My first attempt at #MakeOverMonday

Recently, I stumbled on MakeOverMonday and their weekly challenge that centers around creating visualizations of datasets which cuts across several topics. I joined the challenge on the 34th week and the dataset this time was about access to contraceptives by women in the Lower-Middle income countries(LMICs).

It was titled Sexual and Reproductive Health and Rights. Organizing the dataset was very easy since it was a clean dataset which made the preparation stage very short, it had 7 fields and about over 500 rows. The following steps were what I took to create the in tableau visualization:

I did this by reading the article attached to the dataset which was provided by the host. I got to know the structure of the data and why it was structured that way. I tried to answer questions such as:

a. Are women in these countries interested in contraceptives? If No I excluded them and If yes I asked another question:

b. Are there contraceptives readily available to women in each of the countries/sub-continents/continents? If NO then they have unmet needs which is a problem, also if YES:

c. Are they actually allowed to use these contraceptives ?

d. What are the methods being used? No method? Traditional Method (Which is next to no method)? or the ideal Modern methods?

I came to understand that the data has to do with how much access women have to contraceptives. There were four continents represented in the data which are: Africa, Asia, Latin America and the Caribbean and Oceania.These continents were divided into sub-continents and the sub-continents into countries.

I did this by first trying to play around with the metrics that will be needed in my analysis for a good visualization. I finally settled for the following visualization, a packed bubbles chart, a side by side bar chart, a map and a pie-chart to create my dashboard.

  1. I used the bubble chart to show the sub-continents and the total number of countries in those continents. For instance, the sub-continents in Africa are Western Africa(64 countries showing women between the ages of 15–49 has only 16 percent of the women using contraceptives), Eastern Africa(74 countries showing women between the ages of 15–49 has 18 percent of the women using contraceptives), Southern Africa(20 countries and just 5 percent of the women use both contraceptives) and Northern Africa(28 countries and 7 percent of the women use contraceptives).
  2. The map was used for showing a comparison between women that want to get pregnant and those that don’t, for each represented country in each continent. For instance, we can see from the map that in Mexico which is located in the Latin America and the Caribbean continent, only 37 percent of the women want to get pregnant. The other 63 percent do not.
  3. The side by side bar chart was used as the filter. It shows the continents, the sub-continents and their countries. Clicking on a bar highlights every other information on the dashboard that is related to a sub-continent and all the countries under that sub-continent.
  4. Finally, the pie-chart just shows the percentage distribution of women in all the Lower-Middle income continents.

While this is not all I did to get my final visualization, indulge me to divert my main aim of writing this article because it is my very first. You see, during my EDA(exploratory data analysis) I got stuck and angry, I was so unsure of how to proceed. I had what you would call a road block. Here’s what I did, I took a deep breath. I tried to remind myself of what works for me and that is taking it one step at a time so even when I knew that I had like three deadlines in two days and didn’t want to miss #MakeOverMonday, I tried to keep it together and I told myself that this is not an impossible task, and this is the only reason I have to get it done.

This is not one of the best visualizations you will see, it probably has a lot of flaws but I actually put in a lot of time from the limited amount of time I had and made sure I turned in my submission. While I didn’t have one of the best visualizations, it still fetched me the highest amount of views on Tableau public I have ever had and this tells me two things I can get better and that I am a step in the right direction.

Furthermore, I attended the review for week 33 challenge and I was able to pick up a couple of new ideas and the things I could have done better. For example, telling a summarized written story about my visualizations would have been better and more insightful.

Below is the final result, you can find the interactive version here on tableau public.

In summary, I am putting this dashboard out here to get advice, constructive criticisms and to get better, if I get people kind enough to educate me more.

I will also make sure my next post goes deeper into my thought process and possible technical challenges.

Thank you for reading.

Data Analyst | BI Analyst | Tableau Expert.