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
20230910 S92000003 S08000031 G513H Type 1 All 1493 1415 78 94.8 1 0.1 0 0.0
20220529 S92000003 S08000022 H103H Type 1 Unplanned 229 195 34 85.2 15 6.6 9 3.9
20250504 S92000003 S08000029 F704H Type 1 New planned 68 57 11 83.8 3 4.4 0 0.0
20240331 S92000003 S08000024 S319H Type 1 All 1130 1033 97 91.4 5 0.4 0 0.0
20221120 S92000003 S08000016 B120H Type 1 All 604 472 132 78.1 47 7.8 31 5.1

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
20150816 S08000020 N101H Type 1 Unplanned 1146 1026 120 7 0
20220306 S08000019 V217H Type 1 All 1151 795 356 68 3
20170611 S08000031 G107H Type 1 All 1741 1599 142 2 0
20150607 S08000022 H202H Type 1 All 587 560 27 3 0
20240107 S08000028 W107H Type 1 Unplanned 114 103 11 0 0

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
2016-11-13 S08000031 G107H Type 1 Unplanned 1726 1457 269 22 0
2021-07-04 S08000024 S314H Type 1 All 2247 1794 453 25 2
2020-07-19 S08000022 H212H Type 1 All 175 171 4 0 0
2017-11-05 S08000030 T101H Type 1 All 1010 970 40 1 0
2021-08-08 S08000024 S314H Type 1 All 2465 1423 1042 215 59

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
S08000024 NHS Lothian 20140401 NA S92000003
S08000021 NHS Greater Glasgow and Clyde 20140401 20190331 S92000003
S08000031 NHS Greater Glasgow and Clyde 20190401 NA S92000003
S08000023 NHS Lanarkshire 20140401 20190331 S92000003
S08000016 NHS Borders 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 loc_name postcode long lat
2021-03-07 NHS Borders B120H Type 1 467 Borders General Hospital TD6 9BS -2.741945 55.59548
2019-12-01 NHS Grampian N411H Type 1 569 Dr Gray’s Hospital IV301SN -3.329946 57.64538
2016-07-24 NHS Lanarkshire L308H Type 1 1275 University Hospital Wishaw ML2 0DP -3.941738 55.77369
2025-09-21 NHS Highland H212H Type 1 196 Belford Hospital PH336BS -5.104701 56.81941
2022-11-13 NHS Fife F704H Type 1 1381 Victoria Hospital (NHS Fife) KY2 5AH -3.160138 56.12511

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)

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)

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