library(ggplot2)
library(data.table)Load CGM data for every patient
setwd("~/Downloads/CGM/dexcom_20210301/")
cgm_files = grep("dexcom_clean_pop_", list.files("."), value = T)
cgm_data = rbindlist(lapply(cgm_files, fread))
cgm_data[, dexcom_date := as.Date(ts)]cgm_data_days = cgm_data[,.(
num_bg_recodings = .N
), by = c('pop', 'record_id', 'sex', 'dob', 'insurance_type','dexcom_date')]
cgm_data_days[, wk := as.Date(cut(dexcom_date, "week"))]t1d = cgm_data_days[, .(
num_bg_recodings = sum(num_bg_recodings),
num_dates = uniqueN(dexcom_date),
num_weeks = uniqueN(wk)
), by = c('pop', 'record_id', 'sex', 'dob', 'insurance_type')]Add demographics
t1d[, dob := as.Date(dob, "%m/%d/%y")]
t1d[, age := floor(as.numeric((as.Date('2020-05-25') - dob))/365)]
t1d[, sex := as.factor(sex)]
t1d[, insurance_type := as.factor(insurance_type)]Load Redcap data on pump use
redcap_4t_p = fread("~/Downloads/CGM/4TStudyTeamworkTarge_DATA_2021-03-06_1131.csv")
redcap_4t_p = redcap_4t_p[,.(pop = "4T", pump = max(fcoalesce(as.numeric(cgm_pump_use),0))), by='record_id']
redcap_pilot_p = fread("~/Downloads/CGM/CGMPilotStudyCGMInNe_DATA_2021-03-03_2025.csv")
redcap_pilot_p = redcap_pilot_p[,.(pop = "Pilot", pump = max(fcoalesce(as.numeric(pump_use),0))), by='record_id']
redcap_tips_p = fread("~/Downloads/CGM/CGMTips_DATA_2021-03-06_1131.csv")
redcap_tips_p = redcap_tips_p[,.(pop = "TIPS", pump = max(fcoalesce(as.numeric(pump_use),0))), by='record_id']
redcap_pump = rbind(redcap_4t_p, redcap_pilot_p, redcap_tips_p)
redcap_pump[,.(sum(pump), .N), by='pop']## pop V1 N
## 1: 4T 10 84
## 2: Pilot 67 136
## 3: TIPS 5 76Load Redcap data on contacts
redcap_4t = fread("~/Downloads/CGM/4TStudyTeamworkTarge_DATA_2021-03-06_1131.csv")
redcap_4t = redcap_4t[!is.na(rm_contact_date) | cgm_visit_type %in% c(1,2)]
redcap_4t = unique(redcap_4t[,.(
pop="4T", record_id,
week_contact = as.Date(as.character(cut(fcoalesce(cgm_visitdt,rm_contact_date), "week"))),
in_person_visit = cgm_visit_type == 1,
telehealth_visit = cgm_visit_type == 2,
remote_contact = !is.na(rm_contact_date) & rm_contact_week==1#,
#cgm_pump_use = max(cgm_pump_use, na.rm=T)
)])
redcap_4t = redcap_4t[!is.na(week_contact)]
# str(redcap_4t)
redcap_4t[,.(sum(in_person_visit, na.rm=T), sum(telehealth_visit, na.rm=T), sum(remote_contact, na.rm=T)), by='week_contact']## week_contact V1 V2 V3
## 1: 2020-06-29 4 0 0
## 2: 2020-07-06 6 2 0
## 3: 2020-07-13 4 3 0
## 4: 2020-07-20 1 7 0
## 5: 2020-07-27 3 0 3
## 6: 2020-08-03 2 1 5
## 7: 2020-08-10 4 1 5
## 8: 2020-08-17 2 2 2
## 9: 2020-08-24 2 4 2
## 10: 2020-08-31 0 1 1
## 11: 2020-09-07 1 0 1
## 12: 2020-09-14 5 5 3
## 13: 2020-09-21 7 3 1
## 14: 2020-09-28 0 8 5
## 15: 2020-11-02 0 9 6
## 16: 2020-10-05 5 4 5
## 17: 2020-10-12 4 2 6
## 18: 2020-10-19 5 4 6
## 19: 2020-10-26 3 3 5
## 20: 2020-11-09 7 2 10
## 21: 2020-11-16 6 3 8
## 22: 2020-11-23 1 5 7
## 23: 2020-11-30 3 7 10
## 24: 2020-12-07 1 5 7
## 25: 2020-12-14 6 4 15
## 26: 2020-12-21 1 2 10
## 27: 2020-12-28 4 4 5
## 28: 2021-01-04 6 4 16
## 29: 2021-01-18 1 5 13
## 30: 2021-01-11 2 8 19
## 31: 2021-02-01 2 4 10
## 32: 2020-06-22 0 1 0
## 33: 2020-06-15 1 0 0
## 34: 2021-01-25 3 7 12
## 35: 2021-02-08 1 4 3
## 36: 2021-02-15 5 0 0
## 37: 2021-02-22 2 0 0
## 38: 2021-03-01 1 0 0
## week_contact V1 V2 V3redcap_pilot = fread("~/Downloads/CGM/CGMPilotStudyCGMInNe_DATA_2021-03-03_2025.csv")
redcap_pilot = redcap_pilot[!is.na(visit_date) | !is.na(date_contact)]
redcap_pilot = unique(redcap_pilot[,.(
pop="Pilot",record_id,
week_contact = as.Date(as.character(cut(fcoalesce(as.Date(visit_date), as.Date(date_contact, "%m/%d/%y")), "week"))),
in_person_visit = as.numeric(visit_type) == 1,
telehealth_visit = as.numeric(visit_type) == 2,
remote_contact = !is.na(date_contact) & contact_week==1
)])
redcap_pilot = redcap_pilot[!is.na(week_contact)]
# str(redcap_pilot)
redcap_pilot[,.(sum(in_person_visit, na.rm=T), sum(telehealth_visit, na.rm=T), sum(remote_contact, na.rm=T)), by='week_contact']## week_contact V1 V2 V3
## 1: 2018-10-08 0 0 1
## 2: 2018-11-12 1 0 0
## 3: 2019-01-28 0 0 0
## 4: 2019-04-29 1 0 6
## 5: 2019-09-30 7 2 26
## ---
## 136: 2018-06-11 0 0 0
## 137: 2021-01-04 0 0 11
## 138: 2021-02-08 0 0 4
## 139: 2002-11-11 0 0 1
## 140: 2029-02-12 0 0 0redcap_tips = fread("~/Downloads/CGM/CGMTips_DATA_2021-03-06_1131.csv")
redcap_tips = redcap_tips[!is.na(visit_date) | !is.na(date_contact)]
redcap_tips = unique(redcap_tips[,.(
pop="TIPS", record_id,
week_contact = as.Date(as.character(cut(fcoalesce(as.Date(visit_date), as.Date(date_contact, "%m-%d-%y")), "week"))),
in_person_visit = as.numeric(visit_type) == 1,
telehealth_visit = as.numeric(visit_type) == 2,
remote_contact = !is.na(date_contact) & contact_week==1)])
#str(redcap_tips)
redcap_tips = redcap_tips[!is.na(week_contact)]
redcap_tips[,.(sum(in_person_visit, na.rm=T), sum(telehealth_visit, na.rm=T), sum(remote_contact, na.rm=T)), by='week_contact']## week_contact V1 V2 V3
## 1: 2020-10-26 2 0 2
## 2: 2020-09-14 0 0 1
## 3: 2020-10-12 3 2 3
## 4: 2020-12-07 1 3 5
## 5: 2020-01-13 0 0 8
## 6: 2020-08-31 0 1 1
## 7: 2020-11-23 1 4 2
## 8: 2021-01-18 1 3 0
## 9: 2020-10-19 2 0 1
## 10: 2020-11-16 3 4 1
## 11: 2020-12-14 1 7 4
## 12: 2020-12-21 1 2 3
## 13: 2020-12-28 2 0 0
## 14: 2021-01-04 1 3 0
## 15: 2020-09-28 0 1 0
## 16: 2020-11-30 2 2 2
## 17: 2020-09-07 2 0 1
## 18: 2020-10-05 1 0 1
## 19: 2020-11-02 3 3 2
## 20: 2020-09-21 1 0 0
## 21: 2020-11-09 1 2 2
## 22: 2021-01-11 1 2 0
## 23: 2020-01-06 0 0 3
## 24: 2020-01-20 0 0 3
## week_contact V1 V2 V3# Combine contacts
redcap_combo = rbind(redcap_pilot, redcap_4t, redcap_tips)
# str(redcap_combo)
contacts_agg = redcap_combo[,.(
in_person_visits = sum(in_person_visit, na.rm=T),
telehealth_visits = sum(telehealth_visit, na.rm=T),
remote_contacts = sum(remote_contact, na.rm=T)
), by=c('pop','record_id')]
contacts_agg## pop record_id in_person_visits telehealth_visits remote_contacts
## 1: Pilot 1 2 5 2
## 2: Pilot 2 3 2 0
## 3: Pilot 3 2 4 0
## 4: Pilot 4 3 2 0
## 5: Pilot 5 3 2 0
## ---
## 244: TIPS 40 0 0 1
## 245: TIPS 41 0 2 1
## 246: TIPS 43 0 1 1
## 247: TIPS 45 0 1 1
## 248: TIPS 50 0 0 1t1dc = merge(t1d, contacts_agg, by=c('pop','record_id'), all.x=T)
t1dc = merge(t1dc, redcap_pump, by=c('pop','record_id'), all.x=T)
t1dc[is.na(in_person_visits), in_person_visits:=0]
t1dc[is.na(telehealth_visits), telehealth_visits:=0]
t1dc[is.na(remote_contacts), remote_contacts:=0]
t1dc[is.na(pump), pump:=0]Try using Table1
library(table1)##
## Attaching package: 'table1'## The following objects are masked from 'package:base':
##
## units, units<-table1(
~ sex + insurance_type + age + num_bg_recodings + num_dates + num_weeks + in_person_visits + telehealth_visits + remote_contacts + pump | pop,
render.continuous=c(.="Mean (SD)", "Median [IQR]"="Median [Q1, Q3]"),
data=t1dc,
overall = "Total",
droplevels = TRUE)| 4T (N=58) |
Pilot (N=129) |
TIPS (N=63) |
Total (N=250) |
|
|---|---|---|---|---|
| sex | ||||
| Female | 28 (48.3%) | 60 (46.5%) | 34 (54.0%) | 122 (48.8%) |
| Male | 30 (51.7%) | 69 (53.5%) | 29 (46.0%) | 128 (51.2%) |
| insurance_type | ||||
| Both | 1 (1.7%) | 0 (0%) | 3 (4.8%) | 4 (1.6%) |
| Private | 39 (67.2%) | 100 (77.5%) | 5 (7.9%) | 144 (57.6%) |
| Public | 18 (31.0%) | 28 (21.7%) | 55 (87.3%) | 101 (40.4%) |
| Missing | 0 (0%) | 1 (0.8%) | 0 (0%) | 1 (0.4%) |
| age | ||||
| Mean (SD) | 10.4 (4.60) | 10.0 (4.57) | 13.0 (4.46) | 10.9 (4.70) |
| Median [IQR] | 11.0 [8.00, 14.0] | 10.0 [7.00, 14.0] | 13.0 [11.0, 16.0] | 11.0 [8.00, 14.0] |
| num_bg_recodings | ||||
| Mean (SD) | 39800 (53500) | 125000 (62700) | 45700 (73500) | 85000 (75500) |
| Median [IQR] | 33500 [19200, 46100] | 116000 [81900, 160000] | 20100 [8820, 39900] | 66300 [23300, 129000] |
| num_dates | ||||
| Mean (SD) | 148 (196) | 471 (220) | 195 (284) | 327 (277) |
| Median [IQR] | 126 [69.3, 166] | 430 [323, 628] | 90.0 [47.0, 194] | 258 [98.0, 502] |
| num_weeks | ||||
| Mean (SD) | 22.9 (28.2) | 71.7 (30.7) | 32.8 (42.3) | 50.6 (40.0) |
| Median [IQR] | 20.0 [11.0, 27.0] | 66.0 [53.0, 96.0] | 17.0 [10.0, 34.0] | 41.0 [17.3, 75.8] |
| in_person_visits | ||||
| Mean (SD) | 1.59 (0.956) | 2.84 (1.80) | 0.460 (1.12) | 1.95 (1.79) |
| Median [IQR] | 1.00 [1.00, 2.00] | 3.00 [2.00, 4.00] | 0 [0, 0] | 2.00 [1.00, 3.00] |
| telehealth_visits | ||||
| Mean (SD) | 2.12 (1.57) | 2.38 (1.61) | 0.508 (0.965) | 1.85 (1.66) |
| Median [IQR] | 2.00 [1.00, 3.00] | 2.00 [1.00, 3.00] | 0 [0, 1.00] | 2.00 [0, 3.00] |
| remote_contacts | ||||
| Mean (SD) | 3.47 (4.13) | 14.6 (14.1) | 0.651 (1.08) | 8.52 (12.1) |
| Median [IQR] | 2.00 [0, 5.75] | 13.0 [0, 26.0] | 0 [0, 1.00] | 2.00 [0, 14.0] |
| pump | ||||
| Mean (SD) | 0.172 (0.381) | 0.504 (0.502) | 0.0794 (0.272) | 0.320 (0.467) |
| Median [IQR] | 0 [0, 0] | 1.00 [0, 1.00] | 0 [0, 0] | 0 [0, 1.00] |