Tutorial - Using LUMA data in R

There are many programming languages used for scientific data analysis. Here, we show examples using the R language as it is an open source project and can easily be implemented on all operating systems. It is a powerful, language providing a long list of useful functions and packages that can make life easier for scientific programming.

Getting started with R
  1. Download R (follow link)
  2. Choose an editor: Even though R comes with a script editor, many other choices are avalable which have better features, e.g. script highlighting. One ediot we like to use is RStudio (follow link)
  3. Check out some online introductions on R, e.g. this one (follow link)

Many useful tools and tutorials can be found on the web on how to do data analysis with R, so here we just give a few hints on how to get started when using LUMA data. These tutorials address students with no/small experience with R and give an introduction in different variables types and file formats. Please always download the relevant materials and go through the code provided in order to explore the functions and commands.

Text framed in boxes is the R code that you should type into the R console. Read the instructions carefully.

Exercise 1: How to import data from a text file? (follow link)

This exercise uses data stored as a table in a .csv file, download example
It will show you

Exercise 2: How to import data from a KUMA NetCDF file and convert it to a text (ascii) file?