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How to work with Johns Hopkins University COVID-19 Data in MongoDB Atlas

Aaron Bassett, Joe Karlsson, Mark Smith, Maxime BeugnetPublished Feb 17, 2022 • Updated May 31, 2022
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TL;DR

Our MongoDB Cluster is running in version 4.4.1.
You can connect to it using MongoDB Compass, the Mongo Shell, SQL or any MongoDB driver supporting at least MongoDB 4.4 with the following URI:
readonly is the username and the password, they are not meant to be replaced.

Updates

December 10th, 2020
May 13th, 2020
  • Renamed the field "city" to "county" and "cities" to "counties" where appropriate. They contains the data from the column "Admin2" in JHU CSVs.
May 6th, 2020
  • The covid19 database now has 5 collections. More details in our
    README.md
    .
  • The covid19.statistics collection is renamed covid19.global_and_us for more clarity.
  • Maxime's Charts
    are now using the covid19.global_and_us collection.
  • The dataset is updated hourly so any commit done by JHU will be reflected at most one hour later in our cluster.

Table of Contents

Introduction

As the COVID-19 pandemic has swept the globe, the work of
JHU (Johns Hopkins University)
and its
COVID-19 dashboard
has become vitally important in keeping people informed about the progress of the virus in their communities, in their countries, and in the world.
JHU not only publishes their dashboard, but
they make the data powering it freely available for anyone to use
. However, their data is delivered as flat CSV files which you need to download each time to then query. We've set out to make that up-to-date data more accessible so people could build other analyses and applications directly on top of the data set.
We are now hosting a service with a frequently updated copy of the JHU data in MongoDB Atlas, our database in the cloud. This data is free for anyone to query using the MongoDB Query language and/or SQL. We also support a
variety of BI tools directly
so you can query the data with Tableau, Qlik and Excel.
With the MongoDB COVID-19 dataset there will be no more manual downloads and no more frequent format changes. With this data set, this service will deliver a consistent JSON and SQL view every day with no downstream
ETL
required.
None of the actual data is modified. It is simply structured to make it easier to query by placing it within a
MongoDB Atlas
cluster and by creating some convenient APIs.

The MongoDB Dataset

All the data we use to create the MongoDB COVID-19 dataset comes from the
JHU
dataset. In their turn, here are the sources they are using:
  • the World Health Organization,
  • the National Health Commission of the People's Republic of China,
  • the United States Centre for Disease Control,
  • the Australia Government Department of Health,
  • the European Centre for Disease Prevention and Control,
  • and many others.
Using the CSV files they provide, we are producing two different databases in our cluster.
  • covid19jhu contains the raw CSV files imported with the
    mongoimport
    tool,
  • covid19 contains the same dataset but with a clean MongoDB schema design with all the good practices we are recommending.
Here is an example of a document in the covid19 database:
The document above was obtained by joining together the file UID_ISO_FIPS_LookUp_Table.csv and the CSV files time series you can find in
this folder
.
Some fields might not exist in all the documents because they are not relevant or are just not provided by
JHU
. If you want more details, run a schema analysis with
MongoDB Compass
on the different collections available.
If you would prefer to host the data yourself, the scripts required to download and transform the JHU data are open-source. You can
view them and instructions for how to use them on our GitHub repository
.
In the covid19 database, you will find 5 collections which are detailed in our
Github repository README.md file
.
  • metadata
  • global (the data from the time series global files)
  • us_only (the data from the time series US files)
  • global_and_us (the most complete one)
  • countries_summary (same as global but countries are grouped in a single doc for each date)

Get Started

You can begin exploring the data right away without any MongoDB or programming experience using
MongoDB Charts
or
MongoDB Compass
.
In the following sections, we will also show you how to consume this dataset using the Java, Node.js and Python drivers.
We will show you how to perform the following queries in each language:
  • Retrieve the last 5 days of data for a given place,
  • Retrieve all the data for the last day,
  • Make a geospatial query to retrieve data within a certain distance of a given place.
Explore the Dataset with MongoDB Charts
With
Charts
, you can create visualisations of the data using any of the pre-built graphs and charts. You can then
arrange this into a unique dashboard
, or embed the charts in your pages or blogs.
If you want to create your own MongoDB Charts dashboard, you will need to set up your own
Free MongoDB Atlas
cluster and import the dataset in your cluster using the
import scripts
or use mongoexport & mongoimport or mongodump & mongorestore. See this section for more details:
Take a copy of the data
.
Explore the Dataset with MongoDB Compass
Compass
allows you to dig deeper into the data using the
MongoDB Query Language
or via the
Aggregation Pipeline
visual editor. Perform a range of operations on the data,
including mathematical, comparison and groupings
. Create documents that provide unique insights and interpretations. You can use the output from your pipelines as
data-sources for your Charts
.
Screencast showing some of the features of MongoDB Compass for exploring COVID-19 Data
For MongoDB Compass or your driver, you can use this connection string.
Explore the Dataset with the MongoDB Shell
Because we store the data in MongoDB, you can also access it via the
MongoDB Shell
or using
any of our drivers
. We've limited access to these collections to 'read-only'. You can find the connection strings for the shell and Compass below, as well as driver examples for
Java
,
Node.js
, and
Python
to get you started.
Accessing the Data with Java
Our Java examples are available in our
Github Repository Java folder
.
With the MongoDB Driver
Here is the main class of our Java example. Of course, you need the three POJOs from the
Java Github folder
to make this work.
Accessing the Data with Node.js
Our Node.js examples are available in our
Github Repository Node.js folder
.
With the MongoDB Driver
Check out the instructions in the
Node.js folder
.
Accessing the Data with Python
Our Python examples are available in our
Github Repository Python folder
.
With the MongoDB Driver
See all the instructions to get started in the
Python folder
.
Accessing the Data with Golang
Our Golang examples are available in our
Github Repository Golang folder
.
With the MongoDB Driver
See all the instructions to get started in the
Golang folder
.
Accessing the Data with Google Colaboratory
If you have a Google account, a great way to get started is with our
Google Colab Notebook
.
The sample code shows how to install pymongo and use it to connect to the MongoDB COVID-19 dataset. There are some example queries which show how to query the data and display it in the notebook, and the last example demonstrates how to display a chart using Pandas & Matplotlib!
How to render a chart in a Colab notebook
If you want to modify the notebook, you can take a copy by selecting "Save a copy in Drive ..." from the "File" menu, and then you'll be free to edit the copy.
Accessing the Data with Business Intelligence Tools
You can get lots of value from the dataset without any programming at all. We've enabled the
Atlas BI Connector
, which exposes an SQL interface to MongoDB's document structure. This means you can use data analysis and dashboarding tools like
Tableau
,
Qlik Sense
, and even
MySQL Workbench
to analyze, visualise and extract understanding from the data.
Here's an example of a visualisation produced in a few clicks with Tableau:
COVID-19 World map in Tableau.
Tableau is a powerful data visualisation and dashboard tool, and can be connected to our COVID-19 data in a few steps. We've written a
short tutorial
to get you up and running.
Accessing the Data with any SQL tool
As mentioned above, the
Atlas BI Connector
is activated so you can connect any SQL tool to this cluster using the following connection information:
  • Server: covid-19-biconnector.hip2i.mongodb.net,
  • Port: 27015,
  • Database: covid19 or covid19jhu (depends which version of the dataset you want to see),
  • Username: readonly or readonly?source=admin,
  • Password: readonly.
Take a copy of the data
Accessing our copy of this data in a read-only database is useful, but it won't be enough if you want to integrate it with other data within a single MongoDB cluster. You can obtain a copy of the database, either to use offline using a different tool outside of MongoDB, or to load into your own MongoDB instance. mongoexport is a command-line tool that produces a
JSONL
or CSV export of data stored in a MongoDB instance. First, follow these
instructions to install the MongoDB Database Tools
.
Now you can run the following in your console to download the metadata and global_and_us collections as jsonl files in your current directory:
Use the --jsonArray option if you prefer to work with a JSON array rather than a
JSONL
file.
Documentation for all the features of mongoexport is available on the
MongoDB website
and with the command mongoexport --help.
Once you have the data on your computer, you can use it directly with local tools, or load it into your own MongoDB instance using
mongoimport
.
Note that you cannot run these commands against our cluster because the user we gave you (readonly:readonly) doesn't have write permission on this cluster.
Read our
Getting Your Free MongoDB Atlas Cluster blog post
if you want to know more.
Another smart way to duplicate the dataset in your own cluster would be to use mongodump and mongorestore. Apart from being more efficient, it will also grab the indexes definition along with the data.

Wrap up

We see the value and importance of making this data as readily available to everyone as possible, so we're not stopping here. Over the coming days, we'll be adding a GraphQL and REST API, as well as making the data available within Excel and Google Sheets.
We've also launched an
Atlas credits program
for anyone working on detecting, understanding, and stopping the spread of COVID-19.
If you are having any problems accessing the data or have other data sets you would like to host please contact us on
the MongoDB community
. We would also love to showcase any services you build on top of this data set. Finally please send in PRs for any code changes you would like to make to the examples.
You can also reach out to the authors directly (
Aaron Bassett
,
Joe Karlsson
,
Mark Smith
, and
Maxime Beugnet
) on Twitter.

Sources


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