The repository aims at unifying COVID-19 datasets across different sources in order to simplify the data acquisition process and the subsequent analysis. You are welcome to join and contribute by extending the number of supporting data sources as a joint effort against COVID-19.
The data are available to the end-user via the R package COVID19 or in csv format (see below or on Kaggle).
Provide the research community with a unified data hub by collecting worldwide fine-grained data merged with demographics, air pollution, and other exogenous variables helpful for a better understanding of COVID-19.
The data are collected with the R package COVID19. For R users, the COVID19 package is the recommended way to interact with the dataset. For non R users, the data are provided in csv format and regularly updated (see below or on Kaggle).
Whether or not you are an R user... take part in the data collection! Your contribution will be gratefully acknowledged. See how to contribute.
Simple, yet effective R package to acquire tidy format datasets of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic. The data are downloaded in real-time, cleaned and matched with exogenous variables.
# Install COVID19
install.packages("COVID19")
# Load COVID19
require("COVID19")# Diamond Princess
d1 <- diamond()
# World
w1 <- world("country") # data by country
w2 <- world("state") # data by state
# US
u1 <- us("country") # data by country
u1 <- us("state") # data by state
# Italy
i1 <- italy("country") # data by country
i2 <- italy("state") # data by region
i3 <- italy("city") # data by city
# Switzerland
s1 <- switzerland("country") # data by country
s2 <- switzerland("state") # data by canton
# Liechtenstein
l1 <- liechtenstein() # data by countryCSV datasets of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic. The files are generated with the R package COVID19 and updated daily. The following table shows the data coverage for each variable in each file.
| deaths | confirmed | tests | pop | pop_14 | pop_15_64 | pop_65 | pop_age | pop_density | pop_death_rate | |
|---|---|---|---|---|---|---|---|---|---|---|
| cumulative number of COVID19 deaths | cumulative number of COVID19 confirmed cases | cumulative number of COVID19 tests | total population | population ages 0-14 (% of total population)* | population ages 15-64 (% of total population)** | population ages 65+ (% of total population) | median age of population | population density per km2 | population mortality rate | |
| World | ||||||||||
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| US | ||||||||||
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| Italy | ||||||||||
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| Switzerland | ||||||||||
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| Liechtenstein | ||||||||||
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| Diamond Princess | ||||||||||
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* Switzerland: ages 0-19
** Switzerland: ages 20-64
The following sources are gratefully acknowledged for making the data available to the public.
* Switzerland: ages 0-19
** Switzerland: ages 20-64
The following people have contributed to the data collection as a joint effort against COVID-19.
* Switzerland: ages 0-19
** Switzerland: ages 20-64
- Monitoring the advancement of the COVID–19 contagion in the regions of Italy (code)
Emanuele Guidotti, “Coronavirus COVID-19 (2019-nCoV) Epidemic Datasets.” Kaggle, doi: 10.34740/KAGGLE/DS/574488.