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get_nsw_data.R
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# returns NSW incidence tibble
get_nsw_data <- function() {
# NSW cases by source scraped from chart on 31/3/2020
# figures pre 9/3/2020 should be stable
nsw_cases_from_chart <- tribble(~notification_date, ~overseas_pix, ~local_contacts_pix, ~local_no_contacts_pix, ~under_invest_pix,
"2020-01-22",4,0,0,0,
"2020-01-23",0,0,0,0,
"2020-01-24",4,0,0,0,
"2020-01-25",9,0,0,0,
"2020-01-26",0,0,0,0,
"2020-01-27",0,0,0,0,
"2020-01-28",0,0,0,0,
"2020-01-29",0,0,0,0,
"2020-01-30",0,0,0,0,
"2020-01-31",0,0,0,0,
"2020-02-01",0,0,0,0,
"2020-02-02",0,0,0,0,
"2020-02-03",0,0,0,0,
"2020-02-04",0,0,0,0,
"2020-02-05",0,0,0,0,
"2020-02-06",0,0,0,0,
"2020-02-07",0,0,0,0,
"2020-02-08",0,0,0,0,
"2020-02-09",0,0,0,0,
"2020-02-10",0,0,0,0,
"2020-02-11",0,0,0,0,
"2020-02-12",0,0,0,0,
"2020-02-13",0,0,0,0,
"2020-02-14",0,0,0,0,
"2020-02-15",0,0,0,0,
"2020-02-16",0,0,0,0,
"2020-02-17",0,0,0,0,
"2020-02-18",0,0,0,0,
"2020-02-19",0,0,0,0,
"2020-02-20",0,0,0,0,
"2020-02-21",4,0,0,0,
"2020-02-22",0,0,0,0,
"2020-02-23",0,0,0,0,
"2020-02-24",0,0,0,0,
"2020-02-25",0,0,0,0,
"2020-02-26",0,4,0,0,
"2020-02-27",4,0,0,0,
"2020-02-28",0,0,0,0,
"2020-02-29",4,0,0,0,
"2020-03-01",5,4,0,0,
"2020-03-02",5,4,0,0,
"2020-03-03",26,4,0,0,
"2020-03-04",10,30,0,0,
"2020-03-05",15,30,0,0,
"2020-03-06",11,24,0,0,
"2020-03-07",5,16,14,0,
"2020-03-08",15,26,4,0,
"2020-03-09",26,14,0,0,
"2020-03-10",30,0,0,0,
"2020-03-11",52,26,24,0,
"2020-03-12",57,42,19,0,
"2020-03-13",72,73,15,0,
"2020-03-14",57,49,25,0,
"2020-03-15",94,69,15,0,
"2020-03-16",181,64,6,4,
"2020-03-17",124,106,41,0,
"2020-03-18",192,111,15,0,
"2020-03-19",290,80,25,0,
"2020-03-20",420,178,68,4,
"2020-03-21",363,85,68,14,
"2020-03-22",715,116,74,9,
"2020-03-23",658,177,100,4,
"2020-03-24",699,189,89,14,
"2020-03-25",673,158,116,14,
"2020-03-26",643,162,142,35,
"2020-03-27",544,168,225,24,
"2020-03-28",384,95,84,45,
"2020-03-29",213,80,89,107,
"2020-03-30",83,12,39,77)
pix150 <- 776
nsw_cases_from_chart <- nsw_cases_from_chart %>%
mutate(notification_date = ymd(notification_date),
overseas = round(150*overseas_pix/pix150),
local_contacts = round(150*local_contacts_pix/pix150),
local_no_contacts = round(150*local_no_contacts_pix/pix150),
under_invest = round(150*under_invest_pix/pix150)) %>%
mutate(total = overseas + local_contacts + local_no_contacts + under_invest) %>%
mutate(imported = overseas,
local_plus_uix = local_contacts + local_no_contacts + under_invest,
local_known_only = local_contacts + local_no_contacts + under_invest)
nsw_cases_from_chart_compare <- nsw_cases_from_chart %>%
rename(OS=overseas,
LC=local_contacts,
LNC=local_no_contacts,
UIX=under_invest) %>%
select(notification_date, OS, LC, LNC, UIX) %>%
pivot_longer(-notification_date,
names_to="source",
values_to="n") %>%
mutate(provenance = "health.nsw.gov.au")
# fetch latest data
nsw_incidence_by_source_url <- paste0("https://data.nsw.gov.au/data/dataset",
"/c647a815-5eb7-4df6-8c88-f9c537a4f21e",
"/resource/2f1ba0f3-8c21-4a86-acaf-444be4401a6d",
"/download/covid-19-cases-by-notification-date",
"-and-likely-source-of-infection.csv")
colspec <- cols(notification_date = col_date(format = ""),
likely_source_of_infection = col_character())
nsw_incidence_by_source <- read_csv(nsw_incidence_by_source_url,
col_types = colspec) %>%
rename(source=likely_source_of_infection) %>%
mutate(source=case_when(
source == paste0("Locally acquired - contact of a ",
"confirmed case and/or in a known cluster") ~ "LC",
source == "Locally acquired - contact not identified" ~ "LNC",
source == "Overseas" ~ "OS",
source == "Interstate" ~ "IS",
source == "Under investigation" ~ "UIX",
TRUE ~ "OTH")) %>%
group_by(notification_date, source) %>%
summarise(n=n()) %>%
ungroup() %>%
mutate(provenance = "data.nsw.gov.au")
nsw_incidence_by_source %>%
bind_rows(nsw_cases_from_chart_compare) %>%
ggplot(aes(x=notification_date, y=n, fill=provenance)) +
geom_col(position = "dodge") +
facet_grid(source~.)
nsw_incidence <- nsw_incidence_by_source %>%
bind_rows(nsw_cases_from_chart_compare %>%
filter(notification_date < ymd("2020-03-09"),
notification_date >= ymd("2020-02-29"))) %>%
mutate(source = factor(source)) %>%
pivot_wider(id_cols = c(notification_date, provenance),
names_from = source,
values_from = n,
values_fill = list(n = 0)) %>%
arrange(notification_date)
return(nsw_incidence)
}
# returns NSW incidence by geography tibble
get_raw_nsw_geo_data <- function() {
nsw_incidence_by_geo_url <- paste0("https://data.nsw.gov.au/data/dataset/",
"aefcde60-3b0c-4bc0-9af1-6fe652944ec2/resource/",
"21304414-1ff1-4243-a5d2-f52778048b29/download/",
"covid-19-cases-by-notification-date-and",
"-postcode-local-health-district-and-",
"local-government-area.csv")
colspec <- cols(
notification_date = col_date(format = ""),
postcode = col_character(),
lhd_2010_code = col_character(),
lhd_2010_name = col_character(),
lga_code19 = col_character(),
lga_name19 = col_character())
read_csv(nsw_incidence_by_geo_url, col_types = colspec)
}
get_nsw_postcode_incidence <- function(df) {
df %>%
group_by(notification_date, postcode) %>%
summarise(n=n()) %>%
ungroup() %>%
mutate(provenance = "data.nsw.gov.au")
}
get_nsw_lga_incidence <- function(df) {
df %>%
group_by(notification_date, lga_name19) %>%
summarise(n=n()) %>%
ungroup() %>%
mutate(provenance = "data.nsw.gov.au")
}
get_nsw_lhd_incidence <- function(df) {
df %>%
group_by(notification_date, lhd_2010_name) %>%
summarise(n=n()) %>%
ungroup() %>%
mutate(provenance = "data.nsw.gov.au")
}
# returns NSW incidence by source by geography tibble
get_raw_nsw_source_geo_data <- function() {
nsw_incidence_by_source_geo_url <- paste0("https://data.nsw.gov.au/data/",
"dataset/97ea2424-abaf-4f3e-a9f2",
"-b5c883f42b6a/resource/2776dbb8-",
"f807-4fb2-b1ed-184a6fc2c8aa/",
"download/covid-19-cases-by-",
"notification-date-location-and-",
"likely-source-of-infection.csv")
colspec <- cols(
notification_date = col_date(format = ""),
postcode = col_double(),
likely_source_of_infection = col_character(),
lhd_2010_code = col_character(),
lhd_2010_name = col_character(),
lga_code19 = col_double(),
lga_name19 = col_character())
read_csv(nsw_incidence_by_source_geo_url, col_types = colspec) %>%
rename(source=likely_source_of_infection) %>%
mutate(source=case_when(
source == paste0("Locally acquired - contact of a ",
"confirmed case and/or in a known cluster") ~ "LC",
source == "Locally acquired - contact not identified" ~ "LNC",
source == "Overseas" ~ "OS",
source == "Interstate" ~ "IS",
source == "Under investigation" ~ "UIX",
TRUE ~ "OTH"))
}
# returns NSW incidence by source by geography tibble
get_raw_nsw_testing_geo_result_data <- function() {
nsw_testing_by_geo_result_url <- paste0("https://data.nsw.gov.au/data/dataset/5424aa3b-550d-4637-ae50-7f458ce327f4/resource/cfff5b74-6e2b-4883-bc44-311a214459d5/download/covid-19-tests-by-date-and-location-and-result.csv")
colspec <- cols(
notification_date = col_date(format = ""),
postcode = col_double(),
likely_source_of_infection = col_character(),
lhd_2010_code = col_character(),
lhd_2010_name = col_character(),
lga_code19 = col_double(),
lga_name19 = col_character())
read_csv(nsw_incidence_by_source_geo_url, col_types = colspec) %>%
rename(source=likely_source_of_infection) %>%
mutate(source=case_when(
source == paste0("Locally acquired - contact of a ",
"confirmed case and/or in a known cluster") ~ "LC",
source == "Locally acquired - contact not identified" ~ "LNC",
source == "Overseas" ~ "OS",
source == "Interstate" ~ "IS",
source == "Under investigation" ~ "UIX",
TRUE ~ "OTH"))
}
get_nsw_postcode_incidence <- function(df) {
df %>%
group_by(notification_date, postcode) %>%
summarise(n=n()) %>%
ungroup() %>%
mutate(provenance = "data.nsw.gov.au")
}
get_nsw_lga_incidence <- function(df) {
df %>%
group_by(notification_date, lga_name19) %>%
summarise(n=n()) %>%
ungroup() %>%
mutate(provenance = "data.nsw.gov.au")
}
get_nsw_lhd_incidence <- function(df) {
df %>%
group_by(notification_date, lhd_2010_name) %>%
summarise(n=n()) %>%
ungroup() %>%
mutate(provenance = "data.nsw.gov.au")
}
get_nsw_source_postcode_incidence <- function(df) {
df %>%
group_by(notification_date, source, postcode) %>%
summarise(n=n()) %>%
ungroup() %>%
mutate(provenance = "data.nsw.gov.au")
}
get_nsw_source_lga_incidence <- function(df) {
df %>%
group_by(notification_date, source, lga_code19, lga_name19) %>%
summarise(n=n()) %>%
ungroup() %>%
mutate(provenance = "data.nsw.gov.au")
}
get_nsw_source_lhd_incidence <- function(df) {
df %>%
group_by(notification_date, source, lhd_2010_name) %>%
summarise(n=n()) %>%
ungroup() %>%
mutate(provenance = "data.nsw.gov.au")
}
# checks
'
raw_nsw_geo_data <- get_raw_nsw_geo_data()
nsw_lhd_incidence <- get_nsw_lhd_incidence(raw_nsw_geo_data)
nsw_source_incidence <- get_nsw_data()
nsw_postcode_incidence <- get_nsw_postcode_incidence(raw_nsw_geo_data)
raw_nsw_source_geo_data <- get_raw_nsw_source_geo_data()
nsw_source_postcode_incidence <- get_nsw_source_postcode_incidence(raw_nsw_source_geo_data)
a <- nsw_postcode_incidence %>%
group_by(notification_date) %>%
summarise(postcode_n = sum(n)) %>%
left_join(nsw_source_incidence %>%
group_by(notification_date) %>%
summarise(source_n = sum(LC + OS + IS + LNC + UIX))
) %>%
left_join(nsw_source_postcode_incidence %>%
group_by(notification_date) %>%
summarise(source_postcode_n = sum(n)))
'
raw_nsw_geo_data <- get_raw_nsw_geo_data()
nsw_lhd_incidence <- get_nsw_lhd_incidence(raw_nsw_geo_data)
raw_nsw_source_geo_data <- get_raw_nsw_source_geo_data()
nsw_source_lga_incidence <- get_nsw_source_lga_incidence(raw_nsw_source_geo_data)