Normally when we visualize monthly precipitation anomalies, we simply use a bar graph indicating negative and positive values with red and blue. However, it does not explain the general context of these anomalies. For example, what was the highest or lowest anomaly in each month? In principle, we could use a *boxplot* to visualize the distribution of the anomalies, but in this particular case they would not fit aesthetically, so we should look for an alternative. Here I present a very useful graphic form.
Recently I created a map of the distribution of gas stations and electric charging stations in Europe. How can you obtain this data? Well, in this case I used points of interest (POIs) from the database of *Open Street Maps* (OSM). Obviously OSM not only contains the streets and highways, but also information that can be useful when we use a map such as locations of hospitals or gas stations.