Scope of the possible with R

Welcome

• this session is a non-technical overview designed for service leads
• we’ll get going properly at 15.05
• if you can’t access the chat, you might need to join our Teams channel:
  

The KIND network

• a social learning space for staff working with knowledge, information, and data across health, social care, and housing in Scotland
• we offer social support, free training, mentoring, community events, …
• Teams channel / mailing list

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
  

Load that data

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

Removing data

ae_activity <- ae_activity |>
    select(!c(country, contains("perc_")))

Tidying data

ae_activity <- ae_activity |>
    mutate(date = ymd(date))

Subset data

• we’ll take a random selection of 5 health boards to keep things tidy

ae_activity <- ae_activity |>
    filter(hb %in% boards)

Basic plots

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

Skill development

Chat, queries, questions

• Teams channel: tinyurl.com/kindnetwork
• weekly email update: tinyurl.com/kindupdate
• get in touch: brendan.clarke2@nhs.scot
• session feedback link at resource page