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02-createtemporal.R
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02-createtemporal.R
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# ##==============================================================================
# Analysis filename: 02-createtemporal.R. BRANCH!!
# Project: OC evaluation
# Author: Heavily lifted from W. Hulme Tutorial example 3. Minor adaptations: Martina Fonseca
# Date: 17/12/2020 (updated: 02/02/2021)
# Version: R
# Description: Produce timeline of of GP consultation and OC instance rates
# Output to csv files
# Datasets used: various 'measures*' files
# Datasets created: 'measures_gpc_pop.csv'
# Other output: TBA
# Log file: logs\log-02-createtemporal.txt
#
## ==============================================================================
## open log connection to file
sink(here::here("logs", "log-02-createtemporal.txt"))
## library
library(tidyverse)
library(here)
library(svglite)
# create directory for saving plots, if not existent
if (!dir.exists(here::here("output", "plots"))){
dir.create(here::here("output", "plots"))
}
# create directory for saving plots, if not existent
if (!dir.exists(here::here("output", "tables"))){
dir.create(here::here("output", "tables"))
}
## Redactor code (W.Hulme)
redactor <- function(n, threshold=6,e_overwrite=NA_integer_){
# given a vector of frequencies, this returns a boolean vector that is TRUE if
# a) the frequency is <= the redaction threshold and
# b) if the sum of redacted frequencies in a) is still <= the threshold, then the
# next largest frequency is also redacted
n <- as.integer(n)
leq_threshold <- dplyr::between(n, 1, threshold)
n_sum <- sum(n)
# redact if n is less than or equal to redaction threshold
redact <- leq_threshold
# also redact next smallest n if sum of redacted n is still less than or equal to threshold
if((sum(n*leq_threshold) <= threshold) & any(leq_threshold)){
redact[which.min(dplyr::if_else(leq_threshold, n_sum+1L, n))] = TRUE
}
n_redacted <- if_else(redact, e_overwrite, n)
}
# create look-up table to iterate over
# n_meas=10
# md_tbl <- tibble(
# measure = c("gpc", "OC_Y1f3b", "OC_XUkjp", "OC_XaXcK","OC_XVCTw","OC_XUuWQ","OC_XV1pT","OC_computerlink","OC_alertreceived","OC_Y22b4"),
# measure_col=c("gp_consult_count", "OC_Y1f3b", "OC_XUkjp", "OC_XaXcK","OC_XVCTw","OC_XUuWQ","OC_XV1pT","OC_computerlink","OC_alertreceived","OC_Y22b4"),
# measure_label = c("GPconsult", "Y1f3b", "XUkjp", "XaXcK","XVCTw","XUuWQ","XV1pT","ComputerLink","AlertReceived","Y22b4"),
# by = rep("practice",1,n_meas),
# by_label = rep("by practice",1,n_meas),
# id = paste0(measure, "_", by),
# numerator = measure,
# denominator = "population",
# group_by = rep("practice",1,n_meas)
# )
# n_meas=10
# md_tbl <- tibble(
# measure = c("gpc","snomed_1068881000000101","snomed_978871000000104","snomed_325991000000105","snomed_325911000000101","OC_Y1f3b","OC_Y22b4","OC_XaXcK","OC_computerlink","OC_alertreceived"),
# measure_col=c("gp_consult_count","snomed_1068881000000101","snomed_978871000000104","snomed_325991000000105","snomed_325911000000101" ,"OC_Y1f3b","OC_Y22b4","OC_XaXcK","OC_computerlink","OC_alertreceived"),
# measure_label = c("GPconsult","eConsultation via online application","Consultation via multimedia","Assessment via multimedia encounter type","Consultation via multimedia encounter type","OC_Y1f3b","OC_Y22b4","OC_XaXcK","OC_computerlink","OC_alertreceived"),
# by = rep("practice",1,n_meas),
# by_label = rep("by practice",1,n_meas),
# id = paste0(measure, "_", by),
# numerator = measure,
# denominator = "population",
# group_by = rep("practice",1,n_meas)
# )
n_meas=9
md_tbl <- tibble(
measure = c("gpc","snomed_OCall","snomed_1068881000000101","OC_OC10","OC_Y1f3b","OC_Y22b4","OC_XaXcK","OC_computerlink","OC_alertreceived"),
measure_col=c("gp_consult_count","snomed_OCall","snomed_1068881000000101","OC_OC10","OC_Y1f3b","OC_Y22b4","OC_XaXcK","OC_computerlink","OC_alertreceived"),
measure_label = c("GPconsult","OC-relevant snomed codes","eConsultation via online application","OC-relevant ctv3 codes","OC_Y1f3b","OC_Y22b4","OC_XaXcK","OC_computerlink","OC_alertreceived"),
by = rep("practice",1,n_meas),
by_label = rep("by practice",1,n_meas),
id = paste0(measure, "_", by),
numerator = measure,
denominator = "population",
group_by = rep("practice",1,n_meas)
)
print("> Tibble creation")
## import measures data from look-up
measures <- md_tbl %>%
mutate(
data = map(id, ~read_csv(here::here("output","measures", glue::glue("measure_{.}.csv")))),
)
p_saving <- function(id,data) {
write.csv(paste0(here::here("output","measures"),"/red_measure_",id,".csv"))
return(data)
}
# Create redacted measures and save
measures <- measures %>%
mutate(
redacted_data = pmap(lst(id,measure_col,data),
function(id,measure_col,data) {
redacted_data <- data %>% mutate_at(vars(measure_col),redactor)
redacted_data$value <- ifelse(is.na(redacted_data %>% select(measure_col)),NA,redacted_data$value)
write.csv(redacted_data,paste0(here::here("output","tables"),"/redacted_measure_",id,".csv"))
return(redacted_data)
}
)
)
#measures_m <- measures %>% mutate(no_2020_events = map(data, ~ (.) %>% filter(as.numeric(format(date,'%Y'))==2020)) )
#measures_m <- measures %>% mutate(no_2020_events = map(data, ~ (.) %>% filter(as.numeric(format(date,'%Y'))==2020) %>% select(value) %>% sum() ))
measures <- measures %>% mutate(no_2020_events = pmap(lst( data, measure_col),
function(data, measure_col){
data %>% filter(as.numeric(format(date,'%Y'))==2020) %>% select(measure_col) %>% sum()
}
))
#measures_m <- measures %>% mutate(no_2020_events = map(data, ~ (.) %>% group_by(date)))
measures_gpc_pratice <- measures$data[[match("gpc_practice",measures$id)]]
measures_gpc_pop <-
measures_gpc_pratice %>%
group_by(date) %>%
summarise(population=sum(population),gp_consult_count=sum(gp_consult_count),value=gp_consult_count/population)
write.csv(measures_gpc_pop,paste0(here::here("output"),"/measures_gpc_pop.csv")) # National monthly GP consultation instances. Suppression not needed.
measures_gpc_pop %>% mutate(value_10000 = value*10000) %>%
ggplot()+
geom_line(aes_string(x="date", y="value_10000"), alpha=0.2, colour='blue', size=0.25)+
scale_x_date(date_breaks = "1 month", labels = scales::date_format("%Y-%m"))+
labs(
x=NULL, y=NULL,
title="GP consultation instances",
subtitle = glue::glue("GP consulation rate per 10,000 patients")
)+
theme_bw()+
theme(
panel.border = element_blank(),
axis.text.x = element_text(angle = 70, vjust = 1, hjust=1),
)
ggsave(
units = "cm",
height = 10,
width = 15,
limitsize=FALSE,
filename = str_c("plot_overall_gpc_pop.svg"),
path = here::here("output", "plots")) # National monthly GP consultation instances. Suppression not needed.
#### Excluding practices with no code instances over full tenor of study period
#mydata <- measures$data[[1]]
#mydata <- rbind(mydata, mydata %>% group_by(date) %>% summarise(gp_consult_count=0,population=10000,value=0,practice=999) )
#mydata <- mydata %>% group_by(practice) %>% mutate(code_present = ifelse(sum(value,na.rm=T)>0,1,0) ) %>% ungroup()
#mydata <- mydata %>% group_by(practice) %>% filter(sum(value,na.rm=T)>0)
measures <- measures %>% mutate(
data_ori=data, # data with all practices
data = map(data, ~ (.) %>% group_by(practice) %>% filter(sum(value,na.rm=T)>0)), # data with only practices with at least an observation in the study period (affects deciles)
no_prac = map(data, ~(.) %>% .$practice %>% n_distinct(na.rm=T) ),
no_prac_univ = map(data_ori, ~(.) %>% .$practice %>% n_distinct(na.rm=T))
)
quibble <- function(x, q = c(0.25, 0.5, 0.75)) {
## function that takes a vector and returns a tibble of quantiles - default is quartile
tibble("{{ x }}" := quantile(x, q), "{{ x }}_q" := q)
}
# v_median <- function(v_quantiles) {
# ### function that takes quantiles and extracts median
# v_quantiles %>% filter(mvalue_q==0.5) %>% .$mvalue
# }
v_median <- function(x) {
tibble(median := quantile(x,0.5))
}
v_idr <- function(x){
tibble(IDR := quantile(x,0.9)-quantile(x,0.1))
}
str_medidrnarrative <- function(mydata_idr){
a<- mydata_idr %>%
summarise(date,medchange = (median - lag(median,12))/lag(median,12)*100 ) %>%
mutate(classification=case_when(
between(medchange,-15,15) ~ "no change",
medchange>15 ~ "increase",
medchange<(-60) ~ "large drop",
medchange<(-15) ~ "drop",
TRUE ~ NA_character_,
) )
paste0("Change in median from 2019: April ",
round(as.numeric(a[a$date=="2020-04-01","medchange"]),1),"% (",a[a$date=="2020-04-01","classification"],"); ",
"September ",round(as.numeric(a[a$date=="2020-09-01","medchange"]),1),"% (",a[a$date=="2020-09-01","classification"],"); ",
"December ", round(as.numeric(a[a$date=="2020-12-01","medchange"]),1),"% (",a[a$date=="2020-12-01","classification"],");")
}
flag_run=T
if(flag_run){
## generate plots for each measure within the data frame
measures_plots <- measures %>%
mutate(
data_quantiles = map(data, ~ (.) %>% group_by(date) %>% summarise(quibble(value, seq(0.1,0.9,0.1)))),
#data_median = map(data_quantiles, ~ (.) %>% group_by(date) %>% filter(value_q==0.5) %>% transmute(median=value)),
data_idr = map(data, ~ (.) %>% group_by(date) %>% summarise(v_idr(value*1000),v_median(value*1000))),
plot_by = pmap(lst( group_by, data, measure_label, by_label),
function(group_by, data, measure_label, by_label){
data %>% mutate(value_1000 = value*1000) %>%
ggplot()+
geom_line(aes_string(x="date", y="value_1000", group=group_by), alpha=0.2, colour='blue', size=0.25)+
scale_x_date(date_breaks = "1 month", labels = scales::date_format("%Y-%m"))+
labs(
x=NULL, y=NULL,
title=glue::glue("{measure_label} measurement"),
subtitle = glue::glue("{by_label}, per 10,000 patients")
)+
theme_bw()+
theme(
panel.border = element_blank(),
axis.line.x = element_line(colour = "black"),
axis.text.x = element_text(angle = 70, vjust = 1, hjust=1),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
)
}
),
plot_quantiles = pmap(lst( group_by, data_quantiles, measure_label, by_label),
function(group_by, data_quantiles, measure_label, by_label){
data_quantiles %>% mutate(value_1000 = value*1000) %>%
ggplot()+
geom_line(aes(x=date, y=value_1000, group=value_q, linetype=value_q==0.5, size=value_q==0.5), colour='blue')+
scale_linetype_manual(breaks=c(TRUE, FALSE), values=c("solid", "dotted"), guide=FALSE)+
scale_size_manual(breaks=c(TRUE, FALSE), values=c(1, 0.4), guide=FALSE)+
scale_x_date(date_breaks = "1 month", labels = scales::date_format("%Y-%m"))+
labs(
x=NULL, y=NULL,
title=glue::glue("{measure_label} instances per 1000 patients"),
subtitle = glue::glue("quantiles {by_label}")
)+
theme_bw()+
theme(
panel.border = element_blank(),
axis.line.x = element_line(colour = "black"),
axis.text.x = element_text(angle = 70, vjust = 1, hjust=1),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
#axis.line.y = element_blank(),
)
}
),
plot_quantiles2 = pmap(lst( group_by, data_quantiles, measure_label, by_label,data_idr,no_2020_events,no_prac,no_prac_univ),
function(group_by, data_quantiles, measure_label, by_label,data_idr,no_2020_events,no_prac,no_prac_univ){
data_quantiles %>% mutate(value_1000 = value*1000) %>%
ggplot()+
geom_line(aes(x=date, y=value_1000, group=value_q, linetype=value_q==0.5, size=value_q==0.5), colour='blue')+
scale_linetype_manual(breaks=c(TRUE,FALSE), values=c("solid", "dashed"), guide=FALSE,labels=c("median","decile"))+
scale_size_manual(breaks=c(TRUE, FALSE), values=c(1, 0.5), guide=FALSE)+
scale_x_date(date_breaks = "1 month", labels = scales::date_format("%Y-%m"))+
labs(
x=NULL,
y="rate per 1,000",
linetype="metric",
title=glue::glue("{measure_label}"),
subtitle = paste0(
"Practices included: ",
no_prac, " (",round(no_prac/no_prac_univ*100,1),"%)",
"; 2020 events: ",
paste0(round(no_2020_events/1000,1),"k"),
"; 2020 patients: ",
"TBA"
),
caption=paste0("Feb median: ",
round(data_idr %>% filter(date=="2020-02-01") %>% .$median ,1),
" (IDR ",
round(data_idr %>% filter(date=="2020-02-01") %>% .$IDR ,1),"), ",
"April median: ",
round(data_idr %>% filter(date=="2020-04-01") %>% .$median ,1),
" (IDR ",
round(data_idr %>% filter(date=="2020-04-01") %>% .$IDR ,1),"),\n ",
"September median: ",
round(data_idr %>% filter(date=="2020-09-01") %>% .$median ,1),
" (IDR ",
round(data_idr %>% filter(date=="2020-09-01") %>% .$IDR ,1),"), ",
"December median: ",
round(data_idr %>% filter(date=="2020-12-01") %>% .$median ,1),
" (IDR ",
round(data_idr %>% filter(date=="2020-12-01") %>% .$IDR ,1),")\n",
str_medidrnarrative(data_idr)
)
)+
theme_bw()+
theme(
panel.border = element_blank(),
axis.line.x = element_line(colour = "black"),
axis.text.x = element_text(angle = 70, vjust = 1, hjust=1),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
axis.line.y = element_blank(),
plot.caption = element_text(color = "gray64", size=7)
)
}
)
)
## plot the charts (by variable)
# measures_plots %>%
# transmute(
# plot = plot_by,
# units = "cm",
# height = 10,
# width = 15,
# limitsize=FALSE,
# filename = str_c("plot_each_", id, ".svg"),
# path = here::here("output", "plots"),
# ) %>%
# pwalk(ggsave)
## plot the charts (by quantile)
measures_plots %>%
transmute(
plot = plot_quantiles2,
units = "cm",
height = 10,
width = 15,
limitsize=FALSE,
filename = str_c("plot_quantiles_", id, ".svg"),
path = here::here("output", "plots"),
) %>%
pwalk(ggsave)
}
## close log connection
sink()