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02_describe_service_use.R
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02_describe_service_use.R
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################################################################################
########## DESCRIBE SERVICE USE ##########
################################################################################
# Time periods: 1 month, 3 months, 1 year
# Versions for people with complete GP record
# Convert stats to zero if n_atleast1 is zero
# Service use by cohort
# Significance test difference in service use means by cohort
# Service use by quarter
# Service use by cohort and place of death
# Significance test difference in service use means by cohort for each place of death
# Service use by quarter and place of death
################################################################################
########## Libraries ##########
library("tidyverse")
library("lubridate")
################################################################################
########## Save location ##########
fs::dir_create(here::here("output", "describe_service_use"))
fs::dir_create(here::here("output", "describe_service_use", "plots"))
fs::dir_create(here::here("output", "describe_service_use", "complete_gp_history"))
fs::dir_create(here::here("output", "describe_service_use", "complete_gp_history", "plots"))
################################################################################
########## NT chart style ##########
# Nuffield Trust colour list
NT_colours <- c(
`NT ink` = "#271544",
`white` = "#FFFFFF",
`NT iris` = "#AC8ACF",
`cool black` = "#0E1B26",
`cool dark grey` = "#556370",
`cool mid grey` = "#9AA0AA",
`cool light grey` = "#F4F4F4",
`bright purple` = "#9F67FF",
`light purple 1` = "#D3C4FC",
`light purple 2` = "#B39DFF",
`dark purple 1` = "#7140EA",
`dark purple 2` = "#49148C",
`bright blue` = "#0066F4",
`light blue 1` = "#99DBFF",
`light blue 2` = "#63B2FF",
`dark blue 1` = "#005AC7",
`dark blue 2` = "#192889",
`bright red` = "#FF6B57",
`light red 1` = "#FFCFC9",
`light red 2` = "#FF997F",
`dark red 1` = "#B71C1C",
`dark red 2` = "#700C28",
`bright yellow` = "#EABE17",
`light yellow 1` = "#FDEA9D",
`light yellow 2` = "#F4D05A",
`dark yellow 1` = "#DD931C",
`dark yellow 2` = "#B26605",
`bright green` = "#00C27A",
`light green 1` = "#8BF8BD",
`light green 2` = "#39DA91",
`dark green 1` = "#00823F",
`dark green 2` = "#195442",
`bright cyan` = "#4DCFF5",
`light cyan 1` = "#9EF7FF",
`light cyan 2` = "#6AE8F9",
`dark cyan 1` = "#008CB3",
`dark cyan 2` = "#004C70"
)
NT_colour <- function(index = NULL, named = FALSE){
if(is.null(index)){
index <- names(NT_colours)
}
return_value <- NT_colours[index]
if (!named) {
names(return_value) <- NULL
}
return(return_value)
}
####################################
# NT colour palette
NT_palette <- function(NT_theme = NULL, reverse = FALSE, ...) {
function(n) {
stopifnot(n <= 5 | (n <= 12 & (is.null(NT_theme) | NT_theme == "bright")))
colour_indices <-
if (n == 1 & is.null(NT_theme)) { "bright purple" }
else if (n == 2 & is.null(NT_theme)) { c("bright purple", "bright green") }
else if (n == 3 & is.null(NT_theme)) { c("bright purple", "bright green", "bright blue") }
else if (n == 4 & is.null(NT_theme)) { c("bright purple", "bright green", "bright blue", "bright yellow") }
else if (n == 5 & is.null(NT_theme)) { c("bright purple", "bright green", "bright blue", "bright yellow", "bright red") }
else if (n == 6 & is.null(NT_theme)) { c("bright purple", "bright green", "bright blue", "bright yellow", "bright red", "bright cyan") }
else if (n == 7 & is.null(NT_theme)) { c("bright purple", "bright green", "bright blue", "bright yellow", "bright red", "bright cyan", "light purple 1") }
else if (n == 8 & is.null(NT_theme)) { c("bright purple", "bright green", "bright blue", "bright yellow", "bright red", "bright cyan", "light purple 1", "light green 1") }
else if (n == 9 & is.null(NT_theme)) { c("bright purple", "bright green", "bright blue", "bright yellow", "bright red", "bright cyan", "light purple 1", "light green 1", "light blue 1") }
else if (n == 10 & is.null(NT_theme)) { c("bright purple", "bright green", "bright blue", "bright yellow", "bright red", "bright cyan", "light purple 1", "light green 1", "light blue 1", "light yellow 1") }
else if (n == 11 & is.null(NT_theme)) { c("bright purple", "bright green", "bright blue", "bright yellow", "bright red", "bright cyan", "light purple 1", "light green 1", "light blue 1", "light yellow 1", "light red 1") }
else if (n == 12 & is.null(NT_theme)) { c("bright purple", "bright green", "bright blue", "bright yellow", "bright red", "bright cyan", "light purple 1", "light green 1", "light blue 1", "light yellow 1", "light red 1", "light cyan 1") }
else if (n == 1 & NT_theme == "bright") { "bright purple" }
else if (n == 2 & NT_theme == "bright") { c("bright purple", "bright green") }
else if (n == 3 & NT_theme == "bright") { c("bright purple", "bright green", "bright blue") }
else if (n == 4 & NT_theme == "bright") { c("bright purple", "bright green", "bright blue", "bright yellow") }
else if (n == 5 & NT_theme == "bright") { c("bright purple", "bright green", "bright blue", "bright yellow", "bright red") }
else if (n == 6 & NT_theme == "bright") { c("bright purple", "bright green", "bright blue", "bright yellow", "bright red", "bright cyan") }
else if (n == 7 & NT_theme == "bright") { c("bright purple", "bright green", "bright blue", "bright yellow", "bright red", "bright cyan", "light purple 1") }
else if (n == 8 & NT_theme == "bright") { c("bright purple", "bright green", "bright blue", "bright yellow", "bright red", "bright cyan", "light purple 1", "light green 1") }
else if (n == 9 & NT_theme == "bright") { c("bright purple", "bright green", "bright blue", "bright yellow", "bright red", "bright cyan", "light purple 1", "light green 1", "light blue 1") }
else if (n == 10 & NT_theme == "bright") { c("bright purple", "bright green", "bright blue", "bright yellow", "bright red", "bright cyan", "light purple 1", "light green 1", "light blue 1", "light yellow 1") }
else if (n == 11 & NT_theme == "bright") { c("bright purple", "bright green", "bright blue", "bright yellow", "bright red", "bright cyan", "light purple 1", "light green 1", "light blue 1", "light yellow 1", "light red 1") }
else if (n == 12 & NT_theme == "bright") { c("bright purple", "bright green", "bright blue", "bright yellow", "bright red", "bright cyan", "light purple 1", "light green 1", "light blue 1", "light yellow 1", "light red 1", "light cyan 1") }
else if (n == 1 & NT_theme == "purple") { "bright purple" }
else if (n == 2 & NT_theme == "purple") { c("dark purple 2", "bright purple") }
else if (n == 3 & NT_theme == "purple") { c("dark purple 2", "bright purple", "light purple 2") }
else if (n == 4 & NT_theme == "purple") { c("dark purple 2", "dark purple 1", "bright purple", "light purple 2") }
else if (n == 5 & NT_theme == "purple") { c("dark purple 2", "dark purple 1", "bright purple", "light purple 2", "light purple 1") }
else if (n == 1 & NT_theme == "blue") { "bright blue" }
else if (n == 2 & NT_theme == "blue") { c("dark blue 2", "bright blue") }
else if (n == 3 & NT_theme == "blue") { c("dark blue 2", "bright blue", "light blue 2") }
else if (n == 4 & NT_theme == "blue") { c("dark blue 2", "dark blue 1", "bright blue", "light blue 2") }
else if (n == 5 & NT_theme == "blue") { c("dark blue 2", "dark blue 1", "bright blue", "light blue 2", "light blue 1") }
else if (n == 1 & NT_theme == "red") { "bright red" }
else if (n == 2 & NT_theme == "red") { c("dark red 2", "bright red") }
else if (n == 3 & NT_theme == "red") { c("dark red 2", "bright red", "light red 2") }
else if (n == 4 & NT_theme == "red") { c("dark red 2", "dark red 1", "bright red", "light red 2") }
else if (n == 5 & NT_theme == "red") { c("dark red 2", "dark red 1", "bright red", "light red 2", "light red 1") }
else if (n == 1 & NT_theme == "yellow") { "bright yellow" }
else if (n == 2 & NT_theme == "yellow") { c("dark yellow 2", "bright yellow") }
else if (n == 3 & NT_theme == "yellow") { c("dark yellow 2", "bright yellow", "light yellow 2") }
else if (n == 4 & NT_theme == "yellow") { c("dark yellow 2", "dark yellow 1", "bright yellow", "light yellow 2") }
else if (n == 5 & NT_theme == "yellow") { c("dark yellow 2", "dark yellow 1", "bright yellow", "light yellow 2", "light yellow 1") }
else if (n == 1 & NT_theme == "green") { "bright green" }
else if (n == 2 & NT_theme == "green") { c("dark green 2", "bright green") }
else if (n == 3 & NT_theme == "green") { c("dark green 2", "bright green", "light green 2") }
else if (n == 4 & NT_theme == "green") { c("dark green 2", "dark green 1", "bright green", "light green 2") }
else if (n == 5 & NT_theme == "green") { c("dark green 2", "dark green 1", "bright green", "light green 2", "light green 1") }
else if (n == 1 & NT_theme == "cyan") { "bright cyan" }
else if (n == 2 & NT_theme == "cyan") { c("dark cyan 2", "bright cyan") }
else if (n == 3 & NT_theme == "cyan") { c("dark cyan 2", "bright cyan", "light cyan 2") }
else if (n == 4 & NT_theme == "cyan") { c("dark cyan 2", "dark cyan 1", "bright cyan", "light cyan 2") }
else if (n == 5 & NT_theme == "cyan") { c("dark cyan 2", "dark cyan 1", "bright cyan", "light cyan 2", "light cyan 1") }
return_colours <- NT_colour(colour_indices)
if (reverse) {
return_colours <- rev(NT_colour(colour_indices))
}
return(return_colours)
}
}
####################################
# NT colour scale
scale_colour_NT <- function(palette = NT_palette(NT_theme = NULL, reverse = FALSE, ...), ...) {
ggplot2::discrete_scale(
aesthetics = "colour",
scale_name = "NT1",
palette = palette,
na.value = "#9AA0AA",
...
)
}
####################################
# NT fill scale
scale_fill_NT <- function(palette = NT_palette(NT_theme = NULL, reverse = FALSE, ...), ...) {
ggplot2::discrete_scale(
aesthetics = "fill",
scale_name = "NT2",
palette = palette,
na.value = "#9AA0AA",
...
)
}
####################################
# NT ggplot theme
NT_style <- function(){
font <- "TT Arial"
family <- "sans"
theme_minimal() %+replace%
theme(
# Background elements
panel.background = element_rect(fill = "#F4F4F4", colour = "#F4F4F4"),
panel.border = element_blank(),
plot.background = element_rect(fill = "#F4F4F4", colour = "#F4F4F4"),
plot.margin = margin(t = 0.5, r = 0.5, b = 0.5, l = 0.5, unit ="cm"),
# Grid elements
axis.ticks = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_line(colour = "#9AA0AA", size = 0.3),
panel.grid.minor = element_blank(),
panel.spacing = unit(0.5, "cm"),
# Text elements
axis.text.x = element_text(colour = "#9AA0AA", size = 8, family = "sans", vjust = 0),
axis.text.y = element_text(colour = "#9AA0AA", size = 8, family = "sans"),
axis.title.x = element_text(margin = margin(t = 0.3, r = 0, b = 0, l = 0, unit ="cm"), colour = "#271544", size = 8, face = "bold"),
axis.title.y = element_text(margin = margin(t = 0, r = 0.3, b = 0, l = 0, unit ="cm"), colour = "#271544", size = 8, face = "bold", angle = 90),
legend.text = element_text(colour = "#271544", size = 8, face = "bold", family = "sans"),
legend.title = element_blank(),
plot.caption = element_text(margin = margin(t = 0.3, r = 0, b = 0, l = 0, unit ="cm"), colour = "#271544", size = 8, hjust = 1, vjust = 1),
plot.title = element_text(margin = margin(t = 0, r = 0, b = 0.3, l = 0, unit ="cm"), colour = "#271544", size = 10, face = "bold", hjust = 0),
plot.title.position = "plot",
strip.text = element_text(margin = margin(t = 0, r = 0, b = 0.3, l = 0, unit ="cm"), colour = "#271544", size = 8, face = "bold"),
# Legend elements
legend.background = element_blank(),
legend.box.background = element_blank(),
legend.box.margin = margin(t = 0, r = 0, b = 0, l = 0, unit ="cm"),
legend.key = element_blank(),
legend.key.size = unit(0.4, "cm"),
legend.margin = margin(t = 0, r = 0, b = 0, l = 0, unit ="cm"),
legend.position = "bottom",
legend.spacing.x = unit(0.1, "cm"),
legend.spacing.y = unit(0.1, "cm")
)
}
################################################################################
########## Import data ##########
# Convert dod to date variable
# Create cohort flag and additional flag to identify deaths in our revised 9 month cohort period
# Death quarter variable starting in March so it is quarters of the cohort period rather than calendar or fiscal quarters
# Create grouped and renamed characteristic variables
# Join on region and LA imd quintile based on patient address
# Join on LA imd quintile based on GP address
df_input <- arrow::read_feather(file = here::here("output", "input.feather")) %>%
mutate(dod_ons = as_date(dod_ons)
, cohort = case_when(dod_ons >= as_date("2019-03-01") & dod_ons <= as_date("2020-02-29") ~ 0
, dod_ons >= as_date("2020-03-01") & dod_ons <= as_date("2021-02-28") ~ 1
, TRUE ~ NA_real_)
, study_cohort = case_when(dod_ons >= as_date("2019-06-01") & dod_ons <= as_date("2020-02-29") ~ 0
, dod_ons >= as_date("2020-06-01") & dod_ons <= as_date("2021-02-28") ~ 1
, TRUE ~ NA_real_)
, study_quarter = case_when(month(dod_ons) %in% c(3, 4, 5) & year(dod_ons) == 2019 ~ 1
, month(dod_ons) %in% c(6, 7, 8) & year(dod_ons) == 2019 ~ 2
, month(dod_ons) %in% c(9, 10, 11) & year(dod_ons) == 2019 ~ 3
, (month(dod_ons) == 12 & year(dod_ons) == 2019) | (month(dod_ons) %in% c(1, 2) & year(dod_ons) == 2020) ~ 4
, month(dod_ons) %in% c(3, 4, 5) & year(dod_ons) == 2020 ~ 5
, month(dod_ons) %in% c(6, 7, 8) & year(dod_ons) == 2020 ~ 6
, month(dod_ons) %in% c(9, 10, 11) & year(dod_ons) == 2020 ~ 7
, (month(dod_ons) == 12 & year(dod_ons) == 2020) | (month(dod_ons) %in% c(1, 2) & year(dod_ons) == 2021) ~ 8)
, study_month = floor_date(dod_ons, unit = "month")
, cod_ons_3 = str_sub(cod_ons, 1, 3)
, cod_ons_4 = str_sub(cod_ons, 1, 5)
, pod_ons_new = case_when(pod_ons == "Elsewhere" | pod_ons == "Other communal establishment" ~ "Elsewhere/other"
, TRUE ~ as.character(pod_ons))
, agegrp = case_when(age >= 0 & age <= 10 ~ "00-09"
, age >= 10 & age <= 19 ~ "10-19"
, age >= 20 & age <= 29 ~ "20-29"
, age >= 30 & age <= 39 ~ "30-39"
, age >= 40 & age <= 49 ~ "40-49"
, age >= 50 & age <= 59 ~ "50-59"
, age >= 60 & age <= 69 ~ "60-69"
, age >= 70 & age <= 79 ~ "70-79"
, age >= 80 & age <= 89 ~ "80-89"
, age >= 90 ~ "90+"
, TRUE ~ NA_character_)
, ltc_count = arrow::read_feather(file = here::here("output", "input.feather")) %>% select(starts_with("ltc_")) %>% rowSums()
, ltcgrp = case_when(ltc_count < 5 ~ as.character(ltc_count)
, ltc_count >= 5 ~ "5+"
, TRUE ~ NA_character_)
, codgrp = case_when(cod_ons_4 %in% c("U071", "U072") ~ "Covid-19"
, cod_ons_3 >= "J09" & cod_ons_3 <= "J18" ~ "Flu and pneumonia"
, (cod_ons_3 >= "J00" & cod_ons_3 <= "J08") | (cod_ons_3 >= "J19" & cod_ons_3 <= "J99") ~ "Other respiratory diseases"
, cod_ons_3 %in% c("F01", "F03", "G30") ~ "Dementia and Alzheimer's disease"
, cod_ons_3 >= "I00" & cod_ons_3 <= "I99" ~ "Circulatory diseases"
, cod_ons_3 >= "C00" & cod_ons_3 <= "C99" ~ "Cancer"
, TRUE ~ "All other causes")
, palcare = ltc_palcare1
, nopalcare = ltc_palcare2
, rural_urban = case_when(rural_class %in% c(1, 2, 3, 4) ~ "Urban"
, rural_class %in% c(5, 6, 7, 8) ~ "Rural"
, TRUE ~ NA_character_)) %>%
left_join(read_csv(here::here("docs", "lookups", "msoa_lad_rgn_2020.csv")) %>%
select(msoa11cd, lad20cd, rgn20cd) %>%
rename(region = rgn20cd)
, by = c("msoa" = "msoa11cd")) %>%
left_join(read_csv(here::here("docs", "lookups", "lad_imd_2019.csv")) %>%
rename(imd_quintile_la = imd19_quintile)
, by = "lad20cd") %>%
left_join(read_csv(here::here("docs", "lookups", "msoa_lad_rgn_2020.csv")) %>%
select(msoa11cd, lad20cd) %>%
rename(la_gp = lad20cd)
, by = c("msoa" = "msoa11cd")) %>%
left_join(read_csv(here::here("docs", "lookups", "lad_imd_2019.csv")) %>%
select(lad20cd, imd19_quintile) %>%
rename(imd_quintile_la_gp = imd19_quintile)
, by = "lad20cd") %>%
mutate(imd_quintile_la = case_when(is.na(imd_quintile_la) ~ 0
, TRUE ~ imd_quintile_la)
, imd_quintile_la_gp = case_when(is.na(imd_quintile_la_gp) ~ 0
, TRUE ~ imd_quintile_la_gp))
################################################################################
########## Descriptive stats service use by cohort ##########
# Mean, standard deviation and number of people with at least 1 instance of each activity type
service_use_mean_cohort <- df_input %>%
filter(!is.na(study_cohort)) %>%
select(cohort, ends_with("_1m"), ends_with("_3m"), ends_with("_1y")) %>%
select(-contains("gp_hist")) %>%
pivot_longer(cols = -c(cohort), names_to = "measure", values_to = "value") %>%
group_by(cohort, measure) %>%
summarise(n = n()
, mean = mean(value, na.rm = TRUE)
, sd = sd(value, na.rm = TRUE)
, n_atleast1 = sum(value >= 1, na.rm = TRUE)) %>%
mutate(n = plyr::round_any(n, 10)
, n_atleast1 = plyr::round_any(n_atleast1, 10)
, mean = case_when(n_atleast1 == 0 ~ 0
, TRUE ~ round(mean, 3))
, sd = case_when(n_atleast1 == 0 ~ 0
, TRUE ~ round(sd, 3))) %>%
pivot_wider(names_from = cohort, names_prefix = "cohort_", values_from = c(n, mean, sd, n_atleast1)) %>%
mutate(activity = str_sub(measure, 1, -4)
, period = str_sub(measure, -2, -1)) %>%
arrange(factor(period, levels = c("1m", "3m", "1y")), activity) %>%
mutate(mean_ratio = round((mean_cohort_1/mean_cohort_0),2))
write_csv(service_use_mean_cohort, here::here("output", "describe_service_use", "service_use_mean_cohort.csv"))
# Calculate the same just for people with complete gp history
gp_service_use_mean_cohort <- df_input %>%
filter(!is.na(study_cohort) & gp_hist_1m == TRUE) %>%
select(cohort, ends_with("_1m")) %>%
select(-contains("gp_hist")) %>%
pivot_longer(cols = -c(cohort), names_to = "measure", values_to = "value") %>%
group_by(cohort, measure) %>%
summarise(n = n()
, mean = mean(value, na.rm = TRUE)
, sd = sd(value, na.rm = TRUE)
, n_atleast1 = sum(value >= 1, na.rm = TRUE)) %>%
mutate(n = plyr::round_any(n, 10)
, n_atleast1 = plyr::round_any(n_atleast1, 10)
, mean = case_when(n_atleast1 == 0 ~ 0
, TRUE ~ round(mean, 3))
, sd = case_when(n_atleast1 == 0 ~ 0
, TRUE ~ round(sd, 3))) %>%
pivot_wider(names_from = cohort, names_prefix = "cohort_", values_from = c(n, mean, sd, n_atleast1)) %>%
bind_rows(df_input %>%
filter(!is.na(study_cohort) & gp_hist_3m == TRUE) %>%
select(cohort, ends_with("_3m")) %>%
select(-contains("gp_hist")) %>%
pivot_longer(cols = -c(cohort), names_to = "measure", values_to = "value") %>%
group_by(cohort, measure) %>%
summarise(n = n()
, mean = mean(value, na.rm = TRUE)
, sd = sd(value, na.rm = TRUE)
, n_atleast1 = sum(value >= 1, na.rm = TRUE)) %>%
mutate(n = plyr::round_any(n, 10)
, n_atleast1 = plyr::round_any(n_atleast1, 10)
, mean = case_when(n_atleast1 == 0 ~ 0
, TRUE ~ round(mean, 3))
, sd = case_when(n_atleast1 == 0 ~ 0
, TRUE ~ round(sd, 3))) %>%
pivot_wider(names_from = cohort, names_prefix = "cohort_", values_from = c(n, mean, sd, n_atleast1))) %>%
bind_rows(df_input %>%
filter(!is.na(study_cohort) & gp_hist_1y == TRUE) %>%
select(cohort, ends_with("_1y")) %>%
select(-contains("gp_hist")) %>%
pivot_longer(cols = -c(cohort), names_to = "measure", values_to = "value") %>%
group_by(cohort, measure) %>%
summarise(n = n()
, mean = mean(value, na.rm = TRUE)
, sd = sd(value, na.rm = TRUE)
, n_atleast1 = sum(value >= 1, na.rm = TRUE)) %>%
mutate(n = plyr::round_any(n, 10)
, n_atleast1 = plyr::round_any(n_atleast1, 10)
, mean = case_when(n_atleast1 == 0 ~ 0
, TRUE ~ round(mean, 3))
, sd = case_when(n_atleast1 == 0 ~ 0
, TRUE ~ round(sd, 3))) %>%
pivot_wider(names_from = cohort, names_prefix = "cohort_", values_from = c(n, mean, sd, n_atleast1))) %>%
mutate(activity = str_sub(measure, 1, -4)
, period = str_sub(measure, -2, -1)) %>%
arrange(factor(period, levels = c("1m", "3m", "1y")), activity) %>%
mutate(mean_ratio = round((mean_cohort_1/mean_cohort_0),2))
write_csv(gp_service_use_mean_cohort, here::here("output", "describe_service_use", "complete_gp_history", "gp_service_use_mean_cohort.csv"))
################################################################################
########## Significance test cohorts mean difference ##########
service_use_mean_cohort_sigtest <- tibble(measure = unique(service_use_mean_cohort$measure)) %>%
mutate(dataset_0 = map(measure, function(var) df_input %>%
filter(!is.na(study_cohort)) %>%
select(cohort, ends_with("_1m"), ends_with("_3m"), ends_with("_1y")) %>%
select(-contains("gp_hist")) %>%
pivot_longer(cols = -c(cohort), names_to = "measure", values_to = "value") %>%
filter(cohort == 0 & measure == var))
, dataset_1 = map(measure, function(var) df_input %>%
filter(!is.na(study_cohort)) %>%
select(cohort, ends_with("_1m"), ends_with("_3m"), ends_with("_1y")) %>%
select(-contains("gp_hist")) %>%
pivot_longer(cols = -c(cohort), names_to = "measure", values_to = "value") %>%
filter(cohort == 1 & measure == var))
, normality_pvalue = map2_dbl(dataset_0, dataset_1, function(dset0, dset1) round(ks.test(dset0$value, dset1$value)$p.value, 4))
, equal_variance_pvalue = map2_dbl(dataset_0, dataset_1, function(dset0, dset1) round(var.test(dset0$value, dset1$value)$p.value, 4))
, ttest_pvalue = map2_dbl(dataset_0, dataset_1, function(dset0, dset1) round(t.test(dset0$value, dset1$value, var.equal = TRUE)$p.value, 4))
, welch_pvalue = map2_dbl(dataset_0, dataset_1, function(dset0, dset1) round(t.test(dset0$value, dset1$value, var.equal = FALSE)$p.value, 4))
, wilcox_pvalue = map2_dbl(dataset_0, dataset_1, function(dset0, dset1) round(wilcox.test(dset0$value, dset1$value, exact = FALSE)$p.value, 4))) %>%
select(-dataset_0, -dataset_1)
write_csv(service_use_mean_cohort_sigtest, here::here("output", "describe_service_use", "service_use_mean_cohort_sigtest.csv"))
# Calculate the same just for people with complete gp history
gp_service_use_mean_cohort_sigtest <- tibble(measure = unique(gp_service_use_mean_cohort$measure)) %>%
mutate(dataset_0 = map(measure, function(var) df_input %>%
filter(!is.na(study_cohort) & gp_hist_1m == TRUE) %>%
select(cohort, ends_with("_1m")) %>%
select(-contains("gp_hist")) %>%
pivot_longer(cols = -c(cohort), names_to = "measure", values_to = "value") %>%
bind_rows(df_input %>%
filter(!is.na(study_cohort) & gp_hist_3m == TRUE) %>%
select(cohort, ends_with("_3m")) %>%
select(-contains("gp_hist")) %>%
pivot_longer(cols = -c(cohort), names_to = "measure", values_to = "value")) %>%
bind_rows(df_input %>%
filter(!is.na(study_cohort) & gp_hist_1y == TRUE) %>%
select(cohort, ends_with("_1y")) %>%
select(-contains("gp_hist")) %>%
pivot_longer(cols = -c(cohort), names_to = "measure", values_to = "value")) %>%
filter(cohort == 0 & measure == var))
, dataset_1 = map(measure, function(var) df_input %>%
filter(!is.na(study_cohort) & gp_hist_1m == TRUE) %>%
select(cohort, ends_with("_1m")) %>%
select(-contains("gp_hist")) %>%
pivot_longer(cols = -c(cohort), names_to = "measure", values_to = "value") %>%
bind_rows(df_input %>%
filter(!is.na(study_cohort) & gp_hist_3m == TRUE) %>%
select(cohort, ends_with("_3m")) %>%
select(-contains("gp_hist")) %>%
pivot_longer(cols = -c(cohort), names_to = "measure", values_to = "value")) %>%
bind_rows(df_input %>%
filter(!is.na(study_cohort) & gp_hist_1y == TRUE) %>%
select(cohort, ends_with("_1y")) %>%
select(-contains("gp_hist")) %>%
pivot_longer(cols = -c(cohort), names_to = "measure", values_to = "value")) %>%
filter(cohort == 1 & measure == var))
, normality_pvalue = map2_dbl(dataset_0, dataset_1, function(dset0, dset1) round(ks.test(dset0$value, dset1$value)$p.value, 4))
, equal_variance_pvalue = map2_dbl(dataset_0, dataset_1, function(dset0, dset1) round(var.test(dset0$value, dset1$value)$p.value, 4))
, ttest_pvalue = map2_dbl(dataset_0, dataset_1, function(dset0, dset1) round(t.test(dset0$value, dset1$value, var.equal = TRUE)$p.value, 4))
, welch_pvalue = map2_dbl(dataset_0, dataset_1, function(dset0, dset1) round(t.test(dset0$value, dset1$value, var.equal = FALSE)$p.value, 4))
, wilcox_pvalue = map2_dbl(dataset_0, dataset_1, function(dset0, dset1) round(wilcox.test(dset0$value, dset1$value, exact = FALSE)$p.value, 4))) %>%
select(-dataset_0, -dataset_1)
write_csv(gp_service_use_mean_cohort_sigtest, here::here("output", "describe_service_use", "complete_gp_history", "gp_service_use_mean_cohort_sigtest.csv"))
################################################################################
########## Descriptive stats service use by quarter ##########
service_use_mean_quarter <- df_input %>%
select(study_quarter, ends_with("_1m"), ends_with("_3m"), ends_with("_1y")) %>%
select(-contains("gp_hist")) %>%
pivot_longer(cols = -c(study_quarter), names_to = "measure", values_to = "value") %>%
group_by(study_quarter, measure) %>%
summarise(n = n()
, mean = mean(value, na.rm = TRUE)
, sd = sd(value, na.rm = TRUE)
, n_atleast1 = sum(value >= 1, na.rm = TRUE)) %>%
mutate(n = plyr::round_any(n, 10)
, n_atleast1 = plyr::round_any(n_atleast1, 10)
, mean = case_when(n_atleast1 == 0 ~ 0
, TRUE ~ round(mean, 3))
, sd = case_when(n_atleast1 == 0 ~ 0
, TRUE ~ round(sd, 3))
, activity = str_sub(measure, 1, -4)
, period = str_sub(measure, -2, -1)) %>%
arrange(study_quarter, factor(period, levels = c("1m", "3m", "1y")), activity)
write_csv(service_use_mean_quarter, here::here("output", "describe_service_use", "service_use_mean_quarter.csv"))
# Calculate the same just for people with complete gp history
gp_service_use_mean_quarter <- df_input %>%
filter(gp_hist_1m == TRUE) %>%
select(study_quarter, ends_with("_1m")) %>%
select(-contains("gp_hist")) %>%
pivot_longer(cols = -c(study_quarter), names_to = "measure", values_to = "value") %>%
group_by(study_quarter, measure) %>%
summarise(n = n()
, mean = mean(value, na.rm = TRUE)
, sd = sd(value, na.rm = TRUE)
, n_atleast1 = sum(value >= 1, na.rm = TRUE)) %>%
mutate(n = plyr::round_any(n, 10)
, n_atleast1 = plyr::round_any(n_atleast1, 10)
, mean = case_when(n_atleast1 == 0 ~ 0
, TRUE ~ round(mean, 3))
, sd = case_when(n_atleast1 == 0 ~ 0
, TRUE ~ round(sd, 3))) %>%
bind_rows(df_input %>%
filter(gp_hist_3m == TRUE) %>%
select(study_quarter, ends_with("_3m")) %>%
select(-contains("gp_hist")) %>%
pivot_longer(cols = -c(study_quarter), names_to = "measure", values_to = "value") %>%
group_by(study_quarter, measure) %>%
summarise(n = n()
, mean = mean(value, na.rm = TRUE)
, sd = sd(value, na.rm = TRUE)
, n_atleast1 = sum(value >= 1, na.rm = TRUE)) %>%
mutate(n = plyr::round_any(n, 10)
, n_atleast1 = plyr::round_any(n_atleast1, 10)
, mean = case_when(n_atleast1 == 0 ~ 0
, TRUE ~ round(mean, 3))
, sd = case_when(n_atleast1 == 0 ~ 0
, TRUE ~ sd))) %>%
bind_rows(df_input %>%
filter(gp_hist_1y == TRUE) %>%
select(study_quarter, ends_with("_1y")) %>%
select(-contains("gp_hist")) %>%
pivot_longer(cols = -c(study_quarter), names_to = "measure", values_to = "value") %>%
group_by(study_quarter, measure) %>%
summarise(n = n()
, mean = mean(value, na.rm = TRUE)
, sd = sd(value, na.rm = TRUE)
, n_atleast1 = sum(value >= 1, na.rm = TRUE)) %>%
mutate(n = plyr::round_any(n, 10)
, n_atleast1 = plyr::round_any(n_atleast1, 10)
, mean = case_when(n_atleast1 == 0 ~ 0
, TRUE ~ round(mean, 3))
, sd = case_when(n_atleast1 == 0 ~ 0
, TRUE ~ sd))) %>%
mutate(activity = str_sub(measure, 1, -4)
, period = str_sub(measure, -2, -1)) %>%
arrange(study_quarter, factor(period, levels = c("1m", "3m", "1y")), activity)
write_csv(gp_service_use_mean_quarter, here::here("output", "describe_service_use", "complete_gp_history", "gp_service_use_mean_quarter.csv"))
################################################################################
########## Descriptive stats service use by cohort and place of death ##########
service_use_mean_cohort_pod <- df_input %>%
filter(!is.na(study_cohort)) %>%
select(cohort, pod_ons_new, ends_with("_1m"), ends_with("_3m"), ends_with("_1y")) %>%
select(-contains("gp_hist")) %>%
pivot_longer(cols = -c(cohort, pod_ons_new), names_to = "measure", values_to = "value") %>%
group_by(cohort, pod_ons_new, measure) %>%
summarise(n = n()
, mean = mean(value, na.rm = TRUE)
, sd = sd(value, na.rm = TRUE)
, n_atleast1 = sum(value >= 1, na.rm = TRUE)) %>%
mutate(n = plyr::round_any(n, 10)
, n_atleast1 = plyr::round_any(n_atleast1, 10)
, mean = case_when(n_atleast1 == 0 ~ 0
, TRUE ~ round(mean, 3))
, sd = case_when(n_atleast1 == 0 ~ 0
, TRUE ~ round(sd, 3))) %>%
pivot_wider(names_from = cohort, names_prefix = "cohort_", values_from = c(n, mean, sd, n_atleast1)) %>%
mutate(activity = str_sub(measure, 1, -4)
, period = str_sub(measure, -2, -1)) %>%
arrange(factor(period, levels = c("1m", "3m", "1y")), pod_ons_new, activity) %>%
mutate(mean_ratio = round((mean_cohort_1/mean_cohort_0),2))
write_csv(service_use_mean_cohort_pod, here::here("output", "describe_service_use", "service_use_mean_cohort_pod.csv"))
# Calculate the same just for people with complete gp history
gp_service_use_mean_cohort_pod <- df_input %>%
filter(!is.na(study_cohort) & gp_hist_1m == TRUE) %>%
select(cohort, pod_ons_new, ends_with("_1m")) %>%
select(-contains("gp_hist")) %>%
pivot_longer(cols = -c(cohort, pod_ons_new), names_to = "measure", values_to = "value") %>%
group_by(cohort, pod_ons_new, measure) %>%
summarise(n = n()
, mean = mean(value, na.rm = TRUE)
, sd = sd(value, na.rm = TRUE)
, n_atleast1 = sum(value >= 1, na.rm = TRUE)) %>%
mutate(n = plyr::round_any(n, 10)
, n_atleast1 = plyr::round_any(n_atleast1, 10)
, mean = case_when(n_atleast1 == 0 ~ 0
, TRUE ~ round(mean, 3))
, sd = case_when(n_atleast1 == 0 ~ 0
, TRUE ~ round(sd, 3))) %>%
pivot_wider(names_from = cohort, names_prefix = "cohort_", values_from = c(n, mean, sd, n_atleast1)) %>%
bind_rows(df_input %>%
filter(!is.na(study_cohort) & gp_hist_3m == TRUE) %>%
select(cohort, pod_ons_new, ends_with("_3m")) %>%
select(-contains("gp_hist")) %>%
pivot_longer(cols = -c(cohort, pod_ons_new), names_to = "measure", values_to = "value") %>%
group_by(cohort, pod_ons_new, measure) %>%
summarise(n = n()
, mean = mean(value, na.rm = TRUE)
, sd = sd(value, na.rm = TRUE)
, n_atleast1 = sum(value >= 1, na.rm = TRUE)) %>%
mutate(n = plyr::round_any(n, 10)
, n_atleast1 = plyr::round_any(n_atleast1, 10)
, mean = case_when(n_atleast1 == 0 ~ 0
, TRUE ~ round(mean, 3))
, sd = case_when(n_atleast1 == 0 ~ 0
, TRUE ~ round(sd, 3))) %>%
pivot_wider(names_from = cohort, names_prefix = "cohort_", values_from = c(n, mean, sd, n_atleast1))) %>%
bind_rows(df_input %>%
filter(!is.na(study_cohort) & gp_hist_1y == TRUE) %>%
select(cohort, pod_ons_new, ends_with("_1y")) %>%
select(-contains("gp_hist")) %>%
pivot_longer(cols = -c(cohort, pod_ons_new), names_to = "measure", values_to = "value") %>%
group_by(cohort, pod_ons_new, measure) %>%
summarise(n = n()
, mean = mean(value, na.rm = TRUE)
, sd = sd(value, na.rm = TRUE)
, n_atleast1 = sum(value >= 1, na.rm = TRUE)) %>%
mutate(n = plyr::round_any(n, 10)
, n_atleast1 = plyr::round_any(n_atleast1, 10)
, mean = case_when(n_atleast1 == 0 ~ 0
, TRUE ~ round(mean, 3))
, sd = case_when(n_atleast1 == 0 ~ 0
, TRUE ~ round(sd, 3))) %>%
pivot_wider(names_from = cohort, names_prefix = "cohort_", values_from = c(n, mean, sd, n_atleast1))) %>%
mutate(activity = str_sub(measure, 1, -4)
, period = str_sub(measure, -2, -1)) %>%
arrange(pod_ons_new, factor(period, levels = c("1m", "3m", "1y")), activity) %>%
mutate(mean_ratio = round((mean_cohort_1/mean_cohort_0),2))
write_csv(gp_service_use_mean_cohort_pod, here::here("output", "describe_service_use", "complete_gp_history", "gp_service_use_mean_cohort_pod.csv"))
################################################################################
########## Significance test service use means by cohort for each place of death ##########
# Difference in means between cohorts for each measure and place of death
service_use_mean_cohort_pod_sigtest <- tidyr::expand_grid(measure = unique(service_use_mean_cohort_pod$measure)
, pod_ons_new = unique(service_use_mean_cohort_pod$pod_ons_new)) %>%
mutate(dataset_0 = map2(measure, pod_ons_new, function(var, pod) df_input %>%
filter(!is.na(study_cohort)) %>%
select(cohort, pod_ons_new, ends_with("_1m"), ends_with("_3m"), ends_with("_1y")) %>%
select(-contains("gp_hist")) %>%
pivot_longer(cols = -c(cohort, pod_ons_new), names_to = "measure", values_to = "value") %>%
filter(cohort == 0 & measure == var & pod_ons_new == pod))
, dataset_1 = map2(measure, pod_ons_new, function(var, pod) df_input %>%
filter(!is.na(study_cohort)) %>%
select(cohort, pod_ons_new, ends_with("_1m"), ends_with("_3m"), ends_with("_1y")) %>%
select(-contains("gp_hist")) %>%
pivot_longer(cols = -c(cohort, pod_ons_new), names_to = "measure", values_to = "value")%>%
filter(cohort == 1 & measure == var & pod_ons_new == pod))
, normality_pvalue = map2_dbl(dataset_0, dataset_1, function(dset0, dset1) round(ks.test(dset0$value, dset1$value)$p.value, 4))
, equal_variance_pvalue = map2_dbl(dataset_0, dataset_1, function(dset0, dset1) round(var.test(dset0$value, dset1$value)$p.value, 4))
, ttest_pvalue = map2_dbl(dataset_0, dataset_1, function(dset0, dset1) round(t.test(dset0$value, dset1$value, var.equal = TRUE)$p.value, 4))
, welch_pvalue = map2_dbl(dataset_0, dataset_1, function(dset0, dset1) round(t.test(dset0$value, dset1$value, var.equal = FALSE)$p.value, 4))
, wilcox_pvalue = map2_dbl(dataset_0, dataset_1, function(dset0, dset1) round(wilcox.test(dset0$value, dset1$value, exact = FALSE)$p.value, 4))) %>%
select(-dataset_0, -dataset_1)
write_csv(service_use_mean_cohort_pod_sigtest, here::here("output", "describe_service_use", "service_use_mean_cohort_pod_sigtest.csv"))
# Calculate the same just for people with complete gp history
gp_service_use_mean_cohort_pod_sigtest <- tidyr::expand_grid(measure = unique(service_use_mean_cohort_pod$measure)
, pod_ons_new = unique(service_use_mean_cohort_pod$pod_ons_new)) %>%
mutate(dataset_0 = map2(measure, pod_ons_new, function(var, pod) df_input %>%
filter(!is.na(study_cohort) & gp_hist_1m == TRUE) %>%
select(cohort, ends_with("_1m")) %>%
select(-contains("gp_hist")) %>%
pivot_longer(cols = -c(cohort), names_to = "measure", values_to = "value") %>%
bind_rows(df_input %>%
filter(!is.na(study_cohort) & gp_hist_3m == TRUE) %>%
select(cohort, ends_with("_3m")) %>%
select(-contains("gp_hist")) %>%
pivot_longer(cols = -c(cohort), names_to = "measure", values_to = "value")) %>%
bind_rows(df_input %>%
filter(!is.na(study_cohort) & gp_hist_1y == TRUE) %>%
select(cohort, ends_with("_1y")) %>%
select(-contains("gp_hist")) %>%
pivot_longer(cols = -c(cohort), names_to = "measure", values_to = "value")) %>%
filter(cohort == 0 & measure == var & pod_ons_new == pod))
, dataset_1 = map2(measure, pod_ons_new, function(var, pod) df_input %>%
filter(!is.na(study_cohort) & gp_hist_1m == TRUE) %>%
select(cohort, ends_with("_1m")) %>%
select(-contains("gp_hist")) %>%
pivot_longer(cols = -c(cohort), names_to = "measure", values_to = "value") %>%
bind_rows(df_input %>%
filter(!is.na(study_cohort) & gp_hist_3m == TRUE) %>%
select(cohort, ends_with("_3m")) %>%
select(-contains("gp_hist")) %>%
pivot_longer(cols = -c(cohort), names_to = "measure", values_to = "value")) %>%
bind_rows(df_input %>%
filter(!is.na(study_cohort) & gp_hist_1y == TRUE) %>%
select(cohort, ends_with("_1y")) %>%
select(-contains("gp_hist")) %>%
pivot_longer(cols = -c(cohort), names_to = "measure", values_to = "value")) %>%
filter(cohort == 1 & measure == var & pod_ons_new == pod))
, normality_pvalue = map2_dbl(dataset_0, dataset_1, function(dset0, dset1) round(ks.test(dset0$value, dset1$value)$p.value, 4))
, equal_variance_pvalue = map2_dbl(dataset_0, dataset_1, function(dset0, dset1) round(var.test(dset0$value, dset1$value)$p.value, 4))
, ttest_pvalue = map2_dbl(dataset_0, dataset_1, function(dset0, dset1) round(t.test(dset0$value, dset1$value, var.equal = TRUE)$p.value, 4))
, welch_pvalue = map2_dbl(dataset_0, dataset_1, function(dset0, dset1) round(t.test(dset0$value, dset1$value, var.equal = FALSE)$p.value, 4))
, wilcox_pvalue = map2_dbl(dataset_0, dataset_1, function(dset0, dset1) round(wilcox.test(dset0$value, dset1$value, exact = FALSE)$p.value, 4))) %>%
select(-dataset_0, -dataset_1)
write_csv(gp_service_use_mean_cohort_pod_sigtest, here::here("output", "describe_service_use", "complete_gp_history", "gp_service_use_mean_cohort_pod_sigtest.csv"))
################################################################################
########## Descriptive stats service use by study quarter and place of death ##########
service_use_mean_quarter_pod <- df_input %>%
select(study_quarter, pod_ons_new, ends_with("_1m"), ends_with("_3m"), ends_with("_1y")) %>%
select(-contains("gp_hist")) %>%
pivot_longer(cols = -c(study_quarter, pod_ons_new), names_to = "measure", values_to = "value") %>%
group_by(study_quarter, pod_ons_new, measure) %>%
summarise(n = n()
, mean = mean(value, na.rm = TRUE)
, sd = sd(value, na.rm = TRUE)
, n_atleast1 = sum(value >= 1, na.rm = TRUE)) %>%
mutate(n = plyr::round_any(n, 10)
, n_atleast1 = plyr::round_any(n_atleast1, 10)
, mean = case_when(n_atleast1 == 0 ~ 0
, TRUE ~ round(mean, 3))
, sd = case_when(n_atleast1 == 0 ~ 0
, TRUE ~ sd)
, activity = str_sub(measure, 1, -4)
, period = str_sub(measure, -2, -1)) %>%
arrange(study_quarter, pod_ons_new, factor(period, levels = c("1m", "3m", "1y")), activity)
write_csv(service_use_mean_quarter_pod, here::here("output", "describe_service_use", "service_use_mean_quarter_pod.csv"))
# Calculate the same just for people with complete gp history
gp_service_use_mean_quarter_pod <- df_input %>%
filter(gp_hist_1m == TRUE) %>%
select(study_quarter, pod_ons_new, ends_with("_1m")) %>%
select(-contains("gp_hist")) %>%
pivot_longer(cols = -c(study_quarter, pod_ons_new), names_to = "measure", values_to = "value") %>%
group_by(study_quarter, pod_ons_new, measure) %>%
summarise(n = n()
, mean = mean(value, na.rm = TRUE)
, sd = sd(value, na.rm = TRUE)
, n_atleast1 = sum(value >= 1, na.rm = TRUE)) %>%
mutate(n = plyr::round_any(n, 10)
, n_atleast1 = plyr::round_any(n_atleast1, 10)
, mean = case_when(n_atleast1 == 0 ~ 0
, TRUE ~ round(mean, 3))
, sd = case_when(n_atleast1 == 0 ~ 0
, TRUE ~ round(sd, 3))) %>%
bind_rows(df_input %>%
filter(gp_hist_3m == TRUE) %>%
select(study_quarter, pod_ons_new, ends_with("_3m")) %>%
select(-contains("gp_hist")) %>%
pivot_longer(cols = -c(study_quarter, pod_ons_new), names_to = "measure", values_to = "value") %>%
group_by(study_quarter, pod_ons_new, measure) %>%
summarise(n = n()
, mean = mean(value, na.rm = TRUE)
, sd = sd(value, na.rm = TRUE)
, n_atleast1 = sum(value >= 1, na.rm = TRUE)) %>%
mutate(n = plyr::round_any(n, 10)
, n_atleast1 = plyr::round_any(n_atleast1, 10)
, mean = case_when(n_atleast1 == 0 ~ 0
, TRUE ~ round(mean, 3))
, sd = case_when(n_atleast1 == 0 ~ 0
, TRUE ~ sd))) %>%
bind_rows(df_input %>%
filter(gp_hist_1y == TRUE) %>%
select(study_quarter, pod_ons_new, ends_with("_1y")) %>%
select(-contains("gp_hist")) %>%
pivot_longer(cols = -c(study_quarter, pod_ons_new), names_to = "measure", values_to = "value") %>%
group_by(study_quarter, pod_ons_new, measure) %>%
summarise(n = n()
, mean = mean(value, na.rm = TRUE)
, sd = sd(value, na.rm = TRUE)
, n_atleast1 = sum(value >= 1, na.rm = TRUE)) %>%
mutate(n = plyr::round_any(n, 10)
, n_atleast1 = plyr::round_any(n_atleast1, 10)
, mean = case_when(n_atleast1 == 0 ~ 0
, TRUE ~ round(mean, 3))
, sd = case_when(n_atleast1 == 0 ~ 0
, TRUE ~ sd))) %>%
mutate(activity = str_sub(measure, 1, -4)
, period = str_sub(measure, -2, -1)) %>%
arrange(study_quarter, pod_ons_new, factor(period, levels = c("1m", "3m", "1y")), activity)
write_csv(gp_service_use_mean_quarter_pod, here::here("output", "describe_service_use", "complete_gp_history", "gp_service_use_mean_quarter_pod.csv"))
################################################################################
########## Plots means by study quarter and place of death ##########
service <- unique(service_use_mean_quarter_pod$activity)
pdf("output/describe_service_use/plots/service_use_mean_quarter_pod_1m.pdf")
for (i in service) {
print(
ggplot(service_use_mean_quarter_pod %>%
mutate(pod_ons_new = case_when(pod_ons_new == "Elsewhere/other" ~ "Other"
, TRUE ~ pod_ons_new)
, activity = str_sub(measure, 1, -4)
, period = str_sub(measure, -2, -1)) %>%
filter(activity == i & period == "1m")
, aes(x = study_quarter, y = mean
, colour = factor(pod_ons_new, levels = c("Home", "Care home", "Hospital", "Hospice", "Other")))) +
geom_line(size = 1) +
geom_point(fill = "#F4F4F4", shape = 21, size = 1.5, stroke = 1.3) +
labs(x = "Study quarter", y = "Events per person"
, title = i) +
guides(colour = guide_legend(nrow = 1)) +
scale_colour_NT(palette = NT_palette()) +
scale_x_continuous(expand = c(0, 0.5), breaks = seq(1, 8, 1)) +
scale_y_continuous(expand = c(0, 0), limits = c(0, NA)) +
NT_style()
)
}
dev.off()
pdf("output/describe_service_use/plots/service_use_mean_quarter_pod_3m.pdf")
for (i in service) {
print(
ggplot(service_use_mean_quarter_pod %>%
mutate(pod_ons_new = case_when(pod_ons_new == "Elsewhere/other" ~ "Other"
, TRUE ~ pod_ons_new)
, activity = str_sub(measure, 1, -4)
, period = str_sub(measure, -2, -1)) %>%
filter(activity == i & period == "3m")
, aes(x = study_quarter, y = mean
, colour = factor(pod_ons_new, levels = c("Home", "Care home", "Hospital", "Hospice", "Other")))) +
geom_line(size = 1) +
geom_point(fill = "#F4F4F4", shape = 21, size = 1.5, stroke = 1.3) +
labs(x = "Study quarter", y = "Events per person"
, title = i) +
guides(colour = guide_legend(nrow = 1)) +
scale_colour_NT(palette = NT_palette()) +
scale_x_continuous(expand = c(0, 0.5), breaks = seq(1, 8, 1)) +
scale_y_continuous(expand = c(0, 0), limits = c(0, NA)) +
NT_style()
)
}
dev.off()
pdf("output/describe_service_use/plots/service_use_mean_quarter_pod_1y.pdf")
for (i in service) {
print(
ggplot(service_use_mean_quarter_pod %>%
mutate(pod_ons_new = case_when(pod_ons_new == "Elsewhere/other" ~ "Other"
, TRUE ~ pod_ons_new)
, activity = str_sub(measure, 1, -4)
, period = str_sub(measure, -2, -1)) %>%
filter(activity == i & period == "1y")
, aes(x = study_quarter, y = mean
, colour = factor(pod_ons_new, levels = c("Home", "Care home", "Hospital", "Hospice", "Other")))) +
geom_line(size = 1) +
geom_point(fill = "#F4F4F4", shape = 21, size = 1.5, stroke = 1.3) +
labs(x = "Study quarter", y = "Events per person"
, title = i) +
guides(colour = guide_legend(nrow = 1)) +
scale_colour_NT(palette = NT_palette()) +
scale_x_continuous(expand = c(0, 0.5), breaks = seq(1, 8, 1)) +
scale_y_continuous(expand = c(0, 0), limits = c(0, NA)) +
NT_style()
)
}
dev.off()
# Plot the same for complete GP history
pdf("output/describe_service_use/complete_gp_history/plots/gp_service_use_mean_quarter_pod_1m.pdf")
for (i in service) {
print(
ggplot(gp_service_use_mean_quarter_pod %>%
mutate(pod_ons_new = case_when(pod_ons_new == "Elsewhere/other" ~ "Other"
, TRUE ~ pod_ons_new)
, activity = str_sub(measure, 1, -4)
, period = str_sub(measure, -2, -1)) %>%
filter(activity == i & period == "1m")
, aes(x = study_quarter, y = mean
, colour = factor(pod_ons_new, levels = c("Home", "Care home", "Hospital", "Hospice", "Other")))) +
geom_line(size = 1) +
geom_point(fill = "#F4F4F4", shape = 21, size = 1.5, stroke = 1.3) +
labs(x = "Study quarter", y = "Events per person"
, title = i) +
guides(colour = guide_legend(nrow = 1)) +
scale_colour_NT(palette = NT_palette()) +
scale_x_continuous(expand = c(0, 0.5), breaks = seq(1, 8, 1)) +
scale_y_continuous(expand = c(0, 0), limits = c(0, NA)) +
NT_style()
)
}
dev.off()
pdf("output/describe_service_use/complete_gp_history/plots/gp_service_use_mean_quarter_pod_3m.pdf")
for (i in service) {
print(
ggplot(gp_service_use_mean_quarter_pod %>%
mutate(pod_ons_new = case_when(pod_ons_new == "Elsewhere/other" ~ "Other"
, TRUE ~ pod_ons_new)
, activity = str_sub(measure, 1, -4)
, period = str_sub(measure, -2, -1)) %>%
filter(activity == i & period == "3m")
, aes(x = study_quarter, y = mean
, colour = factor(pod_ons_new, levels = c("Home", "Care home", "Hospital", "Hospice", "Other")))) +
geom_line(size = 1) +
geom_point(fill = "#F4F4F4", shape = 21, size = 1.5, stroke = 1.3) +
labs(x = "Study quarter", y = "Events per person"
, title = i) +
guides(colour = guide_legend(nrow = 1)) +
scale_colour_NT(palette = NT_palette()) +
scale_x_continuous(expand = c(0, 0.5), breaks = seq(1, 8, 1)) +
scale_y_continuous(expand = c(0, 0), limits = c(0, NA)) +
NT_style()
)
}
dev.off()
pdf("output/describe_service_use/complete_gp_history/plots/gp_service_use_mean_quarter_pod_1y.pdf")
for (i in service) {
print(
ggplot(gp_service_use_mean_quarter_pod %>%
mutate(pod_ons_new = case_when(pod_ons_new == "Elsewhere/other" ~ "Other"
, TRUE ~ pod_ons_new)
, activity = str_sub(measure, 1, -4)
, period = str_sub(measure, -2, -1)) %>%
filter(activity == i & period == "1y")
, aes(x = study_quarter, y = mean
, colour = factor(pod_ons_new, levels = c("Home", "Care home", "Hospital", "Hospice", "Other")))) +
geom_line(size = 1) +
geom_point(fill = "#F4F4F4", shape = 21, size = 1.5, stroke = 1.3) +
labs(x = "Study quarter", y = "Events per person"
, title = i) +
guides(colour = guide_legend(nrow = 1)) +
scale_colour_NT(palette = NT_palette()) +
scale_x_continuous(expand = c(0, 0.5), breaks = seq(1, 8, 1)) +
scale_y_continuous(expand = c(0, 0), limits = c(0, NA)) +
NT_style()
)
}
dev.off()
################################################################################
########## Plots proportion at least 1 by study quarter and place of death ##########
pdf("output/describe_service_use/plots/service_use_prop_quarter_pod_1m.pdf")
for (i in service) {
print(
ggplot(service_use_mean_quarter_pod %>%
mutate(pod_ons_new = case_when(pod_ons_new == "Elsewhere/other" ~ "Other"