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Introduction to R

What is R?

  • R is a programming language for statistical computing but...
  • ... you can use it without any knowledge of statistics - to do data manipulation, data visualization, and more (file handling, automatization, web scraping, ...)
  • it is open-source, free and multiplatform
  • https://cran.r-project.org/
  • R can be used in two main ways:
  • interactively writing commands in the console (command line)
  • the code can be stored in scripts - files with code (.R, .r), and can be run entirely or partially
  • generally, the scripts can be written in any text editor, but there are specialized software (IDE - Integrated Development Environment) to make writing and running code easier:
  • the most popular IDE for R is RStudio, but recently same company (Posit) also developes the Positron based on IDE VS Code
  • other general IDEs used for R: VS Code, Vim, Emacs, Jupyter Notebook ...
  • remember R ≠ RStudio
  • R - programming language (you can run R and execute R commands in terminal)
  • RStudio - software application than helps you to write and run R code

Why programming for spatial data handling?

  • why write programs/scritps when I can already can do the same in Excel, QGIS, ArcGIS, ... ?

  • practically, you can do the same, but with programming you writing the "reciepe" how to do it, which can leads to:

    • reproducibility - run the code again, share the workflow, collaborate when coding
    • flexibility - do the same task with different data or parameters repeatedly with minimal effort

Example

Imagine this GIS task: Get mean evelation for 1 km area around occurrence of specific species A. So the task will be something like:

  1. read point vector data (shapefile, geopackage, ...)
  2. retrieve only points with specific attribute (species is species A)
  3. do 1 km buffer around the points
  4. read the raster data (DEM)
  5. extract raster values for the buffer areas, and calculate the mean
  6. write it to a file

But what if you need...

  • do this for all other species in the dataset ...
  • ... and for multiple buffer distances ...
  • and for other 100 raster tiffs in the direcotry or on servers (monthly temperatures, percipitation, pressure, ...)

You can also do this in desktop applications, but as you can imagine, it will be time consuming and inefficient. With writing the workflow in R, you can easily change the parameters, set multiple values run it on multiple datasets, with minimal effort compared to manual work.

Installation of R

  • Download R from https://cran.r-project.org/
    • choose your system (Windows, macOS or Linux) and follow the instruction
    • for windows: choose base distribution, than click Download R-4.x.x for Windows and follow the installation wizard

After installation, you can run R with inbuild IDE Rgui or directly in terminal. But in the course we will use RStudio, and I recommend this to all beginners.

Rstudio

RStudio is the most popular IDE and de facto standard for R programming. It is free and open-source, and you can download it from https://rstudio.com/products/rstudio/download/. Just download the installer for your system and follow the installation wizard. Than you can run RStudio and start coding.

Note

You have to have R installed before you install RStudio.

We won't go too much into details for now, and just show the basic interface and useful settings for start. Other settings will be covered during the course.

Interface

The GUI consists of four main panes, which can be customized and rearranged. But for now we will focus on two of them:

  • Console - here you can write and run code interactively
  • Source - here you can write and save your scripts (files with code), and also send the parts of the code to the console