R Literacy for Digital Soil Mapping

This section provides a structured introduction to using R, with a focus on skills relevant to digital soil mapping workflows. The material is split into short, focused parts that cover everything from core syntax to exploratory data analysis, graphics, and model building.

Part 1

  • R basics: commands, expressions, assignments, operators, objects
  • Data types and data structures
  • Functions, arguments, and packages
  • Getting help in R

Part 2

  • Vectors, matrices, and arrays
  • Vector arithmetic and common functions
  • Basic use of matrix and array objects

Part 3

  • Creating and working with data frames
  • Importing and exporting data

Part 4

  • Basic R graphics

Part 5

  • Understanding object modes, classes, attributes, and coercion
  • Indexing, subsetting, sorting, and locating data
  • Working with factors and combining datasets

Part 6

  • Exploratory data analysis
  • Summary statistics
  • Histograms, boxplots, and Q-Q plots
  • Cumulative probability plots

Part 7

  • Basics of linear modelling in R

Part 8

This section introduces how to construct your own functions in R. Functions are central to R programming — they encapsulate reusable tasks and allow you to build logic for problem-solving.

The example used here shows how to programmatically design a soil sampling sequence along a toposequence. While such a task may feel intuitive when done mentally, translating it into R code requires structured logic, creativity, and problem-solving — all essential skills for digital soil mapping.