nCov2019: an R package for studying the COVID-19 coronavirus pandemic
Background The global spreading of the COVID-19 coronavirus is still a serious public health challenge. Although there are a large number of public resources that provide statistics data, tools for retrospective historical data and convenient visualization are still valuable. To provide convenient a...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2021-06-01
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| Series: | PeerJ |
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| Online Access: | https://peerj.com/articles/11421.pdf |
| _version_ | 1797425482820485120 |
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| author | Tianzhi Wu Erqiang Hu Xijin Ge Guangchuang Yu |
| author_facet | Tianzhi Wu Erqiang Hu Xijin Ge Guangchuang Yu |
| author_sort | Tianzhi Wu |
| collection | DOAJ |
| description | Background The global spreading of the COVID-19 coronavirus is still a serious public health challenge. Although there are a large number of public resources that provide statistics data, tools for retrospective historical data and convenient visualization are still valuable. To provide convenient access to data and visualization on the pandemic we developed an R package, nCov2019 (https://github.com/YuLab-SMU/nCov2019). Methods We collect stable and reliable data of COVID-19 cases from multiple authoritative and up-to-date sources, and aggregate the most recent and historical data for each country or even province. Medical progress information, including global vaccine development and therapeutics candidates, were also collected and can be directly accessed in our package. The nCov2019 package provides an R language interfaces and designed functions for data operation and presentation, a set of interfaces to fetch data subset intuitively, visualization methods, and a dashboard with no extra coding requirement for data exploration and interactive analysis. Results As of January 14, 2021, the global health crisis is still serious. The number of confirmed cases worldwide has reached 91,268,983. Following the USA, India has reached 10 million confirmed cases. Multiple peaks are observed in many countries. Under the efforts of researchers, 51 vaccines and 54 drugs are under development and 14 of these vaccines are already in the pre-clinical phase. Discussion The nCov2019 package provides detailed statistics data, visualization functions and the Shiny web application, which allows researchers to keep abreast of the latest epidemic spread overview. |
| first_indexed | 2024-03-09T08:16:47Z |
| format | Article |
| id | doaj.art-87d778b02c234a2dafdc6a4a915b1f38 |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T08:16:47Z |
| publishDate | 2021-06-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-87d778b02c234a2dafdc6a4a915b1f382023-12-02T22:01:40ZengPeerJ Inc.PeerJ2167-83592021-06-019e1142110.7717/peerj.11421nCov2019: an R package for studying the COVID-19 coronavirus pandemicTianzhi Wu0Erqiang Hu1Xijin Ge2Guangchuang Yu3Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, ChinaDepartment of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, ChinaDepartment of Mathematics and Statistics, South Dakota State University, Brookings, United StatesDepartment of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, ChinaBackground The global spreading of the COVID-19 coronavirus is still a serious public health challenge. Although there are a large number of public resources that provide statistics data, tools for retrospective historical data and convenient visualization are still valuable. To provide convenient access to data and visualization on the pandemic we developed an R package, nCov2019 (https://github.com/YuLab-SMU/nCov2019). Methods We collect stable and reliable data of COVID-19 cases from multiple authoritative and up-to-date sources, and aggregate the most recent and historical data for each country or even province. Medical progress information, including global vaccine development and therapeutics candidates, were also collected and can be directly accessed in our package. The nCov2019 package provides an R language interfaces and designed functions for data operation and presentation, a set of interfaces to fetch data subset intuitively, visualization methods, and a dashboard with no extra coding requirement for data exploration and interactive analysis. Results As of January 14, 2021, the global health crisis is still serious. The number of confirmed cases worldwide has reached 91,268,983. Following the USA, India has reached 10 million confirmed cases. Multiple peaks are observed in many countries. Under the efforts of researchers, 51 vaccines and 54 drugs are under development and 14 of these vaccines are already in the pre-clinical phase. Discussion The nCov2019 package provides detailed statistics data, visualization functions and the Shiny web application, which allows researchers to keep abreast of the latest epidemic spread overview.https://peerj.com/articles/11421.pdfR-packageVisualizationCovid-19Data-acquisition |
| spellingShingle | Tianzhi Wu Erqiang Hu Xijin Ge Guangchuang Yu nCov2019: an R package for studying the COVID-19 coronavirus pandemic PeerJ R-package Visualization Covid-19 Data-acquisition |
| title | nCov2019: an R package for studying the COVID-19 coronavirus pandemic |
| title_full | nCov2019: an R package for studying the COVID-19 coronavirus pandemic |
| title_fullStr | nCov2019: an R package for studying the COVID-19 coronavirus pandemic |
| title_full_unstemmed | nCov2019: an R package for studying the COVID-19 coronavirus pandemic |
| title_short | nCov2019: an R package for studying the COVID-19 coronavirus pandemic |
| title_sort | ncov2019 an r package for studying the covid 19 coronavirus pandemic |
| topic | R-package Visualization Covid-19 Data-acquisition |
| url | https://peerj.com/articles/11421.pdf |
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