visualization

It is very common to see relief maps with shadow effects, also known as ‘hillshade’, which generates visual depth. How can we create these effects in R and how to include them in ggplot2?

Space-time information is vital in many disciplines, especially in climatology or meteorology, and this makes it necessary to have a format that allows a multidimensional structure. It is also important that this format has a high degree of interchange compatibility and can store a large number of data.

In April of this year, I made an animation of the 24-hour average temperature of January 2020, also showing the day-night cycle. My biggest problem was finding a way to project correctly the area at night without breaking the geometry. The easiest solution I found was rasterising the night polygon and then reprojecting it. Indeed, a vector approach could be used, but I have preferred to use raster data here.

The climate of a place is usually presented through climographs that combine monthly precipitation and temperature in a single chart. However, it is also interesting to visualize the climate on a daily scale showing the thermal amplitude and the daily average temperature. To do this, the averages for each day of the year of the daily minimums, maximums and averages are calculated. The annual climate cycle presents a good opportunity to use a radial or polar which allows us to clearly visualize seasonal patterns.