Dashboard week is one of the most signature sessions in The Data School training. In this session, the cohort will be given one or several specific datasets and create a corresponding dashboard. The aiming of dashboard week is to develop excellent time management and data visualisation skills.
Today is the last day of dashboard week and the topic is the Food price index and inflation. The dataset was from FAO, which is a specialized agency of the United Nations that leads international efforts to defeat hunger. The dataset is very comprehensive that contains much information such as price index and consumer price indices from 2000 to 2020.
The original data set has already been cleaned by the coach so I could start to process them immediately. The origin dataset only contains the Food Price inflation and I think it is good to compare it to General Price Inflation. So the first task I did is calculating it based on the documentation from FAO. Then it also took some time to allocate countries to their corresponding country, as in the dataset each country belongs to multiple groups. In addition, I input the price index data as well to determine the most expensive food in a country.
Below is the screenshot of my dashboard:
I tried to figure out the typical pattern of consumption and inflation of different kinds of countries. On the left-hand side is a big map that shows inflation. On the right-hand side, the first line chart provides information about the comparison between food price inflation and general inflation, followed by the most three expensive items. The last bar chart is the comparison between the actual value and average value in a specific country group. The insight I found is in terms of general inflation, developed countries are more stable than developing countries. Moreover, this kind of country has higher indices but lower inflation. Developing countries show the opposite pattern.