The figure is created and copyrighted by datacamp, and is only symbolically used in this post. See here for the details on this figure.
R is the most advanced tool in the market for spatiotemporal geostatistics (with the ‘gstat’ and ‘spacetime’ packages) and raster data analyses (with the ‘raster’ package). It offers an unique ground for storing and integrating space and time attributes of data, and enables their implementation and integration through the analyses. Raster data analysis has not only become faster and more efficient than before but also more intuitive. The thing I love about R is it’s capability of co-interfacing with a wide range of software that is much more vast than Python. In fact, it provides packages to co-interface with Python itself (with the ‘rpython’ package for example), and also allows for using Python’s spatial modules (with the ‘RPyGeo’ package). Moreover, R is much more user friendly than Python in terms of compact coding, i.e. integrating several functions in one line of code. People may argue that Python has the advantage in software development, but I have developed two tools so far using R and without needing Python.
A few recent discussions in Quora and datacamp also eventually voted for R.
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