To help Datawrapper understand and correctly display your data, you need to clean it first. We recommend that you do this in your favorite spreadsheet application. However, sometimes things need to be done quickly, so this tutorial explains how you can do this in Datawrapper.
First you need to know that Datawrapper supports three different column types for text, numbers and dates. For text columns no cleaning is required, as these values are not needed to be parsed. Text columns is the fallback if number and date parsing fails.
Here’s a typical example of a dataset with different column types. As you see the date column is shown in green and the number columns are shown in blue. You probably also noticed the two cells with a light red background, these are cells that could not be parsed correctly.
The last year value could not be parsed because it also contains the text “(thru October 15)“. And the number 1740 accidentally contains a space (yes, things like this happen). As a result, the chart doesn’t look as it should. The mis-parsed number is not displayed at all, and the x-axis cannot be displayed as proper date axis.
Now to fix this you just need to click the cells twice and enter the corrected value. The operation was successful when the red background disappears.
Now the line chart looks much better. Of course now the chart is lacking the information that the data for 2013 is only recorded thru October 15. We recommend to put this in chart description.
Yesterday we released the next version of Datawrapper: 1.6. This post explains some of the new features, we hope you like them.
Inline editing of chart title, description and labels
We simplified annotation of charts by supporting inline editing of chart title, description and labels directly in the chart. The changes are stored immediately. Using the inline editing is optional as you can still use the data table or the other UI elements to achieve the same effect.
Quicker navigation through your recent charts
The My Charts menu point now opens a drop down menu of your nine most recent charts to let you navigate to them more quickly.
Rounding numbers to significant digits
Datawrapper now supports a smarter way of rounding numbers using significant digits. This is useful especially if the numbers in your dataset vary across different orders of magnitude and you want to show a constant precision. In contrast, rounding to a fixed number of decimal places has the negative side-effect of either cutting of important details or adding useless zeros.
2 decimal places
2 significant digits
Logarithmic scales in line charts
The line chart in Datawrapper is now able to show data on a logarithmic scale. You can let the user switch between log and linear scales, just as you can see in this chart. The setting will be available only for charts were it makes sense (positive values which at least differ in three orders of magnitudes).
Support for OAuth sign-in
Datawrapper now supports sign-in using the OAuth standard. This not only makes it easier for new users to sign up if they already have accounts on Twitter or GitHub, but it also showcases how, for instance, a new organization can use their own user database instead of forcing all their editors to create new accounts. To see how this is implemented please take a look at the source code of the plugin that handles the Twitter sign-in.
Here’s a brief list of other things we changed in Datawrapper 1.6.
- improved language in email communication
- enabled data attribution in visualize step
- added column oauth_signin to user table (see migrate sql)
- renamed chart data file to data.csv
- plugin.php can now install plugins from git urls
- display chart id in gallery/mycharts if there’s no title
- using [insert title here] as default chart title
- bugfix: chart height with empty titles
Additionally, as with every other release, we spent some extra time on refactoring the code base and updating the dependencies to their most recent versions.
- updated Propel ORM to 1.6.8
- updated Twig to 1.13.2
We hope you will enjoy the release. Please let us know if you find any bugs or other unexpected behavior.
With the release 1.5.3 we are introducing a simple way to feed data into Datawrapper.
Why would I do this?
For a moment, imagine that you are running a data hub, a software for collaborating on gathering, sharing and using data. Naturally you would like to see all that data being used in the wild, for instance in visualizations embedded into blogs or shared in social media. And you know that tools like Datawrapper are created for that exact purpose. But to visualize your data in Datawrapper, your users would have to go to a number of steps:
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Explaning how to use the new data engine introduced with Datawrapper 1.5.
First of all: Why the change? In previous versions of Datawrapper there was no option to change anything in the dataset once you uploaded it from a spreadsheet program. Instead, if a label, a number or data needed to be changed, you would have to go back to the spreadsheet, change there, then upload again and so forth.
No more. Since Version 1.5 (released late August 2013) you can use a versatile data editor in Datawrapper. This has two main benefits: First, small corrections and changes can be solved very quickly. Secondly, the data imported into Datawrapper can be interpreted much better by the tool, which is the foundation of better and more functional charts.
With great power comes great responsibility. So, in order to make the most of this feature, some knowledge about how to pre-format data and labels is key. Here is the tutorial to do that, it will take five minutes to read through and apply to your next chart.
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After two months and +600 commits, we are very proud to finally release the next major milestone of Datawrapper. Here’s a brief overview what has been changed:
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Seit der Aktualisierung auf die Version 1.0 im November 2012 hat die Zahl der “Visits” auf Diagramme, die mit Datawrapper erstellt und eingebettet wurden, stark zugenommen. In der letzten Maiwoche hat das ABZV-Projekt eine wichtige Marke erreicht: 10 Millionen Visits auf Datawrapper-Diagramme, allein im Jahr 2013.
Since the upgrade to version 1.0 in November 2012 traffic generated by charts created with Datawrapper is increasing on a month by month basis. So, a few days ago we crossed an important threshold: 10 Million visits to Datawrapper charts, just in 2013.
We are proud to announce our next major release of Datawrapper. This post briefly describes the new features:
Automatic time series detection
If the first column of your dataset contains valid dates, Datawrapper will now recognize them automatically. This allows us to improve the rendering of date values in axis labels, including localization of month names and date formats.
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This post describes the new time series features of Datawrapper 1.3, and especially what date formats are detected and how this affects the rendering of your charts.
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This year, Gregor Aisch attended the “International Journalism Festival 2013″ in Perugia. For one of his sessions he put together a brief tutorial showing how to successfully create a chart from data through all steps: Search, filter/clean, visualize, publish.
The example shows that Datawrapper can by now handle more complex visualizations, where selection of colors is important to get a clear message out.
Link: School of Data Tutorial