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Two commonly used programming languages in geosciences are R and Python. Both languages come with inherent advantages and disadvantages. While Python is a generalpurpose language with a readable syntax, R is built by statisticians and encompasses their specific language. In contrast with more nominative and static programming languages, like C++ or Java, both R and Python are heavily used in non-informatics disciplines, due to their relative ease of use. Notwithstanding the lower performance of both languages compared to the aforementioned languages, they allow fast prototyping and implementation of algorithms in research contexts. However, depending on the aim of the research, (geo-)scientists will frequently decide between using Python, favoring replicability and accessibility, or R, containing fast-programmable cutting-edge reporting tools. One of the main differences between both languages is that, for Python, with five libraries one can complete most research (Numpy, Pandas, Scipy, Scikit-learn, and Matplotlib), while for R, one should find the right package in the +12.000 available packages on CRAN. The availability of the many packages for R is at the same time one of its greatest strengths. In this research, three common analyses in geosciences are performed in both R and Python. The first analysis highlights the unsupervised classification of (Sentinel-2) satellite imagery. The second series of analysis concerns the transformation of planimetric coordinates from one coordinate reference system to another system. As a final analysis, a series of standard topographic parameters are derived for an equidistant digital elevation model extracted from ASTER- and SRTM- data (EU-DEM). Next to the quantitative benchmarking of the processing time for each analysis, the output of each calculation is investigated as a qualitative benchmark, with a focus on the comprehensibility of the numerical/character output and the visualizations. [ABSTRACT FROM AUTHOR]