coronadatascraper

A scraper that pulls coronavirus case data from verified sources.

Where's the data?

http://blog.lazd.net/coronadatascraper/

Running the scraper

First, fork the repository so you're ready to contribute back.

Before following these instructions, install yarn.

1. Clone, init submodules, and add upstream

Replace yourusername below with your Github username:

git clone --recursive git@github.com:yourusername/coronadatascraper.git
cd coronadatascraper
git remote add upstream git@github.com:lazd/coronadatascraper.git

If you've already cloned without --recrusive, run:

git submodule init
git submodule update

2. Install dependencies

yarn install

3. Run the scraper

yarn start

4. Pull from upstream often

This gets you the latest scrapers, as well as the cache so we're not hammering servers.

git pull upstream master --recurse-submodules

Note: If you are encountering issues updating a submodule such as Could not access submodule, you may need to update your fork using:

git submodule update --init --recursive

Re-generating old data

To re-generate old data from cache (or timeseries), run:

yarn start --date=2020-3-12

Generating timeseries data

To generate timeseries data in dist/timeseries*.*, run:

yarn timeseries

Building the website

To build the website and all data into dist/:

yarn build

Contributing

Contributions for any place in the world are welcome. See the community-curated list of verified data sources to find a new datasource to add, and be sure to update the "Scraped?" column when you do.

Write clean and clear code, and please ensure to follow the criteria below for sources. Send a pull request with your scraper, and be sure to run the scraper first with the instructions above to make sure the data is valid.

Writing a scraper

Scrapers can pull JSON, CSV, or good ol' HTML down and are written in a sort of modular way, with a handful of helpers available to clean up the data. Scrapers can pull in data for anything -- cities, counties, states, countries, or collections thereof. See the existing scrapers for ideas on how to deal with different ways of data being presented.

Start by opening up scrapers.js and adding a new object at the top of the array.

Make sure you have the following properties:

• url - The source of the data
• scraper - An async function that scrapes data and returns objects, defined below

Add the following directly to the scraper object if the data you're pulling in is specific to a given location:

• city - The city name
• county - The county or parish
• state - The state, province, or region
• country - ISO 3166-1 alpha-3 country code

Your scraper should return a data object, or an array of objects, with some of the following information:

• city - The city name (not required if defined on scraper object)
• county - The county or parish (not required if defined on scraper object)
• state - The state, province, or region (not required if defined on scraper object)
• country - ISO 316 6-1 alpha-3 country code
• cases - Total number of cases
• deaths - Total number of deaths
• recovered - Total number recovered
• tested - Total number tested
• population - The estimated population of the location
• coordinates - Array of coordinates [longitude, latitude]

Everything defined on the scraper object except the scraper function and properties that start with _ will be added to the objects returned by your scraper.

Sample scraper

Here's the scraper for Indiana that gets data from a CSV:

  {
    url: 'https://opendata.arcgis.com/datasets/d14de7e28b0448ab82eb36d6f25b1ea1_0.csv',
    country: 'USA',
    state: 'IN',
    scraper: async function() {
      let data = await fetch.csv(this.url);

      let counties = [];
      for (let county of data) {
        counties.push({
          county: parse.string(county.COUNTYNAME) + ' County',
          cases: parse.number(county.Total_Positive),
          deaths: parse.number(county.Total_Deaths),
          tested: parse.number(county.Total_Tested)
        });
      }

      return counties;
    }
  },

You can see that country and state are already defined on the object, and all the scraper has to do is pull down the CSV and return an array of objects.

Here's the scraper for Oregon that pulls data from a HTML table:

  {
    state: 'OR',
    country: 'USA',
    url: 'https://www.oregon.gov/oha/PH/DISEASESCONDITIONS/DISEASESAZ/Pages/emerging-respiratory-infections.aspx',
    scraper: async function() {
      let counties = [];
      let $ = await fetch.page(this.url);

      let $table = $('table[summary="Cases by County in Oregon for COVID-19"]');

      let $trs = $table.find('tbody > tr:not(:first-child):not(:last-child)');

      $trs.each((index, tr) => {
        let $tr = $(tr);
        let county = parse.string($tr.find('td:first-child').text()) + ' County';
        let cases = parse.number($tr.find('td:nth-child(2)').text());
        counties.push({
          county: county,
          cases: cases
        });
      });

      return counties;
    }
  },

It first finds the table with the [summary] attribute, then iterates over each of the rows extracting county names and cases (skipping the first and last rows), and finally, returns an array of objects.

Library functions

See library functions for API of the available library/utility functions you can use in your scraper.

Of course, if something is missing, yarn add it as a dependency and import it!

Making sure your scraper doesn't break

It's a tough challenge to write scrapers that will work when websites are inevitably updated. Here are some tips:

• Write your scraper so it handles aggregate data with a single scraper entry (i.e. find a table, process the table)
• Try not to hardcode county or city names, instead let the data on the page populate that
• Try to make your scraper less brittle by generated class names (i.e. CSS modules)
• When targeting elements, don't assume order will be the same (i.e. if there are multiple .count elements, don't assume the second one is deaths, verify it by parsing the label)

Generating data retroactively

If your datasource has timeseries data, you can include its data in retroactive regeneration (prior to this project's inception) by checking for process.env['SCRAPE_DATE']. This date is your target date; get it in whatever format you need, and only return results from your timeseries dataset from that date. See the JHU scraper for an example.

What to do if a scraper breaks?

Scrapers need to be able to operate correctly on old data, so updates to scrapers must be backwards compatible. If you know the date the site broke, you can have two implementations (or more) of a scraper in the same function:

{
    state: 'LA',
    country: 'USA',
    scraper: async function() {
      let counties = [];
      if (datetime.scrapeDateIsBefore('2020-3-14')) {
        // Use the old table
        this.url = 'http://ldh.la.gov/Coronavirus/';

        let $ = await fetch.page(this.url);

        let $table = $('p:contains("Louisiana Cases")')
                      .nextAll('table')
                      .find('tbody > tr:not(:last-child)');

        $trs.each((index, tr) => {
          counties.push(...);
        });
      }
      else {
        // Use the new CSV file
        this.url = 'https://opendata.arcgis.com/datasets/cba425c2e5b8421c88827dc0ec8c663b_0.csv';

        let data = await fetch.csv(this.url);

        for (let county of data) {
          counties.push(...);
        }
      }

      // Add state data
      counties.push(transform.sumData(counties));

      return counties;
    }
  },

As you can see, you can change this.url within your function (but be sure to set it every time).

You can also use datetime.scrapeDateIsAfter() for more complex customization.

Criteria for sources

Any source added to the scraper must meet the following criteria:

1. Sources must be government or health organizations

No news articles, no aggregated sources.

2. Sources must provide the number of cases at a bare minimum

Additional data is welcome.

3. Presumptive cases are considered confirmed

In keeping with other datasets, presumptive cases should be considered part of the case total.

License

This project is licensed under the permissive BSD 2-clause license.

The data produced by this project is public domain.

Attribution

Please cite this project if you use it in your visualization or reporting.