Automated Data Processing with R

Important: The assessment in this module is based on a 20 to 30 minute online discussion of assignment solutions. The appointment for the online call is arranged between student and module instructor after the submission of assignment solutions.

There are plenty of reasons to learn programming with R. R is among the most popular analytical scripting languages with diverse applications such as statistical analysis, geographic information analysis, machine learning and data visualization. R is free and open source, does have more comprehensive functionality than most proprietary solutions, is compatible with most popular operating systems and benefits from a large community. Upon the completion of this module, you will have the fundamental skills to make use of the wide R ecosystem.

See a short presentation of the module and recent updates by the lecturer (webinar recording):

3 months

English

3 ECTS

The module is free of charge for UNIGIS students working to meet their elective subject requirements. Included are:

  • all related study materials
  • supervision and assessment
  • module accreditation according to the curriculum

ClubUNIGIS members can register at a price of € 175,-. Included are:

  • all related study materials
  • supervision and assessment
  • course certificate upon completion of the module

Upon the completion of this module, you will have the fundamental skills in R programming as a basis for more advanced methods like Geospatial Data Analysis (is covered by the module “Spatial Statistics” in the MSc program) and Machine Learning. Specific learning contents are

  • R IDE and R interpreter
  • Simple data types and operators
  • Variable declaration and libraries
  • Data Structures (vectors, matrices etc.)
  • Spatial data structures (simple feature and raster objects)
  • Control structure (i.e. conditions and loops)
  • Definition of custom functions
  • Manipulating tabular data using ‘dplyr’
  • Read and write data
  • Vector and raster data manipulation
  • Plots and maps
  • R Markdown notebooks

Core UNIGIS modules 1-3 or equivalent understanding of GIS, data modeling, data structures and data acquisition. Prior experiences with scripting languages is a plus. If you are not sure whether you qualify, please contact martin.loidl@plus.ac.at.

This is an exclusively english language module, hence any kind of communication with the module lecturer should be in English. A discussion forum is maintained in Moodle in order to support efficient module instruction. You are requested to submit all your questions related to this module to this forum only. The lecturer will check all incoming comments on a regular basis. He will answer your questions or provide you with pointers for solving your problems. The module is delivered in form of an instructed self-study that is based on an explorative learning process. Theoretical concepts are complemented with practice oriented examples demonstrated with help of multimedia elements. Upon completion of the module students are requested to evaluate the module, which is a part of our quality assurance.

R, RStudio (IDE)

The assessment in this module is based on a 20 to 30 minute online discussion of assignment solutions. Assignment solutions must be submitted in written format to the Dropbox within the required time period. If assignments are submitted late, the lecturer is not obligated to grade them.

Lesson 1: Setup and IDE, R interpreter, simple data types, operators

Lesson 2: Variables, algorithms (functions), libraries

Lesson 3: Data structures such as vectors, matrices, arrays, lists and data frames

Lesson 4: Spatial data structures such as simple features and raster data objects

Lesson 5: Control structures such as if, else, loops and conditional statements

Lesson 6: Defining functions, return functions, variable scope

Lesson 7: Data manipulation using dplyr (queries, statistical summaries and joins)

Lesson 8: Read and write data, data APIs

Lesson 9: Vector and raster data operations (e.g. raster algebra, vector overlay)

Lesson 10: Plots and maps (ggplot library)

Lesson 11: Data reports by means of R Markdown