This workflow demonstrates how R can easily interact with PostgreSQL Database, and it extender PostGIS working in RStudio. It also shows how to write and run structured and spatial queries in R. It further demonstrates how to set up the necessary environment via a practical code session.

This resource seeks to introduce geospatial student like myself to model building using the Random Forest Algorithm. It demonstrates the procedure for model building which includes data partitioning (train and test data), training of the model using the train data, tuning of the model, assessment of cross-validation results, evaluation of the model using the test data. It also further introduces the concept feature selection mainly the recursive feature elimination(rfe). Lastly, it demonstrates how to make spatial prediction with the built model. All this made possible with the caret package in R.

This resource seeks to simulate what was done in the eBook QGIS and Open Data for Hydrological Applications-Exercise Manual. (Exercise 4: Map Algebra) with the R programming language. It Introduces student like myself to the concept Map Algebra, which is highly needed to handle effectively Raster data (WCS). In the workflow is demonstrated how to use the Raster package in R and also how to visualize the output of geospatial analysis using the tmap package. Many thanks and appreciation to Dr. Hans van der Kwast the author of QGIS and Open Data for Hydrological Applications-Exercise Manual.

This resource outlines the basic workflow of data analysis, the use of the sf package in handling Vector (WFS) data and how the R programming language can be effectively use in the GIS setting. Further, this resource demonstrates how to chart using the ggplot2 package in R and some intense data wrangling procedures using some packages from the tidyverse suite.