRUE, TRUE, NA,
"CovidNew", TRUE, TRUE, NA,
"CovidTotal", TRUE, TRUE, NA,
"BedsUsedOther", TRUE, TRUE, "BedsUsed - CovidTotal",
"BedsAvailable", TRUE, TRUE, NA,
"CovidICUTotal", TRUE, FALSE, NA,
"BedsICUUsedOther", TRUE, FALSE, "BedsICUUsed - CovidICUTotal",
"BedsICUAvailable", TRUE, FALSE, NA,
"CovidVentilatorsUsed", TRUE, FALSE, NA,
"VentilatorsUsedOther", TRUE, FALSE, "VentilatorsUsed - CovidVentilatorsUsed",
"VentilatorsAvailable", TRUE, FALSE, NA,
"HospitalsReporting", TRUE, TRUE, NA,
"HospitalsTotal", TRUE, TRUE, NA,
"BedsInpatientUsedCovid", TRUE, FALSE, NA,
"BedsInpatientTotal", TRUE, FALSE, NA
)
cdtHosp7DayRollingData <- rollSummaryXDays(df = cdtHospData, numDays = 7, varTable = varTable)
cdtHosp14DayRollingData <- rollSummaryXDays(df = cdtHospData, numDays = 14, varTable = varTable)
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
out <- list(
'cdtData' = cdtData, 'cdtNRData' = cdtNRData, 'hospData' = hospData, 'vaccData' = vaccData, "vaccCDCData" = vaccCDCData,
'cdtHospData' = cdtHospData,
'cdtHosp7DayRollingData' = cdtHosp7DayRollingData, 'cdtHosp14DayRollingData' = cdtHosp14DayRollingData
)
return(out)
}
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