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.
We usually work with different data sources, and sometimes we can find tables distributed over several Excel sheets. In this post we are going to import the average daily temperature of Madrid and Berlin which is found in two Excel files with sheets for each year between 2000 and 2005.
In this post, I will show how we can download and work directly with data from climatic reanalysis in R. These kind of datasets are combination of forcast models and data assimilation systems, which allows us to create corrected global grids of recent history of the atmosphere, land surface, and oceans.