How to decide between short and long tables

Datawrapper offers two different kinds of tables, and it can be tricky to know when to use which one: The Short Table or the Long Table? This article tries to help. Here's a summary:

When to use the Short Table When to use the Long Table
Good choice if all the data is important to your readers Good choice if your readers should find their own insights in the data
should have less than 20 rows (only 10 will be shown immediately) can have up to 1000 rows
you can create mini bar charts for your data and make it more comparable readers can search through your table
the data can be paginated
cozy layout: each row gets a lot of space compressed layout: we designed the table in a way to show lots of information on little space
often makes a statement
can exist in the middle of the article

doesn't make a statement
makes sense at the end of an article, to let readers look through the data themselves

Here we look at examples of good use cases for the Short Table and the Long Table in detail: 

A use case for Short Tables

Let's take some data about the pay gap in UK companies that have more than 5000 employees. There are more than 500 companies in the UK with more than 5000 employees – but not all of them are interesting to all readers. If we dig in the data, we will find that the pay gap is the highest in banking companies. That's something we can show well with a short table.

Note that we don't show all 508 companies here, but just the ones that are relevant to the readers.  We show readers what's interesting about the data – we don't give them the the full data set and say "Look for yourself". A headline can help to enforce the statement we want to make:

A use case for Long Tables

A Short Table shows what all readers will find interesting. A Long Table can show what some readers will find interesting. If we have information that is close to readers' personal lives, like company data (they might work there), sports data (they might be fan of one certain club) or city data (they might live in one of the cities/neighbourhoods in the data), then it makes sense to let readers look through the entire data set. Long tables enable readers to find themselves in the data.

Note that we give readers the option to search through the data themselves, so that they can filter for the company they work at, companies they know (like "Amazon") , or for categories of companies, like "Banks" or "University":

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