Even though aggregation is one of the main types of transformations that happen literally all the time in a project, there are many other common other common and useful transformations. Here I am going to show you some of the most common and useful ones. The first one is transformations related to attributes that encode information on time and date. Very often in a project, you are confronted with the problem of creating a visual representation of a dataset that contains some time-oriented information. So time and dates or time and dates at the same time. So in this case, it's very common to have aggregation at different levels according to at what level of details you want to conduct your analysis. So times and dates have these interesting properties that they have a hierarchical structure. So you can go from seconds to minutes to hours, days, weeks, months, years, and so on. You can imagine this as being different resolutions, you can conduct your analysis at different levels of resolution. And you can imagine that this also has an impact on the way this information is visualized. Let me give you an example, here once again, I'm using a bar chart to show a trend over time, something that changes over time. But again, according to the different resolution you choose, you can spot different trends. So you can go for months to weeks to days and as you can see, there are different patterns that emerge according to the resolution that you choose. There's never one single resolution that is optimal. It depends on the specific question that you have and on the specific project you’re working on. So it’s very important to keep in mind when you have time and deeds to look at what is the impact of using different resolutions which ultimately translating to different aggregation levels. The same thing happens when you are analyzing and visualizing spacial data or geographical data. For instance, you can go from analysis at the zip code level, in a city, to a county, to the state, and so on. And in general, every time you have spacial data, you have the option of conducting your analysis and creating visual presentations at different levels of resolution. Here is an example, I have a map where the quantities are mapped at the level of state, all the states in the United States. But I can represent exactly the same information at the county or zip code level. So going from here to here what changes is the resolution. Another very common type of transformation that happens when you have spatial data or more precisely geographical data, is going from the name of a location to the geographical coordinates. Or the other way around, going from the geographical coordinates to the name of this location. This is very, very common and is called typically geo-coding and decoding, very common. For instance here, I have a dataset with different cities in the United States. And for every city, I have one pair of values. One for the latitude and one for the longitude. And as you can imagine, this is very useful when you have to create a map in every single location you want to visualize some quantities or attributes coming from your dataset.
