Recently there is often a small partner asked where in the background can get the latest outbreak data or historical data analysis to do, then this article will put together a few ideas to provide data of the epidemic, with a look!
1.GitHub
As a good developer, in the face of challenges of course, to learn to look for ideas GitHub, just search on GitHub epidemic data will be able to see God do a lot of big reptiles epidemic data
Some even project will provide data interface ( https://lab.isaaclin.cn/nCoV/ ), call the port to follow instructions. Note that the request frequency oh
- Advantages: easy, timely updates, two lines of code to retrieve data
- Disadvantages: not stable enough, may be linked, while the data to be re-taken for analysis before washing
2.AkShare
AkShare Python is an open source data interface library, the purpose is to achieve the futures, options, funds and other financial derivative products and alternative data from the data collection, data cleaning and processing, data download tools to meet the financial data scientists based on data science lovers data acquisition demands respect. Then the provision of financial data library is also collected and Netease lilac garden outbreak data available to everyone
It is also very simple to use
import akshare as ak
epidemic_hist_all_df = ak.epidemic_hist_all() #获取全部历史数据
- Advantages: Easy to use, two lines of code to extract data, analysis data can be used directly
- Disadvantages: somewhat cumbersome installation, the speed in general
3.R package: nCov2019
The R package is released hit the corner of Dr R language package called nCoV2019 on Github, you can quickly get this epidemic relevant statistical data. In the R line of code can be installed.
remotes::install_github("GuangchuangYu/nCov2019")Data acquisition is also very simple, just use x [], the country will be able to get the data, if [] to add the name of the province of the province will be able to obtain the latest data. Of course, also supports historical data and global data query (using a similar method).
Even three lines of command will be able to draw the map of the global epidemic, load data packet ➡️ take ➡️ white paint can easily draw the epidemic map
require(nCov2019)
x = get_nCov2019()
plot(x, region='china', chinamap=cn)- Advantages: Easy to use, two lines of code to extract data, analysis data can be used directly
- Disadvantages: the R base language, somewhat cumbersome to install
4. bears will
Annual Chinese R language conference organizers, the epidemic will publish detailed data the previous day's day, ultra-lazy mode you can click to download
https://mp.weixin.qq.com/s/LCpKhkWWDC01ifi2GKxVbw
- Pros: Easy to use, download, organize data has been used to analyze directly
- Disadvantages: low update frequency, manually maintained, updated once a day
5. The new crown pneumonia epidemic analysis platform
If you need immediate epidemic interactive data visualization, you can choose the new crown pneumonia epidemic analysis platform. By the South China Center for Statistical Research team development, South China Center for Scientific Research Team statistics collected by domestic and foreign new virus outbreaks crown data from multi-perspective analysis conducted epidemic, the use of R + Shiny has developed a new crown pneumonia virus outbreaks in real-time interaction analysis platform. Currently open features include basic information, risk assessment, case analysis and international situation.
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