The materials included in this book have been designed for a module focusing on the programming language R as an effective tool for data science for geographers. R is one of the most widely used programming languages. It provides access to a vast repository of programming libraries, covering all aspects of data science, from data wrangling to statistical analysis, from machine learning to data visualisation. That includes various libraries for processing spatial data, performing geographic information analysis, and creating maps. As such, R is a highly versatile, free and open-source tool in geographic information science, which combines the capabilities of traditional GIS software with the advantages of a scripting language and an interface to a vast array of algorithms.
The materials aim to cover the necessary skills in basic programming, data wrangling and reproducible research to tackle sophisticated but non-spatial data analyses. The first part of the module will focus on core programming techniques, data wrangling and practices for reproducible research. The second part of the module will focus on non-spatial data analysis approaches, including statistical analysis and machine learning.
The materials included in this book have been designed for a module focusing on the programming language R as an effective tool for data science for geographers. R is one of the most widely used programming languages. It provides access to a vast repository of programming libraries, covering all aspects of data science, from data wrangling to statistical analysis, from machine learning to data visualisation. That includes various libraries for processing spatial data, performing geographic information analysis, and creating maps. As such, R is a highly versatile, free and open-source tool in geographic information science, which combines the capabilities of traditional GIS software with the advantages of a scripting language and an interface to a vast array of algorithms.
The materials aim to cover the necessary skills in basic programming, data wrangling and reproducible research to tackle sophisticated but non-spatial data analyses. The first part of the module will focus on core programming techniques, data wrangling and practices for reproducible research. The second part of the module will focus on non-spatial data analysis approaches, including statistical analysis and machine learning.