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README.Rmd
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README.Rmd
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---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# Subnational data for the COVID-19 outbreak <img src="man/figures/logo.png" align="right" alt="" width="120" />
[](https://github.com/epiforecasts/covidregionaldata/actions) [](https://app.codecov.io/gh/epiforecasts/covidregionaldata?branch=master) [](https://epiforecasts.io/covidregionaldata/articles/supported-countries.html) [](https://cran.r-project.org/package=covidregionaldata)
[](https://github.com/epiforecasts/covidregionaldata/blob/master/LICENSE.md/) [](https://github.com/epiforecasts/covidregionaldata/graphs/contributors) [](https://makeapullrequest.com/) [](https://GitHub.com/epiforecasts/covidregionaldata/commit/master/)
[](https://doi.org/10.21105/joss.03290) [](https://zenodo.org/badge/latestdoi/271601189)
Interface to subnational and national level COVID-19 data sourced from both official sources, such as Public Health England in the UK, and from other COVID-19 data collections, including the World Health Organisation (WHO), European Centre for Disease Prevention and Control (ECDC), John Hopkins University (JHU), Google Open Data and others. This package is designed to streamline COVID-19 data extraction, cleaning, and processing from a range of data sources in an open and transparent way. This allows users to inspect and scrutinise the data, and tools used to process it, at every step. For all countries supported, data includes a daily time-series of cases and, wherever available, data on deaths, hospitalisations, and tests. National level data is also supported using a range of data sources.
## Installation
Install from CRAN:
```{r, eval = FALSE}
install.packages("covidregionaldata")
```
Install the stable development version of the package with:
```{r, eval = FALSE}
install.packages("covidregionaldata",
repos = "https://epiforecasts.r-universe.dev"
)
```
Install the unstable development version of the package with:
```{r, eval = FALSE}
remotes::install_github("epiforecasts/covidregionaldata")
```
## Quick start
[](https://epiforecasts.io/covidregionaldata/)
Load `covidregionaldata`, `dplyr`, `scales`, and `ggplot2` (all used in this quick start),
```{r, message = FALSE}
library(covidregionaldata)
library(dplyr)
library(ggplot2)
library(scales)
```
### Setup data caching
This package can optionally use a data cache from `memoise` to locally cache downloads. This can be enabled using the following (this will use the temporary directory by default),
```{r}
start_using_memoise()
```
To stop using `memoise` use,
```{r, eval = FALSE}
stop_using_memoise()
```
and to reset the cache (required to download new data),
```{r, eval = FALSE}
reset_cache()
```
### National data
To get worldwide time-series data by country (sourced from the World Health Organisation (WHO) by default but also optionally from the European Centre for Disease Control (ECDC), John Hopkins University, or the Google COVID-19 open data project), use:
```{r}
nots <- get_national_data()
nots
```
This can also be filtered for a country of interest,
```{r}
g7 <- c(
"United States", "United Kingdom", "France", "Germany",
"Italy", "Canada", "Japan"
)
g7_nots <- get_national_data(countries = g7, verbose = FALSE)
```
Using this data we can compare case information between countries, for example here is the number of deaths over time for each country in the G7:
```{r g7_plot, warning = FALSE, message = FALSE}
g7_nots %>%
ggplot() +
aes(x = date, y = deaths_new, col = country) +
geom_line(alpha = 0.4) +
labs(x = "Date", y = "Reported Covid-19 deaths") +
scale_y_continuous(labels = comma) +
theme_minimal() +
theme(legend.position = "top") +
guides(col = guide_legend(title = "Country"))
```
### Subnational data
To get time-series data for subnational regions of a specific country, for example by level 1 region in the UK, use:
```{r}
uk_nots <- get_regional_data(country = "UK", verbose = FALSE)
uk_nots
```
Now we have the data we can create plots, for example the time-series of the number of cases for each region:
```{r uk_plot, warning = FALSE, message = FALSE}
uk_nots %>%
filter(!(region %in% "England")) %>%
ggplot() +
aes(x = date, y = cases_new, col = region) +
geom_line(alpha = 0.4) +
labs(x = "Date", y = "Reported Covid-19 cases") +
scale_y_continuous(labels = comma) +
theme_minimal() +
theme(legend.position = "top") +
guides(col = guide_legend(title = "Region"))
```
See `get_available_datasets()` for supported regions and subregional levels.
To view what datasets we currently have subnational data for, along with their current status, check the
[supported countries](https://epiforecasts.io/covidregionaldata/articles/supported-countries.html) page
or build the [supported countries vignette](vignettes/supported-countries.Rmd).
For further examples see the [quick start vignette](https://github.com/epiforecasts/covidregionaldata/blob/master/vignettes/quickstart.Rmd). Additional subnational data are supported via the `JHU()` and `Google()` classes. Use the `available_regions()` method once these data have been downloaded and cleaned (see their examples) for subnational data they internally support.
## Citation
If using `covidregionaldata` in your work please consider citing it using the following,
```{r, echo = FALSE}
citation("covidregionaldata")
```
## Development
[](https://github.com/epiforecasts/covidregionaldata/wiki/)
This package is the result of work from a number of contributors (see contributors list [here](https://epiforecasts.io/covidregionaldata/authors.html)). We would like to thank the [CMMID COVID-19 working group
](https://cmmid.github.io/groups/ncov-group.html) for insightful comments and feedback.
We welcome contributions and new contributors! We particularly appreciate help adding new data sources for countries at sub-national level, or work on priority problems in the [issues](https://github.com/epiforecasts/covidregionaldata/issues). Please check and add to the issues, and/or add a [pull request](https://github.com/epiforecasts/covidregionaldata/pulls). For more details, start with the [contributing guide](https://github.com/epiforecasts/covidregionaldata/wiki/Contributing). For details of the steps required to add support for a dataset see the [adding data guide](https://github.com/epiforecasts/covidregionaldata/wiki/Adding-Data).