Exploratory Analysis of Surgical Wait Times Dataset
Load the dataset and view a summary of the data:
wait_times <- read.csv(file = "/Users/carandangc/Desktop/ns_surgery_wait_times/Surgical_Wait_Times_20181118.csv", header = TRUE, sep = ",")
summary(wait_times)## Period Specialty
## 12month_rolling:2229 :4890
## 2017_q2 : 466 General Surgery : 557
## 2016_q4 : 463 Orthopaedic : 254
## 2017_q1 : 463 Obstetrics/Gynaecology: 211
## 2018_q2 : 463 Urology : 200
## 2017_q4 : 462 Otolaryngology (ENT) : 151
## (Other) :2161 (Other) : 444
## Procedure
## All : 296
## Hernia Repair (Adult) : 183
## Hernia Repair - Inguinal/Femoral : 165
## Gallbladder Surgery : 164
## Hysterectomy (Cancer Not Suspected): 162
## Hysteroscopy : 150
## (Other) :5587
## Provider Zone Facility
## :4916 :1817 :1817
## Wasserman, Lukas : 21 IWK : 241 Provincial :1322
## Hoogerboord, C Marius : 17 Total :1322 QE2 : 963
## Clark, F Donald : 16 Zone 1: 799 Valley Regional : 397
## Alawashez, Abdulrahim S: 15 Zone 2: 469 Cape Breton Regional: 327
## Archibald, Alison : 14 Zone 3: 742 Dartmouth General : 312
## (Other) :1708 Zone 4:1317 (Other) :1569
## Year Quarter Consult_Median Consult_90th
## Min. :2016 Min. :1.000 Min. : 1.00 Min. : 1.0
## 1st Qu.:2017 1st Qu.:2.000 1st Qu.: 22.00 1st Qu.: 62.0
## Median :2017 Median :3.000 Median : 42.00 Median : 127.0
## Mean :2017 Mean :2.623 Mean : 62.98 Mean : 182.1
## 3rd Qu.:2018 3rd Qu.:3.000 3rd Qu.: 78.00 3rd Qu.: 220.2
## Max. :2018 Max. :4.000 Max. :2032.00 Max. :3282.0
## NA's :1791 NA's :1791 NA's :979 NA's :979
## Surgery_Median Surgery_90th
## Min. : 0.00 Min. : 4.0
## 1st Qu.: 32.00 1st Qu.: 79.0
## Median : 52.00 Median : 137.0
## Mean : 73.88 Mean : 182.4
## 3rd Qu.: 91.00 3rd Qu.: 229.0
## Max. :1167.00 Max. :2025.0
## NA's :149 NA's :149Let’s look at each of the variables from the above summary and visualize them:
print("Period")## [1] "Period"table(wait_times$Period)##
## 12month_rolling 2016_q3 2016_q4 2017_q1
## 2229 461 463 463
## 2017_q2 2017_q3 2017_q4 2018_q1
## 466 458 462 461
## 2018_q2 2018_q3 3month_rolling
## 463 453 328counts <- table(wait_times$Period)
barplot(counts, main="Wait Times", xlab="Period")
From the table and bar graph above, we see that the quarterly wait times (in days) are similar in magnitude, and the 12month_rolling wait time is much larger than the 3month_rolling wait time.
print("Specialty")## [1] "Specialty"table(wait_times$Specialty)##
## All Specialties Cardiac Surgery
## 4890 2 43
## Dental General Surgery Neurosurgery
## 36 557 42
## Obstetrics/Gynaecology Ophthalmology Oral and Maxillofacial
## 211 101 2
## Oral Maxillofacial Orthopaedic Orthopaedic Surgery
## 41 254 2
## Otolaryngology (ENT) Plastic Surgery Thoracic Surgery
## 151 98 32
## Urology Vascular Surgery
## 200 45counts <- table(wait_times$Specialty)
barplot(counts, main="Wait Times", xlab="Specialty")
From the table and bar graph above, we see that General Surgery has the highest wait time, while All Specialties, Oral and Maxillofacial, and Orthopaedic Surgery have the lowest. These lowest values are most likely from an error, and these 3 variables most likely belong to another group (All Specialities most likely is the column with no heading; Oral and Maxillofacial should be with Oral Maxillofacial, and Orthopaedic Surgery should be with Orthopaedic). So the next variable with the lowest wait time is Dental.
print("Procedure")## [1] "Procedure"table(wait_times$Procedure)##
##
## 26
## Adrenal Surgery
## 3
## Aesthetic Surgery
## 23
## All
## 296
## Amputations
## 24
## Aneurysm Repair
## 37
## Angioplasty
## 24
## Ankle - Arthrodesis
## 21
## Ankle - Arthroplasty
## 3
## Ankle - Arthroscopy
## 23
## AV Fistula Creation/Closure
## 38
## Back Surgery (Adult)
## 29
## Back Surgery (Pediatric)
## 22
## Bariatric Surgery (for Weight Loss)
## 21
## Bladder Cancer Surgery
## 82
## Bladder Surgery
## 28
## Bone-Anchored Hearing Aid (Adult)
## 2
## Bone / Cartilage Graft
## 31
## Bowel Resection
## 132
## Bowel Resection - Laparoscopic
## 68
## Bowel Resection - Open
## 99
## Brain Surgery (Adult)
## 27
## Brain Surgery (Pediatric)
## 22
## Breast Augmentation
## 6
## Breast Cancer Surgery
## 113
## Breast Reconstruction
## 26
## Breast Reduction Surgery
## 31
## Cardiac Valve Replacement
## 27
## Cardiovascular Surgery
## 28
## Carotid Endarterectomy
## 24
## Carpal Tunnel Surgery
## 90
## Cataract Surgery
## 133
## Cervical Spine (Neck) Surgery
## 27
## Chest Scopes (Adult)
## 3
## Cleft Lip/Palate (Pediatric)
## 1
## Colorectal Cancer Surgery
## 106
## Congenital Heart Surgery
## 2
## Cornea and External Disease
## 21
## Coronary Artery Bypass Graft
## 27
## Craniotomy for Epilepsy
## 3
## Craniotomy or Craniectomy
## 5
## Cystoscopy
## 128
## Dental Extractions and Restorations (Adult)
## 77
## Dental Extractions and Restorations (Pediatric)
## 93
## Ear Tubes (Adult)
## 18
## Ear Tubes (Pediatric)
## 73
## Endarterectomy
## 26
## Endometrial Ablation
## 72
## ERCP
## 22
## Esophagectomy / Esophageal Surgery
## 23
## Excision Morton's Neuroma
## 2
## Female Incontinence Surgery
## 53
## Foot - Arthrodesis
## 34
## Foot - Arthroplasty
## 14
## Foot - Bunionectomy
## 33
## Foot - Osteotomy
## 12
## Foot - Reconstruction
## 3
## Functional Endoscopic Sinus Surgery (FESS)
## 52
## Gallbladder Surgery
## 164
## Gastric Surgery
## 37
## Gastric Surgery - Laparoscopic
## 29
## Gastrointestinal Tract Surgery
## 120
## Gastrointestinal Tract Surgery - Laparoscopic
## 68
## Glaucoma (Eye Pressure Lowering Surgery)
## 26
## Hand / Upper Extremity
## 38
## Head and Neck Surgery
## 37
## Hemorrhoidectomy
## 31
## Hernia Repair - Incisional
## 82
## Hernia Repair - Inguinal/Femoral
## 165
## Hernia Repair - Laparoscopic Incisional
## 24
## Hernia Repair - Laparoscopic Inguinal Femoral
## 50
## Hernia Repair - Laparoscopic Umbilical
## 17
## Hernia Repair - Laparoscopic Ventral
## 18
## Hernia Repair - Parastomal
## 2
## Hernia Repair - Umbilical
## 107
## Hernia Repair - Ventral
## 24
## Hernia Repair (Adult)
## 183
## Hernia Repair (Pediatric)
## 23
## Hip Arthroscopy
## 35
## Hip Replacement
## 85
## Hip Replacement Revision
## 26
## Hysterectomy (Cancer Not Suspected)
## 162
## Hysterectomy (Cancer Suspected or Proven)
## 28
## Hysteroscopy
## 150
## Insertion Vascular Access Catheter
## 49
## Internal Defib Insertion / Loop Recorder
## 24
## Kidney / Upper Urinary Tract
## 45
## Kidney Removal
## 50
## Kidney Stone Surgery
## 57
## Knee Replacement
## 86
## Knee Replacement - Partial
## 45
## Knee Replacement Revision
## 30
## Knee Scope
## 137
## Knee Scope with ACL Repair
## 40
## Lacrimal Duct Probing
## 24
## Laryngology and Vocal Cord Surgery
## 41
## Liver Surgery
## 24
## Lung Cancer Surgery
## 24
## Lung Surgery
## 24
## Lymph Node Biopsy
## 30
## Male Circumcision (Adult)
## 50
## Male Circumcision (Pediatric)
## 25
## Male Incontinence Surgery
## 3
## Male Reproductive System Surgery
## 129
## Manipulation of Extremity
## 18
## Maxillofacial Deformity Surgery
## 39
## Melanoma Excision
## 28
## Meniscectomy
## 5
## Nerve or Brain Stimulation
## 3
## Nerve Surgery
## 37
## Nissen Fundoplication - Laparoscopic
## 29
## Nissen Fundoplication - Open
## 3
## Nose Surgery - Reconstructive or Aesthetic
## 3
## Oculo Plastic Surgery
## 20
## Orthopaedic Trauma
## 44
## Osteotomy
## 15
## Otoplasty
## 4
## Pacemaker Insertion
## 30
## Palmar Fascia Excision for Dupuytren's Disease
## 39
## Pancreas Surgery
## 23
## Patella Surgery
## 5
## Pelvic Floor Repair
## 43
## Peritoneal Lavage or Catherization
## 22
## Pilonidal Cyst Surgery
## 19
## Prostate Cancer Surgery
## 28
## Prostate Surgery
## 82
## Prostatectomy
## 28
## Removal of Ovaries and/or Fallopian Tubes (Cancer Not Suspected)
## 49
## Removal of Ovaries and/or Fallopian Tubes (Cancer Suspected or Proven)
## 2
## Repair Facial / Orbital Fractures
## 1
## Sacral Nerve Stimulation
## 22
## Salivary Gland Surgery
## 16
## Septoplasty
## 75
## Shoulder Arthroplasty
## 37
## Shoulder Arthroscopy
## 103
## Shoulder Surgery
## 39
## Sinus Surgery
## 15
## Spinal Cord Stimulation Surgery
## 20
## Splenic Surgery
## 3
## Sternotomy / Thoracotomy
## 4
## Strabismus Surgery (Adult)
## 24
## Strabismus Surgery (Pediatric)
## 24
## Surgery for Hearing or Balance Problems of the Ear
## 102
## Tear Duct Surgery
## 20
## Temperomandibular Joint Surgery (TMJ)
## 24
## Temporal Artery Biopsy
## 3
## Tendon / Ligament Repair
## 74
## Thoroscopy / Pleuroscopy
## 3
## Thymectomy
## 3
## Thyroid or Parathyroid Surgery
## 52
## Tonsillectomy and/or Adenoidectomy (Adult)
## 42
## Tonsillectomy and/or Adenoidectomy (Pediatric)
## 62
## Tubal Ligation
## 48
## Varicose Veins
## 23
## Vascular Bypass Surgery
## 22
## Video-Assisted Thoracic Surgery
## 24
## Vitreoretinal Diseases
## 26
## Whipple Procedures
## 5
## Wound Management
## 16counts <- table(wait_times$Procedure)
barplot(counts, main="Wait Times", xlab="Procedure")
In the table and bar graph above, the highest wait times were associated with All and Hernia Repair - Inguinal/Femoral, while there were several less common procedures (ie Temporal Artery Biopsy) which had a wait time of 1.
print("Provider")## [1] "Provider"table(wait_times$Provider)##
##
## 4916
## Abdul Kadir, Najeeb Ahmed
## 12
## Abutu, Natheniel
## 5
## Alant, Jacob D
## 5
## Alawashez, Abdulrahim S
## 15
## Albahiti, Mohamed Tawfik Akif
## 2
## Albert, Daniel N
## 2
## Ali, Idris M
## 6
## Alshareef, Rayan Abdulaziz
## 2
## Amir, Baharak
## 5
## Anderson, Peter A
## 5
## Archibald, Alison
## 14
## Archibald, Curtis
## 5
## Asim, Hammad
## 11
## Atiyah, Abdulrazzak O
## 9
## Bailly, Greg G
## 8
## Balys, Richard L
## 8
## Barry, Sean
## 4
## Baskett, Roger J
## 4
## Belen, Jaime
## 1
## Bell, David G
## 10
## Belliveau, Daniel
## 2
## Bendor-Samuel, Richard
## 2
## Bent, Alfred
## 3
## Bentley, James R
## 5
## Beveridge,William K
## 7
## Bezuhly, Michael
## 8
## Biddulph, Michael Paul
## 6
## Boileau, Louis
## 5
## Boudreau, Dennis Todd
## 6
## Bourget, Louis
## 4
## Bourque, Susan
## 5
## Bouzayen, Renda
## 3
## Brady, James
## 4
## Brennan, Michael
## 11
## Briand, Beth
## 2
## Brien, Donald M
## 10
## Brodarec, Ivan
## 10
## Brooks, Melissa
## 4
## Brown, Charlotte
## 3
## Brown, Timothy
## 2
## Butler, Clay K
## 14
## Butler, Trevor
## 11
## Calverley, Virginia C
## 3
## Carrillo, Monica
## 3
## Casey, Patrick J
## 4
## Chedrawy, Edgar
## 6
## Cheevers, Paul
## 2
## Chiarot, Marco
## 4
## Chokshi, Rashmikant G
## 8
## Christie, Sean D
## 4
## Chun, Samuel S
## 7
## Clague, Nicholas Paul
## 7
## Clark, F Donald
## 16
## Clarke, David B
## 3
## Clarke, Gregory
## 6
## Clifton, Neil
## 7
## Coady, Catherine M
## 4
## Cohen, Elissa
## 3
## Coles, Chad P
## 2
## Comstock, David
## 3
## Coolen, Anna
## 6
## Corsten, P Gerard
## 6
## Cox, Ashley
## 10
## Curry, Philip L
## 9
## Cyr, Philip L
## 4
## Dahrab, Mishari M
## 4
## Dakin, Todd
## 3
## Davidson, Dion
## 9
## Davies, Dafydd Alexander
## 7
## Davis, Benjamin
## 3
## Davis, George R
## 5
## de Saint Sardos, Alexandre Guy William
## 2
## Dempsey, Ian M
## 7
## Dickinson, John D
## 3
## Dickson, Lisa Marie
## 5
## Dodd, Faith
## 8
## Doucet, Jean-Charles
## 6
## Doyle, Ian M
## 3
## Doyle, Tracy
## 2
## Dunbar, Michael J
## 5
## Dunn, Rex S
## 3
## Dyment, Heather
## 3
## Dzierzanowski, Martin
## 10
## Eadie, Brennan
## 3
## Edgar, Dawn C
## 5
## El-Hawary, Rany
## 4
## El-Tahan, Tahmir H.
## 9
## Ellsmere, James C
## 9
## Enright, Jonathan Boyde
## 14
## Faryniuk, Andrea Marie
## 7
## Fossen, Noha
## 5
## French, Daniel Gerard
## 7
## Gajewski, Jerzy B
## 4
## Gala Lopez, Boris
## 10
## Gauthier, Luke Edward
## 3
## George, Stanley
## 2
## Giacomantonio, Carman Anthony
## 4
## Gillis, Megan
## 5
## Gilmour, Donna
## 4
## Glazebrook, Mark A
## 10
## Glennie, Raymond Andrew
## 3
## Goodday, Reginald
## 3
## Grantmyre, John E
## 7
## Gregoire, Curtis
## 5
## Grimshaw, Robert N
## 4
## Gross, Michael
## 6
## Gupta, Rudra Rishi
## 3
## Hamilton, John
## 2
## Hammel, Kenneth
## 2
## Hassan, Abdalla
## 4
## Hayden, David
## 9
## Hayward, Andrew
## 6
## Heisler, Benjamin
## 12
## Helyer, Lucy K
## 8
## Henteleff, Harry J
## 7
## Herman, Christine
## 8
## Hewins, Edward
## 10
## Himmelman, Jeffrey George
## 6
## Hirsch, Gregory M
## 6
## Hong, Paul
## 4
## Hoogerboord, C Marius
## 17
## Houck, Leslie
## 4
## Howatt, Eric
## 9
## Hurley, Richard
## 3
## Hussain, Ahsen Ayyaz
## 5
## Johnson, Kevin
## 10
## Johnson, Liane
## 4
## Johnson, Paul M
## 8
## Johnston, David G
## 7
## Jony, Louai
## 5
## Jorgensen, Sally
## 4
## Joshi, Changulanda
## 6
## Joy, Edward
## 10
## Kapasi, Mustafa
## 2
## Kasi, Anushuya
## 6
## Kelly, Ryan Patrick
## 14
## Kenyon, Chris
## 7
## Kieser, Katharina E
## 4
## Klassen, Dennis R
## 9
## Kujath, Magdalena
## 12
## Lakosha, Hesham
## 2
## Lamey, Alicia
## 12
## Lantz, Andrea
## 3
## Lapierre, Stephanie
## 6
## Laroche, G Robert
## 3
## Lawen, Joseph G
## 11
## LeBlanc, Martin
## 8
## LeBlanc, Robin B
## 7
## Lee, Min S
## 7
## Lee, Winifred W
## 5
## Lefel, Oleg
## 13
## Legay, Douglas A
## 7
## Leighton, Jennifer Laura
## 3
## Leighton, Ross K
## 5
## Lewis, Darrell
## 2
## Livingstone, Scott
## 9
## Logan, Karl John
## 6
## MacDonald, Blair
## 11
## MacDonald, Joseph Gerard
## 6
## MacFarlane, David
## 5
## MacGillivray, Barbara Jean
## 3
## MacIntosh, Alan B
## 3
## MacKean, Gerald L
## 6
## MacLellan, Dawn
## 5
## MacLellan, Jennifer
## 2
## MacQuarrie, Robyn M
## 4
## Malenfant, Deanne
## 3
## Malik, Hatim G
## 4
## Mann, Colin
## 2
## Mason, Ross
## 7
## Massaro, Peter
## 11
## Massoud, Emad A
## 5
## Matei, Anca
## 5
## Mawdsley, Scott D
## 4
## Mayer, Kristine L
## 3
## McCarthy, John Paul
## 12
## McCarthy, Leanne
## 3
## McGibney, Kieron
## 12
## McGory,Rodney W
## 10
## McKenney, Roderick
## 12
## McNeely, P Daniel
## 3
## McPherson, John
## 7
## Mello, Isabel
## 2
## Miller, M Dale
## 13
## Mills, Jessica Louise
## 5
## Minor, Samuel
## 10
## Mishra, Anu
## 2
## Mitchell, Alex D
## 6
## Moodley, Manivasan
## 4
## Morash, Joel
## 11
## Morris, David P
## 3
## Morris, Steven F
## 5
## Mujoomdar, Aneil Arvind
## 10
## Munro, Monica
## 2
## Murdoch, John L
## 13
## Murphy, Christopher
## 12
## Murphy, Jeremy
## 2
## Murphy, Nadia L
## 6
## Murray, Angus
## 5
## Nader, Nabil
## 10
## Nette, Farrell
## 6
## Neumann, Katerina
## 7
## Nicolela, Marcelo
## 3
## O'Brien, Daniel Michael
## 3
## O'Brien, David A
## 7
## O'Joleck, Michael
## 2
## O'Malley, Padraic
## 9
## O'Neill, Brendan
## 4
## O'Neill, Michelle E
## 4
## O'Sullivan, Colleen M
## 12
## Okuboyejo, Fazil
## 5
## Orlik, Benjamin
## 6
## Orr, Andrew C
## 2
## Orrell, Kevin G
## 9
## Osasere, Michael
## 5
## Owen, Stephen Maldwyn
## 6
## Oxner, William
## 2
## Padmore, Dave
## 6
## Paletz, Justin L
## 5
## Palmer, Bruce
## 7
## Parish, Barbara M
## 3
## Pickett, Gwynedd
## 5
## Pierce, Marianne
## 6
## Pillai, Neelakantan G.
## 8
## Pink, Philip
## 7
## Plourde, Madelaine M
## 6
## Porter, Geoffrey A
## 8
## Powell, Joel
## 2
## Proctor, Irma Joan
## 11
## Pugh, Cheryl
## 6
## Puthenparumpil, Jacob
## 8
## Rafuse, Paul E
## 3
## Randle, Elizabeth
## 3
## Reardon, Gerald P
## 7
## Redwan, Hani
## 10
## Rendon, Ricardo
## 10
## Rent, Kenneth
## 12
## Richardson, Christopher Glen
## 6
## Rigby, Matthew Hall
## 5
## Rittenberg, David
## 4
## Robertson, Chad G
## 2
## Robitaille, Johane
## 3
## Rogers, Jamie
## 2
## Romao, Rodrigo Luiz
## 9
## Ross, Ian W
## 4
## Rudd, Michael W
## 8
## Samad, Arif
## 3
## Sandland, Helen
## 4
## Scott, Robert
## 2
## Scott, Stephanie
## 4
## Seamone, Christopher
## 3
## Sequeira, Stanislaus
## 7
## Sharma, Chakshu
## 8
## Shettar, Channabasav
## 3
## Shih, Warren W
## 11
## Shoman, Nael
## 3
## Shuba, Lesya
## 3
## Silver, M. Margaret
## 2
## Sivakumar, Saraswati
## 3
## Smith, Andrew Neil
## 8
## Smith, Anita
## 4
## Smith, Matthew
## 5
## Smith, Philip Michael
## 13
## Smith, Thomas Duncan
## 3
## Stein, John D
## 5
## Stewart, Keir Marshall
## 6
## Stoddart, Todd
## 12
## Swinkles, Stephanie
## 2
## T'ien, Wallace
## 5
## Tan, Alexander
## 2
## Tang, David T
## 5
## Taylor, Benjamin A
## 5
## Taylor, S. Mark
## 7
## Topp, Trevor J
## 8
## Trenholm, J Andrew I
## 7
## Trites, Jonathan R
## 4
## Tynski, Gregory
## 6
## Urquhart, Nathan Alexander
## 10
## Vair, Brett
## 5
## Van eyk, Nancy Ann
## 3
## Wallace, Timothy D
## 8
## Walling, Simon
## 3
## Walsh, Mark J
## 12
## Wasilewski, Leszek J
## 13
## Wasserman, Lukas
## 21
## Watson-Jessome, Jane
## 7
## Weeks, Adrienne
## 2
## Weise, Lutz
## 7
## Wheelock, Margaret
## 8
## Williams, Blair
## 11
## Williams, Jason G
## 6
## Wong, Ivan
## 4
## Wood, Jeremy R
## 5
## Yaffe, Paul
## 10
## Yepes, Horacio
## 4
## Zaki, Amr M
## 2counts <- table(wait_times$Provider)
barplot(counts, main="Wait Times", xlab="Provider")
In the table and bar graph above, Wasserman, Lukas had the highest wait time, and there were several providers who had the lowest wait times.
print("Zone")## [1] "Zone"table(wait_times$Zone)##
## IWK Total Zone 1 Zone 2 Zone 3 Zone 4
## 1817 241 1322 799 469 742 1317counts <- table(wait_times$Zone)
barplot(counts, main="Wait Times", xlab="Zone")
In the table and bar graph above, IWK had the lowest wait time, while Zone 4 had the highest wait time by far.
print("Facility")## [1] "Facility"table(wait_times$Facility)##
## Aberdeen Hospital
## 1817 159
## Cape Breton Regional Colchester East Hants Health Centre
## 327 216
## Cumberland Regional Dartmouth General
## 94 312
## Glace Bay Hants Community Hospital
## 21 42
## Inverness Consolidated Memorial IWK
## 36 241
## New Waterford Northside General
## 65 108
## Provincial QE2
## 1322 963
## Soldiers Memorial South Shore Regional
## 10 233
## St. Marthas Regional Valley Regional
## 185 397
## Yarmouth Regional
## 159counts <- table(wait_times$Facility)
barplot(counts, main="Wait Times", xlab="Facility")
In the table and bar graph above, Soldiers Memorial had the lowest wait time at 10, while QE2 had the highest at 971. This may be secondary to the fact that Soldiers Memorial is a rural community hospital which mainly performs simple procedures, and all the complex procedures in the province are performed at the QE2.
print("Consult Median")## [1] "Consult Median"table(wait_times$Consult_Median)##
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## 8 3 12 26 13 35 81 70 45 58 64 70 90 166 69
## 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
## 91 73 92 74 125 127 106 104 64 63 64 85 104 92 78
## 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45
## 66 60 56 82 102 64 53 39 58 48 71 68 81 57 43
## 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
## 38 37 47 50 56 45 42 50 55 41 61 28 39 44 35
## 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
## 33 43 49 35 30 28 11 36 37 43 18 20 15 25 25
## 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## 30 24 45 13 24 26 19 26 27 22 26 17 21 19 18
## 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105
## 31 22 33 19 14 13 28 24 19 14 9 21 11 15 18
## 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120
## 13 17 10 16 7 14 21 13 13 14 23 14 17 16 14
## 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135
## 10 11 10 8 13 18 11 9 8 6 11 6 10 8 7
## 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150
## 8 5 6 11 7 5 4 7 2 2 9 7 1 6 8
## 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165
## 5 5 7 11 9 6 5 4 7 6 8 2 3 6 7
## 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
## 9 5 4 8 3 5 5 4 4 6 2 2 3 4 2
## 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195
## 5 5 4 4 4 2 1 6 2 2 2 2 1 1 5
## 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210
## 2 1 3 6 1 2 2 4 2 6 2 1 2 2 2
## 211 212 213 214 216 217 219 220 221 222 223 224 225 228 233
## 2 2 3 2 1 2 3 4 5 1 1 1 2 1 1
## 234 235 236 237 239 241 246 247 249 251 252 254 255 259 261
## 1 2 3 3 3 1 2 2 2 2 1 1 2 6 2
## 269 270 272 273 274 276 277 280 281 283 284 285 287 288 290
## 1 3 1 1 1 2 3 2 1 1 1 2 1 2 1
## 291 296 298 300 307 308 311 313 314 316 320 322 326 330 336
## 1 1 2 1 1 1 1 1 3 1 1 1 1 1 2
## 337 340 345 354 356 361 362 365 367 368 371 375 378 389 395
## 2 2 1 1 1 1 1 1 2 1 2 1 2 1 1
## 398 399 400 406 425 427 435 438 440 461 469 476 487 497 506
## 2 1 1 1 1 1 1 1 1 1 2 1 1 1 1
## 525 530 539 549 566 574 612 685 764 821 922 963 982 1124 1217
## 2 1 1 1 2 1 1 1 2 1 1 1 2 1 1
## 1245 1289 1720 2032
## 1 1 2 1counts <- table(wait_times$Consult_Median)
barplot(counts, main="Wait Times", xlab="Consult Median")
The above histogram resembles an exponential distribution, which is in line with queues and wait times.
print("Consult 90th")## [1] "Consult 90th"table(wait_times$Consult_90th)##
## 1 2 3 4 5 7 8 9 10 11 12 13 14 15 16
## 5 1 1 13 3 6 6 7 9 8 6 9 14 17 12
## 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
## 8 10 20 37 31 23 26 12 15 34 28 37 41 26 25
## 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46
## 25 23 36 42 27 25 22 25 24 39 53 45 31 13 19
## 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61
## 22 28 53 41 19 20 34 25 43 64 23 24 23 27 23
## 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76
## 32 42 31 24 20 28 19 35 36 29 23 12 23 23 33
## 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91
## 26 24 15 18 17 23 19 31 30 20 19 19 24 40 51
## 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106
## 24 17 12 16 17 31 28 19 15 14 17 24 11 36 15
## 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121
## 21 17 18 13 17 24 33 8 12 19 16 26 27 25 12
## 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136
## 9 13 15 22 24 15 19 20 14 16 21 27 32 16 15
## 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151
## 13 12 12 23 16 16 16 14 11 28 21 11 12 8 11
## 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166
## 20 18 23 18 14 13 16 11 17 31 11 8 7 12 17
## 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181
## 16 16 11 26 11 10 15 22 24 18 11 11 15 15 21
## 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196
## 27 13 16 9 13 17 12 17 10 24 16 11 11 14 23
## 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211
## 20 14 14 12 7 8 13 13 7 14 12 14 14 11 17
## 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226
## 9 17 15 16 17 13 14 11 3 5 10 12 12 12 8
## 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241
## 10 17 11 11 5 9 10 5 9 6 12 16 9 8 6
## 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256
## 7 10 12 11 8 7 7 4 10 4 5 5 7 6 2
## 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271
## 6 9 15 7 4 4 11 4 6 7 11 13 5 6 2
## 272 273 274 275 276 277 278 279 280 281 282 284 285 286 287
## 5 9 9 6 2 5 9 8 14 5 8 3 5 2 6
## 288 290 291 292 293 294 295 296 297 298 299 300 301 302 303
## 9 1 8 3 10 6 5 7 1 6 5 5 8 3 4
## 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318
## 4 5 2 2 10 1 2 3 3 3 3 7 4 4 5
## 319 320 321 322 324 325 326 327 328 329 330 331 332 333 334
## 2 7 3 6 12 1 7 3 2 10 15 2 6 6 4
## 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349
## 4 9 12 2 5 3 1 3 6 1 4 2 2 4 2
## 350 351 352 356 357 358 359 360 361 363 364 365 366 367 368
## 8 6 4 6 4 3 2 2 2 5 10 7 1 3 2
## 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383
## 1 1 2 8 9 4 4 1 3 6 4 5 1 5 3
## 384 385 386 387 388 389 390 391 392 393 394 395 396 398 399
## 1 1 5 2 6 3 4 2 6 4 3 3 7 4 2
## 400 402 404 405 406 407 408 410 411 412 413 414 415 416 417
## 3 4 3 7 4 4 3 2 3 2 2 2 1 6 1
## 418 419 421 422 423 424 426 427 428 429 430 431 432 434 435
## 1 1 1 1 2 3 4 8 2 3 1 3 9 1 1
## 436 437 439 440 442 443 445 446 447 448 449 453 454 455 456
## 3 7 1 6 3 3 5 1 3 2 3 1 6 4 1
## 458 459 460 461 462 463 464 465 466 468 469 470 471 474 475
## 2 2 2 5 1 5 1 1 1 6 2 1 6 3 4
## 476 477 478 479 481 482 483 485 487 488 490 491 492 493 495
## 1 2 1 2 1 1 3 2 1 1 3 3 5 2 2
## 496 497 498 499 501 502 504 506 507 508 511 512 513 514 515
## 1 3 3 1 2 1 4 2 1 1 1 1 2 1 1
## 517 518 523 529 530 531 534 536 537 538 542 544 545 546 547
## 1 1 1 1 4 4 1 1 1 1 1 1 1 4 3
## 549 552 554 557 558 560 562 565 566 568 574 575 576 578 581
## 1 1 1 1 2 1 1 3 1 1 2 3 2 2 1
## 585 590 591 594 600 601 603 604 605 609 612 617 619 620 627
## 1 1 1 1 2 1 1 1 2 1 1 2 1 3 2
## 637 638 644 645 650 655 656 661 664 681 682 683 685 686 704
## 2 2 1 1 3 3 1 1 1 1 3 1 1 2 1
## 706 710 713 716 722 728 729 735 736 737 750 755 757 758 762
## 1 3 1 1 1 2 3 1 4 4 4 1 3 1 1
## 763 766 783 784 789 790 793 797 811 828 839 840 847 856 870
## 2 1 1 1 1 1 1 1 2 2 1 1 1 1 1
## 877 881 884 916 917 946 951 953 960 998 1000 1002 1050 1051 1052
## 1 1 2 1 2 1 1 2 1 1 1 1 2 2 1
## 1055 1090 1104 1125 1134 1148 1150 1177 1182 1200 1230 1248 1262 1289 1296
## 2 1 3 2 4 1 1 2 1 1 1 1 1 1 1
## 1322 1357 1381 1403 1442 1449 1506 1558 1829 2032 2033 2071 2162 2310 2474
## 1 2 1 2 1 3 1 1 2 1 2 3 1 1 2
## 2490 2544 2599 3047 3187 3282
## 2 2 1 5 2 2counts <- table(wait_times$Consult_90th)
barplot(counts, main="Wait Times", xlab="Consult 90th")
The above histogram resembles an exponential distribution, which is in line with queues and wait times.
print("Surgery Median")## [1] "Surgery Median"table(wait_times$Surgery_Median)##
## 0 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## 1 6 18 5 9 16 15 24 31 18 21 37 52 43 77
## 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
## 62 54 72 60 73 88 87 90 69 67 83 104 86 103 79
## 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45
## 69 79 105 111 77 103 98 87 77 84 87 78 89 66 56
## 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
## 59 76 92 78 62 59 53 64 53 65 60 61 62 34 46
## 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
## 57 56 34 62 43 45 39 30 57 29 43 40 41 35 35
## 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## 47 42 46 37 28 37 20 47 51 33 26 23 15 35 30
## 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105
## 29 25 30 22 30 15 23 30 27 25 22 20 18 21 16
## 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120
## 30 16 12 24 18 21 14 22 17 20 6 14 20 13 17
## 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135
## 17 18 14 15 25 14 25 12 8 8 17 19 17 11 19
## 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150
## 8 4 10 13 8 7 7 10 10 4 11 8 10 11 17
## 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165
## 3 6 11 7 6 5 6 13 3 8 7 9 5 7 5
## 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
## 3 7 8 5 5 12 5 2 8 4 13 7 4 5 4
## 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195
## 6 10 7 7 4 2 6 7 3 4 2 4 5 4 6
## 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210
## 8 13 2 7 3 2 8 7 4 5 1 3 3 10 6
## 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225
## 5 4 4 4 4 3 2 4 3 4 2 3 6 3 6
## 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240
## 1 2 4 2 3 2 3 4 1 1 4 2 3 3 4
## 241 242 243 245 247 248 249 251 252 253 254 255 256 257 258
## 3 2 4 5 1 2 1 2 2 4 3 3 1 4 3
## 260 262 263 264 265 266 267 268 269 270 271 273 274 275 276
## 2 3 5 1 2 1 1 2 2 1 2 5 1 6 2
## 277 279 280 281 282 283 284 285 287 288 292 293 295 296 297
## 1 2 2 2 1 3 5 1 2 2 2 1 2 1 1
## 300 301 302 304 305 307 309 310 311 313 314 315 316 318 319
## 1 4 2 1 1 2 2 1 2 2 1 1 3 1 1
## 320 322 323 324 327 329 330 332 334 336 337 338 343 344 345
## 3 1 2 2 1 2 1 1 1 1 1 1 3 1 1
## 348 349 351 353 354 358 360 363 366 369 370 373 374 389 390
## 1 1 1 1 1 2 1 1 1 1 1 2 2 1 1
## 393 403 405 407 408 412 413 423 430 433 436 437 446 456 462
## 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1
## 467 471 472 475 479 484 491 492 498 500 506 510 513 558 560
## 1 2 2 1 1 1 1 1 1 2 1 1 1 1 1
## 561 567 571 600 608 680 730 739 1167
## 1 1 1 1 1 1 1 1 1counts <- table(wait_times$Surgery_Median)
barplot(counts, main="Wait Times", xlab="Surgery Median")
The above histogram resembles an exponential distribution, which is in line with queues and wait times.
print("Surgery 90th")## [1] "Surgery 90th"table(wait_times$Surgery_90th)##
## 4 5 6 8 9 10 11 12 13 14 15 16 17 18 19
## 4 3 2 6 4 2 2 3 2 4 8 4 2 5 3
## 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
## 17 16 19 25 5 15 14 26 23 22 26 10 17 17 31
## 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49
## 27 32 23 22 11 19 35 23 36 20 23 22 26 29 41
## 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64
## 36 16 24 20 24 32 24 41 27 18 16 22 43 28 49
## 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79
## 27 21 32 41 31 39 46 32 37 23 35 51 30 34 25
## 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94
## 28 23 29 39 37 34 31 14 16 32 39 34 30 35 32
## 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109
## 28 28 34 33 41 36 23 16 25 42 43 47 32 22 17
## 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124
## 24 47 22 27 22 21 24 39 26 25 37 24 16 19 28
## 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139
## 28 24 46 16 9 13 28 30 28 36 28 13 32 18 19
## 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154
## 13 28 30 14 18 27 39 25 34 25 18 23 19 16 16
## 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169
## 25 24 19 21 19 18 22 24 12 15 10 13 31 16 18
## 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184
## 30 18 17 19 22 19 22 22 15 10 16 23 17 30 21
## 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199
## 22 16 8 17 18 18 8 18 18 8 17 18 28 15 15
## 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214
## 8 12 17 17 27 12 12 16 10 16 9 16 8 8 10
## 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229
## 15 21 16 11 18 10 8 17 18 10 12 10 13 17 16
## 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244
## 14 10 25 10 16 8 9 8 9 6 8 8 9 5 12
## 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259
## 4 10 7 4 10 7 10 6 18 9 8 8 10 13 11
## 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274
## 6 8 4 5 4 13 7 5 4 7 7 5 18 9 13
## 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289
## 9 4 5 4 12 7 10 11 6 2 4 5 7 11 8
## 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304
## 4 7 5 8 2 3 8 3 6 3 14 8 16 5 4
## 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319
## 1 3 14 2 9 3 7 13 2 9 2 7 4 7 4
## 320 321 322 323 324 325 326 327 328 329 330 331 333 334 335
## 4 5 6 7 15 6 1 6 5 4 8 5 1 1 6
## 336 337 338 339 342 343 344 345 346 347 348 349 350 351 352
## 9 6 3 8 7 5 4 7 6 2 10 6 9 5 6
## 353 354 355 356 357 358 359 360 361 362 363 364 365 366 368
## 5 3 5 7 11 6 5 11 2 3 5 6 6 7 4
## 369 370 371 372 373 374 375 376 377 379 380 381 382 383 384
## 1 6 4 10 3 2 3 6 1 3 9 3 4 1 4
## 385 386 387 388 389 390 391 392 393 394 395 396 397 398 400
## 3 5 7 1 2 1 8 11 6 1 1 2 4 4 8
## 401 402 403 405 406 407 408 409 410 411 412 413 414 415 416
## 1 5 2 4 1 3 3 3 1 5 4 5 3 3 4
## 417 419 420 421 422 424 425 426 427 428 429 432 433 435 436
## 4 5 1 10 4 2 1 7 3 2 7 1 3 1 2
## 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451
## 9 3 1 7 1 2 4 2 4 3 1 4 9 5 1
## 452 454 455 456 457 459 461 462 463 464 465 467 468 469 470
## 5 2 3 2 3 6 4 1 4 1 3 2 4 1 4
## 471 472 473 474 475 476 477 479 480 482 483 484 485 486 487
## 1 6 1 4 1 4 2 5 1 4 2 9 1 1 1
## 488 489 490 491 492 493 495 496 497 498 499 501 502 503 504
## 3 3 3 5 2 2 1 1 1 3 4 2 1 2 2
## 505 506 507 508 509 510 511 512 514 515 517 518 520 521 522
## 5 2 2 1 5 1 5 3 3 4 2 2 2 4 1
## 523 524 528 530 531 532 534 535 537 538 540 544 545 546 547
## 1 3 2 2 2 1 2 1 1 2 1 2 1 1 2
## 548 549 551 553 554 555 556 557 559 560 561 564 565 566 567
## 2 3 1 1 4 2 1 2 2 1 3 2 4 3 1
## 569 577 578 581 583 587 588 589 590 592 593 594 595 596 597
## 1 1 1 2 1 2 1 2 2 1 2 2 1 1 3
## 602 603 606 608 609 610 611 616 618 622 623 625 627 629 631
## 2 2 1 1 1 3 1 1 2 2 1 1 1 1 2
## 635 638 640 644 645 647 653 655 662 664 668 672 673 675 676
## 1 5 3 1 1 1 1 1 1 1 2 1 1 3 1
## 687 691 692 694 698 699 700 701 704 706 710 711 713 715 718
## 3 1 1 2 1 4 1 1 1 1 1 1 1 2 1
## 719 720 723 729 731 732 740 741 745 746 750 751 753 755 757
## 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1
## 759 764 766 767 773 776 782 789 791 799 800 802 810 811 818
## 1 1 1 1 1 1 1 1 3 1 2 1 1 1 1
## 827 829 835 848 852 853 871 877 882 890 891 907 922 925 926
## 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1
## 928 930 933 939 951 958 967 983 995 1027 1057 1058 1063 1107 1126
## 1 1 2 1 1 1 1 3 1 1 1 1 1 1 1
## 1136 1139 1152 1170 1181 1182 1240 1260 1294 1483 1525 1843 1891 1940 2025
## 2 1 1 1 1 1 2 1 1 1 1 1 2 1 1counts <- table(wait_times$Surgery_90th)
barplot(counts, main="Wait Times", xlab="Surgery 90th")
The above histogram resembles an exponential distribution, which is in line with queues and wait times.
Before we do boxplots of the overall surgery and consult wait times, let’s review what a box plot tells us.
The top and bottom lines of the rectangle are the 3rd and 1st quartiles (Q3 and Q1), respectively. The length of the rectangle from top to bottom is the interquartile range (IQR). The line in the middle of the rectangle is the median (or the 2nd quartile, Q2). The top whisker denotes the maximum value or the 3rd quartile plus 1.5 times the interquartile range (Q3 + 1.5IQR), whichever is smaller. The bottom whisker denotes either the minimum value or the 1st quartile minus 1.5 times the interquartile range (Q1 – 1.5IQR), whichever is larger. (Referenced from https://chemicalstatistician.wordpress.com/2013/05/26/exploratory-data-analysis-variations-of-box-plots-in-r-for-ozone-concentrations-in-new-york-city-and-ozonopolis/)
Now let’s do boxplots of the surgery and consult wait times:
summary(wait_times$Surgery_90th)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 4.0 79.0 137.0 182.4 229.0 2025.0 149# abstract the raw data vector
surgery_wait_90 <- wait_times$Surgery_90th
# remove the missing values
surgery_wait_90 <- surgery_wait_90[!is.na(surgery_wait_90)]
#it appears that R removes the missing values when calculating summary statistics
summary(surgery_wait_90)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 4.0 79.0 137.0 182.4 229.0 2025.0boxplot(surgery_wait_90, ylab = 'Wait Times (days)', main = 'Box Plot of Surgical Wait Times (90th percentile)')
summary(wait_times$Surgery_Median)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 32.00 52.00 73.88 91.00 1167.00 149# abstract the raw data vector
surgery_wait_50 <- wait_times$Surgery_Median
# remove the missing values
surgery_wait_50 <- surgery_wait_50[!is.na(surgery_wait_50)]
boxplot(surgery_wait_50, ylab = 'Wait Times (days)', main = 'Box Plot of Surgical Wait Times (Median)')
# abstract the raw data vector
summary(wait_times$Consult_90th)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.0 62.0 127.0 182.1 220.2 3282.0 979consult_wait_90 <- wait_times$Consult_90th
# remove the missing values
consult_wait_90 <- consult_wait_90[!is.na(consult_wait_90)]
boxplot(consult_wait_90, ylab = 'Wait Times (days)', main = 'Box Plot of Consult Wait Times (90th percentile)')
summary(wait_times$Consult_Median)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.00 22.00 42.00 62.98 78.00 2032.00 979# abstract the raw data vector
consult_wait_50 <- wait_times$Consult_Median
# remove the missing values
consult_wait_50 <- consult_wait_50[!is.na(consult_wait_50)]
boxplot(consult_wait_50, ylab = 'Wait Times (days)', main = 'Box Plot of Consult Wait Times (Median)')
When looking at the box plots for overall wait times, you have to combine the wait time to get a consult with the surgeon after seeing your family doctor, and then add it to the wait time for which you actually receive your procedure (surgery). If you want to get the wait times for most people, then you go with the 90th percentile wait times for consult and surgery. However, it is not as simple as adding these two variables, as the variance will be much bigger when both are added together. This will be important when exploratory analysis is finished and modeling begins.
str(wait_times)## 'data.frame': 6707 obs. of 12 variables:
## $ Period : Factor w/ 11 levels "12month_rolling",..: 3 4 2 2 6 2 7 5 4 2 ...
## $ Specialty : Factor w/ 17 levels "","All Specialties",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Procedure : Factor w/ 159 levels "","Adrenal Surgery",..: 93 42 124 79 104 84 131 69 42 47 ...
## $ Provider : Factor w/ 297 levels "","Abdul Kadir, Najeeb Ahmed",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Zone : Factor w/ 7 levels "","IWK","Total",..: 6 6 3 7 7 7 3 7 6 7 ...
## $ Facility : Factor w/ 19 levels "","Aberdeen Hospital",..: 3 9 13 6 6 6 13 14 12 14 ...
## $ Year : int 2016 2017 2016 2016 2017 2016 2017 2017 2017 2016 ...
## $ Quarter : int 4 1 3 3 3 3 4 2 1 3 ...
## $ Consult_Median: int 21 25 121 68 35 28 70 58 23 31 ...
## $ Consult_90th : int 52 52 228 173 65 60 578 105 175 77 ...
## $ Surgery_Median: int 39 26 69 61 104 30 98 65 30 23 ...
## $ Surgery_90th : int 67 64 160 221 324 59 150 135 135 90 ...Above, this gives information on the dataframe and its variables.
plot(wait_times)
Above is a plot of all the variables in the dataframe, against each other.
The modeling we do next is to incorporate the queue for the consult wait times which feeds into the queue for the surgery wait times, and take into account the exponential distributions of these consult and surgery wait times.