The sars2pack R package provides one-line access to over 40 COVID-related datasets. Datasets are accessed in real time directly from their sources and then transformed to tidy-data form where possible and applicable. The result of each dataset accessor is a ready-to-use R dataset, often a dataframe. Documentation includes dataset descriptions, sources and references, and examples. Online documentation is available in two locations:
- The sars2pack documentation, which includes reference docs and detailed dataset descriptions.
- Extended workflows and use cases, as an online book
- What are the current and historical total, new cases, and deaths of COVID-19 at the city, county, state, national, and international levels?
- How do changes in infection rates differ across locations?
- What are the non-pharmacological interventions in place at the local and national levels?
- In the United States, what is the geographical distribution of healthcare capacity (ICU beds, total beds, doctors, etc.)?
- What are the published values of key epidemic parameters, as curated from the literature?
# If you do not have BiocManager installed:
install.packages('BiocManager')
# Then, if sars2pack is not already installed:
BiocManager::install('seandavi/sars2pack')
After the one-time installation, load the packge to get started.
library(sars2pack)
| name | accessor | data\_type | geographical | geospatial | region | resolution | url |
|---|---|---|---|---|---|---|---|
| United States county-level geographic details | us\_county\_geo\_details | c(“demographics”, “geographic”) | TRUE | TRUE | United States | admin2 | [LINK](https://github.com/josh-byster/fips_lat_long) |
| OECD International Unemployment Data | oecd\_unemployment\_data | c(“economics”, “time series”) | TRUE | FALSE | World | admin0 | [LINK](https://oecd.org) |
| healthdata.org COVID-19 Mobility Observations and Projections | healthdata\_mobility\_data | c(“mobility”, “time series”, “projections”) | TRUE | FALSE | International | c(“admin0”, “admin1”) | [LINK](https://covid19.healthdata.org/projections) |
| healthdata.org COVID-19 Testing Observations and Projections | healthdata\_testing\_data | c(“testing”, “time series”, “projections”) | TRUE | FALSE | International | c(“admin0”, “admin1”) | [LINK](https://covid19.healthdata.org/projections) |
| Our World In Data testing and cases reporting | owid\_data | c(“time series”, “cases”, “deaths”, “testing”) | TRUE | FALSE | World | admin0 | [LINK](https://ourworldindata.org/coronavirus) |
| CovidTracker data | covidtracker\_data | c(“time series”, “cases”, “deaths”, “testing”) | TRUE | FALSE | United States | admin1 | [LINK](https://covidtracking.com/) |
| European CDC world tracking | ecdc\_data | c(“time series”, “cases”, “deaths”) | TRUE | FALSE | World | admin0 | [LINK](https://www.ecdc.europa.eu/en/covid-19) |
| EU data Github aggregator | eu\_data\_cache\_data | c(“time series”, “cases”, “deaths”) | TRUE | FALSE | Europe | c(“admin0”, “admin1”) | [LINK](https://github.com/covid19-eu-zh/covid19-eu-data) |
| USA Facts | usa\_facts\_data | c(“time series”, “cases”, “deaths”) | TRUE | FALSE | United States | admin1 | [LINK](https://usafacts.org/visualizations/coronavirus-covid-19-spread-map/) |
| Johns Hopkins dataset | jhu\_data | c(“time series”, “cases”, “deaths”) | TRUE | FALSE | World | admin0 | [LINK](https://github.com/CSSEGISandData/COVID-19) |
| Johns Hopkins US-centric data | jhu\_us\_data | c(“time series”, “cases”, “deaths”) | TRUE | FALSE | United States | c(“admin1”, “admin2”) | [LINK](https://github.com/CSSEGISandData/COVID-19) |
| New York Times county level data | nytimes\_county\_data | c(“time series”, “cases”, “deaths”) | TRUE | FALSE | United States | admin2 | [LINK](https://raw.githubusercontent.com/nytimes/covid-19-data) |
| New York Times state level data | nytimes\_state\_data | c(“time series”, “cases”, “deaths”) | TRUE | FALSE | United States | admin1 | [LINK](https://raw.githubusercontent.com/nytimes/covid-19-data) |
| The Economist: Excess deaths during COVID pandemic | economist\_excess\_deaths | c(“time series”, “deaths”, “excess deaths”) | TRUE | FALSE | International | c(“admin0”, “admin1”) | [LINK](https://github.com/TheEconomist/covid-19-excess-deaths-tracker) |
| The : Excess deaths during COVID pandemic | financial\_times\_excess\_deaths | c(“time series”, “deaths”, “excess deaths”) | TRUE | FALSE | International | c(“admin0”, “admin1”) | [LINK](https://github.com/Financial-Times/coronavirus-excess-mortality-data) |
| US CDC excess deaths dataset | cdc\_excess\_deaths | c(“time series”, “deaths”, “excess deaths”) | TRUE | FALSE | United States | admin1 | [LINK](https://www.cdc.gov/nchs/nvss/vsrr/covid19/excess_deaths.html) |
| Descartes Labs Mobility Data | descartes\_mobility\_data | c(“time series”, “mobility”) | TRUE | FALSE | United States | admin1 | [LINK](https://raw.githubusercontent.com/descarteslabs/DL-COVID-19) |
| Apple mobility data from maps | apple\_mobility\_data | c(“time series”, “mobility”) | TRUE | FALSE | World | c(“admin0”, “admin1”, “admin2”, “admin3”) | [LINK](https://www.apple.com/covid19/mobility) |
| Healthdata.org projections of hospital utilization and deaths | healthdata\_projections\_data | c(“time series”, “projections”, “cases”, “deaths”) | TRUE | FALSE | c(“United States”, “World”) | c(“admin1”, “admin2”) | [LINK](http://www.healthdata.org/covid) |
| Healthdata.org mobility data | healthdata\_mobility\_data | c(“time series”, “projections”, “mobility”) | TRUE | FALSE | c(“United States”, “World”) | c(“admin1”, “admin2”) | [LINK](http://www.healthdata.org/covid) |
| United States CDC Social Vulnerability Index | cdc\_social\_vulnerability\_index | demographics | TRUE | FALSE | United States | admin2 | [LINK](https://svi.cdc.gov/) |
| US county health rankings from ‘’ | us\_county\_health\_rankings | demographics | TRUE | FALSE | United States | c(“admin0”, “admin1”, “admin2”) | [LINK](https://www.countyhealthrankings.org) |
| Country metadata from restcountries.eu | country\_metadata | demographics | TRUE | FALSE | World | admin0 | [LINK](https://restcountries.eu) |
| Extensive United States hospital capabilities | us\_hospital\_details | healthcare capacity | TRUE | TRUE | United States | individual hospital | [LINK](https://hub.arcgis.com/datasets/geoplatform::hospitals) |
| Kaiser Family Foundation ICU bed data | kff\_icu\_beds | healthcare capacity | TRUE | TRUE | United States | Individual hospital | [LINK](https://khn.org/news/as-coronavirus-spreads-widely-millions-of-older-americans-live-in-counties-with-no-icu-beds) |
| CovidCare United States Healthcare Capacity | us\_healthcare\_capacity | healthcare capacity | TRUE | TRUE | United States | Individual hospital | [LINK](https://github.com/covidcaremap/covid19-healthsystemcapacity) |
| GISAID metadata from thousands of SARS-CoV-2 sequences | cov\_glue\_lineage\_data | line list | TRUE | FALSE | World | multiple | [LINK](https://github.com/hCoV-2019/lineages) |
| beoutbreakprepared | beoutbreakprepared\_data | line list | TRUE | FALSE | World | patient | [LINK](https://github.com/beoutbreakprepared/nCoV2019) |
| Published epidemic parameters for COVID-19 | param\_estimates\_published | miscellaneous | FALSE | FALSE | list() | list() | [LINK](https://github.com/midas-network/COVID-19/blob/master/parameter_estimates/2019_novel_coronavirus/estimates.csv) |
| Google mobility data | google\_mobility\_data | mobility | TRUE | FALSE | World | c(“admin0”, “admin1”, “admin2”) | [LINK](https://www.google.com/covid19/mobility/) |
| Newick tree from thousands of SARS-CoV-2 sequences | cov\_glue\_newick\_data | phylogenetic | FALSE | FALSE | World | multiple | [LINK](https://github.com/hCoV-2019/lineages) |
| Aggregated projections from US CDC | cdc\_aggregated\_projections | projections | TRUE | FALSE | list() | c(“admin0”, “admin1”) | [LINK](https://www.cdc.gov/coronavirus/2019-ncov/covid-data/forecasting-us.html) |
| CoronaNet government response database | coronanet\_government\_response\_data | public policy | TRUE | FALSE | World | c(“admin0”, “admin1”) | [LINK](https://coronanet-project.org/index.html) |
| Oxford Government Policy Intervention time series | government\_policy\_timeline | public policy | TRUE | FALSE | World | admin0 | [LINK](https://www.bsg.ox.ac.uk/research/research-projects/oxford-covid-19-government-response-tracker) |
| United States social distancing policies | us\_state\_distancing\_policy | public policy | TRUE | FALSE | United States | admin1 | [LINK](https://github.com/COVID19StatePolicy/SocialDistancing/) |
Updated tracking of city, county, state, national, and international confirmed cases, deaths, and testing is critical to driving policy, implementing interventions, and measuring their effectiveness. Case tracking datasets include date, a count of cases, and usually numerous other pieces of information related to location of reporting, etc.
Accessing case-tracking datasets is typically done with one function per dataset. The example here is data from the European Centers for Disease Control, or ECDC.
ecdc = ecdc_data()
Get a quick overview of the dataset.
head(ecdc)
## # A tibble: 6 x 8
## # Groups: location_name, subset [6]
## date location_name iso2c iso3c population_2019 continent subset count
## <date> <chr> <chr> <chr> <dbl> <chr> <chr> <dbl>
## 1 2019-12-31 Afghanistan AF AFG 38041757 Asia confirmed 0
## 2 2019-12-31 Afghanistan AF AFG 38041757 Asia deaths 0
## 3 2019-12-31 Algeria DZ DZA 43053054 Africa confirmed 0
## 4 2019-12-31 Algeria DZ DZA 43053054 Africa deaths 0
## 5 2019-12-31 Armenia AM ARM 2957728 Europe confirmed 0
## 6 2019-12-31 Armenia AM ARM 2957728 Europe deaths 0
The ecdc dataset is just a data.frame (actually, a tibble), so
applying standard R or tidyverse functionality can get answers to basic
questions with little code. The next code block generates a top10 of
countries with the most deaths recorded to date. Note that if you do
this on your own computer, the data will be updated to today’s data
values.
library(dplyr)
top10 = ecdc %>% filter(subset=='deaths') %>%
group_by(location_name) %>%
filter(count==max(count)) %>%
arrange(desc(count)) %>%
head(10) %>% select(-starts_with('iso'),-continent,-subset) %>%
mutate(rate_per_100k = 1e5*count/population_2019)
Finally, present a nice table of those countries:
knitr::kable(
top10,
caption = "Reported COVID-19-related deaths in ten most affected countries.",
format = 'pandoc')
| date | location_name | population_2019 | count | rate_per_100k |
|---|---|---|---|---|
| 2020-07-06 | United_States_of_America | 329064917 | 129947 | 39.489776 |
| 2020-07-06 | Brazil | 211049519 | 64867 | 30.735441 |
| 2020-07-06 | United_Kingdom | 66647112 | 44220 | 66.349462 |
| 2020-07-06 | Italy | 60359546 | 34861 | 57.755570 |
| 2020-07-06 | Mexico | 127575529 | 30639 | 24.016361 |
| 2020-07-04 | France | 67012883 | 29893 | 44.607841 |
| 2020-07-05 | France | 67012883 | 29893 | 44.607841 |
| 2020-07-06 | France | 67012883 | 29893 | 44.607841 |
| 2020-05-24 | Spain | 46937060 | 28752 | 61.256500 |
| 2020-07-06 | India | 1366417756 | 19693 | 1.441214 |
Examine the spread of the pandemic throughout the world by examining cumulative deaths reported for the top 10 countries above.
ecdc_top10 = ecdc %>% filter(location_name %in% top10$location_name & subset=='deaths')
plot_epicurve(ecdc_top10,
filter_expression = count > 10,
color='location_name')
Comparing the features of disease spread is easiest if all curves are shifted to “start” at the same absolute level of infection. In this case, shift the origin for all countries to start at the first time point when more than 100 cumulative cases had been observed. Note how some curves cross others which is evidence of less infection control at the same relative time in the pandemic for that country (eg., Brazil).
ecdc_top10 %>% align_to_baseline(count>100,group_vars=c('location_name')) %>%
plot_epicurve(date_column = 'index',color='location_name')
Pull requests are gladly accepted on Github.
See the Adding new datasets vignette.
- https://github.com/emanuele-guidotti/COVID19
- Top 25 R resources on Novel COVID-19 Coronavirus
- COVID-19 epidemiology with R
- https://github.com/RamiKrispin/coronavirus
- Youtube: Using R to analyze COVID-19
- DataCamp: Visualize the rise of COVID-19 cases globally with ggplot2
- MackLavielle/covidix R package