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Module Specifications

Archived Version 2022 - 2023

Module Title
Module Code
School

Online Module Resources

NFQ level 8 Credit Rating 5
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None
Description

This course introduces the use of the R environment for the implementation of data management, data exploration, basic data analysis and automation of procedures.

Learning Outcomes

1. Analyse a problem and write its solution in the R programming language
2. Read and modify R programming code
3. Demonstrate an understanding of programming constructs and concepts in the R programming language
4. Write programmes using advanced data types (Vectors, Lists, DataFrames) in the R programming language
5. Demonstrate the ability to import, clean and manipulate datasets in R
6. Apply basic data analysis and visualisation to a given dataset in R



Workload Full-time hours per semester
Type Hours Description
Lecture24Formal Lectures
Laboratory24Lab Sessions
Tutorial36Small Group Tutorials
Independent Study41This comprises time for reading, reviewing given and other exercises, group interaction on project, project time and write-up and revision
Total Workload: 125

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Introductory Topics
Introduction to R Programming. R development environment. R Console, Rstudio . Problem solving techniques: Problem analysis and problem solving. Algorithm design. Control structures - sequencing, selection and iteration. Introduction to the Basic Data Types of R: Numerics, Character , Logicals, Arithmetic calculations. Operator precedence. Mathematical and statistical functions. Control structures - If and if/else; While loops; For loops Arrays: Advanced data types: Lists, Dataframes, Matrices, Vectors, Factors and their operations. Modularity: Use of functions Passing arguments between functions. Data analysis: Importing data and reading data in R. Statistical Graphics, gplot

Assessment Breakdown
Continuous Assessment% Examination Weight%
Course Work Breakdown
TypeDescription% of totalAssessment Date
Reassessment Requirement
Resit arrangements are explained by the following categories;
1 = A resit is available for all components of the module
2 = No resit is available for 100% continuous assessment module
3 = No resit is available for the continuous assessment component
Unavailable
Indicative Reading List

  • Jared P. Lander: 2017, R for Everyone: Advanced Analytics and Graphics, 2nd, Addison-Wesley Professional, 978-0-13-4546
  • Norman Matloff: 2011, The Art of R Programming: A Tour of Statistical Software Design, No Starch Press, 401 China Basin Street Suite 108 San Francisco, CAUnited States, 978-1-59327-3
Other Resources

None
Programme or List of Programmes
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