-
Notifications
You must be signed in to change notification settings - Fork 0
/
FINAL_full_data_pipeline.R
321 lines (244 loc) · 19.3 KB
/
FINAL_full_data_pipeline.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
# ****************************************
# The doctor will see you now - Personalised Waiting Times
#
# Final re-factor of all existing data manipulation work: Putting all data preparation into one file
#
# This file will perform the following actions in this order:
# (1) Take as input the clock stops data, incompletes queue data and appropriate lookup tables
# (2) Filter all anomalies from / generally clean the clock stops data and incompletes queue data
# (3) Filter to exclusively 110 Trauma and Orthopaedics cases (i.e: excluding 108 Spinal)
# (4) Filter to exclusively Inpatient cases (i.e: no outpatient clockstops or day case incompletes)
# (5) Assign to every case in both key datasets a Speciality / Service Area based on staff and OPCS codes
# (6) Generate time series of incoming / outgoing cases, separated by Speciality and Priority
# (7) Save all such time series to CSV files, forecasting is then conducted elsewhere
#
# Emma Tarmey
# 18/08/2022
# ****************************************
# ----- Preamble -----
library(dplyr)
library(stringr)
rm(list = ls())
setwd("...")
# ----- Load data from Excel Sheets -----
clock_stops_data <- read.csv("Clock_stops_TandO_2018_Anon.csv")
incompletes_data <- read.csv("20220626_Incompletes_anon.csv")
#procedure_lookup_data <- read.csv("Procedure_decode.csv")
staff_data <- read.csv("staff_names_and_codes.csv")
# trim white-space from data
clock_stops_data <- (clock_stops_data %>% mutate(across(where(is.character), str_trim)))
incompletes_data <- (incompletes_data %>% mutate(across(where(is.character), str_trim)))
#procedure_lookup_data <- (procedure_lookup_data %>% mutate(across(where(is.character), str_trim)))
staff_data <- (staff_data %>% mutate(across(where(is.character), str_trim)))
# clean-up staff lookup data
colnames(staff_data) <- c("Code", "Title", "Firstname", "Surname", "ServiceArea")
staff_data$Code <- gsub('[^[:alnum:] ]', '', staff_data$Code)
staff_data$Code <- gsub('?', '', staff_data$Code)
staff_data_lookup <- staff_data %>% select("Code", "ServiceArea")
# ----- Filter all Anomalies -----
# ----- Filter to exclusively Trauma and Orthopaedics Cases -----
clock_stops_TO_data <- clock_stops_data %>% filter(activity_treatment_function_code == 110)
incompletes_TO_data <- incompletes_data %>% filter(Specialty.code == 110)
# ----- Filter to exclusively Inpatient Cases -----
clock_stops_TO_inpatient_data <- clock_stops_TO_data %>% filter(waiting_list_type == "IRTT")
incompletes_TO_inpatient_data <- incompletes_TO_data %>% filter(AWL.intended.mgmt == "1 - INPATIENT")
# ----- Assign each case a Service Area -----
clock_stops_TO_inpatient_with_service_data <- merge(clock_stops_TO_inpatient_data,
staff_data_lookup,
by.x = "consultant_code",
by.y = "Code",
all.x = TRUE)
incompletes_TO_inpatient_with_service_data <- merge(incompletes_TO_inpatient_data,
staff_data_lookup,
by.x = "Consultant.code",
by.y = "Code",
all.x = TRUE)
# ----- Generate Historical Time Series for each Priority and Service Area -----
# convert dates to numeric values
clock_stops_TO_inpatient_with_service_data$rtt_startdate <- as.numeric(
as.Date(clock_stops_TO_inpatient_with_service_data$rtt_startdate,
format = "%Y-%m-%d",
origin = "1900-01-01"))
clock_stops_TO_inpatient_with_service_data$rtt_enddate <- as.numeric(
as.Date(clock_stops_TO_inpatient_with_service_data$rtt_enddate,
format = "%Y-%m-%d",
origin = "1900-01-01"))
# subset data by service area
foot_ankle_data <- (clock_stops_TO_inpatient_with_service_data %>% filter(ServiceArea == "Foot & Ankle")) %>% select(rtt_startdate, rtt_days_wait, rtt_weeks_wait, rtt_enddate, priority_type_code)
hand_data <- (clock_stops_TO_inpatient_with_service_data %>% filter(ServiceArea == "Hand")) %>% select(rtt_startdate, rtt_days_wait, rtt_weeks_wait, rtt_enddate, priority_type_code)
hip_data <- (clock_stops_TO_inpatient_with_service_data %>% filter(ServiceArea == "Hip")) %>% select(rtt_startdate, rtt_days_wait, rtt_weeks_wait, rtt_enddate, priority_type_code)
hip_knee_data <- (clock_stops_TO_inpatient_with_service_data %>% filter(ServiceArea == "Hip & Knee")) %>% select(rtt_startdate, rtt_days_wait, rtt_weeks_wait, rtt_enddate, priority_type_code)
knee_data <- (clock_stops_TO_inpatient_with_service_data %>% filter(ServiceArea == "Knee")) %>% select(rtt_startdate, rtt_days_wait, rtt_weeks_wait, rtt_enddate, priority_type_code)
shoulder_data <- (clock_stops_TO_inpatient_with_service_data %>% filter(ServiceArea == "Shoulder")) %>% select(rtt_startdate, rtt_days_wait, rtt_weeks_wait, rtt_enddate, priority_type_code)
# subset data by priority level
foot_ankle_priority_1_data <- foot_ankle_data %>% filter(priority_type_code == 1)
foot_ankle_priority_2_data <- foot_ankle_data %>% filter(priority_type_code == 2)
foot_ankle_priority_3_data <- foot_ankle_data %>% filter(priority_type_code == 3)
hand_priority_1_data <- hand_data %>% filter(priority_type_code == 1)
hand_priority_2_data <- hand_data %>% filter(priority_type_code == 2)
hand_priority_3_data <- hand_data %>% filter(priority_type_code == 3)
hip_priority_1_data <- hip_data %>% filter(priority_type_code == 1)
hip_priority_2_data <- hip_data %>% filter(priority_type_code == 2)
hip_priority_3_data <- hip_data %>% filter(priority_type_code == 3)
hip_knee_priority_1_data <- hip_knee_data %>% filter(priority_type_code == 1)
hip_knee_priority_2_data <- hip_knee_data %>% filter(priority_type_code == 2)
hip_knee_priority_3_data <- hip_knee_data %>% filter(priority_type_code == 3)
knee_priority_1_data <- knee_data %>% filter(priority_type_code == 1)
knee_priority_2_data <- knee_data %>% filter(priority_type_code == 2)
knee_priority_3_data <- knee_data %>% filter(priority_type_code == 3)
shoulder_priority_1_data <- shoulder_data %>% filter(priority_type_code == 1)
shoulder_priority_2_data <- shoulder_data %>% filter(priority_type_code == 2)
shoulder_priority_3_data <- shoulder_data %>% filter(priority_type_code == 3)
# isolate frequencies of each occurence to form time-series
# foot ankle priority 1 enter queue
bins <- seq(min(foot_ankle_priority_1_data$rtt_startdate), max(foot_ankle_priority_1_data$rtt_startdate)+7, by=7)
ranges <- paste(head(bins,-1), bins[-1], sep=" - ")
freq <- hist(foot_ankle_priority_1_data$rtt_startdate, breaks=bins, include.lowest=TRUE, plot=TRUE)
foot_ankle_enter_priority_1_timeseries <- data.frame(range = ranges, frequency = freq$counts)
# foot ankle priority 2 enter queue
bins <- seq(min(foot_ankle_priority_2_data$rtt_startdate), max(foot_ankle_priority_2_data$rtt_startdate)+7, by=7)
ranges <- paste(head(bins,-1), bins[-1], sep=" - ")
freq <- hist(foot_ankle_priority_2_data$rtt_startdate, breaks=bins, include.lowest=TRUE, plot=TRUE)
foot_ankle_enter_priority_2_timeseries <- data.frame(range = ranges, frequency = freq$counts)
# foot ankle priority 3 enter queue
bins <- seq(min(foot_ankle_priority_3_data$rtt_startdate), max(foot_ankle_priority_3_data$rtt_startdate)+7, by=7)
ranges <- paste(head(bins,-1), bins[-1], sep=" - ")
freq <- hist(foot_ankle_priority_3_data$rtt_startdate, breaks=bins, include.lowest=TRUE, plot=TRUE)
foot_ankle_enter_priority_3_timeseries <- data.frame(range = ranges, frequency = freq$counts)
# hand priority 1 enter queue
bins <- seq(min(hand_priority_1_data$rtt_startdate), max(hand_priority_1_data$rtt_startdate)+7, by=7)
ranges <- paste(head(bins,-1), bins[-1], sep=" - ")
freq <- hist(hand_priority_1_data$rtt_startdate, breaks=bins, include.lowest=TRUE, plot=TRUE)
hand_enter_priority_1_timeseries <- data.frame(range = ranges, frequency = freq$counts)
# hand priority 2 enter queue
bins <- seq(min(hand_priority_2_data$rtt_startdate), max(hand_priority_2_data$rtt_startdate)+7, by=7)
ranges <- paste(head(bins,-1), bins[-1], sep=" - ")
freq <- hist(hand_priority_2_data$rtt_startdate, breaks=bins, include.lowest=TRUE, plot=TRUE)
hand_enter_priority_2_timeseries <- data.frame(range = ranges, frequency = freq$counts)
# hand priority 3 enter queue
bins <- seq(min(hand_priority_3_data$rtt_startdate), max(hand_priority_3_data$rtt_startdate)+7, by=7)
ranges <- paste(head(bins,-1), bins[-1], sep=" - ")
freq <- hist(hand_priority_3_data$rtt_startdate, breaks=bins, include.lowest=TRUE, plot=TRUE)
hand_enter_priority_3_timeseries <- data.frame(range = ranges, frequency = freq$counts)
# hip priority 1 enter queue
bins <- seq(min(hip_priority_1_data$rtt_startdate), max(hip_priority_1_data$rtt_startdate)+7, by=7)
ranges <- paste(head(bins,-1), bins[-1], sep=" - ")
freq <- hist(hip_priority_1_data$rtt_startdate, breaks=bins, include.lowest=TRUE, plot=TRUE)
hip_enter_priority_1_timeseries <- data.frame(range = ranges, frequency = freq$counts)
# hip priority 2 enter queue
bins <- seq(min(hip_priority_2_data$rtt_startdate), max(hip_priority_2_data$rtt_startdate)+7, by=7)
ranges <- paste(head(bins,-1), bins[-1], sep=" - ")
freq <- hist(hip_priority_2_data$rtt_startdate, breaks=bins, include.lowest=TRUE, plot=TRUE)
hip_enter_priority_2_timeseries <- data.frame(range = ranges, frequency = freq$counts)
# hip priority 3 enter queue
# NOTE: In this particular data-set, there exist no priority 3 hip referrals
# Consequently, a time-series cannot be generated.
#bins <- seq(min(hip_priority_3_data$rtt_startdate), max(hip_priority_3_data$rtt_startdate)+7, by=7)
#ranges <- paste(head(bins,-1), bins[-1], sep=" - ")
#freq <- hist(hip_priority_3_data$rtt_startdate, breaks=bins, include.lowest=TRUE, plot=TRUE)
hip_enter_priority_3_timeseries <- data.frame(range = c(), frequency = c())
# hip knee priority 1 enter queue
bins <- seq(min(hip_knee_priority_1_data$rtt_startdate), max(hip_knee_priority_1_data$rtt_startdate)+7, by=7)
ranges <- paste(head(bins,-1), bins[-1], sep=" - ")
freq <- hist(hip_knee_priority_1_data$rtt_startdate, breaks=bins, include.lowest=TRUE, plot=TRUE)
hip_knee_enter_priority_1_timeseries <- data.frame(range = ranges, frequency = freq$counts)
# hip knee priority 2 enter queue
bins <- seq(min(hip_knee_priority_2_data$rtt_startdate), max(hip_knee_priority_2_data$rtt_startdate)+7, by=7)
ranges <- paste(head(bins,-1), bins[-1], sep=" - ")
freq <- hist(hip_knee_priority_2_data$rtt_startdate, breaks=bins, include.lowest=TRUE, plot=TRUE)
hip_knee_enter_priority_2_timeseries <- data.frame(range = ranges, frequency = freq$counts)
# hip knee priority 3 enter queue
bins <- seq(min(hip_knee_priority_3_data$rtt_startdate), max(hip_knee_priority_3_data$rtt_startdate)+7, by=7)
ranges <- paste(head(bins,-1), bins[-1], sep=" - ")
freq <- hist(hip_knee_priority_3_data$rtt_startdate, breaks=bins, include.lowest=TRUE, plot=TRUE)
hip_knee_enter_priority_3_timeseries <- data.frame(range = ranges, frequency = freq$counts)
# knee priority 1 enter queue
bins <- seq(min(knee_priority_1_data$rtt_startdate), max(knee_priority_1_data$rtt_startdate)+7, by=7)
ranges <- paste(head(bins,-1), bins[-1], sep=" - ")
freq <- hist(knee_priority_1_data$rtt_startdate, breaks=bins, include.lowest=TRUE, plot=TRUE)
knee_enter_priority_1_timeseries <- data.frame(range = ranges, frequency = freq$counts)
# knee priority 2 enter queue
bins <- seq(min(knee_priority_2_data$rtt_startdate), max(knee_priority_2_data$rtt_startdate)+7, by=7)
ranges <- paste(head(bins,-1), bins[-1], sep=" - ")
freq <- hist(knee_priority_2_data$rtt_startdate, breaks=bins, include.lowest=TRUE, plot=TRUE)
knee_enter_priority_2_timeseries <- data.frame(range = ranges, frequency = freq$counts)
# knee priority 3 enter queue
bins <- seq(min(knee_priority_3_data$rtt_startdate), max(knee_priority_3_data$rtt_startdate)+7, by=7)
ranges <- paste(head(bins,-1), bins[-1], sep=" - ")
freq <- hist(knee_priority_3_data$rtt_startdate, breaks=bins, include.lowest=TRUE, plot=TRUE)
knee_enter_priority_3_timeseries <- data.frame(range = ranges, frequency = freq$counts)
# shoulder priority 1 enter queue
bins <- seq(min(shoulder_priority_1_data$rtt_startdate), max(shoulder_priority_1_data$rtt_startdate)+7, by=7)
ranges <- paste(head(bins,-1), bins[-1], sep=" - ")
freq <- hist(shoulder_priority_1_data$rtt_startdate, breaks=bins, include.lowest=TRUE, plot=TRUE)
shoulder_enter_priority_1_timeseries <- data.frame(range = ranges, frequency = freq$counts)
# shoulder priority 2 enter queue
bins <- seq(min(shoulder_priority_2_data$rtt_startdate), max(shoulder_priority_2_data$rtt_startdate)+7, by=7)
ranges <- paste(head(bins,-1), bins[-1], sep=" - ")
freq <- hist(shoulder_priority_2_data$rtt_startdate, breaks=bins, include.lowest=TRUE, plot=TRUE)
shoulder_enter_priority_2_timeseries <- data.frame(range = ranges, frequency = freq$counts)
# shoulder priority 3 enter queue
# NOTE: In this particular data-set, there exist no priority 3 shoulder referrals
# Consequently, a time-series cannot be generated.
#bins <- seq(min(shoulder_priority_3_data$rtt_startdate), max(shoulder_priority_3_data$rtt_startdate)+7, by=7)
#ranges <- paste(head(bins,-1), bins[-1], sep=" - ")
#freq <- hist(shoulder_priority_3_data$rtt_startdate, breaks=bins, include.lowest=TRUE, plot=TRUE)
shoulder_enter_priority_3_timeseries <- data.frame(range = c(), frequency = c())
# foot ankle exit queue
bins <- seq(min(foot_ankle_data$rtt_enddate), max(foot_ankle_data$rtt_enddate)+7, by=7)
ranges <- paste(head(bins,-1), bins[-1], sep=" - ")
freq <- hist(foot_ankle_data$rtt_enddate, breaks=bins, include.lowest=TRUE, plot=TRUE)
foot_ankle_exit_timeseries <- data.frame(range = ranges, frequency = freq$counts)
# hand exit queue
bins <- seq(min(hand_data$rtt_enddate), max(hand_data$rtt_enddate)+7, by=7)
ranges <- paste(head(bins,-1), bins[-1], sep=" - ")
freq <- hist(hand_data$rtt_enddate, breaks=bins, include.lowest=TRUE, plot=TRUE)
hand_exit_timeseries <- data.frame(range = ranges, frequency = freq$counts)
# hip exit queue
bins <- seq(min(hip_data$rtt_enddate), max(hip_data$rtt_enddate)+7, by=7)
ranges <- paste(head(bins,-1), bins[-1], sep=" - ")
freq <- hist(hip_data$rtt_enddate, breaks=bins, include.lowest=TRUE, plot=TRUE)
hip_exit_timeseries <- data.frame(range = ranges, frequency = freq$counts)
# hip knee exit queue
bins <- seq(min(hip_knee_data$rtt_enddate), max(hip_knee_data$rtt_enddate)+7, by=7)
ranges <- paste(head(bins,-1), bins[-1], sep=" - ")
freq <- hist(hip_knee_data$rtt_enddate, breaks=bins, include.lowest=TRUE, plot=TRUE)
hip_knee_exit_timeseries <- data.frame(range = ranges, frequency = freq$counts)
# knee exit queue
bins <- seq(min(knee_data$rtt_enddate), max(knee_data$rtt_enddate)+7, by=7)
ranges <- paste(head(bins,-1), bins[-1], sep=" - ")
freq <- hist(knee_data$rtt_enddate, breaks=bins, include.lowest=TRUE, plot=TRUE)
knee_exit_timeseries <- data.frame(range = ranges, frequency = freq$counts)
# shoulder exit queue
bins <- seq(min(shoulder_data$rtt_enddate), max(shoulder_data$rtt_enddate)+7, by=7)
ranges <- paste(head(bins,-1), bins[-1], sep=" - ")
freq <- hist(shoulder_data$rtt_enddate, breaks=bins, include.lowest=TRUE, plot=TRUE)
shoulder_exit_timeseries <- data.frame(range = ranges, frequency = freq$counts)
# ----- Write Resultant Time-Series to CSV ----
write.csv(clock_stops_TO_inpatient_with_service_data, file = "clock_stops_processed.csv", row.names = TRUE)
write.csv(incompletes_TO_inpatient_with_service_data, file = "incompletes_processed.csv", row.names = TRUE)
setwd("C:/Users/Tarmi/Desktop/University Notes/Semester 3/CORMSIS Project/Data Analysis/ProcessedData/TimeSeries")
write.csv(foot_ankle_enter_priority_1_timeseries, file = "foot_ankle_priority_1_enter_by_week_timeseries.csv", row.names = TRUE)
write.csv(foot_ankle_enter_priority_2_timeseries, file = "foot_ankle_priority_2_enter_by_week_timeseries.csv", row.names = TRUE)
write.csv(foot_ankle_enter_priority_3_timeseries, file = "foot_ankle_priority_3_enter_by_week_timeseries.csv", row.names = TRUE)
write.csv(foot_ankle_exit_timeseries, file = "foot_ankle_priority_all_exit_by_week_timeseries.csv", row.names = TRUE)
write.csv(hand_enter_priority_1_timeseries, file = "hand_priority_1_enter_by_week_timeseries.csv", row.names = TRUE)
write.csv(hand_enter_priority_2_timeseries, file = "hand_priority_2_enter_by_week_timeseries.csv", row.names = TRUE)
write.csv(hand_enter_priority_3_timeseries, file = "hand_priority_3_enter_by_week_timeseries.csv", row.names = TRUE)
write.csv(hand_exit_timeseries, file = "hand_priority_all_exit_by_week_timeseries.csv", row.names = TRUE)
write.csv(hip_enter_priority_1_timeseries, file = "hip_priority_1_enter_by_week_timeseries.csv", row.names = TRUE)
write.csv(hip_enter_priority_2_timeseries, file = "hip_priority_2_enter_by_week_timeseries.csv", row.names = TRUE)
write.csv(hip_enter_priority_3_timeseries, file = "hip_priority_3_enter_by_week_timeseries.csv", row.names = TRUE)
write.csv(hip_exit_timeseries, file = "hip_priority_all_exit_by_week_timeseries.csv", row.names = TRUE)
write.csv(hip_knee_enter_priority_1_timeseries, file = "hip_knee_priority_1_enter_by_week_timeseries.csv", row.names = TRUE)
write.csv(hip_knee_enter_priority_2_timeseries, file = "hip_knee_priority_2_enter_by_week_timeseries.csv", row.names = TRUE)
write.csv(hip_knee_enter_priority_3_timeseries, file = "hip_knee_priority_3_enter_by_week_timeseries.csv", row.names = TRUE)
write.csv(hip_knee_exit_timeseries, file = "hip_knee_priority_all_exit_by_week_timeseries.csv", row.names = TRUE)
write.csv(knee_enter_priority_1_timeseries, file = "knee_priority_1_enter_by_week_timeseries.csv", row.names = TRUE)
write.csv(knee_enter_priority_2_timeseries, file = "knee_priority_2_enter_by_week_timeseries.csv", row.names = TRUE)
write.csv(knee_enter_priority_3_timeseries, file = "knee_priority_3_enter_by_week_timeseries.csv", row.names = TRUE)
write.csv(knee_exit_timeseries, file = "knee_priority_all_exit_by_week_timeseries.csv", row.names = TRUE)
write.csv(shoulder_enter_priority_1_timeseries, file = "shoulder_priority_1_enter_by_week_timeseries.csv", row.names = TRUE)
write.csv(shoulder_enter_priority_2_timeseries, file = "shoulder_priority_2_enter_by_week_timeseries.csv", row.names = TRUE)
write.csv(shoulder_enter_priority_3_timeseries, file = "shoulder_priority_3_enter_by_week_timeseries.csv", row.names = TRUE)
write.csv(shoulder_exit_timeseries, file = "shoulder_priority_all_exit_by_week_timeseries.csv", row.names = TRUE)