Foundation

Working on FPO data, as published in the UK Foundation Programme, “Recruitment Stats and Facts, Interim Report April 2017”.

To do this I’ve used a new package - Tabulizer.

This was probably one of the hardest set-ups I’ve had to do. After downloading several different versions of Java, I had some luck with this set-up guide.

I would say it’s been worth the effort however.

Load in Libraries

library(tabulizer)
library(tidyverse)
library(kableExtra)

Load in Tables

pdf <- "2017_Recruitment_Stats_and_Facts.pdf"
data <- extract_tables(pdf, output = "data.frame", header = TRUE)

Index

Table Title Page
1.1 AFP Fill Rates 6
1.2 Local AFP Applications and FP Preferences by Medical School 7
2.1 Applications to FP 8
2.2 Applicant profile comparison 2016 and 2017 8
2.3 Reserve List Applicants by Medical School 9
2.4 Special Circumstances Granted by Criterion 9

Table: 1.2: Local AFP Applications and FP Preferences by Medical School =======================================================================

example1 <- data[[4]] %>%
  filter(X == "University of Glasgow") %>%
  select(-X) %>%
  rename("Number Applied to Local AUoA" = Number) %>%
  rename("% Applied to Local AUoA" = X.1) %>%
  rename("Number Preferenced Home UoA" = Number.1) %>%
  rename("% Preferenced Home UoA" = X.) %>%
  kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
example1
Number Applied to Local AUoA % Applied to Local AUoA Number Preferenced Home UoA % Preferenced Home UoA
30 75% 215 78%

Table 2.7: Local Applications by Medical School ===============================================

Number Applied Number who Preferenced Home UoA % who Preferenced Home UoA
275 215 78%

Table 2.8: FP Programme Preferences ===================================

Number of Applicants who Preferenced UoA First Number of Applicants who Preferenced UoA Second Number of Applicants who Preferenced UoA Third
865 331 233

Table 2.9: FP Total Scores by Medical School ============================================

FP 2016: SJT + EPM (deciles) (Max. score: 100) FP 2017: SJT + EPM (deciles) (Max. score: 100)
79.68 6.12 95.15 65.54 78.20 5.31 88.06 63.59

Table 2.10: Application Results by Medical School =================================================

Number Applied Number Applied to FP (excl. AFP Offers) Percentage of Total Applicants in FP Allocation Allocated to Primary List Percentage Allocated to Primary List
275 256 93% 254 99%

Table 2.11: Preference Allocation Results Information by Medical School =======================================================================

Number Allocated to First Preference % Allocated to First Preference Number Allocated to Top 5 Preference Percentage Allocated to Top 5 Preference Percentage Allocated Lower than Top 5 Preference
215 85% 244 96% 4%

Table 2.12: FP Application Results by Foundation School =======================================================

Places 1st pref apps % 1st pref apps Non-1st pref spaces No. allocated via spec circs % allocated via spec circs No. allocated to 1st pref No. allocated to top 5 prefs % allocated to 1st pref % allocated to top 5 prefs Lowest pref allocated Lowest allocated score (excluding s/c) Lowest s/c allocated score
792 785 99% 7 11 1% 740 778 93% 98% 11 70.74 70.53

Table 2.13: SJT scores by medical school (max. score = 50). SJT scores were scaled according to the distribution of EPM decile scores =====================================================================================================================================

FP 2014: SJT Score (only) FP 2015: SJT Score (only) FP 2016: SJT Score (only) FP 2017: SJT Score (only)
38.93 3.82 47.22 26.08 38.56 3.31 48.25 25.17 39.59 4.36 49.15 26.92 39.77 3.70 48.017 26.142

Table 3.3: Applications by Medical School =========================================

Number Applied for AFP Number Offered AFP % Offered AFP Number Accepted Offer % Accepted Offer
40 25 63% 19 76%