covid19.nhs.data/README.Rmd at main · epiforecasts/covid19.nhs.data · GitHub

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---
output: github_document
---

```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/",
  eval = TRUE
)
```

# NHS trust level Covid-19 data aggregated to a range of spatial scales

[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental)
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[![DOI](https://zenodo.org/badge/312314841.svg)](https://zenodo.org/badge/latestdoi/312314841)

This package contains a many-to-many mapping between local authority districts and NHS Acute Trusts in England; details of this mapping (including a summary of the methods and a quick-start guide) can be found in [vignettes/mapping-summary](https://epiforecasts.io/covid19.nhs.data/articles/mapping_summary.html).

This package also has functionality to download trust-level hospital admissions data, published weekly on the [NHS COVID-19 Hospital Activity](https://www.england.nhs.uk/statistics/statistical-work-areas/covid-19-hospital-activity/) webpage. Data published on date `YYYY-MM-DD` can be downloaded using the function `get_admissions(release_date = "YYYY-MM-DD")`. This function can also be used to return estimated admissions by upper-tier and lower-tier local authorities. See the quick start below, the vignettes, and the package documentation for more.

## Installation

Install the stable development version of the package from our [r-universe](https://epiforecasts.r-universe.dev):

```{r, eval = FALSE}
install.packages(
  "covid19.nhs.data",
  repos = c(ropensci = 'https://epiforecasts.r-universe.dev',
            CRAN = 'https://cloud.r-project.org')
)
```

Or from GitHub:

```{r, eval = FALSE}
remotes::install_github("epiforecasts/covid19.nhs.data")
```

## Quick start

Load the package.

```{r}
library(covid19.nhs.data)
```

Download the latest admissions mapped to lower-tier local authority (LTLA) using the default mapping. *Note: This data is updated weekly each Thursday and the mapping is a probabilistic estimate.*

```{r}
adm <- get_admissions("ltla")
```

Map the latest available estimates by LTLA using one of the built in package shapefiles.

```{r map}
map_admissions(adm, england_ltla_shape)
```

Plot the time series of estimated admissions in an example LTLA (here Derby).

```{r timeseries}
library(ggplot2)
library(dplyr)

adm %>%
  filter(geo_name %in% "Derby") %>%
  ggplot(aes(x = date, y = admissions)) +
  geom_col(width = 0.9, col = "grey50", fill = "grey85") +
  theme_minimal() +
  labs(x = "Date", y = "Daily Hospital Admissions",
       title = "Covid-19 Admissions in Derby",
       subtitle = "Estimated using a probabilistic mapping from NHS Trusts to lower-tier local authority level")
```