UK
nameUk <- map(uk, "name")
ukdf <- map(uk$K02000001, enframe)
ukdf_names <- map(uk$K02000001[2:32], colnames) %>%
enframe()
create_metrics <- function(i){
j <- i -1
ukdf[[i]] %>%
unnest("value") %>%
mutate(metric = ukdf_names$name[j]) %>%
group_by(name) %>%
mutate(id = row_number(),
type = ifelse(id %% 2 == 0, "val", "date")) %>%
spread(type, value) %>%
fill(date, .direction = "down") %>%
fill(val, .direction = "up") %>%
select(-id) %>%
distinct() %>%
mutate(date = as.Date(date),
val = as.numeric(val))
}
uk_metrics <- map_dfr(2:32, create_metrics)
UK summary
date_range_uk <- uk_metrics %>%
group_by(metric) %>%
summarise(range = range(date)) %>%
mutate(ranges = c("start", "end")) %>%
spread(ranges, range) %>%
select(metric, start, end)
range_plot <- function(df){
df %>%
ggplot() +
geom_point(aes(start, forcats::fct_rev(metric)), colour = "blue") +
geom_point(aes(end, forcats::fct_rev(metric)), colour = "goldenrod") +
geom_segment(aes(y = metric, yend = metric, x = start, xend = end), lty = "dotted" ) +
labs(title = "Date ranges for UK dashboard metrics",
y = "") +
theme(plot.title.position = "plot",
plot.background = element_blank(),
axis.text.y = element_text(size = 8))
}
ukp <- range_plot(date_range_uk)
ggplotly(ukp)
Regions
nameRegions <- map(regions, "name")
regiondf <- map(regions$E12000005, enframe)
regiondf_names <- map(regions$E12000005[2:13], colnames) %>%
enframe()
create_metrics_region <- function(i){
j <- i -1
regiondf[[i]] %>%
unnest("value") %>%
mutate(metric = regiondf_names$name[j]) %>%
group_by(name) %>%
mutate(id = row_number(),
type = ifelse(id %% 2 == 0, "val", "date")) %>%
spread(type, value) %>%
fill(date, .direction = "down") %>%
fill(val, .direction = "up") %>%
select(-id) %>%
distinct() %>%
mutate(date = as.Date(date),
val = as.numeric(val))
}
region_metrics <- map_dfr(2:13, create_metrics)
date_range_region <- region_metrics %>%
group_by(metric) %>%
summarise(range = range(date)) %>%
mutate(ranges = c("start", "end")) %>%
spread(ranges, range) %>%
select(metric, start, end)
range_plot <- function(df){
df %>%
ggplot() +
geom_point(aes(start, forcats::fct_rev(metric)), colour = "blue") +
geom_point(aes(end, forcats::fct_rev(metric)), colour = "goldenrod") +
geom_segment(aes(y = metric, yend = metric, x = start, xend = end), lty = "dotted" ) +
labs(title = "Date ranges for dashboard metrics",
y = "") +
theme(plot.title.position = "plot",
plot.background = element_blank(),
axis.text.y = element_text(size = 8))
}
rp <- range_plot(date_range_region)
ggplotly(rp)
England
nameNations <- map(nations, "name")
englanddf <- map(nations$E92000001, enframe)
englanddf_names <- map(nations$E92000001[2:length(englanddf)], colnames) %>%
enframe()
create_metrics_england <- function(i){
j <- i -1
englanddf[[i]] %>%
unnest("value") %>%
mutate(metric = englanddf_names$name[j]) %>%
group_by(name) %>%
mutate(id = row_number(),
type = ifelse(id %% 2 == 0, "val", "date")) %>%
spread(type, value) %>%
fill(date, .direction = "down") %>%
fill(val, .direction = "up") %>%
select(-id) %>%
distinct() %>%
mutate(date = as.Date(date),
val = as.numeric(val))
}
england_metrics <- map_dfr(2:length(englanddf), create_metrics_england)
england_metrics %>%
#count(metric)
filter(metric == "newAdmissions")
date_range_england <- england_metrics %>%
group_by(metric) %>%
summarise(range = range(date)) %>%
mutate(ranges = c("start", "end")) %>%
spread(ranges, range) %>%
select(metric, start, end)
range_plot <- function(df){
df %>%
ggplot() +
geom_point(aes(start, forcats::fct_rev(metric)), colour = "blue") +
geom_point(aes(end, forcats::fct_rev(metric)), colour = "goldenrod") +
geom_segment(aes(y = metric, yend = metric, x = start, xend = end), lty = "dotted" ) +
labs(title = "Date ranges for dashboard metrics",
y = "") +
theme(plot.title.position = "plot",
plot.background = element_blank(),
axis.text.y = element_text(size = 8))
}
ep <- range_plot(date_range_england)
ggplotly(ep)
Scotland
nameNations <- map(nations, "name")
scotdf <- map(nations$S92000003, enframe)
scotdf_names <- map(nations$S92000003[2:length(scotdf)], colnames) %>%
enframe()
create_metrics_scotland <- function(i){
j <- i -1
scotdf[[i]] %>%
unnest("value") %>%
mutate(metric = scotdf_names$name[j]) %>%
group_by(name) %>%
mutate(id = row_number(),
type = ifelse(id %% 2 == 0, "val", "date")) %>%
spread(type, value) %>%
fill(date, .direction = "down") %>%
fill(val, .direction = "up") %>%
select(-id) %>%
distinct() %>%
mutate(date = as.Date(date),
val = as.numeric(val))
}
scotland_metrics <- map_dfr(2:length(scotdf), create_metrics_scotland)
sm <- data.table::setDT(scotland_metrics)
sm <- sm[, .(metric, date, val)] %>%
filter(!str_detect(metric, "[Mm]ale")) %>%
spread(metric, val, fill = 0)
sm %>%
write_csv("dashboard_scotland.csv")
c <- sm %>%
select(date, cumPillarOnePeopleTestedByPublishDate, cumCasesBySpecimenDate) %>%
mutate(pos_case_rate = cumCasesBySpecimenDate/cumPillarOnePeopleTestedByPublishDate) %>%
filter(cumPillarOnePeopleTestedByPublishDate > 0) %>%
ggplot(aes(date, pos_case_rate)) +
geom_line() +
ggtitle("Scotland: positivity rate")
d <- sm %>%
select(date, cumPillarOnePeopleTestedByPublishDate, newCasesBySpecimenDate) %>%
#mutate(pos_case_rate = cumCasesBySpecimenDate/cumPillarOnePeopleTestedByPublishDate) %>%
filter(cumPillarOnePeopleTestedByPublishDate > 0) %>%
ggplot(aes(date, newCasesBySpecimenDate)) +
geom_col() +
geom_smooth(method = "gam", se = FALSE) +
ggtitle("Scotland: new cases")
c + d
date_range_scotland <- scotland_metrics %>%
group_by(metric) %>%
summarise(range = range(date)) %>%
mutate(ranges = c("start", "end")) %>%
spread(ranges, range) %>%
select(metric, start, end)
range_plot <- function(df){
df %>%
ggplot() +
geom_point(aes(start, forcats::fct_rev(metric)), colour = "blue") +
geom_point(aes(end, forcats::fct_rev(metric)), colour = "goldenrod") +
geom_segment(aes(y = metric, yend = metric, x = start, xend = end), lty = "dotted" ) +
labs(title = "Date ranges for dashboard metrics",
y = "") +
theme(plot.title.position = "plot",
plot.background = element_blank(),
axis.text.y = element_text(size = 8))
}
sp <- range_plot(date_range_scotland)
ggplotly(sp)
Wales
nameNations <- map(nations, "name")
walesdf <- map(nations$W92000004, enframe)
walesdf_names <- map(nations$W92000004[2:length(walesdf)], colnames) %>%
enframe()
create_metrics_wales <- function(i){
j <- i -1
walesdf[[i]] %>%
unnest("value") %>%
mutate(metric = walesdf_names$name[j]) %>%
group_by(name) %>%
mutate(id = row_number(),
type = ifelse(id %% 2 == 0, "val", "date")) %>%
spread(type, value) %>%
fill(date, .direction = "down") %>%
fill(val, .direction = "up") %>%
select(-id) %>%
distinct() %>%
mutate(date = as.Date(date),
val = as.numeric(val))
}
wales_metrics <- map_dfr(2:length(walesdf), create_metrics_wales)
wm <- data.table::setDT(wales_metrics)
wm <- wm[, .(metric, date, val)] %>%
filter(!str_detect(metric, "[Mm]ale")) %>%
spread(metric, val, fill = 0)
wm %>%
write_csv("dashboard_wales.csv")
a <- wm %>%
select(date, cumPillarOnePeopleTestedByPublishDate, cumCasesBySpecimenDate) %>%
mutate(pos_case_rate = cumCasesBySpecimenDate/cumPillarOnePeopleTestedByPublishDate) %>%
filter(cumPillarOnePeopleTestedByPublishDate > 0) %>%
ggplot(aes(date, pos_case_rate)) +
geom_line() +
ggtitle("Wales: positivity rate")
b <- wm %>%
select(date, cumPillarOnePeopleTestedByPublishDate, newCasesBySpecimenDate) %>%
#mutate(pos_case_rate = cumCasesBySpecimenDate/cumPillarOnePeopleTestedByPublishDate) %>%
filter(cumPillarOnePeopleTestedByPublishDate > 0) %>%
ggplot(aes(date, newCasesBySpecimenDate)) +
geom_col() +
geom_smooth(method = "gam", se = FALSE) +
ggtitle("Wales: new cases")
a + b
date_range_wales <- wales_metrics %>%
group_by(metric) %>%
summarise(range = range(date)) %>%
mutate(ranges = c("start", "end")) %>%
spread(ranges, range) %>%
select(metric, start, end)
range_plot <- function(df){
df %>%
ggplot() +
geom_point(aes(start, forcats::fct_rev(metric)), colour = "blue") +
geom_point(aes(end, forcats::fct_rev(metric)), colour = "goldenrod") +
geom_segment(aes(y = metric, yend = metric, x = start, xend = end), lty = "dotted" ) +
labs(title = "Date ranges for dashboard metrics",
y = "") +
theme(plot.title.position = "plot",
plot.background = element_blank(),
axis.text.y = element_text(size = 8))
}
wp <- range_plot(date_range_wales)
ggplotly(wp)
Northern Ireland
nameNations <- map(nations, "name")
nidf <- map(nations$N92000002, enframe)
nidf_names <- map(nations$N92000002[2:length(nidf)], colnames) %>%
enframe()
create_metrics_ni <- function(i){
j <- i -1
nidf[[i]] %>%
unnest("value") %>%
mutate(metric = nidf_names$name[j]) %>%
group_by(name) %>%
mutate(id = row_number(),
type = ifelse(id %% 2 == 0, "val", "date")) %>%
spread(type, value) %>%
fill(date, .direction = "down") %>%
fill(val, .direction = "up") %>%
select(-id) %>%
distinct() %>%
mutate(date = as.Date(date),
val = as.numeric(val))
}
ni_metrics <- map_dfr(2:length(nidf), create_metrics_ni)
ni_metrics %>%
#count(metric)
filter(metric == "newAdmissions")
date_range_ni <- ni_metrics %>%
group_by(metric) %>%
summarise(range = range(date)) %>%
mutate(ranges = c("start", "end")) %>%
spread(ranges, range) %>%
select(metric, start, end)
range_plot <- function(df){
df %>%
ggplot() +
geom_point(aes(start, forcats::fct_rev(metric)), colour = "blue") +
geom_point(aes(end, forcats::fct_rev(metric)), colour = "goldenrod") +
geom_segment(aes(y = metric, yend = metric, x = start, xend = end), lty = "dotted" ) +
labs(title = "Date ranges for dashboard metrics",
y = "") +
theme(plot.title.position = "plot",
plot.background = element_blank(),
axis.text.y = element_text(size = 8))
}
nip <- range_plot(date_range_ni)
ggplotly(nip)
LA - England
nameLA <- map(la, "name")
eladf <- map(la$E06000018, enframe)
eladf_names <- map(la$E06000018[2:length(eladf)], colnames) %>%
enframe()
create_metrics_ela <- function(i){
j <- i -1
eladf[[i]] %>%
unnest("value") %>%
mutate(metric = eladf_names$name[j]) %>%
group_by(name) %>%
mutate(id = row_number(),
type = ifelse(id %% 2 == 0, "val", "date")) %>%
spread(type, value) %>%
fill(date, .direction = "down") %>%
fill(val, .direction = "up") %>%
select(-id) %>%
distinct() %>%
mutate(date = as.Date(date),
val = as.numeric(val))
}
ela_metrics <- map_dfr(2:length(eladf), create_metrics_ela)
date_range_ela <- ela_metrics %>%
group_by(metric) %>%
summarise(range = range(date)) %>%
mutate(ranges = c("start", "end")) %>%
spread(ranges, range) %>%
select(metric, start, end)
range_plot <- function(df){
df %>%
ggplot() +
geom_point(aes(start, forcats::fct_rev(metric)), colour = "blue") +
geom_point(aes(end, forcats::fct_rev(metric)), colour = "goldenrod") +
geom_segment(aes(y = metric, yend = metric, x = start, xend = end), lty = "dotted" ) +
labs(title = "Date ranges for dashboard metrics",
y = "") +
theme(plot.title.position = "plot",
plot.background = element_blank(),
axis.text.y = element_text(size = 8))
}
elap <- range_plot(date_range_ela)
ggplotly(elap)
LA - Wales
nameLA <- map(la, "name")
wladf <- map(la$W06000024, enframe)
wladf_names <- map(la$W06000024[2:length(wladf)], colnames) %>%
enframe()
create_metrics_wla <- function(i){
j <- i -1
wladf[[i]] %>%
unnest("value") %>%
mutate(metric = wladf_names$name[j]) %>%
group_by(name) %>%
mutate(id = row_number(),
type = ifelse(id %% 2 == 0, "val", "date")) %>%
spread(type, value) %>%
fill(date, .direction = "down") %>%
fill(val, .direction = "up") %>%
select(-id) %>%
distinct() %>%
mutate(date = as.Date(date),
val = as.numeric(val))
}
wla_metrics <- map_dfr(2:length(wladf), create_metrics_wla)
date_range_wla <- wla_metrics %>%
group_by(metric) %>%
summarise(range = range(date)) %>%
mutate(ranges = c("start", "end")) %>%
spread(ranges, range) %>%
select(metric, start, end)
range_plot <- function(df){
df %>%
ggplot() +
geom_point(aes(start, forcats::fct_rev(metric)), colour = "blue") +
geom_point(aes(end, forcats::fct_rev(metric)), colour = "goldenrod") +
geom_segment(aes(y = metric, yend = metric, x = start, xend = end), lty = "dotted" ) +
labs(title = "Date ranges for dashboard metrics",
y = "") +
theme(plot.title.position = "plot",
plot.background = element_blank(),
axis.text.y = element_text(size = 8))
}
wlap <- range_plot(date_range_wla)
ggplotly(wlap)
LA - Scotland
nameLA <- map(la, "name")
sladf <- map(la$S12000005, enframe)
sladf_names <- map(la$S12000041[2:length(sladf)], colnames) %>%
enframe()
create_metrics_sla <- function(i){
j <- i -1
sladf[[i]] %>%
unnest("value") %>%
mutate(metric = sladf_names$name[j]) %>%
group_by(name) %>%
mutate(id = row_number(),
type = ifelse(id %% 2 == 0, "val", "date")) %>%
spread(type, value) %>%
fill(date, .direction = "down") %>%
fill(val, .direction = "up") %>%
select(-id) %>%
distinct() %>%
mutate(date = as.Date(date),
val = as.numeric(val))
}
sla_metrics <- map_dfr(2:length(sladf), create_metrics_sla)
date_range_sla <- sla_metrics %>%
group_by(metric) %>%
summarise(range = range(date)) %>%
mutate(ranges = c("start", "end")) %>%
spread(ranges, range) %>%
select(metric, start, end)
range_plot <- function(df){
df %>%
ggplot() +
geom_point(aes(start, forcats::fct_rev(metric)), colour = "blue") +
geom_point(aes(end, forcats::fct_rev(metric)), colour = "goldenrod") +
geom_segment(aes(y = metric, yend = metric, x = start, xend = end), lty = "dotted" ) +
labs(title = "Date ranges for dashboard metrics",
y = "") +
theme(plot.title.position = "plot",
plot.background = element_blank(),
axis.text.y = element_text(size = 8))
}
slap <- range_plot(date_range_sla)
ggplotly(slap)
NHS regions
nameNHSR <- map(nhs_regions, "name")
nhsrdf <- map(nhs_regions$E40000006, enframe)
nhsrdf_names <- map(nhs_regions$E40000006[2:length(nhsrdf)], colnames) %>%
enframe()
create_metrics_nhsr <- function(i){
j <- i -1
nhsrdf[[i]] %>%
unnest("value") %>%
mutate(metric = nhsrdf_names$name[j]) %>%
group_by(name) %>%
mutate(id = row_number(),
type = ifelse(id %% 2 == 0, "val", "date")) %>%
spread(type, value) %>%
fill(date, .direction = "down") %>%
fill(val, .direction = "up") %>%
select(-id) %>%
distinct() %>%
mutate(date = as.Date(date),
val = as.numeric(val))
}
nhsr_metrics <- map_dfr(2:length(nhsrdf), create_metrics_nhsr)
date_range_nhsr <- nhsr_metrics %>%
group_by(metric) %>%
summarise(range = range(date)) %>%
mutate(ranges = c("start", "end")) %>%
spread(ranges, range) %>%
select(metric, start, end)
range_plot <- function(df){
df %>%
ggplot() +
geom_point(aes(start, forcats::fct_rev(metric)), colour = "blue") +
geom_point(aes(end, forcats::fct_rev(metric)), colour = "goldenrod") +
geom_segment(aes(y = metric, yend = metric, x = start, xend = end), lty = "dotted" ) +
labs(title = "Date ranges for dashboard metrics",
y = "") +
theme(plot.title.position = "plot",
plot.background = element_blank(),
axis.text.y = element_text(size = 8))
}
g <- range_plot(date_range_nhsr)
ggplotly(g)