Scope of the possible with R

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Published

January 21, 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

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
20170903	S92000003	S08000031	C313H	Emergency Department	605	570	35	94.2	0	0.0	0	0.0
20220731	S92000003	S08000020	N101H	Emergency Department	980	528	452	53.9	86	8.8	19	1.9
20190512	S92000003	S08000022	H212H	Emergency Department	270	239	31	88.5	1	0.4	0	0.0
20211024	S92000003	S08000022	H103H	Emergency Department	158	141	17	89.2	2	1.3	0	0.0
20180506	S92000003	S08000017	Y144H	Emergency Department	231	218	13	94.4	1	0.4	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
20151101	S08000028	W107H	Emergency Department	89	88	1	0	0
20190217	S08000032	L106H	Emergency Department	1357	1202	155	9	6
20240519	S08000015	A210H	Emergency Department	713	468	245	127	86
20180617	S08000026	Z102H	Emergency Department	171	164	7	0	0
20230820	S08000032	L106H	Emergency Department	1367	780	587	148	43

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
2024-04-14	S08000025	R103H	Emergency Department	174	159	15	0	0
2015-08-30	S08000022	H202H	Emergency Department	735	694	41	5	2
2017-06-04	S08000024	S314H	Emergency Department	2346	2299	47	6	1
2023-02-12	S08000024	S319H	Emergency Department	1108	973	135	6	0
2021-12-19	S08000028	W107H	Emergency Department	64	64	0	0	0

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
2016-03-20	S08000020	N121H	Emergency Department	359	355	4	0	0
2022-07-10	S08000030	T202H	Emergency Department	470	449	21	1	0
2024-04-28	S08000029	F704H	Emergency Department	1413	948	465	59	2
2018-10-14	S08000028	W107H	Emergency Department	125	122	3	0	0
2016-05-29	S08000020	N121H	Emergency Department	385	380	5	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