STIN300 Statistical Programming in R

This is an intensive course where you will use the programming language R to apply your statistical skills to scientific data. If you have no prior experience with programming, be prepared to put in significant effort, see "Recommended prerequisites."

Course participants are usually Master’s or PhD students who have chosen a research topic. Use your own data if possible, or ask your supervisor for a dataset. If you haven’t chosen a research topic yet, we can help you find a dataset.

You will write a report in R Markdown, which will include data visualization and statistical analysis of your dataset. Your report will be fully reproducible and contain executable code. It will serve as a valuable starting point for your future work and facilitate discussions with, for example, a supervisor.

You can learn from free online textbooks, daily tutorial documents, and by asking effective questions in the Discussions section on Canvas.

We emphasize visualization, as well as structuring and manipulating tabular data. The course also covers: operators, variables, data types, and basic data structures, control structures such as loops and conditional statements, file and text processing and user-defined functions.

Upon completion of the course the students should be capable of performing statistical analyses using a programming approach in R. The students should be able to visualize and manipulate data and make their own functions utilizing/modifying available functions in order to solve specific statistical problems. The students should also be able to present the output from statistical analyses in an accessible and scientific form using text and graphics.

KNOWLEDGE: Students will acquire

• an understanding of how programming can automate demanding statistical computations.
• a working knowledge of concepts, syntax and conventions for describing, fitting and interpreting statistical models in R.

SKILLS: Students will be able to

• interpret output from R's functions for statistical modelling, such as lm().
• read in data from various file formats including Excel, comma-separated text, and FASTA.
• develop their own functions which use existing functions, to solve nontrivial challenges more efficiently than by nonstructured programming.
• present results of statistical analysis in a scientific, clear form through reproducible, executable reports which weave together expository text, program code, and output such as tables and graphics.
• troubleshoot problems by locating errors, reproducing them on a small subset of the data, step through code line by line, etc.
• orient themselves in documentation for R packages that implements statistical methods the student knows.

GENERAL COMPETENCES: Students will be well prepared to apply statistical methods in R on datasets they encounter in later studies and working life. This includes loading data into R, transforming it to a structure that the analysis function can use, run analyses with appropriate settings, and interpret and present the results in a form that is useful to the end user.