BÄ°S771 - INTRODUCTION TO R SOFTWARE
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| INTRODUCTION TO R SOFTWARE | BÄ°S771 | 3rd Semester | 2 | 2 | 3 | 7 |
| Prequisites | None | |||||
| Course language | Turkish | |||||
| Course type | Must | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Drill and Practice | |||||
| Instructor (s) | PROF. ERDEM KARABULUT, PhD. - PROF. PINAR ÖZDEMİR, PhD., DR. ÖĞR.ÜYESİ OSMAN DAĞ | |||||
| Course objective | Open source R software, which is one of the most widely used software, will be taught. usage of basic statistical analysis, construction of single and multivariate graphs, adding new data and/or variables to existing graphs, generating data from theoretical distributions which are used in simulation studies, will be discussed. one sample, paired-independent two and k sample hypothesis tests, linear regression and interpretation of their results will also be discussed. | |||||
| Learning outcomes |
| |||||
| Course Content | - Installation of R and Its Package, - Basic Concepts of R Language - Import Data From Other Programmes, - Usage of Functions with R, - Descriptive Statistics with R, - Basic Graphs of R Language, - Basic and Advaced Graphics Functions, - Theoretical Distributions and Generating Artifical Data, - Hypothesis Tests, Linear Regression Analysis and Applications with R | |||||
| References | 1. W.N. Veneables, D.M. Smithand the R Development Core Team, An Introduction to R, R project, 2008 2. Michael J. Crawley, The R Book, John Wiley & Sons Inc., 2007. 3. Joaquim P.Marques de Sa, Applied Statistics Using SPSS, STATISTICA, MATLAB and R, Springer Berlin Heidelberg, New York,2007. 4. Brian S. Everitt, An R and S-Plus Companion to Multivariate Analysis, Springer-Verlag London Linmited, 2005. 5. Robert Gentleman et al., Bioinformatics and Computational Biology Solutions Using R and Bioconductor, 2005 6. Phil Spector, Data Manipulation with R, Springer- Verlag, 2008 7. Peter Delgaard, Introductry Statistics with R, Springer-Verlag, 2002 | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Installation of R and Its Package |
| Week 2 | Basic Concepts of R Language-1 (Vectors, Matrices, Lists, Electronic Tables |
| Week 3 | Basic Concepts of R Language-2 |
| Week 4 | Manipulation,Revision of Data and Import Data From Other Programmes-1 |
| Week 5 | Manipulation,Revision of Data and Import Data From Other Programmes-2 |
| Week 6 | Writing R Functions |
| Week 7 | Midterm exam |
| Week 8 | Descriptive Statistics with R |
| Week 9 | Basic Graphs of R Language |
| Week 10 | Basic and Advaced Graphics Functions |
| Week 11 | Midterm exam |
| Week 12 | Theoretical Distributions and Generating Artifical Data |
| Week 13 | Hypothesis Tests |
| Week 14 | Linear Regression Analysis and Applications with R |
| Week 15 | Preparation to final exam |
| Week 16 | Final Exam |
Assesment methods
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 0 | 0 |
| Laboratory | 0 | 0 |
| Application | 0 | 0 |
| Field activities | 0 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 5 | 10 |
| Presentation | 0 | 0 |
| Project | 0 | 0 |
| Seminar | 0 | 0 |
| Midterms | 2 | 40 |
| Final exam | 1 | 50 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 7 | 50 |
| Percentage of final exam contributing grade succes | 1 | 50 |
| Total | 100 | |
WORKLOAD AND ECTS CALCULATION
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 14 | 4 | 56 |
| Laboratory | 0 | 0 | 0 |
| Application | 0 | 0 | 0 |
| Specific practical training | 0 | 0 | 0 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 14 | 7 | 98 |
| Presentation / Seminar Preparation | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Homework assignment | 5 | 5 | 25 |
| Midterms (Study duration) | 2 | 10 | 20 |
| Final Exam (Study duration) | 1 | 11 | 11 |
| Total Workload | 36 | 37 | 210 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1. A person who has a degree in Biostatistics, PhD: Has the knowledge to lead research planning, execution, and finalization, staying updated on literature and current studies. | X | ||||
| 2. Has sufficient information in the field, produces notable publications by addressing gaps in literature, both theoretically and practically. | X | ||||
| 3. Asks questions about presentations, seminars, and studies at conferences or seminars, with a critical perspective. | X | ||||
| 4. Has theoretical and practical knowledge of statistics at the level of expertise to determine the appropriate statistical analysis and examine the results in-depth. | X | ||||
| 5. Be proficient in computer use and statistical software, ensuring data suitability and recommending solutions for data management and analysis methods. | X | ||||
| 6. Effectively conducts analysis issues through active participation in discussions, exchanging information with the thesis advisor, and presenting seminars. | X | ||||
| 7. Provides method suggestions in consultancy, does research planning, prepares research reports. | X | ||||
| 8. Maintains scientific accuracy and ethical values, remaining careful against any conscious or unconscious biases throughout the study. | X | ||||
| 9. Be able to present oral presentation and poster in national or international conferences. | X | ||||
| 10. Be able to write a research project proposal independently, take part in a project, write a scientific study report. | X | ||||
| 11. Be able to attend multidisciplinary studies, collaborate professionally in group settings and gain the ability to assign individuals in the group. | X | ||||
| 12. Integrates diverse disciplines to analyze and synthesize information, offering solutions. | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest