In eebrown/data2019nCoV: Data on the COVID-19 Outbreak
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
For additional data and details please see this R package's GitHub repository.
library(data2019nCoV)
library(ggplot2)
library(tidyr)
library(dplyr)
library(scales)
library(data.table)
daily_change <- function(series) {
change <- c(series, NA) - c(NA, series)
change <- change[-1]
change <- change[-length(change)]
return(change)
}
ON_cumulative$LastUpdated[length(ON_cumulative$LastUpdated)]
ON_mohreports$date[length(ON_mohreports$date)]
ON_status$date[length(ON_status$date)]
The source of this data is the official Ontario Government's website (https://www.ontario.ca/page/2019-novel-coronavirus). Data is included from page updates that were captured by the Internet Archive (https://web.archive.org/web/*/https://www.ontario.ca/page/2019-novel-coronavirus).
# Red lines reflect government mandated closures/restrictions
# Green lines reflect government loosinging of closures/restrictions
openings <- function() {
# Banning gatherings over 250 people https://news.ontario.ca/mtc/en/2020/03/statement-from-minister-elliott-and-minister-macleod-on-the-2019-novel-coronavirus-covid-19-1.html
abline(v = as.POSIXct("2020-03-13"), col = "orange")
# Schools remain closed after march break https://news.ontario.ca/opo/en/2020/03/title.html
abline(v = as.POSIXct("2020-03-14"), col = "red")
# Provincial closure of non-essential businesses
abline(v = as.POSIXct("2020-03-24"), col = "red")
# https://news.ontario.ca/opo/en/2020/05/certain-businesses-allowed-to-reopen-under-strict-safety-guidelines.html
abline(v = as.POSIXct("2020-05-04"), col = "yellow") # many businesses opening
#https://news.ontario.ca/mtc/en/2020/05/ontario-eases-restrictions-on-professional-sports-training-facilities.html
abline(v = as.POSIXct("2020-05-08"), col = "yellow") #pro sports
# http://www.ontarioparks.com/covid19 Reopening parks May 11, 2020
abline(v = as.POSIXct("2020-05-11"), col = "yellow") #parks
# pre-phase 1 (marinas, golf courses...)
abline(v = as.POSIXct("2020-05-16"), col = "yellow") #parks
# Phase 1 opening
abline(v = as.POSIXct("2020-05-19"), col = "green") # phase 1
# Phase 2 opening
abline(v = as.POSIXct("2020-06-12"), col = "green") # phase 2 (with exceptions)
# phase 2 - remaining
abline(v = as.POSIXct("2020-06-19"), col = "green")
abline(v = as.POSIXct("2020-06-19"), col = "green") #niagara...
abline(v = as.POSIXct("2020-06-24"), col = "green") #toronto,peel...
abline(v = as.POSIXct("2020-06-25"), col = "green") #windsor
# PHase 3 opening
abline(v = as.POSIXct("2020-07-17"), col = "green") # phase 3 except GTA
abline(v = as.POSIXct("2020-07-31"), col = "green") # phase 3 GTA
# Phase 3 - surgical measures
abline(v = as.POSIXct("2020-09-19"), col = "orange") # lowered limits of private gatherings
abline(v = as.POSIXct("2020-09-25"), col = "orange") #tighter hours on bars and restaurants
}
plot(ON_status$date, ON_status$cases,
main = "Cumulative Confirmed COVID-19 Cases in Ontario",
xlab = "Date",
ylab = "Cases",
type = "b")
plot(ON_status$date, ON_status$cases,
main = "Cumulative COVID-19 Cases in Ontario (Semilog.)",
xlab = "Date",
ylab = "Confirmed Cases",
type = "b",
log = "y")
plot(ON_status$date[-1], daily_change(ON_status$tested_patients),
main = "Change in COVID-19 Patients Tested",
xlab = "Date",
ylab = "Change in Patients Tests",
type = "b")
par(mfrow=c(2,1))
plot(ON_status$date, ON_status$positive,
main = "Active COVID-19 Cases in Ontario",
xlab = "Date",
ylab = "Confirmed Cases (Active)",
type = "l")
openings()
plot(ON_status$date[-1], daily_change(ON_status$cases),
main = "New COVID-19 Cases in Ontario",
xlab = "Date",
ylab = "New Confirmed Cases",
type = "l")
openings()
# colours <- c("red", "blue", "black", "magenta", "green")
#
# matplot(ON_mohreports$date, cbind(
# ( (ON_mohreports$deaths / ON_mohreports$cases) * 100 ),
# ( (ON_mohreports$severity_hospitalized / ON_mohreports$cases) * 100 ),
# ( (ON_mohreports$severity_icu / ON_mohreports$cases) * 100 ),
# ( (ON_mohreports$deaths_ltc_residents / ON_mohreports$cases_ltc_residents) * 100 ),
# ( (ON_mohreports$deaths_hospital_pts / ON_mohreports$cases_hospital_pts) * 100 )
# ),
#
# main = "Ontario Severity and Outcomes",
# xlab = "Date (2020)",
# ylab = "Outcome (Percent)",
# type = "l",
# col = colours,
# lty = c("solid", "solid", "solid", "solid", "solid"),
# ylim = c(0,40),
# ylog = TRUE,
# xaxt="n")
# dates<-format(ON_mohreports$date,"%b %d")
# axis(1, at=ON_mohreports$date, labels=dates)
# legend(x="top",
# legend = c("CFR (Overall)",
# "Hospitalized (Cumulative)",
# "ICU (Cumulative)",
# "CFR (LTC Residents)",
# "CFR (Hospital Outbreak Patients)"),
# col = colours,
# lty = c("solid", "solid", "solid", "solid"), pch=18)
#
#
# colours <- c("red", "blue", "black", "magenta", "green", "brown", "orange", "purple", "pink",
# "tomato1", "yellow")
#
# matplot(ON_mohreports$date, cbind(
# ( (ON_mohreports$deaths_80andover / ON_mohreports$age_80plus) * 100 ),
# ( (ON_mohreports$deaths_ltc_residents / ON_mohreports$cases_ltc_residents) * 100 ),
# ( (ON_mohreports$deaths_rh_residents / ON_mohreports$rh_residents) * 100 ),
# ( (ON_mohreports$deaths_hospital_pts / ON_mohreports$cases_hospital_pts) * 100 ),
# ( (ON_mohreports$deaths_60to79 / ON_mohreports$age_60to79) * 100 ),
# ( (ON_mohreports$deaths / ON_mohreports$cases) * 100 ),
# ( (ON_mohreports$deaths_40to59 / ON_mohreports$age_40to59) * 100 ),
# ( (ON_mohreports$deaths_20to39 / ON_mohreports$age_20to39) * 100 ),
# ( (ON_mohreports$deaths_hospital_staff / ON_mohreports$cases_hospital_staff) * 100 ),
# ( (ON_mohreports$deaths_ltc_staff / ON_mohreports$cases_ltc_staff) * 100 ),
# ( (ON_mohreports$deaths_19under / ON_mohreports$age_19under) * 100 )
# ),
#
# main = "Ontario Case Fatality Rates",
# xlab = "Date (2020)",
# ylab = "CFR (Percent)",
# type = "l",
# col = colours,
# lty = c("solid"),
# ylim = c(0,40),
# ylog = FALSE,
# xaxt="n")
# dates<-format(ON_mohreports$date,"%b %d")
# axis(1, at=ON_mohreports$date, labels=dates)
# legend(x="topleft",
# legend = c("Age 80+",
# "LTC residents",
# "RH residents",
# "Hospital outbreak patients",
# "Age 60-79",
# "CFR (Overall)",
# "Age 40-59",
# "Age 20-39",
# "Hospital staff",
# "LTC staff",
# "Age 19 and under"
# ),
# col = colours,
# lty = c("solid"), pch=18)
#
#
# # by age
# matplot(ON_mohreports$date[-1], cbind(
# ( frollmean(daily_change(ON_mohreports$age_19under), 7) ),
# ( frollmean(daily_change(ON_mohreports$age_20to39), 7) ),
# ( frollmean(daily_change(ON_mohreports$age_40to59), 7) ),
# ( frollmean(daily_change(ON_mohreports$age_60to79), 7) ),
# ( frollmean(daily_change(ON_mohreports$age_80plus), 7) )
# ),
#
# main = "Ontario New Cases by Age",
# xlab = "Date (2020)",
# ylab = "New Cases",
# type = "l",
# col = colours,
# lty = c("solid"),
# #ylim = c(0,30),
# ylog = FALSE,
# xaxt="n")
# dates<-format(ON_mohreports$date[-1],"%b %d")
# axis(1, at=ON_mohreports$date[-1], labels=dates)
# legend(x="topleft",
# legend = c("<20",
# "20-39",
# "40-59",
# "60-79",
# ">79"
# ),
# col = colours,
# lty = c("solid"), pch=18)
# plot(ON_mohreports$date,
# ON_mohreports$cases_female /
# (ON_mohreports$cases_male + ON_mohreports$cases_female),
# main = "Ontario COVID-19 Cases - Gender",
# ylab = "Proportion of Female Cases",
# xlab = "Date",
# type = "l")
# ON_forplot <- rename(ON_mohreports,
# Ontario = cases, Toronto = cases_phu_toronto, Peel = cases_phu_peel,
# York = cases_phu_york, Ottawa = cases_phu_ottawa, Durham = cases_phu_durham,
# Waterloo = cases_phu_waterloo, Hamilton = cases_phu_hamilton,
# Windsor... = cases_phu_windsoressex, Middlesex... = cases_phu_middlesexlondon,
# Halton = cases_phu_halton, Niagara = cases_phu_niagara,
# Simcoe... = cases_phu_simcoemuskoka, Haliburton... = cases_phu_haliburtonkawarthapineridge,
# Lambton = cases_phu_lambton, Wellington... = cases_phu_wellingtondufferinguelph,
# Kingston... = cases_phu_kingstonfrontenaclennoxaddington,
# Haldimand... = cases_phu_haldimandnorfolk, Peterborough = cases_phu_peterborough,
# Leeds... = cases_phu_leedsgrenvillelanark, Brant = cases_phu_brant,
# Eastern = cases_phu_easternontario, Porcupine = cases_phu_porcupine,
# Sudbury = cases_phu_sudbury, Hastings... = cases_phu_hastingsprinceedward,
# Grey... = cases_phu_greybruce, Southwestern = cases_phu_southwestern,
# Chatham... = cases_phu_chathamkent, ThunderBay = cases_phu_thunderbay,
# Renfrew = cases_phu_renfrew, Algoma = cases_phu_algoma,
# HuronPerth = cases_phu_huronperth, NorthBay... = cases_phu_northbayparrysound,
# Northwestern = cases_phu_northwestern, Timiskaming = cases_phu_timiskaming)
#
#
# gather(ON_forplot, key, value,
# Ontario, Toronto, Peel, York, Ottawa, Durham, Waterloo, Hamilton,
# Windsor..., Middlesex..., Halton, Niagara, Simcoe..., Haliburton...,
# Lambton, Wellington..., Kingston..., Haldimand..., Peterborough,
# Leeds..., Brant, Eastern, Porcupine, Sudbury, Hastings...,
# Grey..., Southwestern, Chatham..., ThunderBay, Renfrew, Algoma,
# HuronPerth, NorthBay..., Northwestern, Timiskaming
# ) %>%
# ggplot(aes(x=date, y=value, col=key)) +
# geom_path() +
# scale_y_continuous(trans = 'log10', labels = comma) +
# theme(legend.position="bottom") +
# labs(title = "Ontario COVID-19 Cases by Public Health Unit",
# x = "Date",
# y = "Confirmed Cases") +
# guides(shape = guide_legend(override.aes = list(size = 0.5))) +
# guides(color = guide_legend(override.aes = list(size = 0.5))) +
# theme(legend.text = element_text(size = 7)) +
# theme(legend.title = element_blank())
#
# gather(ON_mohreports, key, value,
# cases,deaths,
# severity_hospitalized,severity_icu,
# cases_ltc_residents, cases_ltc_staff,
# cases_hospital_pts, deaths_hospital_pts,
# deaths_ltc_residents, cases_hcp
# ) %>%
# ggplot(aes(x=date, y=value, col=key)) +
# geom_line() +
# guides(shape = guide_legend(override.aes = list(size = 0.5))) +
# scale_y_continuous(trans = 'log10', labels = comma) +
# theme(legend.position="right") +
# labs(title = "Ontario COVID-19 Cases (Semilog.)",
# x = "Date",
# y = "Confirmed Cases") +
# theme(legend.title = element_blank())
# par(mfrow=c(3,2))
#
# plot(ON_mohreports$date[-1], frollmean(daily_change(ON_mohreports$cases), 7),
# type = "l",
# xlab = "Date",
# ylab = "New Cases")
#
# plot(ON_mohreports$date[-1], frollmean(daily_change(ON_mohreports$deaths), 7),
# type = "l",
# xlab = "Date",
# ylab = "New Deaths")
#
# plot(ON_mohreports$date[-1], frollmean(daily_change(ON_mohreports$severity_hospitalized), 7),
# type = "l",
# xlab = "Date",
# ylab = "New Hospitalized Cases")
#
# plot(ON_mohreports$date[-1], frollmean(daily_change(ON_mohreports$severity_icu), 7),
# type = "l",
# xlab = "Date",
# ylab = "New ICU Cases")
#
# plot(ON_mohreports$date[-1], frollmean(daily_change(ON_mohreports$cases_ltc_residents), 7),
# type = "l",
# xlab = "Date",
# ylab = "Change LTC Cases")
# plot(ON_mohreports$date[-1], daily_change(ON_mohreports$cases_phu_toronto),
# type = "p",
# xlab = "Date",
# ylab = "New Cases in Toronto",
# ylim = c(0, 400))
# lines(ON_mohreports$date[-1], frollmean(daily_change(ON_mohreports$cases_phu_toronto), 7), col="red")
Which days of the week do tests get reported?
weekdays <- as.factor(as.numeric(as.Date(ON_status$date))%% 7)
levels(weekdays) <- c("Thurs", "Fri", "Sat", "Sun", "Mon", "Tues", "Wed")
plot(weekdays, ON_status$tests_last_day,
ylab="Tests reported", xlab="Day of Report (by 3/4 pm)")
eebrown/data2019nCoV documentation built on Dec. 10, 2020, 2:17 p.m.
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knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
For additional data and details please see this R package's GitHub repository.
library(data2019nCoV) library(ggplot2) library(tidyr) library(dplyr) library(scales) library(data.table) daily_change <- function(series) { change <- c(series, NA) - c(NA, series) change <- change[-1] change <- change[-length(change)] return(change) } ON_cumulative$LastUpdated[length(ON_cumulative$LastUpdated)] ON_mohreports$date[length(ON_mohreports$date)] ON_status$date[length(ON_status$date)]
The source of this data is the official Ontario Government's website (https://www.ontario.ca/page/2019-novel-coronavirus). Data is included from page updates that were captured by the Internet Archive (https://web.archive.org/web/*/https://www.ontario.ca/page/2019-novel-coronavirus).
# Red lines reflect government mandated closures/restrictions # Green lines reflect government loosinging of closures/restrictions openings <- function() { # Banning gatherings over 250 people https://news.ontario.ca/mtc/en/2020/03/statement-from-minister-elliott-and-minister-macleod-on-the-2019-novel-coronavirus-covid-19-1.html abline(v = as.POSIXct("2020-03-13"), col = "orange") # Schools remain closed after march break https://news.ontario.ca/opo/en/2020/03/title.html abline(v = as.POSIXct("2020-03-14"), col = "red") # Provincial closure of non-essential businesses abline(v = as.POSIXct("2020-03-24"), col = "red") # https://news.ontario.ca/opo/en/2020/05/certain-businesses-allowed-to-reopen-under-strict-safety-guidelines.html abline(v = as.POSIXct("2020-05-04"), col = "yellow") # many businesses opening #https://news.ontario.ca/mtc/en/2020/05/ontario-eases-restrictions-on-professional-sports-training-facilities.html abline(v = as.POSIXct("2020-05-08"), col = "yellow") #pro sports # http://www.ontarioparks.com/covid19 Reopening parks May 11, 2020 abline(v = as.POSIXct("2020-05-11"), col = "yellow") #parks # pre-phase 1 (marinas, golf courses...) abline(v = as.POSIXct("2020-05-16"), col = "yellow") #parks # Phase 1 opening abline(v = as.POSIXct("2020-05-19"), col = "green") # phase 1 # Phase 2 opening abline(v = as.POSIXct("2020-06-12"), col = "green") # phase 2 (with exceptions) # phase 2 - remaining abline(v = as.POSIXct("2020-06-19"), col = "green") abline(v = as.POSIXct("2020-06-19"), col = "green") #niagara... abline(v = as.POSIXct("2020-06-24"), col = "green") #toronto,peel... abline(v = as.POSIXct("2020-06-25"), col = "green") #windsor # PHase 3 opening abline(v = as.POSIXct("2020-07-17"), col = "green") # phase 3 except GTA abline(v = as.POSIXct("2020-07-31"), col = "green") # phase 3 GTA # Phase 3 - surgical measures abline(v = as.POSIXct("2020-09-19"), col = "orange") # lowered limits of private gatherings abline(v = as.POSIXct("2020-09-25"), col = "orange") #tighter hours on bars and restaurants } plot(ON_status$date, ON_status$cases, main = "Cumulative Confirmed COVID-19 Cases in Ontario", xlab = "Date", ylab = "Cases", type = "b") plot(ON_status$date, ON_status$cases, main = "Cumulative COVID-19 Cases in Ontario (Semilog.)", xlab = "Date", ylab = "Confirmed Cases", type = "b", log = "y") plot(ON_status$date[-1], daily_change(ON_status$tested_patients), main = "Change in COVID-19 Patients Tested", xlab = "Date", ylab = "Change in Patients Tests", type = "b") par(mfrow=c(2,1)) plot(ON_status$date, ON_status$positive, main = "Active COVID-19 Cases in Ontario", xlab = "Date", ylab = "Confirmed Cases (Active)", type = "l") openings() plot(ON_status$date[-1], daily_change(ON_status$cases), main = "New COVID-19 Cases in Ontario", xlab = "Date", ylab = "New Confirmed Cases", type = "l") openings()
# colours <- c("red", "blue", "black", "magenta", "green") # # matplot(ON_mohreports$date, cbind( # ( (ON_mohreports$deaths / ON_mohreports$cases) * 100 ), # ( (ON_mohreports$severity_hospitalized / ON_mohreports$cases) * 100 ), # ( (ON_mohreports$severity_icu / ON_mohreports$cases) * 100 ), # ( (ON_mohreports$deaths_ltc_residents / ON_mohreports$cases_ltc_residents) * 100 ), # ( (ON_mohreports$deaths_hospital_pts / ON_mohreports$cases_hospital_pts) * 100 ) # ), # # main = "Ontario Severity and Outcomes", # xlab = "Date (2020)", # ylab = "Outcome (Percent)", # type = "l", # col = colours, # lty = c("solid", "solid", "solid", "solid", "solid"), # ylim = c(0,40), # ylog = TRUE, # xaxt="n") # dates<-format(ON_mohreports$date,"%b %d") # axis(1, at=ON_mohreports$date, labels=dates) # legend(x="top", # legend = c("CFR (Overall)", # "Hospitalized (Cumulative)", # "ICU (Cumulative)", # "CFR (LTC Residents)", # "CFR (Hospital Outbreak Patients)"), # col = colours, # lty = c("solid", "solid", "solid", "solid"), pch=18) # # # colours <- c("red", "blue", "black", "magenta", "green", "brown", "orange", "purple", "pink", # "tomato1", "yellow") # # matplot(ON_mohreports$date, cbind( # ( (ON_mohreports$deaths_80andover / ON_mohreports$age_80plus) * 100 ), # ( (ON_mohreports$deaths_ltc_residents / ON_mohreports$cases_ltc_residents) * 100 ), # ( (ON_mohreports$deaths_rh_residents / ON_mohreports$rh_residents) * 100 ), # ( (ON_mohreports$deaths_hospital_pts / ON_mohreports$cases_hospital_pts) * 100 ), # ( (ON_mohreports$deaths_60to79 / ON_mohreports$age_60to79) * 100 ), # ( (ON_mohreports$deaths / ON_mohreports$cases) * 100 ), # ( (ON_mohreports$deaths_40to59 / ON_mohreports$age_40to59) * 100 ), # ( (ON_mohreports$deaths_20to39 / ON_mohreports$age_20to39) * 100 ), # ( (ON_mohreports$deaths_hospital_staff / ON_mohreports$cases_hospital_staff) * 100 ), # ( (ON_mohreports$deaths_ltc_staff / ON_mohreports$cases_ltc_staff) * 100 ), # ( (ON_mohreports$deaths_19under / ON_mohreports$age_19under) * 100 ) # ), # # main = "Ontario Case Fatality Rates", # xlab = "Date (2020)", # ylab = "CFR (Percent)", # type = "l", # col = colours, # lty = c("solid"), # ylim = c(0,40), # ylog = FALSE, # xaxt="n") # dates<-format(ON_mohreports$date,"%b %d") # axis(1, at=ON_mohreports$date, labels=dates) # legend(x="topleft", # legend = c("Age 80+", # "LTC residents", # "RH residents", # "Hospital outbreak patients", # "Age 60-79", # "CFR (Overall)", # "Age 40-59", # "Age 20-39", # "Hospital staff", # "LTC staff", # "Age 19 and under" # ), # col = colours, # lty = c("solid"), pch=18) # # # # by age # matplot(ON_mohreports$date[-1], cbind( # ( frollmean(daily_change(ON_mohreports$age_19under), 7) ), # ( frollmean(daily_change(ON_mohreports$age_20to39), 7) ), # ( frollmean(daily_change(ON_mohreports$age_40to59), 7) ), # ( frollmean(daily_change(ON_mohreports$age_60to79), 7) ), # ( frollmean(daily_change(ON_mohreports$age_80plus), 7) ) # ), # # main = "Ontario New Cases by Age", # xlab = "Date (2020)", # ylab = "New Cases", # type = "l", # col = colours, # lty = c("solid"), # #ylim = c(0,30), # ylog = FALSE, # xaxt="n") # dates<-format(ON_mohreports$date[-1],"%b %d") # axis(1, at=ON_mohreports$date[-1], labels=dates) # legend(x="topleft", # legend = c("<20", # "20-39", # "40-59", # "60-79", # ">79" # ), # col = colours, # lty = c("solid"), pch=18)
# plot(ON_mohreports$date, # ON_mohreports$cases_female / # (ON_mohreports$cases_male + ON_mohreports$cases_female), # main = "Ontario COVID-19 Cases - Gender", # ylab = "Proportion of Female Cases", # xlab = "Date", # type = "l")
# ON_forplot <- rename(ON_mohreports, # Ontario = cases, Toronto = cases_phu_toronto, Peel = cases_phu_peel, # York = cases_phu_york, Ottawa = cases_phu_ottawa, Durham = cases_phu_durham, # Waterloo = cases_phu_waterloo, Hamilton = cases_phu_hamilton, # Windsor... = cases_phu_windsoressex, Middlesex... = cases_phu_middlesexlondon, # Halton = cases_phu_halton, Niagara = cases_phu_niagara, # Simcoe... = cases_phu_simcoemuskoka, Haliburton... = cases_phu_haliburtonkawarthapineridge, # Lambton = cases_phu_lambton, Wellington... = cases_phu_wellingtondufferinguelph, # Kingston... = cases_phu_kingstonfrontenaclennoxaddington, # Haldimand... = cases_phu_haldimandnorfolk, Peterborough = cases_phu_peterborough, # Leeds... = cases_phu_leedsgrenvillelanark, Brant = cases_phu_brant, # Eastern = cases_phu_easternontario, Porcupine = cases_phu_porcupine, # Sudbury = cases_phu_sudbury, Hastings... = cases_phu_hastingsprinceedward, # Grey... = cases_phu_greybruce, Southwestern = cases_phu_southwestern, # Chatham... = cases_phu_chathamkent, ThunderBay = cases_phu_thunderbay, # Renfrew = cases_phu_renfrew, Algoma = cases_phu_algoma, # HuronPerth = cases_phu_huronperth, NorthBay... = cases_phu_northbayparrysound, # Northwestern = cases_phu_northwestern, Timiskaming = cases_phu_timiskaming) # # # gather(ON_forplot, key, value, # Ontario, Toronto, Peel, York, Ottawa, Durham, Waterloo, Hamilton, # Windsor..., Middlesex..., Halton, Niagara, Simcoe..., Haliburton..., # Lambton, Wellington..., Kingston..., Haldimand..., Peterborough, # Leeds..., Brant, Eastern, Porcupine, Sudbury, Hastings..., # Grey..., Southwestern, Chatham..., ThunderBay, Renfrew, Algoma, # HuronPerth, NorthBay..., Northwestern, Timiskaming # ) %>% # ggplot(aes(x=date, y=value, col=key)) + # geom_path() + # scale_y_continuous(trans = 'log10', labels = comma) + # theme(legend.position="bottom") + # labs(title = "Ontario COVID-19 Cases by Public Health Unit", # x = "Date", # y = "Confirmed Cases") + # guides(shape = guide_legend(override.aes = list(size = 0.5))) + # guides(color = guide_legend(override.aes = list(size = 0.5))) + # theme(legend.text = element_text(size = 7)) + # theme(legend.title = element_blank()) # # gather(ON_mohreports, key, value, # cases,deaths, # severity_hospitalized,severity_icu, # cases_ltc_residents, cases_ltc_staff, # cases_hospital_pts, deaths_hospital_pts, # deaths_ltc_residents, cases_hcp # ) %>% # ggplot(aes(x=date, y=value, col=key)) + # geom_line() + # guides(shape = guide_legend(override.aes = list(size = 0.5))) + # scale_y_continuous(trans = 'log10', labels = comma) + # theme(legend.position="right") + # labs(title = "Ontario COVID-19 Cases (Semilog.)", # x = "Date", # y = "Confirmed Cases") + # theme(legend.title = element_blank())
# par(mfrow=c(3,2)) # # plot(ON_mohreports$date[-1], frollmean(daily_change(ON_mohreports$cases), 7), # type = "l", # xlab = "Date", # ylab = "New Cases") # # plot(ON_mohreports$date[-1], frollmean(daily_change(ON_mohreports$deaths), 7), # type = "l", # xlab = "Date", # ylab = "New Deaths") # # plot(ON_mohreports$date[-1], frollmean(daily_change(ON_mohreports$severity_hospitalized), 7), # type = "l", # xlab = "Date", # ylab = "New Hospitalized Cases") # # plot(ON_mohreports$date[-1], frollmean(daily_change(ON_mohreports$severity_icu), 7), # type = "l", # xlab = "Date", # ylab = "New ICU Cases") # # plot(ON_mohreports$date[-1], frollmean(daily_change(ON_mohreports$cases_ltc_residents), 7), # type = "l", # xlab = "Date", # ylab = "Change LTC Cases")
# plot(ON_mohreports$date[-1], daily_change(ON_mohreports$cases_phu_toronto), # type = "p", # xlab = "Date", # ylab = "New Cases in Toronto", # ylim = c(0, 400)) # lines(ON_mohreports$date[-1], frollmean(daily_change(ON_mohreports$cases_phu_toronto), 7), col="red")
Which days of the week do tests get reported?
weekdays <- as.factor(as.numeric(as.Date(ON_status$date))%% 7) levels(weekdays) <- c("Thurs", "Fri", "Sat", "Sun", "Mon", "Tues", "Wed") plot(weekdays, ON_status$tests_last_day, ylab="Tests reported", xlab="Day of Report (by 3/4 pm)")
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