Background
When I started out, it took me at least two weeks to get a good, share-worthy dashboard ready. At one point in our training at the Data School, you are supposed to churn out one dashboard a day. Yes, that’s right. Essentially, you are given a data set at the start of the day and you make a publish-worthy dashboard from it by the end of the day.
For our first day, we were tasked with creating one from a dataset of the Himalayan mountain expeditions and peaks in Nepal. There are xx number of expeditions with over xx peaks. It’s a large enough data set with several columns containing data on all the expeditions taken place, the people who attempted them, their nationalities, and many many more interesting data points.
While scrolling through the names of the peaks, one particular set caught my eye – the Annapurna peaks. I had heard of these peaks before and knew that they were very challenging to climb. So I decided to focus on these peaks for my dashboard.
How high are the Annapurna Peaks?
I created a set in tableau with peaks that belong to this range and used Tableau’s awesome Sets functionality to analyse the data. I wanted to know how high these peaks are from the rest of the Nepalian Himalayas and I created a box plot to visualise the distribution of the peak heights of both these groups. I dragged the Set I created one th columns and height on the rows, disaggregated it with the names of the peaks and then selected the box plot option. Through this visual I found that the Annapurna peaks lie in the higher ranges of the peaks with the lowest peak greater than the upper hinge of the rest of the mountains in the data set. This means that the lowest peak is taller than 50% of the peaks in the rest of the data set.
How dangerous are Annapurna Peaks?
Then I created two charts that show how dangerous the Annapurna peaks are to climb. You might think a simple calculation of the number of deaths that have occured while climbing a peak will tell us how dangeorous it is. In this case, the following chart shows that more people have died climing Mt. Everest than any of the Annapurna peaks. However, we are not using the right metric to analyse our data in this case. We need to normalise these numbers. And i found that the standard measure used by mountaineers to calculate how dangerous a climb is, is by the Fatality to Summut ratio. It is basically the proportion of the number of people died divided by the number of people successfully reached the summit. I calculated that for my dataset and found some stunning results. More people die trying to scale the Annapurna range compared to Mt. Everest.
I created a parameter to switch between the two visuals I created so it would be easier to understand the difference in using the right measure.
Then I wanted to know how long it takes to climb these mountains compared to the rest of the peaks in the dataset. For this I created two bar charts. One showed the median of the number of days each expedition took, while the other showed the average height gained per day.
I think this gave a good idea of what it means to scale the Annapurna peaks and how treacherous they can be.
You can view my dashboard here: my tableau public dashboard