In this graphic, I am evaluating the trends in the sales of two types of clothing: Jeans and Tennis shoes, over time in two cities, Medellin and Bogota. Each data point represents a specific combination of City, Type of clothing, Quantity sold (Y-axis), and YearMonth (X-axis). I've chosen to use points instead of lines because it facilitates the visualization of the city variations, and using lines might complicate the understanding of the plot. The choice of using distinct and not overly bright colors aids in maintaining a user-friendly experience. In the bar plot, I represent the total quantity sold, and the interaction allows us to easily discern which type of clothing sold the most during a selected time period. The encoding of points and colors in the scatter plot is effective because it enables users to observe a trend, notably an increase in sales in the final months of the year. The bar plot is effective for showing the highest-selling item. The interactive elements enhance the plot as they help users gain a better understanding of the data. When we view the plot in its entirety, we can discern that Jeans consistently sell more than Tennis shoes. However, when we interact with the brush tool and select the months from March to July of each year, we can observe changes in the bars. This interaction is effective because it allows us to explore the described relationship and make comparisons between two time periods with ease. The brush interaction is a wise choice because it simplifies the selection of a specific time frame, which would be challenging or impossible with other methods, such as selecting individual data points. Another useful interaction in the plot is the ability to select a specific bar, which then displays only the data points corresponding to that type of clothing. This feature enables a more detailed examination of the quantity sold for each type individually, aiding in a more comprehensive analysis of the data.