Tracking vaccinations in the Netherlands

The COVID-19 vaccinations started on January 6th in the Netherlands. The Dutch government (“Rijksoverheid”) has received its share of criticism regarding the speed of vaccination so far. It is lagging behind its vaccination roadmap as laid out in December last year. For the first few weeks Rijksoverheid did not have an online vaccination tracker. The number of administered vaccines would be communicated in other ways, such as through Twitter by the Minister of Health, Welfare, and Sport.

Starting January 27th, Rijksoverheid started to list the number of administered vaccines alongside its “Coronadasboard“. Every day the number of (estimated) administered vaccines gets updated, the number is a cumulative number of vaccinations administered since January 6th. The dashboard created by Rijksoverheid currently does not display the number of vaccines over time. I think this has to do with the fact that they do not have the actual number of vaccines administered by the day. This might be caused by a delay in reporting of administered vaccines by different institutions. Furthermore, the number of administered vaccines is an estimation based on the vaccines that are delivered to vaccination locations.

Due to the lack of information about administered vaccines over time, I have created a dashboard that can be seen on As explained above, these are not the actual administered vaccines by the day. These numbers are the daily reported numbers by Rijksoverheid. It does not give us an actual representation of vaccination speed, instead it gives us somewhat of an idea of what is currently happening.

Below is a screenshot of the dashboard as seen on Please visit the website for the interactive version.

Click on the image to go to the interactive version.


Analysis: Dutch Elections

Using data from Wikipedia, I have created an analysis of the Dutch Elections. Wikipedia often has articles that include tables with interesting data. I simply copy and paste this data into an Excel sheet, clean it, and import it into Tableau.

The full dashboard and analysis is available on my Tableau Public profile. Unfortunately, I am unable to insert any HTML code that includes Javascript on this WordPress site, therefore I can’t embed the dashboard into this post.

The non interactive dashboard can be seen below. The interactive version can be see on my Tableau Public profile.

Click on the dashboard to be redirected to the interactive version.

Data Analysis: Women in politics and education around the world

I have been using Python for data analysis for about a year now. In the last couple of months, I have started to work with Tableau. Tableau is a visualization tool, which I find to be a very different experience from using the Python Panda’s library. The visualizations for today’s analysis can be found below and on Tableau Public.

Datasets and data prep

The UN has interesting (and free) datasets available on their website that look at countries around the world. For today’s data analysis, I first used a dataset that lists the share (%) of women in parliament around the world. And second, I used a dataset that provides the ratios between girls and boys for different levels of education (primary, secondary, and tertiary). Before I created graphs in Tableau, I slightly changed the format of the datasets using Tableau Prep. The country/region variable included different levels of countries and regions. Therefore, both continents (e.g. Africa) and countries (e.g. India) were included in the variable. I created a variable that only includes regions on a country level. Furthermore, the ‘share of women’ in parliament was in a format which Tableau won’t recognize as a percentage. This meant that I had to create a new variable for the share of women as well.


See the interactive visualization here. The first map displays the share of women in parliament around the world. You can select a specific year using the dropdown menu next to the map. Hover over the map to see the percentage, year, and country name. The higher the percentage, the higher share of women in that country’s parliament.

The second map displays the ratio (girls:boys) in education around the world. Using the dorpdown menus, you can filter for year and education level. A ratio above 1 indicates that there are more women in that level of education than men. A ratio below 1 indicates the opposite, more men in education than women.

The third graph displays the datapoints for each country, based on the share of women in parliament and the ratio of girls to boys in education. Countries below the horizontal reference line (of 1), have more men than women in enrolled in education. Countries above this reference line have more women enrolled. The countries to the left of the vertical reference line have a share of less than 50% women in parliament. Countries on the right side of the vertical reference line have parliaments that are made up of at least 50% women.

The third graph can be filtered using the ‘select education level’ filter (next to the second map). When you select different education levels, you will see that the share of women in parliament does not necessarily correlate with the ratio of girls to boys in education.

See interactive version here.