Introduction to R Programming STATS4044

  • Academic Session: 2019-20
  • School: School of Mathematics and Statistics
  • Credits: 10
  • Level: Level 4 (SCQF level 10)
  • Typically Offered: Semester 1
  • Available to Visiting Students: No
  • Available to Erasmus Students: No

Short Description

The course introduces students to programming in the statistical software environment R.

Timetable

10 2-hour practicals, and 10 hours of lectures.

Requirements of Entry

The normal requirement is that students should have been admitted to an Honours- or Master's-level programme in Statistics.

Excluded Courses

Statistics 3R: Introduction to R Programming [STATS3017]

Introduction to R Programming (Level M) [STATS5020]

Assessment

Programming Tasks (100%) consisting of in-lab and homework assessments.

Are reassessment opportunities available for all summative assessments? Not applicable for Honours courses

Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

Course Aims

To introduce students to the key concepts and ideas of a statistical computing environment; to train students in programming tools using the R computing environment; to provide computational skills which will support other Level 3 and Level 4 courses; and to introduce students to fundamental concepts in (scientific) programming in general.

Intended Learning Outcomes of Course

By the end of this course students will be able to:

■ recognise and make appropriate use of different types of data structures;

■ use R to create sophisticated figures and graphs;

■ identify and implement appropriate control structures to solve a particular programming problem; and

■ design and write functions in R and implement simple iterative algorithms.

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.