Vizzing for Social Good

Last week we finally had our first Viz For Social Good Amsterdam local chapter meetup and it was, well at least I though, fantastic! The positive energy, feedback and the crowd that lingered behind makes me believe that I was not the only person that felt this way.

The event:

The event was wonderful, if I do say so myself. This was the first local chapter installment of Viz for Social Good in Amsterdam and was run in conjunction with Data Plus Women NL. We ran a mini hackathon, which also included a training session for those who are new to Tableau.

We started with an introduction to the Viz for Social Good project as a whole, Chloe’s inspiration and how this community project has grown from a one woman operation to an award winning venture with its own board and chapter leaders in APAC, EMEA and North America, all centered around people harnessing the power of data visualization for social change. We then introduced the charity and explained how to take part in the project, all outlined here, if you are interested.

The charity we worked with for this small hackathon, was Kiron, a Berlin based NGO that helps refugees get a better education, with the aim of making them more ready for the job market and increasing integration (click here to find out more about Kiron).

The one lesson that stood out to me from the last mini hackathons, was that, while we encourage people with all levels of data viz skills (and all tools) to come along and take part, sometime, we forget that, that range of level of skill also means, there will be some attendees, who are new to Tableau. As a data community advocate, I decided, it was time to do something about this, and so we would also have a training session.

The training:

I happened to mention in our Data Plus Women planning, that I would like to have a Tableau training session as part of this hackathon. Not realizing that the angel that is Rachel Costa, would come and completely blow all of my expectations out of the water. I was thinking, quick demo, show them how to make a few charts, but Rachel, she had bigger and better plans.

Rachel used the Kiron data, to create a nice simple data viz. She then used the hour or so that we had for the hackathon, to walk our three newbies to Tableau, how to make each chart and bring them together into a dashboard. I love this, because, well at least I hope, while those not so new at Tableau were able to chat and throw ideas around, those that were new, also were able to come out of the session with a real tangible result. I am so warmed by the thought that this event really was able to provide a platform for anyone at any level of data viz, to take part in a project like Viz for Social Good and have everyone feel like they can be involved and useful.

My thoughts:

While what we do to help different NGOs can be a game changer for the NGO, what I found really important here was the community fostering. Some people can make fantastic data visualization with what seems like very little effort and no pushing. In our session the skill levels ranged from Alteryx Ace and Iron Viz finalists all the way to data viz newbies. I am not one of those people that find it so easy to create a stunning viz and so I find it very important to surround myself with driven people, who I can learn from, who encourage me and push me to be better at what I do. By bringing all these people and skill levels together, by helping people find this community of like minds, I hope we will inspire more people to want to help out our Viz For Social Good cause.

If I’m honest, the excitement and collaboration of the data viz community was something I have been missing since moving to The Netherlands (aside from when I went to the Tableau Conference Europe). This event, is the first time that I felt this community, this #datafam feeling here in Amsterdam, and I really hope that this is something we can grow!

My advice for future VfSG social vizzing session holders:

1. Relax, you’re doing something awesome, for an institution that helps people less fortunate and for a community of eager data visualizers. Nothing needs to be perfect, having a good time and making people feel welcome, engaged. Sparking an interest is what matters here.

2. If you can have a hands on training session, I would highly recommend it!

3. If it’s your first session, keep the event small. 20 people or less. And keep the setting informal.

4. Reach out. Any one of your VfSG board members and local chapter leaders will be more than happy to lend a helping hand, or moral support or answer any questions you may have.

Closing thoughts:

This event would not have been possible without the fantastic team I work with at Data Plus Women NL. I could not organize this without Emma Pavan, and Rachel Costa completely transformed the event with her Tableau training session. Maryse Monen did a great job of getting the event seen on twitter. Our ground crew Jess, Aline and Chris did a great job.

I hope that with this mini hackathon, we got a few more people on board on the #VizForSocialGood project and I hope to see some submissions pop up on twitter soon. The deadline for the Kiron project is not until July the 31st, so we have plenty of time to continue to help out with this worthy cause!


For last week’s #MakeoverMonday the topic was land used for the production a single gram of protein for different food types. While my mission to try a new Tableau feature continues, I chose transparent charts this week, which, at least I think, is befitting to the topic, as this data visualization aims to create some transparency in what the real cost of our food is.

The environment is a very prevalent issue on my mind. Admittedly I’m not the biggest advocate, but I try to do my part. It is for this reason that I found this topic so interesting. But of course, the fact that over 68% of land usage goes to the rearing of cattle, lead me to other questions, such as, what is the usage of other resources, like water, and how does this relate to what we actually pay for our produce?

What I saw, by comparing a few different data set, all from, is that, of the food produces measured, beef has the lowest value for money. It also uses the highest amount of land for the production of one single gram of protein. In addition to this, it has one of the highest usage of water for the production of that one gram of protein, this probably also has to do with the irrigation of the fields that the cows graze on. So, not only does beef cost the most from our wallets, it also has an extremely high cost to the planet. For the meat lovers, pork and chicken are more sustainable, while fruit and vegetables may use up quite a bit more water, all in all, they are a more earth friendly option.

Transparency – of course, I chose the simplest of simple new Tableau tricks for this week. And once I figured out where to click, it really was super simple. It’s just a matter of selecting the chart you’d like to make transparent, right licking, selecting the format options from the data pane, selecting the pain bucket and changing the worksheet colour to ‘None’. And now you have a chart with your image as the background!


And of course, here is the viz if you would like to explore. Clicking on the image will take you to the Tableau Public Link.

Screen Shot 2018-12-16 at 22.36.50

Happy Holidays!

Testing… Testing … 1 2 3 …

So, it’s been quite some time since my last blog post and A LOT has changed!

In March I finished my journey through the Data School, and my my, what a journey it was. I can’t even explain how much I learned about data viz and about myself. If ever you feel like you want a change in your life or to grow into data viz, I can not recommend joining The Data School at The Information Lab enough.

I’ve since moved back to The Netherlands after 12 years and am crazy happy to be near my family again. I’ve started a position at adidas and I’m loving it, except that they have decided to drop Tableau.

So, I’m on a mission to keep on top of my Tableau game, which, quite frankly is lacking right now. The plan, is to help organize the local chapters of Viz for Social Good and Tiny Tableau Talks in Amsterdam. On the smaller scale and to keep learning, I’ll try to take part in Makeover Monday more, so I can attempt to keep up with all the great new features Tableau has and to try to build charts I haven’t made before.

To kick off the new regime, here is the MM I did last week. Since I haven’t been able to use the new features in Tableau much, here is my first go at a stepped line chart 🙂



Just the thought of spiders, fills me with fear. The same can be said when I was at client site and I was asked to make a spider chart. It’s one of those charts I thought, yes it looks kinda cool, I don’t personally find it the best way to represent data and really, is it really worth the effort?

Well, this time I just had to do it. And you know what, I actually really enjoyed the challenge!

And a challenge it was. I know a few people have blogged about how to make a spider/radar chart or other round kinds of charts, but because I knew it wasn’t going to be easy, my mind went on a serious mental block and I couldn’t even read the posts. Eventually after going through the process in baby steps, I got the gist of it. If you are anything like me, this may be helpful. I’m going to break my process down into baby steps if you want to follow along.

The Data

I can’t seem to upload files here, so here is a screen shot of the sample data I used to create the Spider:

the samples data

The Data Prep/Alteryx Part

steps 1 to 8

1. The data is split into streams. The ‘current’ (apples sold) and the ‘ambition’ (target number) scores.

2. The first row (in both cases) is replicated and repeated at the end of the data set in order to form a full circle.

3. Create a path ID with will give direction to the lines between the points for each set of scores.

4. Name these sets of scores! I called them ‘current’ and ‘ambition’.

5. Bring both sets of scores back together using a union.

6. Find out what the highest score and the highest path number is.
The highest score will be used to draw the outline of the web and the highest path number is used later to calculate which values will be positive and which will be negative (because it’s a circular format, we will need negative numbers to make a full circle).

7. Append the highest score and the highest path number to the rest of the data as new fields.

8. Because I decided to make an outline for my chart, we now have to generate an extra set of rows (to one of the data streams, I chose the ‘current’ steam). This just creates a duplicate of what you already have, with an indication of if it’s the first or the second row.

steps 9 on

9. Sort these just to make life a little easier to follow.

10. Name and define the radar outline.
Since we have created a second lot of rows for the outline,
IF row count = 2 THEN Call it radar outline
and IF row count = 2 THEN fill in the max number for all values
(please see the formulae in the workflow)

11. Bring the ‘current’, ‘radar outline’ and the ‘ambition’ streams all together.

12. Create a copy of the value that you want to plot. Save it for later (this is really just so you can use them as labels in Tableau).

13. OK, this is where it started to bend my brain. Bring on the math.

To create the circluar effect, the first thing we need to do is make the latter half of the data negative. Luckily we we already have the max path so we can just use these  formulae to update the ‘value’:

IF [Path] > ([Max_Path]/2)
THEN -[Value]
ELSE [Value]
IF [Path] = [Max_Path]
THEN -[Value]
ELSE [Value]

14. Next we have to work out angles

– Work out the number of dimensions:

– Break the circle into segments:
360/[Number of Dimensions]

– Angles through the circle:

– Work out the radians (for some reason this just works better for calculating the SIN and COS than degrees)
[Angle Through Circle] * (PI()/180)

15. Now we have to calculate the adjustment for the X and Y coordinates and then apply this adjustment:

X adjustment: SIN([Radians])
Y adjustment: COS([Radians])

And then multiply X and Y by those adjustments. These are the values you will be plotting.

Again, unfortunately I can’t seem to upload the Alteryx file, but if you need it, feel free to  leave a comment or tweet me @amanda_patist.

The Tableau Part


The tableau bit isn’t hard, you just need to know what the tricks are. This frustrated me to no end.

The dotsCreating the dots: 

  1. plot the avgerage x and y.
  2. Bring day to detail and status to colour.

the lines

Creating the lines: 

  1. Duplicate your Y axis.
  2. Move path to the dimensions.
  3. Change the marks to a line.
  4. Bring Path to the line path.
  5. Remove day from detail if it’s there.
  6. Dual and synchronise the Y axis.

Now you can format to your heart’s content and then… TADAAAA…

Finished product

Feel free to have a peak at the workbook here:

blog 2



Social Good

Putting the ‘Social’ in #VizforSocialGood

cropped and filtered

A little while ago, I gave a talk at the Tiny Tableau Talks organised by Emma Kosh. Here I had the opportunity to share with other Tableau users my appreciation for the Tableau community and the projects that I have taken part in that mean the most to me. I talked about Chloe Tseng’s #VizforSocialGood project and  the Tableau Public #VizforSocialGood vs. Hacking Open Data Initiative hackathon, for which I was the the lead of Team Climate Change.

I was overwhelmed by the positive feedback I received and the questions and interest around the Data School and the #VizforSocialGood project. By far the best question was from Alina Cristea, who essentially asked me, if people meet up to work on the project together. My answer to this was, ‘not yet, but we most definitely should!’

So I chatted to Chloe and set about planning an evening where people could get together and put the ‘Social’ into #VizforsocialGood. And what a success it was! A team of attendees of the Tableau User Group and members of the Data School came together on a Tuesday evening to put their vizzing skills to good use, helping a London based project called May Project Gardens. The project  aims to educate and empower young people to be healthy, entrepreneurial and grow their communities. An idea I can fully get behind! Our part was to visualise survey data on how participants felt about the programs they put on.

Here is the viz I submitted for the #VizforSoacialGood project: #VfSG MPG.png

Huge, HUGE thank you to Tom Brown and The Information Lab for hosting and for sponsoring out pizza! It wouldn’t be any kind of Tableau get together without pizza!

Also thank you to Tableau Public, who gave us some freebies… which I forgot and will bring to the next event…. Fingers crossed there will be another session, that people enjoyed the social vizzing and the vizzing to help as much as I did.

And just before I say goodbye, check out some of the other fantastic entries by my fellow Data Schoolers (click the thumbnails for links to the vizzes):

Anna Noble:


Pablo Saenz de Tejada:


Nicco Cirone:


In case you’d like any more information on the things I’ve covered:

Bump Charts

How to make a bump chart in Tableau (with a side of politics)


Finished product


Recently the Netherlands held their general elections. This was a tense time of year for me, having watched the UK vote to leave the EU and seeing Trump become president. Maybe I’m a bit of a hippie or an idealist, but I am not very much a fan of the current popular xenophobic opinions. Sure, the world is going through some uncertain times, but wouldn’t it be better if we just worked together?

I thought I’d have a look to see how the political climate has changed in The Netherlands over time. The results I found still a little alarming, the, what some may call the “racist” PVV party, has gone up in rankings, but luckily, has not reached the top!

Enough of that, it’s time to viz. So what I wanted to do with my viz, was see how each of the political parties ranked over the years. I got my data from the almighty Wikipedia. They have a page for each election year, showing how many seats each party has won. I copied and pasted these into an excel file. Simples!

Now to make the bump chart:

  1. Bring the measure (or dimensions) you want to splits your ranks by (in my case the year) to columns as a discrete field.
  2. Bring the field containing the members you want to rank to colours. In my case this is the ‘Party’.

steps 1 and 2

  1. To apply the ranking, you will need to create a calculated field for the measure you want to rank. To do this I’ve used the Rank Unique function.


Step 3

  1. Bring this new rank field to the rows shelf. This will look a little messy to start with.
  2. You now need to change the ‘Compute Using’ to your dimension of choice, which for me is ‘Party’. In case you aren’t sure how to do this, click on the triangle on the right-hand side of your rank pill and select, compute using, then your field.

step 4

  1. Almost there! To create the “bumps” you will need to make this a dual axis chart and change one of your marks to a circle. Done!


step 6

  1. Format etc. to your liking!

See the final viz here!

#VizForSocialGood: UNICEF Refugee Crisis

I wish I could be the person who can actually fix the world, but I’m not. I’m not a world leader and my voice is not the loudest. What I can do, is make vizzes. I can play my tiny part by helping to inform people on the issues that affect our world.

It was my personal New Year’s resolution to make more vizzes on issues that are important to me and have more meaning. Mostly really so I can learn more about them. But as luck would have it I stumbled upon a post on twitter from @TableauPublic encouraging people to join the conversation on what their #DataResolutions were. So naturally I joined the conversation and almost instantly got contacted by @datachloe who told me about her #VizForSocialGood project (find out more at!

The first project I participated in was on a data set provided by UNICEF, describing the flow of refugees from 2005 to 2015. Here is my attempt at putting together a viz about it.

dashboard-imageClicking on the image will take you to the viz.

I hope to continue contributing to this project while getting better at finding and telling stories with data and most of all opening eyes to issues that affect our world, our planet and our people.

Blue vs. Green

Understanding How Tableau Thinks: Discrete vs. Continuous Data


This week I’m doing my very first Tableau training session. In explaining how Tableau works, or how it thinks, which is important for really leveraging the power of the program, one must understand, the difference between the blue and the green pills. This blog is also taken from my data school days and will be very much like Tom Brown’s blog post, but here goes my interpretation…

When you look at your Tableau Desktop window, you will notice that your data has been split into ‘fields’ and sit in either a ‘dimensions’, ie. what you are measuring, or ‘measures’ ie. the measurement that was made, shelf, in the data pane to the left. These fields are either blue or green and when brought into the chart building view, they are called pills (because they look like pills) and retain their blue or green colour.

The colours actually represent the type of data in the field. The blue pills contain discrete data, and the green pills contain continuous data. The blue discrete field contains a finite number of values, so in the example there are only a finite number of product categories. The green continuous fields contain an infinite number of values ie.  the number of sales could be infinite.  

A field being discrete or continuous, impacts every aspect of functionality in the analysis, from the way the data is displayed, to the behind the scenes processing of the data and understanding how this differs is essential to understanding how Tableau works. I’ll explain these differences below, using the Superstore Sales Sample data that you get with Tableau Desktop. I will use them in columns and rows to build a chart, in filters to streamline data and in colour, to add levels of detail or highlights to your presentation.

  • Columns and rows


  • When a discrete field is added to a column or row, it will be displayed as a ‘header’, something that is used to divide your data.
  • If you do the same with a continuous field, an axis is drawn which will give you an aggregation of the field you have selected for the entire data set. For examples, if you are looking at the Superstore Sales example data set, provided by Tableau, and you have put sum of ‘sales’ on the column or row shelf, you will be given the sum of all the sales in the entire data set ie. over x amount of years, for x amount of product types, in one column or row, respectively.
  • Now by using both, you bring in your continuous data, and can start breaking this down by the categories set by the discrete data, for example, breaking down the sum of sales, by the year or (sub)category of items sold.

Pro tip: You can sort your data really easily by pressing the little  button on the axis  you want to sort by.

Knowing this, you can make almost any variation of chart in tableau. Bringing multiple discrete fields into the view, gives you a nested division of the discrete fields. For example if you bring in category and subcategory.


Bringing in more than one continuous field, gives multiple axes for the same discrete fields. For example if I bring in both sales and profit for my product category, I will now essentially have to mini graphs.


You can make a scatter plot, by plotting two continuous fields against each other, making two axes.


Alternatively you can make a table, by placing your discrete fields on the columns and shelves to create headers and placing a continuous field on the details on the marks card to flesh the table out.


  • Filters

What happens when you put discrete fields and continuous fields on filters, again, really shows what the difference between discrete and continuous data is.

  • Placing a discrete field on filters will bring up a dialog box, which allows you to choose ‘members’ of the discrete fields.
  • When placing a continuous field on the filter shelf, you first have to give an indication of whether or not you want your data aggregated and if so, how. You are then prompted to select a range from your continuous data.
  • Colour

Once you have created a chart, you can use colour to show another layer of information.

  • A discrete field will essentially put a grouping on by colouring members of that group, or break down a bar into subcategories, depending on what granularity you colour by. In the example, of sales by subcategory, if you bring category to the colours shelf, all subcategories within one category will be given one colour.


  • Bringing a continuous field to the colours shelf, will give you a divergent colour scale for that particular field. For example, if you were to bring profit to the same view, you then get the sales bars coloured by a profit scale, with the least profitable in red and the most profitable in green. These colours can of course be changed!


Hopefully that has cleared up the distinction between the blue and the green things in Tableau!

Best Practices

Best Practices in Data Visualisation:  A Review of ‘Storytelling with Data’ and my Before and After.

One of the biggest mistakes I made when applying for my position at the data school (well at least in my opinion) was that I made my visualisations far too complicated. I fell into that trap that I think all Tableau newbies fall into. I was so impressed with all the things that I could do with Tableau, that I just wanted to cram in everything I had done into one dashboard.

The first week in the data school was all about best practices in data visualisation and one of the books that I read was ‘Storytelling with Data – a data visualization guide for business professionals’, written by Cole Nussbaumer Knaflic. Here is a review of that book:

Storytelling with Data: A book review

‘Storytelling with Data’ is a great book for anyone who is just starting their data visualization journey. The basic gist of the book, is to keep your visualisations simple and to the point, a message that has now been thoroughly homed in, via this book and every other form of data visualisation best practice advice I have received.

The book is broken down into 10 chapters, of which 5 make up what I would consider the the core of the book. These teach you how to put your data into context, guide you through choosing effective visuals, teaching you that ‘clutter is your enemy’, how to focus the attention of your reader and to ‘think like a designer’, using size, colour, positioning and most importantly, simplicity to make an effective visual. Chapter 6 walks you through how to tie a story together and the rest put the lessons learned in the first 5 chapters into play.

What I appreciated most about this book, is that it practices as it preaches. Storytelling with Data is written well, in a very easy to follow manner, thoroughly driving home the points that it wishes the reader to take home. Because the book implements all the highlighting or rather ‘leveraging preattentive attributes’ that it describes, it makes it very easy to get all of the important information, without having to take too long to read the book and also makes it easy to find the tips you have learned and want to revisit.

My second favourite things about this book is the graphic examples. Nothing brings home the point about best practices of visualisation better than a visualisation! In particular I enjoyed the before and after visualisations, which not only brought home what I could be doing better and also became a fun game towards the end, seeing how much I had learned and if I could find all the elements that needed improving! The book even gives you a treasure trove of great sources of ‘inspiration through good examples’ by data viz gurus.

In closing, this book is great data visualization noobs like me. It gave me a fresh, concise and effective way to tackle the data visualisation challenges that have and are still to come my way!

In the second week of the data school, we were asked to make a ‘reviz’ of the data we had submitted for our initial applications. I was able to apply so many of the tips I had learned from ‘Storytelling with Data’. Have a look for yourself here:

Link: Before


Link: After


The Data Viz Community and Resources to get you Started


This article is recycled from the blog I wrote at the data school (also included as one of the resources for learning tableau), although slightly updated. But in building the blog from scratch, it really is the best place to start. So here it goes…..

I still consider myself relatively new to the data visualisation scene. It has, after all, only been 6 months that I have been using Tableau and Alteryx. Whilst I still have a long, long way to go, I have learned a lot at my time at the data school (and on my first placement). Now, while the training I received from the Information Lab, was second to none, one of the things made me really love the data viz world, is the data viz community. The Tableau community in particular is incredibly warm, welcoming and ready to help you learn.

If you aren’t already, get on twitter and get on tableau public. Twitter has lots and lots of ‘lists’ to show you good data viz people to follow and on tableau public, aside from showing off your own work, you can find people whose style you like, follow them and have a constant source of inspiration!

Here are a few of the websites, blogs and books I found that really helped me gets started and ones that I have found later on in my journey that I wish I had found earlier.


Websites and Blogs:


  • ‘Storytelling with Data – a data visualization guide for business professionals’ by Cole Nussbaumer Knaflic – book review soon to come!
  • ‘Golden Rules for Great Business Charts – 50 practical tips for business professionals’ by Laszlo Zsom
  • ‘Now you see it – Simple visualization techniques for quantitative analysis’ by Stephen Few
  • ‘The Truthful Art’ by Alberto Cairo

This is of course, just the tip of the iceberg. There is so much help out there on the interwebs to get you started and so much to keep challenging you through your data viz learning experience.

Hope this helps!