Data Analysis for Scientists, using R:

LÍF 632M Graduate & Undergraduate 4 ECTS
Period: Summer 2015 June 1 - 5

Dr. Niall McGinty

and Teaching material



Purpose and Contents

The week-long course will take students through the whole process
of collecting, analysing and publishing data using a modern computer

Empahsis will be placed on seeing the computer as a tool that improves the integrity of data, making it easier to replicate studies and guarantee correctness; and on working in a manner that makes it easy to transfer numerical methods and results into academic publications.

We will begin by discussing how data should be collected to make it suitable for analysis, how files on the computer should be organised, methods to deal with the large number of different data sources dealt with by most scientists, and ensuring that data integrity is retained and that an audit trail is recorded.

We will then learn how to analyse data using R. After installing the
(free) software on their own computers, students will learn to write
short scripts to do their analysis. This will ensure that their
methods can be repeated with alternative data, and that an accurate
record of statisical methods and data sources is available when
writing scientific papers.

Finally, the process of using R to produce professional quality
figures will be explained.

The course will end with three days for the students to analyse their
own data, producing results and figures aimed at a particular journal.

For more information, e-mail Niall McGinty at

Course Prerequisites
The Course is open to both undergraduate and graduate students. The student must be numerically and computer literate. The student should have some familiar with statistical methods (statistical tests, linear models, some other models). The student must have familiarity with computer spreadsheet systems (e.g. Microsoft Excel) and should have experience with another statistical package (e.g. Minitab).

Learning Outcomes

By the end of this course, the student should be able to:

Store many data files on a computer in an organised manner.

Keep track of the source of data, changes to data and methods recorded in laboratory notebooks.

Understand the differences between types of data files.

Install R on a personal computer.

Load data from different sources into R, and perform statistical analyses.

Implement data analyses using R script files.

Write up data analyses and results for a journal, based on R script files.

Produce figures to publication standard using R.





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