Looking at Specialty Training Competition Ratios (2)

- 11 mins

Since my last post, some new data from Round 1 of the 2016 Specialty Training Applications surfaced on Twitter. This offered up the opportunity to update my graphs to include the most current data.

Unfortunately, the data was presented in a picture format, so I had to hand-transcribe onto a spreadsheet, and appended it to the bottom of the data I scraped from my previous post. If you want to see the spreadsheet, I have uploaded it here.

But let’s fire up R.

#Load the required packages
require(dplyr) #To wrangle data
require(ggplot2) #To plot graphs
require(knitr)

#The data
CompetitionRatios <- read.csv("https://raw.githubusercontent.com/dannyjnwong/dannyjnwong.github.io/master/data/CompetitionRatios2013-2016.csv")

#Let's ensure the ratios are accurate
CompetitionRatios <- mutate(CompetitionRatios, Ratio = Applicants/Posts)
CompetitionRatios$Year <- as.factor(CompetitionRatios$Year)
CompetitionRatios$Applicants <- as.numeric(CompetitionRatios$Applicants)
CompetitionRatios$Posts <- as.numeric(CompetitionRatios$Posts)

#Let's see the table
kable(CompetitionRatios)
Specialty Applicants Posts Ratio Year
ACCS EM 534 203 2.630542 2013
Anaesthetics 1189 478 2.487448 2013
Broad-Based Training 429 52 8.250000 2013
Cardiothoracic Surgery (Pilot) 68 6 11.333333 2013
Clinical Radiology 751 185 4.059459 2013
Core Medical Training 3088 1209 2.554177 2013
Core Psychiatry Training 650 437 1.487414 2013
Core Surgical Training 1296 676 1.917160 2013
General Practice 6447 2787 2.313240 2013
Histopathology 154 120 1.283333 2013
Medical Microbiology & Virology 108 21 5.142857 2013
Neurosurgery 183 37 4.945946 2013
Obstetrics and Gynaecology 591 204 2.897059 2013
Ophthalmology 323 71 4.549296 2013
Paediatrics 793 360 2.202778 2013
Public Health 602 70 8.600000 2013
ACCS EM 759 363 2.090909 2014
Anaesthetics 1262 595 2.121008 2014
Broad-Based Training 258 42 6.142857 2014
Cardiothoracic Surgery (Pilot) 72 7 10.285714 2014
Clinical Radiology 798 227 3.515419 2014
Community Sexual and Reproductive Health 33 7 4.714286 2014
Core Medical Training 3065 1468 2.087875 2014
Core Psychiatry Training 643 497 1.293763 2014
Core Surgical Training 1370 625 2.192000 2014
General Practice 5477 3391 1.615158 2014
Histopathology 165 93 1.774193 2014
Medical Microbiology 50 14 3.571429 2014
Neurosurgery 159 24 6.625000 2014
Obstetrics and Gynaecology 583 240 2.429167 2014
Ophthalmology 353 82 4.304878 2014
Oral and Maxillo Facial Surgery (Pilot) 33 4 8.250000 2014
Paediatrics 814 435 1.871264 2014
Public Health 686 78 8.794872 2014
ACCS EM 881 363 2.426997 2015
Anaesthetics 1294 629 2.057234 2015
Broad-Based Training 363 83 4.373494 2015
Cardiothoracic Surgery 68 8 8.500000 2015
Clinical Radiology 917 247 3.712551 2015
Community Sexual and Reproductive Health 100 2 50.000000 2015
Core Medical Training 2632 1550 1.698065 2015
Core Psychiatry Training 662 466 1.420601 2015
Core Surgical Training 1396 604 2.311258 2015
General Practice 5112 3612 1.415282 2015
Histopathology 189 79 2.392405 2015
Neurosurgery 169 30 5.633333 2015
Obstetrics and Gynaecology 599 238 2.516807 2015
Ophthalmology 374 95 3.936842 2015
Oral and Maxillo Facial Surgery 27 5 5.400000 2015
Paediatrics 801 446 1.795964 2015
Public Health 724 88 8.227273 2015
ACCS EM 760 294 2.585034 2016
Anaesthetics 1263 552 2.288044 2016
Cardiothoracic Surgery 61 6 10.166667 2016
Clinical Radiology 1074 212 5.066038 2016
Community Sexual and Reproductive Health 121 5 24.200000 2016
Core Medical Training 2516 1572 1.600509 2016
Core Surgical Training 1622 558 2.906810 2016
General Practice 4863 3790 1.283114 2016
Histopathology 209 83 2.518072 2016
Neurosurgery 169 25 6.760000 2016
Obstetrics and Gynaecology 551 181 3.044199 2016
Ophthalmology 436 90 4.844444 2016
Oral and Maxillo Facial Surgery 19 5 3.800000 2016
Paediatrics 708 411 1.722628 2016
Public Health 738 69 10.695652 2016
Core Psychiatry Training 745 490 1.520408 2016

As you can see I have already formatted the data in the long table format. So we can go straight to graphing. We will visualise the Acute Specialties.

#Filter the specialties to analyse
CompetitionRatios2 <- CompetitionRatios %>% filter(Specialty == "Anaesthetics" | Specialty == "Core Medical Training" | Specialty == "Core Surgical Training" | Specialty == "General Practice" | Specialty == "Paediatrics" | Specialty == "ACCS EM" | Specialty == "Obstetrics and Gynaecology")

ggplot(data=CompetitionRatios2, aes(x=Year, y=Ratio, group = Specialty, colour = Specialty)) +
  geom_line()

center

#ggsave("Ratios2013-2016.png", width = 6, height = 4, units = "in", type = "cairo-png")
ggplot(data=CompetitionRatios2, aes(x=Year, y=Applicants, group = Specialty, colour = Specialty)) +
  geom_line()

center

#ggsave("Applicants2013-2016.png", width = 6, height = 4, units = "in", type = "cairo-png")
ggplot(data=CompetitionRatios2, aes(x=Year, y=Posts, group = Specialty, colour = Specialty)) +
  geom_line()

center

#ggsave("Posts2013-2016.png", width = 6, height = 4, units = "in", type = "cairo-png")

CompetitionRatios3 <- CompetitionRatios %>% group_by(Year) %>% summarise(TotalApplicants = sum(Applicants), TotalPosts = sum(Posts)) %>% mutate(MeanRatio = TotalApplicants/TotalPosts)

kable(CompetitionRatios3)
Year TotalApplicants TotalPosts MeanRatio
2013 17206 6916 2.487854
2014 16580 8192 2.023926
2015 16308 8545 1.908484
2016 15855 8343 1.900395
ggplot(data=CompetitionRatios3, aes(x=Year, y=TotalApplicants, group=0)) + 
  geom_line()

center

We therefore see that there’s been a 7.8519121% reduction in Total Applicants between 2013 to 2016. Also, it is particularly bad in GP (24.5695672% reduction) and CMT (18.5233161% reduction) during the same time-frame.

Danny Wong

Danny Wong

Anaesthetist & Health Services Researcher at the NIAA-HSRC & UCL-DAHR

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