Creating a User-Friendly Coronavirus Dashboard in R for Your Country

Creating a User-Friendly Coronavirus Dashboard in R for Your Country

Introduction:

The Novel COVID-19 Coronavirus has become a global phenomenon, dominating headlines and affecting societies worldwide. As the virus continues to spread, data scientists and professionals are using their expertise and resources to analyze its impact. In an effort to provide useful and accessible information, I have collected and shared the best R resources on the Coronavirus in a previous article. However, many readers expressed interest in creating their own dashboards specific to their countries. To address this, I have created a simple yet powerful Coronavirus dashboard adapted to Belgium. This dashboard features key measures, comparisons with other European countries, a world map of confirmed cases and deaths, and more. If you want to build your own dashboard, follow the steps provided. This dashboard aims to make data visualization and analysis accessible to everyone, from beginners to experts, and serves as a valuable tool in understanding and monitoring the virus.

Full Article: Creating a User-Friendly Coronavirus Dashboard in R for Your Country

A Step-by-Step Guide to Creating a Coronavirus Dashboard for Your Country

The Novel COVID-19 Coronavirus has become the most talked-about topic in recent times. News outlets and media platforms are constantly reporting on the number of new cases and deaths in different countries, emphasizing the importance of staying home to stay safe. The virus has captured the attention of not just governments and media, but also data scientists and professionals who are using their skills to analyze the spread of the virus. This has resulted in a wide range of applications, dashboards, blog posts, videos, datasets, and code that examine the expansion of COVID-19 and its impact on the population.

As someone who is passionate about data, I have come across numerous valuable resources on the Coronavirus. However, these resources were scattered across the internet and often overshadowed by a plethora of other information, such as alarming headlines and stories about infected celebrities or companies claiming to contribute to healthcare agencies. To address this issue, I decided to gather the best resources on the Coronavirus using my favorite statistical program, R, and shared them in a previous article. Although there are many other excellent resources available online (such as the popular dashboard by the Johns Hopkins Coronavirus Resource Center), my expertise lies in evaluating resources created using R.

Through this process, I have discovered numerous excellent resources on the Coronavirus, and I continuously receive data visualizations and analyses from scientists around the world, which I include in my collection. Alongside the R resources, I have also received numerous questions from readers on how to create their own dashboards or build country-specific dashboards. As a result, I decided to create a dashboard specifically for my country, Belgium, and provide a detailed guide on how to build it. Most of these questions came from beginners in R, as experienced users likely already know how to create a dashboard or can use the resources I have collected as inspiration for their own work. Additionally, due to the urgency surrounding the Coronavirus, many interested users wanted to develop their own dashboards as quickly as possible.

Based on these considerations, I concluded that creating a simple yet powerful and visually appealing dashboard, rather than a Shiny app, would be the best approach. Shiny apps allow users to interact with the outputs and visualizations by modifying inputs in a user-friendly manner. However, dashboards are static and cannot be modified by the end-user. The advantage of a dashboard over a Shiny app is that it is significantly easier to code, especially if you are already proficient in R Markdown.

Among the various visualizations I have come across, one dashboard stands out for its simplicity, comprehensiveness, and high-quality visualizations. I decided to create a Coronavirus dashboard based on this existing dashboard by Rami Krispin, which has a license permitting adaptation and sharing. I tailored the dashboard to be specific to Belgium and kept it simple and to the point by removing some visualizations and tables.

The dashboard is divided into several sections that can be selected from the top. The summary section provides key statistics on the Coronavirus in Belgium, including total cases, active cases, and deaths. It also includes a plot displaying the cumulative number of active cases and deaths from January 22, 2020, to the latest available date. The comparison section offers a comparison of the number of daily new cases and the distribution of cases by type in Belgium compared to other European countries (which can be modified in the code). The map section presents a world map showing confirmed cases and deaths. Users can choose to display or hide specific types of cases and zoom in or out to suit their needs. The about section provides additional information about the data, the dashboard itself, and how frequently it is updated.

I believe this simple dashboard can be easily adapted to any country by users of all levels, from beginners to experts. It effectively communicates key information about the virus through visualizations. Additionally, all plots in the dashboard are generated using the plotly package, which allows for enhanced plots with additional information displayed when hovering over them.

If you wish to create your own country-specific dashboard, follow these steps:

1. Open the dashboard using the provided link.
2. View the entire source code by clicking the “Source code” button in the top right corner of the dashboard or access the code on GitHub.
3. Copy the code.
4. Open a new R Markdown file (.Rmd) and enter any desired title and author (these details will be replaced in the next step). Select HTML as the output format and click OK.
5. Delete any existing template code and paste the code you copied in step 3.
6. Ensure that the required packages are installed by running the following command:
“`R
install.packages(c(“devtools”, “flexdashboard”, “leaflet”, “leafpop”))
devtools::install_github(“RamiKrispin/coronavirus”, force = TRUE)
“`
If prompted to update packages, select option 3 for “None.”
7. In the code, replace “Belgium” with the name of your country. Refer to the list of available countries in the dataset and format the name accordingly.
8. That’s it! You now have the basis for your own country-specific Coronavirus dashboard.

In conclusion, the Coronavirus has captured the world’s attention, and data professionals are channeling their expertise into analyzing its spread. To make sense of the overwhelming amount of information available, I have curated a collection of valuable resources on the Coronavirus, with a specific focus on those developed using R. In response to reader inquiries, I have also created a simple dashboard tailored to Belgium, along with a step-by-step guide on how to build your own country-specific dashboard. I hope this guide empowers individuals of all levels to contribute to our collective understanding of the virus and its impact.

Summary: Creating a User-Friendly Coronavirus Dashboard in R for Your Country

The Novel COVID-19 Coronavirus has become a major topic of discussion in the media and among individuals worldwide. Data scientists and professionals are using their expertise to analyze and understand the virus’s impact. To help gather and organize resources related to COVID-19, I have collected a list of the best resources available on R, my favorite statistical program. These resources include applications, dashboards, datasets, and code that analyze the virus’s spread and expansion. Additionally, I have created a simple and visually appealing dashboard specifically for Belgium, which can serve as a guide for others who want to create their own country-specific dashboard. The dashboard provides key measures and visualizations to communicate important information about the virus. By following a few simple steps, anyone can create their own customized dashboard using R Markdown. The code and instructions for creating the dashboard are available in this article.

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