Help for package NHSRdatasets

Type:

Package

Title:

NHS and Healthcare-Related Data for Education and Training

Version:

1.0.0

Maintainer:

Zoë Turner <zoe.turner3@nhs.net>

Description:

Free United Kingdom National Health Service (NHS) and other healthcare, or population health-related data for education and training purposes. This package contains synthetic data based on real healthcare datasets, or cuts of open-licenced official data. This package exists to support skills development in the NHS-R community: https://nhsrcommunity.com/.

License:

CC0

Language:

en-GB

Encoding:

UTF-8

LazyData:

true

Depends:

R (≥ 4.1.0)

BugReports:

https://github.com/nhs-r-community/NHSRdatasets/issues

Suggests:

caret, dplyr, e1071, forcats, ggplot2, ggrepel, httr2, kableExtra, knitr, lattice, lme4, lmtest, lubridate, magrittr, MASS, ModelMetrics, purrr, rcmdcheck, readr, rmarkdown, rsample, scales, stringr, tibble, tidyr, varhandle

VignetteBuilder:

knitr

URL:

https://github.com/nhs-r-community/NHSRdatasets, https://nhs-r-community.github.io/NHSRdatasets/

Config/roxygen2/version:

8.1.0

NeedsCompilation:

Packaged:

2026-09-08 15:53:47 UTC; Zoe.Turner

Author:

Zoë Turner

[aut, cre], Chris Mainey

[aut], Tom Jemmett

[aut], Fran Barton

[aut], Gary Hutson

[aut], NHS-R community [cph]

Repository:

CRAN

Date/Publication:

2026-09-08 17:50:13 UTC

Hospital Length of Stay (LOS) Data

Description

Artificially generated hospital data. Fictional patients at 10 fictional hospitals, with LOS, Age and Date status data Data were generate to learn Generalized Linear Models (GLM) concepts, modelling either Death or LOS.

Usage

data(LOS_model)

Format

Data frame with five columns

ID: A fictional patient ID number
Organisation: A factor representing one of ten fictional hospital trusts, for example Trust1
Age: Age in years of each fictional patient
LOS: In-hospital length of stay in days. The difference between admission and discharge date in dates
Death: Binary for death status: 0 = survived, 1= died in hospital

Source

Generated by Chris Mainey, Feb-2019

Examples

data(LOS_model)

model1 <- glm(Death ~ Age + LOS, data = LOS_model, family = "binomial")
summary(model1)

# Now with an Age, LOS, and Age*LOS interaction.
model2 <- glm(Death ~ Age * LOS, data = LOS_model, family = "binomial")
summary(model2)

NHS England Accident & Emergency Attendances and Admissions

Description

Reported attendances, 4 hour breaches and admissions for all A&E departments in England for the years 2016/17 through 2018/19 (Apr-Mar). The data has been tidied to be easily usable within the tidyverse of packages.

Usage

data(ae_attendances)

Format

Tibble with six columns

period: The month that this data relates to
org_code: The ODS code for this provider
type: The department type. either 1, 2 or other
attendances: the number of patients who attended this department in this month
breaches: the number of patients who breaches the 4 hour target in this month
admissions: the number of patients admitted from A&E to the hospital in this month

Details

Data sourced from NHS England Statistical Work Areas which is available under the Open Government Licence v3.0

Source

NHS England Statistical Work Areas

Examples

data(ae_attendances)

library(dplyr)
library(ggplot2)
library(scales)

# Create a plot of the performance for England over time
ae_attendances %>%
  group_by(period) %>%
  summarise_at(vars(attendances, breaches), sum) %>%
  mutate(performance = 1 - breaches / attendances) %>%
  ggplot(aes(period, performance)) +
  geom_hline(yintercept = 0.95, linetype = "dashed") +
  geom_line() +
  geom_point() +
  scale_y_continuous(labels = percent) +
  labs(title = "4 Hour performance over time")

# Now produce a plot showing the performance of each trust
ae_attendances %>%
  group_by(org_code) %>%
  # select organisations that have a type 1 department
  filter(any(type == "1")) %>%
  summarise_at(vars(attendances, breaches), sum) %>%
  arrange(desc(attendances)) %>%
  mutate(
    performance = 1 - breaches / attendances,
    overall_performance = 1 - sum(breaches) / sum(attendances),
    rank = rank(-performance, ties.method = "first") / n()
  ) %>%
  ggplot(aes(rank, performance)) +
  geom_vline(xintercept = c(0.25, 0.5, 0.75), linetype = "dotted") +
  geom_hline(yintercept = 0.95, colour = "red") +
  geom_hline(aes(yintercept = overall_performance), linetype = "dotted") +
  geom_point() +
  scale_y_continuous(labels = percent) +
  theme_minimal() +
  theme(
    panel.grid = element_blank(),
    axis.text.x = element_blank()
  ) +
  labs(
    title = "4 Hour performance by trust",
    subtitle = "Apr-16 through Mar-19",
    x = "", y = ""
  )

AphA (Association of Professional Healthcare Analysts) CPD Survey Responses

Description

Full raw data from the AphA CPD Survey

Usage

apha_cpd_survey

Format

This tidied raw data is available here as a tibble with 38 columns (blank or superfluous columns from the raw data were removed) and 237 rows (1 per respondent ID).

Variables have been named using a "controlled language" approach informed by Emily Riederer's "Column Names as Contracts" https://emilyriederer.netlify.app/post/column-name-contracts/.

*_id: Columns ending in "_id" are numeric and represent a unique ID for that response.
*_dttm: Columns ending in "_dttm" are in datetime format.
*_cat: Columns ending in "_cat" contain categorical data, though in some cases this is mixed with free text responses and may require tidying if you need it to be strictly categorical/factor data.
*_n: Columns ending in "_n" are theoretically counts, but in this tibble they may be mixed with non-numeric values and so the columns are in character format.
*_ind: Columns ending in "_ind" are theoretically indicator values with 2 main value options (Yes/No). These are in character format, but should be convertible to 1/0 or TRUE/FALSE values, if desired, with minimal wrangling.
*_txt: Columns ending in "_txt" contain free text responses and are in character format.

Multi-part questions have column name stubs with sequential letters. For example, "q20a_", "q20b_" and so on. For formatting consistency, questions with a single part still have a column name stub with the letter a, for example "q01a_".

Original survey questions (lightly edited) are provided as variable labels using the {labelled} package https://larmarange.github.io/labelled/. These labels provide more descriptive context for the "clean" column names. Variable labels can be viewed using labelled::get_variable_labels (apha_cpd_survey).

Survey press release web page: Association of Professional Healthcare Analysts (AphA) ltnws/nhs-at-risk-of-losing-a-generation-of-data-analysts/

Source

Association of Professional Healthcare Analysts (AphA), documents/cpd-survey-results-raw-data/

The survey of NHS and other healthcare data analysts was conducted in July 2022. The results data is made available in this package with the permission of AphA.

International COVID-19 reported infection and death data

Description

Reported COVID-19 infections, and deaths, collected and collated by the European Centre for Disease Prevention and Control (ECDC, provided by day and country). Data were collated and published up to 14th December 2020, and have been tidied so they are easily usable within the 'tidyverse' of packages.

Usage

data(covid19)

Format

Tibble with seven columns

date_reported: The date cases were reported
contient: A 'factor' for the geographical continent in which the reporting country is located.
countries_and_territories: A 'factor' for the country or territory reporting the data.
countries_territory_code: A 'factor' for the a three-letter country or territory code.
population_2019: The reported population of the country for 2019, taken from Eurostat for Europe and the World Bank for the rest of the world.
cases: The reported number of positive cases.
deaths: The reported number of deaths.

Details

Data sourced from European Centre for Disease Prevention and Control which is available under the open licence, compatible with the CC BY 4.0 license, further details available at ECDC.

Source

European Centre for Disease Prevention and Control

Examples

data(covid19)

library(dplyr)
library(ggplot2)
library(scales)

# Create a plot of the performance for England over time
covid19 |>
  filter(countries_and_territories ==
    c("United_Kingdom", "Italy", "France", "Germany", "Spain")) |>
  ggplot(aes(
    x = date_reported,
    y = cases,
    col = countries_and_territories
  )) +
  geom_line() +
  scale_color_discrete("Country") +
  scale_y_continuous(labels = comma) +
  labs(
    y = "Cases",
    x = "Date",
    title = "Covid-19 cases for selected countries",
    alt = "A plot of covid-19 cases in France, Germany, Italy, Spain & the UK"
  ) +
  theme_minimal()

Deaths registered weekly in England and Wales, provisional

Description

Provisional counts of the number of deaths registered in England and Wales, by age, sex and region, from week commencing 8th January 2010 to 3rd April 2020.

Usage

data(ons_mortality)

Format

Data frame with five columns

category_1: character, containing the names of the groups for counts, for example "Total deaths", "all ages".
category_2: character, subcategory of names of groups where necessary, for example details of region: "East", details of age bands "15-44".
counts: numeric, numbers of deaths in whole numbers and average numbers with decimal points. To retain the integrity of the format this column data is left as character.
date: date, format is yyyy-mm-dd; all dates are a Friday.
week_no: integer, each week in a year is numbered sequentially.

Details

Source and licence acknowledgement

This data has been made available through Office of National Statistics under the Open Government Licence https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/

Source

Collected by Zoë Turner, Apr-2020 from https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/weeklyprovisionalfiguresondeathsregisteredinenglandandwales

Examples

data(ons_mortality)

library(dplyr)
library(tidyr)

# create a dataset that is "wide" with each date as a column
ons_mortality |>
  select(-week_no) |>
  pivot_wider(
    names_from = date,
    values_from = counts
  )

ONS Mid-2023 Population Estimate for UK

Description

ONS Population Estimates for Mid-year 2023 National and subnational mid-year population estimates for the UK and its constituent countries by administrative area, age and sex (including components of population change, median age and population density).

Usage

data(ons_uk_population_2023)

Format

Tibble with six columns

sex: male or female
code: country/geography code
name: country of the UK
geography: Country
age: year of age
count: the number of people in this group

Details

ONS Estimates of the population for the UK, England, Wales, Scotland, and Northern Ireland

Source

https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/datasets/populationestimatesforukenglandandwalesscotlandandnorthernireland

Examples

data(ons_uk_population_2023)


library(dplyr)
library(tidyr)

# create a dataset that has total population by age groups for England
ons_uk_population_2023 |>
  filter(name == "ENGLAND") |>
  mutate(age_group = case_when(
    as.numeric(age) <= 17 ~ "0-17",
    as.numeric(age) >= 18 & as.numeric(age) <= 64 ~ "18-64",
    as.numeric(age) >= 65 ~ "65+",
    age == "90+" ~ "65+"
  )) |>
  group_by(age_group) |>
  summarise(count = sum(count))

Stranded Patient (Patients flagged as having a greater than 7 day Length of Stay) Model

Description

This model is to be used as a machine learning classification model, for supervised learning. The binary outcome is stranded vs not stranded patients.

Usage

data(stranded_data)

Format

Tibble with nine columns (1 x outcome and 8 predictors)

stranded.label: Outcome variable - whether the patient is stranded or not
age: Patient age on admission
care.home.referral: Whether than have been referred from a care home
medicallysafe: Medically safe for discharge - means the patient is assessed as safe, but has not been discharged yet
hcop: Indicates whether they have been triaged from a Health Care for Older People specialty
mental_health_care: Flag to indicate whether they need mental health support and care
periods_of_previous_care: Count of the number of previous spells of care
admit_date: Date they were admitted to hospital
frailty_index: An initial index assessment to say if the patient is frail or not. This is needed for alignment of service provision.

Source

Synthetically generated by Gary Hutson, Mar-2021.

Examples

library(dplyr)

data(stranded_data)

stranded_data |>
  glimpse()

Synthetic National Early Warning Scores Data

Description

Synthetic NEWS data to show as the results of the NHSR_synpop package. These datasets have been synthetically generated by this package to be utilised in the NHSRDatasets package.

Usage

data(synthetic_news_data)

Format

Tibble with twelve columns

male: character string containing gender code
age: age of patient
NEWS: National Early Warning Score (NEWS)
syst: Systolic BP - Systolic BP result
dias: Diastolic Blood Pressure - result on NEWS scale
temp: Temperature of patient
pulse: Pulse of the patient
resp: Level of response from the patient
sat: SATS(Oxygen Saturation Levels) of the patient
sup: Suppressed Oxygen score
alert: Level of alertness of patient
died: Indicator to monitor patient death

Source

Generated by Dr. Muhammed Faisal and created by Gary Hutson, Mar-2021

Examples


library(dplyr)

data("synthetic_news_data")

synthetic_news_data |>
  glimpse()

Welcome to ClientVPS Mirrors

Package {NHSRdatasets}

Hospital Length of Stay (LOS) Data

Description

Usage

Format

Source

Examples

NHS England Accident & Emergency Attendances and Admissions

Description

Usage

Format

Details

Source

Examples

AphA (Association of Professional Healthcare Analysts) CPD Survey Responses

Description

Usage

Format

Source

International COVID-19 reported infection and death data

Description

Usage

Format

Details

Source

Examples

Deaths registered weekly in England and Wales, provisional

Description

Usage

Format

Details

Source

Examples

ONS Mid-2023 Population Estimate for UK

Description

Usage

Format

Details

Source

Examples

Stranded Patient (Patients flagged as having a greater than 7 day Length of Stay) Model

Description

Usage

Format

Source

Examples

Synthetic National Early Warning Scores Data

Description

Usage

Format

Source

Examples