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COVID-19/2019-nCoV Time Series Infection Data Warehouse

简体中文 | English

COVID-19/2019-nCoV time series infection data warehouse, the data source is Ding Xiang Yuan.

Researchers
Recently, many college teachers and students contacted me, hoping to use these data for scientific research. However, not everyone is familiar with the use of APIs and the format of JSON, so here is the data warehouse to publish the latest data in CSV format, which can be easily processed and loaded by most software.

The data is obtained by COVID-19 Infection Data Realtime Crawler.

At 0:00 every day, the program will execute on time and the data will be pushed into Release.

Due to the limitation of the server's bandwidth, starting from March 19, 2020, /nCoV/api/overall and /nCoV/api/area do not response time-series data. You can fetch time-series data in json folder. If you call the API with latest=0, please modify the request parameters, otherwise, you do not need to do any modification.

Data customization is not accepted. If you have more requirements for data, please handle it on your own.

Data Description

  1. As mentioned in Issue #21, Some data are duplicated. For example, in Henan Province, there is a city-level document recording Nanyang (Dengzhou inclusive) and Dengzhou. Therefore, the data of "Dengzhou" will be double-counted once during the summation.

Noise Data

  1. At present, some time series data in Zhejiang and Hubei are found containing noises. The possible reason is the manually processed data were recorded by mistake.
  2. Issue #110 reported the reversal of the number of confirmed cases in Changchun and Jilin City in Jilin Province, updated on March 15 by Dingxiang Yuan. To ensure data integrity, I have not modified the data, so please adjust it manually when you use it if considered necessary.

The crawler just crawl what it sees, do not deal with any noise data. Therefore, if you use the data for scientific research, please preprocess and clean the data properly.

In the meantime, I opened an issue for you to report the potential noise data. I will check and remove them periodically.

Reference

Packages

  1. If you would like to analyze the data with R, you can refer to pzhaonet/ncovr. This project will help you to directly load data into R from either GitHub Data Warehouse or API.

Analysis

  1. jianxu305/nCov2019_analysis
    Features: See here.
  2. lyupin/Visualize-DXY-2019-nCov-Data
    Features: See here.
  3. Avens666/COVID-19-2019-nCoV-Infection-Data-cleaning-
    Features: See here

Wish you all the best.