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Scope of the possible with R
R
overview
Session materials
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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
One small bit of cheating: renaming
Preview
date | country | hb | loc | type | attend | n_within | n_4 | perc_4 | n_8 | perc_8 | n_12 | perc_12 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
20160410 | S92000003 | S08000024 | S308H | Emergency Department | 1008 | 954 | 54 | 94.6 | 7 | 0.7 | 1 | 0.1 |
20161016 | S92000003 | S08000031 | C313H | Emergency Department | 574 | 564 | 10 | 98.3 | 0 | 0.0 | 0 | 0.0 |
20171001 | S92000003 | S08000031 | C418H | Emergency Department | 1266 | 1120 | 146 | 88.5 | 12 | 0.9 | 0 | 0.0 |
20210411 | S92000003 | S08000015 | A210H | Emergency Department | 556 | 449 | 107 | 80.8 | 34 | 6.1 | 12 | 2.2 |
20170108 | S92000003 | S08000030 | T202H | Emergency Department | 476 | 461 | 15 | 96.8 | 0 | 0.0 | 0 | 0.0 |
Removing data
ae_activity <- ae_activity |>
select(!c(country, contains("perc_")))
date | hb | loc | type | attend | n_within | n_4 | n_8 | n_12 |
---|---|---|---|---|---|---|---|---|
20210808 | S08000029 | F704H | Emergency Department | 1200 | 1049 | 151 | 7 | 0 |
20170723 | S08000022 | H212H | Emergency Department | 224 | 218 | 6 | 1 | 0 |
20220529 | S08000031 | G107H | Emergency Department | 1709 | 1193 | 516 | 63 | 2 |
20161016 | S08000015 | A210H | Emergency Department | 781 | 744 | 37 | 2 | 0 |
20221127 | S08000024 | S308H | Emergency Department | 1058 | 690 | 368 | 164 | 81 |
Tidying data
ae_activity <- ae_activity |>
mutate(date = lubridate::ymd(date))
date | hb | loc | type | attend | n_within | n_4 | n_8 | n_12 |
---|---|---|---|---|---|---|---|---|
2021-01-17 | S08000017 | Y146H | Emergency Department | 335 | 311 | 24 | 1 | 0 |
2018-07-01 | S08000032 | L308H | Emergency Department | 1506 | 1336 | 170 | 19 | 0 |
2019-09-01 | S08000031 | C418H | Emergency Department | 1385 | 1115 | 270 | 37 | 1 |
2018-12-23 | S08000020 | N411H | Emergency Department | 479 | 460 | 19 | 2 | 0 |
2023-10-01 | S08000022 | C121H | Emergency Department | 169 | 149 | 20 | 3 | 1 |
Subset data
- we’ll take a random selection of 5 health boards to keep things tidy
date | hb | loc | type | attend | n_within | n_4 | n_8 | n_12 |
---|---|---|---|---|---|---|---|---|
2020-01-05 | S08000020 | N101H | Emergency Department | 1250 | 1044 | 206 | 11 | 1 |
2022-02-13 | S08000015 | A111H | Emergency Department | 1059 | 759 | 300 | 137 | 94 |
2019-05-12 | S08000032 | L302H | Emergency Department | 1299 | 1153 | 146 | 10 | 0 |
2020-10-25 | S08000026 | Z102H | Emergency Department | 112 | 109 | 3 | 0 | 0 |
2023-03-19 | S08000019 | V217H | Emergency Department | 1115 | 500 | 615 | 274 | 88 |
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