School is hard, and that’s no secret! It takes many years to learn the fundamental principles of this world, and even longer to specialize in a field of interest. School Life Expectancy (SLE) is a look at how many years a person can be expected to be a student. It spans from primary (Pre-K – 8th grade), secondary(9th-12th grade) and tertiary (trade school, associates, undergrad, grad degrees, etc.) schooling. This measurement is usually compared between countries and sex / gender and provides a useful look at the trends of schooling. I am interested in trends over the years for SLE based on gender / sex and to see which countries have the longest SLE. This report will take a look at some of these metrics between based on data from the United Nations research. You can find my Tableau Dashboard below.
To create my visualizations the data visualization software Tableau was used. Tableau is a really useful tool for creating complex visualizations with a lot of data. My data set can be found on the UN’s data website found here. My data features 6 columns and 1824 rows.
As mentioned above, to make my charts I utilized the program Tableau which greatly helped with managing the data I got from the UN website. To start, for inspiration I searched what other visualizations have been made with the same data. OurWorldData.org has a visualization simply titled, “School Life Expectancy, 2017”, and at first glance I didn’t think much of it until digging a bit further and seeing all the features and different ways to view the data. Not only is there a map with individualized miniature viz’s showing the trend line of SLE per country on hover, but also many other sheets with customizable line and bar graphs. In terms of inspiration, I truly inspired by the different ways in which Our World Data presented its data as well as the interactivity of it.
Looking towards my visualization, I knew I wanted to utilize the various types of graphs that are available on Tableau, though the hard part was figuring out how to set up my data to do so. Since my data featured location information, a map seemed logical to have. For all of my visualizations I computed the average SLE by country or gender accordingly, allowing me to compare more metrics and dimensions. The map showcased overall average SLE for each country I had data for. Using color to distinguish between higher and lower SLE values. For my line chart, I compared gender and Average SLE and color marked male vs female SLE over time. Since the graph was well above the graph origin I decided to use an an overall average to act as an axis and reference point. Lastly, to accompany the map visualization, I decided to draw attention to some countries with the lowest and highest average SLE values on each continent for comparison. I used a treemap which I thought did a great job showing the scale between the largest and smallest values for this metric. Color is also used to show the range of Average SLE values amongst the countries.
My comparison of SLE by gender was a bit surprising to me, I would think that overall, across the world SLE would be lower for female-identifying individuals than the male-identifying individuals due to the many barriers across the globe that prevent non-male-identifying individuals from having access to tertiary schooling such as college and masters programs. The opposite seemed to be the case in that female individuals were consistently in school longer than male individuals. Makes me think about what occupations are being sought after. It would be interesting to see a break down of what fields people study broken down by sex or gender. The world map and treemap were a bit shocking to see the drastic difference by color, scale, and geography. Seeing the different colors on each continent is what led to me making the treemap. I would like to see more functionality on my graph like Our World Data’s visualization.
First! Tableau was really hard for me to learn, I honestly am looking forward to spending a bit more time with it, and seeing what my classmates have done to pick their brain about how they ended up with their final visualizations. However, I can definitely tell that Tableau is super useful in that I was able to visually represent a large dataset so fast! Also looking back at my data, I was really unsure of what I wanted to display, and how I wanted to display it. I need to work figuring out what story my data tells and what’s the best way to present this story. Additionally, as mentioned in the results, I did not really appreciate that the data I had was segmented by two genders because it feels as though people are either being excluded from the dataset or improperly represented. Lastly, I am unsure of how to add tabs to show close ups of the individual charts.. currently seeking YouTube and Google for answers.
Data.UN School Life Expectancy
Nation Master School Life Expectancy