breastPatientsDetail
2024-02-07
Data Source: Mater Dive Port 7
Load library package
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## wineLoad file & some variables values
- Re-code values of Referral.Source, Eligibility, Area.of.Residence, Booking.Type, Reason.for.Cancellation
- Calculate difference of dates, difference of Home and Present address
main <- read.csv("BreastPatientsDetail_Clinic.csv")
main <- subset(main, Attendance.Year < 2024)
## View variable types
main %>% glimpse() #glimpse from library(dplyr)## Rows: 91,169
## Columns: 45
## $ Record.Type <chr> "Attendance", "Attendance", "Cancellation…
## $ Clinic.Code <int> 932, 757, 757, 757, 771, 527, 757, 757, 7…
## $ Clinic.Type <chr> "MED : MEDICAL", "SBC : SYMPTOMATIC BREAS…
## $ NurseFlag <chr> "N", "N", "N", "N", "N", "N", "N", "N", "…
## $ Medical.Record.Number <int> 177, 527, 527, 527, 527, 2194, 2329, 2329…
## $ Gender <chr> "Male", "Female", "Female", "Female", "Fe…
## $ Patient.Name <chr> "CLAVIN JOHN", "PHELAN MAUREEN", "PHELAN …
## $ Attendance.Day <chr> "Monday", "Tuesday", "Tuesday", "Tuesday"…
## $ Attendance.MonthYear <chr> "07-2022", "07-2023", "11-2023", "11-2023…
## $ Attendance.Date <chr> "11/07/2022", "25/07/2023", "07/11/2023",…
## $ Attendance.Type.Description <chr> "NEW", "NEW", "RETURN", "RETURN", "NEW", …
## $ Referral.Source <chr> "G : GP", "W : WARD", "C : CLINIC", "C : …
## $ Clinical.Specialty.Group <chr> "Cancer", "Cancer", "Cancer", "Cancer", "…
## $ Specialty.Description.Mater <chr> "General Surgery: Breast", "General Surge…
## $ Consultant <chr> "BARRYM : BARRY MR. MITCHELL JOHN", "BARR…
## $ Insurance.Scheme <chr> "U : UNKNOWN", "D : MEDICAL CARD HOLDER",…
## $ Eligibility <chr> "01 : CAT 1 (MEDICAL CARD)", "01 : CAT 1 …
## $ Age.at.Attendance <int> 83, 87, 87, 87, 86, 75, 83, 83, 83, 84, 8…
## $ Referring.Hospital <chr> ":", ":", ":", ":", ":", ":", ":", ":", "…
## $ Area.of.Residence <chr> "0109 : DUBLIN 9", "0109 : DUBLIN 9", "01…
## $ Attendance.Year <int> 2022, 2023, 2023, 2023, 2023, 2021, 2020,…
## $ Attendance.Month <chr> "July", "July", "November", "November", "…
## $ Age.at.Attendance.Cat.HSE <chr> "75 - 84", "85 >=", "85 >=", "85 >=", "85…
## $ Pathway.Number <int> 56683148, 57193702, 77706400, 57193702, 5…
## $ Present.Address <chr> "174 GRACEPARK HTS., DRUMCONDRA, DUBLIN 9…
## $ Home.Address <chr> "174 GRACEPARK HEIGHTS, DRUMCONDRA, DUBLI…
## $ Home.Phone.No <chr> "087 6193827", "8309505", "8309505", "830…
## $ Mobile.Phone.No <chr> "087 6193837", "085 8597622", "085 859762…
## $ Appointment.Date...Time <chr> "", "", "20231107 12:00:00", "", "", "", …
## $ X <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ Booking.Type <chr> "N : NEW", "W : WARD", "R : RETURN", "R :…
## $ Booked.Date <chr> "2022/06/23", "2023/06/28", "2023/07/25",…
## $ Booked.Time <chr> "7:22:00", "11:14:00", "12:39:00", "10:16…
## $ Cancelled.Indicator <chr> "", "", "Rescheduled Appt", "", "", "", "…
## $ Cancellation.Group <chr> "N/A", "N/A", "Patient", "N/A", "N/A", "N…
## $ Reason.For.Cancellation <chr> "", "", "PF : PERSONAL/FAMILY REASONS", "…
## $ Reason.for.Cancellation.Desc <chr> "", "", "PERSONAL/FAMILY REASONS", "", ""…
## $ Reason.for.Cancel.Text <chr> "", "", "3 months as per loop", "", "", "…
## $ Rebooked.Indicator..HIS. <chr> "", "", "Yes", "", "", "", "", "Yes", "Ye…
## $ Hospital.Catchment <chr> "Mater Catchment", "Mater Catchment", "Ma…
## $ No.Attendances <int> 1, 1, NA, 1, 1, 1, 1, NA, NA, NA, 1, NA, …
## $ No.New.Attendances <int> 1, 1, NA, 0, 1, 1, 0, NA, NA, NA, 1, NA, …
## $ No.Return.Attendances <int> 0, 0, NA, 1, 0, 0, 1, NA, NA, NA, 0, NA, …
## $ No.DNAs <int> NA, NA, 0, NA, NA, NA, NA, 0, 0, 1, NA, 0…
## $ No.Cancels <int> NA, NA, 1, NA, NA, NA, NA, 1, 1, 0, NA, 1…head(main)## Record.Type Clinic.Code Clinic.Type NurseFlag
## 1 Attendance 932 MED : MEDICAL N
## 2 Attendance 757 SBC : SYMPTOMATIC BREAST CLINIC N
## 3 Cancellation 757 SBC : SYMPTOMATIC BREAST CLINIC N
## 4 Attendance 757 SBC : SYMPTOMATIC BREAST CLINIC N
## 6 Attendance 771 TRI : TRIPLE ASSESSMENT CLINIC N
## 7 Attendance 527 TRI : TRIPLE ASSESSMENT CLINIC N
## Medical.Record.Number Gender Patient.Name Attendance.Day
## 1 177 Male CLAVIN JOHN Monday
## 2 527 Female PHELAN MAUREEN Tuesday
## 3 527 Female PHELAN MAUREEN Tuesday
## 4 527 Female PHELAN MAUREEN Tuesday
## 6 527 Female PHELAN MAUREEN Tuesday
## 7 2194 Female O BRIEN DEIRDRE Thursday
## Attendance.MonthYear Attendance.Date Attendance.Type.Description
## 1 07-2022 11/07/2022 NEW
## 2 07-2023 25/07/2023 NEW
## 3 11-2023 07/11/2023 RETURN
## 4 11-2023 28/11/2023 RETURN
## 6 06-2023 27/06/2023 NEW
## 7 02-2021 18/02/2021 NEW
## Referral.Source Clinical.Specialty.Group Specialty.Description.Mater
## 1 G : GP Cancer General Surgery: Breast
## 2 W : WARD Cancer General Surgery: Breast
## 3 C : CLINIC Cancer General Surgery: Breast
## 4 C : CLINIC Cancer General Surgery: Breast
## 6 W : WARD Cancer General Surgery: Breast
## 7 G : GP Cancer General Surgery: Breast
## Consultant Insurance.Scheme
## 1 BARRYM : BARRY MR. MITCHELL JOHN U : UNKNOWN
## 2 BARRYM : BARRY MR. MITCHELL JOHN D : MEDICAL CARD HOLDER
## 3 BARRYM : BARRY MR. MITCHELL JOHN D : MEDICAL CARD HOLDER
## 4 BARRYM : BARRY MR. MITCHELL JOHN D : MEDICAL CARD HOLDER
## 6 BARRYM : BARRY MR. MITCHELL JOHN D : MEDICAL CARD HOLDER
## 7 KELLM : KELL PROFESSOR MALCOLM D : MEDICAL CARD HOLDER
## Eligibility Age.at.Attendance Referring.Hospital
## 1 01 : CAT 1 (MEDICAL CARD) 83 :
## 2 01 : CAT 1 (MEDICAL CARD) 87 :
## 3 01 : CAT 1 (MEDICAL CARD) 87 :
## 4 01 : CAT 1 (MEDICAL CARD) 87 :
## 6 01 : CAT 1 (MEDICAL CARD) 86 :
## 7 02 : CAT 2 (NON MEDICAL CARD) 75 :
## Area.of.Residence Attendance.Year Attendance.Month Age.at.Attendance.Cat.HSE
## 1 0109 : DUBLIN 9 2022 July 75 - 84
## 2 0109 : DUBLIN 9 2023 July 85 >=
## 3 0109 : DUBLIN 9 2023 November 85 >=
## 4 0109 : DUBLIN 9 2023 November 85 >=
## 6 0109 : DUBLIN 9 2023 June 85 >=
## 7 0109 : DUBLIN 9 2021 February 75 - 84
## Pathway.Number Present.Address
## 1 56683148 174 GRACEPARK HTS., DRUMCONDRA, DUBLIN 9
## 2 57193702 11 BOTANIC ROAD, GLASNEVIN, DUBLIN 9
## 3 77706400 11 BOTANIC ROAD, GLASNEVIN, DUBLIN 9
## 4 57193702 11 BOTANIC ROAD, GLASNEVIN, DUBLIN 9
## 6 57154091 11 BOTANIC ROAD, GLASNEVIN, DUBLIN 9
## 7 56054716 41 COULTRY DRIVE, SANTRY, DUBLIN 9
## Home.Address Home.Phone.No Mobile.Phone.No
## 1 174 GRACEPARK HEIGHTS, DRUMCONDRA, DUBLIN 9 087 6193827 087 6193837
## 2 11 BOTANIC ROAD, GLASNEVIN, DUBLIN 9 8309505 085 8597622
## 3 11 BOTANIC ROAD, GLASNEVIN, DUBLIN 9 8309505 085 8597622
## 4 11 BOTANIC ROAD, GLASNEVIN, DUBLIN 9 8309505 085 8597622
## 6 11 BOTANIC ROAD, GLASNEVIN, DUBLIN 9 8309505 085 8597622
## 7 41 COULTRY DRIVE, SANTRY, DUBLIN 9 NO LANDLINE 089 6144182
## Appointment.Date...Time X Booking.Type Booked.Date Booked.Time
## 1 NA N : NEW 2022/06/23 7:22:00
## 2 NA W : WARD 2023/06/28 11:14:00
## 3 20231107 12:00:00 NA R : RETURN 2023/07/25 12:39:00
## 4 NA R : RETURN 2023/10/26 10:16:00
## 6 NA W : WARD 2023/06/26 11:43:00
## 7 NA N : NEW 2021/02/08 10:32:00
## Cancelled.Indicator Cancellation.Group Reason.For.Cancellation
## 1 N/A
## 2 N/A
## 3 Rescheduled Appt Patient PF : PERSONAL/FAMILY REASONS
## 4 N/A
## 6 N/A
## 7 N/A
## Reason.for.Cancellation.Desc Reason.for.Cancel.Text Rebooked.Indicator..HIS.
## 1
## 2
## 3 PERSONAL/FAMILY REASONS 3 months as per loop Yes
## 4
## 6
## 7
## Hospital.Catchment No.Attendances No.New.Attendances No.Return.Attendances
## 1 Mater Catchment 1 1 0
## 2 Mater Catchment 1 1 0
## 3 Mater Catchment NA NA NA
## 4 Mater Catchment 1 0 1
## 6 Mater Catchment 1 1 0
## 7 Mater Catchment 1 1 0
## No.DNAs No.Cancels
## 1 NA NA
## 2 NA NA
## 3 0 1
## 4 NA NA
## 6 NA NA
## 7 NA NA#sample <- main %>% group_by(main$Record.Type)
#samplePatients <- sample_frac(main,0.1,replace = F)
## Remove NAs or impute NAs if necessary
allMissing = is.na(main)
counts = colSums(allMissing)
counts [counts>0]## Home.Phone.No X No.Attendances
## 6 91169 35872
## No.New.Attendances No.Return.Attendances No.DNAs
## 35872 35872 55297
## No.Cancels
## 55297# main <- na.omit(main)
print(is.data.frame(main))## [1] TRUE## Transforming data
# main_group <- main_processed %>% group_by(main_processed$Medical.Record.Number) #library(moments)
# main_group$freq_MRN_recode <- ifelse(count(main_group$Medical.Record.Number >= 34)==1,1,0)
#library(plyr)
main$Clinic.Type_recode <- mapvalues(main$Clinic.Type, from = c("SBC : SYMPTOMATIC BREAST CLINIC","TRI : TRIPLE ASSESSMENT CLINIC","MED : MEDICAL","FRA : FAMILY RISK ASSESSMENT","GEN : GENERAL"), to = c("SBC","TRI","MED","FRA","GEN"))
main$Consultant_recode <- mapvalues(main$Consultant, from = c("WALSSI : WALSH MS SIUN","BARRYM : BARRY MR. MITCHELL JOHN","KELLM : KELL PROFESSOR MALCOLM","STOKES : STOKES PROF. MAURICE","HEENEY : HEENEY MS. ANNA"), to = c("WALSSI","BARRYM","KELLM","STOKES","HEENEY"))
main$Insurance.Scheme_recode <- mapvalues(main$Insurance.Scheme, from = c("U : UNKNOWN","D : MEDICAL CARD HOLDER","V : VHI","G : GARDA SCHEME","I : IRISH LIFE HEALTH","S : SELF","B : LAYA HEALTHCARE","O : OTHER","J : GLOHEALTH","E : E.S.B. SCHEME","C : BLUE CROSS","P : PRISON OFFICERS","H : HOSPITAL SATURDAY FUND","A : ARMY SCHEME","M : MEDISHIELD"), to = c("U","D","V","G","I","S","B","O","J","E","C","P","H","A","M"))
main$Hospital.Catchment_recode <- mapvalues(main$Hospital.Catchment, from = c("Mater Catchment","National Catchment","Connolly Catchment","James Catchment","Tallaght Catchment","Beaumont Catchment","International Catchment","Vincents Catchment"), to = c("Mater","National","Connolly","James","Tallaght","Beaumont","International","Vincents"))
main$Attendance.Type_recode <- mapvalues(main$Attendance.Type.Description, from = c("WALK IN - NEW","ADD ON - NEW","WALK IN - RETURN","ADD ON - RETURN","Return Home Visit","Return Virtual Phone","VIRTUAL NEW","New Virtual Phone","New Virtual Video","Return Virtual Video"), to = c("NEW","NEW","RETURN","RETURN","RETURN","VIRTUAL(New_Return)","VIRTUAL(New_Return)","VIRTUAL(New_Return)","VIRTUAL(New_Return)","VIRTUAL(New_Return)"))
main$Referral.Source_recode <- mapvalues(main$Referral.Source, from = c("C : CLINIC","G : GP","W : WARD","N : BREAST CHECK (NBSP)","H : OTHER HOSPITAL","S : SELF","O : OTHER CONSULTANT","R : ROOMS","A : EMERGENCY DEPT","B : HEALTH BOARD REFERRAL","F : FAMILY PLANNING","K : ED SMITHFIELD","V : VASCULAR LAB","X : HEALTH CENTRE REFERRAL","Q : REFERRED FROM CAWT INITIATIVE","D : DENTIST"), to = c("CLINIC","GP","WARD","BREAST CHECK","Elsew outside Mater","Elsew outside Mater","OTHER CONSULTANT","Elsew of Mater","EMERGENCY DEPT","Elsew outside Mater","Elsew of Mater","Elsew of Mater","Elsew of Mater","Elsew outside Mater","Elsew outside Mater","Elsew of Mater"))
main$Eligibility_recode <- mapvalues(main$Eligibility, from = c("02 : CAT 2 (NON MEDICAL CARD)","01 : CAT 1 (MEDICAL CARD)","08 : ELIGIBILITY UNKNOWN","12 : DAY CASE - EXEMPT STAT. CHG.","19 : STAFF EXEMPT-STATUTORY CHARGE","35 : INFECTIOUS OR SUSPECTED INFECT","11 : ANTI D/RESEARCH/TRIAL","23 : NATIONAL COLORECTAL SCREENING","16 : NTPF - EXEMPT STAT. CHG.","06 : NON EU-VISITOR","24 : DIABETIC RETINA TREATMENT","07 : RTA","17 : ARMY - EXEMPT STAT. CHG.","10 : UK/NI - EXEMPT STAT.CHG.","21 : THE GOVERNOR - PRISONERS","13 : LONG STAY - EXEMPT STAT. CHG.","25 : UKRAINIAN CITIZEN","20 : HAA CARD HEALTH AMENDMENT ACT","18 : EHIC - EXEMPT STAT. CHG.","05 : EU-VISITOR NO EHIC","26 : U16 EXEMPT STAT. CHARGE","30 : PENDING PATIENT DETAILS"), to = c("NON MEDICAL CARD","MEDICAL CARD","ELIGIBILITY UNKNOWN","EXEMPT","EXEMPT","ACUTE UNCLASSIFIED","RESEARCH/NATIONAL PROG.","RESEARCH/NATIONAL PROG.","EXEMPT","NON MEDICAL CARD","NON ACUTE UNCLASSIFIED","ACUTE UNCLASSIFIED","EXEMPT","EXEMPT","EXEMPT","EXEMPT","EXEMPT","RESEARCH/NATIONAL PROG.","EXEMPT","NON MEDICAL CARD","EXEMPT","NON MEDICAL CARD"))
dublin_nth <- c("0100","0101","0103","0105","0107","0109","0111","0113","0115","0117")
dublin_sth <- c("0200","0202","0204","0206","0208","0210","0212","0214","0216","0218","0220","0222","0224")
outside_irl <- c("3303","3310","3350","3501","3600")
other_eastern.midland_region <- c("2300","3100","2200","2400","0300","0500","2500","0400")
northern.western_region <- c("2900","3000","2600","2000","2700","1900","2800","2100")
southern_region <- c("0700","0600","1000","1700","1200","1300","1500","1600","0800","1101")
main <- main %>% #library(plyr)
mutate(Area.of.Residence_recode = case_when(
substring(main$Area.of.Residence,0,4) %in% dublin_nth ~ "DUBLIN NTH",
substring(main$Area.of.Residence,0,4) %in% dublin_sth ~ "DUBLIN STH",
substring(main$Area.of.Residence,0,4) %in% other_eastern.midland_region ~ "EASTERN & MIDLAND REGION (excl.Dublin,Meath)",
substring(main$Area.of.Residence,0,4) %in% northern.western_region ~ "NORTHERN WESTERN REGION",
substring(main$Area.of.Residence,0,4) %in% southern_region ~ "SOUTHERN REGION",
substring(main$Area.of.Residence,0,4) %in% outside_irl ~ "OUTSIDE IRELAND",
substring(main$Area.of.Residence,0,4) == "3200" ~ "Meath",
substring(main$Area.of.Residence,0,4) == "0000" ~ "UNKNOWN",
TRUE ~ "Other counties"
))
head (main$Area.of.Residence_recode,10)## [1] "DUBLIN NTH" "DUBLIN NTH" "DUBLIN NTH" "DUBLIN NTH" "DUBLIN NTH"
## [6] "DUBLIN NTH" "DUBLIN NTH" "DUBLIN NTH" "DUBLIN NTH" "DUBLIN NTH"table(main$Area.of.Residence_recode)##
## DUBLIN NTH
## 41299
## DUBLIN STH
## 2338
## EASTERN & MIDLAND REGION (excl.Dublin,Meath)
## 21068
## Meath
## 14424
## NORTHERN WESTERN REGION
## 9766
## OUTSIDE IRELAND
## 24
## SOUTHERN REGION
## 2063
## UNKNOWN
## 187main$Booking.Type_recode <- ifelse(main$Booking.Type == ":", ifelse(!duplicated(main$Medical.Record.Number), "N : NEW", "R : RETURN"), main$Booking.Type)
main$Booking.Type_recode <- mapvalues(main$Booking.Type_recode, from = c("N : NEW","R : RETURN","W : WARD"), to =c("NEW","RETURN","WARD"))
main$Reason.for.Cancellation_recode <- mapvalues(main$Reason.for.Cancellation.Desc, from = c("NO SHOW","Patient no longer requires A","TEMPLATE / BOOKING DIARY AMEND","Appt brought forward by Hosp","Clinic reduced on Cons Instru","PERSONAL/FAMILY REASONS","PATIENT CANCELLED APPOINTMENT","Covid 19 Outbreak Hosp Canc","Appt issued incorrectly by hosp","PATIENT ILL","APP CANCELLED - AWAIT RESULTS","PATIENT ON HOLIDAYS","Appt date time not suitable","appointment deferred as per ANP instruction","CONSULTANT ABSENT ANNUAL LEAVE","BOOKING DIARY MANAGEMENT","EARLIER PRIVATE APPOINTMENT","TIME BLOCKED OUT","Covid 19 Outbreak Pat Canc","PATIENT IN OTHER HEALTH CARE","Appointment deferred as per Consultant instruction","inappropriate appointment","Patient requested earlier appt","PATIENT CANCELLED NO REASON","Patient deceased","EARLIER APPOINTMENT IN OTHER PUBLIC HOSPITAL","PATIENT FORGOT APPOINTMENT","PT INDICATES APPT NO REQUIRED","PATIENT INPATIENT MMUH","DEATH IN FAMILY","Adverse weather","PAT ATTENDED OPD AT MMUH","transfer to new consultant","PATIENT ATTEND OPD AT MMUH","ED Requested earlier appointment","CONSULTANT ABSENT - SICK LEAVE","patient attends GP for care","tferred to family hx breast","Consultant on Secondment","GP REQUEST EARLIER APPOINTMENT","validation cancelled by pt","ANOTHER CLINIC SAME DAY","CHRISTMAS PERIOD CLOSURE","CONSULTANT AT MEET/CONFER/EXAM","CONSULTANT RETIREMENT"), to = c("No show","By Patient","By Hospital","By Hospital","By Consultant/Advanced Nurse","By Patient","By Patient","By Covid","By Hospital","By Patient-health conditions","By Hospital","By Patient","By Patient","By Consultant/Advanced Nurse","By Consultant/Advanced Nurse","By Hospital","By Patient","By Hospital","By Covid","By Patient-health conditions","By Consultant/Advanced Nurse","By Patient","By Patient","By Patient","By Patient-health conditions","By Patient-health conditions","By Patient","By Patient","By Patient-health conditions","By Patient","By Patient","By Patient-health conditions","By Consultant/Advanced Nurse","By Patient-health conditions","By Hospital","By Consultant/Advanced Nurse","By Patient-health conditions","By Patient-health conditions","By Consultant/Advanced Nurse","By Patient-health conditions","By Patient","By Patient-health conditions","By Hospital","By Consultant/Advanced Nurse","By Consultant/Advanced Nurse"))
# main$Referring.Hospital <-
### Date calculation from Appointment/ Booking to Attendance
main$Attendance.Date <- as.Date(main$Attendance.Date, format="%d/%m/%y")
main$Appointment.Date <- as.POSIXct(main$Appointment.Date...Time,format="%Y%m%d %H:%M:%S",tz=Sys.timezone())
main$Appointment.Date <- as.Date(main$Appointment.Date, format="%d/%m/%y")
main$appointmentMonthYear <- substring(main$Appointment.Date,0,7)
main$appointmentDay <- weekdays(main$Appointment.Date)
main$Booked.Date <- as.POSIXct(main$Booked.Date,format="%Y/%m/%d",tz=Sys.timezone())
main$Booked.Date_new <- as.Date(main$Booked.Date, format="%d/%m/%y")
main$bookedMonthYear <- substring(main$Booked.Date,0,7)
main$bookedDay <- weekdays(main$Booked.Date)
main$daysDiff_attendanceAppoint <- difftime(main$Appointment.Date,main$Attendance.Date,units="days")
main$daysDiff_attendanceBooked <- difftime(main$Booked.Date,main$Attendance.Date,units="days")
main$daysDiff_AppointBooked <- difftime(main$Appointment.Date,main$Booked.Date,units="days")
main$addressDiff <- ifelse(main$Present.Address == main$Home.Address, 0, 1)
main$Rebooked.Indicator <- main$Rebooked.Indicator..HIS.
## Create dataset for modelling
# main.df <- main %>% select(-rownames,-) #when there are multiple files to combine with same variables
write.csv(main[,c("Record.Type","Clinic.Code","Clinic.Type_recode","NurseFlag","Medical.Record.Number","Gender","Attendance.Day","Attendance.MonthYear","Attendance.Date","Attendance.Type_recode","Attendance.Year","Attendance.Month","Referral.Source_recode","Consultant_recode","Insurance.Scheme_recode","Eligibility_recode","Age.at.Attendance","Age.at.Attendance.Cat.HSE","Pathway.Number","Present.Address","Home.Address","Appointment.Date","appointmentMonthYear","appointmentDay","Area.of.Residence_recode","Referring.Hospital","Booking.Type_recode","Booked.Date_new","bookedMonthYear","bookedDay","Cancellation.Group","Reason.for.Cancellation_recode","Rebooked.Indicator","Hospital.Catchment_recode","No.Attendances","No.New.Attendances","No.Cancels","No.DNAs","daysDiff_attendanceAppoint","daysDiff_attendanceBooked","daysDiff_AppointBooked","addressDiff")],"breastDetails.csv")
main_processed <- read.csv("breastDetails.csv")
## Convert all character variables to factor
# main_processed <- main_processed %>% mutate_if(is.character,as.factor)
# main_processed_tibble <- as_tibble(main_processed) #library(tidyverse), tibble never changes [the type of the inputs, the names of variables], it only recycles inputs of length 1, and never creates row.names()Inspecting data
Inspecting new and re-coded variables
str(main_processed)## 'data.frame': 91169 obs. of 43 variables:
## $ X : int 1 2 3 4 6 7 8 9 10 11 ...
## $ Record.Type : chr "Attendance" "Attendance" "Cancellation" "Attendance" ...
## $ Clinic.Code : int 932 757 757 757 771 527 757 757 757 757 ...
## $ Clinic.Type_recode : chr "MED" "SBC" "SBC" "SBC" ...
## $ NurseFlag : chr "N" "N" "N" "N" ...
## $ Medical.Record.Number : int 177 527 527 527 527 2194 2329 2329 2329 2329 ...
## $ Gender : chr "Male" "Female" "Female" "Female" ...
## $ Attendance.Day : chr "Monday" "Tuesday" "Tuesday" "Tuesday" ...
## $ Attendance.MonthYear : chr "07-2022" "07-2023" "11-2023" "11-2023" ...
## $ Attendance.Date : chr "2020-07-11" "2020-07-25" "2020-11-07" "2020-11-28" ...
## $ Attendance.Type_recode : chr "NEW" "NEW" "RETURN" "RETURN" ...
## $ Attendance.Year : int 2022 2023 2023 2023 2023 2021 2020 2020 2020 2021 ...
## $ Attendance.Month : chr "July" "July" "November" "November" ...
## $ Referral.Source_recode : chr "GP" "WARD" "CLINIC" "CLINIC" ...
## $ Consultant_recode : chr "BARRYM" "BARRYM" "BARRYM" "BARRYM" ...
## $ Insurance.Scheme_recode : chr "U" "D" "D" "D" ...
## $ Eligibility_recode : chr "MEDICAL CARD" "MEDICAL CARD" "MEDICAL CARD" "MEDICAL CARD" ...
## $ Age.at.Attendance : int 83 87 87 87 86 75 83 83 83 84 ...
## $ Age.at.Attendance.Cat.HSE : chr "75 - 84" "85 >=" "85 >=" "85 >=" ...
## $ Pathway.Number : int 56683148 57193702 77706400 57193702 57154091 56054716 53722951 76605449 76606058 76503073 ...
## $ Present.Address : chr "174 GRACEPARK HTS., DRUMCONDRA, DUBLIN 9" "11 BOTANIC ROAD, GLASNEVIN, DUBLIN 9" "11 BOTANIC ROAD, GLASNEVIN, DUBLIN 9" "11 BOTANIC ROAD, GLASNEVIN, DUBLIN 9" ...
## $ Home.Address : chr "174 GRACEPARK HEIGHTS, DRUMCONDRA, DUBLIN 9" "11 BOTANIC ROAD, GLASNEVIN, DUBLIN 9" "11 BOTANIC ROAD, GLASNEVIN, DUBLIN 9" "11 BOTANIC ROAD, GLASNEVIN, DUBLIN 9" ...
## $ Appointment.Date : chr NA NA "2023-11-07" NA ...
## $ appointmentMonthYear : chr NA NA "2023-11" NA ...
## $ appointmentDay : chr NA NA "Tuesday" NA ...
## $ Area.of.Residence_recode : chr "DUBLIN NTH" "DUBLIN NTH" "DUBLIN NTH" "DUBLIN NTH" ...
## $ Referring.Hospital : chr ":" ":" ":" ":" ...
## $ Booking.Type_recode : chr "NEW" "WARD" "RETURN" "RETURN" ...
## $ Booked.Date_new : chr "2022-06-22" "2023-06-27" "2023-07-24" "2023-10-25" ...
## $ bookedMonthYear : chr "2022-06" "2023-06" "2023-07" "2023-10" ...
## $ bookedDay : chr "Thursday" "Wednesday" "Tuesday" "Thursday" ...
## $ Cancellation.Group : chr "N/A" "N/A" "Patient" "N/A" ...
## $ Reason.for.Cancellation_recode: chr "" "" "By Patient" "" ...
## $ Rebooked.Indicator : chr "" "" "Yes" "" ...
## $ Hospital.Catchment_recode : chr "Mater" "Mater" "Mater" "Mater" ...
## $ No.Attendances : int 1 1 NA 1 1 1 1 NA NA NA ...
## $ No.New.Attendances : int 1 1 NA 0 1 1 0 NA NA NA ...
## $ No.Cancels : int NA NA 1 NA NA NA NA 1 1 0 ...
## $ No.DNAs : int NA NA 0 NA NA NA NA 0 0 1 ...
## $ daysDiff_attendanceAppoint : int NA NA 1095 NA NA NA NA 0 0 365 ...
## $ daysDiff_attendanceBooked : num 712 1068 990 1062 1094 ...
## $ daysDiff_AppointBooked : num NA NA 105 NA NA ...
## $ addressDiff : int 1 0 0 0 0 0 0 0 0 0 ...summary(main_processed)## X Record.Type Clinic.Code Clinic.Type_recode
## Min. : 1 Length:91169 Min. : 424.0 Length:91169
## 1st Qu.:23157 Class :character 1st Qu.: 526.0 Class :character
## Median :46297 Mode :character Median : 771.0 Mode :character
## Mean :46278 Mean : 803.8
## 3rd Qu.:69384 3rd Qu.:1133.0
## Max. :92713 Max. :1335.0
##
## NurseFlag Medical.Record.Number Gender Attendance.Day
## Length:91169 Min. : 177 Length:91169 Length:91169
## Class :character 1st Qu.: 802073 Class :character Class :character
## Mode :character Median :1089184 Mode :character Mode :character
## Mean :1144338
## 3rd Qu.:1251444
## Max. :9999896
##
## Attendance.MonthYear Attendance.Date Attendance.Type_recode Attendance.Year
## Length:91169 Length:91169 Length:91169 Min. :2020
## Class :character Class :character Class :character 1st Qu.:2021
## Mode :character Mode :character Mode :character Median :2022
## Mean :2022
## 3rd Qu.:2023
## Max. :2023
##
## Attendance.Month Referral.Source_recode Consultant_recode
## Length:91169 Length:91169 Length:91169
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## Insurance.Scheme_recode Eligibility_recode Age.at.Attendance
## Length:91169 Length:91169 Min. : 0.00
## Class :character Class :character 1st Qu.: 40.00
## Mode :character Mode :character Median : 51.00
## Mean : 51.31
## 3rd Qu.: 62.00
## Max. :105.00
##
## Age.at.Attendance.Cat.HSE Pathway.Number Present.Address
## Length:91169 Min. :51392528 Length:91169
## Class :character 1st Qu.:56100157 Class :character
## Mode :character Median :56937195 Mode :character
## Mean :64424672
## 3rd Qu.:76844763
## Max. :77878693
##
## Home.Address Appointment.Date appointmentMonthYear appointmentDay
## Length:91169 Length:91169 Length:91169 Length:91169
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Area.of.Residence_recode Referring.Hospital Booking.Type_recode
## Length:91169 Length:91169 Length:91169
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## Booked.Date_new bookedMonthYear bookedDay Cancellation.Group
## Length:91169 Length:91169 Length:91169 Length:91169
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Reason.for.Cancellation_recode Rebooked.Indicator Hospital.Catchment_recode
## Length:91169 Length:91169 Length:91169
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## No.Attendances No.New.Attendances No.Cancels No.DNAs
## Min. :1 Min. :0.00 Min. :0.00 Min. :0.00
## 1st Qu.:1 1st Qu.:0.00 1st Qu.:1.00 1st Qu.:0.00
## Median :1 Median :1.00 Median :1.00 Median :0.00
## Mean :1 Mean :0.55 Mean :0.77 Mean :0.23
## 3rd Qu.:1 3rd Qu.:1.00 3rd Qu.:1.00 3rd Qu.:0.00
## Max. :2 Max. :1.00 Max. :1.00 Max. :1.00
## NA's :35872 NA's :35872 NA's :55297 NA's :55297
## daysDiff_attendanceAppoint daysDiff_attendanceBooked daysDiff_AppointBooked
## Min. : 0.0 Min. :-1055.0 Min. : 0.00
## 1st Qu.: 365.0 1st Qu.: 169.0 1st Qu.: 11.04
## Median : 730.0 Median : 366.0 Median : 25.04
## Mean : 580.1 Mean : 505.9 Mean : 90.63
## 3rd Qu.: 731.0 3rd Qu.: 729.0 3rd Qu.:150.00
## Max. :1096.0 Max. : 1112.0 Max. :672.00
## NA's :55297 NA's :2310 NA's :55297
## addressDiff
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.1789
## 3rd Qu.:0.0000
## Max. :1.0000
## head(main_processed)## X Record.Type Clinic.Code Clinic.Type_recode NurseFlag Medical.Record.Number
## 1 1 Attendance 932 MED N 177
## 2 2 Attendance 757 SBC N 527
## 3 3 Cancellation 757 SBC N 527
## 4 4 Attendance 757 SBC N 527
## 5 6 Attendance 771 TRI N 527
## 6 7 Attendance 527 TRI N 2194
## Gender Attendance.Day Attendance.MonthYear Attendance.Date
## 1 Male Monday 07-2022 2020-07-11
## 2 Female Tuesday 07-2023 2020-07-25
## 3 Female Tuesday 11-2023 2020-11-07
## 4 Female Tuesday 11-2023 2020-11-28
## 5 Female Tuesday 06-2023 2020-06-27
## 6 Female Thursday 02-2021 2020-02-18
## Attendance.Type_recode Attendance.Year Attendance.Month
## 1 NEW 2022 July
## 2 NEW 2023 July
## 3 RETURN 2023 November
## 4 RETURN 2023 November
## 5 NEW 2023 June
## 6 NEW 2021 February
## Referral.Source_recode Consultant_recode Insurance.Scheme_recode
## 1 GP BARRYM U
## 2 WARD BARRYM D
## 3 CLINIC BARRYM D
## 4 CLINIC BARRYM D
## 5 WARD BARRYM D
## 6 GP KELLM D
## Eligibility_recode Age.at.Attendance Age.at.Attendance.Cat.HSE Pathway.Number
## 1 MEDICAL CARD 83 75 - 84 56683148
## 2 MEDICAL CARD 87 85 >= 57193702
## 3 MEDICAL CARD 87 85 >= 77706400
## 4 MEDICAL CARD 87 85 >= 57193702
## 5 MEDICAL CARD 86 85 >= 57154091
## 6 NON MEDICAL CARD 75 75 - 84 56054716
## Present.Address
## 1 174 GRACEPARK HTS., DRUMCONDRA, DUBLIN 9
## 2 11 BOTANIC ROAD, GLASNEVIN, DUBLIN 9
## 3 11 BOTANIC ROAD, GLASNEVIN, DUBLIN 9
## 4 11 BOTANIC ROAD, GLASNEVIN, DUBLIN 9
## 5 11 BOTANIC ROAD, GLASNEVIN, DUBLIN 9
## 6 41 COULTRY DRIVE, SANTRY, DUBLIN 9
## Home.Address Appointment.Date
## 1 174 GRACEPARK HEIGHTS, DRUMCONDRA, DUBLIN 9 <NA>
## 2 11 BOTANIC ROAD, GLASNEVIN, DUBLIN 9 <NA>
## 3 11 BOTANIC ROAD, GLASNEVIN, DUBLIN 9 2023-11-07
## 4 11 BOTANIC ROAD, GLASNEVIN, DUBLIN 9 <NA>
## 5 11 BOTANIC ROAD, GLASNEVIN, DUBLIN 9 <NA>
## 6 41 COULTRY DRIVE, SANTRY, DUBLIN 9 <NA>
## appointmentMonthYear appointmentDay Area.of.Residence_recode
## 1 <NA> <NA> DUBLIN NTH
## 2 <NA> <NA> DUBLIN NTH
## 3 2023-11 Tuesday DUBLIN NTH
## 4 <NA> <NA> DUBLIN NTH
## 5 <NA> <NA> DUBLIN NTH
## 6 <NA> <NA> DUBLIN NTH
## Referring.Hospital Booking.Type_recode Booked.Date_new bookedMonthYear
## 1 : NEW 2022-06-22 2022-06
## 2 : WARD 2023-06-27 2023-06
## 3 : RETURN 2023-07-24 2023-07
## 4 : RETURN 2023-10-25 2023-10
## 5 : WARD 2023-06-25 2023-06
## 6 : NEW 2021-02-08 2021-02
## bookedDay Cancellation.Group Reason.for.Cancellation_recode
## 1 Thursday N/A
## 2 Wednesday N/A
## 3 Tuesday Patient By Patient
## 4 Thursday N/A
## 5 Monday N/A
## 6 Monday N/A
## Rebooked.Indicator Hospital.Catchment_recode No.Attendances
## 1 Mater 1
## 2 Mater 1
## 3 Yes Mater NA
## 4 Mater 1
## 5 Mater 1
## 6 Mater 1
## No.New.Attendances No.Cancels No.DNAs daysDiff_attendanceAppoint
## 1 1 NA NA NA
## 2 1 NA NA NA
## 3 NA 1 0 1095
## 4 0 NA NA NA
## 5 1 NA NA NA
## 6 1 NA NA NA
## daysDiff_attendanceBooked daysDiff_AppointBooked addressDiff
## 1 711.9583 NA 1
## 2 1067.9583 NA 0
## 3 989.9583 105.0417 0
## 4 1061.9583 NA 0
## 5 1093.9583 NA 0
## 6 356.0000 NA 0any(is.na(main_processed))## [1] TRUEcolSums(is.na(main_processed))## X Record.Type
## 0 0
## Clinic.Code Clinic.Type_recode
## 0 0
## NurseFlag Medical.Record.Number
## 0 0
## Gender Attendance.Day
## 0 0
## Attendance.MonthYear Attendance.Date
## 0 0
## Attendance.Type_recode Attendance.Year
## 0 0
## Attendance.Month Referral.Source_recode
## 0 0
## Consultant_recode Insurance.Scheme_recode
## 0 0
## Eligibility_recode Age.at.Attendance
## 0 0
## Age.at.Attendance.Cat.HSE Pathway.Number
## 0 0
## Present.Address Home.Address
## 0 0
## Appointment.Date appointmentMonthYear
## 55297 55297
## appointmentDay Area.of.Residence_recode
## 55297 0
## Referring.Hospital Booking.Type_recode
## 0 0
## Booked.Date_new bookedMonthYear
## 2310 2310
## bookedDay Cancellation.Group
## 2310 0
## Reason.for.Cancellation_recode Rebooked.Indicator
## 0 0
## Hospital.Catchment_recode No.Attendances
## 0 35872
## No.New.Attendances No.Cancels
## 35872 55297
## No.DNAs daysDiff_attendanceAppoint
## 55297 55297
## daysDiff_attendanceBooked daysDiff_AppointBooked
## 2310 55297
## addressDiff
## 0Creating descriptive statistics (2020-2023)
Outcome (response variable): Record.Type (including Attendance, Cancellation, DNA)
- Total No. of observations: 91169
- No. of Attendance: 55297; New Attendance: 30512
- No. of Cancellation: 27610
- No. of DNA: 8262
Dependable variables (with highest frequency of value):
- Clinic relevance
- Clinic code :757 (12.79%), 1134 (12.72%)
- Clinic type: Symptomatic breast clinic (48.66%)
- M.R.N: 51 times is the highest visit
- Referral source: Clinic (52.71%)
- Consultant: WAL (27%), BAR (26%)
- Insurance scheme: Unknown (36.57%)
- Eligibility: Non medical card (53.08%)
- Booking Type: Return (57.4%)
- Hospital catchment: National catchment (57.91%)
- Date relevance
Attendance Day: Tuesday (26.6%)
Attendance Month-Year: 2021-09 (2.96%)
Attendance Month: September (10.2%)
Attendance Year: 2021 (27%)
Attendance Type: Return (49.64%)
Appointment Day: Tuesday (27.8%)
Appointment Month-Year: 2021-08 (3.42%)
Booked Day: Tuesday (22.57%)
Booked Month-Year: 2022-10 (2.78%)
Days difference between Attendance and Appointment: Mean 580, Median 730 [0-1096]
Days difference between Attendance and Booked day: Mean 506, Median 366 [<0,1112]
Days difference between Booked day and Appointment: Mean 91, Median 25 [0-672]
- Patient relevance
- Gender: Female (98%)
- Age at attendance cat.HSE: 45-54 (23.7%), Average 51 [0-105]
- Area of residence: Dublin north (45.3%)
- Difference of Home and Present address: No (82%)
Cancellation (N=27610)
- Cancellation Group: Hospital (57.53%)
- Reason for cancellation: by hospital (38.62%)
- Rebooked indicator: Yes (74.5%)
## Outcome description
### Convert dependent variable (outcome, response var.) to factor type if not yet
unique(main_processed$Record.Type)## [1] "Attendance" "Cancellation" "DNA"cbind(sort(table(main_processed$Record.Type),decreasing=TRUE),sort(100*round(prop.table(table(main_processed$Record.Type=="Attendance")),5),deparse.level=2,decreasing=TRUE))## Warning in cbind(sort(table(main_processed$Record.Type), decreasing = TRUE), :
## number of rows of result is not a multiple of vector length (arg 2)## [,1] [,2]
## Attendance 55297 60.653
## Cancellation 27610 39.347
## DNA 8262 60.653cbind(sort(table(main_processed$Record.Type=="Attendance"),decreasing=TRUE),sort(100*round(prop.table(table(main_processed$Record.Type=="Attendance")),5),deparse.level=2,decreasing=TRUE))## [,1] [,2]
## TRUE 55297 60.653
## FALSE 35872 39.347cbind(sort(table(main_processed$Record.Type=="Cancellation"),decreasing=TRUE),sort(100*round(prop.table(table(main_processed$Record.Type=="Cancellation")),5),deparse.level=2,decreasing=TRUE))## [,1] [,2]
## FALSE 63559 69.716
## TRUE 27610 30.284cbind(sort(table(main_processed$Record.Type=="DNA"),decreasing=TRUE),sort(100*round(prop.table(table(main_processed$Record.Type=="DNA")),5),deparse.level=2,decreasing=TRUE))## [,1] [,2]
## FALSE 82907 90.938
## TRUE 8262 9.062table(main_processed$Record.Type,main_processed$Attendance.MonthYear)##
## 01-2020 01-2021 01-2022 01-2023 02-2020 02-2021 02-2022 02-2023
## Attendance 1403 976 1020 1304 1280 1117 1026 1163
## Cancellation 113 508 505 575 265 414 620 547
## DNA 46 186 253 165 96 215 262 104
##
## 03-2020 03-2021 03-2022 03-2023 04-2020 04-2021 04-2022 04-2023
## Attendance 690 1332 1071 1205 290 1164 1074 1005
## Cancellation 686 525 671 562 562 450 446 573
## DNA 67 174 247 139 14 187 273 98
##
## 05-2020 05-2021 05-2022 05-2023 06-2020 06-2021 06-2022 06-2023
## Attendance 564 1109 1215 1390 1132 1200 1033 1180
## Cancellation 250 448 640 665 420 504 750 614
## DNA 33 227 271 120 43 281 220 100
##
## 07-2020 07-2021 07-2022 07-2023 08-2020 08-2021 08-2022 08-2023
## Attendance 1472 1198 991 1038 1336 1412 1225 1267
## Cancellation 478 641 636 657 491 993 594 799
## DNA 148 327 181 97 112 235 234 112
##
## 09-2020 09-2021 09-2022 09-2023 10-2020 10-2021 10-2022 10-2023
## Attendance 1496 1475 1419 1206 1224 1251 1170 1226
## Cancellation 547 859 558 774 657 615 600 564
## DNA 164 365 261 133 133 265 225 118
##
## 11-2020 11-2021 11-2022 11-2023 12-2020 12-2021 12-2022 12-2023
## Attendance 1307 1299 1451 1180 1026 887 919 879
## Cancellation 671 700 699 683 546 523 430 582
## DNA 99 300 255 94 111 239 167 66## Predictor description (categorical)
### Data frame for nominal/ binary values of variables
unique(main_processed$Clinic.Code)## [1] 932 757 771 527 1134 1132 424 441 526 1333 934 1133 1334 1335 933
## [16] 1187 768 440unique(main_processed$Clinic.Type_recode)## [1] "MED" "SBC" "TRI" "FRA" "GEN"unique(main_processed$NurseFlag)## [1] "N" "Y"#unique(main_processed$Medical.Record.Number)
unique(main_processed$Gender)## [1] "Male" "Female" "Unknown"unique(main_processed$Attendance.Day)## [1] "Monday" "Tuesday" "Thursday" "Friday" "Wednesday" "Sunday"
## [7] "Saturday"unique(main_processed$Attendance.MonthYear)## [1] "07-2022" "07-2023" "11-2023" "06-2023" "02-2021" "06-2020" "09-2020"
## [8] "06-2021" "10-2021" "12-2021" "11-2020" "07-2020" "08-2021" "08-2022"
## [15] "01-2023" "05-2021" "02-2023" "03-2023" "10-2023" "09-2022" "04-2023"
## [22] "06-2022" "02-2020" "10-2022" "10-2020" "01-2020" "07-2021" "09-2021"
## [29] "08-2023" "11-2022" "02-2022" "09-2023" "01-2022" "11-2021" "03-2022"
## [36] "12-2022" "05-2023" "12-2023" "05-2022" "04-2021" "04-2022" "08-2020"
## [43] "05-2020" "04-2020" "01-2021" "03-2021" "03-2020" "12-2020"unique(main_processed$Attendance.Type_recode)## [1] "NEW" "RETURN" "VIRTUAL RETURN"
## [4] "VIRTUAL(New_Return)"unique(main_processed$Attendance.Year)## [1] 2022 2023 2021 2020unique(main_processed$Attendance.Month)## [1] "July" "November" "June" "February" "September" "October"
## [7] "December" "August" "January" "May" "March" "April"unique(main_processed$Referral.Source_recode)## [1] "GP" "WARD" "CLINIC"
## [4] "BREAST CHECK" "Elsew outside Mater" "Elsew of Mater"
## [7] "EMERGENCY DEPT" "OTHER CONSULTANT"unique(main_processed$Consultant_recode)## [1] "BARRYM" "KELLM" "WALSSI" "STOKES" "HEENEY"unique(main_processed$Insurance.Scheme_recode)## [1] "U" "D" "V" "G" "I" "S" "B" "O" "J" "E" "C" "P" "H" "A" "M"unique(main_processed$Eligibility_recode)## [1] "MEDICAL CARD" "NON MEDICAL CARD"
## [3] "ACUTE UNCLASSIFIED" "NON ACUTE UNCLASSIFIED"
## [5] "EXEMPT" "ELIGIBILITY UNKNOWN"
## [7] "RESEARCH/NATIONAL PROG."unique(main_processed$Age.at.Attendance.Cat.HSE)## [1] "75 - 84" "85 >=" "65 - 74" "55 - 64" "45 - 54" "35 - 44" "25 - 34"
## [8] "15 - 24" "10 - 14" "0 - 4"unique(main_processed$Area.of.Residence_recode)## [1] "DUBLIN NTH"
## [2] "EASTERN & MIDLAND REGION (excl.Dublin,Meath)"
## [3] "Meath"
## [4] "DUBLIN STH"
## [5] "SOUTHERN REGION"
## [6] "NORTHERN WESTERN REGION"
## [7] "UNKNOWN"
## [8] "OUTSIDE IRELAND"unique(main_processed$Referring.Hospital)## [1] ":"
## [2] "1111 : MATER PRIVATE HOSPITAL"
## [3] "0100 : ST MARYS HOSPITAL"
## [4] "0908 : MATER MISERICORDIAE UNIVERSITY"
## [5] "0403 : OUR LADYS HOSPITAL"
## [6] "0202 : MULLINGAR MIDLAND REGIONAL"
## [7] "0932 : ROTUNDA HOSPITAL"
## [8] "0947 : ST LUKES HOSPITAL"
## [9] "0923 : BEAUMONT HOSPITAL"
## [10] "0203 : TULLAMORE MIDLAND REGIONAL"
## [11] "0108 : CONNOLLY HOSPITAL"
## [12] "0904 : ST JAMES HOSPITAL"
## [13] "1290 : VHI SWIFTCARE CLINIC SWORDS"
## [14] "0402 : CAVAN GENERAL HOSPITAL"
## [15] "9060 : PRIVATE HOSPITAL"
## [16] "0922 : OUR LADY OF LOURDES HOSPITAL"
## [17] "0102 : NAAS GENERAL HOSPITAL"
## [18] "9061 : BONS SECOURS PRIVATE HOSPITAL"
## [19] "0930 : COOMBE WOMENS AND INFANTS"
## [20] "0600 : WATERFORD UNIVERSITY HOSPITAL"
## [21] "0601 : ST LUKES GENERAL HOSPITAL"
## [22] "9099 : OTHER ACUTE HOSPITAL"
## [23] "1307 : BEACON HOSPITAL"
## [24] "0941 : CRUMLIN OUR LADYS CHILDRENS"
## [25] "9067 : HERMITAGE MEDICAL CLINIC"
## [26] "1291 : VHI SWIFTCARE CLINIC DUNDRUM"
## [27] "0954 : CLONTARF HOSPITAL"
## [28] "0910 : ST VINCENTS UNIVERSITY"
## [29] "0931 : NATIONAL MATERNITY HOSPITAL"
## [30] "0940 : TEMPLE STREET CHILDRENS"
## [31] "0800 : GALWAY UNIVERSITY HOSPITAL"
## [32] "0605 : WEXFORD GENERAL HOSPITAL"
## [33] "0201 : PORTLAOISE MIDLAND REGIONAL"
## [34] "1270 : TALLAGHT HOSPITAL AMNCH"
## [35] "9063 : ST VINCENTS PRIVATE HOSPITAL"
## [36] "9092 : ST VINCENTS HOSPITAL"
## [37] "0300 : LIMERICK UNIVERSITY HOSPITAL"
## [38] "9082 : BEAUMONT PRIVATE CLINIC"unique(main_processed$Booking.Type_recode)## [1] "NEW" "WARD" "RETURN"unique(main_processed$Cancellation.Group)## [1] "N/A" "Patient" "Hospital" "DNA" "Validation"unique(main_processed$Reason.for.Cancellation_recode)## [1] "" "By Patient"
## [3] "By Hospital" "No show"
## [5] "By Consultant/Advanced Nurse" "By Patient-health conditions"
## [7] "By Covid"unique(main_processed$Rebooked.Indicator)## [1] "" "Yes" "No"unique(main_processed$Hospital.Catchment_recode)## [1] "Mater" "National" "Connolly" "James"
## [5] "Tallaght" "Beaumont" "International" "Vincents"options(digits=2)
cbind(sort(table(main_processed$Clinic.Type_recode),decreasing=TRUE),sort(100*round(prop.table(table(main_processed$Clinic.Type_recode)),5),deparse.level=2,decreasing=TRUE))## [,1] [,2]
## SBC 44365 48.662
## TRI 33896 37.179
## MED 12744 13.978
## FRA 140 0.154
## GEN 24 0.026cbind(sort(table(main_processed$Clinic.Code),decreasing=TRUE),sort(100*round(prop.table(table(main_processed$Clinic.Code)),5),deparse.level=2,decreasing=TRUE))## [,1] [,2]
## 757 11656 12.785
## 1134 11592 12.715
## 441 11255 12.345
## 424 10396 11.403
## 1133 9120 10.003
## 771 8645 9.482
## 527 7262 7.965
## 526 4020 4.409
## 1132 3592 3.940
## 932 2956 3.242
## 933 2941 3.226
## 1334 2377 2.607
## 934 2070 2.271
## 1333 1938 2.126
## 1335 1185 1.300
## 1187 115 0.126
## 768 25 0.027
## 440 24 0.026cbind(sort(table(main_processed$NurseFlag),decreasing=TRUE),sort(100*round(prop.table(table(main_processed$NurseFlag)),5),deparse.level=2,decreasing=TRUE))## [,1] [,2]
## N 91144 99.973
## Y 25 0.027head(cbind(sort(table(main_processed$Medical.Record.Number),decreasing=TRUE),sort(100*round(prop.table(table(main_processed$Medical.Record.Number)),5),deparse.level=2,decreasing=TRUE)),10) #group freq of MRN at 34## [,1] [,2]
## 305504 51 0.056
## 1045718 51 0.056
## 805761 48 0.053
## 522374 46 0.050
## 247389 43 0.047
## 995525 41 0.045
## 1225996 41 0.045
## 1228628 40 0.044
## 331885 39 0.043
## 802696 39 0.043cbind(sort(table(main_processed$Referral.Source_recode),decreasing=TRUE),sort(100*round(prop.table(table(main_processed$Referral.Source_recode)),5),deparse.level=2,decreasing=TRUE))## [,1] [,2]
## CLINIC 48054 52.709
## GP 31775 34.853
## WARD 7806 8.562
## BREAST CHECK 1805 1.980
## Elsew outside Mater 1339 1.469
## OTHER CONSULTANT 251 0.275
## Elsew of Mater 79 0.087
## EMERGENCY DEPT 60 0.066#cbind(sort(table(main_processed$Referring.Hospital),decreasing=TRUE),sort(100*round(prop.table(table(main_processed$Referring.Hospital)),5),deparse.level=2,decreasing=TRUE))
cbind(sort(table(main_processed$Consultant_recode),decreasing=TRUE),sort(100*round(prop.table(table(main_processed$Consultant_recode)),5),deparse.level=2,decreasing=TRUE))## [,1] [,2]
## WALSSI 24419 27
## BARRYM 23257 26
## KELLM 20599 23
## STOKES 17394 19
## HEENEY 5500 6cbind(sort(table(main_processed$Insurance.Scheme_recode),decreasing=TRUE),sort(100*round(prop.table(table(main_processed$Insurance.Scheme_recode)),5),deparse.level=2,decreasing=TRUE))## [,1] [,2]
## U 33336 36.565
## D 24765 27.164
## V 15664 17.181
## B 6705 7.354
## I 6133 6.727
## S 2695 2.956
## G 619 0.679
## O 509 0.558
## E 207 0.227
## P 195 0.214
## J 136 0.149
## H 102 0.112
## A 71 0.078
## C 25 0.027
## M 7 0.008cbind(sort(table(main_processed$Eligibility_recode),decreasing=TRUE),sort(100*round(prop.table(table(main_processed$Eligibility_recode)),5),deparse.level=2,decreasing=TRUE))## [,1] [,2]
## NON MEDICAL CARD 48391 53.078
## MEDICAL CARD 36394 39.919
## ELIGIBILITY UNKNOWN 3378 3.705
## EXEMPT 2409 2.642
## RESEARCH/NATIONAL PROG. 313 0.343
## ACUTE UNCLASSIFIED 247 0.271
## NON ACUTE UNCLASSIFIED 37 0.041cbind(sort(table(main_processed$Booking.Type_recode),decreasing=TRUE),sort(100*round(prop.table(table(main_processed$Booking.Type_recode)),5),deparse.level=2,decreasing=TRUE))## [,1] [,2]
## RETURN 52302 57.4
## NEW 33029 36.2
## WARD 5838 6.4cbind(sort(table(main_processed$bookedDay),decreasing=TRUE),sort(100*round(prop.table(table(main_processed$bookedDay)),5),deparse.level=2,decreasing=TRUE))## [,1] [,2]
## Tuesday 20059 22.574
## Monday 19961 22.464
## Wednesday 17863 20.103
## Thursday 16775 18.878
## Friday 13938 15.686
## Saturday 248 0.279
## Sunday 15 0.017cbind(sort(table(main_processed$bookedMonthYear),decreasing=TRUE),sort(100*round(prop.table(table(main_processed$bookedMonthYear)),5),deparse.level=2,decreasing=TRUE))## [,1] [,2]
## 2022-10 2470 2.780
## 2021-09 2409 2.711
## 2020-11 2406 2.708
## 2020-08 2391 2.691
## 2020-07 2354 2.649
## 2021-08 2330 2.622
## 2022-05 2322 2.613
## 2020-09 2243 2.524
## 2021-03 2241 2.522
## 2020-01 2142 2.411
## 2022-06 2138 2.406
## 2020-02 2067 2.326
## 2022-03 2058 2.316
## 2020-10 2055 2.313
## 2021-07 2043 2.299
## 2022-07 2037 2.292
## 2022-09 2032 2.287
## 2021-10 2030 2.285
## 2020-06 2023 2.277
## 2021-04 2007 2.259
## 2021-02 1969 2.216
## 2023-05 1943 2.187
## 2021-06 1938 2.181
## 2023-01 1914 2.154
## 2023-04 1902 2.140
## 2023-03 1891 2.128
## 2022-02 1867 2.101
## 2021-05 1850 2.082
## 2022-11 1842 2.073
## 2021-11 1822 2.050
## 2023-06 1791 2.016
## 2023-07 1791 2.016
## 2021-12 1738 1.956
## 2022-04 1713 1.928
## 2022-01 1710 1.924
## 2023-02 1685 1.896
## 2022-08 1607 1.808
## 2023-08 1591 1.790
## 2020-12 1577 1.775
## 2022-12 1437 1.617
## 2023-09 1398 1.573
## 2023-11 1334 1.501
## 2021-01 1251 1.408
## 2023-10 1223 1.376
## 2020-05 1111 1.250
## 2020-03 835 0.940
## 2019-12 523 0.589
## 2020-04 501 0.564
## 2023-12 230 0.259
## 2019-11 210 0.236
## 2019-08 156 0.176
## 2019-07 151 0.170
## 2019-10 148 0.167
## 2019-09 104 0.117
## 2019-01 89 0.100
## 2019-02 66 0.074
## 2019-06 64 0.072
## 2019-03 40 0.045
## 2019-05 33 0.037
## 2019-04 15 0.017
## 2017-08 1 0.001cbind(sort(table(main_processed$appointmentDay),decreasing=TRUE),sort(100*round(prop.table(table(main_processed$appointmentDay)),5),deparse.level=2,decreasing=TRUE))## [,1] [,2]
## Tuesday 9971 27.8
## Friday 8555 23.8
## Monday 7543 21.0
## Thursday 6825 19.0
## Wednesday 2978 8.3cbind(sort(table(main_processed$appointmentMonthYear),decreasing=TRUE),sort(100*round(prop.table(table(main_processed$appointmentMonthYear)),5),deparse.level=2,decreasing=TRUE))## [,1] [,2]
## 2021-08 1228 3.42
## 2021-09 1224 3.41
## 2021-11 1000 2.79
## 2022-06 970 2.70
## 2021-07 968 2.70
## 2022-11 954 2.66
## 2022-03 918 2.56
## 2022-05 911 2.54
## 2023-08 911 2.54
## 2023-09 907 2.53
## 2022-02 882 2.46
## 2021-10 880 2.45
## 2022-08 828 2.31
## 2022-10 825 2.30
## 2022-09 819 2.28
## 2022-07 817 2.28
## 2020-10 790 2.20
## 2021-06 785 2.19
## 2023-05 785 2.19
## 2023-11 777 2.17
## 2020-11 770 2.15
## 2021-12 762 2.12
## 2022-01 758 2.11
## 2023-07 754 2.10
## 2020-03 753 2.10
## 2023-01 740 2.06
## 2022-04 719 2.00
## 2023-06 714 1.99
## 2020-09 711 1.98
## 2023-03 701 1.95
## 2021-03 699 1.95
## 2021-01 694 1.93
## 2023-10 682 1.90
## 2021-05 675 1.88
## 2023-04 671 1.87
## 2020-12 657 1.83
## 2023-02 651 1.81
## 2023-12 648 1.81
## 2021-04 637 1.78
## 2021-02 629 1.75
## 2020-07 626 1.75
## 2020-08 603 1.68
## 2022-12 597 1.66
## 2020-04 576 1.61
## 2020-06 463 1.29
## 2020-02 361 1.01
## 2020-05 283 0.79
## 2020-01 159 0.44cbind(sort(table(main_processed$Attendance.Type_recode),decreasing=TRUE),sort(100*round(prop.table(table(main_processed$Attendance.Type_recode)),5),deparse.level=2,decreasing=TRUE))## [,1] [,2]
## RETURN 45770 50.20
## NEW 41788 45.84
## VIRTUAL RETURN 3001 3.29
## VIRTUAL(New_Return) 610 0.67cbind(sort(table(main_processed$Attendance.Day),decreasing=TRUE),sort(100*round(prop.table(table(main_processed$Attendance.Day)),5),deparse.level=2,decreasing=TRUE))## [,1] [,2]
## Tuesday 24254 26.603
## Friday 20068 22.012
## Thursday 18942 20.777
## Monday 17732 19.450
## Wednesday 10169 11.154
## Saturday 3 0.003
## Sunday 1 0.001cbind(sort(table(main_processed$Attendance.MonthYear),decreasing=TRUE),sort(100*round(prop.table(table(main_processed$Attendance.MonthYear)),5),deparse.level=2,decreasing=TRUE))## [,1] [,2]
## 09-2021 2699 2.96
## 08-2021 2640 2.90
## 11-2022 2405 2.64
## 11-2021 2299 2.52
## 09-2022 2238 2.46
## 09-2020 2207 2.42
## 08-2023 2178 2.39
## 05-2023 2175 2.39
## 07-2021 2166 2.38
## 10-2021 2131 2.34
## 05-2022 2126 2.33
## 09-2023 2113 2.32
## 07-2020 2098 2.30
## 11-2020 2077 2.28
## 08-2022 2053 2.25
## 01-2023 2044 2.24
## 03-2021 2031 2.23
## 10-2020 2014 2.21
## 06-2022 2003 2.20
## 10-2022 1995 2.19
## 03-2022 1989 2.18
## 06-2021 1985 2.18
## 11-2023 1957 2.15
## 08-2020 1939 2.13
## 02-2022 1908 2.09
## 10-2023 1908 2.09
## 03-2023 1906 2.09
## 06-2023 1894 2.08
## 02-2023 1814 1.99
## 07-2022 1808 1.98
## 04-2021 1801 1.98
## 04-2022 1793 1.97
## 07-2023 1792 1.97
## 05-2021 1784 1.96
## 01-2022 1778 1.95
## 02-2021 1746 1.92
## 12-2020 1683 1.85
## 04-2023 1676 1.84
## 01-2021 1670 1.83
## 12-2021 1649 1.81
## 02-2020 1641 1.80
## 06-2020 1595 1.75
## 01-2020 1562 1.71
## 12-2023 1527 1.68
## 12-2022 1516 1.66
## 03-2020 1443 1.58
## 04-2020 866 0.95
## 05-2020 847 0.93cbind(sort(table(main_processed$Attendance.Month),decreasing=TRUE),sort(100*round(prop.table(table(main_processed$Attendance.Month)),5),deparse.level=2,decreasing=TRUE))## [,1] [,2]
## September 9257 10.2
## August 8810 9.7
## November 8738 9.6
## October 8048 8.8
## July 7864 8.6
## June 7477 8.2
## March 7369 8.1
## February 7109 7.8
## January 7054 7.7
## May 6932 7.6
## December 6375 7.0
## April 6136 6.7cbind(sort(table(main_processed$Attendance.Year),decreasing=TRUE),sort(100*round(prop.table(table(main_processed$Attendance.Year)),5),deparse.level=2,decreasing=TRUE))## [,1] [,2]
## 2021 24601 27
## 2022 23612 26
## 2023 22984 25
## 2020 19972 22cbind(sort(table(main_processed$No.Attendances),decreasing=TRUE),sort(100*round(prop.table(table(main_processed$No.Attendances)),5),deparse.level=2,decreasing=TRUE))## [,1] [,2]
## 1 55289 99.986
## 2 8 0.014cbind(sort(table(main_processed$No.New.Attendances),decreasing=TRUE),sort(100*round(prop.table(table(main_processed$No.New.Attendances)),5),deparse.level=2,decreasing=TRUE))## [,1] [,2]
## 1 30512 55
## 0 24785 45cbind(sort(table(main_processed$No.Cancels),decreasing=TRUE),sort(100*round(prop.table(table(main_processed$No.Cancels)),5),deparse.level=2,decreasing=TRUE))## [,1] [,2]
## 1 27610 77
## 0 8262 23cbind(sort(table(main_processed$Cancellation.Group),decreasing=TRUE),sort(100*round(prop.table(table(main_processed$Cancellation.Group)),5),deparse.level=2,decreasing=TRUE))## [,1] [,2]
## N/A 55297 60.7
## Hospital 15883 17.4
## Patient 10311 11.3
## DNA 8262 9.1
## Validation 1416 1.6cbind(sort(table(main_processed$Reason.for.Cancellation_recode),decreasing=TRUE),sort(100*round(prop.table(table(main_processed$Reason.for.Cancellation_recode)),5),deparse.level=2,decreasing=TRUE))## [,1] [,2]
## 55297 60.7
## By Hospital 10662 11.7
## By Patient 10466 11.5
## No show 8262 9.1
## By Consultant/Advanced Nurse 4125 4.5
## By Patient-health conditions 1203 1.3
## By Covid 1154 1.3cbind(sort(table(main_processed$Rebooked.Indicator),decreasing=TRUE),sort(100*round(prop.table(table(main_processed$Rebooked.Indicator)),5),deparse.level=2,decreasing=TRUE))## [,1] [,2]
## 55297 61
## Yes 20570 23
## No 15302 17cbind(sort(table(main_processed$Hospital.Catchment_recode),decreasing=TRUE),sort(100*round(prop.table(table(main_processed$Hospital.Catchment_recode)),5),deparse.level=2,decreasing=TRUE))## [,1] [,2]
## National 52799 57.91
## Mater 22205 24.36
## Connolly 9963 10.93
## Beaumont 3673 4.03
## James 970 1.06
## Vincents 966 1.06
## Tallaght 397 0.43
## International 196 0.21cbind(sort(table(main_processed$Gender),decreasing=TRUE),sort(100*round(prop.table(table(main_processed$Gender)),5),deparse.level=2,decreasing=TRUE))## [,1] [,2]
## Female 89511 98.181
## Male 1653 1.813
## Unknown 5 0.005cbind(sort(table(main_processed$Area.of.Residence_recode),decreasing=TRUE),sort(100*round(prop.table(table(main_processed$Area.of.Residence_recode)),5),deparse.level=2,decreasing=TRUE))## [,1] [,2]
## DUBLIN NTH 41299 45.299
## EASTERN & MIDLAND REGION (excl.Dublin,Meath) 21068 23.109
## Meath 14424 15.821
## NORTHERN WESTERN REGION 9766 10.712
## DUBLIN STH 2338 2.564
## SOUTHERN REGION 2063 2.263
## UNKNOWN 187 0.205
## OUTSIDE IRELAND 24 0.026cbind(sort(table(main_processed$addressDiff),decreasing=TRUE),sort(100*round(prop.table(table(main_processed$addressDiff)),5),deparse.level=2,decreasing=TRUE))## [,1] [,2]
## 0 74857 82
## 1 16312 18cbind(sort(table(main_processed$Age.at.Attendance.Cat.HSE),decreasing=TRUE),sort(100*round(prop.table(table(main_processed$Age.at.Attendance.Cat.HSE)),5),deparse.level=2,decreasing=TRUE))## [,1] [,2]
## 45 - 54 21611 23.704
## 35 - 44 19035 20.879
## 55 - 64 18151 19.909
## 65 - 74 12557 13.773
## 25 - 34 9617 10.549
## 75 - 84 5340 5.857
## 15 - 24 3257 3.572
## 85 >= 1437 1.576
## 10 - 14 152 0.167
## 0 - 4 12 0.013## Predictor description (numeric)
summary(main_processed$Age.at.Attendance)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 40 51 51 62 105stat.desc(main_processed$Age.at.Attendance, basic=F)## median mean SE.mean CI.mean.0.95 var std.dev
## 51.000 51.305 0.051 0.100 239.451 15.474
## coef.var
## 0.302skew(main_processed$Age.at.Attendance)## skew (g1) se z p
## 0.1184 0.0081 14.5943 0.0000kurtosis(main_processed$Age.at.Attendance)## [1] -0.43summary(main_processed$daysDiff_attendanceAppoint)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0 365 730 580 731 1096 55297stat.desc(main_processed$daysDiff_attendanceAppoint, basic=F)## median mean SE.mean CI.mean.0.95 var std.dev
## 7.3e+02 5.8e+02 2.0e+00 4.0e+00 1.5e+05 3.9e+02
## coef.var
## 6.7e-01skew(main_processed$daysDiff_attendanceAppoint)## Warning in skew(main_processed$daysDiff_attendanceAppoint): Missing
## observations are removed from a vector.## skew (g1) se z p
## -8.0e-02 1.3e-02 -6.1e+00 7.9e-10kurtosis(main_processed$daysDiff_attendanceAppoint)## [1] -1.2summary(main_processed$daysDiff_attendanceBooked)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -1055 169 366 506 729 1112 2310stat.desc(main_processed$daysDiff_attendanceBooked, basic=F)## median mean SE.mean CI.mean.0.95 var std.dev
## 3.7e+02 5.1e+02 1.3e+00 2.6e+00 1.6e+05 4.0e+02
## coef.var
## 7.9e-01skew(main_processed$daysDiff_attendanceBooked)## Warning in skew(main_processed$daysDiff_attendanceBooked): Missing observations
## are removed from a vector.## skew (g1) se z p
## -0.0163 0.0082 -1.9809 0.0476kurtosis(main_processed$daysDiff_attendanceBooked)## [1] -1.2summary(main_processed$daysDiff_AppointBooked)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0 11 25 91 150 672 55297stat.desc(main_processed$daysDiff_AppointBooked, basic=F)## median mean SE.mean CI.mean.0.95 var std.dev
## 2.5e+01 9.1e+01 6.4e-01 1.3e+00 1.5e+04 1.2e+02
## coef.var
## 1.3e+00skew(main_processed$daysDiff_AppointBooked)## Warning in skew(main_processed$daysDiff_AppointBooked): Missing observations
## are removed from a vector.## skew (g1) se z p
## 1.470 0.013 113.660 0.000kurtosis(main_processed$daysDiff_AppointBooked)## [1] 0.74
## Warning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(density)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: Use of `main_processed$Age.at.Attendance` is discouraged.
## ℹ Use `Age.at.Attendance` instead.
## Use of `main_processed$Age.at.Attendance` is discouraged.
## ℹ Use `Age.at.Attendance` instead.
## Warning: Use of `main_processed$Record.Type` is discouraged.
## ℹ Use `Record.Type` instead.
## Warning: Removed 35872 rows containing non-finite values (`stat_count()`).
## <ggproto object: Class ScaleDiscrete, Scale, gg>
## aesthetics: fill
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## limits: NULL
## make_sec_title: function
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## train: function
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## transform: function
## transform_df: function
## super: <ggproto object: Class ScaleDiscrete, Scale, gg>
## Warning: Removed 2310 rows containing non-finite values (`stat_count()`).
## Warning: Removed 55297 rows containing non-finite values (`stat_count()`).
## Warning: Removed 3001 rows containing non-finite values (`stat_count()`).
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Correlation of outcome and predictor (nominal and nominal)
Total No. correlation
## Record Type & Clinic relevance
#library(dplyr)
CrossTable(main_processed$Clinic.Type_recode, main_processed$Record.Type, digits=2, fisher=F, chisq=TRUE, expected=TRUE)## Warning in chisq.test(t, correct = FALSE, ...): Chi-squared approximation may
## be incorrect##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 91169
##
##
## | main_processed$Record.Type
## main_processed$Clinic.Type_recode | Attendance | Cancellation | DNA | Row Total |
## ----------------------------------|--------------|--------------|--------------|--------------|
## FRA | 62 | 65 | 13 | 140 |
## | 84.91 | 42.40 | 12.69 | |
## | 6.18 | 12.05 | 0.01 | |
## | 0.44 | 0.46 | 0.09 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## ----------------------------------|--------------|--------------|--------------|--------------|
## GEN | 14 | 1 | 9 | 24 |
## | 14.56 | 7.27 | 2.17 | |
## | 0.02 | 5.41 | 21.42 | |
## | 0.58 | 0.04 | 0.38 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## ----------------------------------|--------------|--------------|--------------|--------------|
## MED | 8523 | 2288 | 1933 | 12744 |
## | 7729.66 | 3859.45 | 1154.90 | |
## | 81.43 | 639.84 | 524.24 | |
## | 0.67 | 0.18 | 0.15 | 0.14 |
## | 0.15 | 0.08 | 0.23 | |
## | 0.09 | 0.03 | 0.02 | |
## ----------------------------------|--------------|--------------|--------------|--------------|
## SBC | 21852 | 19214 | 3299 | 44365 |
## | 26908.83 | 13435.68 | 4020.49 | |
## | 950.30 | 2485.10 | 129.47 | |
## | 0.49 | 0.43 | 0.07 | 0.49 |
## | 0.40 | 0.70 | 0.40 | |
## | 0.24 | 0.21 | 0.04 | |
## ----------------------------------|--------------|--------------|--------------|--------------|
## TRI | 24846 | 6042 | 3008 | 33896 |
## | 20559.04 | 10265.21 | 3071.75 | |
## | 893.91 | 1737.47 | 1.32 | |
## | 0.73 | 0.18 | 0.09 | 0.37 |
## | 0.45 | 0.22 | 0.36 | |
## | 0.27 | 0.07 | 0.03 | |
## ----------------------------------|--------------|--------------|--------------|--------------|
## Column Total | 55297 | 27610 | 8262 | 91169 |
## | 0.61 | 0.30 | 0.09 | |
## ----------------------------------|--------------|--------------|--------------|--------------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 7488 d.f. = 8 p = 0
##
##
## CrossTable(main_processed$Clinic.Code, main_processed$Record.Type, digits=2, fisher=F, chisq=TRUE, expected=TRUE)## Warning in chisq.test(t, correct = FALSE, ...): Chi-squared approximation may
## be incorrect##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 91169
##
##
## | main_processed$Record.Type
## main_processed$Clinic.Code | Attendance | Cancellation | DNA | Row Total |
## ---------------------------|--------------|--------------|--------------|--------------|
## 424 | 5728 | 3758 | 910 | 10396 |
## | 6305.52 | 3148.37 | 942.12 | |
## | 52.89 | 118.05 | 1.09 | |
## | 0.55 | 0.36 | 0.09 | 0.11 |
## | 0.10 | 0.14 | 0.11 | |
## | 0.06 | 0.04 | 0.01 | |
## ---------------------------|--------------|--------------|--------------|--------------|
## 440 | 14 | 1 | 9 | 24 |
## | 14.56 | 7.27 | 2.17 | |
## | 0.02 | 5.41 | 21.42 | |
## | 0.58 | 0.04 | 0.38 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## ---------------------------|--------------|--------------|--------------|--------------|
## 441 | 5508 | 4725 | 1022 | 11255 |
## | 6826.53 | 3408.51 | 1019.96 | |
## | 254.67 | 508.48 | 0.00 | |
## | 0.49 | 0.42 | 0.09 | 0.12 |
## | 0.10 | 0.17 | 0.12 | |
## | 0.06 | 0.05 | 0.01 | |
## ---------------------------|--------------|--------------|--------------|--------------|
## 526 | 3089 | 526 | 405 | 4020 |
## | 2438.26 | 1217.43 | 364.30 | |
## | 173.67 | 392.70 | 4.55 | |
## | 0.77 | 0.13 | 0.10 | 0.04 |
## | 0.06 | 0.02 | 0.05 | |
## | 0.03 | 0.01 | 0.00 | |
## ---------------------------|--------------|--------------|--------------|--------------|
## 527 | 5295 | 1408 | 559 | 7262 |
## | 4404.64 | 2199.25 | 658.10 | |
## | 179.98 | 284.68 | 14.92 | |
## | 0.73 | 0.19 | 0.08 | 0.08 |
## | 0.10 | 0.05 | 0.07 | |
## | 0.06 | 0.02 | 0.01 | |
## ---------------------------|--------------|--------------|--------------|--------------|
## 757 | 5527 | 5477 | 652 | 11656 |
## | 7069.75 | 3529.95 | 1056.30 | |
## | 336.66 | 1073.95 | 154.75 | |
## | 0.47 | 0.47 | 0.06 | 0.13 |
## | 0.10 | 0.20 | 0.08 | |
## | 0.06 | 0.06 | 0.01 | |
## ---------------------------|--------------|--------------|--------------|--------------|
## 768 | 17 | 7 | 1 | 25 |
## | 15.16 | 7.57 | 2.27 | |
## | 0.22 | 0.04 | 0.71 | |
## | 0.68 | 0.28 | 0.04 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## ---------------------------|--------------|--------------|--------------|--------------|
## 771 | 6228 | 1658 | 759 | 8645 |
## | 5243.48 | 2618.09 | 783.44 | |
## | 184.86 | 352.08 | 0.76 | |
## | 0.72 | 0.19 | 0.09 | 0.09 |
## | 0.11 | 0.06 | 0.09 | |
## | 0.07 | 0.02 | 0.01 | |
## ---------------------------|--------------|--------------|--------------|--------------|
## 932 | 1994 | 581 | 381 | 2956 |
## | 1792.91 | 895.21 | 267.88 | |
## | 22.55 | 110.28 | 47.77 | |
## | 0.67 | 0.20 | 0.13 | 0.03 |
## | 0.04 | 0.02 | 0.05 | |
## | 0.02 | 0.01 | 0.00 | |
## ---------------------------|--------------|--------------|--------------|--------------|
## 933 | 1976 | 565 | 400 | 2941 |
## | 1783.81 | 890.66 | 266.52 | |
## | 20.71 | 119.08 | 66.85 | |
## | 0.67 | 0.19 | 0.14 | 0.03 |
## | 0.04 | 0.02 | 0.05 | |
## | 0.02 | 0.01 | 0.00 | |
## ---------------------------|--------------|--------------|--------------|--------------|
## 934 | 1374 | 361 | 335 | 2070 |
## | 1255.52 | 626.89 | 187.59 | |
## | 11.18 | 112.77 | 115.84 | |
## | 0.66 | 0.17 | 0.16 | 0.02 |
## | 0.02 | 0.01 | 0.04 | |
## | 0.02 | 0.00 | 0.00 | |
## ---------------------------|--------------|--------------|--------------|--------------|
## 1132 | 2420 | 621 | 551 | 3592 |
## | 2178.67 | 1087.82 | 325.52 | |
## | 26.73 | 200.33 | 156.19 | |
## | 0.67 | 0.17 | 0.15 | 0.04 |
## | 0.04 | 0.02 | 0.07 | |
## | 0.03 | 0.01 | 0.01 | |
## ---------------------------|--------------|--------------|--------------|--------------|
## 1133 | 3835 | 4704 | 581 | 9120 |
## | 5531.58 | 2761.94 | 826.48 | |
## | 520.35 | 1365.56 | 72.91 | |
## | 0.42 | 0.52 | 0.06 | 0.10 |
## | 0.07 | 0.17 | 0.07 | |
## | 0.04 | 0.05 | 0.01 | |
## ---------------------------|--------------|--------------|--------------|--------------|
## 1134 | 8406 | 2154 | 1032 | 11592 |
## | 7030.93 | 3510.57 | 1050.50 | |
## | 268.93 | 524.21 | 0.33 | |
## | 0.73 | 0.19 | 0.09 | 0.13 |
## | 0.15 | 0.08 | 0.12 | |
## | 0.09 | 0.02 | 0.01 | |
## ---------------------------|--------------|--------------|--------------|--------------|
## 1187 | 45 | 58 | 12 | 115 |
## | 69.75 | 34.83 | 10.42 | |
## | 8.78 | 15.42 | 0.24 | |
## | 0.39 | 0.50 | 0.10 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## ---------------------------|--------------|--------------|--------------|--------------|
## 1333 | 1254 | 550 | 134 | 1938 |
## | 1175.46 | 586.91 | 175.63 | |
## | 5.25 | 2.32 | 9.87 | |
## | 0.65 | 0.28 | 0.07 | 0.02 |
## | 0.02 | 0.02 | 0.02 | |
## | 0.01 | 0.01 | 0.00 | |
## ---------------------------|--------------|--------------|--------------|--------------|
## 1334 | 1828 | 296 | 253 | 2377 |
## | 1441.73 | 719.86 | 215.41 | |
## | 103.49 | 249.57 | 6.56 | |
## | 0.77 | 0.12 | 0.11 | 0.03 |
## | 0.03 | 0.01 | 0.03 | |
## | 0.02 | 0.00 | 0.00 | |
## ---------------------------|--------------|--------------|--------------|--------------|
## 1335 | 759 | 160 | 266 | 1185 |
## | 718.74 | 358.87 | 107.39 | |
## | 2.25 | 110.21 | 234.27 | |
## | 0.64 | 0.14 | 0.22 | 0.01 |
## | 0.01 | 0.01 | 0.03 | |
## | 0.01 | 0.00 | 0.00 | |
## ---------------------------|--------------|--------------|--------------|--------------|
## Column Total | 55297 | 27610 | 8262 | 91169 |
## | 0.61 | 0.30 | 0.09 | |
## ---------------------------|--------------|--------------|--------------|--------------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 8627 d.f. = 34 p = 0
##
##
## CrossTable(main_processed$NurseFlag, main_processed$Record.Type, digits=2, fisher=F, chisq=TRUE, expected=TRUE)## Warning in chisq.test(t, correct = FALSE, ...): Chi-squared approximation may
## be incorrect##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 91169
##
##
## | main_processed$Record.Type
## main_processed$NurseFlag | Attendance | Cancellation | DNA | Row Total |
## -------------------------|--------------|--------------|--------------|--------------|
## N | 55280 | 27603 | 8261 | 91144 |
## | 55281.84 | 27602.43 | 8259.73 | |
## | 0.00 | 0.00 | 0.00 | |
## | 0.61 | 0.30 | 0.09 | 1.00 |
## | 1.00 | 1.00 | 1.00 | |
## | 0.61 | 0.30 | 0.09 | |
## -------------------------|--------------|--------------|--------------|--------------|
## Y | 17 | 7 | 1 | 25 |
## | 15.16 | 7.57 | 2.27 | |
## | 0.22 | 0.04 | 0.71 | |
## | 0.68 | 0.28 | 0.04 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## -------------------------|--------------|--------------|--------------|--------------|
## Column Total | 55297 | 27610 | 8262 | 91169 |
## | 0.61 | 0.30 | 0.09 | |
## -------------------------|--------------|--------------|--------------|--------------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 0.97 d.f. = 2 p = 0.61
##
##
## CrossTable(main_processed$Referral.Source_recode, main_processed$Record.Type, digits=2, fisher=F, chisq=TRUE, expected=TRUE)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 91169
##
##
## | main_processed$Record.Type
## main_processed$Referral.Source_recode | Attendance | Cancellation | DNA | Row Total |
## --------------------------------------|--------------|--------------|--------------|--------------|
## BREAST CHECK | 948 | 801 | 56 | 1805 |
## | 1094.79 | 546.63 | 163.57 | |
## | 19.68 | 118.36 | 70.75 | |
## | 0.53 | 0.44 | 0.03 | 0.02 |
## | 0.02 | 0.03 | 0.01 | |
## | 0.01 | 0.01 | 0.00 | |
## --------------------------------------|--------------|--------------|--------------|--------------|
## CLINIC | 25225 | 18845 | 3984 | 48054 |
## | 29146.33 | 14552.87 | 4354.79 | |
## | 527.57 | 1265.89 | 31.57 | |
## | 0.52 | 0.39 | 0.08 | 0.53 |
## | 0.46 | 0.68 | 0.48 | |
## | 0.28 | 0.21 | 0.04 | |
## --------------------------------------|--------------|--------------|--------------|--------------|
## Elsew of Mater | 57 | 18 | 4 | 79 |
## | 47.92 | 23.92 | 7.16 | |
## | 1.72 | 1.47 | 1.39 | |
## | 0.72 | 0.23 | 0.05 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## --------------------------------------|--------------|--------------|--------------|--------------|
## Elsew outside Mater | 1052 | 212 | 75 | 1339 |
## | 812.15 | 405.51 | 121.34 | |
## | 70.84 | 92.34 | 17.70 | |
## | 0.79 | 0.16 | 0.06 | 0.01 |
## | 0.02 | 0.01 | 0.01 | |
## | 0.01 | 0.00 | 0.00 | |
## --------------------------------------|--------------|--------------|--------------|--------------|
## EMERGENCY DEPT | 54 | 3 | 3 | 60 |
## | 36.39 | 18.17 | 5.44 | |
## | 8.52 | 12.67 | 1.09 | |
## | 0.90 | 0.05 | 0.05 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## --------------------------------------|--------------|--------------|--------------|--------------|
## GP | 23181 | 4917 | 3677 | 31775 |
## | 19272.58 | 9622.87 | 2879.54 | |
## | 792.61 | 2301.31 | 220.85 | |
## | 0.73 | 0.15 | 0.12 | 0.35 |
## | 0.42 | 0.18 | 0.45 | |
## | 0.25 | 0.05 | 0.04 | |
## --------------------------------------|--------------|--------------|--------------|--------------|
## OTHER CONSULTANT | 182 | 57 | 12 | 251 |
## | 152.24 | 76.01 | 22.75 | |
## | 5.82 | 4.76 | 5.08 | |
## | 0.73 | 0.23 | 0.05 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## --------------------------------------|--------------|--------------|--------------|--------------|
## WARD | 4598 | 2757 | 451 | 7806 |
## | 4734.60 | 2364.00 | 707.40 | |
## | 3.94 | 65.33 | 92.93 | |
## | 0.59 | 0.35 | 0.06 | 0.09 |
## | 0.08 | 0.10 | 0.05 | |
## | 0.05 | 0.03 | 0.00 | |
## --------------------------------------|--------------|--------------|--------------|--------------|
## Column Total | 55297 | 27610 | 8262 | 91169 |
## | 0.61 | 0.30 | 0.09 | |
## --------------------------------------|--------------|--------------|--------------|--------------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 5734 d.f. = 14 p = 0
##
##
## #CrossTable(main_processed$Referring.Hospital, main_processed$Record.Type, digits=2, fisher=F, chisq=TRUE, expected=TRUE)
CrossTable(main_processed$Consultant_recode, main_processed$Record.Type, digits=2, fisher=F, chisq=TRUE, expected=TRUE)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 91169
##
##
## | main_processed$Record.Type
## main_processed$Consultant_recode | Attendance | Cancellation | DNA | Row Total |
## ---------------------------------|--------------|--------------|--------------|--------------|
## BARRYM | 13749 | 7716 | 1792 | 23257 |
## | 14106.14 | 7043.25 | 2107.62 | |
## | 9.04 | 64.26 | 47.26 | |
## | 0.59 | 0.33 | 0.08 | 0.26 |
## | 0.25 | 0.28 | 0.22 | |
## | 0.15 | 0.08 | 0.02 | |
## ---------------------------------|--------------|--------------|--------------|--------------|
## HEENEY | 3841 | 1006 | 653 | 5500 |
## | 3335.93 | 1665.64 | 498.43 | |
## | 76.47 | 261.24 | 47.94 | |
## | 0.70 | 0.18 | 0.12 | 0.06 |
## | 0.07 | 0.04 | 0.08 | |
## | 0.04 | 0.01 | 0.01 | |
## ---------------------------------|--------------|--------------|--------------|--------------|
## KELLM | 12999 | 5731 | 1869 | 20599 |
## | 12493.97 | 6238.29 | 1866.74 | |
## | 20.41 | 41.25 | 0.00 | |
## | 0.63 | 0.28 | 0.09 | 0.23 |
## | 0.24 | 0.21 | 0.23 | |
## | 0.14 | 0.06 | 0.02 | |
## ---------------------------------|--------------|--------------|--------------|--------------|
## STOKES | 10002 | 5620 | 1772 | 17394 |
## | 10550.03 | 5267.67 | 1576.29 | |
## | 28.47 | 23.57 | 24.30 | |
## | 0.58 | 0.32 | 0.10 | 0.19 |
## | 0.18 | 0.20 | 0.21 | |
## | 0.11 | 0.06 | 0.02 | |
## ---------------------------------|--------------|--------------|--------------|--------------|
## WALSSI | 14706 | 7537 | 2176 | 24419 |
## | 14810.93 | 7395.15 | 2212.92 | |
## | 0.74 | 2.72 | 0.62 | |
## | 0.60 | 0.31 | 0.09 | 0.27 |
## | 0.27 | 0.27 | 0.26 | |
## | 0.16 | 0.08 | 0.02 | |
## ---------------------------------|--------------|--------------|--------------|--------------|
## Column Total | 55297 | 27610 | 8262 | 91169 |
## | 0.61 | 0.30 | 0.09 | |
## ---------------------------------|--------------|--------------|--------------|--------------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 648 d.f. = 8 p = 9.6e-135
##
##
## CrossTable(main_processed$Insurance.Scheme_recode, main_processed$Record.Type, digits=2, fisher=F, chisq=TRUE, expected=TRUE)## Warning in chisq.test(t, correct = FALSE, ...): Chi-squared approximation may
## be incorrect##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 91169
##
##
## | main_processed$Record.Type
## main_processed$Insurance.Scheme_recode | Attendance | Cancellation | DNA | Row Total |
## ---------------------------------------|--------------|--------------|--------------|--------------|
## A | 43 | 23 | 5 | 71 |
## | 43.06 | 21.50 | 6.43 | |
## | 0.00 | 0.10 | 0.32 | |
## | 0.61 | 0.32 | 0.07 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## ---------------------------------------|--------------|--------------|--------------|--------------|
## B | 4152 | 2139 | 414 | 6705 |
## | 4066.80 | 2030.57 | 607.63 | |
## | 1.78 | 5.79 | 61.70 | |
## | 0.62 | 0.32 | 0.06 | 0.07 |
## | 0.08 | 0.08 | 0.05 | |
## | 0.05 | 0.02 | 0.00 | |
## ---------------------------------------|--------------|--------------|--------------|--------------|
## C | 16 | 7 | 2 | 25 |
## | 15.16 | 7.57 | 2.27 | |
## | 0.05 | 0.04 | 0.03 | |
## | 0.64 | 0.28 | 0.08 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## ---------------------------------------|--------------|--------------|--------------|--------------|
## D | 14801 | 7711 | 2253 | 24765 |
## | 15020.79 | 7499.94 | 2244.28 | |
## | 3.22 | 5.94 | 0.03 | |
## | 0.60 | 0.31 | 0.09 | 0.27 |
## | 0.27 | 0.28 | 0.27 | |
## | 0.16 | 0.08 | 0.02 | |
## ---------------------------------------|--------------|--------------|--------------|--------------|
## E | 126 | 67 | 14 | 207 |
## | 125.55 | 62.69 | 18.76 | |
## | 0.00 | 0.30 | 1.21 | |
## | 0.61 | 0.32 | 0.07 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## ---------------------------------------|--------------|--------------|--------------|--------------|
## G | 372 | 191 | 56 | 619 |
## | 375.44 | 187.46 | 56.10 | |
## | 0.03 | 0.07 | 0.00 | |
## | 0.60 | 0.31 | 0.09 | 0.01 |
## | 0.01 | 0.01 | 0.01 | |
## | 0.00 | 0.00 | 0.00 | |
## ---------------------------------------|--------------|--------------|--------------|--------------|
## H | 64 | 33 | 5 | 102 |
## | 61.87 | 30.89 | 9.24 | |
## | 0.07 | 0.14 | 1.95 | |
## | 0.63 | 0.32 | 0.05 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## ---------------------------------------|--------------|--------------|--------------|--------------|
## I | 3742 | 2006 | 385 | 6133 |
## | 3719.87 | 1857.34 | 555.79 | |
## | 0.13 | 11.90 | 52.48 | |
## | 0.61 | 0.33 | 0.06 | 0.07 |
## | 0.07 | 0.07 | 0.05 | |
## | 0.04 | 0.02 | 0.00 | |
## ---------------------------------------|--------------|--------------|--------------|--------------|
## J | 75 | 54 | 7 | 136 |
## | 82.49 | 41.19 | 12.32 | |
## | 0.68 | 3.99 | 2.30 | |
## | 0.55 | 0.40 | 0.05 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## ---------------------------------------|--------------|--------------|--------------|--------------|
## M | 4 | 1 | 2 | 7 |
## | 4.25 | 2.12 | 0.63 | |
## | 0.01 | 0.59 | 2.94 | |
## | 0.57 | 0.14 | 0.29 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## ---------------------------------------|--------------|--------------|--------------|--------------|
## O | 358 | 110 | 41 | 509 |
## | 308.73 | 154.15 | 46.13 | |
## | 7.86 | 12.64 | 0.57 | |
## | 0.70 | 0.22 | 0.08 | 0.01 |
## | 0.01 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## ---------------------------------------|--------------|--------------|--------------|--------------|
## P | 121 | 59 | 15 | 195 |
## | 118.27 | 59.05 | 17.67 | |
## | 0.06 | 0.00 | 0.40 | |
## | 0.62 | 0.30 | 0.08 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## ---------------------------------------|--------------|--------------|--------------|--------------|
## S | 1712 | 824 | 159 | 2695 |
## | 1634.61 | 816.17 | 244.23 | |
## | 3.66 | 0.08 | 29.74 | |
## | 0.64 | 0.31 | 0.06 | 0.03 |
## | 0.03 | 0.03 | 0.02 | |
## | 0.02 | 0.01 | 0.00 | |
## ---------------------------------------|--------------|--------------|--------------|--------------|
## U | 20245 | 9306 | 3785 | 33336 |
## | 20219.38 | 10095.61 | 3021.01 | |
## | 0.03 | 61.76 | 193.21 | |
## | 0.61 | 0.28 | 0.11 | 0.37 |
## | 0.37 | 0.34 | 0.46 | |
## | 0.22 | 0.10 | 0.04 | |
## ---------------------------------------|--------------|--------------|--------------|--------------|
## V | 9466 | 5079 | 1119 | 15664 |
## | 9500.73 | 4743.75 | 1419.52 | |
## | 0.13 | 23.69 | 63.62 | |
## | 0.60 | 0.32 | 0.07 | 0.17 |
## | 0.17 | 0.18 | 0.14 | |
## | 0.10 | 0.06 | 0.01 | |
## ---------------------------------------|--------------|--------------|--------------|--------------|
## Column Total | 55297 | 27610 | 8262 | 91169 |
## | 0.61 | 0.30 | 0.09 | |
## ---------------------------------------|--------------|--------------|--------------|--------------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 555 d.f. = 28 p = 2.6e-99
##
##
## CrossTable(main_processed$Eligibility_recode, main_processed$Record.Type, digits=2, fisher=F, chisq=TRUE, expected=TRUE)## Warning in chisq.test(t, correct = FALSE, ...): Chi-squared approximation may
## be incorrect##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 91169
##
##
## | main_processed$Record.Type
## main_processed$Eligibility_recode | Attendance | Cancellation | DNA | Row Total |
## ----------------------------------|--------------|--------------|--------------|--------------|
## ACUTE UNCLASSIFIED | 136 | 78 | 33 | 247 |
## | 149.81 | 74.80 | 22.38 | |
## | 1.27 | 0.14 | 5.03 | |
## | 0.55 | 0.32 | 0.13 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## ----------------------------------|--------------|--------------|--------------|--------------|
## ELIGIBILITY UNKNOWN | 1000 | 1108 | 1270 | 3378 |
## | 2048.87 | 1023.01 | 306.12 | |
## | 536.94 | 7.06 | 3034.90 | |
## | 0.30 | 0.33 | 0.38 | 0.04 |
## | 0.02 | 0.04 | 0.15 | |
## | 0.01 | 0.01 | 0.01 | |
## ----------------------------------|--------------|--------------|--------------|--------------|
## EXEMPT | 1425 | 768 | 216 | 2409 |
## | 1461.14 | 729.55 | 218.31 | |
## | 0.89 | 2.03 | 0.02 | |
## | 0.59 | 0.32 | 0.09 | 0.03 |
## | 0.03 | 0.03 | 0.03 | |
## | 0.02 | 0.01 | 0.00 | |
## ----------------------------------|--------------|--------------|--------------|--------------|
## MEDICAL CARD | 22233 | 11064 | 3097 | 36394 |
## | 22074.16 | 11021.71 | 3298.13 | |
## | 1.14 | 0.16 | 12.27 | |
## | 0.61 | 0.30 | 0.09 | 0.40 |
## | 0.40 | 0.40 | 0.37 | |
## | 0.24 | 0.12 | 0.03 | |
## ----------------------------------|--------------|--------------|--------------|--------------|
## NON ACUTE UNCLASSIFIED | 21 | 14 | 2 | 37 |
## | 22.44 | 11.21 | 3.35 | |
## | 0.09 | 0.70 | 0.55 | |
## | 0.57 | 0.38 | 0.05 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## ----------------------------------|--------------|--------------|--------------|--------------|
## NON MEDICAL CARD | 30302 | 14468 | 3621 | 48391 |
## | 29350.73 | 14654.93 | 4385.33 | |
## | 30.83 | 2.38 | 133.22 | |
## | 0.63 | 0.30 | 0.07 | 0.53 |
## | 0.55 | 0.52 | 0.44 | |
## | 0.33 | 0.16 | 0.04 | |
## ----------------------------------|--------------|--------------|--------------|--------------|
## RESEARCH/NATIONAL PROG. | 180 | 110 | 23 | 313 |
## | 189.84 | 94.79 | 28.36 | |
## | 0.51 | 2.44 | 1.01 | |
## | 0.58 | 0.35 | 0.07 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## ----------------------------------|--------------|--------------|--------------|--------------|
## Column Total | 55297 | 27610 | 8262 | 91169 |
## | 0.61 | 0.30 | 0.09 | |
## ----------------------------------|--------------|--------------|--------------|--------------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 3774 d.f. = 12 p = 0
##
##
## CrossTable(main_processed$Booking.Type_recode, main_processed$Record.Type, digits=2, fisher=F, chisq=TRUE, expected=TRUE)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 91169
##
##
## | main_processed$Record.Type
## main_processed$Booking.Type_recode | Attendance | Cancellation | DNA | Row Total |
## -----------------------------------|--------------|--------------|--------------|--------------|
## NEW | 24236 | 5013 | 3780 | 33029 |
## | 20033.18 | 10002.64 | 2993.18 | |
## | 881.72 | 2488.99 | 206.83 | |
## | 0.73 | 0.15 | 0.11 | 0.36 |
## | 0.44 | 0.18 | 0.46 | |
## | 0.27 | 0.05 | 0.04 | |
## -----------------------------------|--------------|--------------|--------------|--------------|
## RETURN | 27715 | 20419 | 4168 | 52302 |
## | 31722.88 | 15839.36 | 4739.76 | |
## | 506.36 | 1324.12 | 68.97 | |
## | 0.53 | 0.39 | 0.08 | 0.57 |
## | 0.50 | 0.74 | 0.50 | |
## | 0.30 | 0.22 | 0.05 | |
## -----------------------------------|--------------|--------------|--------------|--------------|
## WARD | 3346 | 2178 | 314 | 5838 |
## | 3540.94 | 1768.00 | 529.06 | |
## | 10.73 | 95.08 | 87.42 | |
## | 0.57 | 0.37 | 0.05 | 0.06 |
## | 0.06 | 0.08 | 0.04 | |
## | 0.04 | 0.02 | 0.00 | |
## -----------------------------------|--------------|--------------|--------------|--------------|
## Column Total | 55297 | 27610 | 8262 | 91169 |
## | 0.61 | 0.30 | 0.09 | |
## -----------------------------------|--------------|--------------|--------------|--------------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 5670 d.f. = 4 p = 0
##
##
## CrossTable(main_processed$Hospital.Catchment_recode, main_processed$Record.Type, digits=2, fisher=F, chisq=TRUE, expected=TRUE)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 91169
##
##
## | main_processed$Record.Type
## main_processed$Hospital.Catchment_recode | Attendance | Cancellation | DNA | Row Total |
## -----------------------------------------|--------------|--------------|--------------|--------------|
## Beaumont | 2098 | 1258 | 317 | 3673 |
## | 2227.80 | 1112.35 | 332.86 | |
## | 7.56 | 19.07 | 0.76 | |
## | 0.57 | 0.34 | 0.09 | 0.04 |
## | 0.04 | 0.05 | 0.04 | |
## | 0.02 | 0.01 | 0.00 | |
## -----------------------------------------|--------------|--------------|--------------|--------------|
## Connolly | 6237 | 2806 | 920 | 9963 |
## | 6042.89 | 3017.24 | 902.88 | |
## | 6.24 | 14.79 | 0.32 | |
## | 0.63 | 0.28 | 0.09 | 0.11 |
## | 0.11 | 0.10 | 0.11 | |
## | 0.07 | 0.03 | 0.01 | |
## -----------------------------------------|--------------|--------------|--------------|--------------|
## International | 108 | 60 | 28 | 196 |
## | 118.88 | 59.36 | 17.76 | |
## | 1.00 | 0.01 | 5.90 | |
## | 0.55 | 0.31 | 0.14 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## -----------------------------------------|--------------|--------------|--------------|--------------|
## James | 593 | 266 | 111 | 970 |
## | 588.34 | 293.76 | 87.90 | |
## | 0.04 | 2.62 | 6.07 | |
## | 0.61 | 0.27 | 0.11 | 0.01 |
## | 0.01 | 0.01 | 0.01 | |
## | 0.01 | 0.00 | 0.00 | |
## -----------------------------------------|--------------|--------------|--------------|--------------|
## Mater | 13697 | 6299 | 2209 | 22205 |
## | 13468.06 | 6724.65 | 2012.28 | |
## | 3.89 | 26.94 | 19.23 | |
## | 0.62 | 0.28 | 0.10 | 0.24 |
## | 0.25 | 0.23 | 0.27 | |
## | 0.15 | 0.07 | 0.02 | |
## -----------------------------------------|--------------|--------------|--------------|--------------|
## National | 31787 | 16527 | 4485 | 52799 |
## | 32024.33 | 15989.87 | 4784.80 | |
## | 1.76 | 18.04 | 18.78 | |
## | 0.60 | 0.31 | 0.08 | 0.58 |
## | 0.57 | 0.60 | 0.54 | |
## | 0.35 | 0.18 | 0.05 | |
## -----------------------------------------|--------------|--------------|--------------|--------------|
## Tallaght | 240 | 96 | 61 | 397 |
## | 240.79 | 120.23 | 35.98 | |
## | 0.00 | 4.88 | 17.40 | |
## | 0.60 | 0.24 | 0.15 | 0.00 |
## | 0.00 | 0.00 | 0.01 | |
## | 0.00 | 0.00 | 0.00 | |
## -----------------------------------------|--------------|--------------|--------------|--------------|
## Vincents | 537 | 298 | 131 | 966 |
## | 585.91 | 292.55 | 87.54 | |
## | 4.08 | 0.10 | 21.57 | |
## | 0.56 | 0.31 | 0.14 | 0.01 |
## | 0.01 | 0.01 | 0.02 | |
## | 0.01 | 0.00 | 0.00 | |
## -----------------------------------------|--------------|--------------|--------------|--------------|
## Column Total | 55297 | 27610 | 8262 | 91169 |
## | 0.61 | 0.30 | 0.09 | |
## -----------------------------------------|--------------|--------------|--------------|--------------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 201 d.f. = 14 p = 3.3e-35
##
##
## CrossTable(main_processed$bookedDay, main_processed$Record.Type, digits=2, fisher=F, chisq=TRUE, expected=TRUE)## Warning in chisq.test(t, correct = FALSE, ...): Chi-squared approximation may
## be incorrect##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 88859
##
##
## | main_processed$Record.Type
## main_processed$bookedDay | Attendance | Cancellation | DNA | Row Total |
## -------------------------|--------------|--------------|--------------|--------------|
## Friday | 8390 | 4345 | 1203 | 13938 |
## | 8311.29 | 4330.77 | 1295.94 | |
## | 0.75 | 0.05 | 6.67 | |
## | 0.60 | 0.31 | 0.09 | 0.16 |
## | 0.16 | 0.16 | 0.15 | |
## | 0.09 | 0.05 | 0.01 | |
## -------------------------|--------------|--------------|--------------|--------------|
## Monday | 11561 | 6484 | 1916 | 19961 |
## | 11902.83 | 6202.22 | 1855.95 | |
## | 9.82 | 12.80 | 1.94 | |
## | 0.58 | 0.32 | 0.10 | 0.22 |
## | 0.22 | 0.23 | 0.23 | |
## | 0.13 | 0.07 | 0.02 | |
## -------------------------|--------------|--------------|--------------|--------------|
## Saturday | 153 | 65 | 30 | 248 |
## | 147.88 | 77.06 | 23.06 | |
## | 0.18 | 1.89 | 2.09 | |
## | 0.62 | 0.26 | 0.12 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## -------------------------|--------------|--------------|--------------|--------------|
## Sunday | 10 | 4 | 1 | 15 |
## | 8.94 | 4.66 | 1.39 | |
## | 0.12 | 0.09 | 0.11 | |
## | 0.67 | 0.27 | 0.07 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## -------------------------|--------------|--------------|--------------|--------------|
## Thursday | 10226 | 4935 | 1614 | 16775 |
## | 10003.00 | 5212.28 | 1559.72 | |
## | 4.97 | 14.75 | 1.89 | |
## | 0.61 | 0.29 | 0.10 | 0.19 |
## | 0.19 | 0.18 | 0.20 | |
## | 0.12 | 0.06 | 0.02 | |
## -------------------------|--------------|--------------|--------------|--------------|
## Tuesday | 11838 | 6367 | 1854 | 20059 |
## | 11961.27 | 6232.67 | 1865.06 | |
## | 1.27 | 2.90 | 0.07 | |
## | 0.59 | 0.32 | 0.09 | 0.23 |
## | 0.22 | 0.23 | 0.22 | |
## | 0.13 | 0.07 | 0.02 | |
## -------------------------|--------------|--------------|--------------|--------------|
## Wednesday | 10809 | 5410 | 1644 | 17863 |
## | 10651.78 | 5550.34 | 1660.88 | |
## | 2.32 | 3.55 | 0.17 | |
## | 0.61 | 0.30 | 0.09 | 0.20 |
## | 0.20 | 0.20 | 0.20 | |
## | 0.12 | 0.06 | 0.02 | |
## -------------------------|--------------|--------------|--------------|--------------|
## Column Total | 52987 | 27610 | 8262 | 88859 |
## | 0.60 | 0.31 | 0.09 | |
## -------------------------|--------------|--------------|--------------|--------------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 68 d.f. = 12 p = 6.4e-10
##
##
## CrossTable(main_processed$bookedMonthYear, main_processed$Record.Type, digits=2, fisher=F, chisq=TRUE, expected=TRUE)## Warning in chisq.test(t, correct = FALSE, ...): Chi-squared approximation may
## be incorrect##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 88859
##
##
## | main_processed$Record.Type
## main_processed$bookedMonthYear | Attendance | Cancellation | DNA | Row Total |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2017-08 | 1 | 0 | 0 | 1 |
## | 0.60 | 0.31 | 0.09 | |
## | 0.27 | 0.31 | 0.09 | |
## | 1.00 | 0.00 | 0.00 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2019-01 | 89 | 0 | 0 | 89 |
## | 53.07 | 27.65 | 8.28 | |
## | 24.32 | 27.65 | 8.28 | |
## | 1.00 | 0.00 | 0.00 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2019-02 | 66 | 0 | 0 | 66 |
## | 39.36 | 20.51 | 6.14 | |
## | 18.04 | 20.51 | 6.14 | |
## | 1.00 | 0.00 | 0.00 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2019-03 | 40 | 0 | 0 | 40 |
## | 23.85 | 12.43 | 3.72 | |
## | 10.93 | 12.43 | 3.72 | |
## | 1.00 | 0.00 | 0.00 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2019-04 | 15 | 0 | 0 | 15 |
## | 8.94 | 4.66 | 1.39 | |
## | 4.10 | 4.66 | 1.39 | |
## | 1.00 | 0.00 | 0.00 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2019-05 | 33 | 0 | 0 | 33 |
## | 19.68 | 10.25 | 3.07 | |
## | 9.02 | 10.25 | 3.07 | |
## | 1.00 | 0.00 | 0.00 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2019-06 | 64 | 0 | 0 | 64 |
## | 38.16 | 19.89 | 5.95 | |
## | 17.49 | 19.89 | 5.95 | |
## | 1.00 | 0.00 | 0.00 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2019-07 | 151 | 0 | 0 | 151 |
## | 90.04 | 46.92 | 14.04 | |
## | 41.27 | 46.92 | 14.04 | |
## | 1.00 | 0.00 | 0.00 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2019-08 | 156 | 0 | 0 | 156 |
## | 93.02 | 48.47 | 14.50 | |
## | 42.63 | 48.47 | 14.50 | |
## | 1.00 | 0.00 | 0.00 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2019-09 | 104 | 0 | 0 | 104 |
## | 62.02 | 32.31 | 9.67 | |
## | 28.42 | 32.31 | 9.67 | |
## | 1.00 | 0.00 | 0.00 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2019-10 | 148 | 0 | 0 | 148 |
## | 88.25 | 45.99 | 13.76 | |
## | 40.45 | 45.99 | 13.76 | |
## | 1.00 | 0.00 | 0.00 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2019-11 | 210 | 0 | 0 | 210 |
## | 125.22 | 65.25 | 19.53 | |
## | 57.39 | 65.25 | 19.53 | |
## | 1.00 | 0.00 | 0.00 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2019-12 | 523 | 0 | 0 | 523 |
## | 311.87 | 162.50 | 48.63 | |
## | 142.94 | 162.50 | 48.63 | |
## | 1.00 | 0.00 | 0.00 | 0.01 |
## | 0.01 | 0.00 | 0.00 | |
## | 0.01 | 0.00 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2020-01 | 1318 | 689 | 135 | 2142 |
## | 1277.28 | 665.56 | 199.16 | |
## | 1.30 | 0.83 | 20.67 | |
## | 0.62 | 0.32 | 0.06 | 0.02 |
## | 0.02 | 0.02 | 0.02 | |
## | 0.01 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2020-02 | 999 | 932 | 136 | 2067 |
## | 1232.56 | 642.25 | 192.19 | |
## | 44.26 | 130.72 | 16.43 | |
## | 0.48 | 0.45 | 0.07 | 0.02 |
## | 0.02 | 0.03 | 0.02 | |
## | 0.01 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2020-03 | 281 | 486 | 68 | 835 |
## | 497.91 | 259.45 | 77.64 | |
## | 94.50 | 197.83 | 1.20 | |
## | 0.34 | 0.58 | 0.08 | 0.01 |
## | 0.01 | 0.02 | 0.01 | |
## | 0.00 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2020-04 | 300 | 178 | 23 | 501 |
## | 298.75 | 155.67 | 46.58 | |
## | 0.01 | 3.20 | 11.94 | |
## | 0.60 | 0.36 | 0.05 | 0.01 |
## | 0.01 | 0.01 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2020-05 | 661 | 388 | 62 | 1111 |
## | 662.49 | 345.21 | 103.30 | |
## | 0.00 | 5.30 | 16.51 | |
## | 0.59 | 0.35 | 0.06 | 0.01 |
## | 0.01 | 0.01 | 0.01 | |
## | 0.01 | 0.00 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2020-06 | 1271 | 613 | 139 | 2023 |
## | 1206.32 | 628.58 | 188.10 | |
## | 3.47 | 0.39 | 12.81 | |
## | 0.63 | 0.30 | 0.07 | 0.02 |
## | 0.02 | 0.02 | 0.02 | |
## | 0.01 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2020-07 | 1411 | 647 | 296 | 2354 |
## | 1403.70 | 731.43 | 218.87 | |
## | 0.04 | 9.75 | 27.18 | |
## | 0.60 | 0.27 | 0.13 | 0.03 |
## | 0.03 | 0.02 | 0.04 | |
## | 0.02 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2020-08 | 1503 | 717 | 171 | 2391 |
## | 1425.76 | 742.92 | 222.31 | |
## | 4.18 | 0.90 | 11.84 | |
## | 0.63 | 0.30 | 0.07 | 0.03 |
## | 0.03 | 0.03 | 0.02 | |
## | 0.02 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2020-09 | 1345 | 688 | 210 | 2243 |
## | 1337.51 | 696.94 | 208.55 | |
## | 0.04 | 0.11 | 0.01 | |
## | 0.60 | 0.31 | 0.09 | 0.03 |
## | 0.03 | 0.02 | 0.03 | |
## | 0.02 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2020-10 | 1191 | 686 | 178 | 2055 |
## | 1225.41 | 638.52 | 191.07 | |
## | 0.97 | 3.53 | 0.89 | |
## | 0.58 | 0.33 | 0.09 | 0.02 |
## | 0.02 | 0.02 | 0.02 | |
## | 0.01 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2020-11 | 1413 | 833 | 160 | 2406 |
## | 1434.71 | 747.59 | 223.71 | |
## | 0.33 | 9.76 | 18.14 | |
## | 0.59 | 0.35 | 0.07 | 0.03 |
## | 0.03 | 0.03 | 0.02 | |
## | 0.02 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2020-12 | 902 | 470 | 205 | 1577 |
## | 940.37 | 490.00 | 146.63 | |
## | 1.57 | 0.82 | 23.24 | |
## | 0.57 | 0.30 | 0.13 | 0.02 |
## | 0.02 | 0.02 | 0.02 | |
## | 0.01 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2021-01 | 686 | 434 | 131 | 1251 |
## | 745.98 | 388.71 | 116.32 | |
## | 4.82 | 5.28 | 1.85 | |
## | 0.55 | 0.35 | 0.10 | 0.01 |
## | 0.01 | 0.02 | 0.02 | |
## | 0.01 | 0.00 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2021-02 | 989 | 794 | 186 | 1969 |
## | 1174.12 | 611.80 | 183.08 | |
## | 29.19 | 54.26 | 0.05 | |
## | 0.50 | 0.40 | 0.09 | 0.02 |
## | 0.02 | 0.03 | 0.02 | |
## | 0.01 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2021-03 | 1317 | 736 | 188 | 2241 |
## | 1336.32 | 696.32 | 208.37 | |
## | 0.28 | 2.26 | 1.99 | |
## | 0.59 | 0.33 | 0.08 | 0.03 |
## | 0.02 | 0.03 | 0.02 | |
## | 0.01 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2021-04 | 1114 | 662 | 231 | 2007 |
## | 1196.78 | 623.61 | 186.61 | |
## | 5.73 | 2.36 | 10.56 | |
## | 0.56 | 0.33 | 0.12 | 0.02 |
## | 0.02 | 0.02 | 0.03 | |
## | 0.01 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2021-05 | 1013 | 622 | 215 | 1850 |
## | 1103.16 | 574.83 | 172.01 | |
## | 7.37 | 3.87 | 10.74 | |
## | 0.55 | 0.34 | 0.12 | 0.02 |
## | 0.02 | 0.02 | 0.03 | |
## | 0.01 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2021-06 | 947 | 766 | 225 | 1938 |
## | 1155.64 | 602.17 | 180.19 | |
## | 37.67 | 44.57 | 11.14 | |
## | 0.49 | 0.40 | 0.12 | 0.02 |
## | 0.02 | 0.03 | 0.03 | |
## | 0.01 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2021-07 | 1099 | 733 | 211 | 2043 |
## | 1218.25 | 634.79 | 189.96 | |
## | 11.67 | 15.19 | 2.33 | |
## | 0.54 | 0.36 | 0.10 | 0.02 |
## | 0.02 | 0.03 | 0.03 | |
## | 0.01 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2021-08 | 1270 | 786 | 274 | 2330 |
## | 1389.39 | 723.97 | 216.64 | |
## | 10.26 | 5.31 | 15.19 | |
## | 0.55 | 0.34 | 0.12 | 0.03 |
## | 0.02 | 0.03 | 0.03 | |
## | 0.01 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2021-09 | 1402 | 631 | 376 | 2409 |
## | 1436.50 | 748.52 | 223.99 | |
## | 0.83 | 18.45 | 103.17 | |
## | 0.58 | 0.26 | 0.16 | 0.03 |
## | 0.03 | 0.02 | 0.05 | |
## | 0.02 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2021-10 | 1211 | 572 | 247 | 2030 |
## | 1210.50 | 630.76 | 188.75 | |
## | 0.00 | 5.47 | 17.98 | |
## | 0.60 | 0.28 | 0.12 | 0.02 |
## | 0.02 | 0.02 | 0.03 | |
## | 0.01 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2021-11 | 975 | 576 | 271 | 1822 |
## | 1086.47 | 566.13 | 169.41 | |
## | 11.44 | 0.17 | 60.92 | |
## | 0.54 | 0.32 | 0.15 | 0.02 |
## | 0.02 | 0.02 | 0.03 | |
## | 0.01 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2021-12 | 953 | 510 | 275 | 1738 |
## | 1036.38 | 540.03 | 161.60 | |
## | 6.71 | 1.67 | 79.58 | |
## | 0.55 | 0.29 | 0.16 | 0.02 |
## | 0.02 | 0.02 | 0.03 | |
## | 0.01 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2022-01 | 992 | 546 | 172 | 1710 |
## | 1019.68 | 531.33 | 158.99 | |
## | 0.75 | 0.41 | 1.06 | |
## | 0.58 | 0.32 | 0.10 | 0.02 |
## | 0.02 | 0.02 | 0.02 | |
## | 0.01 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2022-02 | 1057 | 583 | 227 | 1867 |
## | 1113.30 | 580.11 | 173.59 | |
## | 2.85 | 0.01 | 16.43 | |
## | 0.57 | 0.31 | 0.12 | 0.02 |
## | 0.02 | 0.02 | 0.03 | |
## | 0.01 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2022-03 | 1177 | 598 | 283 | 2058 |
## | 1227.19 | 639.46 | 191.35 | |
## | 2.05 | 2.69 | 43.90 | |
## | 0.57 | 0.29 | 0.14 | 0.02 |
## | 0.02 | 0.02 | 0.03 | |
## | 0.01 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2022-04 | 1000 | 514 | 199 | 1713 |
## | 1021.47 | 532.26 | 159.27 | |
## | 0.45 | 0.63 | 9.91 | |
## | 0.58 | 0.30 | 0.12 | 0.02 |
## | 0.02 | 0.02 | 0.02 | |
## | 0.01 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2022-05 | 1242 | 841 | 239 | 2322 |
## | 1384.62 | 721.48 | 215.90 | |
## | 14.69 | 19.80 | 2.47 | |
## | 0.53 | 0.36 | 0.10 | 0.03 |
## | 0.02 | 0.03 | 0.03 | |
## | 0.01 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2022-06 | 1234 | 674 | 230 | 2138 |
## | 1274.90 | 664.31 | 198.79 | |
## | 1.31 | 0.14 | 4.90 | |
## | 0.58 | 0.32 | 0.11 | 0.02 |
## | 0.02 | 0.02 | 0.03 | |
## | 0.01 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2022-07 | 1249 | 575 | 213 | 2037 |
## | 1214.67 | 632.93 | 189.40 | |
## | 0.97 | 5.30 | 2.94 | |
## | 0.61 | 0.28 | 0.10 | 0.02 |
## | 0.02 | 0.02 | 0.03 | |
## | 0.01 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2022-08 | 964 | 490 | 153 | 1607 |
## | 958.26 | 499.32 | 149.42 | |
## | 0.03 | 0.17 | 0.09 | |
## | 0.60 | 0.30 | 0.10 | 0.02 |
## | 0.02 | 0.02 | 0.02 | |
## | 0.01 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2022-09 | 1267 | 564 | 201 | 2032 |
## | 1211.69 | 631.38 | 188.93 | |
## | 2.52 | 7.19 | 0.77 | |
## | 0.62 | 0.28 | 0.10 | 0.02 |
## | 0.02 | 0.02 | 0.02 | |
## | 0.01 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2022-10 | 1497 | 721 | 252 | 2470 |
## | 1472.87 | 767.47 | 229.66 | |
## | 0.40 | 2.81 | 2.17 | |
## | 0.61 | 0.29 | 0.10 | 0.03 |
## | 0.03 | 0.03 | 0.03 | |
## | 0.02 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2022-11 | 1115 | 537 | 190 | 1842 |
## | 1098.39 | 572.34 | 171.27 | |
## | 0.25 | 2.18 | 2.05 | |
## | 0.61 | 0.29 | 0.10 | 0.02 |
## | 0.02 | 0.02 | 0.02 | |
## | 0.01 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2022-12 | 828 | 494 | 115 | 1437 |
## | 856.89 | 446.50 | 133.61 | |
## | 0.97 | 5.05 | 2.59 | |
## | 0.58 | 0.34 | 0.08 | 0.02 |
## | 0.02 | 0.02 | 0.01 | |
## | 0.01 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2023-01 | 1252 | 551 | 111 | 1914 |
## | 1141.33 | 594.71 | 177.96 | |
## | 10.73 | 3.21 | 25.20 | |
## | 0.65 | 0.29 | 0.06 | 0.02 |
## | 0.02 | 0.02 | 0.01 | |
## | 0.01 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2023-02 | 1014 | 577 | 94 | 1685 |
## | 1004.77 | 523.56 | 156.67 | |
## | 0.08 | 5.46 | 25.07 | |
## | 0.60 | 0.34 | 0.06 | 0.02 |
## | 0.02 | 0.02 | 0.01 | |
## | 0.01 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2023-03 | 1161 | 589 | 141 | 1891 |
## | 1127.61 | 587.57 | 175.82 | |
## | 0.99 | 0.00 | 6.90 | |
## | 0.61 | 0.31 | 0.07 | 0.02 |
## | 0.02 | 0.02 | 0.02 | |
## | 0.01 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2023-04 | 1217 | 578 | 107 | 1902 |
## | 1134.17 | 590.98 | 176.85 | |
## | 6.05 | 0.29 | 27.59 | |
## | 0.64 | 0.30 | 0.06 | 0.02 |
## | 0.02 | 0.02 | 0.01 | |
## | 0.01 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2023-05 | 1276 | 562 | 105 | 1943 |
## | 1158.62 | 603.72 | 180.66 | |
## | 11.89 | 2.88 | 31.68 | |
## | 0.66 | 0.29 | 0.05 | 0.02 |
## | 0.02 | 0.02 | 0.01 | |
## | 0.01 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2023-06 | 1192 | 482 | 117 | 1791 |
## | 1067.98 | 556.49 | 166.52 | |
## | 14.40 | 9.97 | 14.73 | |
## | 0.67 | 0.27 | 0.07 | 0.02 |
## | 0.02 | 0.02 | 0.01 | |
## | 0.01 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2023-07 | 1191 | 470 | 130 | 1791 |
## | 1067.98 | 556.49 | 166.52 | |
## | 14.17 | 13.44 | 8.01 | |
## | 0.66 | 0.26 | 0.07 | 0.02 |
## | 0.02 | 0.02 | 0.02 | |
## | 0.01 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2023-08 | 1002 | 492 | 97 | 1591 |
## | 948.72 | 494.35 | 147.93 | |
## | 2.99 | 0.01 | 17.53 | |
## | 0.63 | 0.31 | 0.06 | 0.02 |
## | 0.02 | 0.02 | 0.01 | |
## | 0.01 | 0.01 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2023-09 | 929 | 358 | 111 | 1398 |
## | 833.63 | 434.38 | 129.98 | |
## | 10.91 | 13.43 | 2.77 | |
## | 0.66 | 0.26 | 0.08 | 0.02 |
## | 0.02 | 0.01 | 0.01 | |
## | 0.01 | 0.00 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2023-10 | 823 | 337 | 63 | 1223 |
## | 729.28 | 380.01 | 113.71 | |
## | 12.04 | 4.87 | 22.62 | |
## | 0.67 | 0.28 | 0.05 | 0.01 |
## | 0.02 | 0.01 | 0.01 | |
## | 0.01 | 0.00 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2023-11 | 959 | 284 | 91 | 1334 |
## | 795.47 | 414.50 | 124.03 | |
## | 33.62 | 41.08 | 8.80 | |
## | 0.72 | 0.21 | 0.07 | 0.02 |
## | 0.02 | 0.01 | 0.01 | |
## | 0.01 | 0.00 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2023-12 | 178 | 44 | 8 | 230 |
## | 137.15 | 71.46 | 21.39 | |
## | 12.17 | 10.56 | 8.38 | |
## | 0.77 | 0.19 | 0.03 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## Column Total | 52987 | 27610 | 8262 | 88859 |
## | 0.60 | 0.31 | 0.09 | |
## -------------------------------|--------------|--------------|--------------|--------------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 2956 d.f. = 120 p = 0
##
##
## ## Record Type & Attendance relevance
CrossTable(main_processed$Attendance.Day, main_processed$Record.Type, digits=2, fisher=F, chisq=TRUE, expected=TRUE)## Warning in chisq.test(t, correct = FALSE, ...): Chi-squared approximation may
## be incorrect##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 91169
##
##
## | main_processed$Record.Type
## main_processed$Attendance.Day | Attendance | Cancellation | DNA | Row Total |
## ------------------------------|--------------|--------------|--------------|--------------|
## Friday | 11513 | 6862 | 1693 | 20068 |
## | 12171.90 | 6077.48 | 1818.62 | |
## | 35.67 | 101.27 | 8.68 | |
## | 0.57 | 0.34 | 0.08 | 0.22 |
## | 0.21 | 0.25 | 0.20 | |
## | 0.13 | 0.08 | 0.02 | |
## ------------------------------|--------------|--------------|--------------|--------------|
## Monday | 10189 | 5532 | 2011 | 17732 |
## | 10755.04 | 5370.03 | 1606.93 | |
## | 29.79 | 4.89 | 101.61 | |
## | 0.57 | 0.31 | 0.11 | 0.19 |
## | 0.18 | 0.20 | 0.24 | |
## | 0.11 | 0.06 | 0.02 | |
## ------------------------------|--------------|--------------|--------------|--------------|
## Saturday | 3 | 0 | 0 | 3 |
## | 1.82 | 0.91 | 0.27 | |
## | 0.77 | 0.91 | 0.27 | |
## | 1.00 | 0.00 | 0.00 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## ------------------------------|--------------|--------------|--------------|--------------|
## Sunday | 1 | 0 | 0 | 1 |
## | 0.61 | 0.30 | 0.09 | |
## | 0.26 | 0.30 | 0.09 | |
## | 1.00 | 0.00 | 0.00 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## ------------------------------|--------------|--------------|--------------|--------------|
## Thursday | 12117 | 5229 | 1596 | 18942 |
## | 11488.95 | 5736.47 | 1716.58 | |
## | 34.33 | 44.89 | 8.47 | |
## | 0.64 | 0.28 | 0.08 | 0.21 |
## | 0.22 | 0.19 | 0.19 | |
## | 0.13 | 0.06 | 0.02 | |
## ------------------------------|--------------|--------------|--------------|--------------|
## Tuesday | 14283 | 7958 | 2013 | 24254 |
## | 14710.85 | 7345.18 | 2197.97 | |
## | 12.44 | 51.13 | 15.57 | |
## | 0.59 | 0.33 | 0.08 | 0.27 |
## | 0.26 | 0.29 | 0.24 | |
## | 0.16 | 0.09 | 0.02 | |
## ------------------------------|--------------|--------------|--------------|--------------|
## Wednesday | 7191 | 2029 | 949 | 10169 |
## | 6167.83 | 3079.62 | 921.54 | |
## | 169.73 | 358.42 | 0.82 | |
## | 0.71 | 0.20 | 0.09 | 0.11 |
## | 0.13 | 0.07 | 0.11 | |
## | 0.08 | 0.02 | 0.01 | |
## ------------------------------|--------------|--------------|--------------|--------------|
## Column Total | 55297 | 27610 | 8262 | 91169 |
## | 0.61 | 0.30 | 0.09 | |
## ------------------------------|--------------|--------------|--------------|--------------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 980 d.f. = 12 p = 3.2e-202
##
##
## CrossTable(main_processed$Attendance.Month, main_processed$Record.Type, digits=2, fisher=F, chisq=TRUE, expected=TRUE)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 91169
##
##
## | main_processed$Record.Type
## main_processed$Attendance.Month | Attendance | Cancellation | DNA | Row Total |
## --------------------------------|--------------|--------------|--------------|--------------|
## April | 3533 | 2031 | 572 | 6136 |
## | 3721.69 | 1858.25 | 556.06 | |
## | 9.57 | 16.06 | 0.46 | |
## | 0.58 | 0.33 | 0.09 | 0.07 |
## | 0.06 | 0.07 | 0.07 | |
## | 0.04 | 0.02 | 0.01 | |
## --------------------------------|--------------|--------------|--------------|--------------|
## August | 5240 | 2877 | 693 | 8810 |
## | 5343.56 | 2668.06 | 798.39 | |
## | 2.01 | 16.36 | 13.91 | |
## | 0.59 | 0.33 | 0.08 | 0.10 |
## | 0.09 | 0.10 | 0.08 | |
## | 0.06 | 0.03 | 0.01 | |
## --------------------------------|--------------|--------------|--------------|--------------|
## December | 3711 | 2081 | 583 | 6375 |
## | 3866.65 | 1930.63 | 577.72 | |
## | 6.27 | 11.71 | 0.05 | |
## | 0.58 | 0.33 | 0.09 | 0.07 |
## | 0.07 | 0.08 | 0.07 | |
## | 0.04 | 0.02 | 0.01 | |
## --------------------------------|--------------|--------------|--------------|--------------|
## February | 4586 | 1846 | 677 | 7109 |
## | 4311.84 | 2152.92 | 644.24 | |
## | 17.43 | 43.75 | 1.67 | |
## | 0.65 | 0.26 | 0.10 | 0.08 |
## | 0.08 | 0.07 | 0.08 | |
## | 0.05 | 0.02 | 0.01 | |
## --------------------------------|--------------|--------------|--------------|--------------|
## January | 4703 | 1701 | 650 | 7054 |
## | 4278.48 | 2136.26 | 639.25 | |
## | 42.12 | 88.68 | 0.18 | |
## | 0.67 | 0.24 | 0.09 | 0.08 |
## | 0.09 | 0.06 | 0.08 | |
## | 0.05 | 0.02 | 0.01 | |
## --------------------------------|--------------|--------------|--------------|--------------|
## July | 4699 | 2412 | 753 | 7864 |
## | 4769.77 | 2381.57 | 712.66 | |
## | 1.05 | 0.39 | 2.28 | |
## | 0.60 | 0.31 | 0.10 | 0.09 |
## | 0.08 | 0.09 | 0.09 | |
## | 0.05 | 0.03 | 0.01 | |
## --------------------------------|--------------|--------------|--------------|--------------|
## June | 4545 | 2288 | 644 | 7477 |
## | 4535.05 | 2264.37 | 677.59 | |
## | 0.02 | 0.25 | 1.66 | |
## | 0.61 | 0.31 | 0.09 | 0.08 |
## | 0.08 | 0.08 | 0.08 | |
## | 0.05 | 0.03 | 0.01 | |
## --------------------------------|--------------|--------------|--------------|--------------|
## March | 4298 | 2444 | 627 | 7369 |
## | 4469.54 | 2231.66 | 667.80 | |
## | 6.58 | 20.20 | 2.49 | |
## | 0.58 | 0.33 | 0.09 | 0.08 |
## | 0.08 | 0.09 | 0.08 | |
## | 0.05 | 0.03 | 0.01 | |
## --------------------------------|--------------|--------------|--------------|--------------|
## May | 4278 | 2003 | 651 | 6932 |
## | 4204.49 | 2099.32 | 628.20 | |
## | 1.29 | 4.42 | 0.83 | |
## | 0.62 | 0.29 | 0.09 | 0.08 |
## | 0.08 | 0.07 | 0.08 | |
## | 0.05 | 0.02 | 0.01 | |
## --------------------------------|--------------|--------------|--------------|--------------|
## November | 5237 | 2753 | 748 | 8738 |
## | 5299.88 | 2646.25 | 791.86 | |
## | 0.75 | 4.31 | 2.43 | |
## | 0.60 | 0.32 | 0.09 | 0.10 |
## | 0.09 | 0.10 | 0.09 | |
## | 0.06 | 0.03 | 0.01 | |
## --------------------------------|--------------|--------------|--------------|--------------|
## October | 4871 | 2436 | 741 | 8048 |
## | 4881.38 | 2437.29 | 729.33 | |
## | 0.02 | 0.00 | 0.19 | |
## | 0.61 | 0.30 | 0.09 | 0.09 |
## | 0.09 | 0.09 | 0.09 | |
## | 0.05 | 0.03 | 0.01 | |
## --------------------------------|--------------|--------------|--------------|--------------|
## September | 5596 | 2738 | 923 | 9257 |
## | 5614.68 | 2803.43 | 838.90 | |
## | 0.06 | 1.53 | 8.43 | |
## | 0.60 | 0.30 | 0.10 | 0.10 |
## | 0.10 | 0.10 | 0.11 | |
## | 0.06 | 0.03 | 0.01 | |
## --------------------------------|--------------|--------------|--------------|--------------|
## Column Total | 55297 | 27610 | 8262 | 91169 |
## | 0.61 | 0.30 | 0.09 | |
## --------------------------------|--------------|--------------|--------------|--------------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 329 d.f. = 22 p = 1.3e-56
##
##
## CrossTable(main_processed$Attendance.Year, main_processed$Record.Type, digits=2, fisher=F, chisq=TRUE, expected=TRUE)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 91169
##
##
## | main_processed$Record.Type
## main_processed$Attendance.Year | Attendance | Cancellation | DNA | Row Total |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2020 | 13220 | 5686 | 1066 | 19972 |
## | 12113.68 | 6048.40 | 1809.92 | |
## | 101.04 | 21.71 | 305.77 | |
## | 0.66 | 0.28 | 0.05 | 0.22 |
## | 0.24 | 0.21 | 0.13 | |
## | 0.15 | 0.06 | 0.01 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2021 | 14420 | 7180 | 3001 | 24601 |
## | 14921.32 | 7450.27 | 2229.41 | |
## | 16.84 | 9.80 | 267.04 | |
## | 0.59 | 0.29 | 0.12 | 0.27 |
## | 0.26 | 0.26 | 0.36 | |
## | 0.16 | 0.08 | 0.03 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2022 | 13614 | 7149 | 2849 | 23612 |
## | 14321.46 | 7150.76 | 2139.79 | |
## | 34.95 | 0.00 | 235.06 | |
## | 0.58 | 0.30 | 0.12 | 0.26 |
## | 0.25 | 0.26 | 0.34 | |
## | 0.15 | 0.08 | 0.03 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## 2023 | 14043 | 7595 | 1346 | 22984 |
## | 13940.55 | 6960.57 | 2082.88 | |
## | 0.75 | 57.83 | 260.69 | |
## | 0.61 | 0.33 | 0.06 | 0.25 |
## | 0.25 | 0.28 | 0.16 | |
## | 0.15 | 0.08 | 0.01 | |
## -------------------------------|--------------|--------------|--------------|--------------|
## Column Total | 55297 | 27610 | 8262 | 91169 |
## | 0.61 | 0.30 | 0.09 | |
## -------------------------------|--------------|--------------|--------------|--------------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 1311 d.f. = 6 p = 3.5e-280
##
##
## CrossTable(main_processed$Attendance.MonthYear, main_processed$Record.Type, digits=2, fisher=F, chisq=TRUE, expected=TRUE)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 91169
##
##
## | main_processed$Record.Type
## main_processed$Attendance.MonthYear | Attendance | Cancellation | DNA | Row Total |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 01-2020 | 1403 | 113 | 46 | 1562 |
## | 947.40 | 473.04 | 141.55 | |
## | 219.09 | 274.04 | 64.50 | |
## | 0.90 | 0.07 | 0.03 | 0.02 |
## | 0.03 | 0.00 | 0.01 | |
## | 0.02 | 0.00 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 01-2021 | 976 | 508 | 186 | 1670 |
## | 1012.91 | 505.75 | 151.34 | |
## | 1.34 | 0.01 | 7.94 | |
## | 0.58 | 0.30 | 0.11 | 0.02 |
## | 0.02 | 0.02 | 0.02 | |
## | 0.01 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 01-2022 | 1020 | 505 | 253 | 1778 |
## | 1078.42 | 538.46 | 161.13 | |
## | 3.16 | 2.08 | 52.38 | |
## | 0.57 | 0.28 | 0.14 | 0.02 |
## | 0.02 | 0.02 | 0.03 | |
## | 0.01 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 01-2023 | 1304 | 575 | 165 | 2044 |
## | 1239.75 | 619.01 | 185.23 | |
## | 3.33 | 3.13 | 2.21 | |
## | 0.64 | 0.28 | 0.08 | 0.02 |
## | 0.02 | 0.02 | 0.02 | |
## | 0.01 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 02-2020 | 1280 | 265 | 96 | 1641 |
## | 995.32 | 496.97 | 148.71 | |
## | 81.42 | 108.27 | 18.68 | |
## | 0.78 | 0.16 | 0.06 | 0.02 |
## | 0.02 | 0.01 | 0.01 | |
## | 0.01 | 0.00 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 02-2021 | 1117 | 414 | 215 | 1746 |
## | 1059.01 | 528.77 | 158.23 | |
## | 3.18 | 24.91 | 20.37 | |
## | 0.64 | 0.24 | 0.12 | 0.02 |
## | 0.02 | 0.01 | 0.03 | |
## | 0.01 | 0.00 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 02-2022 | 1026 | 620 | 262 | 1908 |
## | 1157.26 | 577.83 | 172.91 | |
## | 14.89 | 3.08 | 45.90 | |
## | 0.54 | 0.32 | 0.14 | 0.02 |
## | 0.02 | 0.02 | 0.03 | |
## | 0.01 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 02-2023 | 1163 | 547 | 104 | 1814 |
## | 1100.25 | 549.36 | 164.39 | |
## | 3.58 | 0.01 | 22.18 | |
## | 0.64 | 0.30 | 0.06 | 0.02 |
## | 0.02 | 0.02 | 0.01 | |
## | 0.01 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 03-2020 | 690 | 686 | 67 | 1443 |
## | 875.23 | 437.00 | 130.77 | |
## | 39.20 | 141.87 | 31.10 | |
## | 0.48 | 0.48 | 0.05 | 0.02 |
## | 0.01 | 0.02 | 0.01 | |
## | 0.01 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 03-2021 | 1332 | 525 | 174 | 2031 |
## | 1231.87 | 615.08 | 184.06 | |
## | 8.14 | 13.19 | 0.55 | |
## | 0.66 | 0.26 | 0.09 | 0.02 |
## | 0.02 | 0.02 | 0.02 | |
## | 0.01 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 03-2022 | 1071 | 671 | 247 | 1989 |
## | 1206.39 | 602.36 | 180.25 | |
## | 15.20 | 7.82 | 24.72 | |
## | 0.54 | 0.34 | 0.12 | 0.02 |
## | 0.02 | 0.02 | 0.03 | |
## | 0.01 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 03-2023 | 1205 | 562 | 139 | 1906 |
## | 1156.05 | 577.22 | 172.73 | |
## | 2.07 | 0.40 | 6.59 | |
## | 0.63 | 0.29 | 0.07 | 0.02 |
## | 0.02 | 0.02 | 0.02 | |
## | 0.01 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 04-2020 | 290 | 562 | 14 | 866 |
## | 525.26 | 262.26 | 78.48 | |
## | 105.37 | 342.57 | 52.98 | |
## | 0.33 | 0.65 | 0.02 | 0.01 |
## | 0.01 | 0.02 | 0.00 | |
## | 0.00 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 04-2021 | 1164 | 450 | 187 | 1801 |
## | 1092.37 | 545.42 | 163.21 | |
## | 4.70 | 16.69 | 3.47 | |
## | 0.65 | 0.25 | 0.10 | 0.02 |
## | 0.02 | 0.02 | 0.02 | |
## | 0.01 | 0.00 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 04-2022 | 1074 | 446 | 273 | 1793 |
## | 1087.51 | 543.00 | 162.49 | |
## | 0.17 | 17.33 | 75.16 | |
## | 0.60 | 0.25 | 0.15 | 0.02 |
## | 0.02 | 0.02 | 0.03 | |
## | 0.01 | 0.00 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 04-2023 | 1005 | 573 | 98 | 1676 |
## | 1016.55 | 507.57 | 151.88 | |
## | 0.13 | 8.44 | 19.12 | |
## | 0.60 | 0.34 | 0.06 | 0.02 |
## | 0.02 | 0.02 | 0.01 | |
## | 0.01 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 05-2020 | 564 | 250 | 33 | 847 |
## | 513.73 | 256.51 | 76.76 | |
## | 4.92 | 0.17 | 24.95 | |
## | 0.67 | 0.30 | 0.04 | 0.01 |
## | 0.01 | 0.01 | 0.00 | |
## | 0.01 | 0.00 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 05-2021 | 1109 | 448 | 227 | 1784 |
## | 1082.05 | 540.27 | 161.67 | |
## | 0.67 | 15.76 | 26.40 | |
## | 0.62 | 0.25 | 0.13 | 0.02 |
## | 0.02 | 0.02 | 0.03 | |
## | 0.01 | 0.00 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 05-2022 | 1215 | 640 | 271 | 2126 |
## | 1289.49 | 643.85 | 192.66 | |
## | 4.30 | 0.02 | 31.85 | |
## | 0.57 | 0.30 | 0.13 | 0.02 |
## | 0.02 | 0.02 | 0.03 | |
## | 0.01 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 05-2023 | 1390 | 665 | 120 | 2175 |
## | 1319.21 | 658.69 | 197.10 | |
## | 3.80 | 0.06 | 30.16 | |
## | 0.64 | 0.31 | 0.06 | 0.02 |
## | 0.03 | 0.02 | 0.01 | |
## | 0.02 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 06-2020 | 1132 | 420 | 43 | 1595 |
## | 967.42 | 483.04 | 144.54 | |
## | 28.00 | 8.23 | 71.34 | |
## | 0.71 | 0.26 | 0.03 | 0.02 |
## | 0.02 | 0.02 | 0.01 | |
## | 0.01 | 0.00 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 06-2021 | 1200 | 504 | 281 | 1985 |
## | 1203.97 | 601.15 | 179.89 | |
## | 0.01 | 15.70 | 56.84 | |
## | 0.60 | 0.25 | 0.14 | 0.02 |
## | 0.02 | 0.02 | 0.03 | |
## | 0.01 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 06-2022 | 1033 | 750 | 220 | 2003 |
## | 1214.89 | 606.60 | 181.52 | |
## | 27.23 | 33.90 | 8.16 | |
## | 0.52 | 0.37 | 0.11 | 0.02 |
## | 0.02 | 0.03 | 0.03 | |
## | 0.01 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 06-2023 | 1180 | 614 | 100 | 1894 |
## | 1148.77 | 573.59 | 171.64 | |
## | 0.85 | 2.85 | 29.90 | |
## | 0.62 | 0.32 | 0.05 | 0.02 |
## | 0.02 | 0.02 | 0.01 | |
## | 0.01 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 07-2020 | 1472 | 478 | 148 | 2098 |
## | 1272.51 | 635.37 | 190.13 | |
## | 31.28 | 38.98 | 9.33 | |
## | 0.70 | 0.23 | 0.07 | 0.02 |
## | 0.03 | 0.02 | 0.02 | |
## | 0.02 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 07-2021 | 1198 | 641 | 327 | 2166 |
## | 1313.75 | 655.96 | 196.29 | |
## | 10.20 | 0.34 | 87.04 | |
## | 0.55 | 0.30 | 0.15 | 0.02 |
## | 0.02 | 0.02 | 0.04 | |
## | 0.01 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 07-2022 | 991 | 636 | 181 | 1808 |
## | 1096.61 | 547.54 | 163.85 | |
## | 10.17 | 14.29 | 1.80 | |
## | 0.55 | 0.35 | 0.10 | 0.02 |
## | 0.02 | 0.02 | 0.02 | |
## | 0.01 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 07-2023 | 1038 | 657 | 97 | 1792 |
## | 1086.91 | 542.70 | 162.40 | |
## | 2.20 | 24.07 | 26.33 | |
## | 0.58 | 0.37 | 0.05 | 0.02 |
## | 0.02 | 0.02 | 0.01 | |
## | 0.01 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 08-2020 | 1336 | 491 | 112 | 1939 |
## | 1176.07 | 587.21 | 175.72 | |
## | 21.75 | 15.76 | 23.11 | |
## | 0.69 | 0.25 | 0.06 | 0.02 |
## | 0.02 | 0.02 | 0.01 | |
## | 0.01 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 08-2021 | 1412 | 993 | 235 | 2640 |
## | 1601.25 | 799.51 | 239.24 | |
## | 22.37 | 46.83 | 0.08 | |
## | 0.53 | 0.38 | 0.09 | 0.03 |
## | 0.03 | 0.04 | 0.03 | |
## | 0.02 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 08-2022 | 1225 | 594 | 234 | 2053 |
## | 1245.21 | 621.74 | 186.05 | |
## | 0.33 | 1.24 | 12.36 | |
## | 0.60 | 0.29 | 0.11 | 0.02 |
## | 0.02 | 0.02 | 0.03 | |
## | 0.01 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 08-2023 | 1267 | 799 | 112 | 2178 |
## | 1321.03 | 659.59 | 197.38 | |
## | 2.21 | 29.46 | 36.93 | |
## | 0.58 | 0.37 | 0.05 | 0.02 |
## | 0.02 | 0.03 | 0.01 | |
## | 0.01 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 09-2020 | 1496 | 547 | 164 | 2207 |
## | 1338.62 | 668.38 | 200.00 | |
## | 18.50 | 22.04 | 6.48 | |
## | 0.68 | 0.25 | 0.07 | 0.02 |
## | 0.03 | 0.02 | 0.02 | |
## | 0.02 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 09-2021 | 1475 | 859 | 365 | 2699 |
## | 1637.03 | 817.38 | 244.59 | |
## | 16.04 | 2.12 | 59.28 | |
## | 0.55 | 0.32 | 0.14 | 0.03 |
## | 0.03 | 0.03 | 0.04 | |
## | 0.02 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 09-2022 | 1419 | 558 | 261 | 2238 |
## | 1357.42 | 677.77 | 202.81 | |
## | 2.79 | 21.16 | 16.69 | |
## | 0.63 | 0.25 | 0.12 | 0.02 |
## | 0.03 | 0.02 | 0.03 | |
## | 0.02 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 09-2023 | 1206 | 774 | 133 | 2113 |
## | 1281.60 | 639.91 | 191.49 | |
## | 4.46 | 28.10 | 17.86 | |
## | 0.57 | 0.37 | 0.06 | 0.02 |
## | 0.02 | 0.03 | 0.02 | |
## | 0.01 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 10-2020 | 1224 | 657 | 133 | 2014 |
## | 1221.56 | 609.93 | 182.51 | |
## | 0.00 | 3.63 | 13.43 | |
## | 0.61 | 0.33 | 0.07 | 0.02 |
## | 0.02 | 0.02 | 0.02 | |
## | 0.01 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 10-2021 | 1251 | 615 | 265 | 2131 |
## | 1292.52 | 645.36 | 193.12 | |
## | 1.33 | 1.43 | 26.76 | |
## | 0.59 | 0.29 | 0.12 | 0.02 |
## | 0.02 | 0.02 | 0.03 | |
## | 0.01 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 10-2022 | 1170 | 600 | 225 | 1995 |
## | 1210.03 | 604.17 | 180.79 | |
## | 1.32 | 0.03 | 10.81 | |
## | 0.59 | 0.30 | 0.11 | 0.02 |
## | 0.02 | 0.02 | 0.03 | |
## | 0.01 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 10-2023 | 1226 | 564 | 118 | 1908 |
## | 1157.26 | 577.83 | 172.91 | |
## | 4.08 | 0.33 | 17.44 | |
## | 0.64 | 0.30 | 0.06 | 0.02 |
## | 0.02 | 0.02 | 0.01 | |
## | 0.01 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 11-2020 | 1307 | 671 | 99 | 2077 |
## | 1259.77 | 629.01 | 188.22 | |
## | 1.77 | 2.80 | 42.29 | |
## | 0.63 | 0.32 | 0.05 | 0.02 |
## | 0.02 | 0.02 | 0.01 | |
## | 0.01 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 11-2021 | 1299 | 700 | 300 | 2299 |
## | 1394.42 | 696.24 | 208.34 | |
## | 6.53 | 0.02 | 40.32 | |
## | 0.57 | 0.30 | 0.13 | 0.03 |
## | 0.02 | 0.03 | 0.04 | |
## | 0.01 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 11-2022 | 1451 | 699 | 255 | 2405 |
## | 1458.71 | 728.34 | 217.95 | |
## | 0.04 | 1.18 | 6.30 | |
## | 0.60 | 0.29 | 0.11 | 0.03 |
## | 0.03 | 0.03 | 0.03 | |
## | 0.02 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 11-2023 | 1180 | 683 | 94 | 1957 |
## | 1186.98 | 592.67 | 177.35 | |
## | 0.04 | 13.77 | 39.17 | |
## | 0.60 | 0.35 | 0.05 | 0.02 |
## | 0.02 | 0.02 | 0.01 | |
## | 0.01 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 12-2020 | 1026 | 546 | 111 | 1683 |
## | 1020.79 | 509.69 | 152.52 | |
## | 0.03 | 2.59 | 11.30 | |
## | 0.61 | 0.32 | 0.07 | 0.02 |
## | 0.02 | 0.02 | 0.01 | |
## | 0.01 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 12-2021 | 887 | 523 | 239 | 1649 |
## | 1000.17 | 499.39 | 149.44 | |
## | 12.81 | 1.12 | 53.68 | |
## | 0.54 | 0.32 | 0.14 | 0.02 |
## | 0.02 | 0.02 | 0.03 | |
## | 0.01 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 12-2022 | 919 | 430 | 167 | 1516 |
## | 919.50 | 459.11 | 137.38 | |
## | 0.00 | 1.85 | 6.38 | |
## | 0.61 | 0.28 | 0.11 | 0.02 |
## | 0.02 | 0.02 | 0.02 | |
## | 0.01 | 0.00 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## 12-2023 | 879 | 582 | 66 | 1527 |
## | 926.18 | 462.44 | 138.38 | |
## | 2.40 | 30.91 | 37.86 | |
## | 0.58 | 0.38 | 0.04 | 0.02 |
## | 0.02 | 0.02 | 0.01 | |
## | 0.01 | 0.01 | 0.00 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
## Column Total | 55297 | 27610 | 8262 | 91169 |
## | 0.61 | 0.30 | 0.09 | |
## ------------------------------------|--------------|--------------|--------------|--------------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 3422 d.f. = 94 p = 0
##
##
## CrossTable(main_processed$Attendance.Type_recode, main_processed$Record.Type, digits=2, fisher=F, chisq=TRUE, expected=TRUE)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 91169
##
##
## | main_processed$Record.Type
## main_processed$Attendance.Type_recode | Attendance | Cancellation | DNA | Row Total |
## --------------------------------------|--------------|--------------|--------------|--------------|
## NEW | 30503 | 7191 | 4094 | 41788 |
## | 25345.80 | 12655.25 | 3786.95 | |
## | 1049.35 | 2359.34 | 24.90 | |
## | 0.73 | 0.17 | 0.10 | 0.46 |
## | 0.55 | 0.26 | 0.50 | |
## | 0.33 | 0.08 | 0.04 | |
## --------------------------------------|--------------|--------------|--------------|--------------|
## RETURN | 21183 | 20419 | 4168 | 45770 |
## | 27761.01 | 13861.18 | 4147.81 | |
## | 1558.67 | 3102.55 | 0.10 | |
## | 0.46 | 0.45 | 0.09 | 0.50 |
## | 0.38 | 0.74 | 0.50 | |
## | 0.23 | 0.22 | 0.05 | |
## --------------------------------------|--------------|--------------|--------------|--------------|
## VIRTUAL RETURN | 3001 | 0 | 0 | 3001 |
## | 1820.21 | 908.84 | 271.96 | |
## | 766.00 | 908.84 | 271.96 | |
## | 1.00 | 0.00 | 0.00 | 0.03 |
## | 0.05 | 0.00 | 0.00 | |
## | 0.03 | 0.00 | 0.00 | |
## --------------------------------------|--------------|--------------|--------------|--------------|
## VIRTUAL(New_Return) | 610 | 0 | 0 | 610 |
## | 369.99 | 184.73 | 55.28 | |
## | 155.70 | 184.73 | 55.28 | |
## | 1.00 | 0.00 | 0.00 | 0.01 |
## | 0.01 | 0.00 | 0.00 | |
## | 0.01 | 0.00 | 0.00 | |
## --------------------------------------|--------------|--------------|--------------|--------------|
## Column Total | 55297 | 27610 | 8262 | 91169 |
## | 0.61 | 0.30 | 0.09 | |
## --------------------------------------|--------------|--------------|--------------|--------------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 10437 d.f. = 6 p = 0
##
##
## ## Record Type & difference of days
bartlett.test(main_processed$daysDiff_attendanceAppoint,main_processed$Record.Type)##
## Bartlett test of homogeneity of variances
##
## data: main_processed$daysDiff_attendanceAppoint and main_processed$Record.Type
## Bartlett's K-squared = 400, df = 1, p-value <2e-16kruskal.test(main_processed$daysDiff_attendanceAppoint ~ main_processed$Record.Type, data=main_processed)##
## Kruskal-Wallis rank sum test
##
## data: main_processed$daysDiff_attendanceAppoint by main_processed$Record.Type
## Kruskal-Wallis chi-squared = 21, df = 1, p-value = 4e-06dunn_result <- dunn.test(main_processed$daysDiff_attendanceAppoint, g=main_processed$Record.Type, method="bonferroni")## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 21.4254, df = 1, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | Cancella
## ---------+-----------
## DNA | 4.628757
## | 0.0000*
##
## alpha = 0.05
## Reject Ho if p <= alpha/2bartlett.test(main_processed$daysDiff_attendanceBooked,main_processed$Record.Type)##
## Bartlett test of homogeneity of variances
##
## data: main_processed$daysDiff_attendanceBooked and main_processed$Record.Type
## Bartlett's K-squared = 396, df = 2, p-value <2e-16kruskal.test(main_processed$daysDiff_attendanceBooked ~ main_processed$Record.Type, data=main_processed)##
## Kruskal-Wallis rank sum test
##
## data: main_processed$daysDiff_attendanceBooked by main_processed$Record.Type
## Kruskal-Wallis chi-squared = 393, df = 2, p-value <2e-16dunn_result <- dunn.test(main_processed$daysDiff_attendanceBooked, g=main_processed$Record.Type, method="bonferroni")## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 392.5006, df = 2, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | Attendan Cancella
## ---------+----------------------
## Cancella | 19.77196
## | 0.0000*
## |
## DNA | 5.473924 -6.539591
## | 0.0000* 0.0000*
##
## alpha = 0.05
## Reject Ho if p <= alpha/2## Record Type & Patient characteristics relevance
CrossTable(main_processed$Gender, main_processed$Record.Type, digits=2, fisher=F, chisq=TRUE, expected=TRUE)## Warning in chisq.test(t, correct = FALSE, ...): Chi-squared approximation may
## be incorrect##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 91169
##
##
## | main_processed$Record.Type
## main_processed$Gender | Attendance | Cancellation | DNA | Row Total |
## ----------------------|--------------|--------------|--------------|--------------|
## Female | 54247 | 27241 | 8023 | 89511 |
## | 54291.37 | 27107.88 | 8111.75 | |
## | 0.04 | 0.65 | 0.97 | |
## | 0.61 | 0.30 | 0.09 | 0.98 |
## | 0.98 | 0.99 | 0.97 | |
## | 0.60 | 0.30 | 0.09 | |
## ----------------------|--------------|--------------|--------------|--------------|
## Male | 1048 | 367 | 238 | 1653 |
## | 1002.60 | 500.60 | 149.80 | |
## | 2.06 | 35.66 | 51.93 | |
## | 0.63 | 0.22 | 0.14 | 0.02 |
## | 0.02 | 0.01 | 0.03 | |
## | 0.01 | 0.00 | 0.00 | |
## ----------------------|--------------|--------------|--------------|--------------|
## Unknown | 2 | 2 | 1 | 5 |
## | 3.03 | 1.51 | 0.45 | |
## | 0.35 | 0.16 | 0.66 | |
## | 0.40 | 0.40 | 0.20 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## ----------------------|--------------|--------------|--------------|--------------|
## Column Total | 55297 | 27610 | 8262 | 91169 |
## | 0.61 | 0.30 | 0.09 | |
## ----------------------|--------------|--------------|--------------|--------------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 92 d.f. = 4 p = 3.9e-19
##
##
## CrossTable(main_processed$Age.at.Attendance.Cat.HSE, main_processed$Record.Type, digits=2, fisher=F, chisq=TRUE, expected=TRUE) #age group## Warning in chisq.test(t, correct = FALSE, ...): Chi-squared approximation may
## be incorrect##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 91169
##
##
## | main_processed$Record.Type
## main_processed$Age.at.Attendance.Cat.HSE | Attendance | Cancellation | DNA | Row Total |
## -----------------------------------------|--------------|--------------|--------------|--------------|
## 0 - 4 | 0 | 7 | 5 | 12 |
## | 7.28 | 3.63 | 1.09 | |
## | 7.28 | 3.12 | 14.08 | |
## | 0.00 | 0.58 | 0.42 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## -----------------------------------------|--------------|--------------|--------------|--------------|
## 10 - 14 | 103 | 38 | 11 | 152 |
## | 92.19 | 46.03 | 13.77 | |
## | 1.27 | 1.40 | 0.56 | |
## | 0.68 | 0.25 | 0.07 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## -----------------------------------------|--------------|--------------|--------------|--------------|
## 15 - 24 | 2035 | 742 | 480 | 3257 |
## | 1975.48 | 986.36 | 295.16 | |
## | 1.79 | 60.54 | 115.76 | |
## | 0.62 | 0.23 | 0.15 | 0.04 |
## | 0.04 | 0.03 | 0.06 | |
## | 0.02 | 0.01 | 0.01 | |
## -----------------------------------------|--------------|--------------|--------------|--------------|
## 25 - 34 | 5909 | 2318 | 1390 | 9617 |
## | 5833.03 | 2912.45 | 871.52 | |
## | 0.99 | 121.33 | 308.45 | |
## | 0.61 | 0.24 | 0.14 | 0.11 |
## | 0.11 | 0.08 | 0.17 | |
## | 0.06 | 0.03 | 0.02 | |
## -----------------------------------------|--------------|--------------|--------------|--------------|
## 35 - 44 | 11825 | 5368 | 1842 | 19035 |
## | 11545.35 | 5764.64 | 1725.01 | |
## | 6.77 | 27.29 | 7.93 | |
## | 0.62 | 0.28 | 0.10 | 0.21 |
## | 0.21 | 0.19 | 0.22 | |
## | 0.13 | 0.06 | 0.02 | |
## -----------------------------------------|--------------|--------------|--------------|--------------|
## 45 - 54 | 13179 | 6739 | 1693 | 21611 |
## | 13107.78 | 6544.77 | 1958.45 | |
## | 0.39 | 5.76 | 35.98 | |
## | 0.61 | 0.31 | 0.08 | 0.24 |
## | 0.24 | 0.24 | 0.20 | |
## | 0.14 | 0.07 | 0.02 | |
## -----------------------------------------|--------------|--------------|--------------|--------------|
## 55 - 64 | 10736 | 6065 | 1350 | 18151 |
## | 11009.18 | 5496.92 | 1644.90 | |
## | 6.78 | 58.71 | 52.87 | |
## | 0.59 | 0.33 | 0.07 | 0.20 |
## | 0.19 | 0.22 | 0.16 | |
## | 0.12 | 0.07 | 0.01 | |
## -----------------------------------------|--------------|--------------|--------------|--------------|
## 65 - 74 | 7540 | 4130 | 887 | 12557 |
## | 7616.23 | 3802.81 | 1137.95 | |
## | 0.76 | 28.15 | 55.34 | |
## | 0.60 | 0.33 | 0.07 | 0.14 |
## | 0.14 | 0.15 | 0.11 | |
## | 0.08 | 0.05 | 0.01 | |
## -----------------------------------------|--------------|--------------|--------------|--------------|
## 75 - 84 | 3259 | 1647 | 434 | 5340 |
## | 3238.89 | 1617.19 | 483.93 | |
## | 0.12 | 0.55 | 5.15 | |
## | 0.61 | 0.31 | 0.08 | 0.06 |
## | 0.06 | 0.06 | 0.05 | |
## | 0.04 | 0.02 | 0.00 | |
## -----------------------------------------|--------------|--------------|--------------|--------------|
## 85 >= | 711 | 556 | 170 | 1437 |
## | 871.59 | 435.19 | 130.23 | |
## | 29.59 | 33.54 | 12.15 | |
## | 0.49 | 0.39 | 0.12 | 0.02 |
## | 0.01 | 0.02 | 0.02 | |
## | 0.01 | 0.01 | 0.00 | |
## -----------------------------------------|--------------|--------------|--------------|--------------|
## Column Total | 55297 | 27610 | 8262 | 91169 |
## | 0.61 | 0.30 | 0.09 | |
## -----------------------------------------|--------------|--------------|--------------|--------------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 1004 d.f. = 18 p = 8e-202
##
##
## bartlett.test(main_processed$Age.at.Attendance,main_processed$Record.Type) # unequal variances##
## Bartlett test of homogeneity of variances
##
## data: main_processed$Age.at.Attendance and main_processed$Record.Type
## Bartlett's K-squared = 121, df = 2, p-value <2e-16kruskal.test(main_processed$Age.at.Attendance ~ main_processed$Record.Type, data=main_processed)##
## Kruskal-Wallis rank sum test
##
## data: main_processed$Age.at.Attendance by main_processed$Record.Type
## Kruskal-Wallis chi-squared = 629, df = 2, p-value <2e-16dunn_result <- dunn.test(main_processed$Age.at.Attendance, g=main_processed$Record.Type, method="bonferroni") #run library(dunn.test) for this test## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 629.039, df = 2, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | Attendan Cancella
## ---------+----------------------
## Cancella | -15.87345
## | 0.0000*
## |
## DNA | 15.69074 24.08614
## | 0.0000* 0.0000*
##
## alpha = 0.05
## Reject Ho if p <= alpha/2CrossTable(main_processed$Area.of.Residence_recode, main_processed$Record.Type, digits=2, fisher=F, chisq=TRUE, expected=TRUE)## Warning in chisq.test(t, correct = FALSE, ...): Chi-squared approximation may
## be incorrect##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 91169
##
##
## | main_processed$Record.Type
## main_processed$Area.of.Residence_recode | Attendance | Cancellation | DNA | Row Total |
## ---------------------------------------------|--------------|--------------|--------------|--------------|
## DUBLIN NTH | 25186 | 12203 | 3910 | 41299 |
## | 25049.20 | 12507.16 | 3742.64 | |
## | 0.75 | 7.40 | 7.48 | |
## | 0.61 | 0.30 | 0.09 | 0.45 |
## | 0.46 | 0.44 | 0.47 | |
## | 0.28 | 0.13 | 0.04 | |
## ---------------------------------------------|--------------|--------------|--------------|--------------|
## DUBLIN STH | 1374 | 661 | 303 | 2338 |
## | 1418.07 | 708.05 | 211.88 | |
## | 1.37 | 3.13 | 39.19 | |
## | 0.59 | 0.28 | 0.13 | 0.03 |
## | 0.02 | 0.02 | 0.04 | |
## | 0.02 | 0.01 | 0.00 | |
## ---------------------------------------------|--------------|--------------|--------------|--------------|
## EASTERN & MIDLAND REGION (excl.Dublin,Meath) | 12594 | 6689 | 1785 | 21068 |
## | 12778.44 | 6380.32 | 1909.24 | |
## | 2.66 | 14.93 | 8.09 | |
## | 0.60 | 0.32 | 0.08 | 0.23 |
## | 0.23 | 0.24 | 0.22 | |
## | 0.14 | 0.07 | 0.02 | |
## ---------------------------------------------|--------------|--------------|--------------|--------------|
## Meath | 8955 | 4223 | 1246 | 14424 |
## | 8748.63 | 4368.22 | 1307.14 | |
## | 4.87 | 4.83 | 2.86 | |
## | 0.62 | 0.29 | 0.09 | 0.16 |
## | 0.16 | 0.15 | 0.15 | |
## | 0.10 | 0.05 | 0.01 | |
## ---------------------------------------------|--------------|--------------|--------------|--------------|
## NORTHERN WESTERN REGION | 5948 | 3002 | 816 | 9766 |
## | 5923.40 | 2957.58 | 885.02 | |
## | 0.10 | 0.67 | 5.38 | |
## | 0.61 | 0.31 | 0.08 | 0.11 |
## | 0.11 | 0.11 | 0.10 | |
## | 0.07 | 0.03 | 0.01 | |
## ---------------------------------------------|--------------|--------------|--------------|--------------|
## OUTSIDE IRELAND | 10 | 13 | 1 | 24 |
## | 14.56 | 7.27 | 2.17 | |
## | 1.43 | 4.52 | 0.63 | |
## | 0.42 | 0.54 | 0.04 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## ---------------------------------------------|--------------|--------------|--------------|--------------|
## SOUTHERN REGION | 1127 | 762 | 174 | 2063 |
## | 1251.28 | 624.77 | 186.96 | |
## | 12.34 | 30.14 | 0.90 | |
## | 0.55 | 0.37 | 0.08 | 0.02 |
## | 0.02 | 0.03 | 0.02 | |
## | 0.01 | 0.01 | 0.00 | |
## ---------------------------------------------|--------------|--------------|--------------|--------------|
## UNKNOWN | 103 | 57 | 27 | 187 |
## | 113.42 | 56.63 | 16.95 | |
## | 0.96 | 0.00 | 5.96 | |
## | 0.55 | 0.30 | 0.14 | 0.00 |
## | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.00 | |
## ---------------------------------------------|--------------|--------------|--------------|--------------|
## Column Total | 55297 | 27610 | 8262 | 91169 |
## | 0.61 | 0.30 | 0.09 | |
## ---------------------------------------------|--------------|--------------|--------------|--------------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 161 d.f. = 14 p = 5.4e-27
##
##
## CrossTable(main_processed$addressDiff, main_processed$Record.Type, digits=2, fisher=F, chisq=TRUE, expected=TRUE)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 91169
##
##
## | main_processed$Record.Type
## main_processed$addressDiff | Attendance | Cancellation | DNA | Row Total |
## ---------------------------|--------------|--------------|--------------|--------------|
## 0 | 45893 | 22567 | 6397 | 74857 |
## | 45403.23 | 22670.01 | 6783.76 | |
## | 5.28 | 0.47 | 22.05 | |
## | 0.61 | 0.30 | 0.09 | 0.82 |
## | 0.83 | 0.82 | 0.77 | |
## | 0.50 | 0.25 | 0.07 | |
## ---------------------------|--------------|--------------|--------------|--------------|
## 1 | 9404 | 5043 | 1865 | 16312 |
## | 9893.77 | 4939.99 | 1478.24 | |
## | 24.24 | 2.15 | 101.19 | |
## | 0.58 | 0.31 | 0.11 | 0.18 |
## | 0.17 | 0.18 | 0.23 | |
## | 0.10 | 0.06 | 0.02 | |
## ---------------------------|--------------|--------------|--------------|--------------|
## Column Total | 55297 | 27610 | 8262 | 91169 |
## | 0.61 | 0.30 | 0.09 | |
## ---------------------------|--------------|--------------|--------------|--------------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 155 d.f. = 2 p = 1.8e-34
##
##
## Cancellation & DNA correlation
- Clinic relevance: Consultant, Insurance scheme, Eligibility, Hospital catchment, Clinic type, Clinic code, Referral source, Booking Type
- Date relevance: booked Day, booked Month-Year, appointment Day, appointment Month-Year, daysDiff_AppointBooked
- Patient relevance: Gender, Age at attendance, Area of residence, address difference
- Cancellation group: Reason for cancellation, Rebooked indicator
# cancellation (No.Cancels=1) & DNA (No.Cancels=0)
## clinic relevance
CrossTable(main_processed$Clinic.Type_recode, main_processed$No.Cancels, digits=2, fisher=F, chisq=TRUE, expected=TRUE)## Warning in chisq.test(t, correct = FALSE, ...): Chi-squared approximation may
## be incorrect##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 35872
##
##
## | main_processed$No.Cancels
## main_processed$Clinic.Type_recode | 0 | 1 | Row Total |
## ----------------------------------|-----------|-----------|-----------|
## FRA | 13 | 65 | 78 |
## | 17.96 | 60.04 | |
## | 1.37 | 0.41 | |
## | 0.17 | 0.83 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ----------------------------------|-----------|-----------|-----------|
## GEN | 9 | 1 | 10 |
## | 2.30 | 7.70 | |
## | 19.47 | 5.83 | |
## | 0.90 | 0.10 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ----------------------------------|-----------|-----------|-----------|
## MED | 1933 | 2288 | 4221 |
## | 972.18 | 3248.82 | |
## | 949.60 | 284.16 | |
## | 0.46 | 0.54 | 0.12 |
## | 0.23 | 0.08 | |
## | 0.05 | 0.06 | |
## ----------------------------------|-----------|-----------|-----------|
## SBC | 3299 | 19214 | 22513 |
## | 5185.17 | 17327.83 | |
## | 686.12 | 205.31 | |
## | 0.15 | 0.85 | 0.63 |
## | 0.40 | 0.70 | |
## | 0.09 | 0.54 | |
## ----------------------------------|-----------|-----------|-----------|
## TRI | 3008 | 6042 | 9050 |
## | 2084.39 | 6965.61 | |
## | 409.26 | 122.47 | |
## | 0.33 | 0.67 | 0.25 |
## | 0.36 | 0.22 | |
## | 0.08 | 0.17 | |
## ----------------------------------|-----------|-----------|-----------|
## Column Total | 8262 | 27610 | 35872 |
## | 0.23 | 0.77 | |
## ----------------------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 2684 d.f. = 4 p = 0
##
##
## CrossTable(main_processed$Clinic.Code, main_processed$No.Cancels, digits=2, fisher=F, chisq=TRUE, expected=TRUE)## Warning in chisq.test(t, correct = FALSE, ...): Chi-squared approximation may
## be incorrect##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 35872
##
##
## | main_processed$No.Cancels
## main_processed$Clinic.Code | 0 | 1 | Row Total |
## ---------------------------|-----------|-----------|-----------|
## 424 | 910 | 3758 | 4668 |
## | 1075.13 | 3592.87 | |
## | 25.36 | 7.59 | |
## | 0.19 | 0.81 | 0.13 |
## | 0.11 | 0.14 | |
## | 0.03 | 0.10 | |
## ---------------------------|-----------|-----------|-----------|
## 440 | 9 | 1 | 10 |
## | 2.30 | 7.70 | |
## | 19.47 | 5.83 | |
## | 0.90 | 0.10 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ---------------------------|-----------|-----------|-----------|
## 441 | 1022 | 4725 | 5747 |
## | 1323.64 | 4423.36 | |
## | 68.74 | 20.57 | |
## | 0.18 | 0.82 | 0.16 |
## | 0.12 | 0.17 | |
## | 0.03 | 0.13 | |
## ---------------------------|-----------|-----------|-----------|
## 526 | 405 | 526 | 931 |
## | 214.43 | 716.57 | |
## | 169.37 | 50.68 | |
## | 0.44 | 0.56 | 0.03 |
## | 0.05 | 0.02 | |
## | 0.01 | 0.01 | |
## ---------------------------|-----------|-----------|-----------|
## 527 | 559 | 1408 | 1967 |
## | 453.04 | 1513.96 | |
## | 24.78 | 7.42 | |
## | 0.28 | 0.72 | 0.05 |
## | 0.07 | 0.05 | |
## | 0.02 | 0.04 | |
## ---------------------------|-----------|-----------|-----------|
## 757 | 652 | 5477 | 6129 |
## | 1411.62 | 4717.38 | |
## | 408.77 | 122.32 | |
## | 0.11 | 0.89 | 0.17 |
## | 0.08 | 0.20 | |
## | 0.02 | 0.15 | |
## ---------------------------|-----------|-----------|-----------|
## 768 | 1 | 7 | 8 |
## | 1.84 | 6.16 | |
## | 0.39 | 0.12 | |
## | 0.12 | 0.88 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ---------------------------|-----------|-----------|-----------|
## 771 | 759 | 1658 | 2417 |
## | 556.68 | 1860.32 | |
## | 73.53 | 22.00 | |
## | 0.31 | 0.69 | 0.07 |
## | 0.09 | 0.06 | |
## | 0.02 | 0.05 | |
## ---------------------------|-----------|-----------|-----------|
## 932 | 381 | 581 | 962 |
## | 221.57 | 740.43 | |
## | 114.72 | 34.33 | |
## | 0.40 | 0.60 | 0.03 |
## | 0.05 | 0.02 | |
## | 0.01 | 0.02 | |
## ---------------------------|-----------|-----------|-----------|
## 933 | 400 | 565 | 965 |
## | 222.26 | 742.74 | |
## | 142.14 | 42.53 | |
## | 0.41 | 0.59 | 0.03 |
## | 0.05 | 0.02 | |
## | 0.01 | 0.02 | |
## ---------------------------|-----------|-----------|-----------|
## 934 | 335 | 361 | 696 |
## | 160.30 | 535.70 | |
## | 190.39 | 56.97 | |
## | 0.48 | 0.52 | 0.02 |
## | 0.04 | 0.01 | |
## | 0.01 | 0.01 | |
## ---------------------------|-----------|-----------|-----------|
## 1132 | 551 | 621 | 1172 |
## | 269.93 | 902.07 | |
## | 292.66 | 87.57 | |
## | 0.47 | 0.53 | 0.03 |
## | 0.07 | 0.02 | |
## | 0.02 | 0.02 | |
## ---------------------------|-----------|-----------|-----------|
## 1133 | 581 | 4704 | 5285 |
## | 1217.24 | 4067.76 | |
## | 332.55 | 99.51 | |
## | 0.11 | 0.89 | 0.15 |
## | 0.07 | 0.17 | |
## | 0.02 | 0.13 | |
## ---------------------------|-----------|-----------|-----------|
## 1134 | 1032 | 2154 | 3186 |
## | 733.80 | 2452.20 | |
## | 121.19 | 36.26 | |
## | 0.32 | 0.68 | 0.09 |
## | 0.12 | 0.08 | |
## | 0.03 | 0.06 | |
## ---------------------------|-----------|-----------|-----------|
## 1187 | 12 | 58 | 70 |
## | 16.12 | 53.88 | |
## | 1.05 | 0.32 | |
## | 0.17 | 0.83 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ---------------------------|-----------|-----------|-----------|
## 1333 | 134 | 550 | 684 |
## | 157.54 | 526.46 | |
## | 3.52 | 1.05 | |
## | 0.20 | 0.80 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.00 | 0.02 | |
## ---------------------------|-----------|-----------|-----------|
## 1334 | 253 | 296 | 549 |
## | 126.45 | 422.55 | |
## | 126.66 | 37.90 | |
## | 0.46 | 0.54 | 0.02 |
## | 0.03 | 0.01 | |
## | 0.01 | 0.01 | |
## ---------------------------|-----------|-----------|-----------|
## 1335 | 266 | 160 | 426 |
## | 98.12 | 327.88 | |
## | 287.26 | 85.96 | |
## | 0.62 | 0.38 | 0.01 |
## | 0.03 | 0.01 | |
## | 0.01 | 0.00 | |
## ---------------------------|-----------|-----------|-----------|
## Column Total | 8262 | 27610 | 35872 |
## | 0.23 | 0.77 | |
## ---------------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 3122 d.f. = 17 p = 0
##
##
## CrossTable(main_processed$NurseFlag, main_processed$No.Cancels, digits=2, fisher=F, chisq=TRUE, expected=TRUE)## Warning in chisq.test(t, correct = TRUE, ...): Chi-squared approximation may be
## incorrect
## Warning in chisq.test(t, correct = TRUE, ...): Chi-squared approximation may be
## incorrect##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 35872
##
##
## | main_processed$No.Cancels
## main_processed$NurseFlag | 0 | 1 | Row Total |
## -------------------------|-----------|-----------|-----------|
## N | 8261 | 27603 | 35864 |
## | 8260.16 | 27603.84 | |
## | 0.00 | 0.00 | |
## | 0.23 | 0.77 | 1.00 |
## | 1.00 | 1.00 | |
## | 0.23 | 0.77 | |
## -------------------------|-----------|-----------|-----------|
## Y | 1 | 7 | 8 |
## | 1.84 | 6.16 | |
## | 0.39 | 0.12 | |
## | 0.12 | 0.88 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## -------------------------|-----------|-----------|-----------|
## Column Total | 8262 | 27610 | 35872 |
## | 0.23 | 0.77 | |
## -------------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 0.5 d.f. = 1 p = 0.48
##
## Pearson's Chi-squared test with Yates' continuity correction
## ------------------------------------------------------------
## Chi^2 = 0.083 d.f. = 1 p = 0.77
##
## CrossTable(main_processed$Referral.Source_recode, main_processed$No.Cancels, digits=2, fisher=F, chisq=TRUE, expected=TRUE)## Warning in chisq.test(t, correct = FALSE, ...): Chi-squared approximation may
## be incorrect##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 35872
##
##
## | main_processed$No.Cancels
## main_processed$Referral.Source_recode | 0 | 1 | Row Total |
## --------------------------------------|-----------|-----------|-----------|
## BREAST CHECK | 56 | 801 | 857 |
## | 197.38 | 659.62 | |
## | 101.27 | 30.30 | |
## | 0.07 | 0.93 | 0.02 |
## | 0.01 | 0.03 | |
## | 0.00 | 0.02 | |
## --------------------------------------|-----------|-----------|-----------|
## CLINIC | 3984 | 18845 | 22829 |
## | 5257.95 | 17571.05 | |
## | 308.67 | 92.36 | |
## | 0.17 | 0.83 | 0.64 |
## | 0.48 | 0.68 | |
## | 0.11 | 0.53 | |
## --------------------------------------|-----------|-----------|-----------|
## Elsew of Mater | 4 | 18 | 22 |
## | 5.07 | 16.93 | |
## | 0.22 | 0.07 | |
## | 0.18 | 0.82 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## --------------------------------------|-----------|-----------|-----------|
## Elsew outside Mater | 75 | 212 | 287 |
## | 66.10 | 220.90 | |
## | 1.20 | 0.36 | |
## | 0.26 | 0.74 | 0.01 |
## | 0.01 | 0.01 | |
## | 0.00 | 0.01 | |
## --------------------------------------|-----------|-----------|-----------|
## EMERGENCY DEPT | 3 | 3 | 6 |
## | 1.38 | 4.62 | |
## | 1.89 | 0.57 | |
## | 0.50 | 0.50 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## --------------------------------------|-----------|-----------|-----------|
## GP | 3677 | 4917 | 8594 |
## | 1979.36 | 6614.64 | |
## | 1456.02 | 435.70 | |
## | 0.43 | 0.57 | 0.24 |
## | 0.45 | 0.18 | |
## | 0.10 | 0.14 | |
## --------------------------------------|-----------|-----------|-----------|
## OTHER CONSULTANT | 12 | 57 | 69 |
## | 15.89 | 53.11 | |
## | 0.95 | 0.29 | |
## | 0.17 | 0.83 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## --------------------------------------|-----------|-----------|-----------|
## WARD | 451 | 2757 | 3208 |
## | 738.86 | 2469.14 | |
## | 112.15 | 33.56 | |
## | 0.14 | 0.86 | 0.09 |
## | 0.05 | 0.10 | |
## | 0.01 | 0.08 | |
## --------------------------------------|-----------|-----------|-----------|
## Column Total | 8262 | 27610 | 35872 |
## | 0.23 | 0.77 | |
## --------------------------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 2576 d.f. = 7 p = 0
##
##
## # CrossTable(main_processed$Referring.Hospital, main_processed$No.Cancels, digits=2, fisher=F, chisq=TRUE, expected=TRUE)
CrossTable(main_processed$Consultant_recode, main_processed$No.Cancels, digits=2, fisher=F, chisq=TRUE, expected=TRUE)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 35872
##
##
## | main_processed$No.Cancels
## main_processed$Consultant_recode | 0 | 1 | Row Total |
## ---------------------------------|-----------|-----------|-----------|
## BARRYM | 1792 | 7716 | 9508 |
## | 2189.87 | 7318.13 | |
## | 72.29 | 21.63 | |
## | 0.19 | 0.81 | 0.27 |
## | 0.22 | 0.28 | |
## | 0.05 | 0.22 | |
## ---------------------------------|-----------|-----------|-----------|
## HEENEY | 653 | 1006 | 1659 |
## | 382.10 | 1276.90 | |
## | 192.06 | 57.47 | |
## | 0.39 | 0.61 | 0.05 |
## | 0.08 | 0.04 | |
## | 0.02 | 0.03 | |
## ---------------------------------|-----------|-----------|-----------|
## KELLM | 1869 | 5731 | 7600 |
## | 1750.42 | 5849.58 | |
## | 8.03 | 2.40 | |
## | 0.25 | 0.75 | 0.21 |
## | 0.23 | 0.21 | |
## | 0.05 | 0.16 | |
## ---------------------------------|-----------|-----------|-----------|
## STOKES | 1772 | 5620 | 7392 |
## | 1702.52 | 5689.48 | |
## | 2.84 | 0.85 | |
## | 0.24 | 0.76 | 0.21 |
## | 0.21 | 0.20 | |
## | 0.05 | 0.16 | |
## ---------------------------------|-----------|-----------|-----------|
## WALSSI | 2176 | 7537 | 9713 |
## | 2237.09 | 7475.91 | |
## | 1.67 | 0.50 | |
## | 0.22 | 0.78 | 0.27 |
## | 0.26 | 0.27 | |
## | 0.06 | 0.21 | |
## ---------------------------------|-----------|-----------|-----------|
## Column Total | 8262 | 27610 | 35872 |
## | 0.23 | 0.77 | |
## ---------------------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 360 d.f. = 4 p = 1.4e-76
##
##
## CrossTable(main_processed$Insurance.Scheme_recode, main_processed$No.Cancels, digits=2, fisher=F, chisq=TRUE, expected=TRUE)## Warning in chisq.test(t, correct = FALSE, ...): Chi-squared approximation may
## be incorrect##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 35872
##
##
## | main_processed$No.Cancels
## main_processed$Insurance.Scheme_recode | 0 | 1 | Row Total |
## ---------------------------------------|-----------|-----------|-----------|
## A | 5 | 23 | 28 |
## | 6.45 | 21.55 | |
## | 0.33 | 0.10 | |
## | 0.18 | 0.82 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ---------------------------------------|-----------|-----------|-----------|
## B | 414 | 2139 | 2553 |
## | 588.00 | 1965.00 | |
## | 51.49 | 15.41 | |
## | 0.16 | 0.84 | 0.07 |
## | 0.05 | 0.08 | |
## | 0.01 | 0.06 | |
## ---------------------------------------|-----------|-----------|-----------|
## C | 2 | 7 | 9 |
## | 2.07 | 6.93 | |
## | 0.00 | 0.00 | |
## | 0.22 | 0.78 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ---------------------------------------|-----------|-----------|-----------|
## D | 2253 | 7711 | 9964 |
## | 2294.90 | 7669.10 | |
## | 0.76 | 0.23 | |
## | 0.23 | 0.77 | 0.28 |
## | 0.27 | 0.28 | |
## | 0.06 | 0.21 | |
## ---------------------------------------|-----------|-----------|-----------|
## E | 14 | 67 | 81 |
## | 18.66 | 62.34 | |
## | 1.16 | 0.35 | |
## | 0.17 | 0.83 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ---------------------------------------|-----------|-----------|-----------|
## G | 56 | 191 | 247 |
## | 56.89 | 190.11 | |
## | 0.01 | 0.00 | |
## | 0.23 | 0.77 | 0.01 |
## | 0.01 | 0.01 | |
## | 0.00 | 0.01 | |
## ---------------------------------------|-----------|-----------|-----------|
## H | 5 | 33 | 38 |
## | 8.75 | 29.25 | |
## | 1.61 | 0.48 | |
## | 0.13 | 0.87 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ---------------------------------------|-----------|-----------|-----------|
## I | 385 | 2006 | 2391 |
## | 550.69 | 1840.31 | |
## | 49.85 | 14.92 | |
## | 0.16 | 0.84 | 0.07 |
## | 0.05 | 0.07 | |
## | 0.01 | 0.06 | |
## ---------------------------------------|-----------|-----------|-----------|
## J | 7 | 54 | 61 |
## | 14.05 | 46.95 | |
## | 3.54 | 1.06 | |
## | 0.11 | 0.89 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ---------------------------------------|-----------|-----------|-----------|
## M | 2 | 1 | 3 |
## | 0.69 | 2.31 | |
## | 2.48 | 0.74 | |
## | 0.67 | 0.33 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ---------------------------------------|-----------|-----------|-----------|
## O | 41 | 110 | 151 |
## | 34.78 | 116.22 | |
## | 1.11 | 0.33 | |
## | 0.27 | 0.73 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ---------------------------------------|-----------|-----------|-----------|
## P | 15 | 59 | 74 |
## | 17.04 | 56.96 | |
## | 0.25 | 0.07 | |
## | 0.20 | 0.80 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ---------------------------------------|-----------|-----------|-----------|
## S | 159 | 824 | 983 |
## | 226.40 | 756.60 | |
## | 20.07 | 6.00 | |
## | 0.16 | 0.84 | 0.03 |
## | 0.02 | 0.03 | |
## | 0.00 | 0.02 | |
## ---------------------------------------|-----------|-----------|-----------|
## U | 3785 | 9306 | 13091 |
## | 3015.10 | 10075.90 | |
## | 196.59 | 58.83 | |
## | 0.29 | 0.71 | 0.36 |
## | 0.46 | 0.34 | |
## | 0.11 | 0.26 | |
## ---------------------------------------|-----------|-----------|-----------|
## V | 1119 | 5079 | 6198 |
## | 1427.52 | 4770.48 | |
## | 66.68 | 19.95 | |
## | 0.18 | 0.82 | 0.17 |
## | 0.14 | 0.18 | |
## | 0.03 | 0.14 | |
## ---------------------------------------|-----------|-----------|-----------|
## Column Total | 8262 | 27610 | 35872 |
## | 0.23 | 0.77 | |
## ---------------------------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 514 d.f. = 14 p = 8.2e-101
##
##
## CrossTable(main_processed$Eligibility_recode, main_processed$No.Cancels, digits=2, fisher=F, chisq=TRUE, expected=TRUE)## Warning in chisq.test(t, correct = FALSE, ...): Chi-squared approximation may
## be incorrect##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 35872
##
##
## | main_processed$No.Cancels
## main_processed$Eligibility_recode | 0 | 1 | Row Total |
## ----------------------------------|-----------|-----------|-----------|
## ACUTE UNCLASSIFIED | 33 | 78 | 111 |
## | 25.57 | 85.43 | |
## | 2.16 | 0.65 | |
## | 0.30 | 0.70 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ----------------------------------|-----------|-----------|-----------|
## ELIGIBILITY UNKNOWN | 1270 | 1108 | 2378 |
## | 547.70 | 1830.30 | |
## | 952.57 | 285.05 | |
## | 0.53 | 0.47 | 0.07 |
## | 0.15 | 0.04 | |
## | 0.04 | 0.03 | |
## ----------------------------------|-----------|-----------|-----------|
## EXEMPT | 216 | 768 | 984 |
## | 226.63 | 757.37 | |
## | 0.50 | 0.15 | |
## | 0.22 | 0.78 | 0.03 |
## | 0.03 | 0.03 | |
## | 0.01 | 0.02 | |
## ----------------------------------|-----------|-----------|-----------|
## MEDICAL CARD | 3097 | 11064 | 14161 |
## | 3261.55 | 10899.45 | |
## | 8.30 | 2.48 | |
## | 0.22 | 0.78 | 0.39 |
## | 0.37 | 0.40 | |
## | 0.09 | 0.31 | |
## ----------------------------------|-----------|-----------|-----------|
## NON ACUTE UNCLASSIFIED | 2 | 14 | 16 |
## | 3.69 | 12.31 | |
## | 0.77 | 0.23 | |
## | 0.12 | 0.88 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ----------------------------------|-----------|-----------|-----------|
## NON MEDICAL CARD | 3621 | 14468 | 18089 |
## | 4166.24 | 13922.76 | |
## | 71.36 | 21.35 | |
## | 0.20 | 0.80 | 0.50 |
## | 0.44 | 0.52 | |
## | 0.10 | 0.40 | |
## ----------------------------------|-----------|-----------|-----------|
## RESEARCH/NATIONAL PROG. | 23 | 110 | 133 |
## | 30.63 | 102.37 | |
## | 1.90 | 0.57 | |
## | 0.17 | 0.83 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ----------------------------------|-----------|-----------|-----------|
## Column Total | 8262 | 27610 | 35872 |
## | 0.23 | 0.77 | |
## ----------------------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 1348 d.f. = 6 p = 4.3e-288
##
##
## CrossTable(main_processed$Booking.Type_recode, main_processed$No.Cancels, digits=2, fisher=F, chisq=TRUE, expected=TRUE)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 35872
##
##
## | main_processed$No.Cancels
## main_processed$Booking.Type_recode | 0 | 1 | Row Total |
## -----------------------------------|-----------|-----------|-----------|
## NEW | 3780 | 5013 | 8793 |
## | 2025.19 | 6767.81 | |
## | 1520.52 | 455.00 | |
## | 0.43 | 0.57 | 0.25 |
## | 0.46 | 0.18 | |
## | 0.11 | 0.14 | |
## -----------------------------------|-----------|-----------|-----------|
## RETURN | 4168 | 20419 | 24587 |
## | 5662.85 | 18924.15 | |
## | 394.60 | 118.08 | |
## | 0.17 | 0.83 | 0.69 |
## | 0.50 | 0.74 | |
## | 0.12 | 0.57 | |
## -----------------------------------|-----------|-----------|-----------|
## WARD | 314 | 2178 | 2492 |
## | 573.95 | 1918.05 | |
## | 117.74 | 35.23 | |
## | 0.13 | 0.87 | 0.07 |
## | 0.04 | 0.08 | |
## | 0.01 | 0.06 | |
## -----------------------------------|-----------|-----------|-----------|
## Column Total | 8262 | 27610 | 35872 |
## | 0.23 | 0.77 | |
## -----------------------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 2641 d.f. = 2 p = 0
##
##
## CrossTable(main_processed$Hospital.Catchment_recode, main_processed$No.Cancels, digits=2, fisher=F, chisq=TRUE, expected=TRUE)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 35872
##
##
## | main_processed$No.Cancels
## main_processed$Hospital.Catchment_recode | 0 | 1 | Row Total |
## -----------------------------------------|-----------|-----------|-----------|
## Beaumont | 317 | 1258 | 1575 |
## | 362.75 | 1212.25 | |
## | 5.77 | 1.73 | |
## | 0.20 | 0.80 | 0.04 |
## | 0.04 | 0.05 | |
## | 0.01 | 0.04 | |
## -----------------------------------------|-----------|-----------|-----------|
## Connolly | 920 | 2806 | 3726 |
## | 858.17 | 2867.83 | |
## | 4.46 | 1.33 | |
## | 0.25 | 0.75 | 0.10 |
## | 0.11 | 0.10 | |
## | 0.03 | 0.08 | |
## -----------------------------------------|-----------|-----------|-----------|
## International | 28 | 60 | 88 |
## | 20.27 | 67.73 | |
## | 2.95 | 0.88 | |
## | 0.32 | 0.68 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## -----------------------------------------|-----------|-----------|-----------|
## James | 111 | 266 | 377 |
## | 86.83 | 290.17 | |
## | 6.73 | 2.01 | |
## | 0.29 | 0.71 | 0.01 |
## | 0.01 | 0.01 | |
## | 0.00 | 0.01 | |
## -----------------------------------------|-----------|-----------|-----------|
## Mater | 2209 | 6299 | 8508 |
## | 1959.55 | 6548.45 | |
## | 31.75 | 9.50 | |
## | 0.26 | 0.74 | 0.24 |
## | 0.27 | 0.23 | |
## | 0.06 | 0.18 | |
## -----------------------------------------|-----------|-----------|-----------|
## National | 4485 | 16527 | 21012 |
## | 4839.46 | 16172.54 | |
## | 25.96 | 7.77 | |
## | 0.21 | 0.79 | 0.59 |
## | 0.54 | 0.60 | |
## | 0.13 | 0.46 | |
## -----------------------------------------|-----------|-----------|-----------|
## Tallaght | 61 | 96 | 157 |
## | 36.16 | 120.84 | |
## | 17.06 | 5.11 | |
## | 0.39 | 0.61 | 0.00 |
## | 0.01 | 0.00 | |
## | 0.00 | 0.00 | |
## -----------------------------------------|-----------|-----------|-----------|
## Vincents | 131 | 298 | 429 |
## | 98.81 | 330.19 | |
## | 10.49 | 3.14 | |
## | 0.31 | 0.69 | 0.01 |
## | 0.02 | 0.01 | |
## | 0.00 | 0.01 | |
## -----------------------------------------|-----------|-----------|-----------|
## Column Total | 8262 | 27610 | 35872 |
## | 0.23 | 0.77 | |
## -----------------------------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 137 d.f. = 7 p = 2.6e-26
##
##
## CrossTable(main_processed$bookedDay, main_processed$No.Cancels, digits=2, fisher=F, chisq=TRUE, expected=TRUE)## Warning in chisq.test(t, correct = FALSE, ...): Chi-squared approximation may
## be incorrect##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 35872
##
##
## | main_processed$No.Cancels
## main_processed$bookedDay | 0 | 1 | Row Total |
## -------------------------|-----------|-----------|-----------|
## Friday | 1203 | 4345 | 5548 |
## | 1277.81 | 4270.19 | |
## | 4.38 | 1.31 | |
## | 0.22 | 0.78 | 0.15 |
## | 0.15 | 0.16 | |
## | 0.03 | 0.12 | |
## -------------------------|-----------|-----------|-----------|
## Monday | 1916 | 6484 | 8400 |
## | 1934.68 | 6465.32 | |
## | 0.18 | 0.05 | |
## | 0.23 | 0.77 | 0.23 |
## | 0.23 | 0.23 | |
## | 0.05 | 0.18 | |
## -------------------------|-----------|-----------|-----------|
## Saturday | 30 | 65 | 95 |
## | 21.88 | 73.12 | |
## | 3.01 | 0.90 | |
## | 0.32 | 0.68 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## -------------------------|-----------|-----------|-----------|
## Sunday | 1 | 4 | 5 |
## | 1.15 | 3.85 | |
## | 0.02 | 0.01 | |
## | 0.20 | 0.80 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## -------------------------|-----------|-----------|-----------|
## Thursday | 1614 | 4935 | 6549 |
## | 1508.36 | 5040.64 | |
## | 7.40 | 2.21 | |
## | 0.25 | 0.75 | 0.18 |
## | 0.20 | 0.18 | |
## | 0.04 | 0.14 | |
## -------------------------|-----------|-----------|-----------|
## Tuesday | 1854 | 6367 | 8221 |
## | 1893.45 | 6327.55 | |
## | 0.82 | 0.25 | |
## | 0.23 | 0.77 | 0.23 |
## | 0.22 | 0.23 | |
## | 0.05 | 0.18 | |
## -------------------------|-----------|-----------|-----------|
## Wednesday | 1644 | 5410 | 7054 |
## | 1624.67 | 5429.33 | |
## | 0.23 | 0.07 | |
## | 0.23 | 0.77 | 0.20 |
## | 0.20 | 0.20 | |
## | 0.05 | 0.15 | |
## -------------------------|-----------|-----------|-----------|
## Column Total | 8262 | 27610 | 35872 |
## | 0.23 | 0.77 | |
## -------------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 21 d.f. = 6 p = 0.002
##
##
## main_processed_subset <- subset(main_processed, year(main_processed$Booked.Date_new) > 2019) #create the subset for cancellations (from the hospital) have started since CoVID
CrossTable(main_processed_subset$bookedMonthYear, main_processed_subset$No.Cancels, digits=2, fisher=F, chisq=TRUE, expected=TRUE)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 35872
##
##
## | main_processed_subset$No.Cancels
## main_processed_subset$bookedMonthYear | 0 | 1 | Row Total |
## --------------------------------------|-----------|-----------|-----------|
## 2020-01 | 135 | 689 | 824 |
## | 189.78 | 634.22 | |
## | 15.81 | 4.73 | |
## | 0.16 | 0.84 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.00 | 0.02 | |
## --------------------------------------|-----------|-----------|-----------|
## 2020-02 | 136 | 932 | 1068 |
## | 245.98 | 822.02 | |
## | 49.17 | 14.71 | |
## | 0.13 | 0.87 | 0.03 |
## | 0.02 | 0.03 | |
## | 0.00 | 0.03 | |
## --------------------------------------|-----------|-----------|-----------|
## 2020-03 | 68 | 486 | 554 |
## | 127.60 | 426.40 | |
## | 27.84 | 8.33 | |
## | 0.12 | 0.88 | 0.02 |
## | 0.01 | 0.02 | |
## | 0.00 | 0.01 | |
## --------------------------------------|-----------|-----------|-----------|
## 2020-04 | 23 | 178 | 201 |
## | 46.29 | 154.71 | |
## | 11.72 | 3.51 | |
## | 0.11 | 0.89 | 0.01 |
## | 0.00 | 0.01 | |
## | 0.00 | 0.00 | |
## --------------------------------------|-----------|-----------|-----------|
## 2020-05 | 62 | 388 | 450 |
## | 103.64 | 346.36 | |
## | 16.73 | 5.01 | |
## | 0.14 | 0.86 | 0.01 |
## | 0.01 | 0.01 | |
## | 0.00 | 0.01 | |
## --------------------------------------|-----------|-----------|-----------|
## 2020-06 | 139 | 613 | 752 |
## | 173.20 | 578.80 | |
## | 6.75 | 2.02 | |
## | 0.18 | 0.82 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.00 | 0.02 | |
## --------------------------------------|-----------|-----------|-----------|
## 2020-07 | 296 | 647 | 943 |
## | 217.19 | 725.81 | |
## | 28.60 | 8.56 | |
## | 0.31 | 0.69 | 0.03 |
## | 0.04 | 0.02 | |
## | 0.01 | 0.02 | |
## --------------------------------------|-----------|-----------|-----------|
## 2020-08 | 171 | 717 | 888 |
## | 204.52 | 683.48 | |
## | 5.49 | 1.64 | |
## | 0.19 | 0.81 | 0.02 |
## | 0.02 | 0.03 | |
## | 0.00 | 0.02 | |
## --------------------------------------|-----------|-----------|-----------|
## 2020-09 | 210 | 688 | 898 |
## | 206.83 | 691.17 | |
## | 0.05 | 0.01 | |
## | 0.23 | 0.77 | 0.03 |
## | 0.03 | 0.02 | |
## | 0.01 | 0.02 | |
## --------------------------------------|-----------|-----------|-----------|
## 2020-10 | 178 | 686 | 864 |
## | 199.00 | 665.00 | |
## | 2.22 | 0.66 | |
## | 0.21 | 0.79 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.00 | 0.02 | |
## --------------------------------------|-----------|-----------|-----------|
## 2020-11 | 160 | 833 | 993 |
## | 228.71 | 764.29 | |
## | 20.64 | 6.18 | |
## | 0.16 | 0.84 | 0.03 |
## | 0.02 | 0.03 | |
## | 0.00 | 0.02 | |
## --------------------------------------|-----------|-----------|-----------|
## 2020-12 | 205 | 470 | 675 |
## | 155.47 | 519.53 | |
## | 15.78 | 4.72 | |
## | 0.30 | 0.70 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.01 | 0.01 | |
## --------------------------------------|-----------|-----------|-----------|
## 2021-01 | 131 | 434 | 565 |
## | 130.13 | 434.87 | |
## | 0.01 | 0.00 | |
## | 0.23 | 0.77 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.00 | 0.01 | |
## --------------------------------------|-----------|-----------|-----------|
## 2021-02 | 186 | 794 | 980 |
## | 225.71 | 754.29 | |
## | 6.99 | 2.09 | |
## | 0.19 | 0.81 | 0.03 |
## | 0.02 | 0.03 | |
## | 0.01 | 0.02 | |
## --------------------------------------|-----------|-----------|-----------|
## 2021-03 | 188 | 736 | 924 |
## | 212.81 | 711.19 | |
## | 2.89 | 0.87 | |
## | 0.20 | 0.80 | 0.03 |
## | 0.02 | 0.03 | |
## | 0.01 | 0.02 | |
## --------------------------------------|-----------|-----------|-----------|
## 2021-04 | 231 | 662 | 893 |
## | 205.67 | 687.33 | |
## | 3.12 | 0.93 | |
## | 0.26 | 0.74 | 0.02 |
## | 0.03 | 0.02 | |
## | 0.01 | 0.02 | |
## --------------------------------------|-----------|-----------|-----------|
## 2021-05 | 215 | 622 | 837 |
## | 192.78 | 644.22 | |
## | 2.56 | 0.77 | |
## | 0.26 | 0.74 | 0.02 |
## | 0.03 | 0.02 | |
## | 0.01 | 0.02 | |
## --------------------------------------|-----------|-----------|-----------|
## 2021-06 | 225 | 766 | 991 |
## | 228.25 | 762.75 | |
## | 0.05 | 0.01 | |
## | 0.23 | 0.77 | 0.03 |
## | 0.03 | 0.03 | |
## | 0.01 | 0.02 | |
## --------------------------------------|-----------|-----------|-----------|
## 2021-07 | 211 | 733 | 944 |
## | 217.42 | 726.58 | |
## | 0.19 | 0.06 | |
## | 0.22 | 0.78 | 0.03 |
## | 0.03 | 0.03 | |
## | 0.01 | 0.02 | |
## --------------------------------------|-----------|-----------|-----------|
## 2021-08 | 274 | 786 | 1060 |
## | 244.14 | 815.86 | |
## | 3.65 | 1.09 | |
## | 0.26 | 0.74 | 0.03 |
## | 0.03 | 0.03 | |
## | 0.01 | 0.02 | |
## --------------------------------------|-----------|-----------|-----------|
## 2021-09 | 376 | 631 | 1007 |
## | 231.93 | 775.07 | |
## | 89.49 | 26.78 | |
## | 0.37 | 0.63 | 0.03 |
## | 0.05 | 0.02 | |
## | 0.01 | 0.02 | |
## --------------------------------------|-----------|-----------|-----------|
## 2021-10 | 247 | 572 | 819 |
## | 188.63 | 630.37 | |
## | 18.06 | 5.40 | |
## | 0.30 | 0.70 | 0.02 |
## | 0.03 | 0.02 | |
## | 0.01 | 0.02 | |
## --------------------------------------|-----------|-----------|-----------|
## 2021-11 | 271 | 576 | 847 |
## | 195.08 | 651.92 | |
## | 29.55 | 8.84 | |
## | 0.32 | 0.68 | 0.02 |
## | 0.03 | 0.02 | |
## | 0.01 | 0.02 | |
## --------------------------------------|-----------|-----------|-----------|
## 2021-12 | 275 | 510 | 785 |
## | 180.80 | 604.20 | |
## | 49.08 | 14.69 | |
## | 0.35 | 0.65 | 0.02 |
## | 0.03 | 0.02 | |
## | 0.01 | 0.01 | |
## --------------------------------------|-----------|-----------|-----------|
## 2022-01 | 172 | 546 | 718 |
## | 165.37 | 552.63 | |
## | 0.27 | 0.08 | |
## | 0.24 | 0.76 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.00 | 0.02 | |
## --------------------------------------|-----------|-----------|-----------|
## 2022-02 | 227 | 583 | 810 |
## | 186.56 | 623.44 | |
## | 8.77 | 2.62 | |
## | 0.28 | 0.72 | 0.02 |
## | 0.03 | 0.02 | |
## | 0.01 | 0.02 | |
## --------------------------------------|-----------|-----------|-----------|
## 2022-03 | 283 | 598 | 881 |
## | 202.91 | 678.09 | |
## | 31.61 | 9.46 | |
## | 0.32 | 0.68 | 0.02 |
## | 0.03 | 0.02 | |
## | 0.01 | 0.02 | |
## --------------------------------------|-----------|-----------|-----------|
## 2022-04 | 199 | 514 | 713 |
## | 164.22 | 548.78 | |
## | 7.37 | 2.20 | |
## | 0.28 | 0.72 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.01 | 0.01 | |
## --------------------------------------|-----------|-----------|-----------|
## 2022-05 | 239 | 841 | 1080 |
## | 248.74 | 831.26 | |
## | 0.38 | 0.11 | |
## | 0.22 | 0.78 | 0.03 |
## | 0.03 | 0.03 | |
## | 0.01 | 0.02 | |
## --------------------------------------|-----------|-----------|-----------|
## 2022-06 | 230 | 674 | 904 |
## | 208.21 | 695.79 | |
## | 2.28 | 0.68 | |
## | 0.25 | 0.75 | 0.03 |
## | 0.03 | 0.02 | |
## | 0.01 | 0.02 | |
## --------------------------------------|-----------|-----------|-----------|
## 2022-07 | 213 | 575 | 788 |
## | 181.49 | 606.51 | |
## | 5.47 | 1.64 | |
## | 0.27 | 0.73 | 0.02 |
## | 0.03 | 0.02 | |
## | 0.01 | 0.02 | |
## --------------------------------------|-----------|-----------|-----------|
## 2022-08 | 153 | 490 | 643 |
## | 148.10 | 494.90 | |
## | 0.16 | 0.05 | |
## | 0.24 | 0.76 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.00 | 0.01 | |
## --------------------------------------|-----------|-----------|-----------|
## 2022-09 | 201 | 564 | 765 |
## | 176.19 | 588.81 | |
## | 3.49 | 1.05 | |
## | 0.26 | 0.74 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.01 | 0.02 | |
## --------------------------------------|-----------|-----------|-----------|
## 2022-10 | 252 | 721 | 973 |
## | 224.10 | 748.90 | |
## | 3.47 | 1.04 | |
## | 0.26 | 0.74 | 0.03 |
## | 0.03 | 0.03 | |
## | 0.01 | 0.02 | |
## --------------------------------------|-----------|-----------|-----------|
## 2022-11 | 190 | 537 | 727 |
## | 167.44 | 559.56 | |
## | 3.04 | 0.91 | |
## | 0.26 | 0.74 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.01 | 0.01 | |
## --------------------------------------|-----------|-----------|-----------|
## 2022-12 | 115 | 494 | 609 |
## | 140.26 | 468.74 | |
## | 4.55 | 1.36 | |
## | 0.19 | 0.81 | 0.02 |
## | 0.01 | 0.02 | |
## | 0.00 | 0.01 | |
## --------------------------------------|-----------|-----------|-----------|
## 2023-01 | 111 | 551 | 662 |
## | 152.47 | 509.53 | |
## | 11.28 | 3.38 | |
## | 0.17 | 0.83 | 0.02 |
## | 0.01 | 0.02 | |
## | 0.00 | 0.02 | |
## --------------------------------------|-----------|-----------|-----------|
## 2023-02 | 94 | 577 | 671 |
## | 154.54 | 516.46 | |
## | 23.72 | 7.10 | |
## | 0.14 | 0.86 | 0.02 |
## | 0.01 | 0.02 | |
## | 0.00 | 0.02 | |
## --------------------------------------|-----------|-----------|-----------|
## 2023-03 | 141 | 589 | 730 |
## | 168.13 | 561.87 | |
## | 4.38 | 1.31 | |
## | 0.19 | 0.81 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.00 | 0.02 | |
## --------------------------------------|-----------|-----------|-----------|
## 2023-04 | 107 | 578 | 685 |
## | 157.77 | 527.23 | |
## | 16.34 | 4.89 | |
## | 0.16 | 0.84 | 0.02 |
## | 0.01 | 0.02 | |
## | 0.00 | 0.02 | |
## --------------------------------------|-----------|-----------|-----------|
## 2023-05 | 105 | 562 | 667 |
## | 153.62 | 513.38 | |
## | 15.39 | 4.61 | |
## | 0.16 | 0.84 | 0.02 |
## | 0.01 | 0.02 | |
## | 0.00 | 0.02 | |
## --------------------------------------|-----------|-----------|-----------|
## 2023-06 | 117 | 482 | 599 |
## | 137.96 | 461.04 | |
## | 3.18 | 0.95 | |
## | 0.20 | 0.80 | 0.02 |
## | 0.01 | 0.02 | |
## | 0.00 | 0.01 | |
## --------------------------------------|-----------|-----------|-----------|
## 2023-07 | 130 | 470 | 600 |
## | 138.19 | 461.81 | |
## | 0.49 | 0.15 | |
## | 0.22 | 0.78 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.00 | 0.01 | |
## --------------------------------------|-----------|-----------|-----------|
## 2023-08 | 97 | 492 | 589 |
## | 135.66 | 453.34 | |
## | 11.02 | 3.30 | |
## | 0.16 | 0.84 | 0.02 |
## | 0.01 | 0.02 | |
## | 0.00 | 0.01 | |
## --------------------------------------|-----------|-----------|-----------|
## 2023-09 | 111 | 358 | 469 |
## | 108.02 | 360.98 | |
## | 0.08 | 0.02 | |
## | 0.24 | 0.76 | 0.01 |
## | 0.01 | 0.01 | |
## | 0.00 | 0.01 | |
## --------------------------------------|-----------|-----------|-----------|
## 2023-10 | 63 | 337 | 400 |
## | 92.13 | 307.87 | |
## | 9.21 | 2.76 | |
## | 0.16 | 0.84 | 0.01 |
## | 0.01 | 0.01 | |
## | 0.00 | 0.01 | |
## --------------------------------------|-----------|-----------|-----------|
## 2023-11 | 91 | 284 | 375 |
## | 86.37 | 288.63 | |
## | 0.25 | 0.07 | |
## | 0.24 | 0.76 | 0.01 |
## | 0.01 | 0.01 | |
## | 0.00 | 0.01 | |
## --------------------------------------|-----------|-----------|-----------|
## 2023-12 | 8 | 44 | 52 |
## | 11.98 | 40.02 | |
## | 1.32 | 0.40 | |
## | 0.15 | 0.85 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## --------------------------------------|-----------|-----------|-----------|
## Column Total | 8262 | 27610 | 35872 |
## | 0.23 | 0.77 | |
## --------------------------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 746 d.f. = 47 p = 1.7e-126
##
##
## CrossTable(main_processed$appointmentDay, main_processed$No.Cancels, digits=2, fisher=F, chisq=TRUE, expected=TRUE)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 35872
##
##
## | main_processed$No.Cancels
## main_processed$appointmentDay | 0 | 1 | Row Total |
## ------------------------------|-----------|-----------|-----------|
## Friday | 1693 | 6862 | 8555 |
## | 1970.38 | 6584.62 | |
## | 39.05 | 11.68 | |
## | 0.20 | 0.80 | 0.24 |
## | 0.20 | 0.25 | |
## | 0.05 | 0.19 | |
## ------------------------------|-----------|-----------|-----------|
## Monday | 2011 | 5532 | 7543 |
## | 1737.30 | 5805.70 | |
## | 43.12 | 12.90 | |
## | 0.27 | 0.73 | 0.21 |
## | 0.24 | 0.20 | |
## | 0.06 | 0.15 | |
## ------------------------------|-----------|-----------|-----------|
## Thursday | 1596 | 5229 | 6825 |
## | 1571.93 | 5253.07 | |
## | 0.37 | 0.11 | |
## | 0.23 | 0.77 | 0.19 |
## | 0.19 | 0.19 | |
## | 0.04 | 0.15 | |
## ------------------------------|-----------|-----------|-----------|
## Tuesday | 2013 | 7958 | 9971 |
## | 2296.51 | 7674.49 | |
## | 35.00 | 10.47 | |
## | 0.20 | 0.80 | 0.28 |
## | 0.24 | 0.29 | |
## | 0.06 | 0.22 | |
## ------------------------------|-----------|-----------|-----------|
## Wednesday | 949 | 2029 | 2978 |
## | 685.89 | 2292.11 | |
## | 100.93 | 30.20 | |
## | 0.32 | 0.68 | 0.08 |
## | 0.11 | 0.07 | |
## | 0.03 | 0.06 | |
## ------------------------------|-----------|-----------|-----------|
## Column Total | 8262 | 27610 | 35872 |
## | 0.23 | 0.77 | |
## ------------------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 284 d.f. = 4 p = 3.3e-60
##
##
## CrossTable(main_processed$appointmentMonthYear, main_processed$No.Cancels, digits=2, fisher=F, chisq=TRUE, expected=TRUE)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 35872
##
##
## | main_processed$No.Cancels
## main_processed$appointmentMonthYear | 0 | 1 | Row Total |
## ------------------------------------|-----------|-----------|-----------|
## 2020-01 | 46 | 113 | 159 |
## | 36.62 | 122.38 | |
## | 2.40 | 0.72 | |
## | 0.29 | 0.71 | 0.00 |
## | 0.01 | 0.00 | |
## | 0.00 | 0.00 | |
## ------------------------------------|-----------|-----------|-----------|
## 2020-02 | 96 | 265 | 361 |
## | 83.15 | 277.85 | |
## | 1.99 | 0.59 | |
## | 0.27 | 0.73 | 0.01 |
## | 0.01 | 0.01 | |
## | 0.00 | 0.01 | |
## ------------------------------------|-----------|-----------|-----------|
## 2020-03 | 67 | 686 | 753 |
## | 173.43 | 579.57 | |
## | 65.31 | 19.54 | |
## | 0.09 | 0.91 | 0.02 |
## | 0.01 | 0.02 | |
## | 0.00 | 0.02 | |
## ------------------------------------|-----------|-----------|-----------|
## 2020-04 | 14 | 562 | 576 |
## | 132.66 | 443.34 | |
## | 106.14 | 31.76 | |
## | 0.02 | 0.98 | 0.02 |
## | 0.00 | 0.02 | |
## | 0.00 | 0.02 | |
## ------------------------------------|-----------|-----------|-----------|
## 2020-05 | 33 | 250 | 283 |
## | 65.18 | 217.82 | |
## | 15.89 | 4.75 | |
## | 0.12 | 0.88 | 0.01 |
## | 0.00 | 0.01 | |
## | 0.00 | 0.01 | |
## ------------------------------------|-----------|-----------|-----------|
## 2020-06 | 43 | 420 | 463 |
## | 106.64 | 356.36 | |
## | 37.98 | 11.36 | |
## | 0.09 | 0.91 | 0.01 |
## | 0.01 | 0.02 | |
## | 0.00 | 0.01 | |
## ------------------------------------|-----------|-----------|-----------|
## 2020-07 | 148 | 478 | 626 |
## | 144.18 | 481.82 | |
## | 0.10 | 0.03 | |
## | 0.24 | 0.76 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.00 | 0.01 | |
## ------------------------------------|-----------|-----------|-----------|
## 2020-08 | 112 | 491 | 603 |
## | 138.88 | 464.12 | |
## | 5.20 | 1.56 | |
## | 0.19 | 0.81 | 0.02 |
## | 0.01 | 0.02 | |
## | 0.00 | 0.01 | |
## ------------------------------------|-----------|-----------|-----------|
## 2020-09 | 164 | 547 | 711 |
## | 163.76 | 547.24 | |
## | 0.00 | 0.00 | |
## | 0.23 | 0.77 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.00 | 0.02 | |
## ------------------------------------|-----------|-----------|-----------|
## 2020-10 | 133 | 657 | 790 |
## | 181.95 | 608.05 | |
## | 13.17 | 3.94 | |
## | 0.17 | 0.83 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.00 | 0.02 | |
## ------------------------------------|-----------|-----------|-----------|
## 2020-11 | 99 | 671 | 770 |
## | 177.35 | 592.65 | |
## | 34.61 | 10.36 | |
## | 0.13 | 0.87 | 0.02 |
## | 0.01 | 0.02 | |
## | 0.00 | 0.02 | |
## ------------------------------------|-----------|-----------|-----------|
## 2020-12 | 111 | 546 | 657 |
## | 151.32 | 505.68 | |
## | 10.74 | 3.21 | |
## | 0.17 | 0.83 | 0.02 |
## | 0.01 | 0.02 | |
## | 0.00 | 0.02 | |
## ------------------------------------|-----------|-----------|-----------|
## 2021-01 | 186 | 508 | 694 |
## | 159.84 | 534.16 | |
## | 4.28 | 1.28 | |
## | 0.27 | 0.73 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.01 | 0.01 | |
## ------------------------------------|-----------|-----------|-----------|
## 2021-02 | 215 | 414 | 629 |
## | 144.87 | 484.13 | |
## | 33.95 | 10.16 | |
## | 0.34 | 0.66 | 0.02 |
## | 0.03 | 0.01 | |
## | 0.01 | 0.01 | |
## ------------------------------------|-----------|-----------|-----------|
## 2021-03 | 174 | 525 | 699 |
## | 160.99 | 538.01 | |
## | 1.05 | 0.31 | |
## | 0.25 | 0.75 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.00 | 0.01 | |
## ------------------------------------|-----------|-----------|-----------|
## 2021-04 | 187 | 450 | 637 |
## | 146.71 | 490.29 | |
## | 11.06 | 3.31 | |
## | 0.29 | 0.71 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.01 | 0.01 | |
## ------------------------------------|-----------|-----------|-----------|
## 2021-05 | 227 | 448 | 675 |
## | 155.47 | 519.53 | |
## | 32.92 | 9.85 | |
## | 0.34 | 0.66 | 0.02 |
## | 0.03 | 0.02 | |
## | 0.01 | 0.01 | |
## ------------------------------------|-----------|-----------|-----------|
## 2021-06 | 281 | 504 | 785 |
## | 180.80 | 604.20 | |
## | 55.53 | 16.62 | |
## | 0.36 | 0.64 | 0.02 |
## | 0.03 | 0.02 | |
## | 0.01 | 0.01 | |
## ------------------------------------|-----------|-----------|-----------|
## 2021-07 | 327 | 641 | 968 |
## | 222.95 | 745.05 | |
## | 48.56 | 14.53 | |
## | 0.34 | 0.66 | 0.03 |
## | 0.04 | 0.02 | |
## | 0.01 | 0.02 | |
## ------------------------------------|-----------|-----------|-----------|
## 2021-08 | 235 | 993 | 1228 |
## | 282.83 | 945.17 | |
## | 8.09 | 2.42 | |
## | 0.19 | 0.81 | 0.03 |
## | 0.03 | 0.04 | |
## | 0.01 | 0.03 | |
## ------------------------------------|-----------|-----------|-----------|
## 2021-09 | 365 | 859 | 1224 |
## | 281.91 | 942.09 | |
## | 24.49 | 7.33 | |
## | 0.30 | 0.70 | 0.03 |
## | 0.04 | 0.03 | |
## | 0.01 | 0.02 | |
## ------------------------------------|-----------|-----------|-----------|
## 2021-10 | 265 | 615 | 880 |
## | 202.68 | 677.32 | |
## | 19.16 | 5.73 | |
## | 0.30 | 0.70 | 0.02 |
## | 0.03 | 0.02 | |
## | 0.01 | 0.02 | |
## ------------------------------------|-----------|-----------|-----------|
## 2021-11 | 300 | 700 | 1000 |
## | 230.32 | 769.68 | |
## | 21.08 | 6.31 | |
## | 0.30 | 0.70 | 0.03 |
## | 0.04 | 0.03 | |
## | 0.01 | 0.02 | |
## ------------------------------------|-----------|-----------|-----------|
## 2021-12 | 239 | 523 | 762 |
## | 175.50 | 586.50 | |
## | 22.97 | 6.87 | |
## | 0.31 | 0.69 | 0.02 |
## | 0.03 | 0.02 | |
## | 0.01 | 0.01 | |
## ------------------------------------|-----------|-----------|-----------|
## 2022-01 | 253 | 505 | 758 |
## | 174.58 | 583.42 | |
## | 35.22 | 10.54 | |
## | 0.33 | 0.67 | 0.02 |
## | 0.03 | 0.02 | |
## | 0.01 | 0.01 | |
## ------------------------------------|-----------|-----------|-----------|
## 2022-02 | 262 | 620 | 882 |
## | 203.14 | 678.86 | |
## | 17.05 | 5.10 | |
## | 0.30 | 0.70 | 0.02 |
## | 0.03 | 0.02 | |
## | 0.01 | 0.02 | |
## ------------------------------------|-----------|-----------|-----------|
## 2022-03 | 247 | 671 | 918 |
## | 211.43 | 706.57 | |
## | 5.98 | 1.79 | |
## | 0.27 | 0.73 | 0.03 |
## | 0.03 | 0.02 | |
## | 0.01 | 0.02 | |
## ------------------------------------|-----------|-----------|-----------|
## 2022-04 | 273 | 446 | 719 |
## | 165.60 | 553.40 | |
## | 69.66 | 20.84 | |
## | 0.38 | 0.62 | 0.02 |
## | 0.03 | 0.02 | |
## | 0.01 | 0.01 | |
## ------------------------------------|-----------|-----------|-----------|
## 2022-05 | 271 | 640 | 911 |
## | 209.82 | 701.18 | |
## | 17.84 | 5.34 | |
## | 0.30 | 0.70 | 0.03 |
## | 0.03 | 0.02 | |
## | 0.01 | 0.02 | |
## ------------------------------------|-----------|-----------|-----------|
## 2022-06 | 220 | 750 | 970 |
## | 223.41 | 746.59 | |
## | 0.05 | 0.02 | |
## | 0.23 | 0.77 | 0.03 |
## | 0.03 | 0.03 | |
## | 0.01 | 0.02 | |
## ------------------------------------|-----------|-----------|-----------|
## 2022-07 | 181 | 636 | 817 |
## | 188.17 | 628.83 | |
## | 0.27 | 0.08 | |
## | 0.22 | 0.78 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.01 | 0.02 | |
## ------------------------------------|-----------|-----------|-----------|
## 2022-08 | 234 | 594 | 828 |
## | 190.70 | 637.30 | |
## | 9.83 | 2.94 | |
## | 0.28 | 0.72 | 0.02 |
## | 0.03 | 0.02 | |
## | 0.01 | 0.02 | |
## ------------------------------------|-----------|-----------|-----------|
## 2022-09 | 261 | 558 | 819 |
## | 188.63 | 630.37 | |
## | 27.76 | 8.31 | |
## | 0.32 | 0.68 | 0.02 |
## | 0.03 | 0.02 | |
## | 0.01 | 0.02 | |
## ------------------------------------|-----------|-----------|-----------|
## 2022-10 | 225 | 600 | 825 |
## | 190.01 | 634.99 | |
## | 6.44 | 1.93 | |
## | 0.27 | 0.73 | 0.02 |
## | 0.03 | 0.02 | |
## | 0.01 | 0.02 | |
## ------------------------------------|-----------|-----------|-----------|
## 2022-11 | 255 | 699 | 954 |
## | 219.72 | 734.28 | |
## | 5.66 | 1.69 | |
## | 0.27 | 0.73 | 0.03 |
## | 0.03 | 0.03 | |
## | 0.01 | 0.02 | |
## ------------------------------------|-----------|-----------|-----------|
## 2022-12 | 167 | 430 | 597 |
## | 137.50 | 459.50 | |
## | 6.33 | 1.89 | |
## | 0.28 | 0.72 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.00 | 0.01 | |
## ------------------------------------|-----------|-----------|-----------|
## 2023-01 | 165 | 575 | 740 |
## | 170.44 | 569.56 | |
## | 0.17 | 0.05 | |
## | 0.22 | 0.78 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.00 | 0.02 | |
## ------------------------------------|-----------|-----------|-----------|
## 2023-02 | 104 | 547 | 651 |
## | 149.94 | 501.06 | |
## | 14.07 | 4.21 | |
## | 0.16 | 0.84 | 0.02 |
## | 0.01 | 0.02 | |
## | 0.00 | 0.02 | |
## ------------------------------------|-----------|-----------|-----------|
## 2023-03 | 139 | 562 | 701 |
## | 161.45 | 539.55 | |
## | 3.12 | 0.93 | |
## | 0.20 | 0.80 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.00 | 0.02 | |
## ------------------------------------|-----------|-----------|-----------|
## 2023-04 | 98 | 573 | 671 |
## | 154.54 | 516.46 | |
## | 20.69 | 6.19 | |
## | 0.15 | 0.85 | 0.02 |
## | 0.01 | 0.02 | |
## | 0.00 | 0.02 | |
## ------------------------------------|-----------|-----------|-----------|
## 2023-05 | 120 | 665 | 785 |
## | 180.80 | 604.20 | |
## | 20.45 | 6.12 | |
## | 0.15 | 0.85 | 0.02 |
## | 0.01 | 0.02 | |
## | 0.00 | 0.02 | |
## ------------------------------------|-----------|-----------|-----------|
## 2023-06 | 100 | 614 | 714 |
## | 164.45 | 549.55 | |
## | 25.26 | 7.56 | |
## | 0.14 | 0.86 | 0.02 |
## | 0.01 | 0.02 | |
## | 0.00 | 0.02 | |
## ------------------------------------|-----------|-----------|-----------|
## 2023-07 | 97 | 657 | 754 |
## | 173.66 | 580.34 | |
## | 33.84 | 10.13 | |
## | 0.13 | 0.87 | 0.02 |
## | 0.01 | 0.02 | |
## | 0.00 | 0.02 | |
## ------------------------------------|-----------|-----------|-----------|
## 2023-08 | 112 | 799 | 911 |
## | 209.82 | 701.18 | |
## | 45.60 | 13.65 | |
## | 0.12 | 0.88 | 0.03 |
## | 0.01 | 0.03 | |
## | 0.00 | 0.02 | |
## ------------------------------------|-----------|-----------|-----------|
## 2023-09 | 133 | 774 | 907 |
## | 208.90 | 698.10 | |
## | 27.58 | 8.25 | |
## | 0.15 | 0.85 | 0.03 |
## | 0.02 | 0.03 | |
## | 0.00 | 0.02 | |
## ------------------------------------|-----------|-----------|-----------|
## 2023-10 | 118 | 564 | 682 |
## | 157.08 | 524.92 | |
## | 9.72 | 2.91 | |
## | 0.17 | 0.83 | 0.02 |
## | 0.01 | 0.02 | |
## | 0.00 | 0.02 | |
## ------------------------------------|-----------|-----------|-----------|
## 2023-11 | 94 | 683 | 777 |
## | 178.96 | 598.04 | |
## | 40.33 | 12.07 | |
## | 0.12 | 0.88 | 0.02 |
## | 0.01 | 0.02 | |
## | 0.00 | 0.02 | |
## ------------------------------------|-----------|-----------|-----------|
## 2023-12 | 66 | 582 | 648 |
## | 149.25 | 498.75 | |
## | 46.43 | 13.89 | |
## | 0.10 | 0.90 | 0.02 |
## | 0.01 | 0.02 | |
## | 0.00 | 0.02 | |
## ------------------------------------|-----------|-----------|-----------|
## Column Total | 8262 | 27610 | 35872 |
## | 0.23 | 0.77 | |
## ------------------------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 1385 d.f. = 47 p = 2.7e-259
##
##
## ## booking relevance
### difference between Booked and Appointment factors in Cancellation
CrossTable(main_processed$Rebooked.Indicator, main_processed$No.Cancels, digits=2, fisher=T, chisq=TRUE, expected=TRUE)## Warning in chisq.test(t, correct = FALSE, ...): Chi-squared approximation may
## be incorrect##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 35872
##
##
## | main_processed$No.Cancels
## main_processed$Rebooked.Indicator | 0 | 1 | Row Total |
## ----------------------------------|-----------|-----------|-----------|
## | 0 | 0 | 0 |
## | 0.00 | 0.00 | |
## | NaN | NaN | |
## | NaN | NaN | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ----------------------------------|-----------|-----------|-----------|
## No | 8262 | 7040 | 15302 |
## | 3524.34 | 11777.66 | |
## | 6368.69 | 1905.76 | |
## | 0.54 | 0.46 | 0.43 |
## | 1.00 | 0.25 | |
## | 0.23 | 0.20 | |
## ----------------------------------|-----------|-----------|-----------|
## Yes | 0 | 20570 | 20570 |
## | 4737.66 | 15832.34 | |
## | 4737.66 | 1417.69 | |
## | 0.00 | 1.00 | 0.57 |
## | 0.00 | 0.75 | |
## | 0.00 | 0.57 | |
## ----------------------------------|-----------|-----------|-----------|
## Column Total | 8262 | 27610 | 35872 |
## | 0.23 | 0.77 | |
## ----------------------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = NaN d.f. = 2 p = NaN
##
##
##
## Fisher's Exact Test for Count Data
## ------------------------------------------------------------
## Alternative hypothesis: two.sided
## p = 0
##
## leveneTest(daysDiff_AppointBooked ~ as.factor(No.Cancels), data=main_processed) #leveneTest is Ha## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 516 <2e-16 ***
## 35870
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1t.test(daysDiff_AppointBooked ~ No.Cancels, var.equal=F, data=main_processed)##
## Welch Two Sample t-test
##
## data: daysDiff_AppointBooked by No.Cancels
## t = -27, df = 15525, p-value <2e-16
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## -40 -35
## sample estimates:
## mean in group 0 mean in group 1
## 62 99## patient characteristics relevance
CrossTable(main_processed$Gender, main_processed$No.Cancels, digits=2, fisher=TRUE, chisq=TRUE, expected=TRUE)## Warning in chisq.test(t, correct = FALSE, ...): Chi-squared approximation may
## be incorrect##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 35872
##
##
## | main_processed$No.Cancels
## main_processed$Gender | 0 | 1 | Row Total |
## ----------------------|-----------|-----------|-----------|
## Female | 8023 | 27241 | 35264 |
## | 8121.97 | 27142.03 | |
## | 1.21 | 0.36 | |
## | 0.23 | 0.77 | 0.98 |
## | 0.97 | 0.99 | |
## | 0.22 | 0.76 | |
## ----------------------|-----------|-----------|-----------|
## Male | 238 | 367 | 605 |
## | 139.34 | 465.66 | |
## | 69.85 | 20.90 | |
## | 0.39 | 0.61 | 0.02 |
## | 0.03 | 0.01 | |
## | 0.01 | 0.01 | |
## ----------------------|-----------|-----------|-----------|
## Unknown | 1 | 2 | 3 |
## | 0.69 | 2.31 | |
## | 0.14 | 0.04 | |
## | 0.33 | 0.67 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ----------------------|-----------|-----------|-----------|
## Column Total | 8262 | 27610 | 35872 |
## | 0.23 | 0.77 | |
## ----------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 92 d.f. = 2 p = 8.2e-21
##
##
##
## Fisher's Exact Test for Count Data
## ------------------------------------------------------------
## Alternative hypothesis: two.sided
## p = 2e-19
##
## CrossTable(main_processed$Age.at.Attendance.Cat.HSE, main_processed$No.Cancels, digits=2, fisher=F, chisq=TRUE, expected=TRUE) # age group## Warning in chisq.test(t, correct = FALSE, ...): Chi-squared approximation may
## be incorrect##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 35872
##
##
## | main_processed$No.Cancels
## main_processed$Age.at.Attendance.Cat.HSE | 0 | 1 | Row Total |
## -----------------------------------------|-----------|-----------|-----------|
## 0 - 4 | 5 | 7 | 12 |
## | 2.76 | 9.24 | |
## | 1.81 | 0.54 | |
## | 0.42 | 0.58 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## -----------------------------------------|-----------|-----------|-----------|
## 10 - 14 | 11 | 38 | 49 |
## | 11.29 | 37.71 | |
## | 0.01 | 0.00 | |
## | 0.22 | 0.78 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## -----------------------------------------|-----------|-----------|-----------|
## 15 - 24 | 480 | 742 | 1222 |
## | 281.45 | 940.55 | |
## | 140.07 | 41.91 | |
## | 0.39 | 0.61 | 0.03 |
## | 0.06 | 0.03 | |
## | 0.01 | 0.02 | |
## -----------------------------------------|-----------|-----------|-----------|
## 25 - 34 | 1390 | 2318 | 3708 |
## | 854.02 | 2853.98 | |
## | 336.38 | 100.66 | |
## | 0.37 | 0.63 | 0.10 |
## | 0.17 | 0.08 | |
## | 0.04 | 0.06 | |
## -----------------------------------------|-----------|-----------|-----------|
## 35 - 44 | 1842 | 5368 | 7210 |
## | 1660.60 | 5549.40 | |
## | 19.82 | 5.93 | |
## | 0.26 | 0.74 | 0.20 |
## | 0.22 | 0.19 | |
## | 0.05 | 0.15 | |
## -----------------------------------------|-----------|-----------|-----------|
## 45 - 54 | 1693 | 6739 | 8432 |
## | 1942.05 | 6489.95 | |
## | 31.94 | 9.56 | |
## | 0.20 | 0.80 | 0.24 |
## | 0.20 | 0.24 | |
## | 0.05 | 0.19 | |
## -----------------------------------------|-----------|-----------|-----------|
## 55 - 64 | 1350 | 6065 | 7415 |
## | 1707.81 | 5707.19 | |
## | 74.97 | 22.43 | |
## | 0.18 | 0.82 | 0.21 |
## | 0.16 | 0.22 | |
## | 0.04 | 0.17 | |
## -----------------------------------------|-----------|-----------|-----------|
## 65 - 74 | 887 | 4130 | 5017 |
## | 1155.51 | 3861.49 | |
## | 62.39 | 18.67 | |
## | 0.18 | 0.82 | 0.14 |
## | 0.11 | 0.15 | |
## | 0.02 | 0.12 | |
## -----------------------------------------|-----------|-----------|-----------|
## 75 - 84 | 434 | 1647 | 2081 |
## | 479.29 | 1601.71 | |
## | 4.28 | 1.28 | |
## | 0.21 | 0.79 | 0.06 |
## | 0.05 | 0.06 | |
## | 0.01 | 0.05 | |
## -----------------------------------------|-----------|-----------|-----------|
## 85 >= | 170 | 556 | 726 |
## | 167.21 | 558.79 | |
## | 0.05 | 0.01 | |
## | 0.23 | 0.77 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.00 | 0.02 | |
## -----------------------------------------|-----------|-----------|-----------|
## Column Total | 8262 | 27610 | 35872 |
## | 0.23 | 0.77 | |
## -----------------------------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 873 d.f. = 9 p = 4.7e-182
##
##
## leveneTest(Age.at.Attendance ~ as.factor(No.Cancels), data=main_processed)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 147 <2e-16 ***
## 35870
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1t.test(Age.at.Attendance ~ No.Cancels, var.equal=FALSE, data=main_processed) #leveneTest is Ha##
## Welch Two Sample t-test
##
## data: Age.at.Attendance by No.Cancels
## t = -22, df = 12624, p-value <2e-16
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## -4.9 -4.1
## sample estimates:
## mean in group 0 mean in group 1
## 48 53CrossTable(main_processed$Area.of.Residence_recode, main_processed$No.Cancels, digits=2, fisher=F, chisq=TRUE, expected=TRUE) # remove Na or impute where no residence## Warning in chisq.test(t, correct = FALSE, ...): Chi-squared approximation may
## be incorrect##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 35872
##
##
## | main_processed$No.Cancels
## main_processed$Area.of.Residence_recode | 0 | 1 | Row Total |
## ---------------------------------------------|-----------|-----------|-----------|
## DUBLIN NTH | 3910 | 12203 | 16113 |
## | 3711.13 | 12401.87 | |
## | 10.66 | 3.19 | |
## | 0.24 | 0.76 | 0.45 |
## | 0.47 | 0.44 | |
## | 0.11 | 0.34 | |
## ---------------------------------------------|-----------|-----------|-----------|
## DUBLIN STH | 303 | 661 | 964 |
## | 222.03 | 741.97 | |
## | 29.53 | 8.84 | |
## | 0.31 | 0.69 | 0.03 |
## | 0.04 | 0.02 | |
## | 0.01 | 0.02 | |
## ---------------------------------------------|-----------|-----------|-----------|
## EASTERN & MIDLAND REGION (excl.Dublin,Meath) | 1785 | 6689 | 8474 |
## | 1951.72 | 6522.28 | |
## | 14.24 | 4.26 | |
## | 0.21 | 0.79 | 0.24 |
## | 0.22 | 0.24 | |
## | 0.05 | 0.19 | |
## ---------------------------------------------|-----------|-----------|-----------|
## Meath | 1246 | 4223 | 5469 |
## | 1259.61 | 4209.39 | |
## | 0.15 | 0.04 | |
## | 0.23 | 0.77 | 0.15 |
## | 0.15 | 0.15 | |
## | 0.03 | 0.12 | |
## ---------------------------------------------|-----------|-----------|-----------|
## NORTHERN WESTERN REGION | 816 | 3002 | 3818 |
## | 879.36 | 2938.64 | |
## | 4.56 | 1.37 | |
## | 0.21 | 0.79 | 0.11 |
## | 0.10 | 0.11 | |
## | 0.02 | 0.08 | |
## ---------------------------------------------|-----------|-----------|-----------|
## OUTSIDE IRELAND | 1 | 13 | 14 |
## | 3.22 | 10.78 | |
## | 1.53 | 0.46 | |
## | 0.07 | 0.93 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ---------------------------------------------|-----------|-----------|-----------|
## SOUTHERN REGION | 174 | 762 | 936 |
## | 215.58 | 720.42 | |
## | 8.02 | 2.40 | |
## | 0.19 | 0.81 | 0.03 |
## | 0.02 | 0.03 | |
## | 0.00 | 0.02 | |
## ---------------------------------------------|-----------|-----------|-----------|
## UNKNOWN | 27 | 57 | 84 |
## | 19.35 | 64.65 | |
## | 3.03 | 0.91 | |
## | 0.32 | 0.68 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ---------------------------------------------|-----------|-----------|-----------|
## Column Total | 8262 | 27610 | 35872 |
## | 0.23 | 0.77 | |
## ---------------------------------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 93 d.f. = 7 p = 2.7e-17
##
##
## CrossTable(main_processed$addressDiff, main_processed$No.Cancels, digits=2, fisher=TRUE, chisq=TRUE, expected=TRUE)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 35872
##
##
## | main_processed$No.Cancels
## main_processed$addressDiff | 0 | 1 | Row Total |
## ---------------------------|-----------|-----------|-----------|
## 0 | 6397 | 22567 | 28964 |
## | 6670.96 | 22293.04 | |
## | 11.25 | 3.37 | |
## | 0.22 | 0.78 | 0.81 |
## | 0.77 | 0.82 | |
## | 0.18 | 0.63 | |
## ---------------------------|-----------|-----------|-----------|
## 1 | 1865 | 5043 | 6908 |
## | 1591.04 | 5316.96 | |
## | 47.17 | 14.12 | |
## | 0.27 | 0.73 | 0.19 |
## | 0.23 | 0.18 | |
## | 0.05 | 0.14 | |
## ---------------------------|-----------|-----------|-----------|
## Column Total | 8262 | 27610 | 35872 |
## | 0.23 | 0.77 | |
## ---------------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 76 d.f. = 1 p = 3e-18
##
## Pearson's Chi-squared test with Yates' continuity correction
## ------------------------------------------------------------
## Chi^2 = 76 d.f. = 1 p = 3.4e-18
##
##
## Fisher's Exact Test for Count Data
## ------------------------------------------------------------
## Sample estimate odds ratio: 0.77
##
## Alternative hypothesis: true odds ratio is not equal to 1
## p = 8.4e-18
## 95% confidence interval: 0.72 0.81
##
## Alternative hypothesis: true odds ratio is less than 1
## p = 4.9e-18
## 95% confidence interval: 0 0.81
##
## Alternative hypothesis: true odds ratio is greater than 1
## p = 1
## 95% confidence interval: 0.73 Inf
##
##
## CrossTable(main_processed$Reason.for.Cancellation_recode, main_processed$Cancellation.Group, digits=2, fisher=F, chisq=TRUE, expected=TRUE)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 91169
##
##
## | main_processed$Cancellation.Group
## main_processed$Reason.for.Cancellation_recode | DNA | Hospital | N/A | Patient | Validation | Row Total |
## ----------------------------------------------|------------|------------|------------|------------|------------|------------|
## | 0 | 0 | 55297 | 0 | 0 | 55297 |
## | 5011.18 | 9633.56 | 33539.45 | 6253.96 | 858.85 | |
## | 5011.18 | 9633.56 | 14114.45 | 6253.96 | 858.85 | |
## | 0.00 | 0.00 | 1.00 | 0.00 | 0.00 | 0.61 |
## | 0.00 | 0.00 | 1.00 | 0.00 | 0.00 | |
## | 0.00 | 0.00 | 0.61 | 0.00 | 0.00 | |
## ----------------------------------------------|------------|------------|------------|------------|------------|------------|
## By Consultant/Advanced Nurse | 0 | 4125 | 0 | 0 | 0 | 4125 |
## | 373.82 | 718.64 | 2501.95 | 466.53 | 64.07 | |
## | 373.82 | 16146.29 | 2501.95 | 466.53 | 64.07 | |
## | 0.00 | 1.00 | 0.00 | 0.00 | 0.00 | 0.05 |
## | 0.00 | 0.26 | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.05 | 0.00 | 0.00 | 0.00 | |
## ----------------------------------------------|------------|------------|------------|------------|------------|------------|
## By Covid | 0 | 957 | 0 | 197 | 0 | 1154 |
## | 104.58 | 201.04 | 699.94 | 130.51 | 17.92 | |
## | 104.58 | 2842.51 | 699.94 | 33.87 | 17.92 | |
## | 0.00 | 0.83 | 0.00 | 0.17 | 0.00 | 0.01 |
## | 0.00 | 0.06 | 0.00 | 0.02 | 0.00 | |
## | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 | |
## ----------------------------------------------|------------|------------|------------|------------|------------|------------|
## By Hospital | 0 | 10662 | 0 | 0 | 0 | 10662 |
## | 966.22 | 1857.48 | 6466.85 | 1205.85 | 165.60 | |
## | 966.22 | 41733.75 | 6466.85 | 1205.85 | 165.60 | |
## | 0.00 | 1.00 | 0.00 | 0.00 | 0.00 | 0.12 |
## | 0.00 | 0.67 | 0.00 | 0.00 | 0.00 | |
## | 0.00 | 0.12 | 0.00 | 0.00 | 0.00 | |
## ----------------------------------------------|------------|------------|------------|------------|------------|------------|
## By Patient | 0 | 132 | 0 | 8939 | 1395 | 10466 |
## | 948.46 | 1823.33 | 6347.97 | 1183.68 | 162.55 | |
## | 948.46 | 1568.89 | 6347.97 | 50811.86 | 9344.14 | |
## | 0.00 | 0.01 | 0.00 | 0.85 | 0.13 | 0.11 |
## | 0.00 | 0.01 | 0.00 | 0.87 | 0.99 | |
## | 0.00 | 0.00 | 0.00 | 0.10 | 0.02 | |
## ----------------------------------------------|------------|------------|------------|------------|------------|------------|
## By Patient-health conditions | 0 | 7 | 0 | 1175 | 21 | 1203 |
## | 109.02 | 209.58 | 729.66 | 136.06 | 18.68 | |
## | 109.02 | 195.81 | 729.66 | 7933.50 | 0.29 | |
## | 0.00 | 0.01 | 0.00 | 0.98 | 0.02 | 0.01 |
## | 0.00 | 0.00 | 0.00 | 0.11 | 0.01 | |
## | 0.00 | 0.00 | 0.00 | 0.01 | 0.00 | |
## ----------------------------------------------|------------|------------|------------|------------|------------|------------|
## No show | 8262 | 0 | 0 | 0 | 0 | 8262 |
## | 748.73 | 1439.36 | 5011.18 | 934.41 | 128.32 | |
## | 75393.73 | 1439.36 | 5011.18 | 934.41 | 128.32 | |
## | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.09 |
## | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | |
## | 0.09 | 0.00 | 0.00 | 0.00 | 0.00 | |
## ----------------------------------------------|------------|------------|------------|------------|------------|------------|
## Column Total | 8262 | 15883 | 55297 | 10311 | 1416 | 91169 |
## | 0.09 | 0.17 | 0.61 | 0.11 | 0.02 | |
## ----------------------------------------------|------------|------------|------------|------------|------------|------------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 270558 d.f. = 24 p = 0
##
##
## CrossTable(main_processed$Cancellation.Group, main_processed$Rebooked.Indicator, digits=2, fisher=F, chisq=TRUE, expected=TRUE)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 91169
##
##
## | main_processed$Rebooked.Indicator
## main_processed$Cancellation.Group | | No | Yes | Row Total |
## ----------------------------------|-----------|-----------|-----------|-----------|
## DNA | 0 | 8262 | 0 | 8262 |
## | 5011.18 | 1386.71 | 1864.11 | |
## | 5011.18 | 34087.54 | 1864.11 | |
## | 0.00 | 1.00 | 0.00 | 0.09 |
## | 0.00 | 0.54 | 0.00 | |
## | 0.00 | 0.09 | 0.00 | |
## ----------------------------------|-----------|-----------|-----------|-----------|
## Hospital | 0 | 2338 | 13545 | 15883 |
## | 9633.56 | 2665.84 | 3583.60 | |
## | 9633.56 | 40.32 | 27689.88 | |
## | 0.00 | 0.15 | 0.85 | 0.17 |
## | 0.00 | 0.15 | 0.66 | |
## | 0.00 | 0.03 | 0.15 | |
## ----------------------------------|-----------|-----------|-----------|-----------|
## N/A | 55297 | 0 | 0 | 55297 |
## | 33539.45 | 9281.17 | 12476.38 | |
## | 14114.45 | 9281.17 | 12476.38 | |
## | 1.00 | 0.00 | 0.00 | 0.61 |
## | 1.00 | 0.00 | 0.00 | |
## | 0.61 | 0.00 | 0.00 | |
## ----------------------------------|-----------|-----------|-----------|-----------|
## Patient | 0 | 4023 | 6288 | 10311 |
## | 6253.96 | 1730.62 | 2326.42 | |
## | 6253.96 | 3036.49 | 6746.05 | |
## | 0.00 | 0.39 | 0.61 | 0.11 |
## | 0.00 | 0.26 | 0.31 | |
## | 0.00 | 0.04 | 0.07 | |
## ----------------------------------|-----------|-----------|-----------|-----------|
## Validation | 0 | 679 | 737 | 1416 |
## | 858.85 | 237.66 | 319.48 | |
## | 858.85 | 819.55 | 545.62 | |
## | 0.00 | 0.48 | 0.52 | 0.02 |
## | 0.00 | 0.04 | 0.04 | |
## | 0.00 | 0.01 | 0.01 | |
## ----------------------------------|-----------|-----------|-----------|-----------|
## Column Total | 55297 | 15302 | 20570 | 91169 |
## | 0.61 | 0.17 | 0.23 | |
## ----------------------------------|-----------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 132459 d.f. = 8 p = 0
##
##
## CrossTable(main_processed$Reason.for.Cancellation_recode, main_processed$Rebooked.Indicator, digits=2, fisher=F, chisq=TRUE, expected=TRUE)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 91169
##
##
## | main_processed$Rebooked.Indicator
## main_processed$Reason.for.Cancellation_recode | | No | Yes | Row Total |
## ----------------------------------------------|-----------|-----------|-----------|-----------|
## | 55297 | 0 | 0 | 55297 |
## | 33539.45 | 9281.17 | 12476.38 | |
## | 14114.45 | 9281.17 | 12476.38 | |
## | 1.00 | 0.00 | 0.00 | 0.61 |
## | 1.00 | 0.00 | 0.00 | |
## | 0.61 | 0.00 | 0.00 | |
## ----------------------------------------------|-----------|-----------|-----------|-----------|
## By Consultant/Advanced Nurse | 0 | 140 | 3985 | 4125 |
## | 2501.95 | 692.35 | 930.70 | |
## | 2501.95 | 440.66 | 10023.32 | |
## | 0.00 | 0.03 | 0.97 | 0.05 |
## | 0.00 | 0.01 | 0.19 | |
## | 0.00 | 0.00 | 0.04 | |
## ----------------------------------------------|-----------|-----------|-----------|-----------|
## By Covid | 0 | 1030 | 124 | 1154 |
## | 699.94 | 193.69 | 260.37 | |
## | 699.94 | 3611.00 | 71.43 | |
## | 0.00 | 0.89 | 0.11 | 0.01 |
## | 0.00 | 0.07 | 0.01 | |
## | 0.00 | 0.01 | 0.00 | |
## ----------------------------------------------|-----------|-----------|-----------|-----------|
## By Hospital | 0 | 1258 | 9404 | 10662 |
## | 6466.85 | 1789.53 | 2405.61 | |
## | 6466.85 | 157.88 | 20359.64 | |
## | 0.00 | 0.12 | 0.88 | 0.12 |
## | 0.00 | 0.08 | 0.46 | |
## | 0.00 | 0.01 | 0.10 | |
## ----------------------------------------------|-----------|-----------|-----------|-----------|
## By Patient | 0 | 4140 | 6326 | 10466 |
## | 6347.97 | 1756.64 | 2361.39 | |
## | 6347.97 | 3233.70 | 6656.30 | |
## | 0.00 | 0.40 | 0.60 | 0.11 |
## | 0.00 | 0.27 | 0.31 | |
## | 0.00 | 0.05 | 0.07 | |
## ----------------------------------------------|-----------|-----------|-----------|-----------|
## By Patient-health conditions | 0 | 472 | 731 | 1203 |
## | 729.66 | 201.91 | 271.43 | |
## | 729.66 | 361.27 | 778.14 | |
## | 0.00 | 0.39 | 0.61 | 0.01 |
## | 0.00 | 0.03 | 0.04 | |
## | 0.00 | 0.01 | 0.01 | |
## ----------------------------------------------|-----------|-----------|-----------|-----------|
## No show | 0 | 8262 | 0 | 8262 |
## | 5011.18 | 1386.71 | 1864.11 | |
## | 5011.18 | 34087.54 | 1864.11 | |
## | 0.00 | 1.00 | 0.00 | 0.09 |
## | 0.00 | 0.54 | 0.00 | |
## | 0.00 | 0.09 | 0.00 | |
## ----------------------------------------------|-----------|-----------|-----------|-----------|
## Column Total | 55297 | 15302 | 20570 | 91169 |
## | 0.61 | 0.17 | 0.23 | |
## ----------------------------------------------|-----------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 139275 d.f. = 12 p = 0
##
##
## New Attendance correlation
- Clinic relevance: Consultant, Insurance scheme, Eligibility, Hospital catchment, Clinic type, Clinic code, Nurse flag, Referral source, Booking Type
- Date relevance: daysDiff_attendanceBooked daysDiff_AppointBooked
- Patient relevance: Gender, Age at attendance, Area of residence
- Cancellation group: Reason for cancellation, Rebooked indicator
## clinic relevance
CrossTable(main_processed$Clinic.Type_recode, main_processed$No.New.Attendances, digits=2, fisher=F, chisq=TRUE, expected=TRUE)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 55297
##
##
## | main_processed$No.New.Attendances
## main_processed$Clinic.Type_recode | 0 | 1 | Row Total |
## ----------------------------------|-----------|-----------|-----------|
## FRA | 6 | 56 | 62 |
## | 27.79 | 34.21 | |
## | 17.08 | 13.88 | |
## | 0.10 | 0.90 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ----------------------------------|-----------|-----------|-----------|
## GEN | 2 | 12 | 14 |
## | 6.28 | 7.72 | |
## | 2.91 | 2.37 | |
## | 0.14 | 0.86 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ----------------------------------|-----------|-----------|-----------|
## MED | 76 | 8447 | 8523 |
## | 3820.14 | 4702.86 | |
## | 3669.66 | 2980.87 | |
## | 0.01 | 0.99 | 0.15 |
## | 0.00 | 0.28 | |
## | 0.00 | 0.15 | |
## ----------------------------------|-----------|-----------|-----------|
## SBC | 17003 | 4849 | 21852 |
## | 9794.42 | 12057.58 | |
## | 5305.44 | 4309.63 | |
## | 0.78 | 0.22 | 0.40 |
## | 0.69 | 0.16 | |
## | 0.31 | 0.09 | |
## ----------------------------------|-----------|-----------|-----------|
## TRI | 7698 | 17148 | 24846 |
## | 11136.37 | 13709.63 | |
## | 1061.60 | 862.34 | |
## | 0.31 | 0.69 | 0.45 |
## | 0.31 | 0.56 | |
## | 0.14 | 0.31 | |
## ----------------------------------|-----------|-----------|-----------|
## Column Total | 24785 | 30512 | 55297 |
## | 0.45 | 0.55 | |
## ----------------------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 18226 d.f. = 4 p = 0
##
##
## CrossTable(main_processed$Clinic.Code, main_processed$No.New.Attendances, digits=2, fisher=F, chisq=TRUE, expected=TRUE)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 55297
##
##
## | main_processed$No.New.Attendances
## main_processed$Clinic.Code | 0 | 1 | Row Total |
## ---------------------------|-----------|-----------|-----------|
## 424 | 4345 | 1383 | 5728 |
## | 2567.38 | 3160.62 | |
## | 1230.80 | 999.78 | |
## | 0.76 | 0.24 | 0.10 |
## | 0.18 | 0.05 | |
## | 0.08 | 0.03 | |
## ---------------------------|-----------|-----------|-----------|
## 440 | 2 | 12 | 14 |
## | 6.28 | 7.72 | |
## | 2.91 | 2.37 | |
## | 0.14 | 0.86 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ---------------------------|-----------|-----------|-----------|
## 441 | 4766 | 742 | 5508 |
## | 2468.77 | 3039.23 | |
## | 2137.60 | 1736.38 | |
## | 0.87 | 0.13 | 0.10 |
## | 0.19 | 0.02 | |
## | 0.09 | 0.01 | |
## ---------------------------|-----------|-----------|-----------|
## 526 | 834 | 2255 | 3089 |
## | 1384.54 | 1704.46 | |
## | 218.91 | 177.82 | |
## | 0.27 | 0.73 | 0.06 |
## | 0.03 | 0.07 | |
## | 0.02 | 0.04 | |
## ---------------------------|-----------|-----------|-----------|
## 527 | 2151 | 3144 | 5295 |
## | 2373.30 | 2921.70 | |
## | 20.82 | 16.91 | |
## | 0.41 | 0.59 | 0.10 |
## | 0.09 | 0.10 | |
## | 0.04 | 0.06 | |
## ---------------------------|-----------|-----------|-----------|
## 757 | 4317 | 1210 | 5527 |
## | 2477.29 | 3049.71 | |
## | 1366.22 | 1109.79 | |
## | 0.78 | 0.22 | 0.10 |
## | 0.17 | 0.04 | |
## | 0.08 | 0.02 | |
## ---------------------------|-----------|-----------|-----------|
## 768 | 1 | 16 | 17 |
## | 7.62 | 9.38 | |
## | 5.75 | 4.67 | |
## | 0.06 | 0.94 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ---------------------------|-----------|-----------|-----------|
## 771 | 1747 | 4481 | 6228 |
## | 2791.49 | 3436.51 | |
## | 390.82 | 317.46 | |
## | 0.28 | 0.72 | 0.11 |
## | 0.07 | 0.15 | |
## | 0.03 | 0.08 | |
## ---------------------------|-----------|-----------|-----------|
## 932 | 10 | 1984 | 1994 |
## | 893.74 | 1100.26 | |
## | 873.85 | 709.84 | |
## | 0.01 | 0.99 | 0.04 |
## | 0.00 | 0.07 | |
## | 0.00 | 0.04 | |
## ---------------------------|-----------|-----------|-----------|
## 933 | 18 | 1958 | 1976 |
## | 885.67 | 1090.33 | |
## | 850.04 | 690.49 | |
## | 0.01 | 0.99 | 0.04 |
## | 0.00 | 0.06 | |
## | 0.00 | 0.04 | |
## ---------------------------|-----------|-----------|-----------|
## 934 | 10 | 1364 | 1374 |
## | 615.85 | 758.15 | |
## | 596.01 | 484.14 | |
## | 0.01 | 0.99 | 0.02 |
## | 0.00 | 0.04 | |
## | 0.00 | 0.02 | |
## ---------------------------|-----------|-----------|-----------|
## 1132 | 28 | 2392 | 2420 |
## | 1084.68 | 1335.32 | |
## | 1029.41 | 836.19 | |
## | 0.01 | 0.99 | 0.04 |
## | 0.00 | 0.08 | |
## | 0.00 | 0.04 | |
## ---------------------------|-----------|-----------|-----------|
## 1133 | 2504 | 1331 | 3835 |
## | 1718.91 | 2116.09 | |
## | 358.58 | 291.28 | |
## | 0.65 | 0.35 | 0.07 |
## | 0.10 | 0.04 | |
## | 0.05 | 0.02 | |
## ---------------------------|-----------|-----------|-----------|
## 1134 | 2920 | 5486 | 8406 |
## | 3767.70 | 4638.30 | |
## | 190.73 | 154.93 | |
## | 0.35 | 0.65 | 0.15 |
## | 0.12 | 0.18 | |
## | 0.05 | 0.10 | |
## ---------------------------|-----------|-----------|-----------|
## 1187 | 5 | 40 | 45 |
## | 20.17 | 24.83 | |
## | 11.41 | 9.27 | |
## | 0.11 | 0.89 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ---------------------------|-----------|-----------|-----------|
## 1333 | 1071 | 183 | 1254 |
## | 562.06 | 691.94 | |
## | 460.83 | 374.34 | |
## | 0.85 | 0.15 | 0.02 |
## | 0.04 | 0.01 | |
## | 0.02 | 0.00 | |
## ---------------------------|-----------|-----------|-----------|
## 1334 | 46 | 1782 | 1828 |
## | 819.34 | 1008.66 | |
## | 729.92 | 592.92 | |
## | 0.03 | 0.97 | 0.03 |
## | 0.00 | 0.06 | |
## | 0.00 | 0.03 | |
## ---------------------------|-----------|-----------|-----------|
## 1335 | 10 | 749 | 759 |
## | 340.20 | 418.80 | |
## | 320.49 | 260.34 | |
## | 0.01 | 0.99 | 0.01 |
## | 0.00 | 0.02 | |
## | 0.00 | 0.01 | |
## ---------------------------|-----------|-----------|-----------|
## Column Total | 24785 | 30512 | 55297 |
## | 0.45 | 0.55 | |
## ---------------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 19564 d.f. = 17 p = 0
##
##
## CrossTable(main_processed$NurseFlag, main_processed$No.New.Attendances, digits=2, fisher=F, chisq=TRUE, expected=TRUE)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 55297
##
##
## | main_processed$No.New.Attendances
## main_processed$NurseFlag | 0 | 1 | Row Total |
## -------------------------|-----------|-----------|-----------|
## N | 24784 | 30496 | 55280 |
## | 24777.38 | 30502.62 | |
## | 0.00 | 0.00 | |
## | 0.45 | 0.55 | 1.00 |
## | 1.00 | 1.00 | |
## | 0.45 | 0.55 | |
## -------------------------|-----------|-----------|-----------|
## Y | 1 | 16 | 17 |
## | 7.62 | 9.38 | |
## | 5.75 | 4.67 | |
## | 0.06 | 0.94 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## -------------------------|-----------|-----------|-----------|
## Column Total | 24785 | 30512 | 55297 |
## | 0.45 | 0.55 | |
## -------------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 10 d.f. = 1 p = 0.0012
##
## Pearson's Chi-squared test with Yates' continuity correction
## ------------------------------------------------------------
## Chi^2 = 8.9 d.f. = 1 p = 0.0028
##
## CrossTable(main_processed$Referral.Source_recode, main_processed$No.New.Attendances, digits=2, fisher=F, chisq=TRUE, expected=TRUE)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 55297
##
##
## | main_processed$No.New.Attendances
## main_processed$Referral.Source_recode | 0 | 1 | Row Total |
## --------------------------------------|-----------|-----------|-----------|
## BREAST CHECK | 348 | 600 | 948 |
## | 424.91 | 523.09 | |
## | 13.92 | 11.31 | |
## | 0.37 | 0.63 | 0.02 |
## | 0.01 | 0.02 | |
## | 0.01 | 0.01 | |
## --------------------------------------|-----------|-----------|-----------|
## CLINIC | 20534 | 4691 | 25225 |
## | 11306.25 | 13918.75 | |
## | 7531.36 | 6117.75 | |
## | 0.81 | 0.19 | 0.46 |
## | 0.83 | 0.15 | |
## | 0.37 | 0.08 | |
## --------------------------------------|-----------|-----------|-----------|
## Elsew of Mater | 20 | 37 | 57 |
## | 25.55 | 31.45 | |
## | 1.20 | 0.98 | |
## | 0.35 | 0.65 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## --------------------------------------|-----------|-----------|-----------|
## Elsew outside Mater | 156 | 896 | 1052 |
## | 471.52 | 580.48 | |
## | 211.13 | 171.51 | |
## | 0.15 | 0.85 | 0.02 |
## | 0.01 | 0.03 | |
## | 0.00 | 0.02 | |
## --------------------------------------|-----------|-----------|-----------|
## EMERGENCY DEPT | 8 | 46 | 54 |
## | 24.20 | 29.80 | |
## | 10.85 | 8.81 | |
## | 0.15 | 0.85 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## --------------------------------------|-----------|-----------|-----------|
## GP | 514 | 22667 | 23181 |
## | 10390.10 | 12790.90 | |
## | 9387.52 | 7625.52 | |
## | 0.02 | 0.98 | 0.42 |
## | 0.02 | 0.74 | |
## | 0.01 | 0.41 | |
## --------------------------------------|-----------|-----------|-----------|
## OTHER CONSULTANT | 25 | 157 | 182 |
## | 81.58 | 100.42 | |
## | 39.24 | 31.87 | |
## | 0.14 | 0.86 | 0.00 |
## | 0.00 | 0.01 | |
## | 0.00 | 0.00 | |
## --------------------------------------|-----------|-----------|-----------|
## WARD | 3180 | 1418 | 4598 |
## | 2060.90 | 2537.10 | |
## | 607.69 | 493.63 | |
## | 0.69 | 0.31 | 0.08 |
## | 0.13 | 0.05 | |
## | 0.06 | 0.03 | |
## --------------------------------------|-----------|-----------|-----------|
## Column Total | 24785 | 30512 | 55297 |
## | 0.45 | 0.55 | |
## --------------------------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 32264 d.f. = 7 p = 0
##
##
## #CrossTable(main_processed$Referring.Hospital, main_processed$No.New.Attendances, digits=2, fisher=F, chisq=TRUE, expected=TRUE)
CrossTable(main_processed$Consultant_recode, main_processed$No.New.Attendances, digits=2, fisher=F, chisq=TRUE, expected=TRUE)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 55297
##
##
## | main_processed$No.New.Attendances
## main_processed$Consultant_recode | 0 | 1 | Row Total |
## ---------------------------------|-----------|-----------|-----------|
## BARRYM | 6074 | 7675 | 13749 |
## | 6162.52 | 7586.48 | |
## | 1.27 | 1.03 | |
## | 0.44 | 0.56 | 0.25 |
## | 0.25 | 0.25 | |
## | 0.11 | 0.14 | |
## ---------------------------------|-----------|-----------|-----------|
## HEENEY | 1127 | 2714 | 3841 |
## | 1721.60 | 2119.40 | |
## | 205.36 | 166.81 | |
## | 0.29 | 0.71 | 0.07 |
## | 0.05 | 0.09 | |
## | 0.02 | 0.05 | |
## ---------------------------------|-----------|-----------|-----------|
## KELLM | 6514 | 6485 | 12999 |
## | 5826.36 | 7172.64 | |
## | 81.16 | 65.92 | |
## | 0.50 | 0.50 | 0.24 |
## | 0.26 | 0.21 | |
## | 0.12 | 0.12 | |
## ---------------------------------|-----------|-----------|-----------|
## STOKES | 5613 | 4389 | 10002 |
## | 4483.06 | 5518.94 | |
## | 284.80 | 231.34 | |
## | 0.56 | 0.44 | 0.18 |
## | 0.23 | 0.14 | |
## | 0.10 | 0.08 | |
## ---------------------------------|-----------|-----------|-----------|
## WALSSI | 5457 | 9249 | 14706 |
## | 6591.46 | 8114.54 | |
## | 195.25 | 158.61 | |
## | 0.37 | 0.63 | 0.27 |
## | 0.22 | 0.30 | |
## | 0.10 | 0.17 | |
## ---------------------------------|-----------|-----------|-----------|
## Column Total | 24785 | 30512 | 55297 |
## | 0.45 | 0.55 | |
## ---------------------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 1392 d.f. = 4 p = 4.7e-300
##
##
## CrossTable(main_processed$Insurance.Scheme_recode, main_processed$No.New.Attendances, digits=2, fisher=F, chisq=TRUE, expected=TRUE)## Warning in chisq.test(t, correct = FALSE, ...): Chi-squared approximation may
## be incorrect##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 55297
##
##
## | main_processed$No.New.Attendances
## main_processed$Insurance.Scheme_recode | 0 | 1 | Row Total |
## ---------------------------------------|-----------|-----------|-----------|
## A | 23 | 20 | 43 |
## | 19.27 | 23.73 | |
## | 0.72 | 0.59 | |
## | 0.53 | 0.47 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ---------------------------------------|-----------|-----------|-----------|
## B | 2065 | 2087 | 4152 |
## | 1860.99 | 2291.01 | |
## | 22.36 | 18.17 | |
## | 0.50 | 0.50 | 0.08 |
## | 0.08 | 0.07 | |
## | 0.04 | 0.04 | |
## ---------------------------------------|-----------|-----------|-----------|
## C | 10 | 6 | 16 |
## | 7.17 | 8.83 | |
## | 1.12 | 0.91 | |
## | 0.62 | 0.38 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ---------------------------------------|-----------|-----------|-----------|
## D | 7843 | 6958 | 14801 |
## | 6634.04 | 8166.96 | |
## | 220.31 | 178.96 | |
## | 0.53 | 0.47 | 0.27 |
## | 0.32 | 0.23 | |
## | 0.14 | 0.13 | |
## ---------------------------------------|-----------|-----------|-----------|
## E | 80 | 46 | 126 |
## | 56.48 | 69.52 | |
## | 9.80 | 7.96 | |
## | 0.63 | 0.37 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ---------------------------------------|-----------|-----------|-----------|
## G | 176 | 196 | 372 |
## | 166.74 | 205.26 | |
## | 0.51 | 0.42 | |
## | 0.47 | 0.53 | 0.01 |
## | 0.01 | 0.01 | |
## | 0.00 | 0.00 | |
## ---------------------------------------|-----------|-----------|-----------|
## H | 38 | 26 | 64 |
## | 28.69 | 35.31 | |
## | 3.02 | 2.46 | |
## | 0.59 | 0.41 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ---------------------------------------|-----------|-----------|-----------|
## I | 1822 | 1920 | 3742 |
## | 1677.22 | 2064.78 | |
## | 12.50 | 10.15 | |
## | 0.49 | 0.51 | 0.07 |
## | 0.07 | 0.06 | |
## | 0.03 | 0.03 | |
## ---------------------------------------|-----------|-----------|-----------|
## J | 49 | 26 | 75 |
## | 33.62 | 41.38 | |
## | 7.04 | 5.72 | |
## | 0.65 | 0.35 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ---------------------------------------|-----------|-----------|-----------|
## M | 0 | 4 | 4 |
## | 1.79 | 2.21 | |
## | 1.79 | 1.46 | |
## | 0.00 | 1.00 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ---------------------------------------|-----------|-----------|-----------|
## O | 148 | 210 | 358 |
## | 160.46 | 197.54 | |
## | 0.97 | 0.79 | |
## | 0.41 | 0.59 | 0.01 |
## | 0.01 | 0.01 | |
## | 0.00 | 0.00 | |
## ---------------------------------------|-----------|-----------|-----------|
## P | 76 | 45 | 121 |
## | 54.23 | 66.77 | |
## | 8.74 | 7.10 | |
## | 0.63 | 0.37 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ---------------------------------------|-----------|-----------|-----------|
## S | 822 | 890 | 1712 |
## | 767.35 | 944.65 | |
## | 3.89 | 3.16 | |
## | 0.48 | 0.52 | 0.03 |
## | 0.03 | 0.03 | |
## | 0.01 | 0.02 | |
## ---------------------------------------|-----------|-----------|-----------|
## U | 6768 | 13477 | 20245 |
## | 9074.13 | 11170.87 | |
## | 586.09 | 476.08 | |
## | 0.33 | 0.67 | 0.37 |
## | 0.27 | 0.44 | |
## | 0.12 | 0.24 | |
## ---------------------------------------|-----------|-----------|-----------|
## V | 4865 | 4601 | 9466 |
## | 4242.81 | 5223.19 | |
## | 91.24 | 74.12 | |
## | 0.51 | 0.49 | 0.17 |
## | 0.20 | 0.15 | |
## | 0.09 | 0.08 | |
## ---------------------------------------|-----------|-----------|-----------|
## Column Total | 24785 | 30512 | 55297 |
## | 0.45 | 0.55 | |
## ---------------------------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 1758 d.f. = 14 p = 0
##
##
## CrossTable(main_processed$Eligibility_recode, main_processed$No.New.Attendances, digits=2, fisher=F, chisq=TRUE, expected=TRUE)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 55297
##
##
## | main_processed$No.New.Attendances
## main_processed$Eligibility_recode | 0 | 1 | Row Total |
## ----------------------------------|-----------|-----------|-----------|
## ACUTE UNCLASSIFIED | 58 | 78 | 136 |
## | 60.96 | 75.04 | |
## | 0.14 | 0.12 | |
## | 0.43 | 0.57 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ----------------------------------|-----------|-----------|-----------|
## ELIGIBILITY UNKNOWN | 282 | 718 | 1000 |
## | 448.22 | 551.78 | |
## | 61.64 | 50.07 | |
## | 0.28 | 0.72 | 0.02 |
## | 0.01 | 0.02 | |
## | 0.01 | 0.01 | |
## ----------------------------------|-----------|-----------|-----------|
## EXEMPT | 726 | 699 | 1425 |
## | 638.71 | 786.29 | |
## | 11.93 | 9.69 | |
## | 0.51 | 0.49 | 0.03 |
## | 0.03 | 0.02 | |
## | 0.01 | 0.01 | |
## ----------------------------------|-----------|-----------|-----------|
## MEDICAL CARD | 10861 | 11372 | 22233 |
## | 9965.19 | 12267.81 | |
## | 80.53 | 65.41 | |
## | 0.49 | 0.51 | 0.40 |
## | 0.44 | 0.37 | |
## | 0.20 | 0.21 | |
## ----------------------------------|-----------|-----------|-----------|
## NON ACUTE UNCLASSIFIED | 12 | 9 | 21 |
## | 9.41 | 11.59 | |
## | 0.71 | 0.58 | |
## | 0.57 | 0.43 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ----------------------------------|-----------|-----------|-----------|
## NON MEDICAL CARD | 12719 | 17583 | 30302 |
## | 13581.84 | 16720.16 | |
## | 54.82 | 44.53 | |
## | 0.42 | 0.58 | 0.55 |
## | 0.51 | 0.58 | |
## | 0.23 | 0.32 | |
## ----------------------------------|-----------|-----------|-----------|
## RESEARCH/NATIONAL PROG. | 127 | 53 | 180 |
## | 80.68 | 99.32 | |
## | 26.59 | 21.60 | |
## | 0.71 | 0.29 | 0.00 |
## | 0.01 | 0.00 | |
## | 0.00 | 0.00 | |
## ----------------------------------|-----------|-----------|-----------|
## Column Total | 24785 | 30512 | 55297 |
## | 0.45 | 0.55 | |
## ----------------------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 428 d.f. = 6 p = 2.2e-89
##
##
## CrossTable(main_processed$Booking.Type_recode, main_processed$No.New.Attendances, digits=2, fisher=F, chisq=TRUE, expected=TRUE)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 55297
##
##
## | main_processed$No.New.Attendances
## main_processed$Booking.Type_recode | 0 | 1 | Row Total |
## -----------------------------------|-----------|-----------|-----------|
## NEW | 637 | 23599 | 24236 |
## | 10862.96 | 13373.04 | |
## | 9626.32 | 7819.49 | |
## | 0.03 | 0.97 | 0.44 |
## | 0.03 | 0.77 | |
## | 0.01 | 0.43 | |
## -----------------------------------|-----------|-----------|-----------|
## RETURN | 21771 | 5944 | 27715 |
## | 12422.31 | 15292.69 | |
## | 7035.58 | 5715.02 | |
## | 0.79 | 0.21 | 0.50 |
## | 0.88 | 0.19 | |
## | 0.39 | 0.11 | |
## -----------------------------------|-----------|-----------|-----------|
## WARD | 2377 | 969 | 3346 |
## | 1499.73 | 1846.27 | |
## | 513.16 | 416.84 | |
## | 0.71 | 0.29 | 0.06 |
## | 0.10 | 0.03 | |
## | 0.04 | 0.02 | |
## -----------------------------------|-----------|-----------|-----------|
## Column Total | 24785 | 30512 | 55297 |
## | 0.45 | 0.55 | |
## -----------------------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 31126 d.f. = 2 p = 0
##
##
## CrossTable(main_processed$Hospital.Catchment_recode, main_processed$No.New.Attendances, digits=2, fisher=F, chisq=TRUE, expected=TRUE)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 55297
##
##
## | main_processed$No.New.Attendances
## main_processed$Hospital.Catchment_recode | 0 | 1 | Row Total |
## -----------------------------------------|-----------|-----------|-----------|
## Beaumont | 1194 | 904 | 2098 |
## | 940.36 | 1157.64 | |
## | 68.42 | 55.57 | |
## | 0.57 | 0.43 | 0.04 |
## | 0.05 | 0.03 | |
## | 0.02 | 0.02 | |
## -----------------------------------------|-----------|-----------|-----------|
## Connolly | 2495 | 3742 | 6237 |
## | 2795.52 | 3441.48 | |
## | 32.31 | 26.24 | |
## | 0.40 | 0.60 | 0.11 |
## | 0.10 | 0.12 | |
## | 0.05 | 0.07 | |
## -----------------------------------------|-----------|-----------|-----------|
## International | 54 | 54 | 108 |
## | 48.41 | 59.59 | |
## | 0.65 | 0.52 | |
## | 0.50 | 0.50 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## -----------------------------------------|-----------|-----------|-----------|
## James | 242 | 351 | 593 |
## | 265.79 | 327.21 | |
## | 2.13 | 1.73 | |
## | 0.41 | 0.59 | 0.01 |
## | 0.01 | 0.01 | |
## | 0.00 | 0.01 | |
## -----------------------------------------|-----------|-----------|-----------|
## Mater | 5676 | 8021 | 13697 |
## | 6139.21 | 7557.79 | |
## | 34.95 | 28.39 | |
## | 0.41 | 0.59 | 0.25 |
## | 0.23 | 0.26 | |
## | 0.10 | 0.15 | |
## -----------------------------------------|-----------|-----------|-----------|
## National | 14797 | 16990 | 31787 |
## | 14247.44 | 17539.56 | |
## | 21.20 | 17.22 | |
## | 0.47 | 0.53 | 0.57 |
## | 0.60 | 0.56 | |
## | 0.27 | 0.31 | |
## -----------------------------------------|-----------|-----------|-----------|
## Tallaght | 78 | 162 | 240 |
## | 107.57 | 132.43 | |
## | 8.13 | 6.60 | |
## | 0.32 | 0.68 | 0.00 |
## | 0.00 | 0.01 | |
## | 0.00 | 0.00 | |
## -----------------------------------------|-----------|-----------|-----------|
## Vincents | 249 | 288 | 537 |
## | 240.69 | 296.31 | |
## | 0.29 | 0.23 | |
## | 0.46 | 0.54 | 0.01 |
## | 0.01 | 0.01 | |
## | 0.00 | 0.01 | |
## -----------------------------------------|-----------|-----------|-----------|
## Column Total | 24785 | 30512 | 55297 |
## | 0.45 | 0.55 | |
## -----------------------------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 305 d.f. = 7 p = 6.4e-62
##
##
## CrossTable(main_processed$bookedDay, main_processed$No.New.Attendances, digits=2, fisher=F, chisq=TRUE, expected=TRUE)## Warning in chisq.test(t, correct = FALSE, ...): Chi-squared approximation may
## be incorrect##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 52987
##
##
## | main_processed$No.New.Attendances
## main_processed$bookedDay | 0 | 1 | Row Total |
## -------------------------|-----------|-----------|-----------|
## Friday | 3327 | 5063 | 8390 |
## | 3721.64 | 4668.36 | |
## | 41.85 | 33.36 | |
## | 0.40 | 0.60 | 0.16 |
## | 0.14 | 0.17 | |
## | 0.06 | 0.10 | |
## -------------------------|-----------|-----------|-----------|
## Monday | 5569 | 5992 | 11561 |
## | 5128.23 | 6432.77 | |
## | 37.88 | 30.20 | |
## | 0.48 | 0.52 | 0.22 |
## | 0.24 | 0.20 | |
## | 0.11 | 0.11 | |
## -------------------------|-----------|-----------|-----------|
## Saturday | 16 | 137 | 153 |
## | 67.87 | 85.13 | |
## | 39.64 | 31.60 | |
## | 0.10 | 0.90 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## -------------------------|-----------|-----------|-----------|
## Sunday | 9 | 1 | 10 |
## | 4.44 | 5.56 | |
## | 4.70 | 3.74 | |
## | 0.90 | 0.10 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## -------------------------|-----------|-----------|-----------|
## Thursday | 4270 | 5956 | 10226 |
## | 4536.05 | 5689.95 | |
## | 15.60 | 12.44 | |
## | 0.42 | 0.58 | 0.19 |
## | 0.18 | 0.20 | |
## | 0.08 | 0.11 | |
## -------------------------|-----------|-----------|-----------|
## Tuesday | 5325 | 6513 | 11838 |
## | 5251.11 | 6586.89 | |
## | 1.04 | 0.83 | |
## | 0.45 | 0.55 | 0.22 |
## | 0.23 | 0.22 | |
## | 0.10 | 0.12 | |
## -------------------------|-----------|-----------|-----------|
## Wednesday | 4988 | 5821 | 10809 |
## | 4794.66 | 6014.34 | |
## | 7.80 | 6.22 | |
## | 0.46 | 0.54 | 0.20 |
## | 0.21 | 0.20 | |
## | 0.09 | 0.11 | |
## -------------------------|-----------|-----------|-----------|
## Column Total | 23504 | 29483 | 52987 |
## | 0.44 | 0.56 | |
## -------------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 267 d.f. = 6 p = 1e-54
##
##
## CrossTable(main_processed$bookedMonthYear, main_processed$No.New.Attendances, digits=2, fisher=F, chisq=TRUE, expected=TRUE)## Warning in chisq.test(t, correct = FALSE, ...): Chi-squared approximation may
## be incorrect##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 52987
##
##
## | main_processed$No.New.Attendances
## main_processed$bookedMonthYear | 0 | 1 | Row Total |
## -------------------------------|-----------|-----------|-----------|
## 2017-08 | 0 | 1 | 1 |
## | 0.44 | 0.56 | |
## | 0.44 | 0.35 | |
## | 0.00 | 1.00 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## -------------------------------|-----------|-----------|-----------|
## 2019-01 | 82 | 7 | 89 |
## | 39.48 | 49.52 | |
## | 45.80 | 36.51 | |
## | 0.92 | 0.08 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## -------------------------------|-----------|-----------|-----------|
## 2019-02 | 58 | 8 | 66 |
## | 29.28 | 36.72 | |
## | 28.18 | 22.47 | |
## | 0.88 | 0.12 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## -------------------------------|-----------|-----------|-----------|
## 2019-03 | 38 | 2 | 40 |
## | 17.74 | 22.26 | |
## | 23.13 | 18.44 | |
## | 0.95 | 0.05 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## -------------------------------|-----------|-----------|-----------|
## 2019-04 | 11 | 4 | 15 |
## | 6.65 | 8.35 | |
## | 2.84 | 2.26 | |
## | 0.73 | 0.27 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## -------------------------------|-----------|-----------|-----------|
## 2019-05 | 31 | 2 | 33 |
## | 14.64 | 18.36 | |
## | 18.29 | 14.58 | |
## | 0.94 | 0.06 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## -------------------------------|-----------|-----------|-----------|
## 2019-06 | 59 | 5 | 64 |
## | 28.39 | 35.61 | |
## | 33.01 | 26.31 | |
## | 0.92 | 0.08 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## -------------------------------|-----------|-----------|-----------|
## 2019-07 | 136 | 15 | 151 |
## | 66.98 | 84.02 | |
## | 71.12 | 56.70 | |
## | 0.90 | 0.10 | 0.00 |
## | 0.01 | 0.00 | |
## | 0.00 | 0.00 | |
## -------------------------------|-----------|-----------|-----------|
## 2019-08 | 147 | 9 | 156 |
## | 69.20 | 86.80 | |
## | 87.47 | 69.73 | |
## | 0.94 | 0.06 | 0.00 |
## | 0.01 | 0.00 | |
## | 0.00 | 0.00 | |
## -------------------------------|-----------|-----------|-----------|
## 2019-09 | 92 | 12 | 104 |
## | 46.13 | 57.87 | |
## | 45.60 | 36.36 | |
## | 0.88 | 0.12 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## -------------------------------|-----------|-----------|-----------|
## 2019-10 | 130 | 18 | 148 |
## | 65.65 | 82.35 | |
## | 63.08 | 50.28 | |
## | 0.88 | 0.12 | 0.00 |
## | 0.01 | 0.00 | |
## | 0.00 | 0.00 | |
## -------------------------------|-----------|-----------|-----------|
## 2019-11 | 146 | 64 | 210 |
## | 93.15 | 116.85 | |
## | 29.98 | 23.90 | |
## | 0.70 | 0.30 | 0.00 |
## | 0.01 | 0.00 | |
## | 0.00 | 0.00 | |
## -------------------------------|-----------|-----------|-----------|
## 2019-12 | 160 | 363 | 523 |
## | 231.99 | 291.01 | |
## | 22.34 | 17.81 | |
## | 0.31 | 0.69 | 0.01 |
## | 0.01 | 0.01 | |
## | 0.00 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2020-01 | 479 | 839 | 1318 |
## | 584.64 | 733.36 | |
## | 19.09 | 15.22 | |
## | 0.36 | 0.64 | 0.02 |
## | 0.02 | 0.03 | |
## | 0.01 | 0.02 | |
## -------------------------------|-----------|-----------|-----------|
## 2020-02 | 394 | 605 | 999 |
## | 443.14 | 555.86 | |
## | 5.45 | 4.34 | |
## | 0.39 | 0.61 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2020-03 | 165 | 116 | 281 |
## | 124.65 | 156.35 | |
## | 13.06 | 10.42 | |
## | 0.59 | 0.41 | 0.01 |
## | 0.01 | 0.00 | |
## | 0.00 | 0.00 | |
## -------------------------------|-----------|-----------|-----------|
## 2020-04 | 83 | 217 | 300 |
## | 133.07 | 166.93 | |
## | 18.84 | 15.02 | |
## | 0.28 | 0.72 | 0.01 |
## | 0.00 | 0.01 | |
## | 0.00 | 0.00 | |
## -------------------------------|-----------|-----------|-----------|
## 2020-05 | 341 | 320 | 661 |
## | 293.21 | 367.79 | |
## | 7.79 | 6.21 | |
## | 0.52 | 0.48 | 0.01 |
## | 0.01 | 0.01 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2020-06 | 869 | 402 | 1271 |
## | 563.79 | 707.21 | |
## | 165.23 | 131.72 | |
## | 0.68 | 0.32 | 0.02 |
## | 0.04 | 0.01 | |
## | 0.02 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2020-07 | 817 | 594 | 1411 |
## | 625.89 | 785.11 | |
## | 58.35 | 46.52 | |
## | 0.58 | 0.42 | 0.03 |
## | 0.03 | 0.02 | |
## | 0.02 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2020-08 | 853 | 650 | 1503 |
## | 666.70 | 836.30 | |
## | 52.06 | 41.50 | |
## | 0.57 | 0.43 | 0.03 |
## | 0.04 | 0.02 | |
## | 0.02 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2020-09 | 568 | 777 | 1345 |
## | 596.62 | 748.38 | |
## | 1.37 | 1.09 | |
## | 0.42 | 0.58 | 0.03 |
## | 0.02 | 0.03 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2020-10 | 503 | 688 | 1191 |
## | 528.30 | 662.70 | |
## | 1.21 | 0.97 | |
## | 0.42 | 0.58 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2020-11 | 644 | 769 | 1413 |
## | 626.78 | 786.22 | |
## | 0.47 | 0.38 | |
## | 0.46 | 0.54 | 0.03 |
## | 0.03 | 0.03 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2020-12 | 438 | 464 | 902 |
## | 400.11 | 501.89 | |
## | 3.59 | 2.86 | |
## | 0.49 | 0.51 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2021-01 | 352 | 334 | 686 |
## | 304.30 | 381.70 | |
## | 7.48 | 5.96 | |
## | 0.51 | 0.49 | 0.01 |
## | 0.01 | 0.01 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2021-02 | 426 | 563 | 989 |
## | 438.70 | 550.30 | |
## | 0.37 | 0.29 | |
## | 0.43 | 0.57 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2021-03 | 468 | 849 | 1317 |
## | 584.20 | 732.80 | |
## | 23.11 | 18.42 | |
## | 0.36 | 0.64 | 0.02 |
## | 0.02 | 0.03 | |
## | 0.01 | 0.02 | |
## -------------------------------|-----------|-----------|-----------|
## 2021-04 | 422 | 692 | 1114 |
## | 494.15 | 619.85 | |
## | 10.53 | 8.40 | |
## | 0.38 | 0.62 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2021-05 | 376 | 637 | 1013 |
## | 449.35 | 563.65 | |
## | 11.97 | 9.54 | |
## | 0.37 | 0.63 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2021-06 | 331 | 616 | 947 |
## | 420.07 | 526.93 | |
## | 18.89 | 15.06 | |
## | 0.35 | 0.65 | 0.02 |
## | 0.01 | 0.02 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2021-07 | 434 | 665 | 1099 |
## | 487.49 | 611.51 | |
## | 5.87 | 4.68 | |
## | 0.39 | 0.61 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2021-08 | 653 | 617 | 1270 |
## | 563.35 | 706.65 | |
## | 14.27 | 11.37 | |
## | 0.51 | 0.49 | 0.02 |
## | 0.03 | 0.02 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2021-09 | 547 | 855 | 1402 |
## | 621.90 | 780.10 | |
## | 9.02 | 7.19 | |
## | 0.39 | 0.61 | 0.03 |
## | 0.02 | 0.03 | |
## | 0.01 | 0.02 | |
## -------------------------------|-----------|-----------|-----------|
## 2021-10 | 584 | 627 | 1211 |
## | 537.18 | 673.82 | |
## | 4.08 | 3.25 | |
## | 0.48 | 0.52 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2021-11 | 412 | 563 | 975 |
## | 432.49 | 542.51 | |
## | 0.97 | 0.77 | |
## | 0.42 | 0.58 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2021-12 | 365 | 588 | 953 |
## | 422.73 | 530.27 | |
## | 7.88 | 6.29 | |
## | 0.38 | 0.62 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2022-01 | 428 | 564 | 992 |
## | 440.03 | 551.97 | |
## | 0.33 | 0.26 | |
## | 0.43 | 0.57 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2022-02 | 452 | 605 | 1057 |
## | 468.86 | 588.14 | |
## | 0.61 | 0.48 | |
## | 0.43 | 0.57 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2022-03 | 515 | 662 | 1177 |
## | 522.09 | 654.91 | |
## | 0.10 | 0.08 | |
## | 0.44 | 0.56 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2022-04 | 432 | 568 | 1000 |
## | 443.58 | 556.42 | |
## | 0.30 | 0.24 | |
## | 0.43 | 0.57 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2022-05 | 470 | 772 | 1242 |
## | 550.93 | 691.07 | |
## | 11.89 | 9.48 | |
## | 0.38 | 0.62 | 0.02 |
## | 0.02 | 0.03 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2022-06 | 474 | 760 | 1234 |
## | 547.38 | 686.62 | |
## | 9.84 | 7.84 | |
## | 0.38 | 0.62 | 0.02 |
## | 0.02 | 0.03 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2022-07 | 524 | 725 | 1249 |
## | 554.03 | 694.97 | |
## | 1.63 | 1.30 | |
## | 0.42 | 0.58 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2022-08 | 434 | 530 | 964 |
## | 427.61 | 536.39 | |
## | 0.10 | 0.08 | |
## | 0.45 | 0.55 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2022-09 | 561 | 706 | 1267 |
## | 562.02 | 704.98 | |
## | 0.00 | 0.00 | |
## | 0.44 | 0.56 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2022-10 | 702 | 795 | 1497 |
## | 664.04 | 832.96 | |
## | 2.17 | 1.73 | |
## | 0.47 | 0.53 | 0.03 |
## | 0.03 | 0.03 | |
## | 0.01 | 0.02 | |
## -------------------------------|-----------|-----------|-----------|
## 2022-11 | 466 | 649 | 1115 |
## | 494.59 | 620.41 | |
## | 1.65 | 1.32 | |
## | 0.42 | 0.58 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2022-12 | 360 | 468 | 828 |
## | 367.28 | 460.72 | |
## | 0.14 | 0.12 | |
## | 0.43 | 0.57 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2023-01 | 536 | 716 | 1252 |
## | 555.36 | 696.64 | |
## | 0.68 | 0.54 | |
## | 0.43 | 0.57 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2023-02 | 460 | 554 | 1014 |
## | 449.79 | 564.21 | |
## | 0.23 | 0.18 | |
## | 0.45 | 0.55 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2023-03 | 501 | 660 | 1161 |
## | 515.00 | 646.00 | |
## | 0.38 | 0.30 | |
## | 0.43 | 0.57 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2023-04 | 529 | 688 | 1217 |
## | 539.84 | 677.16 | |
## | 0.22 | 0.17 | |
## | 0.43 | 0.57 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2023-05 | 515 | 761 | 1276 |
## | 566.01 | 709.99 | |
## | 4.60 | 3.66 | |
## | 0.40 | 0.60 | 0.02 |
## | 0.02 | 0.03 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2023-06 | 522 | 670 | 1192 |
## | 528.75 | 663.25 | |
## | 0.09 | 0.07 | |
## | 0.44 | 0.56 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2023-07 | 533 | 658 | 1191 |
## | 528.30 | 662.70 | |
## | 0.04 | 0.03 | |
## | 0.45 | 0.55 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2023-08 | 420 | 582 | 1002 |
## | 444.47 | 557.53 | |
## | 1.35 | 1.07 | |
## | 0.42 | 0.58 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2023-09 | 302 | 627 | 929 |
## | 412.09 | 516.91 | |
## | 29.41 | 23.44 | |
## | 0.33 | 0.67 | 0.02 |
## | 0.01 | 0.02 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2023-10 | 339 | 484 | 823 |
## | 365.07 | 457.93 | |
## | 1.86 | 1.48 | |
## | 0.41 | 0.59 | 0.02 |
## | 0.01 | 0.02 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2023-11 | 354 | 605 | 959 |
## | 425.39 | 533.61 | |
## | 11.98 | 9.55 | |
## | 0.37 | 0.63 | 0.02 |
## | 0.02 | 0.02 | |
## | 0.01 | 0.01 | |
## -------------------------------|-----------|-----------|-----------|
## 2023-12 | 61 | 117 | 178 |
## | 78.96 | 99.04 | |
## | 4.08 | 3.26 | |
## | 0.34 | 0.66 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## -------------------------------|-----------|-----------|-----------|
## Column Total | 23504 | 29483 | 52987 |
## | 0.44 | 0.56 | |
## -------------------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 1826 d.f. = 60 p = 0
##
##
## ## difference between New Attendance and booking
leveneTest(daysDiff_attendanceBooked ~ as.factor(No.New.Attendances), data=main_processed) #leveneTest is Ha## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 59.1 1.6e-14 ***
## 52985
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1t.test(daysDiff_attendanceBooked ~ No.New.Attendances, var.equal=F, data=main_processed)##
## Welch Two Sample t-test
##
## data: daysDiff_attendanceBooked by No.New.Attendances
## t = -18, df = 49014, p-value <2e-16
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## -73 -59
## sample estimates:
## mean in group 0 mean in group 1
## 480 546## patient characteristics relevance
CrossTable(main_processed$Gender, main_processed$No.New.Attendances, digits=2, fisher=TRUE, chisq=TRUE, expected=TRUE)## Warning in chisq.test(t, correct = FALSE, ...): Chi-squared approximation may
## be incorrect##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 55297
##
##
## | main_processed$No.New.Attendances
## main_processed$Gender | 0 | 1 | Row Total |
## ----------------------|-----------|-----------|-----------|
## Female | 24598 | 29649 | 54247 |
## | 24314.37 | 29932.63 | |
## | 3.31 | 2.69 | |
## | 0.45 | 0.55 | 0.98 |
## | 0.99 | 0.97 | |
## | 0.44 | 0.54 | |
## ----------------------|-----------|-----------|-----------|
## Male | 187 | 861 | 1048 |
## | 469.73 | 578.27 | |
## | 170.18 | 138.23 | |
## | 0.18 | 0.82 | 0.02 |
## | 0.01 | 0.03 | |
## | 0.00 | 0.02 | |
## ----------------------|-----------|-----------|-----------|
## Unknown | 0 | 2 | 2 |
## | 0.90 | 1.10 | |
## | 0.90 | 0.73 | |
## | 0.00 | 1.00 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ----------------------|-----------|-----------|-----------|
## Column Total | 24785 | 30512 | 55297 |
## | 0.45 | 0.55 | |
## ----------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 316 d.f. = 2 p = 2.4e-69
##
##
##
## Fisher's Exact Test for Count Data
## ------------------------------------------------------------
## Alternative hypothesis: two.sided
## p = 2.7e-77
##
## CrossTable(main_processed$Age.at.Attendance.Cat.HSE, main_processed$No.New.Attendances, digits=2, fisher=F, chisq=TRUE, expected=TRUE) # age group## Warning in chisq.test(t, correct = FALSE, ...): Chi-squared approximation may
## be incorrect##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 55297
##
##
## | main_processed$No.New.Attendances
## main_processed$Age.at.Attendance.Cat.HSE | 0 | 1 | Row Total |
## -----------------------------------------|-----------|-----------|-----------|
## 0 - 4 | 0 | 0 | 0 |
## | 0.00 | 0.00 | |
## | NaN | NaN | |
## | NaN | NaN | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## -----------------------------------------|-----------|-----------|-----------|
## 10 - 14 | 29 | 74 | 103 |
## | 46.17 | 56.83 | |
## | 6.38 | 5.18 | |
## | 0.28 | 0.72 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## -----------------------------------------|-----------|-----------|-----------|
## 15 - 24 | 333 | 1702 | 2035 |
## | 912.12 | 1122.88 | |
## | 367.69 | 298.68 | |
## | 0.16 | 0.84 | 0.04 |
## | 0.01 | 0.06 | |
## | 0.01 | 0.03 | |
## -----------------------------------------|-----------|-----------|-----------|
## 25 - 34 | 1157 | 4752 | 5909 |
## | 2648.51 | 3260.49 | |
## | 839.94 | 682.29 | |
## | 0.20 | 0.80 | 0.11 |
## | 0.05 | 0.16 | |
## | 0.02 | 0.09 | |
## -----------------------------------------|-----------|-----------|-----------|
## 35 - 44 | 4057 | 7768 | 11825 |
## | 5300.15 | 6524.85 | |
## | 291.58 | 236.85 | |
## | 0.34 | 0.66 | 0.21 |
## | 0.16 | 0.25 | |
## | 0.07 | 0.14 | |
## -----------------------------------------|-----------|-----------|-----------|
## 45 - 54 | 6114 | 7065 | 13179 |
## | 5907.04 | 7271.96 | |
## | 7.25 | 5.89 | |
## | 0.46 | 0.54 | 0.24 |
## | 0.25 | 0.23 | |
## | 0.11 | 0.13 | |
## -----------------------------------------|-----------|-----------|-----------|
## 55 - 64 | 6275 | 4461 | 10736 |
## | 4812.05 | 5923.95 | |
## | 444.77 | 361.28 | |
## | 0.58 | 0.42 | 0.19 |
## | 0.25 | 0.15 | |
## | 0.11 | 0.08 | |
## -----------------------------------------|-----------|-----------|-----------|
## 65 - 74 | 4466 | 3074 | 7540 |
## | 3379.55 | 4160.45 | |
## | 349.27 | 283.71 | |
## | 0.59 | 0.41 | 0.14 |
## | 0.18 | 0.10 | |
## | 0.08 | 0.06 | |
## -----------------------------------------|-----------|-----------|-----------|
## 75 - 84 | 1926 | 1333 | 3259 |
## | 1460.74 | 1798.26 | |
## | 148.19 | 120.38 | |
## | 0.59 | 0.41 | 0.06 |
## | 0.08 | 0.04 | |
## | 0.03 | 0.02 | |
## -----------------------------------------|-----------|-----------|-----------|
## 85 >= | 428 | 283 | 711 |
## | 318.68 | 392.32 | |
## | 37.50 | 30.46 | |
## | 0.60 | 0.40 | 0.01 |
## | 0.02 | 0.01 | |
## | 0.01 | 0.01 | |
## -----------------------------------------|-----------|-----------|-----------|
## Column Total | 24785 | 30512 | 55297 |
## | 0.45 | 0.55 | |
## -----------------------------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = NaN d.f. = 9 p = NaN
##
##
## leveneTest(Age.at.Attendance ~ as.factor(No.New.Attendances), data=main_processed)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 203 <2e-16 ***
## 55295
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1t.test(Age.at.Attendance ~ No.New.Attendances, var.equal=FALSE, data=main_processed) #leveneTest is Ha##
## Welch Two Sample t-test
##
## data: Age.at.Attendance by No.New.Attendances
## t = 67, df = 54558, p-value <2e-16
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## 8.1 8.6
## sample estimates:
## mean in group 0 mean in group 1
## 56 47wilcox.test(main_processed$Age.at.Attendance ~ main_processed$No.New.Attendances)##
## Wilcoxon rank sum test with continuity correction
##
## data: main_processed$Age.at.Attendance by main_processed$No.New.Attendances
## W = 5e+08, p-value <2e-16
## alternative hypothesis: true location shift is not equal to 0CrossTable(main_processed$Area.of.Residence_recode, main_processed$No.New.Attendances, digits=2, fisher=F, chisq=TRUE, expected=TRUE) # remove Na or impute where no residence## Warning in chisq.test(t, correct = FALSE, ...): Chi-squared approximation may
## be incorrect##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 55297
##
##
## | main_processed$No.New.Attendances
## main_processed$Area.of.Residence_recode | 0 | 1 | Row Total |
## ---------------------------------------------|-----------|-----------|-----------|
## DUBLIN NTH | 11084 | 14102 | 25186 |
## | 11288.77 | 13897.23 | |
## | 3.71 | 3.02 | |
## | 0.44 | 0.56 | 0.46 |
## | 0.45 | 0.46 | |
## | 0.20 | 0.26 | |
## ---------------------------------------------|-----------|-----------|-----------|
## DUBLIN STH | 570 | 804 | 1374 |
## | 615.85 | 758.15 | |
## | 3.41 | 2.77 | |
## | 0.41 | 0.59 | 0.02 |
## | 0.02 | 0.03 | |
## | 0.01 | 0.01 | |
## ---------------------------------------------|-----------|-----------|-----------|
## EASTERN & MIDLAND REGION (excl.Dublin,Meath) | 5953 | 6641 | 12594 |
## | 5644.83 | 6949.17 | |
## | 16.82 | 13.67 | |
## | 0.47 | 0.53 | 0.23 |
## | 0.24 | 0.22 | |
## | 0.11 | 0.12 | |
## ---------------------------------------------|-----------|-----------|-----------|
## Meath | 3796 | 5159 | 8955 |
## | 4013.77 | 4941.23 | |
## | 11.82 | 9.60 | |
## | 0.42 | 0.58 | 0.16 |
## | 0.15 | 0.17 | |
## | 0.07 | 0.09 | |
## ---------------------------------------------|-----------|-----------|-----------|
## NORTHERN WESTERN REGION | 2560 | 3388 | 5948 |
## | 2665.99 | 3282.01 | |
## | 4.21 | 3.42 | |
## | 0.43 | 0.57 | 0.11 |
## | 0.10 | 0.11 | |
## | 0.05 | 0.06 | |
## ---------------------------------------------|-----------|-----------|-----------|
## OUTSIDE IRELAND | 6 | 4 | 10 |
## | 4.48 | 5.52 | |
## | 0.51 | 0.42 | |
## | 0.60 | 0.40 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ---------------------------------------------|-----------|-----------|-----------|
## SOUTHERN REGION | 764 | 363 | 1127 |
## | 505.14 | 621.86 | |
## | 132.65 | 107.76 | |
## | 0.68 | 0.32 | 0.02 |
## | 0.03 | 0.01 | |
## | 0.01 | 0.01 | |
## ---------------------------------------------|-----------|-----------|-----------|
## UNKNOWN | 52 | 51 | 103 |
## | 46.17 | 56.83 | |
## | 0.74 | 0.60 | |
## | 0.50 | 0.50 | 0.00 |
## | 0.00 | 0.00 | |
## | 0.00 | 0.00 | |
## ---------------------------------------------|-----------|-----------|-----------|
## Column Total | 24785 | 30512 | 55297 |
## | 0.45 | 0.55 | |
## ---------------------------------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 315 d.f. = 7 p = 3.5e-64
##
##
## CrossTable(main_processed$addressDiff, main_processed$No.New.Attendances, digits=2, fisher=F, chisq=TRUE, expected=TRUE)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Expected N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 55297
##
##
## | main_processed$No.New.Attendances
## main_processed$addressDiff | 0 | 1 | Row Total |
## ---------------------------|-----------|-----------|-----------|
## 0 | 20521 | 25372 | 45893 |
## | 20569.98 | 25323.02 | |
## | 0.12 | 0.09 | |
## | 0.45 | 0.55 | 0.83 |
## | 0.83 | 0.83 | |
## | 0.37 | 0.46 | |
## ---------------------------|-----------|-----------|-----------|
## 1 | 4264 | 5140 | 9404 |
## | 4215.02 | 5188.98 | |
## | 0.57 | 0.46 | |
## | 0.45 | 0.55 | 0.17 |
## | 0.17 | 0.17 | |
## | 0.08 | 0.09 | |
## ---------------------------|-----------|-----------|-----------|
## Column Total | 24785 | 30512 | 55297 |
## | 0.45 | 0.55 | |
## ---------------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 1.2 d.f. = 1 p = 0.26
##
## Pearson's Chi-squared test with Yates' continuity correction
## ------------------------------------------------------------
## Chi^2 = 1.2 d.f. = 1 p = 0.27
##
## Cancellation
# convert the non-numeric variables to numeric values (the lower the number, the higher frequency)
main_processed$Referral.Source_numeric <- mapvalues(main_processed$Referral.Source_recode, from = c("CLINIC","GP","WARD","BREAST CHECK","Elsew outside Mater","OTHER CONSULTANT","Elsew of Mater","EMERGENCY DEPT"), to = c(1:8))
main_processed$Clinic.Type_numeric <- mapvalues(main_processed$Clinic.Type_recode, from = c("SBC","TRI","MED","FRA","GEN"), to = c(1:5))
main_processed$Consultant_numeric <- mapvalues(main_processed$Consultant_recode, from = c("WALSSI","BARRYM","KELLM","STOKES","HEENEY"), to = c(1:5))
main_processed$Insurance.Scheme_numeric <- mapvalues(main_processed$Insurance.Scheme_recode, from = c("U","D","V","B","I","S","G","O","E","P","J","H","A","C","M"), to = c(1:15))
main_processed$Eligibility_numeric <- mapvalues(main_processed$Eligibility_recode, from = c("NON MEDICAL CARD","MEDICAL CARD","ELIGIBILITY UNKNOWN","EXEMPT","RESEARCH/NATIONAL PROG.","ACUTE UNCLASSIFIED","NON ACUTE UNCLASSIFIED"), to = c(1:7))
main_processed$Booking.Type_numeric <- mapvalues(main_processed$Booking.Type_recode, from = c("RETURN","NEW","WARD"), to = c(1:3))
main_processed$Hospital.Catchment_numeric <- mapvalues(main_processed$Hospital.Catchment_recode, from = c("National","Mater","Connolly","Beaumont","James","Vincents","Tallaght","International"), to = c(1:8))
main_processed$appointmentDay_numeric <- mapvalues(main_processed$appointmentDay, from = c("Tuesday","Friday","Monday","Thursday","Wednesday"), to = c(1:5))
main_processed$appointmentMonthYear_numeric <- mapvalues(main_processed$appointmentMonthYear, from = c("2021-08","2021-09","2021-11","2022-06","2021-07","2022-11","2022-03","2022-05","2023-08","2023-09","2022-02","2021-10","2022-08","2022-10","2022-09","2022-07","2020-10","2021-06","2023-05","2023-11","2020-11","2021-12","2022-01","2023-07","2020-03","2023-01","2022-04","2023-06","2020-09","2023-03","2021-03","2021-01","2023-10","2021-05","2023-04","2020-12","2023-02","2023-12","2021-04","2021-02","2020-07","2020-08","2022-12","2020-04","2020-06","2020-02","2020-05","2020-01"), to = c(1:48))
main_processed$bookedDay_numeric <- mapvalues(main_processed$bookedDay, from = c("Tuesday","Monday","Wednesday","Thursday","Friday","Saturday","Sunday"), to = c(1:7))
main_processed$bookedMonthYear_numeric <- mapvalues(main_processed$bookedMonthYear, from = c("2022-10","2021-09","2020-11","2020-08","2020-07","2021-08","2022-05","2020-09","2021-03","2020-01","2022-06","2020-02","2022-03","2020-10","2021-07","2022-07","2022-09","2021-10","2020-06","2021-04","2021-02","2023-05","2021-06","2023-01","2023-04","2023-03","2022-02","2021-05","2022-11","2021-11","2023-06","2023-07","2021-12","2022-04","2022-01","2023-02","2022-08","2023-08","2020-12","2022-12","2023-09","2023-11","2021-01","2023-10","2020-05","2020-03","2019-12","2020-04","2023-12","2019-11","2019-08","2019-07","2019-10","2019-09","2019-01","2019-02","2019-06","2019-03","2019-05","2019-04","2017-08"), to = c(1:61))
main_processed$Gender_numeric <- mapvalues(main_processed$Gender, from = c("Male","Female","Unknown"), to = c(1:3))
main_processed$Area.of.Residence_numeric <- mapvalues(main_processed$Area.of.Residence_recode, from = c("DUBLIN NTH","EASTERN & MIDLAND REGION (excl.Dublin,Meath)","Meath","NORTHERN WESTERN REGION","DUBLIN STH","SOUTHERN REGION","UNKNOWN","OUTSIDE IRELAND"), to = c(1:8))
main_processed$Rebooked.Indicator_numeric <- mapvalues(main_processed$Rebooked.Indicator, from = c("Yes","No"), to = c(1,0))
main_processed$Cancellation.Group_numeric <- mapvalues(main_processed$Cancellation.Group, from = c("Hospital","Patient","DNA","Validation"), to = c(1:4))
main_processed$Reason.for.Cancellation_numeric <- mapvalues(main_processed$Reason.for.Cancellation_recode, from = c("By Hospital","By Patient","No show","By Consultant/Advanced Nurse","By Patient-health conditions","By Covid"), to = c(1:6))
write.csv(main_processed[,c("Gender_numeric","Age.at.Attendance","Area.of.Residence_numeric","addressDiff","No.Cancels","Referral.Source_numeric","Clinic.Code","Clinic.Type_numeric","Consultant_numeric","Insurance.Scheme_numeric","Eligibility_numeric","Hospital.Catchment_numeric","Booking.Type_numeric","Rebooked.Indicator_numeric","Cancellation.Group_numeric","Reason.for.Cancellation_numeric","appointmentDay_numeric","appointmentMonthYear_numeric","bookedDay_numeric","bookedMonthYear_numeric","daysDiff_AppointBooked")],"breastDetails_cancel.csv")
main_processed_cancel <- read.csv("breastDetails_cancel.csv")
main_processed_cancel <- subset(main_processed_cancel, is.na(main_processed_cancel$No.Cancels)==F)
main_processed_cancel <- main_processed_cancel %>% mutate_if(is.character,as.numeric)
cor(main_processed_cancel, method = "spearman")## X Gender_numeric Age.at.Attendance
## X 1.00000 -0.0242 -0.378
## Gender_numeric -0.02424 1.0000 0.028
## Age.at.Attendance -0.37796 0.0279 1.000
## Area.of.Residence_numeric 0.21877 0.0110 0.031
## addressDiff -0.02171 -0.0033 -0.093
## No.Cancels -0.09434 0.0504 0.126
## Referral.Source_numeric 0.21168 -0.0733 -0.208
## Clinic.Code 0.16061 -0.0371 -0.209
## Clinic.Type_numeric 0.24422 -0.0740 -0.325
## Consultant_numeric -0.00092 -0.0098 0.010
## Insurance.Scheme_numeric -0.22355 0.0390 0.179
## Eligibility_numeric 0.00720 -0.0322 0.078
## Hospital.Catchment_numeric -0.18216 -0.0218 -0.086
## Booking.Type_numeric 0.23048 -0.0829 -0.276
## Rebooked.Indicator_numeric -0.13060 0.0512 0.181
## Cancellation.Group_numeric 0.13488 -0.0538 -0.176
## Reason.for.Cancellation_numeric 0.06132 -0.0225 -0.057
## appointmentDay_numeric 0.02286 -0.0044 -0.024
## appointmentMonthYear_numeric -0.01881 -0.0080 -0.018
## bookedDay_numeric 0.02477 -0.0131 -0.021
## bookedMonthYear_numeric 0.05792 -0.0039 -0.017
## daysDiff_AppointBooked -0.14693 0.0506 0.241
## Area.of.Residence_numeric addressDiff
## X 0.21877 -0.0217
## Gender_numeric 0.01102 -0.0033
## Age.at.Attendance 0.03133 -0.0933
## Area.of.Residence_numeric 1.00000 -0.0144
## addressDiff -0.01436 1.0000
## No.Cancels 0.01815 -0.0460
## Referral.Source_numeric -0.02079 0.0249
## Clinic.Code -0.01532 0.0125
## Clinic.Type_numeric -0.01260 0.0521
## Consultant_numeric 0.00209 -0.0061
## Insurance.Scheme_numeric -0.03622 -0.0380
## Eligibility_numeric 0.01106 0.0334
## Hospital.Catchment_numeric -0.60413 0.0337
## Booking.Type_numeric -0.01984 0.0389
## Rebooked.Indicator_numeric 0.00903 -0.0459
## Cancellation.Group_numeric 0.00265 0.0430
## Reason.for.Cancellation_numeric 0.01044 0.0224
## appointmentDay_numeric 0.00253 0.0034
## appointmentMonthYear_numeric 0.00063 0.0042
## bookedDay_numeric -0.00431 -0.0010
## bookedMonthYear_numeric 0.00919 -0.0044
## daysDiff_AppointBooked 0.02376 -0.0126
## No.Cancels Referral.Source_numeric Clinic.Code
## X -0.0943 0.212 0.161
## Gender_numeric 0.0504 -0.073 -0.037
## Age.at.Attendance 0.1258 -0.208 -0.209
## Area.of.Residence_numeric 0.0181 -0.021 -0.015
## addressDiff -0.0460 0.025 0.013
## No.Cancels 1.0000 -0.129 -0.081
## Referral.Source_numeric -0.1292 1.000 0.241
## Clinic.Code -0.0814 0.241 1.000
## Clinic.Type_numeric -0.2696 0.534 0.428
## Consultant_numeric -0.0546 -0.009 -0.580
## Insurance.Scheme_numeric 0.1154 -0.144 -0.139
## Eligibility_numeric -0.1040 0.066 0.043
## Hospital.Catchment_numeric -0.0441 0.028 0.032
## Booking.Type_numeric -0.1844 0.804 0.297
## Rebooked.Indicator_numeric 0.6342 -0.240 -0.117
## Cancellation.Group_numeric -0.6988 0.193 0.220
## Reason.for.Cancellation_numeric -0.4000 0.090 0.248
## appointmentDay_numeric -0.0689 -0.029 -0.378
## appointmentMonthYear_numeric 0.0529 0.014 0.005
## bookedDay_numeric -0.0043 0.024 0.024
## bookedMonthYear_numeric 0.0239 0.040 0.084
## daysDiff_AppointBooked 0.2011 -0.377 -0.227
## Clinic.Type_numeric Consultant_numeric
## X 0.2442 -0.00092
## Gender_numeric -0.0740 -0.00982
## Age.at.Attendance -0.3254 0.01007
## Area.of.Residence_numeric -0.0126 0.00209
## addressDiff 0.0521 -0.00610
## No.Cancels -0.2696 -0.05458
## Referral.Source_numeric 0.5345 -0.00904
## Clinic.Code 0.4285 -0.58024
## Clinic.Type_numeric 1.0000 -0.08266
## Consultant_numeric -0.0827 1.00000
## Insurance.Scheme_numeric -0.2023 0.03017
## Eligibility_numeric 0.1000 -0.00312
## Hospital.Catchment_numeric 0.0327 -0.01137
## Booking.Type_numeric 0.6542 -0.00297
## Rebooked.Indicator_numeric -0.4102 -0.05056
## Cancellation.Group_numeric 0.3613 -0.04320
## Reason.for.Cancellation_numeric 0.2019 -0.17123
## appointmentDay_numeric -0.0463 0.54905
## appointmentMonthYear_numeric -0.0131 -0.01525
## bookedDay_numeric 0.0284 -0.03638
## bookedMonthYear_numeric 0.0083 -0.03961
## daysDiff_AppointBooked -0.4915 0.01307
## Insurance.Scheme_numeric Eligibility_numeric
## X -0.2236 0.0072
## Gender_numeric 0.0390 -0.0322
## Age.at.Attendance 0.1785 0.0781
## Area.of.Residence_numeric -0.0362 0.0111
## addressDiff -0.0380 0.0334
## No.Cancels 0.1154 -0.1040
## Referral.Source_numeric -0.1443 0.0656
## Clinic.Code -0.1395 0.0434
## Clinic.Type_numeric -0.2023 0.1000
## Consultant_numeric 0.0302 -0.0031
## Insurance.Scheme_numeric 1.0000 -0.2713
## Eligibility_numeric -0.2713 1.0000
## Hospital.Catchment_numeric 0.0014 -0.0025
## Booking.Type_numeric -0.1707 0.0887
## Rebooked.Indicator_numeric 0.1299 -0.0851
## Cancellation.Group_numeric -0.1231 0.0808
## Reason.for.Cancellation_numeric -0.0774 0.0562
## appointmentDay_numeric 0.0176 0.0036
## appointmentMonthYear_numeric 0.0099 0.0195
## bookedDay_numeric -0.0125 0.0106
## bookedMonthYear_numeric -0.0244 -0.0049
## daysDiff_AppointBooked 0.1157 -0.0547
## Hospital.Catchment_numeric Booking.Type_numeric
## X -0.1822 0.2305
## Gender_numeric -0.0218 -0.0829
## Age.at.Attendance -0.0856 -0.2755
## Area.of.Residence_numeric -0.6041 -0.0198
## addressDiff 0.0337 0.0389
## No.Cancels -0.0441 -0.1844
## Referral.Source_numeric 0.0276 0.8037
## Clinic.Code 0.0319 0.2968
## Clinic.Type_numeric 0.0327 0.6542
## Consultant_numeric -0.0114 -0.0030
## Insurance.Scheme_numeric 0.0014 -0.1707
## Eligibility_numeric -0.0025 0.0887
## Hospital.Catchment_numeric 1.0000 0.0327
## Booking.Type_numeric 0.0327 1.0000
## Rebooked.Indicator_numeric -0.0307 -0.3027
## Cancellation.Group_numeric 0.0194 0.2653
## Reason.for.Cancellation_numeric 0.0070 0.1314
## appointmentDay_numeric -0.0169 0.0090
## appointmentMonthYear_numeric 0.0123 -0.0064
## bookedDay_numeric 0.0025 0.0287
## bookedMonthYear_numeric -0.0027 0.0377
## daysDiff_AppointBooked -0.0466 -0.4225
## Rebooked.Indicator_numeric
## X -0.1306
## Gender_numeric 0.0512
## Age.at.Attendance 0.1807
## Area.of.Residence_numeric 0.0090
## addressDiff -0.0459
## No.Cancels 0.6342
## Referral.Source_numeric -0.2397
## Clinic.Code -0.1174
## Clinic.Type_numeric -0.4102
## Consultant_numeric -0.0506
## Insurance.Scheme_numeric 0.1299
## Eligibility_numeric -0.0851
## Hospital.Catchment_numeric -0.0307
## Booking.Type_numeric -0.3027
## Rebooked.Indicator_numeric 1.0000
## Cancellation.Group_numeric -0.5996
## Reason.for.Cancellation_numeric -0.3580
## appointmentDay_numeric -0.0894
## appointmentMonthYear_numeric -0.0140
## bookedDay_numeric -0.0199
## bookedMonthYear_numeric 0.0073
## daysDiff_AppointBooked 0.3093
## Cancellation.Group_numeric
## X 0.1349
## Gender_numeric -0.0538
## Age.at.Attendance -0.1756
## Area.of.Residence_numeric 0.0026
## addressDiff 0.0430
## No.Cancels -0.6988
## Referral.Source_numeric 0.1927
## Clinic.Code 0.2204
## Clinic.Type_numeric 0.3613
## Consultant_numeric -0.0432
## Insurance.Scheme_numeric -0.1231
## Eligibility_numeric 0.0808
## Hospital.Catchment_numeric 0.0194
## Booking.Type_numeric 0.2653
## Rebooked.Indicator_numeric -0.5996
## Cancellation.Group_numeric 1.0000
## Reason.for.Cancellation_numeric 0.3849
## appointmentDay_numeric 0.0753
## appointmentMonthYear_numeric -0.0346
## bookedDay_numeric 0.0349
## bookedMonthYear_numeric 0.0122
## daysDiff_AppointBooked -0.3201
## Reason.for.Cancellation_numeric
## X 0.0613
## Gender_numeric -0.0225
## Age.at.Attendance -0.0572
## Area.of.Residence_numeric 0.0104
## addressDiff 0.0224
## No.Cancels -0.4000
## Referral.Source_numeric 0.0896
## Clinic.Code 0.2475
## Clinic.Type_numeric 0.2019
## Consultant_numeric -0.1712
## Insurance.Scheme_numeric -0.0774
## Eligibility_numeric 0.0562
## Hospital.Catchment_numeric 0.0070
## Booking.Type_numeric 0.1314
## Rebooked.Indicator_numeric -0.3580
## Cancellation.Group_numeric 0.3849
## Reason.for.Cancellation_numeric 1.0000
## appointmentDay_numeric -0.0778
## appointmentMonthYear_numeric -0.0076
## bookedDay_numeric -0.0027
## bookedMonthYear_numeric 0.0288
## daysDiff_AppointBooked -0.0300
## appointmentDay_numeric
## X 0.0229
## Gender_numeric -0.0044
## Age.at.Attendance -0.0241
## Area.of.Residence_numeric 0.0025
## addressDiff 0.0034
## No.Cancels -0.0689
## Referral.Source_numeric -0.0290
## Clinic.Code -0.3782
## Clinic.Type_numeric -0.0463
## Consultant_numeric 0.5490
## Insurance.Scheme_numeric 0.0176
## Eligibility_numeric 0.0036
## Hospital.Catchment_numeric -0.0169
## Booking.Type_numeric 0.0090
## Rebooked.Indicator_numeric -0.0894
## Cancellation.Group_numeric 0.0753
## Reason.for.Cancellation_numeric -0.0778
## appointmentDay_numeric 1.0000
## appointmentMonthYear_numeric 0.0238
## bookedDay_numeric 0.0763
## bookedMonthYear_numeric 0.0041
## daysDiff_AppointBooked -0.0770
## appointmentMonthYear_numeric bookedDay_numeric
## X -0.01881 0.0248
## Gender_numeric -0.00798 -0.0131
## Age.at.Attendance -0.01772 -0.0212
## Area.of.Residence_numeric 0.00063 -0.0043
## addressDiff 0.00424 -0.0010
## No.Cancels 0.05286 -0.0043
## Referral.Source_numeric 0.01375 0.0241
## Clinic.Code 0.00501 0.0241
## Clinic.Type_numeric -0.01306 0.0284
## Consultant_numeric -0.01525 -0.0364
## Insurance.Scheme_numeric 0.00988 -0.0125
## Eligibility_numeric 0.01946 0.0106
## Hospital.Catchment_numeric 0.01225 0.0025
## Booking.Type_numeric -0.00636 0.0287
## Rebooked.Indicator_numeric -0.01400 -0.0199
## Cancellation.Group_numeric -0.03461 0.0349
## Reason.for.Cancellation_numeric -0.00761 -0.0027
## appointmentDay_numeric 0.02383 0.0763
## appointmentMonthYear_numeric 1.00000 0.0306
## bookedDay_numeric 0.03059 1.0000
## bookedMonthYear_numeric 0.16667 0.0244
## daysDiff_AppointBooked -0.12315 0.0225
## bookedMonthYear_numeric daysDiff_AppointBooked
## X 0.0579 -0.147
## Gender_numeric -0.0039 0.051
## Age.at.Attendance -0.0169 0.241
## Area.of.Residence_numeric 0.0092 0.024
## addressDiff -0.0044 -0.013
## No.Cancels 0.0239 0.201
## Referral.Source_numeric 0.0404 -0.377
## Clinic.Code 0.0841 -0.227
## Clinic.Type_numeric 0.0083 -0.491
## Consultant_numeric -0.0396 0.013
## Insurance.Scheme_numeric -0.0244 0.116
## Eligibility_numeric -0.0049 -0.055
## Hospital.Catchment_numeric -0.0027 -0.047
## Booking.Type_numeric 0.0377 -0.423
## Rebooked.Indicator_numeric 0.0073 0.309
## Cancellation.Group_numeric 0.0122 -0.320
## Reason.for.Cancellation_numeric 0.0288 -0.030
## appointmentDay_numeric 0.0041 -0.077
## appointmentMonthYear_numeric 0.1667 -0.123
## bookedDay_numeric 0.0244 0.022
## bookedMonthYear_numeric 1.0000 -0.015
## daysDiff_AppointBooked -0.0150 1.000library(corrplot)## Warning: package 'corrplot' was built under R version 4.3.2## corrplot 0.92 loadedcorrplot(cor(main_processed_cancel))
gg_cancellationGroup <- ggplot(main_processed,aes(x=Cancellation.Group)) +
labs(x = "Cancellation Group") +
scale_x_discrete(limits=c("Hospital","Patient","DNA","Validation")) +
geom_bar(colour="blue", fill="lightblue")
gg_cancellationGroup## Warning: Removed 55297 rows containing non-finite values (`stat_count()`).
gg_cancelReason <- ggplot(main_processed,aes(x=Reason.for.Cancellation_recode)) +
labs(x = "Reason for Cancellation") +
scale_x_discrete(limits=c("By Hospital","By Patient","No show","By Consultant/Advanced Nurse","By Patient-health conditions","By Covid"), guide = guide_axis(n.dodge = 2)) +
geom_bar(colour="blue", fill="lightblue")
gg_cancelReason## Warning: Removed 55297 rows containing non-finite values (`stat_count()`).
Logistic Regression model
Build baseline model of cancellations with predictors
- strong correlation between (Referral source and Booking type), (Clinic type and Booking type), (Consultant and Appointment day), (Consultant and Clinic code)
- moderate correlation between (Appointment day and Clinic code), (Clinic type and Clinic code), (Clinic type and Referral source), (Clinic type and No.Cancels), (Clinic type and Age)
- weak correlation between (Booking type and Age), (Booking type and Clinic code)
- Model 1: No.Cancels ~ Clinic.Code + Clinic.Type_numeric + Referral.Source_numeric + Consultant_numeric + Insurance.Scheme_numeric + Eligibility_numeric + Booking.Type_numeric + Hospital.Catchment_numeric + Rebooked.Indicator_numeric + Reason.for.Cancellation_numeric
- AIC: 14333
- AUC: 0.91
- nagelkerke R squared: 0.6
- Confusion matrix: accuracy (0.84), precision (0.32), recall (0.1), prevalence (0.23), detection rate (0.18)
- Model 2: No.Cancels ~ appointmentDay_numeric + appointmentMonthYear_numeric + bookedDay_numeric + bookedMonthYear_numeric + daysDiff_AppointBooked
- AIC: 26316
- AUC: 0.61
- nagelkerke R squared: 0.04
- Confusion matrix: accuracy (0.5), precision (0.77), recall (1), prevalence (0.23), detection rate (0)
- Model 3: No.Cancels ~ Gender_numeric + Age.at.Attendance + Area.of.Residence_numeric + addressDiff
- AIC: 26518
- AUC: 0.59
- nagelkerke R squared: 0.028
- Confusion matrix: accuracy (0.5), precision (0.997), recall (0.77), prevalence (0.23), detection rate (0.002)
- Model: No.Cancels ~ Gender_numeric + Age.at.Attendance + Area.of.Residence_numeric + addressDiff + Referral.Source_numeric + Clinic.Code + Clinic.Type_numeric + Consultant_numeric + Insurance.Scheme_numeric + Eligibility_numeric + Hospital.Catchment_numeric + Booking.Type_numeric + appointmentDay_numeric + appointmentMonthYear_numeric + bookedDay_numeric + bookedMonthYear_numeric
- AIC: 14031
- AUC: 0.91
- nagelkerke R squared: 0.61
- Confusion matrix: accuracy (0.81), precision (0.92), recall (0.88), prevalence (0.23), detection rate (0.17)
- AIC the smallest AIC provides the best model
- AUC (area under the ROC curve) the closer to 1 the more correct predictions
- predict: predict the values based on the previous data behaviors and thus by fitting that data to the model
- plogis: Logistic Cumulative Distribution Function
- Cox and Snell R square: Analysis_Survival_Cox Regression. The coefficients in a Cox regression relate to hazard; a positive coefficient indicates a worse prognosis and a negative coefficient indicates a protective effect of the variable with which it is associated
- nagelkerke: a measure of goodness of fit in logistic regression analysis, a modification of the Cox and Snell R Square. Ranges [0,1], a common rule of thumb (<= 0.2: weak relationship, 0.2-0.4: moderate, >= 0.4: strong relationship).
- confusionMatrix: matrix between actual and predicted values
- vif = 1/(1-Ri^2) = 1/tolerance. Vif >4 or tolearance <0.25: multicollinearity might exist; vif >10 or tolerance <0.1: signigicant multicollinearity that needs to be corrected. Vif interpret if model has problems estimating the coefficient
variables need to be recoded as numeric to run any regression
# Create training and test dataset samples
sample <- sample(c(TRUE,FALSE), nrow(main_processed_cancel), replace=TRUE, prob=c(0.7,0.3))
train <- main_processed_cancel[sample, ]
test <- main_processed_cancel[!sample, ]
# Fit the logistic regression model
## with Clinic relevance
# map probabilities to log-odds, predicted probabilities are within the [0, 1] range
logmodel1 <- glm(formula = No.Cancels ~ Clinic.Code + Clinic.Type_numeric + Referral.Source_numeric + Consultant_numeric + Insurance.Scheme_numeric + Eligibility_numeric + Booking.Type_numeric + Hospital.Catchment_numeric + Rebooked.Indicator_numeric + Reason.for.Cancellation_numeric, family = binomial(link = "logit"), data = train)
summary(logmodel1)##
## Call:
## glm(formula = No.Cancels ~ Clinic.Code + Clinic.Type_numeric +
## Referral.Source_numeric + Consultant_numeric + Insurance.Scheme_numeric +
## Eligibility_numeric + Booking.Type_numeric + Hospital.Catchment_numeric +
## Rebooked.Indicator_numeric + Reason.for.Cancellation_numeric,
## family = binomial(link = "logit"), data = train)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 7.83e-01 1.22e-01 6.44 1.2e-10 ***
## Clinic.Code -1.17e-04 7.89e-05 -1.48 0.13889
## Clinic.Type_numeric -5.41e-02 3.26e-02 -1.66 0.09671 .
## Referral.Source_numeric 1.12e-01 3.36e-02 3.34 0.00084 ***
## Consultant_numeric -9.01e-02 1.72e-02 -5.25 1.5e-07 ***
## Insurance.Scheme_numeric 8.42e-02 1.28e-02 6.60 4.2e-11 ***
## Eligibility_numeric -1.82e-01 2.44e-02 -7.46 8.8e-14 ***
## Booking.Type_numeric 1.99e-01 4.79e-02 4.16 3.2e-05 ***
## Hospital.Catchment_numeric -6.88e-02 1.72e-02 -3.99 6.6e-05 ***
## Rebooked.Indicator_numeric 2.06e+01 1.46e+02 0.14 0.88767
## Reason.for.Cancellation_numeric -2.71e-01 1.83e-02 -14.79 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 27092 on 25110 degrees of freedom
## Residual deviance: 14334 on 25100 degrees of freedom
## AIC: 14356
##
## Number of Fisher Scoring iterations: 19exp(coefficients(logmodel1))## (Intercept) Clinic.Code
## 2.2e+00 1.0e+00
## Clinic.Type_numeric Referral.Source_numeric
## 9.5e-01 1.1e+00
## Consultant_numeric Insurance.Scheme_numeric
## 9.1e-01 1.1e+00
## Eligibility_numeric Booking.Type_numeric
## 8.3e-01 1.2e+00
## Hospital.Catchment_numeric Rebooked.Indicator_numeric
## 9.3e-01 8.6e+08
## Reason.for.Cancellation_numeric
## 7.6e-01# Assess Model Fit
nagelkerke(logmodel1) #library(rcompanion)## $Models
##
## Model: "glm, No.Cancels ~ Clinic.Code + Clinic.Type_numeric + Referral.Source_numeric + Consultant_numeric + Insurance.Scheme_numeric + Eligibility_numeric + Booking.Type_numeric + Hospital.Catchment_numeric + Rebooked.Indicator_numeric + Reason.for.Cancellation_numeric, binomial(link = \"logit\"), train"
## Null: "glm, No.Cancels ~ 1, binomial(link = \"logit\"), train"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.47
## Cox and Snell (ML) 0.40
## Nagelkerke (Cragg and Uhler) 0.60
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -10 -6379 12758 0
##
## $Number.of.observations
##
## Model: 25111
## Null: 25111
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"# Calculate the VIF values of each variable to see if multicollinearity is a problem
vif(logmodel1) #library(regclass), library(car)## Clinic.Code Clinic.Type_numeric
## 1.5 1.6
## Referral.Source_numeric Consultant_numeric
## 1.8 1.2
## Insurance.Scheme_numeric Eligibility_numeric
## 1.1 1.1
## Booking.Type_numeric Hospital.Catchment_numeric
## 2.2 1.0
## Rebooked.Indicator_numeric Reason.for.Cancellation_numeric
## 1.0 1.0# Calculate probability of cancellation for each individual in test dataset
## Obtain predicted probabilities: continuous values (not factors)
predicted1 <- predict(logmodel1, test, type=c("response")) #library(rbenchmark)
## Find the optimal probability
# library(InformationValue)
# Warning in install.packages :
# unable to access index for repository https://cran.rstudio.com/src/contrib:
# cannot open URL 'https://cran.rstudio.com/src/contrib/PACKAGES'
# Warning in install.packages :
# package ‘InformationValue’ is not available for this version of R
# optimal <-optimalCutoff(test$No.Cancels,predicted1)[1]
# optimal
## Convert predicted probabilities to binary predictions
binary_predicted1 <- ifelse(predicted1 >= 0.5, 1, 0) #threshold=0.5
## Create confusion matrix to show predictions compared to the actual defaults
confusionMatrix(factor(binary_predicted1), factor(test$No.Cancels)) #library(caret), test$No.Cancels represents the actual response variable, binary_predictions are the binary predictions based on the threshold## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 2032 986
## 1 451 7292
##
## Accuracy : 0.866
## 95% CI : (0.86, 0.873)
## No Information Rate : 0.769
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.65
##
## Mcnemar's Test P-Value : <2e-16
##
## Sensitivity : 0.818
## Specificity : 0.881
## Pos Pred Value : 0.673
## Neg Pred Value : 0.942
## Prevalence : 0.231
## Detection Rate : 0.189
## Detection Prevalence : 0.280
## Balanced Accuracy : 0.850
##
## 'Positive' Class : 0
## confusion_matrix(logmodel1) #library(regclass)## Predicted 0 Predicted 1 Total
## Actual 0 4721 1058 5779
## Actual 1 2374 16958 19332
## Total 7095 18016 25111confusion_matrix(logmodel1, test) #library(regclass)## Predicted 0 Predicted 1 Total
## Actual 0 2032 451 2483
## Actual 1 986 7292 8278
## Total 3018 7743 10761## Calculate the sensitivity, specificity and total misclassification error if not shown in confusionMatrix
# sensitivity(factor(binary_predicted1), factor(test$No.Cancels))
# specificity(factor(binary_predicted1), factor(test$No.Cancels))
# misClassError(factor(binary_predicted1), factor(test$No.Cancels), threshold=optimal)
## Plot ROC curve which displays the % of True positivity predicted by the model as the prediction probability cutoff is [0,1]
# plot.roc(binary_predicted1, test$No.Cancels) #library(pROC) plot.ROC or plotROC
# lroc(logmodel1,graph=T) #for AUC but has a very loooong $diagnostic.table
## with Date relevance
logmodel2 <- glm(formula = No.Cancels ~ appointmentDay_numeric + appointmentMonthYear_numeric + bookedDay_numeric + bookedMonthYear_numeric + daysDiff_AppointBooked, family = binomial(link = "logit"), data = train)
summary(logmodel2)##
## Call:
## glm(formula = No.Cancels ~ appointmentDay_numeric + appointmentMonthYear_numeric +
## bookedDay_numeric + bookedMonthYear_numeric + daysDiff_AppointBooked,
## family = binomial(link = "logit"), data = train)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 9.25e-01 5.59e-02 16.54 < 2e-16 ***
## appointmentDay_numeric -1.15e-01 1.16e-02 -9.97 < 2e-16 ***
## appointmentMonthYear_numeric 1.22e-02 1.16e-03 10.51 < 2e-16 ***
## bookedDay_numeric 6.28e-06 1.09e-02 0.00 0.99954
## bookedMonthYear_numeric 4.29e-03 1.19e-03 3.59 0.00033 ***
## daysDiff_AppointBooked 3.02e-03 1.49e-04 20.24 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 27092 on 25110 degrees of freedom
## Residual deviance: 26422 on 25105 degrees of freedom
## AIC: 26434
##
## Number of Fisher Scoring iterations: 4# exp(coefficients(logmodel2))
nagelkerke(logmodel2)## $Models
##
## Model: "glm, No.Cancels ~ appointmentDay_numeric + appointmentMonthYear_numeric + bookedDay_numeric + bookedMonthYear_numeric + daysDiff_AppointBooked, binomial(link = \"logit\"), train"
## Null: "glm, No.Cancels ~ 1, binomial(link = \"logit\"), train"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.025
## Cox and Snell (ML) 0.026
## Nagelkerke (Cragg and Uhler) 0.040
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -5 -335 670 1.2e-142
##
## $Number.of.observations
##
## Model: 25111
## Null: 25111
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"# Obtain predicted probabilities: continuous values (not factors)
predicted2 <- predict(logmodel2, test, type=c("response")) #library(rbenchmark)
# Convert predicted probabilities to binary predictions
binary_predicted2 <- ifelse(predicted2 >= 0.5, 1, 0) #threshold=0.5
# Create confusion matrix, library(caret)
confusionMatrix(factor(binary_predicted2), factor(test$No.Cancels)) #test$No.Cancels represents the actual response variable, binary_predictions are the binary predictions based on the threshold## Warning in confusionMatrix.default(factor(binary_predicted2),
## factor(test$No.Cancels)): Levels are not in the same order for reference and
## data. Refactoring data to match.## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 0 0
## 1 2483 8278
##
## Accuracy : 0.769
## 95% CI : (0.761, 0.777)
## No Information Rate : 0.769
## P-Value [Acc > NIR] : 0.505
##
## Kappa : 0
##
## Mcnemar's Test P-Value : <2e-16
##
## Sensitivity : 0.000
## Specificity : 1.000
## Pos Pred Value : NaN
## Neg Pred Value : 0.769
## Prevalence : 0.231
## Detection Rate : 0.000
## Detection Prevalence : 0.000
## Balanced Accuracy : 0.500
##
## 'Positive' Class : 0
## confusion_matrix(logmodel2) #library(regclass)## Predicted levels same as naive model (majority level)## Predicted 1
## Actual 0 5779
## Actual 1 19332confusion_matrix(logmodel2, test) #library(regclass)## Predicted levels same as naive model (majority level)## Predicted 1
## Actual 0 2483
## Actual 1 8278vif(logmodel2)## appointmentDay_numeric appointmentMonthYear_numeric
## 1 1
## bookedDay_numeric bookedMonthYear_numeric
## 1 1
## daysDiff_AppointBooked
## 1## with Patient relevance
logmodel3 <- glm(formula = No.Cancels ~ Gender_numeric + Age.at.Attendance + Area.of.Residence_numeric + addressDiff, family = binomial(link = "logit"), data = train)
summary(logmodel3)##
## Call:
## glm(formula = No.Cancels ~ Gender_numeric + Age.at.Attendance +
## Area.of.Residence_numeric + addressDiff, family = binomial(link = "logit"),
## data = train)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.137027 0.206516 -5.51 3.7e-08 ***
## Gender_numeric 0.732635 0.101127 7.24 4.3e-13 ***
## Age.at.Attendance 0.018097 0.000992 18.24 < 2e-16 ***
## Area.of.Residence_numeric 0.010122 0.011550 0.88 0.38
## addressDiff -0.204957 0.037094 -5.53 3.3e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 27092 on 25110 degrees of freedom
## Residual deviance: 26641 on 25106 degrees of freedom
## AIC: 26651
##
## Number of Fisher Scoring iterations: 4# exp(coefficients(logmodel3))
nagelkerke(logmodel3)## $Models
##
## Model: "glm, No.Cancels ~ Gender_numeric + Age.at.Attendance + Area.of.Residence_numeric + addressDiff, binomial(link = \"logit\"), train"
## Null: "glm, No.Cancels ~ 1, binomial(link = \"logit\"), train"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.017
## Cox and Snell (ML) 0.018
## Nagelkerke (Cragg and Uhler) 0.027
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -4 -225 451 3.1e-96
##
## $Number.of.observations
##
## Model: 25111
## Null: 25111
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"# Obtain predicted probabilities: continuous values (not factors)
predicted3 <- predict(logmodel3, test, type=c("response")) #library(rbenchmark)
# Convert predicted probabilities to binary predictions
binary_predicted3 <- ifelse(predicted3 >= 0.5, 1, 0) #threshold=0.5
# Create confusion matrix, library(caret)
confusionMatrix(factor(binary_predicted3), factor(test$No.Cancels)) #test$No.Cancels represents the actual response variable, binary_predictions are the binary predictions based on the threshold## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 13 12
## 1 2470 8266
##
## Accuracy : 0.769
## 95% CI : (0.761, 0.777)
## No Information Rate : 0.769
## P-Value [Acc > NIR] : 0.496
##
## Kappa : 0.006
##
## Mcnemar's Test P-Value : <2e-16
##
## Sensitivity : 0.00524
## Specificity : 0.99855
## Pos Pred Value : 0.52000
## Neg Pred Value : 0.76993
## Prevalence : 0.23074
## Detection Rate : 0.00121
## Detection Prevalence : 0.00232
## Balanced Accuracy : 0.50189
##
## 'Positive' Class : 0
## confusion_matrix(logmodel3) #library(regclass)## Predicted 0 Predicted 1 Total
## Actual 0 29 5750 5779
## Actual 1 35 19297 19332
## Total 64 25047 25111confusion_matrix(logmodel3, test) #library(regclass)## Predicted 0 Predicted 1 Total
## Actual 0 13 2470 2483
## Actual 1 12 8266 8278
## Total 25 10736 10761vif(logmodel3)## Gender_numeric Age.at.Attendance Area.of.Residence_numeric
## 1 1 1
## addressDiff
## 1## with all predictors
logmodel <- glm(formula = No.Cancels ~ Gender_numeric + Age.at.Attendance + Area.of.Residence_numeric + addressDiff + Referral.Source_numeric + Clinic.Code + Clinic.Type_numeric + Consultant_numeric + Insurance.Scheme_numeric + Eligibility_numeric + Hospital.Catchment_numeric + Booking.Type_numeric + Rebooked.Indicator_numeric + Reason.for.Cancellation_numeric + appointmentDay_numeric + appointmentMonthYear_numeric + bookedDay_numeric + bookedMonthYear_numeric + daysDiff_AppointBooked, family = binomial(link = "logit"), data = train)
summary(logmodel)##
## Call:
## glm(formula = No.Cancels ~ Gender_numeric + Age.at.Attendance +
## Area.of.Residence_numeric + addressDiff + Referral.Source_numeric +
## Clinic.Code + Clinic.Type_numeric + Consultant_numeric +
## Insurance.Scheme_numeric + Eligibility_numeric + Hospital.Catchment_numeric +
## Booking.Type_numeric + Rebooked.Indicator_numeric + Reason.for.Cancellation_numeric +
## appointmentDay_numeric + appointmentMonthYear_numeric + bookedDay_numeric +
## bookedMonthYear_numeric + daysDiff_AppointBooked, family = binomial(link = "logit"),
## data = train)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.75e-01 3.18e-01 -1.81 0.07017 .
## Gender_numeric 4.29e-01 1.32e-01 3.25 0.00114 **
## Age.at.Attendance 3.94e-03 1.35e-03 2.93 0.00343 **
## Area.of.Residence_numeric 2.40e-02 1.55e-02 1.55 0.12115
## addressDiff -1.02e-01 4.98e-02 -2.05 0.03995 *
## Referral.Source_numeric 8.73e-02 3.42e-02 2.55 0.01075 *
## Clinic.Code -1.91e-04 8.17e-05 -2.34 0.01931 *
## Clinic.Type_numeric -3.62e-02 3.53e-02 -1.03 0.30386
## Consultant_numeric -7.84e-02 1.98e-02 -3.95 7.7e-05 ***
## Insurance.Scheme_numeric 8.09e-02 1.30e-02 6.23 4.7e-10 ***
## Eligibility_numeric -1.91e-01 2.50e-02 -7.66 1.9e-14 ***
## Hospital.Catchment_numeric -6.60e-02 1.75e-02 -3.77 0.00016 ***
## Booking.Type_numeric 2.07e-01 4.92e-02 4.20 2.7e-05 ***
## Rebooked.Indicator_numeric 2.07e+01 1.43e+02 0.14 0.88527
## Reason.for.Cancellation_numeric -3.24e-01 1.87e-02 -17.30 < 2e-16 ***
## appointmentDay_numeric 1.26e-02 1.81e-02 0.70 0.48479
## appointmentMonthYear_numeric 2.18e-02 1.54e-03 14.11 < 2e-16 ***
## bookedDay_numeric 2.01e-02 1.47e-02 1.37 0.17017
## bookedMonthYear_numeric 2.62e-04 1.58e-03 0.17 0.86893
## daysDiff_AppointBooked -1.40e-03 2.33e-04 -6.02 1.7e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 27092 on 25110 degrees of freedom
## Residual deviance: 14035 on 25091 degrees of freedom
## AIC: 14075
##
## Number of Fisher Scoring iterations: 19exp(coefficients(logmodel))## (Intercept) Gender_numeric
## 5.6e-01 1.5e+00
## Age.at.Attendance Area.of.Residence_numeric
## 1.0e+00 1.0e+00
## addressDiff Referral.Source_numeric
## 9.0e-01 1.1e+00
## Clinic.Code Clinic.Type_numeric
## 1.0e+00 9.6e-01
## Consultant_numeric Insurance.Scheme_numeric
## 9.2e-01 1.1e+00
## Eligibility_numeric Hospital.Catchment_numeric
## 8.3e-01 9.4e-01
## Booking.Type_numeric Rebooked.Indicator_numeric
## 1.2e+00 9.8e+08
## Reason.for.Cancellation_numeric appointmentDay_numeric
## 7.2e-01 1.0e+00
## appointmentMonthYear_numeric bookedDay_numeric
## 1.0e+00 1.0e+00
## bookedMonthYear_numeric daysDiff_AppointBooked
## 1.0e+00 1.0e+00nagelkerke(logmodel)## $Models
##
## Model: "glm, No.Cancels ~ Gender_numeric + Age.at.Attendance + Area.of.Residence_numeric + addressDiff + Referral.Source_numeric + Clinic.Code + Clinic.Type_numeric + Consultant_numeric + Insurance.Scheme_numeric + Eligibility_numeric + Hospital.Catchment_numeric + Booking.Type_numeric + Rebooked.Indicator_numeric + Reason.for.Cancellation_numeric + appointmentDay_numeric + appointmentMonthYear_numeric + bookedDay_numeric + bookedMonthYear_numeric + daysDiff_AppointBooked, binomial(link = \"logit\"), train"
## Null: "glm, No.Cancels ~ 1, binomial(link = \"logit\"), train"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.48
## Cox and Snell (ML) 0.41
## Nagelkerke (Cragg and Uhler) 0.61
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -19 -6529 13057 0
##
## $Number.of.observations
##
## Model: 25111
## Null: 25111
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"# Obtain predicted probabilities: continuous values (not factors)
predicted <- predict(logmodel, test, type=c("response")) #library(rbenchmark)
# Convert predicted probabilities to binary predictions
binary_predicted <- ifelse(predicted >= 0.5, 1, 0) #threshold=0.5
# Create confusion matrix, library(caret)
confusionMatrix(factor(binary_predicted), factor(test$No.Cancels)) #test$No.Cancels represents the actual response variable, binary_predictions are the binary predictions based on the threshold## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 1878 973
## 1 605 7305
##
## Accuracy : 0.853
## 95% CI : (0.847, 0.86)
## No Information Rate : 0.769
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.607
##
## Mcnemar's Test P-Value : <2e-16
##
## Sensitivity : 0.756
## Specificity : 0.882
## Pos Pred Value : 0.659
## Neg Pred Value : 0.924
## Prevalence : 0.231
## Detection Rate : 0.175
## Detection Prevalence : 0.265
## Balanced Accuracy : 0.819
##
## 'Positive' Class : 0
## confusion_matrix(logmodel) #library(regclass)## Predicted 0 Predicted 1 Total
## Actual 0 4349 1430 5779
## Actual 1 2356 16976 19332
## Total 6705 18406 25111confusion_matrix(logmodel, test) #library(regclass)## Predicted 0 Predicted 1 Total
## Actual 0 1878 605 2483
## Actual 1 973 7305 8278
## Total 2851 7910 10761vif(logmodel)## Gender_numeric Age.at.Attendance
## 1.0 1.2
## Area.of.Residence_numeric addressDiff
## 1.0 1.0
## Referral.Source_numeric Clinic.Code
## 1.9 1.5
## Clinic.Type_numeric Consultant_numeric
## 1.9 1.5
## Insurance.Scheme_numeric Eligibility_numeric
## 1.1 1.1
## Hospital.Catchment_numeric Booking.Type_numeric
## 1.0 2.3
## Rebooked.Indicator_numeric Reason.for.Cancellation_numeric
## 1.0 1.1
## appointmentDay_numeric appointmentMonthYear_numeric
## 1.4 1.1
## bookedDay_numeric bookedMonthYear_numeric
## 1.0 1.1
## daysDiff_AppointBooked
## 1.3#library(epiDisplay), give plot plus AUC
logistic.display(logmodel)##
## Logistic regression predicting No.Cancels
##
## crude OR(95%CI)
## Gender_numeric (cont. var.) 2.21 (1.81,2.68)
##
## Age.at.Attendance (cont. var.) 1.019 (1.017,1.0209)
##
## Area.of.Residence_numeric (cont. var.) 1.01 (0.99,1.04)
##
## addressDiff: 1 vs 0 0.77 (0.72,0.83)
##
## Referral.Source_numeric (cont. var.) 0.85 (0.83,0.88)
##
## Clinic.Code (cont. var.) 0.9995 (0.9994,0.9996)
##
## Clinic.Type_numeric (cont. var.) 0.44 (0.42,0.46)
##
## Consultant_numeric (cont. var.) 0.89 (0.86,0.91)
##
## Insurance.Scheme_numeric (cont. var.) 1.18 (1.15,1.2)
##
## Eligibility_numeric (cont. var.) 0.77 (0.74,0.79)
##
## Hospital.Catchment_numeric (cont. var.) 0.93 (0.9,0.95)
##
## Booking.Type_numeric (cont. var.) 0.62 (0.59,0.65)
##
## Rebooked.Indicator_numeric: 1 vs 0 997648548.86 (0,7.00341769159113e+134)
##
## Reason.for.Cancellation_numeric (cont. var.) 0.63 (0.62,0.65)
##
## appointmentDay_numeric (cont. var.) 0.88 (0.86,0.9)
##
## appointmentMonthYear_numeric (cont. var.) 1.01 (1.01,1.01)
##
## bookedDay_numeric (cont. var.) 1 (0.98,1.02)
##
## bookedMonthYear_numeric (cont. var.) 1.0047 (1.0024,1.007)
##
## daysDiff_AppointBooked (cont. var.) 1.0029 (1.0026,1.0032)
##
## adj. OR(95%CI)
## Gender_numeric (cont. var.) 1.54 (1.19,1.99)
##
## Age.at.Attendance (cont. var.) 1.004 (1.0013,1.0066)
##
## Area.of.Residence_numeric (cont. var.) 1.02 (0.99,1.06)
##
## addressDiff: 1 vs 0 0.9 (0.82,1)
##
## Referral.Source_numeric (cont. var.) 1.09 (1.02,1.17)
##
## Clinic.Code (cont. var.) 0.9998 (0.9996,1)
##
## Clinic.Type_numeric (cont. var.) 0.96 (0.9,1.03)
##
## Consultant_numeric (cont. var.) 0.92 (0.89,0.96)
##
## Insurance.Scheme_numeric (cont. var.) 1.08 (1.06,1.11)
##
## Eligibility_numeric (cont. var.) 0.83 (0.79,0.87)
##
## Hospital.Catchment_numeric (cont. var.) 0.94 (0.9,0.97)
##
## Booking.Type_numeric (cont. var.) 1.23 (1.12,1.35)
##
## Rebooked.Indicator_numeric: 1 vs 0 977733851.95 (0,1.27547984100273e+131)
##
## Reason.for.Cancellation_numeric (cont. var.) 0.72 (0.7,0.75)
##
## appointmentDay_numeric (cont. var.) 1.01 (0.98,1.05)
##
## appointmentMonthYear_numeric (cont. var.) 1.02 (1.02,1.03)
##
## bookedDay_numeric (cont. var.) 1.02 (0.99,1.05)
##
## bookedMonthYear_numeric (cont. var.) 1.0003 (0.9972,1.0034)
##
## daysDiff_AppointBooked (cont. var.) 0.9986 (0.9981,0.9991)
##
## P(Wald's test) P(LR-test)
## Gender_numeric (cont. var.) 0.001 < 0.001
##
## Age.at.Attendance (cont. var.) 0.003 0.003
##
## Area.of.Residence_numeric (cont. var.) 0.121 0.121
##
## addressDiff: 1 vs 0 0.04 0.04
##
## Referral.Source_numeric (cont. var.) 0.011 0.011
##
## Clinic.Code (cont. var.) 0.019 0.019
##
## Clinic.Type_numeric (cont. var.) 0.304 0.304
##
## Consultant_numeric (cont. var.) < 0.001 < 0.001
##
## Insurance.Scheme_numeric (cont. var.) < 0.001 < 0.001
##
## Eligibility_numeric (cont. var.) < 0.001 < 0.001
##
## Hospital.Catchment_numeric (cont. var.) < 0.001 < 0.001
##
## Booking.Type_numeric (cont. var.) < 0.001 < 0.001
##
## Rebooked.Indicator_numeric: 1 vs 0 0.885 < 0.001
##
## Reason.for.Cancellation_numeric (cont. var.) < 0.001 < 0.001
##
## appointmentDay_numeric (cont. var.) 0.485 0.485
##
## appointmentMonthYear_numeric (cont. var.) < 0.001 < 0.001
##
## bookedDay_numeric (cont. var.) 0.17 0.17
##
## bookedMonthYear_numeric (cont. var.) 0.869 0.869
##
## daysDiff_AppointBooked (cont. var.) < 0.001 < 0.001
##
## Log-likelihood = -7017.3041
## No. of observations = 25111
## AIC value = 14074.6082lroc <- lroc(logmodel, title=TRUE, cex.main=1, cex.lab=1, col.lab="blue", cex.axis=1,
lwd=3)
lroc1 <- lroc(logmodel1, add=TRUE, line.col="brown", lty=2)
lroc2 <- lroc(logmodel2, add=TRUE, line.col="darkgreen", lty=2)
lroc3 <- lroc(logmodel3, add=TRUE, line.col="purple", lty=2)
legend("bottomright",legend=c("all predictors", "clinic relevance", "date relevance", "patient relevance"),
lty=1:2, col=c("red","brown","darkgreen","purple"), bg="white")
lrtest(logmodel,logmodel1) #library(lmtest)## Likelihood ratio test for MLE method
## Chi-squared 9 d.f. = 299 , P value = 3.7e-59lrtest(logmodel,logmodel2)## Likelihood ratio test for MLE method
## Chi-squared 14 d.f. = 12387 , P value = 0lrtest(logmodel,logmodel3)## Likelihood ratio test for MLE method
## Chi-squared 15 d.f. = 12607 , P value = 0Comparing factors in models
# anova(logmodel1, logmodel2, logmodel3, logmodel, test="Chisq") #run if there is other ML models
#library(survey)
regTermTest(logmodel,"Referral.Source_numeric")## Wald test for Referral.Source_numeric
## in glm(formula = No.Cancels ~ Gender_numeric + Age.at.Attendance +
## Area.of.Residence_numeric + addressDiff + Referral.Source_numeric +
## Clinic.Code + Clinic.Type_numeric + Consultant_numeric +
## Insurance.Scheme_numeric + Eligibility_numeric + Hospital.Catchment_numeric +
## Booking.Type_numeric + Rebooked.Indicator_numeric + Reason.for.Cancellation_numeric +
## appointmentDay_numeric + appointmentMonthYear_numeric + bookedDay_numeric +
## bookedMonthYear_numeric + daysDiff_AppointBooked, family = binomial(link = "logit"),
## data = train)
## F = 6.5 on 1 and 25091 df: p= 0.01regTermTest(logmodel,"Clinic.Code")## Wald test for Clinic.Code
## in glm(formula = No.Cancels ~ Gender_numeric + Age.at.Attendance +
## Area.of.Residence_numeric + addressDiff + Referral.Source_numeric +
## Clinic.Code + Clinic.Type_numeric + Consultant_numeric +
## Insurance.Scheme_numeric + Eligibility_numeric + Hospital.Catchment_numeric +
## Booking.Type_numeric + Rebooked.Indicator_numeric + Reason.for.Cancellation_numeric +
## appointmentDay_numeric + appointmentMonthYear_numeric + bookedDay_numeric +
## bookedMonthYear_numeric + daysDiff_AppointBooked, family = binomial(link = "logit"),
## data = train)
## F = 5.5 on 1 and 25091 df: p= 0.02regTermTest(logmodel,"Clinic.Type_numeric")## Wald test for Clinic.Type_numeric
## in glm(formula = No.Cancels ~ Gender_numeric + Age.at.Attendance +
## Area.of.Residence_numeric + addressDiff + Referral.Source_numeric +
## Clinic.Code + Clinic.Type_numeric + Consultant_numeric +
## Insurance.Scheme_numeric + Eligibility_numeric + Hospital.Catchment_numeric +
## Booking.Type_numeric + Rebooked.Indicator_numeric + Reason.for.Cancellation_numeric +
## appointmentDay_numeric + appointmentMonthYear_numeric + bookedDay_numeric +
## bookedMonthYear_numeric + daysDiff_AppointBooked, family = binomial(link = "logit"),
## data = train)
## F = 1.1 on 1 and 25091 df: p= 0.3regTermTest(logmodel,"Consultant_numeric")## Wald test for Consultant_numeric
## in glm(formula = No.Cancels ~ Gender_numeric + Age.at.Attendance +
## Area.of.Residence_numeric + addressDiff + Referral.Source_numeric +
## Clinic.Code + Clinic.Type_numeric + Consultant_numeric +
## Insurance.Scheme_numeric + Eligibility_numeric + Hospital.Catchment_numeric +
## Booking.Type_numeric + Rebooked.Indicator_numeric + Reason.for.Cancellation_numeric +
## appointmentDay_numeric + appointmentMonthYear_numeric + bookedDay_numeric +
## bookedMonthYear_numeric + daysDiff_AppointBooked, family = binomial(link = "logit"),
## data = train)
## F = 16 on 1 and 25091 df: p= 8e-05regTermTest(logmodel,"Insurance.Scheme_numeric")## Wald test for Insurance.Scheme_numeric
## in glm(formula = No.Cancels ~ Gender_numeric + Age.at.Attendance +
## Area.of.Residence_numeric + addressDiff + Referral.Source_numeric +
## Clinic.Code + Clinic.Type_numeric + Consultant_numeric +
## Insurance.Scheme_numeric + Eligibility_numeric + Hospital.Catchment_numeric +
## Booking.Type_numeric + Rebooked.Indicator_numeric + Reason.for.Cancellation_numeric +
## appointmentDay_numeric + appointmentMonthYear_numeric + bookedDay_numeric +
## bookedMonthYear_numeric + daysDiff_AppointBooked, family = binomial(link = "logit"),
## data = train)
## F = 39 on 1 and 25091 df: p= 5e-10regTermTest(logmodel,"Eligibility_numeric")## Wald test for Eligibility_numeric
## in glm(formula = No.Cancels ~ Gender_numeric + Age.at.Attendance +
## Area.of.Residence_numeric + addressDiff + Referral.Source_numeric +
## Clinic.Code + Clinic.Type_numeric + Consultant_numeric +
## Insurance.Scheme_numeric + Eligibility_numeric + Hospital.Catchment_numeric +
## Booking.Type_numeric + Rebooked.Indicator_numeric + Reason.for.Cancellation_numeric +
## appointmentDay_numeric + appointmentMonthYear_numeric + bookedDay_numeric +
## bookedMonthYear_numeric + daysDiff_AppointBooked, family = binomial(link = "logit"),
## data = train)
## F = 59 on 1 and 25091 df: p= 2e-14regTermTest(logmodel,"Booking.Type_numeric")## Wald test for Booking.Type_numeric
## in glm(formula = No.Cancels ~ Gender_numeric + Age.at.Attendance +
## Area.of.Residence_numeric + addressDiff + Referral.Source_numeric +
## Clinic.Code + Clinic.Type_numeric + Consultant_numeric +
## Insurance.Scheme_numeric + Eligibility_numeric + Hospital.Catchment_numeric +
## Booking.Type_numeric + Rebooked.Indicator_numeric + Reason.for.Cancellation_numeric +
## appointmentDay_numeric + appointmentMonthYear_numeric + bookedDay_numeric +
## bookedMonthYear_numeric + daysDiff_AppointBooked, family = binomial(link = "logit"),
## data = train)
## F = 18 on 1 and 25091 df: p= 3e-05regTermTest(logmodel,"Hospital.Catchment_numeric")## Wald test for Hospital.Catchment_numeric
## in glm(formula = No.Cancels ~ Gender_numeric + Age.at.Attendance +
## Area.of.Residence_numeric + addressDiff + Referral.Source_numeric +
## Clinic.Code + Clinic.Type_numeric + Consultant_numeric +
## Insurance.Scheme_numeric + Eligibility_numeric + Hospital.Catchment_numeric +
## Booking.Type_numeric + Rebooked.Indicator_numeric + Reason.for.Cancellation_numeric +
## appointmentDay_numeric + appointmentMonthYear_numeric + bookedDay_numeric +
## bookedMonthYear_numeric + daysDiff_AppointBooked, family = binomial(link = "logit"),
## data = train)
## F = 14 on 1 and 25091 df: p= 2e-04regTermTest(logmodel,"appointmentDay_numeric")## Wald test for appointmentDay_numeric
## in glm(formula = No.Cancels ~ Gender_numeric + Age.at.Attendance +
## Area.of.Residence_numeric + addressDiff + Referral.Source_numeric +
## Clinic.Code + Clinic.Type_numeric + Consultant_numeric +
## Insurance.Scheme_numeric + Eligibility_numeric + Hospital.Catchment_numeric +
## Booking.Type_numeric + Rebooked.Indicator_numeric + Reason.for.Cancellation_numeric +
## appointmentDay_numeric + appointmentMonthYear_numeric + bookedDay_numeric +
## bookedMonthYear_numeric + daysDiff_AppointBooked, family = binomial(link = "logit"),
## data = train)
## F = 0.49 on 1 and 25091 df: p= 0.5regTermTest(logmodel,"appointmentMonthYear_numeric")## Wald test for appointmentMonthYear_numeric
## in glm(formula = No.Cancels ~ Gender_numeric + Age.at.Attendance +
## Area.of.Residence_numeric + addressDiff + Referral.Source_numeric +
## Clinic.Code + Clinic.Type_numeric + Consultant_numeric +
## Insurance.Scheme_numeric + Eligibility_numeric + Hospital.Catchment_numeric +
## Booking.Type_numeric + Rebooked.Indicator_numeric + Reason.for.Cancellation_numeric +
## appointmentDay_numeric + appointmentMonthYear_numeric + bookedDay_numeric +
## bookedMonthYear_numeric + daysDiff_AppointBooked, family = binomial(link = "logit"),
## data = train)
## F = 199 on 1 and 25091 df: p= <2e-16regTermTest(logmodel,"bookedDay_numeric")## Wald test for bookedDay_numeric
## in glm(formula = No.Cancels ~ Gender_numeric + Age.at.Attendance +
## Area.of.Residence_numeric + addressDiff + Referral.Source_numeric +
## Clinic.Code + Clinic.Type_numeric + Consultant_numeric +
## Insurance.Scheme_numeric + Eligibility_numeric + Hospital.Catchment_numeric +
## Booking.Type_numeric + Rebooked.Indicator_numeric + Reason.for.Cancellation_numeric +
## appointmentDay_numeric + appointmentMonthYear_numeric + bookedDay_numeric +
## bookedMonthYear_numeric + daysDiff_AppointBooked, family = binomial(link = "logit"),
## data = train)
## F = 1.9 on 1 and 25091 df: p= 0.2regTermTest(logmodel,"bookedMonthYear_numeric")## Wald test for bookedMonthYear_numeric
## in glm(formula = No.Cancels ~ Gender_numeric + Age.at.Attendance +
## Area.of.Residence_numeric + addressDiff + Referral.Source_numeric +
## Clinic.Code + Clinic.Type_numeric + Consultant_numeric +
## Insurance.Scheme_numeric + Eligibility_numeric + Hospital.Catchment_numeric +
## Booking.Type_numeric + Rebooked.Indicator_numeric + Reason.for.Cancellation_numeric +
## appointmentDay_numeric + appointmentMonthYear_numeric + bookedDay_numeric +
## bookedMonthYear_numeric + daysDiff_AppointBooked, family = binomial(link = "logit"),
## data = train)
## F = 0.027 on 1 and 25091 df: p= 0.9regTermTest(logmodel,"Gender_numeric")## Wald test for Gender_numeric
## in glm(formula = No.Cancels ~ Gender_numeric + Age.at.Attendance +
## Area.of.Residence_numeric + addressDiff + Referral.Source_numeric +
## Clinic.Code + Clinic.Type_numeric + Consultant_numeric +
## Insurance.Scheme_numeric + Eligibility_numeric + Hospital.Catchment_numeric +
## Booking.Type_numeric + Rebooked.Indicator_numeric + Reason.for.Cancellation_numeric +
## appointmentDay_numeric + appointmentMonthYear_numeric + bookedDay_numeric +
## bookedMonthYear_numeric + daysDiff_AppointBooked, family = binomial(link = "logit"),
## data = train)
## F = 11 on 1 and 25091 df: p= 0.001# regTermTest(logmodel,"Age.at.Attendance")
regTermTest(logmodel,"Area.of.Residence_numeric")## Wald test for Area.of.Residence_numeric
## in glm(formula = No.Cancels ~ Gender_numeric + Age.at.Attendance +
## Area.of.Residence_numeric + addressDiff + Referral.Source_numeric +
## Clinic.Code + Clinic.Type_numeric + Consultant_numeric +
## Insurance.Scheme_numeric + Eligibility_numeric + Hospital.Catchment_numeric +
## Booking.Type_numeric + Rebooked.Indicator_numeric + Reason.for.Cancellation_numeric +
## appointmentDay_numeric + appointmentMonthYear_numeric + bookedDay_numeric +
## bookedMonthYear_numeric + daysDiff_AppointBooked, family = binomial(link = "logit"),
## data = train)
## F = 2.4 on 1 and 25091 df: p= 0.1regTermTest(logmodel,"addressDiff")## Wald test for addressDiff
## in glm(formula = No.Cancels ~ Gender_numeric + Age.at.Attendance +
## Area.of.Residence_numeric + addressDiff + Referral.Source_numeric +
## Clinic.Code + Clinic.Type_numeric + Consultant_numeric +
## Insurance.Scheme_numeric + Eligibility_numeric + Hospital.Catchment_numeric +
## Booking.Type_numeric + Rebooked.Indicator_numeric + Reason.for.Cancellation_numeric +
## appointmentDay_numeric + appointmentMonthYear_numeric + bookedDay_numeric +
## bookedMonthYear_numeric + daysDiff_AppointBooked, family = binomial(link = "logit"),
## data = train)
## F = 4.2 on 1 and 25091 df: p= 0.04# Find feature/ variable importance from the model
varImp(logmodel) #library(caret)## Overall
## Gender_numeric 3.25
## Age.at.Attendance 2.93
## Area.of.Residence_numeric 1.55
## addressDiff 2.05
## Referral.Source_numeric 2.55
## Clinic.Code 2.34
## Clinic.Type_numeric 1.03
## Consultant_numeric 3.95
## Insurance.Scheme_numeric 6.23
## Eligibility_numeric 7.66
## Hospital.Catchment_numeric 3.77
## Booking.Type_numeric 4.20
## Rebooked.Indicator_numeric 0.14
## Reason.for.Cancellation_numeric 17.30
## appointmentDay_numeric 0.70
## appointmentMonthYear_numeric 14.11
## bookedDay_numeric 1.37
## bookedMonthYear_numeric 0.17
## daysDiff_AppointBooked 6.02# Report model outcome
library(report)## Warning: package 'report' was built under R version 4.3.2report(logmodel)## Registered S3 method overwritten by 'parameters':
## method from
## predict.kmeans rattle## Profiled confidence intervals may take longer time to compute.
## Use `ci_method="wald"` for faster computation of CIs.
## Profiled confidence intervals may take longer time to compute.
## Use `ci_method="wald"` for faster computation of CIs.
## Profiled confidence intervals may take longer time to compute.
## Use `ci_method="wald"` for faster computation of CIs.
## Profiled confidence intervals may take longer time to compute.
## Use `ci_method="wald"` for faster computation of CIs.## We fitted a logistic model (estimated using ML) to predict No.Cancels with
## Gender_numeric, Age.at.Attendance, Area.of.Residence_numeric, addressDiff,
## Referral.Source_numeric, Clinic.Code, Clinic.Type_numeric, Consultant_numeric,
## Insurance.Scheme_numeric, Eligibility_numeric, Hospital.Catchment_numeric,
## Booking.Type_numeric, Rebooked.Indicator_numeric,
## Reason.for.Cancellation_numeric, appointmentDay_numeric,
## appointmentMonthYear_numeric, bookedDay_numeric, bookedMonthYear_numeric and
## daysDiff_AppointBooked (formula: No.Cancels ~ Gender_numeric +
## Age.at.Attendance + Area.of.Residence_numeric + addressDiff +
## Referral.Source_numeric + Clinic.Code + Clinic.Type_numeric +
## Consultant_numeric + Insurance.Scheme_numeric + Eligibility_numeric +
## Hospital.Catchment_numeric + Booking.Type_numeric + Rebooked.Indicator_numeric
## + Reason.for.Cancellation_numeric + appointmentDay_numeric +
## appointmentMonthYear_numeric + bookedDay_numeric + bookedMonthYear_numeric +
## daysDiff_AppointBooked). The model's explanatory power is substantial (Tjur's
## R2 = 0.44). The model's intercept, corresponding to Gender_numeric = 0,
## Age.at.Attendance = 0, Area.of.Residence_numeric = 0, addressDiff = 0,
## Referral.Source_numeric = 0, Clinic.Code = 0, Clinic.Type_numeric = 0,
## Consultant_numeric = 0, Insurance.Scheme_numeric = 0, Eligibility_numeric = 0,
## Hospital.Catchment_numeric = 0, Booking.Type_numeric = 0,
## Rebooked.Indicator_numeric = 0, Reason.for.Cancellation_numeric = 0,
## appointmentDay_numeric = 0, appointmentMonthYear_numeric = 0, bookedDay_numeric
## = 0, bookedMonthYear_numeric = 0 and daysDiff_AppointBooked = 0, is at -0.58
## (95% CI [-1.20, 0.04], p = 0.070). Within this model:
##
## - The effect of Gender numeric is statistically significant and positive (beta
## = 0.43, 95% CI [0.17, 0.69], p = 0.001; Std. beta = 0.06, 95% CI [0.02, 0.09])
## - The effect of Age at Attendance is statistically significant and positive
## (beta = 3.94e-03, 95% CI [1.30e-03, 6.59e-03], p = 0.003; Std. beta = 0.06, 95%
## CI [0.02, 0.10])
## - The effect of Area of Residence numeric is statistically non-significant and
## positive (beta = 0.02, 95% CI [-6.37e-03, 0.05], p = 0.121; Std. beta = 0.03,
## 95% CI [-8.39e-03, 0.07])
## - The effect of addressDiff is statistically significant and negative (beta =
## -0.10, 95% CI [-0.20, -4.78e-03], p = 0.040; Std. beta = -0.04, 95% CI [-0.08,
## -1.88e-03])
## - The effect of Referral Source numeric is statistically significant and
## positive (beta = 0.09, 95% CI [0.02, 0.15], p = 0.011; Std. beta = 0.07, 95% CI
## [0.02, 0.13])
## - The effect of Clinic Code is statistically significant and negative (beta =
## -1.91e-04, 95% CI [-3.52e-04, -3.12e-05], p = 0.019; Std. beta = -0.06, 95% CI
## [-0.11, -9.36e-03])
## - The effect of Clinic Type numeric is statistically non-significant and
## negative (beta = -0.04, 95% CI [-0.11, 0.03], p = 0.304; Std. beta = -0.03, 95%
## CI [-0.07, 0.02])
## - The effect of Consultant numeric is statistically significant and negative
## (beta = -0.08, 95% CI [-0.12, -0.04], p < .001; Std. beta = -0.10, 95% CI
## [-0.14, -0.05])
## - The effect of Insurance Scheme numeric is statistically significant and
## positive (beta = 0.08, 95% CI [0.06, 0.11], p < .001; Std. beta = 0.13, 95% CI
## [0.09, 0.17])
## - The effect of Eligibility numeric is statistically significant and negative
## (beta = -0.19, 95% CI [-0.24, -0.14], p < .001; Std. beta = -0.16, 95% CI
## [-0.19, -0.12])
## - The effect of Hospital Catchment numeric is statistically significant and
## negative (beta = -0.07, 95% CI [-0.10, -0.03], p < .001; Std. beta = -0.07, 95%
## CI [-0.11, -0.04])
## - The effect of Booking Type numeric is statistically significant and positive
## (beta = 0.21, 95% CI [0.11, 0.30], p < .001; Std. beta = 0.13, 95% CI [0.07,
## 0.19])
## - The effect of Rebooked Indicator numeric is statistically non-significant and
## positive (beta = 20.70, 95% CI [204.71, 186.74], p = 0.885; Std. beta = 10.24,
## 95% CI [101.27, 92.38])
## - The effect of Reason for Cancellation numeric is statistically significant
## and negative (beta = -0.32, 95% CI [-0.36, -0.29], p < .001; Std. beta = -0.42,
## 95% CI [-0.46, -0.37])
## - The effect of appointmentDay numeric is statistically non-significant and
## positive (beta = 0.01, 95% CI [-0.02, 0.05], p = 0.485; Std. beta = 0.02, 95%
## CI [-0.03, 0.06])
## - The effect of appointmentMonthYear numeric is statistically significant and
## positive (beta = 0.02, 95% CI [0.02, 0.02], p < .001; Std. beta = 0.29, 95% CI
## [0.25, 0.33])
## - The effect of bookedDay numeric is statistically non-significant and positive
## (beta = 0.02, 95% CI [-8.63e-03, 0.05], p = 0.170; Std. beta = 0.03, 95% CI
## [-0.01, 0.07])
## - The effect of bookedMonthYear numeric is statistically non-significant and
## positive (beta = 2.61e-04, 95% CI [-2.85e-03, 3.37e-03], p = 0.869; Std. beta =
## 3.40e-03, 95% CI [-0.04, 0.04])
## - The effect of daysDiff AppointBooked is statistically significant and
## negative (beta = -1.40e-03, 95% CI [-1.86e-03, -9.47e-04], p < .001; Std. beta
## = -0.17, 95% CI [-0.23, -0.12])
##
## Standardized parameters were obtained by fitting the model on a standardized
## version of the dataset. 95% Confidence Intervals (CIs) and p-values were
## computed using a Wald z-distribution approximation.Decision Tree model
Model of cancellations with predictors
- strong correlation between (Referral source and Booking type), (Clinic type and Booking type), (Consultant and Appointment day), (Consultant and Clinic code)
# Create training and test data
## Define the columns to exclude
cols_to_exclude <- c("Record.Type","NurseFlag","Medical.Record.Number","Attendance.Day","Attendance.MonthYear","Attendance.Date", "Attendance.Type_recode","Attendance.Year","Attendance.Month","Age.at.Attendance.Cat.HSE","Pathway.Number","Present.Address","Home.Address","Appointment.Date","Referring.Hospital","Booked.Date_new", "No.Attendances","No.New.Attendances","Cancellation.Group","Reason.for.Cancellation_recode","No.DNAs","daysDiff_attendanceAppoint","daysDiff_attendanceBooked")
## Subset the data frame by excluding the specified columns
main_processed_cancel_dt <- main_processed[, !names(main_processed) %in% cols_to_exclude]
main_processed_cancel_dt <- subset(main_processed_cancel_dt, is.na(main_processed_cancel_dt$No.Cancels)==F)
sample_dt <- sample(c(TRUE,FALSE), nrow(main_processed_cancel_dt), replace=TRUE, prob=c(0.7,0.3))
train_dt <- main_processed_cancel_dt[sample_dt, ]
test_dt <- main_processed_cancel_dt[!sample_dt, ]
# Train the model
X <- train_dt[, -which(names(train_dt) == "No.Cancels")]
y <- train_dt$No.Cancels
# Fit the decision tree model
cancel_dt <- rpart(y ~ ., data = X, method = "class",cp=0.05) #cp is the complexity parameter, split that not decreasing the overall lack of fit by a factor of cp is pruned, the default value of cp is 0.01
summary(cancel_dt)## Call:
## rpart(formula = y ~ ., data = X, method = "class", cp = 0.05)
## n= 25084
##
## CP nsplit rel error xerror xstd
## 1 1.00 0 1 1 0.012
## 2 0.05 1 0 0 0.000
##
## Variable importance
## Cancellation.Group_numeric Rebooked.Indicator
## 73 10
## Rebooked.Indicator_numeric X
## 10 4
## Eligibility_numeric Eligibility_recode
## 1 1
##
## Node number 1: 25084 observations, complexity param=1
## predicted class=1 expected loss=0.23 P(node) =1
## class counts: 5761 19323
## probabilities: 0.230 0.770
## left son=2 (5761 obs) right son=3 (19323 obs)
## Primary splits:
## Cancellation.Group_numeric splits as RRLR, improve=8900, (0 missing)
## Reason.for.Cancellation_numeric splits as RRLRRR, improve=8900, (0 missing)
## Rebooked.Indicator splits as LR, improve=3600, (0 missing)
## Rebooked.Indicator_numeric splits as LR, improve=3600, (0 missing)
## Booking.Type_recode splits as LRR, improve= 630, (0 missing)
## Surrogate splits:
## Rebooked.Indicator splits as LR, agree=0.80, adj=0.144, (0 split)
## Rebooked.Indicator_numeric splits as LR, agree=0.80, adj=0.144, (0 split)
## X < 91000 to the right, agree=0.78, adj=0.055, (0 split)
## Eligibility_recode splits as RLRRRRR, agree=0.77, adj=0.016, (0 split)
## Eligibility_numeric splits as RRLRRRR, agree=0.77, adj=0.016, (0 split)
##
## Node number 2: 5761 observations
## predicted class=0 expected loss=0 P(node) =0.23
## class counts: 5761 0
## probabilities: 1.000 0.000
##
## Node number 3: 19323 observations
## predicted class=1 expected loss=0 P(node) =0.77
## class counts: 0 19323
## probabilities: 0.000 1.000# Plot the decision tree
rpart.plot(cancel_dt)
cancel_dt$variable.importance## Cancellation.Group_numeric Rebooked.Indicator
## 8876 1282
## Rebooked.Indicator_numeric X
## 1282 488
## Eligibility_numeric Eligibility_recode
## 142 142# Predict the model with actual defaults in test data
cancel_pred_dt <- predict(cancel_dt, test_dt, type="class")
print(cancel_pred_dt)## 13 37 43 50 66 76 78 80 91 113 116 117 131
## 1 1 1 0 1 1 1 1 1 0 1 0 1
## 140 147 155 156 162 173 195 196 208 217 227 244 262
## 0 0 1 1 1 1 1 1 1 1 1 1 1
## 263 291 292 302 333 334 341 356 359 360 367 379 381
## 1 1 1 1 0 0 1 1 0 0 1 0 0
## 382 391 395 397 402 408 417 421 425 426 432 442 455
## 0 1 0 0 1 1 0 1 1 1 1 1 0
## 467 470 475 485 487 497 500 504 509 532 539 561 563
## 1 1 0 0 0 1 1 1 1 0 1 1 1
## 569 576 580 584 585 587 592 595 598 600 604 611 632
## 1 1 1 0 1 1 1 1 1 1 1 1 1
## 642 645 648 653 662 667 669 687 695 696 703 708 710
## 1 1 1 1 0 1 0 1 1 1 1 1 1
## 720 732 746 750 752 756 761 765 766 770 779 781 783
## 0 1 1 0 1 1 1 0 1 0 1 1 0
## 790 798 808 824 830 839 842 843 847 852 855 861 862
## 0 0 0 1 1 1 1 1 1 1 1 1 1
## 864 890 902 904 918 920 929 949 952 954 990 993 997
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1010 1032 1033 1035 1042 1045 1049 1054 1055 1071 1075 1082 1090
## 1 1 1 0 1 1 0 1 1 0 0 1 0
## 1094 1099 1105 1107 1126 1133 1134 1150 1155 1156 1158 1165 1167
## 0 1 1 1 1 1 1 1 1 0 1 1 0
## 1171 1182 1187 1196 1200 1201 1204 1214 1272 1280 1285 1288 1290
## 1 1 0 1 1 1 1 1 1 1 1 1 1
## 1314 1316 1337 1348 1350 1360 1362 1373 1376 1387 1423 1424 1426
## 1 1 0 0 1 1 0 1 1 1 1 1 1
## 1436 1442 1449 1459 1482 1487 1492 1498 1515 1517 1529 1538 1540
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1545 1567 1572 1592 1593 1595 1621 1630 1671 1686 1690 1712 1738
## 0 0 0 1 1 1 1 1 1 0 1 1 1
## 1747 1748 1767 1794 1802 1805 1808 1819 1821 1827 1831 1843 1845
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1850 1853 1865 1867 1872 1891 1920 1943 1952 1956 1965 1969 1986
## 0 0 0 1 1 0 1 1 0 1 0 1 1
## 1992 1995 1998 2004 2016 2020 2022 2031 2041 2057 2077 2079 2086
## 1 1 1 1 1 1 0 1 1 1 0 1 1
## 2090 2116 2119 2149 2151 2160 2161 2162 2165 2177 2179 2184 2187
## 1 1 1 1 0 1 1 0 0 1 0 0 1
## 2195 2201 2202 2207 2236 2256 2278 2291 2306 2307 2313 2347 2372
## 0 1 1 1 1 0 1 1 1 1 1 1 1
## 2376 2387 2395 2409 2442 2444 2447 2450 2453 2469 2478 2480 2489
## 1 1 1 1 1 0 0 1 0 1 1 1 1
## 2491 2504 2506 2516 2518 2539 2543 2588 2594 2604 2608 2609 2621
## 1 1 1 1 1 1 1 1 1 1 0 1 1
## 2627 2647 2682 2688 2703 2706 2719 2733 2740 2764 2778 2792 2797
## 1 1 1 0 1 1 1 1 1 1 0 1 0
## 2801 2804 2823 2828 2860 2882 2886 2893 2907 2914 2915 2933 2935
## 1 1 1 1 0 1 0 1 1 1 1 1 1
## 2937 2938 2944 2956 2961 2968 2990 3007 3009 3014 3019 3032 3036
## 1 1 1 1 1 0 1 1 1 1 1 1 0
## 3058 3063 3064 3068 3075 3076 3083 3090 3098 3106 3110 3132 3137
## 1 1 0 1 1 1 0 0 1 1 1 1 1
## 3147 3159 3172 3175 3177 3189 3190 3200 3209 3235 3250 3260 3264
## 1 1 0 1 1 1 1 1 1 1 1 1 1
## 3268 3270 3272 3275 3287 3292 3313 3323 3326 3328 3338 3343 3361
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 3368 3380 3405 3413 3415 3419 3434 3437 3447 3456 3511 3516 3518
## 0 0 0 1 1 1 1 1 1 1 1 1 0
## 3524 3548 3549 3550 3555 3560 3567 3571 3574 3575 3576 3581 3589
## 1 0 1 1 1 1 1 1 1 1 1 1 1
## 3592 3614 3635 3638 3640 3645 3647 3655 3670 3678 3682 3694 3697
## 0 1 1 0 0 0 0 0 1 1 1 1 0
## 3720 3721 3734 3743 3750 3769 3778 3785 3788 3831 3861 3866 3871
## 1 0 0 0 1 1 1 1 1 1 1 0 1
## 3873 3887 3888 3890 3892 3909 3912 3913 3916 3917 3921 3935 3938
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 3944 3949 3954 3960 3966 3971 3980 3983 3991 4012 4013 4017 4025
## 1 0 1 0 1 1 1 1 1 1 1 0 1
## 4034 4041 4043 4048 4064 4066 4068 4080 4081 4087 4100 4103 4122
## 0 0 0 0 1 0 0 1 1 0 1 1 1
## 4140 4145 4166 4172 4179 4184 4186 4193 4211 4214 4293 4301 4316
## 1 1 1 1 0 1 1 1 1 1 1 1 0
## 4319 4330 4358 4380 4399 4410 4419 4435 4442 4506 4509 4510 4511
## 1 1 1 1 1 1 1 1 1 0 1 1 1
## 4518 4525 4534 4537 4545 4569 4596 4606 4609 4614 4630 4633 4637
## 1 1 1 0 1 1 1 1 1 1 1 1 1
## 4639 4671 4682 4685 4706 4710 4729 4748 4749 4754 4756 4766 4790
## 1 0 1 1 1 1 1 0 1 1 0 1 1
## 4808 4817 4820 4826 4827 4829 4840 4842 4843 4848 4852 4878 4905
## 1 1 1 1 1 1 1 1 0 1 1 1 0
## 4912 4917 4930 4933 4948 4949 4959 4971 4976 4988 4995 4996 4998
## 1 0 1 1 1 1 1 0 1 0 1 1 0
## 5000 5008 5021 5035 5039 5043 5044 5048 5055 5073 5080 5093 5104
## 1 1 1 0 0 1 1 1 1 1 1 1 0
## 5114 5117 5126 5131 5135 5138 5149 5152 5155 5159 5166 5169 5181
## 1 0 1 1 1 1 1 0 1 1 1 1 1
## 5182 5183 5186 5194 5197 5198 5203 5205 5216 5222 5227 5241 5248
## 1 1 1 1 1 1 0 0 1 1 1 1 1
## 5255 5267 5270 5276 5300 5302 5304 5307 5310 5312 5331 5332 5337
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 5352 5360 5361 5374 5376 5392 5400 5428 5431 5433 5434 5448 5467
## 1 1 1 1 1 1 1 1 1 1 1 0 1
## 5468 5472 5475 5476 5490 5496 5518 5524 5527 5549 5552 5557 5561
## 1 1 1 1 1 1 1 1 1 0 1 1 1
## 5568 5578 5583 5603 5605 5611 5619 5634 5649 5658 5661 5662 5665
## 0 1 0 1 1 1 1 1 1 1 0 1 1
## 5670 5695 5706 5710 5720 5721 5724 5744 5748 5752 5762 5764 5770
## 1 0 1 1 1 1 1 1 1 1 0 1 1
## 5782 5788 5801 5828 5839 5840 5865 5870 5874 5876 5893 5913 5915
## 1 1 1 1 1 1 1 0 1 1 1 1 1
## 5917 5924 5931 5938 5951 5954 5960 5964 5990 5999 6008 6028 6037
## 1 1 1 1 0 1 1 1 1 1 1 1 1
## 6044 6053 6057 6070 6073 6078 6083 6091 6107 6120 6121 6139 6144
## 1 1 1 1 1 0 0 1 1 1 1 0 1
## 6163 6164 6171 6189 6191 6200 6201 6243 6251 6262 6267 6286 6289
## 1 0 1 0 1 1 1 1 0 1 1 0 1
## 6320 6327 6334 6337 6341 6342 6347 6351 6354 6356 6361 6365 6391
## 1 1 1 0 1 1 1 1 1 1 1 1 1
## 6397 6431 6444 6451 6462 6464 6467 6483 6498 6504 6534 6540 6548
## 1 1 0 1 1 0 1 1 1 0 0 0 1
## 6552 6565 6578 6580 6588 6604 6608 6615 6643 6648 6649 6665 6677
## 1 1 1 1 1 1 1 1 0 1 1 1 1
## 6684 6694 6699 6701 6712 6714 6726 6734 6739 6744 6746 6749 6751
## 1 1 1 0 1 1 0 1 1 1 1 1 1
## 6754 6759 6787 6791 6805 6823 6825 6829 6840 6842 6844 6851 6876
## 1 1 1 0 1 1 1 1 1 1 1 1 1
## 6879 6881 6894 6905 6906 6926 6935 6953 6955 6964 6988 6990 6991
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 6992 6994 7014 7027 7035 7040 7041 7050 7065 7066 7067 7074 7076
## 1 1 0 1 1 1 1 1 1 0 0 1 1
## 7103 7113 7124 7148 7153 7155 7157 7161 7168 7184 7192 7196 7200
## 0 1 1 0 1 1 1 1 1 1 1 1 1
## 7222 7226 7228 7238 7251 7255 7259 7264 7268 7269 7274 7282 7292
## 1 0 1 1 0 1 0 1 1 0 1 1 1
## 7296 7305 7332 7343 7344 7347 7357 7374 7384 7385 7391 7393 7399
## 1 0 0 1 1 1 0 0 0 0 0 1 1
## 7401 7406 7407 7435 7469 7471 7475 7478 7500 7510 7514 7520 7523
## 1 1 1 1 1 1 0 1 0 1 1 0 1
## 7535 7538 7542 7556 7566 7567 7568 7576 7606 7654 7657 7659 7665
## 1 1 1 1 0 1 1 1 0 0 0 1 1
## 7669 7682 7683 7712 7727 7736 7738 7754 7756 7760 7773 7791 7796
## 1 0 1 1 1 1 1 1 1 1 0 0 1
## 7797 7800 7808 7814 7821 7823 7825 7836 7841 7843 7850 7851 7853
## 1 1 1 1 1 1 1 1 1 0 1 0 0
## 7860 7883 7887 7926 7927 7941 7942 7946 7959 7964 7973 7977 7982
## 0 0 0 1 1 0 1 1 1 1 1 1 1
## 7992 7998 8007 8010 8013 8017 8023 8031 8032 8037 8068 8080 8102
## 1 1 1 1 1 1 1 1 1 0 0 1 1
## 8106 8116 8129 8134 8142 8149 8161 8171 8180 8186 8191 8193 8203
## 1 1 0 1 1 1 1 1 0 1 1 0 1
## 8204 8206 8207 8210 8212 8221 8222 8263 8268 8274 8276 8279 8286
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 8288 8294 8304 8324 8329 8344 8347 8352 8354 8359 8361 8366 8368
## 1 0 0 1 1 1 1 1 1 0 1 0 1
## 8369 8371 8382 8385 8386 8392 8401 8408 8428 8432 8445 8449 8453
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 8474 8476 8488 8508 8511 8526 8547 8554 8559 8581 8597 8603 8618
## 1 1 1 1 1 0 0 1 1 1 0 1 1
## 8631 8632 8635 8636 8638 8646 8648 8649 8651 8654 8660 8664 8679
## 1 1 0 1 1 1 1 1 1 1 1 1 1
## 8684 8688 8698 8713 8728 8729 8734 8737 8743 8751 8754 8755 8760
## 1 1 0 1 1 1 0 1 1 0 0 0 0
## 8771 8786 8794 8795 8797 8798 8800 8801 8803 8812 8813 8815 8816
## 1 1 1 1 0 1 1 1 1 1 1 1 1
## 8832 8843 8845 8856 8858 8889 8891 8893 8902 8911 8969 8976 9000
## 1 1 1 0 1 1 1 1 1 1 1 0 1
## 9006 9013 9040 9045 9058 9074 9080 9090 9094 9099 9100 9114 9118
## 0 1 1 1 1 0 1 1 1 1 1 1 1
## 9130 9131 9142 9143 9170 9180 9198 9201 9207 9220 9223 9235 9250
## 1 0 1 1 1 1 1 1 1 1 0 1 0
## 9252 9254 9262 9269 9273 9281 9282 9303 9308 9318 9321 9334 9346
## 1 0 1 1 0 1 1 1 1 0 1 1 1
## 9353 9374 9375 9376 9388 9404 9412 9414 9423 9425 9429 9430 9448
## 1 1 1 1 1 0 1 0 1 1 1 0 1
## 9454 9468 9469 9470 9473 9487 9499 9507 9510 9514 9528 9540 9543
## 1 1 1 1 1 1 0 1 1 0 1 1 1
## 9551 9556 9567 9573 9591 9599 9640 9654 9660 9665 9679 9692 9697
## 0 1 0 1 1 1 1 0 1 0 1 1 0
## 9701 9720 9721 9737 9745 9756 9761 9765 9771 9783 9788 9793 9796
## 1 1 0 0 1 1 0 1 1 1 1 1 1
## 9799 9802 9806 9818 9823 9830 9851 9853 9854 9867 9872 9892 9897
## 1 1 1 1 1 1 1 1 1 1 0 1 1
## 9903 9904 9908 9913 9916 9919 9922 9938 9942 9956 9960 9964 9967
## 1 1 1 1 1 1 1 1 1 0 1 1 1
## 9986 9990 9997 10010 10012 10026 10034 10040 10047 10060 10065 10067 10076
## 1 1 1 1 0 1 1 1 1 1 0 0 1
## 10080 10081 10082 10130 10141 10152 10154 10162 10170 10176 10181 10185 10188
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 10196 10240 10254 10263 10266 10269 10281 10317 10330 10333 10336 10338 10345
## 1 1 0 0 1 1 1 0 1 1 1 1 1
## 10353 10356 10362 10363 10368 10373 10383 10394 10403 10410 10423 10426 10429
## 1 1 1 1 1 1 0 1 1 1 1 1 1
## 10454 10458 10470 10477 10478 10496 10512 10522 10532 10540 10564 10568 10573
## 1 1 0 1 1 1 1 1 1 1 0 1 1
## 10581 10587 10595 10605 10622 10649 10654 10660 10667 10668 10677 10684 10698
## 0 1 1 0 1 0 1 1 1 1 1 1 1
## 10733 10734 10742 10748 10751 10766 10783 10786 10795 10814 10819 10821 10829
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 10835 10836 10837 10847 10850 10856 10862 10867 10869 10877 10887 10902 10912
## 1 1 0 1 1 0 1 1 1 1 1 1 1
## 10923 10925 10936 10942 10948 10950 10955 10984 10993 10997 11001 11002 11020
## 0 0 1 1 1 1 1 1 1 1 1 0 1
## 11022 11032 11034 11047 11053 11079 11081 11096 11099 11107 11109 11110 11125
## 1 1 0 1 0 1 0 1 1 1 0 1 1
## 11135 11140 11157 11161 11162 11165 11175 11177 11185 11196 11197 11198 11203
## 1 1 1 0 1 1 1 1 1 1 1 0 1
## 11204 11205 11250 11255 11256 11264 11267 11286 11289 11296 11305 11322 11341
## 1 1 0 1 1 1 1 1 1 1 1 1 1
## 11353 11359 11374 11393 11394 11395 11403 11414 11455 11496 11501 11508 11521
## 1 1 0 1 1 1 0 1 1 1 1 0 1
## 11522 11541 11552 11565 11566 11569 11572 11610 11626 11656 11661 11669 11680
## 1 1 0 1 1 1 1 1 1 0 1 1 1
## 11682 11683 11685 11688 11692 11695 11700 11710 11719 11729 11730 11733 11758
## 1 1 1 1 1 1 1 0 1 0 1 1 1
## 11765 11775 11778 11786 11798 11809 11814 11816 11819 11824 11840 11844 11861
## 1 1 1 0 1 1 1 1 1 1 0 1 1
## 11870 11871 11873 11880 11884 11892 11905 11919 11923 11927 11928 11934 11942
## 1 1 1 1 1 1 0 1 0 1 0 0 1
## 11954 11960 11973 11979 11987 11988 11994 11995 11998 12022 12029 12034 12038
## 1 1 1 1 1 1 1 1 1 1 1 0 1
## 12039 12040 12053 12078 12086 12087 12089 12091 12095 12116 12117 12126 12143
## 1 1 0 1 1 1 1 1 1 0 1 1 0
## 12147 12150 12174 12187 12208 12224 12229 12239 12242 12247 12255 12271 12280
## 1 1 1 1 1 1 1 1 1 1 1 1 0
## 12284 12302 12312 12356 12361 12364 12371 12382 12392 12396 12397 12427 12431
## 0 1 0 1 1 1 1 1 1 1 1 0 0
## 12442 12444 12451 12468 12471 12491 12509 12518 12524 12528 12544 12554 12555
## 1 1 1 0 0 0 1 0 1 0 1 1 1
## 12563 12578 12589 12595 12598 12608 12624 12627 12636 12638 12640 12655 12678
## 1 1 0 0 0 0 1 1 1 1 0 1 0
## 12680 12705 12709 12723 12743 12746 12747 12751 12754 12755 12759 12774 12803
## 0 1 1 1 0 1 1 0 0 0 0 1 1
## 12809 12826 12836 12863 12868 12873 12879 12881 12887 12889 12893 12908 12911
## 0 1 1 1 1 1 1 1 1 1 0 1 1
## 12924 12929 12943 12948 12970 12972 12973 12979 12982 12987 12990 13008 13024
## 0 0 0 1 1 1 1 1 0 1 0 0 0
## 13033 13045 13068 13074 13075 13088 13092 13114 13141 13158 13167 13175 13209
## 1 0 1 0 1 0 1 1 0 1 0 0 1
## 13217 13220 13221 13234 13264 13280 13283 13284 13285 13305 13307 13312 13317
## 1 1 1 1 1 1 0 1 1 1 1 0 1
## 13334 13343 13345 13351 13367 13371 13386 13405 13413 13418 13424 13426 13448
## 1 1 1 0 1 1 0 1 0 1 1 1 1
## 13464 13468 13474 13475 13488 13489 13503 13517 13522 13546 13549 13550 13552
## 0 1 1 1 1 1 0 1 0 1 1 1 0
## 13555 13557 13559 13572 13582 13591 13606 13608 13609 13613 13623 13641 13666
## 1 1 1 1 1 1 0 1 0 0 0 1 1
## 13674 13685 13689 13690 13692 13693 13702 13705 13712 13713 13717 13725 13728
## 0 1 1 1 1 1 1 1 1 1 1 1 0
## 13740 13764 13769 13773 13779 13782 13813 13816 13832 13833 13840 13843 13844
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 13845 13848 13886 13891 13907 13930 13931 13939 13942 13947 13948 13961 13969
## 1 1 0 0 1 1 1 1 1 1 1 1 1
## 13979 13981 13992 13993 14000 14003 14007 14011 14013 14023 14028 14030 14051
## 1 0 1 0 1 1 1 1 1 1 1 1 1
## 14063 14068 14077 14078 14100 14117 14133 14151 14181 14193 14198 14200 14204
## 1 1 1 1 1 0 1 1 0 1 1 0 0
## 14224 14243 14251 14273 14276 14294 14299 14301 14302 14309 14315 14317 14341
## 1 1 1 1 0 1 1 1 1 1 1 1 1
## 14347 14353 14358 14368 14371 14372 14377 14379 14382 14390 14403 14406 14409
## 1 1 0 1 1 1 1 1 0 1 1 1 0
## 14414 14419 14420 14437 14445 14447 14454 14463 14487 14519 14527 14532 14539
## 1 0 0 1 1 1 1 1 1 1 0 1 0
## 14542 14545 14562 14564 14568 14569 14582 14606 14614 14636 14637 14640 14643
## 0 0 1 1 1 1 1 1 1 1 0 1 0
## 14647 14651 14654 14664 14667 14684 14688 14689 14690 14701 14702 14704 14706
## 0 1 1 1 1 1 0 1 1 1 1 1 1
## 14725 14726 14742 14763 14771 14777 14781 14787 14798 14808 14812 14830 14835
## 1 1 1 1 1 1 1 0 0 1 1 0 1
## 14837 14848 14856 14864 14876 14877 14884 14910 14915 14920 14924 14927 14930
## 0 1 0 1 1 0 0 0 1 1 0 1 1
## 14941 14942 14947 14952 14978 14993 14998 15000 15008 15029 15046 15054 15056
## 1 1 1 1 0 0 0 0 1 1 1 1 1
## 15069 15070 15079 15084 15096 15106 15108 15109 15116 15126 15135 15144 15160
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 15187 15200 15215 15220 15228 15234 15243 15260 15264 15276 15281 15282 15295
## 0 1 1 1 1 0 1 1 1 1 1 1 1
## 15303 15304 15307 15331 15332 15351 15357 15358 15362 15365 15371 15372 15383
## 1 1 1 0 0 1 1 1 1 1 1 1 1
## 15387 15389 15391 15401 15402 15403 15417 15419 15421 15436 15445 15461 15464
## 0 0 1 1 1 1 1 0 1 1 1 1 1
## 15472 15479 15494 15496 15499 15505 15511 15513 15532 15543 15548 15555 15557
## 1 1 1 0 1 1 0 0 1 1 0 1 1
## 15567 15570 15614 15615 15651 15657 15659 15660 15674 15677 15688 15690 15701
## 1 1 1 1 1 1 0 1 1 1 0 1 1
## 15706 15729 15735 15738 15742 15755 15756 15760 15770 15777 15782 15783 15788
## 1 1 1 1 0 1 1 0 1 1 1 1 0
## 15804 15809 15817 15823 15824 15830 15831 15835 15846 15855 15856 15863 15870
## 1 0 1 0 1 1 1 1 1 1 1 1 1
## 15880 15881 15895 15904 15938 15959 15963 15992 16006 16007 16010 16011 16013
## 1 0 1 1 1 1 1 0 1 1 1 1 1
## 16035 16052 16055 16056 16061 16062 16069 16071 16073 16080 16084 16086 16089
## 1 1 1 0 0 1 1 0 0 1 0 0 1
## 16097 16099 16101 16112 16118 16124 16143 16144 16145 16153 16170 16176 16190
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 16199 16200 16201 16210 16211 16212 16216 16234 16242 16252 16260 16277 16282
## 1 1 0 1 0 0 0 1 1 1 1 1 0
## 16287 16288 16293 16302 16304 16309 16316 16318 16322 16327 16336 16340 16344
## 1 1 1 1 1 1 1 1 0 1 1 1 1
## 16345 16354 16372 16373 16376 16386 16391 16407 16437 16446 16448 16451 16453
## 0 0 1 1 1 1 1 0 0 1 1 1 1
## 16455 16464 16484 16494 16517 16534 16546 16560 16567 16571 16575 16581 16610
## 1 1 0 1 1 0 1 1 1 1 1 1 1
## 16620 16636 16656 16673 16676 16680 16681 16685 16689 16692 16697 16698 16701
## 1 1 1 1 0 0 1 0 0 1 1 0 1
## 16704 16706 16707 16708 16740 16743 16750 16756 16759 16767 16774 16794 16799
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 16803 16804 16818 16827 16840 16847 16849 16852 16854 16856 16869 16871 16886
## 0 1 0 1 0 0 1 1 1 1 1 0 1
## 16897 16903 16911 16917 16925 16926 16929 16942 16945 16956 16967 16974 16992
## 1 0 1 1 1 1 0 1 1 1 0 1 0
## 16996 17009 17014 17015 17038 17046 17058 17063 17066 17074 17086 17093 17125
## 1 1 1 1 1 1 1 1 0 1 1 0 1
## 17129 17132 17141 17142 17144 17156 17165 17170 17177 17179 17213 17232 17243
## 1 1 1 1 1 0 0 1 1 1 1 0 1
## 17259 17265 17266 17267 17277 17294 17303 17306 17310 17320 17327 17328 17329
## 1 1 1 1 1 1 1 1 0 1 1 0 0
## 17341 17354 17368 17393 17399 17402 17407 17414 17418 17420 17438 17442 17455
## 1 1 1 1 0 0 1 1 1 1 0 1 1
## 17464 17473 17479 17487 17494 17498 17507 17515 17533 17538 17539 17540 17552
## 1 1 1 0 1 1 1 1 1 1 1 1 0
## 17570 17571 17577 17580 17595 17599 17622 17633 17638 17647 17648 17654 17661
## 1 1 1 1 1 0 1 1 1 1 1 1 1
## 17667 17680 17685 17686 17691 17699 17702 17713 17721 17723 17724 17730 17747
## 1 1 1 1 1 1 1 1 0 1 1 1 1
## 17751 17776 17777 17798 17799 17801 17812 17820 17823 17825 17835 17847 17857
## 1 1 1 1 1 1 1 1 1 0 1 1 1
## 17866 17877 17888 17891 17897 17902 17918 17936 17938 17939 17941 17948 17955
## 0 1 1 1 1 0 1 1 1 1 1 1 1
## 17966 17974 17977 17978 17983 17996 17997 17999 18002 18040 18056 18059 18061
## 1 1 1 1 1 1 1 1 1 0 1 1 1
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## 1 1 1 1 1 0 1 1 0 0 0 1 1
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## 0 1 0 0 0 0 1 0 1 0 1 1 1
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## 1 1 0 1 1 0 0 1 1 1 1 1 1
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## 1 1 1 1 0 0 1 0 0 0 0 0 1
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## 0 1 1 0 0 0 0 0 0 0 1 1 0
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## 0 1 1 0 1 0 0 0 1 1 1 1 0
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## 0 0 0 0 0 1 0 1 1 1 1 1 1
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## 0 0 0 0 0 0 0 1 0 0 1 1 0
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## 1 0 0 0 0 1 0 1 0 0 1 1 0
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## 0 0 1 0 1 0 0 0 0 0 0 0 0
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## 0 0 1 1 0 0 1 1 1 0 0 1 0
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## 0 1 0 0 0 0 0 0 0 1 0 1 0
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## 0 1 0 1 1 1 0 0 0 0 0 0 0
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## 0 0 0 1 1 0 1 0 0 1 1 0 0
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## 0 1 1 0 0 0 0 0 0 0 0 0 0
## 90603 90605 90608 90609 90611 90612 90617 90621 90622 90623 90629 90630 90647
## 1 0 0 0 0 0 1 1 0 1 0 0 0
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## 0 0 0 1 1 1 0 0 0 0 0 0 0
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## 0 0 1 0 0 0 0 0 0 0 1 1 0
## 90745 90750 90751 90753 90757 90763 90766 90767 90770 90774 90776 90777 90779
## 1 0 1 1 0 1 0 0 1 0 0 0 0
## 90783 90785 90786 90787 90791 90795 90797 90799 90803 90806 90807 90808 90812
## 1 1 1 1 0 0 0 0 0 0 0 0 1
## 90813 90821 90822 90824 90828 90829 90833 90834 90839 90843 90850 90852 90853
## 0 1 0 0 0 0 0 0 1 0 0 0 0
## 90858 90861 90862 90864 90866 90867 90869 90873 90874 90876 90877 90879 90881
## 1 1 0 0 0 0 0 0 1 0 0 0 0
## 90884 90885 90886 90887 90896 90898 90899 90902 90903 90916 90917 90923 90925
## 0 0 0 1 1 0 0 1 0 0 0 0 1
## 90927 90932 90933 90934 90935 90937 90941 90946 90955 90958 90969 90970 90978
## 0 0 0 1 0 0 0 0 0 1 1 1 0
## 90981 90985 90987 90988 90999 91004 91005 91006 91007 91012 91016 91025 91026
## 0 0 0 0 0 0 1 1 0 0 1 0 0
## 91029 91030 91031 91036 91037 91038 91040 91044 91046 91049 91053 91054 91056
## 0 0 0 1 1 0 1 0 0 0 0 0 0
## 91060 91061 91066 91067 91083 91089 91094 91098 91106 91109 91110 91117 91119
## 1 0 0 1 0 0 1 0 0 0 0 0 0
## 91120 91130 91136 91138 91139 91146 91153 91155 91161 91165 91169
## 0 1 0 0 0 0 1 0 0 0 0
## Levels: 0 1# Evaluate the model
CrossTable(test_dt$No.Cancels, cancel_pred_dt,
prop.chisq = FALSE, prob.c = FALSE, prop.r = FALSE,
dnn = c('actual default','predicted default'))##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 10788
##
##
## | predicted default
## actual default | 0 | 1 | Row Total |
## ---------------|-----------|-----------|-----------|
## 0 | 2501 | 0 | 2501 |
## | 1.000 | 0.000 | |
## | 0.232 | 0.000 | |
## ---------------|-----------|-----------|-----------|
## 1 | 0 | 8287 | 8287 |
## | 0.000 | 1.000 | |
## | 0.000 | 0.768 | |
## ---------------|-----------|-----------|-----------|
## Column Total | 2501 | 8287 | 10788 |
## | 0.232 | 0.768 | |
## ---------------|-----------|-----------|-----------|
##
## table_cancel <- data.frame(value = as.factor(test_dt$No.Cancels), pred = cancel_pred_dt)
library(yardstick)## Warning: package 'yardstick' was built under R version 4.3.2##
## Attaching package: 'yardstick'## The following object is masked from 'package:epiDisplay':
##
## kap## The following object is masked from 'package:rcompanion':
##
## accuracy## The following objects are masked from 'package:caret':
##
## precision, recall, sensitivity, specificity## The following object is masked from 'package:readr':
##
## specclass_metrics <- metric_set(accuracy,precision,recall) #metric_set is function of library(yardstick)
class_metrics(table_cancel, truth = value, estimate = pred)## # A tibble: 3 × 3
## .metric .estimator .estimate
## <chr> <chr> <dbl>
## 1 accuracy binary 1
## 2 precision binary 1
## 3 recall binary 1Build baseline model of new attendances with predictors
Note that the echo = FALSE parameter was added to the
code chunk to prevent printing of the R code that generated the
plot.