generated from opensafely/research-template
/
02-createtemporal.R
332 lines (287 loc) · 14.9 KB
/
02-createtemporal.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
# ##==============================================================================
# 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)
)
## 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(0,1,0.1)))),
#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()