| Module Title |
Introduction to R |
| Module Code |
CSC1013 (ITS: CA176) |
|
Faculty |
Engineering & Computing |
School |
Computing |
|
NFQ level |
8 |
Credit Rating |
5 |
|
|
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 |
|---|
| Lecture | 24 | Formal Lectures | | Laboratory | 24 | Lab Sessions | | Tutorial | 36 | Small Group Tutorials | | Independent Study | 41 | This comprises time for reading, reviewing given and other exercises, group interaction on project, project time and write-up and revision |
| Total Workload: 125 |
|
|
| Section Breakdown | | CRN | 20132 | Part of Term | Semester 2 | | Coursework | 50% | Examination Weight | 50% | | Grade Scale | 40PASS | Pass Both Elements | N | | Resit Category | RC1 | Best Mark | N | | Module Co-ordinator | Brian Davis | Module Teacher | Michael Scriney |
|
| Assessment Breakdown |
| Type | Description | % of total | Assessment Date |
|---|
| Laboratory Portfolio | Lab Assignments Students should be able to design an algorithm and write a programme using basic(Numerics, Logicals, Characters) and advanced data types in R (Vectors, Lists, Arrays, DataFrames, Matrcies) . They will demonstrate competition in implicit vectorisation, selection, iteration and functions in R.
They should also be familiar with reading and understanding simple R code. | 5% | Every Week | | Project | Students will work in groups and complete a laboratory project in R, which involves selecting, importing, cleaning and manipulating a data set in R. Students must demonstrate the capacity to apply basic data analysis and visualisation to a given dataset in R. Each student will also provide an individual short reflective learning report on their role in the project. | 30% | Week 9 | | In Class Test | Lab Test. Students sit a lab. exam. after covering Basic and advanced data types(Lists, DataFrames, Vectors, Factor Vectors, Matrices) as well as iteration, selection, user defined and built in functions in R Designed to assess their understanding and ability to implement these features. | 15% | Once per semester | | Formal Examination | End-of-Semester Final Examination | 50% | End-of-Semester |
| Reassessment Requirement Type |
Resit arrangements are explained by the following categories;
RC1: A resit is available for both* components of the module.
RC2: No resit is available for a 100% coursework module.
RC3: No resit is available for the coursework component where there is a coursework and summative examination element.
* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment
|
|
Pre-requisite |
None
|
|
Co-requisite |
None |
| Compatibles |
None |
| Incompatibles |
None |
|
|
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
|
Indicative Reading List
Books:
- Jared P. Lander: 2017, R for Everyone: Advanced Analytics and Graphics, 2nd, Addison-Wesley Professional, 560, 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, 400, 978-1-59327-3
Articles:
None |
Other Resources
None |
|
|
|
|
