Today we are adding a very simple and fast way to create and publish maps. Another new feature is the of Datawrapper Team, as an effective way to use this tool professionally.

There is a bit of a backstory here, stretching back to mid-2012: When we first saw Kartograph, the map library created by Gregor Aisch, we surely wanted this to become a part of  Datawrapper. We wanted to enable everyone to create a meaningful map, in short time – the same we did for basic charts.

But simplicity is hard. It took a lot of work to get this point. Gregor joined Datawrapper as the head developer in 2012. We launched Datawrapper 1.0 in November last year and kept on developing, so today we reached Datawrapper 1.7.

Usage growth for Datawrapper 2013

As a result, we saw incredible usage growth in the last twelve month. The number of “chart views” jumped to roughly 45 million in 2013, up from about 2,5 million in 2012.

Many of the leading newsrooms in the world are using this tool. We saw charts “created with Datawrapper” on websites like the Washington Post, Der Spiegel, The Guardian, Der Standard, Tagesspiegel, Neue Zürcher Zeitung and many others. Usage is worldwide, strongest in Europe, but gradually spreading to the US, Australia, New Zealand and sites in Asia, such as Nepal. 

A recent addition has been the “secret data journalism project” by the Trinity-Mirror Group, by the name of Ampp3d. They did not even call us, just started to use Datawrapper cleverly, changing the colors as needed and quite cleverly just skipping the headline/pre-text feature to have the charts nicely embbeded into their stories.

This is well beyond what we hoped for when we started this.

Here are maps…

Today we are releasing Datawrapper 1.7 with maps as the big new feature. It is labeled beta, as we will add more maps shortly and some additional features. The concept though is the same as for the charts: Datawrapper is designed to create correct, responsive, embeddable visualizations in very short time. This is our main goal: To help journalists working under deadline adding data to their stories.

Quick guide: How to create a map with Datawrapper

Next steps for maps

This is only the start. In the future we want to add more maps of important world regions and countries. One thing though: In order to have maps as part of customized layouts, there is a bit of customization work to be done. On the other side, we can now offer local maps, which would cover just one city, one region, etc. Creating those maps for individual users will be covered by costs per hour, as we did in the past for customized layouts.

Welcome Datawrapper Team

During 2013 we already offered customization services for a fee. In order to create a custom layout, add specific modules, more data input options and so on, we so far offer Datawrapper Pro. This is a fully dedicated server, where we do the service of installing and maintaining Datawrapper for just one brand. For all larger companies interested in adding capabilities for data-driven visualization this is the best and most customizable option. The price per year is €8000 Euros, with unlimited chart views, unlimited users. The only extra costs are the creation of custom maps (though five maps are already included) and specific modules created just for one client, such as workflows from web to print. These are charged based on the hours worked.

What is the difference between Datawrapper Pro und Datawrapper Team?
But not all newsrooms have such big plans. Or budgets. But would still like to use Datawrapper with customizations, maybe with an option to upgrade to Pro later.

This is why Datawrapper Team is the new entry level option for professional use of Datawrapper. Costs are €1200 Euro per year plus a one-time fee for a customized layout with multiple options at €800 Euros. The number of users is limited to five, additional users can be added at €120 per year per user. As with Datawrapper Pro we charge by the hour for other customization services, modules. Custom maps are created for €80 Euros per map, with rebates for higher numbers.

Here is a sample view on how Datawrapper Team works (The Guardian is just used as an example here). One big option is that teams can now work together in the future, share and open each other charts. This means that a growing archive of charts can be used by more widely, making updates of recurring data much simpler.




Customization services for Datawrapper will be managed by Journalism++ Cologne, the recently created local chapter of the network, with help and expertise from the growing group of data journalists and coders.

Contact us via

That’s it for 2013. This was intense, but fun. A big thanks to everyone helping us, using Datawrapper, giving advice, asking questions and caring for data.

See you again in 2014. Take care, get some rest.



Datawrapper Map with highlighted data points

Note: If you want to just test the map feature, we now have one sample dataset just for testing. Just log into Datawrapper to see how it works.

1. Find data

First of all you need a dataset with one row having geo information. These can be country names for example. Datawrapper will automatically read country names, etc. and create a choropleth map in just a few steps. Each map in Datawrapper is designed to accept many of the standard region keys. For the world and continent maps, each country can be addressed using the two and three-letter ISO key. In addition country names are also accepted, in any language supported by the map. You can even mix the keys, so if one country name is not recognized by Datawrapper you can just insert the one country code without having to change your entire dataset.

2. Upload into Datawrapper

Bildschirmfoto 2013-12-20 um 11.34.45
..and check whether Datawrapper understood everything correctly.

Tip: When you click on the upper column (A, B) you can assign a specific format to the data. The map can display both textual and numerical data.

Bildschirmfoto 2013-12-20 um 10.44.28

3. Select “Maps” in the next step

In the second panel (“Refine the chart”) you choose the base map from the list of currently available maps. Then you can select column that stores the geo IDs (map key column) as well as the column that contains the data to be shown on the map (data column). Datawrapper will automatically detect these columns for you, but in some cases you might want to adjust them to fit your dataset.
screenshot 2013-12-20 um 15.23.02


4. Change the color with one click, highlight important elements

Optionally you can also adjust the colors used in the map, or alter the way the data values are classified into the color buckets. You will notice that changing these parameters has a great effect on the overall appearance of the map (and how the story is perceived!). It’s definitely worth taking care of your choropleth maps.

Using the existing highlighting feature you can add simple labels to selected map regions. We recommend to keep it simple and just focus on the regions most important to your story. Keep in mind that maps can get too crowded with labels very easily.

Datawrapper Map with highlighted data points

That’s it. Hit publish and you are done. Let us hear your comments. 

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.

screenshot 2013-12-04 um 13.19.06



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.

screenshot 2013-12-04 um 13.41.19


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.

screenshot 2013-12-04 um 13.49.14

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.

screenshot 2013-11-01 um 16.24.23


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.

Number rounded to
2 decimal places
rounded to
2 significant digits
0.012 0.01 0.012
0.12 0.12 0.12
1.2 1.20 1.2
12 12.00 12
120 120.00 120



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.

screenshot 2013-11-01 um 16.36.07


Minor changes

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
  • refactored JavaScript and CSS out of Twig templates
  • refactored core chart javascript into /js/dw/chart.base.js

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:
Read more »

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.
Read more »

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. 

Mit fast 3,7 Millionen “Visits” auf eingebettete Diagramme allein im Mai haben wir die Marke von 10 Millionen im Jahr 2013 erreicht

Das Open Source Projekt der ABZV, das vor knapp zwei Jahren mit einem weißen Blatt Papier begann, hat damit eine neue Stufe erreicht, sowohl bei der Akzeptanz wie auch der Leistungsfähigkeit.

Die steigende Nutzung in Redaktionen ist eine wichtige Motivation für das Entwicklungsteam, jetzt an weiteren Verbesserungen zu arbeiten. Derzeit beobachten wir starkes Wachstum in zwei Bereichen: Zum einen melden sich immer mehr Medien, um möglichst rasch eine Anpassung der Diagramm-Layouts an das jeweils eigene Erscheinungsbild zu erhalten. Zum anderen werden über die kostenfreie Plattform auch viele Diagramme von einzelnen Nutzern erstellt, die diese dann in Blogs oder andere Webseiten einbinden.

Beide Nutzungsformen sind genau das, was das Team um Datawrapper erreichen wollte: Stärkung und Vereinfachung der journalistischen Nutzung verfügbarer Zahlen, im Verbund mit deutlich vereinfachten Abläufen, um ein Diagramm zu erstellen. Daher bedeutet uns die erreichte Zahl viel.

Daher auch ein großes “Danke!” an alle, die den Datawrapper schon jetzt benutzen.
screenshot 2013-05-29 um 03.11.23
Word cloud von +500 Medien-Webseiten, die Datawrapper nutzen. Die Größe der Namen steht in Bezug zur Zahl der Diagramm-Visits


Derzeit arbeiten wir an einer langen, langen Liste weiterer Module, kleiner Verbesserungen, einer noch schöneren Benutzeroberfläche, sowie natürlich auch an weiteren Diagramm-Typen.

Der Datawrapper wird in Zukunft mehr Eingabe-Optionen für Daten bieten, mehr Visualisierungs-Varianten und mehr Ausgabeoptionen, auch für Arbeitsabläufe vom Web zu Print sowie für Tablets und Mobilgeräte. Kurz: Wir wollen das bereits erfolgreiche Werkzeug weiter verbessern und die Nutzung zugleich super einfach halten. Gregor Aisch, der die Programmierung leitet und bisher fast allein stemmt, hat einen sehr exakten Arbeitsplan für das Jahr 2013 entwickelt – mit vielen Funktionen, die wir am liebsten jetzt gleich schon zur Verfügung stellen würden. Aber das geht nur Schritt für Schritt. Wenn man sich überlegt, wie klein das Team ist, dass an diesem Projekt arbeitet, ist es eine Art Wunder, wie weit wir gekommen sind.

Den Vorteil von “Open Source” verstehen

Ein Aspekt, der für uns und dieses Projekt sehr zentral ist: Viele der bisherigen Nutzer bestätigen uns, dass die “Open Source”-Verfügbarkeit des Quellcodes wichtig ist und neben den Kernfunktionen ein wichtiges Argument für den Einsatz des Datawrapper in Redaktionen ist. Daten zu sammeln, sinnvoll zu analysieren und zu publizieren ist (hoffentlich!) kein Strohfeuer, sondern eine enorme Kraftquelle für guten, vertiefenden Journalismus. Wenn also in einer Redaktion allmählich ein wachsendes Archiv aufbereiteter Zahlen vorliegt, sollte diese Sammlung auch unter voller Kontrolle der Redaktion stehen, nicht unter der Kontrolle der jeweiligen Plattform oder eines externen Anbieters. Genau dies wird durch die optionale Installation der Software auf eigenen oder selbst-kontrollierten Servern gesichert.

Als Zusatz in der deutschen Version dieser Meldung: Wir hatten von Beginn an auf eine internationale Strategie gesetzt, weil die Zahl der Journalisten, die sich bereits jetzt regelmäßig mit Daten-Analysen und Visualisierungen in den neuen Formaten und Möglichkeiten auseinander setzt, derzeit noch sehr klein ist. Dass jetzt so viele große Namen aus der Zeitungs- und Medienwelt Datawrapper nutzen, ist ein rundum positives Zeichen. Es ist einfach toll, Namen wie Le Monde, Guardian, NZZ, Washington Post, Der Standard und viele andere in der Nutzerliste zu sehen. Aber da geht noch was.

Wir sind stolz und für den Moment mal zufrieden, dass wir mit dem Werkzeug einen Beitrag zur Zukunftssicherung des Journalismus leisten. Bei der “New York Times” klatschen sich die Daten-Spezialisten nach der Publikation von Datenprojekten kurz ab und sagen (eher als Scherz): “Journalismus gerettet!”

Noch einmal: Ein vernehmliches Danke. Das macht Spass, so wie es derzeit läuft. Bitte unterstützen Sie uns auch in Zukunft, dieses Projekt für alle Journalisten weiter zu entwickeln.

Das Datawrapper-Team

(@Datawrapper bei Twitter)

  • Mirko Lorenz
  • Gregor Aisch
  • Nicolas Kayser-Bril
  • Anne-Lise Bouyer
  • Pierre Romera
  • Edouard Richard
  • Pierre Bellon

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.

With almost 3.7 million chart visits in May we crossed the mark of 10 Million in 2013

Having started from a blank sheet of paper roughly two years ago this is very exciting. The usage motivates us to make Datawrapper more mature and versatile in future releases. From what we see the drivers for the growth in usage are currently two-fold: For one we are increasingly working very closely with more and more top media brands. Secondly more and more charts are created by single users, who embed the results on their blogs or elsewhere. Both forms of use are what we wanted to achieve, so the ten million visits mark means a lot to us.

So, here is a big “Thank you!” to everyone out there using Datawrapper.


screenshot 2013-05-29 um 03.11.23
Word cloud of +500 media websites using Datawrapper in 2013, sized by chart views.

Future development

Right now we are very busy, working through a long, long list of new and extended modules, little fixes, a better user interface and more chart types. Datawrapper will provide many more options in terms of input of data, when creating charts and will have advanced output options by the end of the year. In short: We want to make use even easier while providing many more options. Gregor Aisch has developed a huge 2013 roadmap – with some features that would be so cool to have right now. But, we are progressing and given the tiny team that is working on this project, it is a small miracle that we got so far.

Understanding the value of open source

One thing that is very important to us is this: The early adopters using Datawrapper value the open source approach. Specifically the journalists get the difference and tell us that this is one big reason to work with Datawrapper. Investigating data is a long-term approach. So when a growing archive of charts and data is created, we think this should be under control of the newsroom, not by the service.

As they (jokingly) say when a big data project is finished at the New York Times: Journalism saved!

Again, thanks everyone. This is fun. Let‘s keep it rolling…

The Datawrapper Team


  • Mirko Lorenz
  • Gregor Aisch
  • Nicolas Kayser-Bril
  • Anne-Lise Bouyer
  • Pierre Romera
  • Edouard Richard
  • Pierre Bellon


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.

Read more »