covid19.analytics: An R Package to Obtain, Analyze and Visualize Data from the 2019 Coronavirus Disease Pandemic

With the emergence of a new pandemic worldwide, novel strategies attempting to understand its evolution and dynamics, empowered by computational techniques such as data analysis and computational modelling, have also emerged. Several initiatives under the umbrella of open science are contributing to tackle this unprecedented situation. In particular, the R Language (Ihaka & Gentleman, 1996; R Core Team, 2016) could be an excellent tool and ecosystem for approaches focusing on open science and reproducible research. Hence it is not surprising that with the onset of the pandemic, a large number of R packages and resources were made available for researchers working in the pandemic.

In this paper, we present the covid19.analytics R package, which allows users to access and analyze worldwide data from publicly available resources.
The covid19.analytics R package allows users to obtain live 1 worldwide data of reported cases from the novel Coronavirus Disease , as well as related datasets, such as genomics data, testing and vaccinations records, and historical pandemics records. It does this by accessing and retrieving data publicly available and published by several sources, such as: • The "COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE)  The package also provides basic analysis and visualization tools and functions to investigate these datasets and other structured in a similar fashion.

Statement of need
One important element to consider in the case of an ongoing and unfolding pandemic is the rapid data accessibility and its prompt availability. One of the main goals of the covid19. analytics package is to make the latest data related to the COVID19 pandemic promptly available to researchers and the scientific community.
As of today, there are a few dozen other packages also in the CRAN repository that allow users to gain access to different datasets about the COVID-19 pandemic. Some packages just provide access to data from specific geographical locations or the approach to the data structure in which the data is presented is different from the one presented here. Nevertheless, having a variety of packages that users can try and probably combine is an important and crucial element in data analysis. Moreover, it has been reported that there are cases of data misuse/misinterpretation due to different issues, such as erroneous metadata or data formats (Ledford & Van Noorden, 2020), in some cases resulting in articles being retracted (Schriml et al., 2020). Therefore providing additional functionalities to check the integrity and consistency of the data, as our covid19.analytics package does, is paramount. This is especially true in a situation where the unfolding of events and data availability is flowing so fast that it sometimes is even hard to keep track of all the changes.
As of today and to the best of our knowledge, there isn't any other R package that provides direct access to genomics data of the COVID-19 virus. The covid19.analytics package is indeed capable of retrieving genomics data, and it does this by incorporating a novel, more reliable and robust way of accessing and designing different pathways to the data sources.
Moreover, the covid19.analytics package offers a modular and versatile approach to the data by allowing users to input their own data for which most of the functions in the package can be applied when the data is structured using a time series format as described in the package documentation.

Functionalities and Main Features
The covid19.analytics package is an open source tool; its main goal is to offer users prompt and quick access to different data sources related to the unfolding pandemic. Its main implementation and API is the R package (Ponce, 2020) which is available on CRAN (Hornik, 2012).
In addition to this, the package has a few more add-ons: • A central GitHub repository, https://github.com/mponce0/covid19.analytics, where the latest development version and source code of the package are available. Users can also submit tickets for bugs, suggestions, or comments using the "issues" tab in this repository.
• A rendered version with live examples and documentation also hosted at GitHub pages, https://mponce0.github.io/covid19.analytics/ • A dashboard for interactive usage of the package with extended capabilities for users without any coding expertise, https://covid19analytics.scinet.utoronto.ca • A backup data repository hosted at GitHub, https://github.com/mponce0/ covid19analytics.datasets, where replicas of the live datasets are stored for redundancy and robust accessibility (see Figure 1). The package also incorporates a dashboard to facilitate access to its functionalities to less experienced users (see Figure 2). This can be accessed interactively in a local session or through the public deployed dashboard at the URL described above. Another unique feature of this package is the ability to incorporate models to estimate the disease spread by using the actual data. The covid19.analytics package allows users to model the dispersion of the disease by implementing a simple Susceptible-Infected-Recovered (SIR) model (Kermack & McKendrick, 1927;Smith et al., 2004). The package can also generate multiple SIR models by varying some of the parameters used to select the actual data to be used in the model and some additional visualizations tools.

covid19.analytics
There are several plotting and visualization functions within the covid19.analytics package, generating both static and interactive visualizations.
Further details, documentation, examples, and tutorials of the covid19.analytics package can be found on the package repository and (Ponce & Sandhel, 2020).
Users can submit bug reports and improvements requests or seek for support using the "issues tracker" tab in the GitHub repository. Contributions from users and the community are welcome and can be made using "pull requests" in the repository as well. We will continue working on adding and developing new features to the package; a glimpse and brief description about these can be found on the repository's Projects area.