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Information
| Unit | INSTITUTE OF MEDICAL SCIENCES |
| BIOSTATISTICS (MEDICINE) (MASTER) (WITHOUT THESIS) (EVENING EDUCATION) | |
| Code | BIST525 |
| Name | Introduction to R Programming |
| Term | 2025-2026 Academic Year |
| Term | Spring |
| Duration (T+A) | 1-2 (T-A) (17 Week) |
| ECTS | 5 ECTS |
| National Credit | 2 National Credit |
| Teaching Language | Türkçe |
| Level | Lisansüstü Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Doç. Dr. YAŞAR SERTDEMİR |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
The aim of this course is to enable students to learn the R programming language at a basic level for use in health research, perform data analysis and visualization, and generate statistical results in a reproducible and transparent manner.
Course Content
This course introduces the fundamentals of the R programming language, the RStudio environment, data types and data structures, data import, data cleaning and transformation, basic programming constructs (functions, loops), script writing, basic statistical analyses, data visualization (ggplot2), applications using health-related datasets, and reporting of analytical outputs. The course is conducted hands-on using real health data.
Course Precondition
No prerequisites. (Basic biostatistics knowledge is recommended.)
Resources
R Yazılımına Giriş Özlem İlk ODTÜ GELİŞTİRME VAKFI YAYINCILIK - AKADEMİK KİTAPLAR
Notes
CRAN (Comprehensive R Archive Network) documentation RStudio learning resources Open-access health datasets and example codes
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Explain the R programming environment and basic syntax. |
| LO02 | Import and manage health-related data in R. |
| LO03 | Write scripts using basic programming constructs. |
| LO04 | Perform basic statistical analyses using R. |
| LO05 | Visualize analytical results using appropriate graphical methods. |
| LO06 | Ensure reproducibility of code and analytical outputs. |
| LO07 | Present R-based results in accordance with scientific reporting standards. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Explain basic biostatistics, probability, and demographic concepts used in health sciences. | |
| PLO02 | Bilgi - Kuramsal, Olgusal | Define research designs, sampling methods, and data types used in health research. | |
| PLO03 | Bilgi - Kuramsal, Olgusal | Explain the foundations of statistical approaches used in healthcare decision-making processes. | |
| PLO04 | Bilgi - Kuramsal, Olgusal | Explain the basic logic of regression, modeling, and advanced statistical methods used in health sciences. | |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Analyze and interpret data obtained from health research using appropriate statistical methods. | 2 |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Perform statistical analyses and generate outputs using statistical software packages. | 4 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Apply basic data science, artificial intelligence, and machine learning applications in health sciences. | 2 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Evaluate multiple regression and survival analysis results in a clinical context. | |
| PLO09 | Beceriler - Bilişsel, Uygulamalı | Analyze genetic and biomedical data using basic analytical approaches. | |
| PLO10 | Beceriler - Bilişsel, Uygulamalı | Apply scale development, validity, and reliability analyses. | |
| PLO11 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Conduct data analysis and reporting within the scope of a term project. | 1 |
| PLO12 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Apply and manage sampling procedures in health studies. | |
| PLO13 | Yetkinlikler - Öğrenme Yetkinliği | Critically evaluate scientific studies from a statistical perspective. | 1 |
| PLO14 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Present analysis results in accordance with ethical principles. | 1 |
| PLO15 | Yetkinlikler - Alana Özgü Yetkinlik | Applies fundamental concepts of epidemiology and health statistics to clinical and field settings. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Course introduction; introduction to R and RStudio | Reading | Öğretim Yöntemleri: Anlatım |
| 2 | Data types and data structures | Reading | Öğretim Yöntemleri: Anlatım |
| 3 | Data import and data manipulation | Reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 4 | Basic programming constructs | Reading | Öğretim Yöntemleri: Anlatım |
| 5 | Writing functions | Reading | Öğretim Yöntemleri: Anlatım |
| 6 | Introduction to data visualization | Reading | Öğretim Yöntemleri: Anlatım |
| 7 | Graphics with ggplot2 | Reading | Öğretim Yöntemleri: Anlatım |
| 8 | Mid-Term Exam | Ölçme Yöntemleri: Sözlü Sınav |
|
| 9 | Basic statistical analyses | Reading | Öğretim Yöntemleri: Anlatım |
| 10 | Data cleaning and transformation | Reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 11 | Applications using health datasets | Reading | Öğretim Yöntemleri: Anlatım |
| 12 | Reproducible analysis and reporting | Reading | Öğretim Yöntemleri: Soru-Cevap, Anlatım |
| 13 | Practical exercises | Reading | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama |
| 14 | General review | Reading | Öğretim Yöntemleri: Soru-Cevap, Tartışma, Anlatım |
| 15 | Project evaluation | Reading | Öğretim Yöntemleri: Soru-Cevap, Tartışma |
| 16 | Term Exams | Ölçme Yöntemleri: Proje / Tasarım, Ödev |
|
| 17 | Term Exams | Ölçme Yöntemleri: Proje / Tasarım, Ödev |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 3 | 42 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 1 | 15 | 15 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 15 | 15 |
| Total Workload (Hour) | 129 | ||
| Total Workload / 25 (h) | 5,16 | ||
| ECTS | 5 ECTS | ||