README.md

In smorsink1/ncov2019: Import and Visualize COVID-19, Zika, and SARS Data

ncov2019

ncov2019 Version 1.0.0

(latest update on 2020-03-23)

ncov2019 is an R package for acquiring and visualizing data on COVID-19 and other diseases (Zika and SARS). It includes functions for scraping and cleaning data on each of these diseases, filtering disease data, and visualizing data on a map or a time-series graph. It also includes data: disease data by location for SARS, Zika, and COVID-19, country population data, and country latitude/longitude data.

Note: this package helps the user interact with reported data. Reported data may not be accurate, potentially due to reporting issues but more prominently due to issues with the availability of testing. Increases in "cases" may be due to increases in the number of infected people, but also may just be a result of more people getting tested. To more fully understand the state of COVID-19, we encourage the user to supplement the data available in this package with data on the amount of testing.

Installation

To install from R, use devtools::install_github("smorsink1/ncov2019")

Functionality

The package functionality has three main phases: importing data, filtering data, and visualizing data.

To import cleaned disease data, use either importCovidData(), importZikaData(), or importSARSData(). Each of the import functions has a from_web argument. For Zika and SARS, getting the data from the web will give the same data as loading the data that is saved in the package (either zika_data.rda or sars_data.rda), so the default is from_web = FALSE. For COVID-19, one needs from_web = TRUE to get the most up-to-date information (this is the default behavior).

To filter disease data, use filterDiseaseData(). While other filtering methods, (such as dplyr::filter(), or using base R), sometimes yield the same results, using filterDiseaseData() is safer to ensure compatibility with the rest of the functions in the package.

To visualize disease data on a map, use mapPlotStatic() to generate a plot for a single date or mapPlotAnimate() to generate an animation across many dates. To visualize disease metrics over time, use the plotTimeSeries() function with specified arguments (see documentation). The behavior of plotTimeSeries() is largely determined by the plot_what argument, which can take on values like "cases", "cases_per_pop", "log_cases", "new_cases", or "growth_factor".

Thanks to

Johns Hopkins University Center for Systems Science and Engineering for COVID-19 data, the CDC for Zika virus data, WHO for SARS data, Google for country coordinate data, and the World Bank for country population data.

More Information

For a deeper view of the functionality of ncov2019, check out the vignette.

smorsink1/ncov2019 documentation built on March 27, 2020, 7:22 p.m.