Shiny on Stats and R

AssociationExplorer: A user-friendly shiny application for exploring associations and visual patterns

Tue, 16 Dec 2025 00:00:00 +0000

I am pleased to announce the publication of our paper “AssociationExplorer: A user-friendly Shiny application for exploring associations and visual patterns” in the journal SoftwareX, together with the official release of the AssociationExplorer2 R package on CRAN.

Both the paper and the software are part of an open-science effort aimed at making exploratory data analysis more accessible to non-technical users.

Why AssociationExplorer?

Exploring multivariate datasets is now central in social sciences, data journalism, and education. However, identifying and interpreting associations between variables often requires programming skills and a solid background in statistics, which can represent a substantial barrier for many users.

AssociationExplorer was designed to lower this barrier by providing an interactive, visual, and statistically grounded tool for exploring associations between quantitative and qualitative variables, without requiring users to write any code.

The application is primarily intended for:

journalists and data journalism practitioners,
teachers and students,
researchers in the exploratory phase of an analysis,
engaged citizens interested in understanding public or survey data.

What does the app do?

AssociationExplorer follows a simple and guided workflow:

Import data (CSV or Excel files)
Interactively select variables of interest
Automatically compute association measures adapted to variable types:
- Pearson’s \(r\) correlation for numeric–numeric pairs,
- Cramer’s V for categorical–categorical pairs,
- the correlation ratio \(\eta\) for mixed numeric–categorical pairs
Filter associations using user-defined thresholds
Visualize results through:
- an interactive correlation network,
- contextual bivariate visualizations (scatter plots, mean plots, and colored contingency tables)

This workflow is designed to support transparent, reactive, and interpretable exploratory data analysis.

A paper published in SoftwareX

The SoftwareX paper provides a detailed description of:

the motivation and intended audience of the tool,
the software architecture,
the methodological choices underlying the association measures,
an illustrative case study based on the European Social Survey,
and perspectives for future development.

Link to the paper: https://doi.org/10.1016/j.softx.2025.102483

In line with the journal’s standards, the code, documentation, and example data are fully open and reproducible.

The R package is also on CRAN

Alongside the paper, an R package is also available on CRAN, making installation and use straightforward:

install.packages("AssociationExplorer2")
library(AssociationExplorer2)
run_associationexplorer()

The CRAN release ensures:

standardized installation,
better integration with existing R workflows,
clearer versioning and dependency management.

Who is it for, and how can it be used?

AssociationExplorer is particularly useful for:

exploratory analysis prior to formal modeling,
teaching concepts related to association and dependence,
data storytelling and journalistic exploration,
the analysis of survey data and public datasets.

The goal is not to replace confirmatory statistical analyses, but to provide a robust tool for understanding the structure of the data before modeling.

Acknowledgements

This work was carried out in collaboration with Cédric Heuchenne, Arnaud Claes, and Antonin Descampe, whom I warmly thank.

The project is supported by the Walloon Region and SPW Recherche within the ODALON research project.

Useful links

📄 SoftwareX paper: https://doi.org/10.1016/j.softx.2025.102483
📦 CRAN package: https://CRAN.R-project.org/package=AssociationExplorer2
💻 GitHub repository:
- of the paper
- of the R package

As always, feedback, bug reports, and suggestions are very welcome.

Thanks for reading!

References

Soetewey, A., Heuchenne, C., Claes, A. and Descampe, A. (2026). AssociationExplorer: A user-friendly shiny application for exploring associations and visual patterns. SoftwareX, 33(102483). https://doi.org/10.1016/j.softx.2025.102483

Soetewey, A., Heuchenne, C., Claes, A. and Descampe, A. (2025). AssociationExplorer2: A User-Friendly ‘shiny’ Application for Exploring Associations and Visual Patterns. R package version 0.1.4, https://github.com/AntoineSoetewey/AssociationExplorer2

Running pace calculator in R Shiny

Mon, 15 Mar 2021 00:00:00 +0000

Introduction

If you are a runner yourself, you are certainly aware of how important preparation is before a race. For the preparation of my first marathon, I used to rely on a training plan.

This running plan was great, but an important information was missing: the running pace. Most of the time, the distance and the time was given, but I needed to figure out the pace myself.

Although the computation is fairly easy, I felt like I was missing a quick way to compute my running pace based on the distance and expected time given by the training plan.

So I started to look for a solution online, but I was never completely satisfied. Some running pace calculators were too detailed (showing way too much information) others were too basic (showing not enough information).

Running pace calculator

I thus decided to create one myself so I could really choose what information would be displayed, and how it would be displayed.

For the runners among you, here is a link to the application:

Running pace calculator

If you follow the blog, you know how much I like R Shiny, so you probably guessed that the calculator is built with this technology.

Note that this running pace calculator is inspired by several calculators I found online. I kept it quite basic so that it goes straight to the point, but most importantly so that it would fit to my needs (which may be different than yours).

How to use it?

I try to keep all my Shiny apps easy to use for everyone. However, here is how to use it in case it is not intuitive enough:

Access the app via this link
Choose the units (kilometers or miles)
Enter the distance you plan to run
Enter the time for which you would like to run that distance

On the right panel (or bottom if you use the app on mobile) you will see:

The pace you will need to respect in order to run the distance within the time you specified
Depending on the units you selected, your pace will be displayed in minutes/km or minutes/miles, and kilometers/hour or miles/hour
The table below displays the splits—the time at each kilometer or mile

Update of January 11, 2023:

Until now, only the conversion from distance and time to pace was possible. For completeness, I have added to following conversions:

Pace and time to distance: enter the pace and the time you plan to run to find the expected running distance.
Pace and distance to time: enter the pace and the distance you plan to run to find the expected running time.

Code

As for all my Shiny apps, the code is available on GitHub. Feel free to open an issue if you find a bug or if you have a suggestion. And if you are proficient in R Shiny, do not hesitate to propose a pull request with your suggestions implemented.

Conclusion

Thanks for reading.

I hope this running pace calculator will be useful if you are a runner, or if you plan to start running.

As always, if you have a question about the app, please add it as a comment so other readers can benefit from the discussion.

Mortgage calculator in R Shiny

Fri, 14 Aug 2020 00:00:00 +0000

Introduction

I recently moved out and bought my first apartment. Of course, I could not pay it entirely with my own savings, so I had to borrow money from the bank. I visited a couple of banks operating in my country and asked for a mortgage.

If you already bought your house or apartment in the past, you know how it goes: the bank analyzes your financial and personal situation and make an offer based on your propensity to repay the bank. You then either accept the offer if you are satisfied with the rate and conditions, or visit another bank if you believe you could receive a better offer. Mortgages and loans are more complicated than that of course, but let’s keep it simple here.

As I kind of like to control and keep a close eye on my personal finances (sometimes a bit too close I must admit), I knew precisely how much I could spend for my monthly mortgage repayment while still being able to cover my living expenses. However, I had no clue how much I could borrow in total for my new apartment given these housing repayments.

I knew I was not the first person in this case, so I looked online if I could find a R script which would answer my question (and potentially also give me the total cost of the housing loan, including the loan amount and the accumulated interests). I finally found a R script created a while ago by Prof. Thomas Girke.

Mortgage calculator

The function in the script was functional and solved my main issue, but I wanted to be able to play more easily with the different settings such as the amount, the duration and the interest rate of the loan.

For this reason, I created a R Shiny app which is available here:

Mortgage calculator

Mortgage calculator in R Shiny

In the meantime, I received an Excel file from a friend working in a Belgian bank which does precisely the same task. I am not an actuary nor an expert in mortgage loan, so with his file I was able to cross check the results and edit the code accordingly.

The app greatly helped me to know the maximum amount I could borrow from the bank by playing with the three main settings of a mortgage, so it gave me a precise price limit when looking for apartments online.

Note that the app can of course be used for any loan, not only for mortgage.

How to use the mortgage calculator?

First, you can find the mortgage calculator here.

I try to keep all my Shiny apps easy to use for everyone. However, here is how to use it in case it is not intuitive enough:

Enter the amount of the loan (i.e., the amount you would like to borrow, do not include downpayment)
Enter the annual interest rate in %
Enter the duration of the loan in years

On the right panel (or bottom if you use the app on mobile) you will see:

a summary repeating the settings you entered,
the total cost of the loan (principal and interests included), and more importantly
the amount of the monthly payments

A plot representing the percentage attributed to the repayment of the interests and the capital is also displayed. You see that (especially in the first years of the loan), the higher the interest rate and the duration of the loan, the higher the percentage of the monthly repayments is attributed to the repayments of the interests.

Finally, the amortization table showing the remaining balance month by month is displayed after the summary and the plot. You can copy, export (in PDF, CSV or Excel) or print this amortization table for further use.

Code of the app

Here is the entire code (or see the last version on GitHub) in case you would like to enhance it (feel free to send me your app if you happen to improve it!).

Conclusion

Thanks for reading.

I hope this mortgage calculator helped you to play with the different settings of a mortgage, and who knows, helped you to decide which house or apartment to buy.

As always, if you have a question or a suggestion related to the topic covered in this article, please add it as a comment so other readers can benefit from the discussion.

Disclosure: Note that this application does not include investment advice or recommendations, nor a financial analysis. This application is intended for information only and you invest at your own risks. I cannot be held liable for any decision made based on the information contained in this application, nor for its use by third parties.

How to publish a Shiny app? An example with shinyapps.io

Fri, 29 May 2020 00:00:00 +0000

Introduction

The COVID-19 virus led many people to create interactive apps and dashboards. A reader recently asked me how to publish a Shiny app she just created. Similarly to a previous article where I show how to upload R code on GitHub, I thought it would be useful to some people to see how I publish my Shiny apps so they could do the same.

Before going through the different steps required to deploy your Shiny app online, you can check the final result with my apps here.

Note 1: The screenshots have been taken on MacOS and I have not tested it on Windows. Do not hesitate to let me know in the comments whether it is similar or not on other operating systems.

Note 2: There are other ways to publish your app (with Docker for example), but the method shown below is easy (in my opinion) and works well.

Prerequisite

I personally use the shinyapps.io platform to deploy my Shiny apps. So in order to follow this guide you will first need to create an account (if you do not already have one).

They offer a free plan, but you are limited to 5 active applications and a monthly usage of 25 active hours.

For your information, if you make your app available to a wide audience, expect to exceed the monthly cap of active hours quite quickly. To increase the monthly limit (or to publish more than 5 apps), you will need to upgrade your plan to a paying one.

Step-by-step guide

Below the steps to follow in pictures.

Step 1: Open RStudio and create a new Shiny app:

Step 2: Give it a name (without space), choose where to save it and click on the Create button:

Step 3: In the same way as when you open a new R Markdown document, the code for a basic Shiny app is created. Run the app by clicking on the Run App button to see the result:

Step 4: The basic app opens, publish it:

Step 5: If it is your first Shiny app, the box “Publish From Account” should be empty. Click on “Add New Account” to link the shinyapps.io account you just created:

Step 6: Click on the first alternative (ShinyApps.io):

Step 7: Click on the link to your ShinyApps account:

Step 8: Click on the Dashboard button to log in into your account:

Step 9: Click on your name and then on Tokens

Step 10: If this is your first app, there should be no token already created. Create one by clicking on the Add Token button. Then Click on the Show button:

Step 11: Click on the Show Secret button:

Step 12: Now the code is complete (nothing is hidden anymore). Click on the Copy to clipboard button:

Step 13: Copy the code and click on the OK button:

Step 14: Go back to RStudio, paste the code in the console and run it:

Your computer is now authorized to deploy applications to your shinyapps.io account.

Step 15: Go back to the window where you can publish your app, choose a title (without space) and click on the Publish button:

Step 16: After several seconds (depending on the weight of your app), the Shiny app should appear in your internet browser:

Step 17: You can now edit the app (or replace the entire code by another of your app), and run the app again by clicking on the Run App button. For this illustration, I just added a link for more information in the side panel:

Step 18: Check that the modifications have been taken into account (the link appears in the side panel as expected) and republish your app:

Step 19: Click on the Publish button:

Step 20: Your app is live! You can now share it and everyone with the link will be able to use it:

Additional notes

Settings of your app

If you need to change the settings of your Shiny app, go to your shinyapps.io dashboard and click on the app you just created to access the settings:

See the different settings in the tabs located at the top of the windows, and see the link to the app next to the URL field:

Publish your dataset

It is often the case that your Shiny app uses a dataset that is not loaded in R by default (it uses one of your dataset that is saved locally on your computer for instance).

To make the app work with your data, you will also need to publish the data when publishing your app (check the checkbox corresponding to your data):

Publish your dataset together with the app

If you would like to have more flexibility, another way to be able to use an external dataset in your app is to host it online:

if you have a website, you can easily publish it through your website
if you do not have a website, you can host the dataset via GitHub (if you do not have an account, it is a good time to create one!)

The last step is then to import the data via that new URL (put the data import in the code of your app).

Conclusion

Thanks for reading.

I hope this tutorial helped you to publish your first Shiny app.

As always, if you have a question or a suggestion related to the topic covered in this article, please add it as a comment so other readers can benefit from the discussion.

Top 100 R resources on COVID-19 Coronavirus

Thu, 12 Mar 2020 00:00:00 +0000

Warning: Some links or resources may have been moved or deleted, and are thus not accessible anymore. If you are the author and would like to update the URL, feel free to contact me so I can update the link.

The Coronavirus is a serious concern around the globe. With its expansion, there are also more and more online resources about it. This article presents a selection of the best R resources on the COVID-19 virus.

This list is by no means exhaustive. I am not aware of all R resources available online about the Coronavirus, so please feel free to let me know in the comments or by contacting me if you believe that another resource (R package, Shiny app, R code, blog posts, datasets, etc.) deserves to be on this list.

R Shiny apps and dashboards

Coronavirus tracker

Developed by John Coene, this Shiny app tracks the spread of the Coronavirus, based on three data sources (John Hopkins, Weixin and DXY Data). The Shiny app, built with shinyMobile (which makes it responsive on different screen sizes), presents in a really nice way the number of deaths, confirmed, suspected and recovered cases by time and region.

The code is available on GitHub.

Coronavirus dashboard from the `{coronavirus} package`

Developed by the author of the {coronavirus} package, this dashboard provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic. The data and dashboard are refreshed on a daily basis.

The code is available on GitHub.

From this dashboard, I created another dashboard specific to Belgium. Feel free to use the code available on GitHub to build one specific to your country. See more details in this article.

Visualization of Covid-19 Cases

Developed by Nico Hahn, this Shiny app uses leaflet, plotly and the data from Johns Hopkins University to visualize the outbreak of the novel Coronavirus and shows data for the entire world or singular countries.

The code is available on GitHub.

Modeling COVID-19 Spread vs Healthcare Capacity

Developed by Dr. Alison Hill, this Shiny app uses an epidemiological model based on the classic SEIR model to describe the spread and clinical progression of COVID-19. It includes different clinical trajectories of infection, interventions to reduce transmission, and comparisons to healthcare capacity.

The code is available on GitHub.

COVID-19 Data Visualization Platform

Developed by Shubhram Pandey, this Shiny app provides a clear visualization of Covid19 impact all over the world and it also provides a sentiment analysis using natural language processing from Twitter.

The code is available on GitHub.

Coronavirus 10-day forecast

Developed by the Spatial Ecology and Evolution Lab, this Shiny app gives a ten-day forecast, by country, on likely numbers of Coronavirus cases and gives citizens a sense of how fast this epidemic is progressing.

See a detailed explanation of the app and how to read it in this blog post. The code is available on GitHub.

Coronavirus (COVID-19) across the world

Developed by Anisa Dhana in collaboration with datascience+, this Shiny app monitors the spread of COVID-19 across the world via a map visualization of the confirmed cases and some graphs on the growth of the virus.

The dataset used is from Johns Hopkins CSSE and part of the code is available in this blog post.

COVID-19 outbreak

Developed by Dr. Thibaut Fabacher in collaboration with the department of Public Health of the Strasbourg University Hospital and the Laboratory of Biostatistics and Medical Informatics of the Strasbourg Medicine Faculty, this Shiny app shows an interactive map for global monitoring of the infection. It focuses on the evolution of the number of cases per country and for a given period in terms of incidence and prevalence.

The code is available on GitHub and this blog post discusses it in more detail.

Flatten the Curve

Developed by Tinu Schneider, this Shiny app illustrates, in an interactive way, the different scenarios behind the #FlattenTheCurve message.

The app has been built upon Michael Höhle’s article and the code is available on GitHub.

Explore the spread of Covid-19

Developed by Joachim Gassen, this Shiny app allows you to visualize confirmed, recovered cases and reported deaths for several countries via one summary graph.

The Shiny app is based on data from:

Johns Hopkins University CSSE team on the spread of the SARS-CoV-2 virus
ACAPS governmental measures database
World Bank

This blog post explains the Shiny app in further details and in particular the {tidycovid19} R package behind it.

Governments and COVID-19

Developed by Sebastian Engel-Wolf, this Shiny app presents in a elegant way the following measurements:

Maximum time of exponential growth in a row
Days to double infections
Exponential growth today
Confirmed cases
Deaths
Population
Confirmed cases on 100,000 inhabitants
Mortality rate

The code is available on GitHub and this article explains it in further details.

Simulating COVID-19 Epidemic in Togo - West Africa

Developed by Dr. Kankoé Sallah, this Shiny app uses SEIR metapopulation model with mobility between catchment areas to describe country level spread of COVID-19 and the impact of interventions in Togo, West Africa.

Covid-19 Prediction

Developed by Manuel Oviedo and Manuel Febrero (Modestya research group of the University of Santiago de Compostela), this Shiny app predicts the growth rate at 5-day horizon using the evolution during the last 15 days of growth rate. Three functional regression models are fitted and re-estimated when new data is available. The app also shows an interactive plot and table for the expected number of accumulated cases and new daily cases to each horizon (for confirmed and deaths responses) by country (from Johns Hopkins CSSE) and Spanish region (from ISCII).

See an explanation of the methodology in the About tab.

Covid-19 Dashboard

Developed by Philippe De Brouwer, this dashboard displays several key measures regarding the outbreak of the virus (by country or for all countries combined), together with some forecasts, a world map and other interactive plots.

Healthcare worker deaths from novel Coronavirus (COVID-19) in the US

Developed by Jonathan Gross, this Shiny app visualizes healthcare worker deaths from Coronavirus (COVID-19) in the US reported in the news. It is updated daily and the code is available on GitHub.

Covid-19 Hospitalizations in Belgium

Developed by Jean-Michel Bodart, this dashboard provides an overview of the evolution of Covid-19-related hospitalizations in Belgium, by region and province.

The code is available on GitHub.

COVIDMINDER: Where you live matters!

Developed by The Rensselaer Institute for Data Exploration and Applications, this Shiny app reveals the regional disparities in outcomes, determinants and medications (e.g., mortality rates, test cases, diabetes, and hospital beds) across United States, with a special focus on New York.

This blog post explains the Shiny app in more detail and the code is available on GitHub.

COVID-19 Canada Data Explorer Tool

Developed by Petr Baranovskiy from Data Enthusiast’s Blog, this Shiny app processes the official dataset available from the Government of Canada and shows several indicators related to the SARS-CoV-2 epidemic in Canada.

This blog post details the application in further detail.

Philippine COVID-19 Case Forecasting

Developed by Jamal Kay Rogers and Yvonne Grace Arandela, this Shiny app provides a 5-day forecast of confirmed positive, deaths, and recoveries of COVID-19 cases in The Philippines.

The app also delivers graphical plots of a 10-day forecast and the daily and cumulated cases of COVID-19 in The Philippines. The data source is Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE).

COVID-19 Case & Death Report Number Corrector

Developed by Matt Maciejewski, this Shiny app focuses on the correction of underreported Covid-19 case and death counts using a reference country based on Lachmann et al. (2020), and via a multiplicative estimator for total deaths and cases. The estimator will be turned into a posterior prediction once data becomes available.

The app is explained in more detail in this article and the code of the Shiny app can be found on GitHub.

Covid-19: the SPQEIR model

Developed by several researchers from the Luxembourg Centre for Systems Biomedicine (LCSB) of the University of Luxembourg, the KU Leuven and the UGent, this Shiny app uses the new SPQEIR model to simulate the impact of various suppression strategies (social distancing, lockdown, protection, etc.) on the development of COVID-19.

The Belgian Covid Cases Tracker

Developed by Patrick Sciortino, this Shiny app aims at estimating the curve of true Covid-19 cases based on the idea that a hospitalized case in time t informs us about an infection that took place a few days earlier.

COVID-19 monitor

Developed by Trafford Data Lab, this Shiny app visualizes daily confirmed Coronavirus cases and deaths in the UK.

It uses the following data sources:

The code can be found on GitHub.

COVID-19 Bulletin Board

Developed by Wei Su, this dashboard shows real-time visualization of the COVID-19 epidemic in Japan. It mainly shows various indicators including, but not limited to, PCR test, positive confirmed, hospital discharge and death, as well as trends in each prefecture in Japan. There are also a variety of charts such as cluster network, new confirmed cases in log scale for users’ reference.

The dashboard is based on this Japanese version (developed by the same author). The code can be found on GitHub.

Covid-19 Statistics Displayer

Created by Carl Sansfaçon, this Wordpress plugin associates R {ggplot2} graphics with ARIMA forecast and PHP coding to show evolution of the confirmed, death and recovered cases in different countries, states/provinces and US cities.

It uses data from the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. The plugin can be installed as a Wordpress plugin.

CoronaMapper

Supported by OxyLabs, created by Paolo Montemurro and Peter, this visualization displays the four days average growth indicator, which clearly shows how a certain statistic of the virus is evolving over time, filtering out the noise.

The website receives data each hour from several official data-sources, and visualizes the historical evolution of COVID19 in an intuitive & interactive way.

CoronaDash

Developed by Peter Laurinec, this Shiny app provides various data mining and visualization techniques for comparing countries’ COVID-19 data statistics as:

extrapolating total confirmed cases by exponential smoothing model,
trajectories of cases/deaths spread,
multidimensional clustering of countries’ data/ statistics - with dendrogram and table of clusters averages,
aggregated views for the whole world,
hierarchical clustering of countries’ trajectories based on DTW distance and preprocessing by SMA (+ normalization), for fast comparison of a large number of countries’ COVID-19 magnitudes and trends.

This blog post explained in further detail the last point of the above list. The code of the app is available on GitHub.

Covidfrance

Developed by Guillaume Pressiat, this Shiny app illustrates the evolution of number of hospitalizations, intensive care units, recoveries and deaths in France (by department).

This blog post presents the application and the code of the app is available on GitHub.

The Impact of COVID-19 on Mobility

Developed by Dimiter Toshkov, this Shiny app shows relative changes in mobility (visits and length of stay) for a specific category of places within a country compared to a baseline. The baseline is computed as the median for the day of the week during the 5-week period between January 3 and February 6, 2020. Hence, the plot shows how mobility has changed relative to the situation in the same country in the beginning of the year.

The author also developed a version for the US states.

Data is from Google Community Mobility Reports.

Coronavirus Analysis Platform

Developed by Khaled M Alqahtani, this Shiny app offers a powerful analysis tools, including:

Descriptive analysis
Growth rate and curve flatting
Cumulative forecast
Daily cases forecasting containing 16 different models
Newspaper analysis
TV analysis

Data is from Johns Hopkins CSSE and some GDELT APIs.

COVID-19 Tracker

Developed by Dr. Magda Bucholc from Ulster University, this dashboard reports cases at the local government district in Northern Ireland and county level across the island of Ireland, providing gender and age breakdowns of reported cases, growth rates, and statistics per 100,000 of the population; it also has daily mobility data from Google and Apple.

More information about this dashboard can be found here.

COVID-19 Overview

Developed by Fabian Dablander, Alexandra Rusu, Marcel Schreiner, and Aleksandar Tomasevic as part of the Science versus Corona project, this dashboard provides an overview of confirmed cases, deaths, and measures that countries have taken to curb the spread of the virus.

For a more information about this dashboard, see this blog post.

COVID-19 Exit Strategies

Developed as part of the Science versus Corona project, this Shiny app compares several alternative exit strategies that either aim to keep the number of infections as low as possible (e.g., contact tracing), or that aim to develop herd immunity without exceeding health care capacity.

The Shiny app is based on the stochastic individual-based SEIR model developed by de Vlas and Coffeng (2020). This post explains the Shiny app in more detail.

Corona Virus Statistics : COVID19

Developed by Dr. Mohammed N. Alenezi, this dashboard displays the latest information about Coronavirus for Kuwait, GCC, and the world. The dashboard contains a collection of different plots and models presented under 8 tabs.

Covid19 Data

Developed by Dhafer Malouche, this dashboard presents statistics for more than 200 countries and regions, with the estimation of the reproduction number \(R(t)\) in the previous 60 days and a Covid19 country classification.

WHO COVID-19 Explorer

Developed by the World Health Organization (WHO), this Shiny app aims to provide frequently updated data visualizations about confirmed cases and deaths at the global, regional and country level.

COVID-19 Scenario Analysis Tool

Developed by the MRC Centre for Global Infectious Disease Analysis (Imperial College London), this dashboard illustrates the epidemic trajectory, the healthcare demand and the \(R_t\) & \(R_{eff}\) measures for many countries over time in interactive plots.

The dashboard uses the squire R package, among others.

Covid-19 track

Developed by Redzuan, this Shiny dashboard analyses, combines and harmonizes the latest Covid-19 data into progress timeline, comparative analysis on various areas, mapping, latest news, forecasting using various models, prediction on Herd Immunity target, summary table, downloadable fact sheet and other features.

R packages

`{nCov2019}`

The {nCov2019} package gives you access to epidemiological data on the Coronavirus outbreak.¹ The package gives real-time statistics, includes historical data and a Shiny app. The vignette explains the main functions and possibilities of the package.

Furthermore, the authors of the package also developed a website with interactive plots and time-series forecasts, which could be useful in informing the public and studying how the virus spread in populous countries.

`{coronavirus}`

Developed by Rami Krispin, the {coronavirus} package provides a tidy format dataset of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic. Pulled from the dataset of John Hopkins, the R package gives a daily summary of the Coronavirus cases by state/province. The data set contains various variables such as confirmed cases, death, and recovered across different countries and states.

More details are available here, a csv format of the package dataset is available here and a summary dashboard is available here.

`{tidycovid19}`

Developed by Joachim Gassen, the {tidycovid19} package allows you to download, tidy and visualize Covid-19 related data (including data on governmental measures) directly from authoritative sources. It also provides a flexible function and an accompanying Shiny app to visualize the spreading of the virus.

The package is available on GitHub and these blog posts here and here explain it in more detail.

R packages from R Epidemics Consortium

These R packages from R Epidemics Consortium allows you to find the most advanced tools used by professional epidemiologists and experts in the domain of analyzing disease outbreaks.

`{covdata}`

Published by Prof. Kieran Healy, the {covdata} package is a R package providing COVID-19 case data from multiple sources:

National level data from the European Centers for Disease Control
State-level data for the United States from the COVID Tracking Project
State-level and county-level data for the United States from the New York Times
Data from the US Centers for Disease Control’s Coronavirus Disease 2019 (COVID-19)-Associated Hospitalization Surveillance Network (COVID-NET)
Data from Apple on relative trends in mobility in cities and countries since mid-January of 2020, based on usage of their Maps application
Data from Google on relative trends in mobility in regions and countries since mid-January of 2020, based on location and activity information

The code is available on GitHub.

`{covid19italy}`

The covid19italy R package provides a tidy format dataset of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) pandemic outbreak in Italy. The package includes the following three datasets:

italy_total: daily summary of the outbreak on the national level
italy_region: daily summary of the outbreak on the region level
italy_province: daily summary of the outbreak on the province level

More information about the package datasets available in this vignette, this blog post, and this supporting dashboard.

Data source: Italy Department of Civil Protection

`{COVID19}`

The goal of COVID-19 Data Hub is to provide the research community with a unified data hub by collecting worldwide fine-grained case data, merged with exogenous variables helpful for a better understanding of COVID-19. Featured by the University of Milano and funded by the Institute for Data Valorization IVADO, Canada.

The package collects COVID-19 data across governmental sources, includes policy measures from Oxford COVID-19 Government Response Tracker, and extends the dataset via an interface to World Bank Open Data, Google Mobility Reports and Apple Mobility Reports.

The package is available on CRAN, it is 100% open source and external contributors are welcomed to join.

`{COVOID}`

COVOID (for COVID-19 Open-source Infection Dynamics project) is a R package for modelling COVID-19 and other infectious diseases using deterministic compartmental models (DCMs).

It contains a built-in Shiny app enabling easy use and demonstration of key concepts to those without R programming backgrounds, along with an expanding API for simulating and estimating homogeneous and age-structured SIR, SEIR and extended models. In particular COVOID allows the simultaneous simulation of age specific (e.g. school closures) and general interventions over varying time intervals.

The code is available on GitHub.

`{cdccovidview}`

Published by Bob Rudis, the {cdccovidview} package can be used to work with the U.S. CDC’s New COVID-19 Trackers: COVIDView and COVID-NET.

`{babsim.hospital}`

Published by several researchers from TH Köln, the {babsim.hospital} package implements a discrete-event simulation model for a hospital resource planning problem. It can be used by health departments to forecast demand for intensive care beds, ventilators, and staff resources.

The team also developed a Shiny app which predicts COVID-19 ICU bed resources in hospitals. The app is available in English and German.

`{EpiLPS}`

Published by several researchers working in the field of epidemiology, the package shows how to smooth epidemic curves and estimate the time-varying reproduction number in a flexible way.

More information can be found in:

the research paper,
the accompanying website, and
this summary written by one of the authors.

R code and blog posts

Analyzing COVID-19 outbreak data with R

Written by Tim Churches, these two articles (part 1 and part 2) explore the R tools and packages that might be used to analyze the COVID-19 data. In particular, the author considers when the pandemic will subside in China, and then turns the analysis on Japan, South Korea, Italy and Iran. He also shows improvements on the cumulative incidence plots that are so common. Moreover, he presents R code to analyze how contagious is the Coronavirus thanks to the classic SIR (Susceptible-Infectious-Recovered) compartmental model of communicable disease outbreaks.²

The code is available on GitHub (part 1 and part 2).

Part 1 is actually based on another shorter blog post by Prof. Dr. Holger K. von Jouanne-Diedrich from Learning Machines. Read his article for a more concise analysis on how to model the outbreak of the Coronavirus and discover how contagious it is. Note that I have personally written an article analyzing COVID-19 in Belgium based on articles from these two authors.

More recently, Tim Churches published a series of other interesting articles:

Modelling the effect of various public health interventions on the local epidemic spread of COVID-19 infection using stochastic individual compartmental models (ICMs) implemented by the {EpiModel} package for R:
- Part 1 (code here)
- Part 2 (code here)
The use of simulations to explore the effects of various interventions on COVID-19 spread
COVID-19 epidemiology with R: in this blog post, the author, using relatively early and partial US data, separates out inbound from community cases, and predicts the next few weeks of incident numbers. He also highlights several R functions to analyze a disease outbreak.
We can “shrink” the COVID-19 curve, rather than just flatten it (in collaboration with Louisa Jorm)

COVID-19 Data Analysis with `{tidyverse}` and `{ggplot2}`

Dr. Yanchang Zhao from RDataMining published a data analysis around the Coronavirus with the {tidyverse} and {ggplot2} packages, for China and world wide.

Both documents are a mix of data cleaning, data processing and visualizations of the confirmed/cured cases and death rates across countries or regions.

COVID-19 cumulative observed case fatality rate over time

Written by Peter Ellis, this article focuses on how the observed case fatality rate of COVID-19 has evolved over time across 7 countries and comments on why the rates vary (low testing rates, age of the population, overwhelmed hospitals, etc.).

The code is available at the end of the article. The data is from John Hopkins and it uses the {coronavirus} package.

More recently, the author published a series of other articles:

Impact of a country’s age breakdown on COVID-19 case fatality rate: it looks at estimated fatalities in different countries according to the age distributions in those countries (based on Italy’s data). The data is from The Istituto Superiore di Sanità (Roma) and all the code is shown in the post.
How to make that crazy Fox News y axis chart with ggplot2 and scales: less about COVID19 than about how a bizarre Fox News graph can be re-created with the correct transformations needed to make its scale appropriate.
Test positivity rates and actual incidence and growth of diseases: this blog post looks at several different ways of accounting for the information given to us by high positive testing rates for COVID-19 and looks at the impact on estimates of effective reproduction number at a point in time.
Incidence of COVID-19 in Texas after adjusting for test positivity: the author examines the trends in COVID-19 cases in Texas, with and without being adjusted by a multiplier of the square root of the test positivity rate.

Covid 19 Tracking

Written by Prof. Kieran Healy, this article discusses how to get an overview of best-available counts of deaths, using the COVID-19 Data from the European Centers for Disease Control.

Code can be found in the article and on GitHub.

More recently, the author published three other articles:

A COVID Small Multiple: this article discusses how to create a small-multiple plot of cases by country, showing the trajectory of the outbreak for a large number of countries, with the background of each small-multiple panel also showing (in grey) the trajectory of every other country for comparison.

Apple’s COVID Mobility Data: this article uses Apple’s time series mobility data for several cities and countries (via the {covdata} package) to plot three modes of getting around: driving, public transit, and walking. The series begins on January 13th indexed to 100 at the beginning of the series, so trends are relative to that baseline.

New Orleans and Normalization: this article responds to a thoughtful post by Dr. Drang regarding an improvement in normalization of the data.

Infectious diseases and nonlinear differential equations

Published by Fabian Dablander, this math intensive blog post explains what SIR and SIRS models take into account and how they calculate their results.

From a pandemic perspective, the author writes “The SIRS model extends the SIR model, allowing the recovered population to become susceptible again (hence the extra ‘S’). It assumes that the susceptible population increases proportional to the recovered population”.

More recently, the author, in collaboration with other researchers, published another blog post outlining a number of excellent visualizations of the COVID19 pandemic, as well as presenting their own dashboard.

Epidemic modelling of COVID-19 in the UK using an SIR model

Published by Thomas Wilding, this blog post applies the SIR model to UK data.

As further extensions to the model, the author suggests:

Using an SEIR model (adding an Exposed compartment for people who are infected but not yet infectious)
Adding a “Q” layer since a lot of people are being Quarantined or isolated
Considering the “hidden”” population that is infected but is denied being tested due to shortage of tests
Feasibility of a second wave / outbreak of the epidemic later in the year (as seen in previous outbreaks, such as Swine Flu)

Data sources:

Modeling Pandemics

Published by Arthur Charpentier, this series of 3 blog post (part 1, part 2, part 3) walks through the SIR model and its parameters, how ODEquations solves it, and generating the reproductive rate. It also gives a mathematical explanation of a model for how quickly a pandemic will return, albeit with diminishing intensity. Last, it explains a model that is more sophisticated than SIR, the SEIR model, and illustrates it with Ebola data.

More recently, the author published another article which examines what proportion of the population in various U.S. states have been tested for the novel Coronavirus and tries to answer the following two questions:

How many people are tested on a daily basis?
What are we actually testing for?

Last but not least, this post reproduces one of his scientific paper entitled “COVID-19 pandemic control: balancing detection policy and lockdown intervention under ICU sustainability”.

COVID-19: The Case of Germany

Published by Prof. Dr. Holger K. von Jouanne-Diedrich from Learning Machines, this blog post uses the SIR model and German data to estimate the duration and severity of the pandemic.

Download the data from Morgenpost.

More recently, the author published other articles:

COVID-19 in the US: Back-of-the-Envelope Calculation of Actual Infections and Future Deaths: Working back from reported deaths from Covid19, the post shows how to estimate infections at a prior date, based on several assumptions about fatality rates and infected periods (and acknowledging many unknowns and data problems).
How to analyze mobility trends with R using anonymized and aggregated Apple’s mobility data available to the public. The article presents a R function to return the data in a well-structured format for countries and major cities, and to visualize the drop in vehicular and pedestrian movement caused by the pandemic.

COVID-19: False Positive Alarm, demonstrating the importance of infection rates on the likelihood that someone testing positive for the Coronavirus is actually positive.

Flatten the COVID-19 Curve

Published by Michael Höhle from Theory meets practice, this blog post discusses why the message of flattening the COVID-19 curve is right, but why some of the visualizations used to show the effect are wrong: Reducing the basic reproduction number does not just stretch the outbreak, it also reduces the final size of the outbreak.

From a pandemic point of view, the author writes “Because of limited health capacities, stretching out the outbreak over a longer time period will ensure, that a larger proportion of those in need of hospital treatment will actually get it. Other advantages of this approach are to win time in order to find better treatment forms and, possibly, to eventually develop a vaccine”.

A Shiny app has also been built upon this article to investigate different scenarios.

In a second article entitled “Effective reproduction number estimation”, Michael Höhle estimates with the {R0} package the time-varying effective reproduction number during an infectious disease outbreak such as COVID-19. Using a single simulated outbreak he compares the performance of three different estimation methods.

More recently, in this article the author looks at “superspreading” in infectious disease transmission from a statistical point of view. He characterises heterogeneity in the offspring distribution [who becomes infected by the superspreader person] by the Gini coefficient instead of the usual dispersion parameter of the negative binomial distribution. This allows us to consider more flexible offspring distributions.

Flattening vs shrinking: the math of #FlattenTheCurve

Published by Ben Bolker and Jonathan Dushoff, this blog post gives a clear explanation of physical distancing and explains how physical distancing makes several beneficial outcomes possible.

The code is available on GitHub.

explainCovid19 challenge

Published by Przemyslaw Biecek, this blog post gives an overview of a model that uses gradient boosting to predict survival based on age, country, and gender. It also shows how older people are more at risk and it lets you play with the model yourself with a modelStudio interactive dashboard.

Data sources:

Google sheet (with most recent data at the end of February)
Kaggle dataset

An R Package to explore the Novel Coronavirus

Published by Patrick Tung via Towards Data Science, this blog post translates into English an R package originally written in Chinese.

Data is collected from Tencent, at https://news.qq.com/zt2020/page/feiyan.htm, which contains one of the most up-to-date public information of the Coronavirus.

Coronavirus model using R – Colombia

Published by Daniel Pena Chavez, this blog post uses the code from Prof. Dr. Holger K. von Jouanne-Diedrich to model height of pandemic in Colombia and projected deaths. The author also points out that a huge number of other variables need to be considered, such as density, climate and government response.

Data is from Rami Krispin’s GitHub.

More recently, the author published another article comparing China and Italy’s rates on log scales.

COVID-19: The Case of Spain

Written by Jose from Diarium - Statistics and R software, this blog post, using data for Spain, applies the SIR model, and then a cubic polynomial regression model to predict infections, hospitalizations, deaths and peak date.

Tidying the new Johns Hopkins Covid-19 time-series datasets

Written by Joachim Gassen, this blog post provides functions and code to deal with different country names and changes on the Johns Hopkins site.

More recently, the author published a series of other interesting articles:

Merge Covid-19 Data with Governmental Interventions Data: this article analyzes five kinds of intervention on the spread of COVID-19.

Scraping Google Covid-19 community movement data from PDF figures: this article explains how to scrape data from a Google site that tracks movements of people. The author uses the {tidycovid19} R package and prepares an analysis of Germany and then across countries.

Covid-19: Explore Your Visualizer Degrees of Freedom: in this article, the author uses COVID-19 data to demonstrate how graphs can communicate very differently, and be manipulated. He shows that getting a ‘neutral’ message to the reader is far from trivial and that visualizations without guidance can be particularly misleading.
{tidycovid19} New data and documentation: A recent update to the {tidycovid19} package brings data on testing, alternative case data, some regional data and proper data documentation. Using all this, you can use the package to explore the associations of (the lifting of) governmental measures, citizen behavior and the Covid-19 spread.
Exploring and Benchmarking Oxford Government Response Data: A post assessing the impact of non-pharmaceutical interventions on the spread of Covid-19 based on the Assessment Capacities Project (ACAPS) and the Oxford Covid-19 Government Response Tracker.

COVID-19 in Belgium

Based on Tim Churches’ article, I published an analysis of the COVID-19 specifically for Belgium. In this article, I also use the most common epidemiological model, the SIR model (to its simplest form), to analyze the outbreak of the disease in the case where there would be no public health intervention. I also show how to compute the reproduction number and I present some additional improvements that can be made to further analyze the epidemic.

The code is available on GitHub, so feel free to use it as starting point for an analysis of the virus outbreak in your own country.

Facts About Coronavirus Disease 2019 (COVID-19) in 5 Charts created with R and ggplot2

Written by Gregory Kanevsky, this blog post compiles some useful facts about COVID-19 into 5 charts, including gauge charts, and discusses R and {ggplot2} techniques used to create them.

Contagiousness of COVID-19 Part I: Improvements of Mathematical Fitting

Written by Martijn Weterings on Learning Machines, this guest post describes the fitting of Covid-19 data with the SIR model and explains tricky parts of the fitting methodology and how we can mitigate some of the problems (e.g., early stopping of the algorithm or an ill-conditioned problem). It provides a very clear explanation of some tweaks to the standard model.

The code is available here.

Coronavirus : spatially smoothed decease in France and decease animation map

Published by Michael Ires, this blog post shows R code on how to use kernel weighted smoothing with arbitrary bounding areas to display a map of deaths from Covid-19 in France.

The author also published two other articles on how to build an animated map of deaths from Covid-19 in France and in Europe.

More recently, the author published an article where he uses the {geogrid} package to show the incidence of Covid19 by French departments as well as spread.

Another “flatten the COVID-19 curve” simulation… in R

Written by Javier Fernandez-Lopez, this blog post shows R code to create static plots and then simulations to demonstrate how social distancing could help to “flat the curve” of COVID-19 infections.

Tracking Covid19 Cases Throughout NJ with R

Written by Kevin Zolea, this blog post shows how to use the {gganimate} package to create an animated time series map showing how Covid19 spread throughout the U.S. state of New Jersey’s counties.

It’s fun to look at the YACM (Yet Another COVID Model)

Written by Adrian Barnett from Median Watch, all the models in this blog post are based on the excellent ordinary differential equation models by Alison Hill. They are microsimulations of those models that make heavy use of the {MicSim} package for running microsimulations in R.

Is COVID-19 as bad as all that? Yes it probably is

Written by Dr. Francis Smart from Econometrics By Simulation, this article explains clearly some of the factors that determine the infections and deaths from COVID19, with different scenarios of seriousness.

Potential Long-Term Intervention Strategies for COVID-19

On this website, several professors and members of Stanford University (Marissa Childs, Morgan Kain, Devin Kirk, Mallory Harris, Jacob Ritchie, Lisa Couper, Isabel Delwel, Nicole Nova, Erin Mordecai) developed a compartmental model of COVID-19 to evaluate possible outcomes of non-pharmaceutical interventions such as social distancing.

The website presents an introduction to the problem, the possibility to play around with the model to predict the effects of COVID intervention strategies (thanks to a Shiny app), the model details, and predictions for Santa Clara County, California.

The code is available on GitHub.

Animations in the time of Coronavirus

Written by Martin Henze from the Heads or Tails blog, this article describes how to extract and prepare the necessary data to animate the spread of the virus over time in Germany, using {gganimate} and {sf} R packages to create animated map visuals.

The author posted the dataset associated with the Germany maps to Kaggle, where he is maintaining it on a daily basis. In addition, he posted a version of the JHU US county level dataset where he added some key demographic info from the US census. This dataset is also updated daily.

COVID-19 Data and Prediction for Michigan

Written by Nagdev Amruthnath, this blog post builds and tests an exponential regression model based on (not much) State of Michigan data.

Data Visualization of COVID-19 in the US

Written by Daniel Reiff, this article examines COVID-19 growth dynamics using exponential and logistic curves.

The spread of COVID-19 across countries visualization with R

Written by Sergey Bryl, this article examines the speed and spreading of the virus across countries. One animated visualization and two stationary charts show:

how long and intensive were previous phases and
compare the effectiveness against COVID-19 for different countries

The code can be found at the end of the article.

Covid-19 and Rural Areas in the U.S

Written by Elliot Meador from Deltanomics, this blog post focuses on cases of Covid-19 in rural areas of the U.S, including whether in the South any particular state appears to be an outlier.

Covid Death Rates: Is the data correct?

Written by Sam Weiss, this post raises questions about the accuracy of reports of case numbers, in that they may fail to backfill for corrected data.

More recently, the author published two articles (part 1 and part 2) in which he finds and visualizes an association between number of people that tested positive for COVID-19 in a country and imports from China. In addition he finds that there are particular industries that are particularly correlated with COVID-19 rates.

COVID-19 Risk Heat Maps with Location Data, Apache Arrow, Markov Chain Modeling, and R Shiny

Written by Filip Stachura, this post describes Appsilon’s solution (CoronaRank) submitted to the recent Pandemic Response Hackathon. Inspired by Google’s PageRank, it uses geolocation data in the Apache Parquet format from Veraset for effective exposure risk assessment using Markov Chain modeling.

COVID-19 Tracker Indonesia

Written by Dio Ariadi from DataWizArt, this post shows some very nice plots on regional variation in COVID-19 cases and deaths in Indonesia, with very neatly integrated R code for each plot.

COVID-19 Projections Using Machine Learning

Developed by Youyang Gu, this website presents an intuitive model that builds machine learning techniques on top of a classic infectious disease model to make COVID-19 infections and deaths projections for the US, all 50 US states, and more than 60 countries.

The code can be found on GitHub.

COVID-19 in Belgium: is it over yet?

Written by myself in collaboration with Prof. Niko Speybroeck and Angel Rosas-Aguirre, this article shows the evolution of the number of hospital admissions and the number of confirmed cases in Belgium (by province and at the national level).

Code of the plots is available in the article.

COVID-19 Cases by Ethnicity

Written by Tommi Suvitaival, this article investigates Covid19 deaths as a function of the percentage of a county’s population that comes from various ethnic backgrounds.

Tennessee COVID-19 Update

Written by Prof. James M. Luther, this document presents a summary of the daily data for the state of Tennessee. The author uses an interactive map, a seven-day rolling average of various metrics, and facet charts.

Simulating Coronavirus Outbreak in Cities with Origin-Destination Matrix and SEIR Model

Written by Yihui Fan, this blog post lays out a step-by-step guide on simulating and visualising the spread of Coronavirus in the Greater Tokyo Area based on Origin-Destination Matrix and SEIR Model.

Another post from the same author focuses on the effectiveness of reducing population movement in managing Coronavirus outbreak.

COVID-19 Population Mobility - How has human mobility changed under the COVID-19 Pandemic?

Written by Kelsey E. Gonzalez, this visualization aims to understand population behavior during the COVID-19 pandemic.

Data is from Cuebiq and the code is available on GitHub.

How to Build COVID-19 Data-Driven Shiny Apps in 5 minutes

Written by Emanuele Guidotti, this tutorial shows how to build a simple yet complete Shiny application using the R Package COVID19: R Interface to COVID-19 Data Hub.

In another post, the author explores the R package {COVID19} in further detail.

Analyzing data from COVID19 R package

Written by Pablo Cánovas, this blog post explores whether deaths from COVID19 are being reported accurately.

It uses data from The Human Mortality Database and the COVID-19 Data Hub.

Body Mass and Risk from COVID-19 and Influenza

Written by Prof. Radford Neal, this blog post looks at data from flu-like illnesses and some preliminary Covid19 data. The author concludes that being underweight and being seriously obese are both risk factors for serious respiratory illness.

More recently, the author published a series of other articles:

The Puzzling Linearity of COVID-19 discussing the fact that for many countries, the linear plots of total cases or total deaths go up exponentially at first, and then approach a straight line that is not horizontal
Seasonality of COVID-19, Other Coronaviruses, and Influenza: this post looks at the evidence for seasonality in influenza and the common cold Coronaviruses, and to what extent one might expect COVID-19 to also be seasonal
Critique of “Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period”: this post analyzes and criticizes an earlier paper by Kissler et al. (2020). See also part 2 and part 3

HMD – Weekly Data

Written by Ronald Richman, this blog post explores the highly improbable level of deaths currently being reported using the Human Mortality Database and its recently begun special time series of weekly death data across 13 countries.

Guest posts on Chris Muir’s blog

Written by Skylar Hara, this blog post examines whether the incidence of Covid19 changed after the Governor of Hawaii issued a stay-at-home order.

Written by Ronja Steinbach, this blog post gathers data on Trump or Clinton states, percentages of minorities, and median household income to answer the following questions:

Does party affiliation of a state in the 2016 election have a significant impact on the incidence rate of the virus in that state?
Does the proportion of the population that is minority and median household income affect the incident rate of the virus across states?

Written by Akemi Santiago, this post shows a statistically different mortality rate between African-Americans in the United States and white Americans.

Written by Masha Rutenberg, this post focuses on the correlation between the population of a country and the number of confirmed infections in the country.

All authors are students of Prof. Chris Muir.

An R View into Epidemiology

Written by Joseph Rickert in R Views, this blog post tracks down R packages that help epidemiology research and shows the number of downloads in recent months for the five most popular packages.

Articles by Rob J Hyndman

Rob J. Hyndman, Professor of Statistics and Head of the Department of Econometrics and Business Statistics at Monash University (Australia), published a series of Covid19 related articles:

Forecasting COVID-19: this blog post does not use R, although Prof. Hyndman is an expert with it, but it does explain some of the problems with time series forecasting or other methods of forecasting
Why log ratios are useful for tracking COVID-19: this post presents the benefits of reporting log-scale graphics
Excess deaths for 2020: the reported COVID19 deaths in each country are often underestimated. One way to explore the true mortality effect of the pandemic is to look at “excess deaths” — the difference between death rates this year and the same time in previous years
Seasonal mortality rates: this post shows how the weekly mortality data published by the Human Mortality Database can be used to explore seasonality in mortality rates. Mortality rates are known to be seasonal due to temperatures and other weather-related effects

Turkey vs. Germany: COVID-19

Written by Selcuk Disci from DataGeeek, this article compares efforts by Turkey and Germany to control the pandemic, and tests several regression models.

Hands-on: How to build an interactive map in R-Shiny: An example for the COVID-19 Dashboard

Written by Sangmeng, this post explains how to build an interactive dashboard with Shiny with an example for the COVID-19 Dashboard.

Modelling COVID-19 in Morocco

Written by Zakariah Gassasse, this blog post presents data on cases of Covid19 in Morocco and applies the SIR model to the data.

More recently, the author published two other articles. The first article provides a short-term forecast of COVID-19 cases and deaths in Morocco by using simple but effective time-series analyses. The second article maps the outbreak to see which regions are suffering the most.

SIR models with Kermack and McKendrick

Written by Pierre Jacob, this blog post is mostly a retrospective look at the origins of the much-used SIR model.

Johns Hopkins Covid-19 Data and R

Written by Steve Miller, these blog posts (part 1 & part 2) showcase the handling of daily data of cases/deaths from Covid-19 in the U.S. published by Johns Hopkins University, and visualize moving averages of cases and deaths.

The author also published a blog post putting unemployment claims in the U.S. in perspective.

Estimating COVID-19’s \(R_t\) in Real-Time

Written by Ramnath Vaidyanathan, this tutorial shows how to estimate \(R_t\), the measure known as effective reproduction number, which is the number of people who become infected per infectious person at time \(t\).

From static to animated time series: the tidyverse way

Written by Giulia Ruggeri, this post goes through the steps necessary to create an animated COVID-19 time series plot.

Sneak peek: new Summit data tool helps clients visualize US areas that are most heavily impacted by the COVID-19 virus

Written by Colby Ziegler, this blog post describes a tool (built primarily in R using the {leaflet}, {tidyverse}, and {tigris} packages) tracking and displaying the total number of confirmed COVID-19 cases and deaths by U.S. county, overlaid with the locations of Certified Community Development Financial Institutions (CDFIs).

How to Reproduce Financial Times Style COVID19 Daily Reporting?

Written by Peyman Kor, this blog post shows how to reproduce in R the Financial Times facet plot by country.

Visualizing COVID cases in Belgium

In this blog post, Koen Hufkens plots cases in Belgium and addresses a challenge in geo-spatial plotting.

A spatio-temporal analysis of the environmental correlates of COVID-19 incidence in Spain

Written by Antonio Paez and several co-authors, this blog post looks at weather, humidity and other factors in Spain to create a SUR model.

Another post by Antonio Paez investigates the incidence of COVID-19 in the United States using Google Community Mobility Reports.

Covid-19 Analysis

Written by Rizami Annuar, this post compares data on cases and deaths in Malaysia to other countries, including correlations.

Australian governments can choose to slow the spread of coronavirus, but they would need to act immediately

Written by Matt Cowgill, this blog post shows that Australia early on had relatively few cases, but the post argues that the country needed to act urgently. Data is from the {gtrendsR} package.

In a more recent post, using Google trends data for key words associated with unemployment, the authors trace the effects of COVID19 in Australia in the early months.

In addition to these posts, a series of other posts have been published by Stephen Duckett and Brendan Coates:

Does Covid raise everyone’s relative risk of dying by a similar amount? More evidence

Written by Prof. David Spiegelhalter (author of the great book “The Art of Statistics”), this post looks at relative mortality rates by age and gender, using data from the U.K.’s Office for National Statistics.

Tracking Coronavirus: Building Parameterized Reports to Analyze Changing Data Sources

In this post, using Johns Hopkins data, Tyler Sanders builds a virus dashboard that can be updated each day with just the click of a button as an example of how to build parameterized reports with R Markdown.

Mapping NZ cases of COVID-19

In this post, using his own choropleth package ({DHBins}) and its hexagonal bins, Thomas Lumley maps cases of COVID19 by Health Boards in New Zealand.

Visualize the Pandemic with R #COVID-19

In this post, Xinhan Qian carries out a variety of explorations with Covid19 data, including the precipitous declines in U.S. movie box office revenue and restaurant reservations.

Exploring the Temporal Evolution of COVID-19 Cases in the United States

Written by Robert Winkelman and Colin Waltz, this post shows clearly how to create animated plots of the spread of COVID19 infections in the United States.

R Data Analysis: COVID-19

Published on the blog of Sharp Sight, this series of blog posts (part 1, 2, 3, 4, 5 and 6) explain how to rename and reorder columns, standardize dates (with the {lubridate} package), merge datasets, take other preparatory steps, plot with the {ggplot2} package and finally, reproduce in R a plot that shows the relative progress of 16 countries in coping with the pandemic.

All posts use the Johns Hopkins data and data from the company’s own collection.

GA COVID-19 Reports

Based on data from the GA Department of Public Health’s report, Andrew Benesh posts a daily analysis of the U.S. state Georgia’s cases, deaths, ICU usage etc.

All his reports are posted on Medium and the code is available here.

Corona in Belgium

Published by bnosac, this post covers the early exploration of the exponential spread of Covid19, with a focus on Belgium and the Netherlands.

The Coronavirus in Italy from the Twitter’s Point of View

Written by the Kode team, these two posts (here and here) use text-mining techniques to analyze tweets in Italy early in the pandemic and on speeches by the Prime Minister of Italy regarding Covid19.

They also published an interactive dashboard (in Italian) allowing to explore the data released daily by the Civil Protection.

Use R and Tidycensus to Look at COVID-19 Risk Factors

Written by René F. Najera, this post uses US census data to identify “overcrowded” areas and considers them in terms of Covid19 risk.

Animating U.S. COVID-19 hotspots over time

Written by Nathan Chaney, this post presents an animated map of the 7-day rolling average of new COVID-19 cases in US. Code for the animated map is available directly at the end of the post.

This post is an extension of his previous post on visualizing COVID-19 in Arkansas.

Understanding COVID19 in Connecticut. It takes a town

Based on these two posts by Nathan Chaney, Chuck Powell shows in his post how to create an animated map of 7-day rolling average of new COVID19 cases per 100,000 in Connecticut (by town).

Data

2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE: this dataset is used by many resources mentioned in this article and has become the gold standard for COVID-19 modeling
World Health Organization (WHO). See also their accompanying Shiny app
COVID-19 Open Research Dataset Challenge (CORD-19) (via Kaggle)
Novel Corona Virus 2019 Dataset: Day level information on Covid-19 affected cases (via Kaggle)
COVID-19 Data from the European Centers for Disease Control
The COVID Tracking Project
Tidying the John Hopkins Covid-19 data to long format and merging some World Bank data
A Short Review of COVID-19 Data Sources
COVID-19 datasets by esri
beoutbreakprepared/nCoV2019: one of the very few non-aggregated dataset available online. Such a dataset of individual-level information on patients with confirmed COVID-19, (including their travel history, location, symptoms, reported onset and confirmation dates and basic demographics) is important to understand, among others, transmissibility, risk of geographic spread, routes of transmission and risk factors for infection
Fighting the Covid-19: All the datasets and data efforts in one place: this post gathers many relevant datasets and data efforts
A Google Sheet which helps with tracking of the local lockdowns in the UK

Other lists or collections of resources

With so many great resources about the Coronavirus, other people also collected and organized similar lists.³ Below some collections I have been fortunate enough to discover:

COVID-19 Blog Post Directory: developed by Connor Rothschild and Rees Morrison, this Shiny app lets users interactively search a collection of over 400 posts by primary topic, post title, date, and whether the post uses a particular mathematical technique or data source. See also the accompanying blog post.
COVID-19 Modelling Resources, Data and Challenges by IDEA (not only R)
GitHub repo covid19-r by Mine Cetinkaya-Rundel (only R)
Amplifying people I trust on COVID-19: written by Jeff Leek from Simply Statistics, this article is a collection of trustworthy people and experts who share good information about the COVID-19 pandemic
Some Select COVID-19 Modeling Resources and More Select COVID-19 Resources by Joseph Rickert, assembling dashboards, Shiny apps, blog posts, packages, datasets, videos and conference proceedings that pertain to the Covid19 pandemic
CovidR Contest: launched by the European R users meeting (eRum), this contest is an open-source contest and pre-conference event, featuring any work done with R around the topic of the COVID-19 pandemic
Turning COVID-19 into a data visualization exercise for your students: written by Daniela Duca, this blog post presents a variety of methods to visualize data, drawing on several blog posts, Shiny apps and dashboards
Outbreak.info is an open source tool built by Scripps Research that standardizes and aggregates COVID-19 data. The interface aggregates journal articles, preprints, datasets, clinical trials, protocols, and other resources in one place, standardizing metadata and applying NLP to make these sources searchable and more accessible. The interactive data dashboards allow for quick comparison of countries, states, counties, and metro areas, and includes an API and R Package for researchers who want to access all of the raw data

I hope that, in addition to my collection, these rich lists done by others will give you enough background materials to analyze the outbreak of COVID-19 on your own (or at least some ideas)!

Non-english resources

This section may be of interested to only a limited number or people, but still, there are great resources in languages other than English. See a collection of them below listed by language:

Japanese:
- Coronavirus infection bulletin: original version of this dashboard
German:
- Developed by Prof. Dr. Helmut Küchenhoff, this CoronaMaps presents the situation of the Coronavirus in the world, in Europe and in Germany via a map and a table
Spanish:
- GitHub repository with official government data and R code used to extract it, see here and here
- COVID-19 en Chile: this Shiny app shows the accumulated confirmed contagion cases and provides an estimate of the growth rate for each municipality
- Developed by Que Oferton, this Shiny app provides an overview of the 2019 Novel Coronavirus COVID-19 epidemic in Central America, including statistics, forecast, SIR and SEIR models. The data and dashboard are refreshed on a daily basis
French:
- Written by Arthur Charpentier, this blog post shows how to quantify excess mortality using french mortality data

Conclusion

Thanks for reading.

I hope you will find these R resources on the COVID-19 Coronavirus useful. Feel free to let me know in the comments if I missed one.

A special thanks to Rees Morrison for his tremendous work on collecting and organizing several articles, which greatly helped in improving the section about blog posts. Read his articles (part 1 and 2) presenting a descriptive analysis of all the posts collected.

Although I have carefully read all resources, inclusion on the list does not mean that I endorse the findings. Moreover, some of the analyses, code, dashboards, packages or datasets might be out of date, so these should not be viewed, by default, as current findings. If you are the author of one of these resources, do not hesitate to contact me if you see any inconsistency or if you would like to remove it from this article.

As always, if you have a question or a suggestion related to the topic covered in this article, please add it as a comment so other readers can benefit from the discussion.

References

de Vlas, Sake J, and Luc E Coffeng. 2020. “A Phased Lift of Control: A Practical Strategy to Achieve Herd Immunity Against Covid-19 at the Country Level.” medRxiv.

Kissler, Stephen M, Christine Tedijanto, Edward Goldstein, Yonatan H Grad, and Marc Lipsitch. 2020. “Projecting the Transmission Dynamics of SARS-CoV-2 Through the Postpandemic Period.” Science 368 (6493): 860–68.

The package has also been the subject of a preprint.↩︎
See more information about this epidemiological model in this post by Marc Choisy.↩︎
Note that unlike my list, collections by others may include resources on COVID-19 using other tools than R.↩︎

A Shiny app for inferential statistics by hand

Wed, 15 Jan 2020 00:00:00 +0000

Statistics is divided into four main branches:

Descriptive statistics
Inferential statistics
Predictive analysis
Exploratory analysis

Descriptive statistics provide a summary of the data; it helps explaining the data in a concise way without losing too much information. Data can be summarized numerically or graphically. See descriptive statistics by hand or in R to learn more about this branch of statistics.

The branch of predictive analysis aims at predicting a dependent variable based on one or several independent variables. Depending on the type of data to be predicted, it often encompasses methods such as linear regression or classification.

Exploratory analyses focus on using graphical approaches to delve into the data and identify the relationships that exist between the different variables in the dataset. They are therefore more akin to data visualization.

Inferential statistics uses a random sample of data taken from a population to make inferences, i.e., to draw conclusions about the population (see the difference between population and sample). In other words, information from the sample is used to make generalizations about the parameter of interest in the population.

The two major tools in inferential statistics are:

confidence intervals, and
hypothesis tests.

Here is a Shiny app which helps you to use these two tools:

Statistics-201

This Shiny app focuses on confidence intervals and hypothesis tests for:

1 and 2 means (with unpaired and paired samples)
1 and 2 proportions
1 and 2 variances

How to use this app?

Follow these steps:

Open the app via this link
Choose the parameter(s) you want to do inference for (i.e., mean(s), proportion(s) or variance(s))
Write your data in Sample. Observations are separated by a comma and the decimal is a point
Set the null and alternative hypothesis
Select the significance level (most of the time \(\alpha = 0.05\))

In the results panel (on the right side or below depending on the size of your screen), you will see:

a recap of your sample together with some appropriate descriptive statistics
the confidence interval
the hypothesis test
the interpretation
and an illustration of the hypothesis test

All formulas, steps and computations to arrive at the final results are also provided.

Code

See the last version on GitHub.

Note that the link may not work if the app has hit the monthly usage limit. Try again later if that is the case.

Conclusion

Thanks for reading.

I hope you will find this app useful to do inferential statistics and in particular confidence interval and hypothesis testing by hand.

If you need to learn more about the structure of a hypothesis test by hand, see this article. See also this flowchart to have an overview of the most common statistical tests.

As always, if you have a question or a suggestion related to the topic covered in this article, please add it as a comment so other readers can benefit from the discussion.

A Shiny app for simple linear regression by hand and in R

Wed, 15 Jan 2020 00:00:00 +0000

Simple linear regression is a statistical method to summarize and study relationships between two variables. When more than two variables are of interest, it is referred as multiple linear regression. See this article on linear regression for more details.

In this article, we focus only on a Shiny app which allows to perform simple linear regression by hand and in R:

Statistics-202

How to use this app?

Follow these steps:

Open the app via this link
Enter your data in the x and y fields. The x field corresponds to the independent variable, while the y field corresponds to the dependent variable
If you do not want to display the confidence interval around the regression line, uncheck the checkbox under Plot
Change the x and y-axis labels for the regression plot if needed

In the results panel (on the right side or below depending on the size of your screen), you will see:

a recap of your dataset together with some appropriate descriptive statistics
the estimates \(\beta_0\), \(\beta_1\) and the regression model computed by hand
the results of the model computed in R
the regression plot with some key measures
the interpretations
and the assumptions to check the validity of the model

All formulas, steps and computations to arrive at the final results are also provided.

Note that although the assumptions are displayed, it is your responsibility to check them to assess the validity of the linear model.

Last but not least, you can download a report of the results (in HTML) by clicking on the Download button, and you can choose whether you want to include the R code or not.

Code

See the last version on GitHub.

Note that the link may not work if the app has hit the monthly usage limit. Try again later if that is the case.

Conclusion

For further details about what is linear regression and when it is used, please see:

this post, and
the numerous resources on the topic available in textbooks and online.

Thanks for reading.

I hope you will find this app useful to do simple linear regression by hand and in R.

As always, if you have a question or a suggestion related to the topic covered in this article, please add it as a comment so other readers can benefit from the discussion.

A practical guide on optimal asset allocation

Tue, 07 Jan 2020 00:00:00 +0000

UPDATE: Due to the limitation in terms of maximum number of Shiny apps that can be published on the free shinyapps.io plan, the Shiny app presented below has been unpublished. However, the code can be found on GitHub.

Introduction

In his book A Random Walk down Wall Street, Burton G. Malkiel advises readers of an optimal asset allocation depending on age. As an amateur investor, I thought it would be useful to develop a Shiny app which depicts his advice for other interested investors. Here is the link to the app:

Optimal asset allocation

How to use this app?

Open the app via this link
Set your age under How old are you?
Indicate how much you are willing to invest

On the right panel (or below depending on the size of your screen) you will see a table and a barplot. The table includes:

the different type of assets which represent the different allocations
the most common tickers for each type of asset
the optimal percentage for each asset
the optimal amount for each asset

In the table, the optimal percentage depends on your age (the older you are, the more conservative your portfolio), while the optimal amount depends on your age and the amount you are willing to invest.

The barplot displays the optimal weight (so the optimal percentage of your portfolio) for each type of asset: stocks, bonds, real estate and cash. This is just a visualization of the column “Optimal percentage” displayed in the table above.

It is also possible to compare your portfolio with the optimal portfolio, as recommended by the author. For this comparison, click on the tab “Compare with your portfolio” above How old are you?. In this tab:

Again, indicate your age
Then indicate your portfolio’s value for each asset (if you do not own a specific asset, leave it equal to 0)

On the right panel (or below depending on the size of your screen) you will see your portfolio’s total amount and a barplot comparing your portfolio and the optimal portfolio according to your age. This visualization will instantly give you a clear overview of what type of asset is overrepresented and underrepresented in your current portfolio compared the the optimal one. This will allow you to easily see how to rebalance your assets in accordance to what the author suggests.

Code

Here is the entire code (or see the last version on GitHub) in case you would like to enhance it.

Note that the link may not work if the app has hit the monthly usage limit. Try again later if that is the case.

Disclosure: Note that this practical guide on asset allocation is based on the book A Random Walk down Wall Street by Burton G. Malkiel. This application does not include investment advice or recommendations, nor a financial analysis. This application is intended for information only and you invest at your own risks. I cannot be held liable for any decision made based on the information contained in this application, nor for its use by third parties.

Conclusion

Thanks for reading.

I hope you will find this app useful to quickly replicate the optimal asset allocation suggested by Burton G. Malkiel in his book A Random Walk down Wall Street. If you want to have more information about this investment strategy, I suggest reading his book.

As always, if you have a question or a suggestion related to the topic covered in this article, please add it as a comment so other readers can benefit from the discussion.

Draw a word cloud with a R Shiny app

Tue, 07 Jan 2020 00:00:00 +0000

Below a Shiny app to help you draw a word cloud:

Word cloud

Word clouds are particularly useful as part of text mining analyses. Moreover, it is also useful to analyze string and character variables for any datasets (see the different data types in R).

How to use this app?

In Word source you can see two examples of word clouds with preloaded texts. If you want to use your own file, choose the option “Upload a file”
When uploading a file, make sure to upload a .csv or .txt file
If it is a .csv file, there should be only one column containing all words or sentences (see example files: example.csv and example.txt)
Choose the language of the text. Stop words in the chosen language will be automatically removed
Numbers and punctuations will be automatically removed as well, regardless of the language
You can remove specific words by specifying them after having clicked on “Remove specific words?”. Indicate one word per line
Change the default options in Maximum number of words and Background color if needed
If you need to display this word cloud in your analyses, you can simply take a screenshot and include it in your document

Code

Here is the entire code (or see the last version on GitHub) in case you would like to enhance it. See an example on how to use this app after the embedded code.

Note that the link may not work if the app has hit the monthly usage limit. Try again later if that is the case.

Conclusion

Thanks for reading.

I hope you will find this app useful to draw word clouds of your texts.

As always, if you have a question or a suggestion related to the topic covered in this article, please add it as a comment so other readers can benefit from the discussion.

How to embed a Shiny app in blogdown?

Tue, 07 Jan 2020 00:00:00 +0000

Step-by-step guide

If you have developed and deployed a Shiny app and would like to embed it in blogdown, follow these steps:

create a new post as usual
add output: html_document if it is not already included in the YAML metadata
insert the following HTML code in the body of the post:

<iframe height="800" width="100%" frameborder="no" src="https://antoinesoetewey.shinyapps.io/statistics-201/"> </iframe>

You should change the URL with the URL of your deployed Shiny app (after src=, do not forget that the URL should start with http:// or https:// and should be surrounded by "). Moreover, you can modify the height, the width and include or not a frame border with the corresponding tags.

Here is an example of the result with one of my Shiny app (link to the app):

Note that the app may not work if it has hit the monthly usage limit. Try again later if that is the case.

Code

Here is the entire .Rmd code in case you would like to use it for your own website:

If you encounter an issue, try loading the following packages in the body of your new post: shiny, widgetframe (you can load a package with the command library(widgetframe)). If one of the package is not installed yet, you can do it with the command install.packages("widgetframe").

Conclusion

Thanks for reading.

I hope this article helped you to embed a Shiny app in your blogdown website.

As always, if you have a question or a suggestion related to the topic covered in this article, please add it as a comment so other readers can benefit from the discussion.

A guide on how to read statistical tables

Mon, 06 Jan 2020 00:00:00 +0000

Below a Shiny app to help you read the main statistical tables:

Statistics-101

This Shiny app helps you to compute probabilities for the main probability distributions.

How to use this app?

Open the app via this link
Choose the distribution
Set the parameter(s) of the distribution (the parameters depend of course on the chosen distribution)
Select whether you want to find the lower tail, upper tail or an interval
Choose the value of x

On the right panel (or below depending on the size of your screen) you will see:

a recap of the data you just entered
the numerical solution (i.e., the probability)
a visualization of the solution
the probability density function together with the mean, the standard deviation and the variance

Example

Here is an example with the most common distribution: the normal distribution.

Suppose the following problem: The cost of weekly maintenance and repair of a business has been observed over a long period of time and turns out to be distributed according to a normal distribution with an average of 402€ and a standard deviation of 22€. Having set a budget of 439€ for next week, what is the probability that the cost exceeds this budget?

To solve this problem, follow these steps in the app:

Choose the normal distribution, as it is said that the costs follow a normal distribution
Set the mean \(\mu\) equal to 402, as it is said that the average cost is 402€
In the statement, the standard deviation is given (and not the variance) so select “Standard deviation \(\sigma\)” and set it equal to 22
We are asked what is the probability the the cost exceeds the budget. Therefore, we look for the probability above a certain x, so select upper tail \(P(X > x)\)
We are now asked to find the probability that the cost exceeds 439€, so set x equal to 439

The solution panel gives a recap of the data:

\[X ∼ \mathcal{N}(\mu = 402, \sigma^2 = 484)\] where \(484 = 22^2\), and the solution: \[P(X > 439) = P(Z > 1.68) = 0.0463\] where \(Z = \frac{X - \mu}{\sigma} = \frac{439 - 402}{22} = 1.68\) and \(Z ∼ \mathcal{N}(\mu = 0, \sigma^2 = 1)\) (known as the standard normal distribution). Thus, the probability that the cost next week exceeds the budget of 439€ is 0.0463, or 4.63%.

It also shows the normal distribution (with \(\mu = 402\) and \(\sigma^2 = 484\)) with the shaded area corresponding to the probability we are looking for. It then gives some details about the density function, the mean, the standard deviation and the variance.

Code

See the last version on GitHub.

Note that the link may not work if the app has hit the monthly usage limit. Try again later if that is the case.

Conclusion

Thanks for reading.

I hope you will find this app useful to compute probabilities for the main distributions.

As always, if you have a question or a suggestion related to the topic covered in this article, please add it as a comment so other readers can benefit from the discussion.

Shiny on Stats and R

AssociationExplorer: A user-friendly shiny application for exploring associations and visual patterns

Why AssociationExplorer?

What does the app do?

A paper published in SoftwareX

The R package is also on CRAN

Who is it for, and how can it be used?

Acknowledgements

Useful links

References

Running pace calculator in R Shiny

Introduction

Running pace calculator

How to use it?

Code

Conclusion

Mortgage calculator in R Shiny

Introduction

Mortgage calculator

How to use the mortgage calculator?

Code of the app

Conclusion

How to publish a Shiny app? An example with shinyapps.io

Introduction

Prerequisite

Step-by-step guide

Additional notes

Settings of your app

Publish your dataset

Conclusion

Top 100 R resources on COVID-19 Coronavirus

R Shiny apps and dashboards

Coronavirus tracker

Coronavirus dashboard from the {coronavirus} package

Visualization of Covid-19 Cases

Modeling COVID-19 Spread vs Healthcare Capacity

COVID-19 Data Visualization Platform

Coronavirus 10-day forecast

Coronavirus (COVID-19) across the world

COVID-19 outbreak

Flatten the Curve

Explore the spread of Covid-19

Governments and COVID-19

Simulating COVID-19 Epidemic in Togo - West Africa

Covid-19 Prediction

Covid-19 Dashboard

Healthcare worker deaths from novel Coronavirus (COVID-19) in the US

Covid-19 Hospitalizations in Belgium

COVIDMINDER: Where you live matters!

COVID-19 Canada Data Explorer Tool

Philippine COVID-19 Case Forecasting

COVID-19 Case & Death Report Number Corrector

Covid-19: the SPQEIR model

The Belgian Covid Cases Tracker

COVID-19 monitor

COVID-19 Bulletin Board

Covid-19 Statistics Displayer

CoronaMapper

CoronaDash

Covidfrance

The Impact of COVID-19 on Mobility

Coronavirus Analysis Platform

COVID-19 Tracker

COVID-19 Overview

COVID-19 Exit Strategies

Corona Virus Statistics : COVID19

Covid19 Data

WHO COVID-19 Explorer

COVID-19 Scenario Analysis Tool

Covid-19 track

R packages

{nCov2019}

{coronavirus}

{tidycovid19}

R packages from R Epidemics Consortium

{covdata}

{covid19italy}

{COVID19}

{COVOID}

{cdccovidview}

Coronavirus dashboard from the `{coronavirus} package`

`{nCov2019}`

`{coronavirus}`

`{tidycovid19}`

`{covdata}`

`{covid19italy}`

`{COVID19}`

`{COVOID}`

`{cdccovidview}`

`{babsim.hospital}`

`{EpiLPS}`

COVID-19 Data Analysis with `{tidyverse}` and `{ggplot2}`