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Johns Hopkins University CSSE released the data set that powers their dashboard on GitHub (https://github.com/CSSEGISandData/COVID-19). If you want to work with that data easily, I created the attached workflow and macro to import the daily data found within that repository. Install the package to the root of the folder that Git creates. This will import the daily data, parse the date fields that change formats halfway through the timeseries, update null latitude and longitude fields, and other general cleansing. With that done, you can experiment with daily worldwide COVID-19 confirmed/deaths/recovered case numbers at the country/region and province/state level, with geocoding available for about 99% of records.
If this workflow is useful, please let us know. If you need help or have improvements to the workflow, please share.
We'd also love for you to share what you create or discover by replying to this thread!
EDIT: The workflow has been updated to better clean and regularize the data. A lot of clean up is being now to country and state names, with merging of duplicates being done, and a locality field being parsed out of values such as "Chicago, IL". Review the new workflow for details. This should improve the quality of the output data significantly, although JHU is still working on upstream issues on their end.
I used what you linked to before and amended it. It worked ok I think. I may have needed to tweak a couple of things. I was using it to help with an Alteryx trial, which has now expired, so no need for it any longer. Very nice tool which was easy to use and perfect for the Covid data kind of task. Given the trial has expired I am now evaluating the Excel-based options to compare, given they are free to use if you already have Office365.
Great!!! If you allow me to add a comment, from a reporting perspective I think Excel and Alteryx may have very similar capabilities (vlookups, Joins, transpose, crosstab, etc) and this works fine, but in my personal experience we found some deficiency while using Excel when using large files, or when having different Files Types because we need to convert everything to an Excel "Type" format so we can cross reference Datasets and finally for the Data accuracy in Alteryx it is easy to identify visually how the data is coming form the source, like leading or trailing spaces, null values, blanks, unique values, etc.