Scope of the possible with R

R
overview
Published

December 9, 2025

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Welcome

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

Session outline

  • Introducing R, and a bit of chat about the aims of this session
  • Practical demo - take some data, load, tidy, analyse, produce outputs
  • Strengths and weaknesses
    • obvious
    • less obvious
  • Alternatives
  • Skill development

Introducing R

  • free and open-source statistical programming language
  • 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
  • yet there’s signficant resistant to using R in parts of Scotland’s health and care sector

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

Hello world!

There are lots of ways to run R. In this session, we’ll demonstrate using the Rstudio Desktop IDE on Windows.

"Hello world!"

[1] “Hello world!”

Packages

R is highly extensible using packages: collections of useful code

library(readr) # plus, nice to see R's folksy daft names for things in action

Create variables

url <- "https://www.opendata.nhs.scot/dataset/0d57311a-db66-4eaa-bd6d-cc622b6cbdfa/resource/a5f7ca94-c810-41b5-a7c9-25c18d43e5a4/download/weekly_ae_activity_20251123.csv"

Load some open data

That’s a link to data about weekly A+E activity. It’s large-ish (approximately 40000 rows)

ae_activity <- read_csv(url)

One small bit of cheating: renaming

names(ae_activity) <- c("date", "country", "hb", "loc", "type", "attend", "n", "n_in_4", "n_4", "perc_4", "n_8", "perc_8", "n_12", "perc_12")

Preview

Preview of data
date country hb loc type attend n n_in_4 n_4 perc_4 n_8 perc_8 n_12 perc_12
20220828 S92000003 S08000026 Z102H Type 1 New planned 4 3 1 75.0 0 0.0 0 0
20190602 S92000003 S08000024 S319H Type 1 All 964 911 53 94.5 1 0.1 0 0
20171112 S92000003 S08000022 C121H Type 1 All 136 136 0 100.0 0 0.0 0 0
20190714 S92000003 S08000031 G107H Type 1 All 1928 1555 373 80.7 10 0.5 0 0
20170611 S92000003 S08000030 T202H Type 1 All 474 469 5 98.9 0 0.0 0 0

Removing data

library(dplyr)

ae_activity <- ae_activity |>
    select(!c(country, contains("perc_")))
Preview of data
date hb loc type attend n n_in_4 n_4 n_8 n_12
20220807 S08000031 G513H Type 1 All 1214 1192 22 0 0
20171015 S08000030 T202H Type 1 All 440 436 4 0 0
20191013 S08000031 C418H Type 1 Unplanned 1316 1013 303 56 5
20241124 S08000017 Y144H Type 1 All 253 216 37 3 0
20240303 S08000015 A210H Type 1 All 601 409 192 111 79

Tidying data

library(lubridate)

ae_activity <- ae_activity |>
    mutate(date = ymd(date))
Preview of data
date hb loc type attend n n_in_4 n_4 n_8 n_12
2022-02-20 S08000016 B120H Type 1 All 616 405 211 101 69
2023-09-24 S08000015 A111H Type 1 All 1194 834 360 158 91
2017-10-22 S08000017 Y146H Type 1 Unplanned 656 591 65 1 0
2018-06-17 S08000026 Z102H Type 1 Unplanned 171 164 7 0 0
2022-01-30 S08000032 L302H Type 1 New planned 8 8 0 0 0

Subset data

We’ll take a selection of 5 health boards to keep things tidy:

boards_sample <- c("NHS Borders", "NHS Fife", "NHS Grampian", "NHS Highland", "NHS Lanarkshire")

Joining data

Those board codes (like S08000020) aren’t very easy to read. Luckily, we can add the proper “NHS Thing & Thing” board names from another data source.

boards <- read_csv("https://www.opendata.nhs.scot/dataset/9f942fdb-e59e-44f5-b534-d6e17229cc7b/resource/652ff726-e676-4a20-abda-435b98dd7bdc/download/hb14_hb19.csv")
NHS boards
HB HBName HBDateEnacted HBDateArchived Country
S08000030 NHS Tayside 20180202 NA S92000003
S08000022 NHS Highland 20140401 NA S92000003
S08000023 NHS Lanarkshire 20140401 20190331 S92000003
S08000018 NHS Fife 20140401 20180201 S92000003
S08000026 NHS Shetland 20140401 NA S92000003

We can do something very similar with the A&E locations:

locs <- read_csv("https://www.opendata.nhs.scot/dataset/a877470a-06a9-492f-b9e8-992f758894d0/resource/1a4e3f48-3d9b-4769-80e9-3ef6d27852fe/download/ae_hospital_site_list_09_09_2025.csv") |>
  select(2:4)

names(locs) <- c("loc_name", "loc", "postcode") # a bit of renaming to make the names easier

And we can add the postcodes:

locs <- locs |>
  rowwise() |>
  mutate(long = PostcodesioR::postcode_lookup(postcode)$longitude,
         lat = PostcodesioR::postcode_lookup(postcode)$latitude) # adding in some location information

We can then join our three datasets together to give us data with the NHS Board names, A&E names, and locations:

ae_activity_locs <- ae_activity |>
  filter(attend == "All") |>
  left_join(boards, by = join_by(hb == HB)) |>
  filter(HBName %in% boards_sample) |>
  select(date, HBName, loc, type, n, contains("n_")) |>
  mutate(date = ymd(date)) |>
  left_join(locs)
Data with NHS board, location, and location names
date HBName loc type n n_in_4 n_4 n_8 n_12 loc_name postcode long lat
2024-09-08 NHS Lanarkshire L302H Type 1 1253 746 507 139 52 University Hospital Hairmyres G75 8RG -4.221963 55.75982
2017-10-29 NHS Highland H212H Type 1 160 153 7 0 0 Belford Hospital PH336BS -5.104701 56.81941
2016-08-21 NHS Highland H103H Type 1 138 128 10 1 0 Caithness General Hospital KW1 5NS -3.095744 58.44133
2017-10-22 NHS Borders B120H Type 1 575 551 24 1 0 Borders General Hospital TD6 9BS -2.741945 55.59548
2020-11-08 NHS Lanarkshire L106H Type 1 1008 782 226 54 9 University Hospital Monklands ML6 0JS -3.999588 55.86588

Basic plots

library(ggplot2)

ae_activity_locs |>
  filter(HBName == "NHS Highland") |>
  ggplot() +
  geom_line(aes(x = date, y = n, colour = loc_name))

Looking across different measures

library(tidyr)

ae_activity_locs |>
  filter(loc_name == "Raigmore Hospital") |>
  select(date, n_in_4:n_12) |>
  pivot_longer(-date) |>
  group_by(date = floor_date(date, "month"), name) |>
  summarise(value = mean(value)) |>
  ggplot(aes(x = date, y = value, colour = name)) +
  geom_line() +
  geom_smooth(se = F)

Making that re-usable

graphmo <- function(hbname = "NHS Highland"){
  
  ae_activity_locs |>
    filter(HBName %in% hbname) |>
    ggplot() +
    geom_line(aes(x = date, y = n, colour = loc_name)) +
    theme(legend.position = "bottom") +
    xlab("Location") +
    labs(colour = "") # hide the label

}

graphmo(c("NHS Grampian", "NHS Fife"))

A rubbish map

We’ve got latitude and longitude information for our A&E sites, which means we can plot them on a map:

ae_activity_locs |>
  ggplot(aes(x = long, y = lat, label = loc_name, colour = HBName)) +
  geom_point() +
  theme_void() +
  theme(legend.position = "none")

That’s not the most useful map I’ve ever seen. Luckily, there’s a package to help us:

Add to a map

ae_activity_locs |>
    leaflet::leaflet() |>
    leaflet::addTiles() |>
    leaflet::addMarkers(~long, ~lat, label = ~loc_name)

Simple map

Then make that map more useful

ae_activity_locs |>
    group_by(loc_name) |>
    summarise(n = sum(n), long = min(long), lat = min(lat)) |>
    mutate(rate = paste(loc_name, "averages", n, "attendees")) |>
    leaflet::leaflet() |>
    leaflet::addTiles() |>
    leaflet::addMarkers(~long, ~lat, label = ~rate)

More useful map

Strengths

R offers enormous scope and flexibility, largely because of two features. First, R is based on the idea of packages, where you’re encouraged to outsource specialist functions to your R installation in a repeatable and standard way. There’s basically a package for everything - something over 20000 at present. Second, R encourages reproducible analytics: the idea being you write your script once, and then run it many times as your data changes, producing standardised outputs by design.

Together, that design makes R a force-multiplier for fancier data work: use packages to replicate your existing work in a reproducible way, then use the time saved in your routine reporting to improve and extend the work. There are other features of code-based analytics which make collaborating and developing more complex projects typically much smoother than they would be in non-code tools like Excel.

Weaknesses

  • it’s code, and it takes some time (months to years) to achieve real fluency
  • potentially harder to learn than some competitor languages and tools (Power BI, Python)
  • very patchy expertise across H+SC Scotland
  • complex IG landscape
  • messy skills development journey