Visualize urban growth

The General Directorate for the Cadastre of Spain has spatial information of the all buildings except for the Basque Country and Navarra. This data set is part of the implementation of [INSPIRE](https://inspire.ec.europa.eu/), the Space Information Infrastructure in Europe. More information can be found [here](http://www.catastro.meh.es/webinspire/index.html). We will use the links (*urls*) in *ATOM* format, which is an RSS type for web feeds, allowing us to obtain the download links for each municipality.

Visualize monthly precipitation anomalies

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.

Tidy correlation tests in R

When we try to estimate the correlation coefficient between multiple variables, the task is more complicated in order to obtain a simple and tidy result. A simple solution is to use the ``tidy()`` function from the *{broom}* package. As an example, in this post we are going to estimate the correlation coefficients between the annual precipitation of several Spanish cities and climate teleconnections indices.

Import Excel sheets with R

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.

Calculating the distance to the sea in R

The distance to the sea is a fundamental variable in geography, especially relevant when it comes to modeling. For example, in interpolations of air temperature, the distance to the sea is usually used as a predictor variable, since there is a casual relationship between the two that explains the spatial variation. How can we estimate the (shortest) distance to the coast in R?