Scope of the possible with R

R
overview
Published

July 2, 2025

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Welcome

  • this session is a non-technical overview designed for service leads

Session outline

  • Why R, and why this session?
  • R demo - take some data, load, tidy, analyse
  • Strengths and weaknesses
    • obvious
    • less obvious
  • Alternatives
  • Skill development

R

  • free and open-source
  • multi-platform
  • large user base
  • prominent in health, industry, biosciences

Why this session?

  • R can be confusing
    • it’s code-based, and most of us don’t have much code experience
    • it’s used for some inherently complicated tasks
    • it’s a big product with lots of add-ons and oddities
  • But R is probably the best general-purpose toolbox we have for data work at present
    • big user base in health and social care
    • focus on health and care-like applications
    • not that hard to learn
    • extensible and flexible
    • capable of enterprise-y, fancy uses

R demo

  • this is about showing what’s possible, and give you a flavour of how R works
  • we won’t explain code in detail during this session
  • using live open data https://www.opendata.nhs.scot/dataset/weekly-accident-and-emergency-activity-and-waiting-times

Load that data

library(readr)
ae_activity <- read_csv("data/weekly_ae_activity_20240609.csv")

One small bit of cheating: renaming

names(ae_activity) <- c("date", "country", "hb", "loc", "type", "attend", "n_within", "n_4", "perc_4", "n_8", "perc_8", "n_12", "perc_12")

Preview

Preview of data
date country hb loc type attend n_within n_4 perc_4 n_8 perc_8 n_12 perc_12
20180603 S92000003 S08000019 V217H Emergency Department 1328 1208 120 91.0 2 0.2 0 0.0
20201018 S92000003 S08000032 L106H Emergency Department 1081 826 255 76.4 22 2.0 6 0.6
20221204 S92000003 S08000022 H202H Emergency Department 624 466 158 74.7 20 3.2 9 1.4
20180729 S92000003 S08000022 H202H Emergency Department 761 713 48 93.7 0 0.0 0 0.0
20160821 S92000003 S08000017 Y146H Emergency Department 721 675 46 93.6 0 0.0 0 0.0

Removing data

ae_activity <- ae_activity |>
    select(!c(country, contains("perc_")))
Preview of data
date hb loc type attend n_within n_4 n_8 n_12
20160807 S08000025 R103H Emergency Department 117 112 5 0 0
20190224 S08000016 B120H Emergency Department 584 570 14 0 0
20160710 S08000020 N411H Emergency Department 510 491 19 3 0
20240107 S08000024 S319H Emergency Department 912 838 74 2 0
20151227 S08000022 H202H Emergency Department 539 522 17 0 0

Tidying data

ae_activity <- ae_activity |>
    mutate(date = lubridate::ymd(date))
Preview of data
date hb loc type attend n_within n_4 n_8 n_12
2015-08-23 S08000031 G513H Emergency Department 951 947 4 0 0
2019-04-07 S08000025 R103H Emergency Department 128 122 6 2 0
2018-09-09 S08000022 H103H Emergency Department 158 152 6 0 0
2016-10-16 S08000031 G107H Emergency Department 1868 1690 178 0 0
2018-04-15 S08000031 G405H Emergency Department 1930 1344 586 89 3

Subset data

  • we’ll take a random selection of 5 health boards to keep things tidy
ae_activity <- ae_activity |>
    filter(hb %in% boards)
Preview of data
date hb loc type attend n_within n_4 n_8 n_12
2018-11-11 S08000026 Z102H Emergency Department 167 165 2 0 0
2018-06-03 S08000030 T202H Emergency Department 575 562 13 0 0
2020-12-20 S08000030 T202H Emergency Department 302 290 12 2 0
2017-02-12 S08000017 Y146H Emergency Department 626 583 43 3 0
2015-06-14 S08000030 T101H Emergency Department 911 887 24 0 0

Basic plots

library(ggplot2)
ae_activity |>
    ggplot() +
    geom_line(aes(x = date, y = attend, colour = hb, group = loc))

Joining data

ae_activity |>
    left_join(read_csv("data/boards_data.csv"), by = c("hb" = "HB")) |>
    select(!any_of(c("_id", "HB", "HBDateEnacted", "HBDateArchived", "Country"))) |>
    ggplot() +
    geom_line(aes(x = date, y = attend, colour = HBName, group = loc))

and again…

Add to a map

ae_activity_loc |>
    leaflet::leaflet() |>
    leaflet::addTiles() |>
    leaflet::addMarkers(~longitude, ~latitude, label = ~HospitalName)

Then make that map more useful

ae_activity_loc |>
    group_by(HospitalName) |>
    summarise(attend = sum(attend), n_within = sum(n_within), longitude = min(longitude), latitude = min(latitude)) |>
    mutate(rate = paste(HospitalName, "averages", scales::percent(round(n_within / attend, 1)))) |>
    leaflet::leaflet() |>
    leaflet::addTiles() |>
    leaflet::addMarkers(~longitude, ~latitude, label = ~rate)

Then add to reports, dashboards…

Strengths

  • enormous scope and flexibility
  • a force-multiplier for fancier data work
    • helps collaboration within teams, between teams, between orgs
    • reproducible analytics
    • modular approaches to large projects
  • decreasing pain curve: the fancier the project, the better

Weaknesses

  • harder to learn than competitors
  • very patchy expertise across H+SC Scotland
  • complex IG landscape
  • messy skills development journey