R Management for Health Training Course

Course Overview

R Management for Health Training Course is designed to equip healthcare professionals, epidemiologists, public health specialists, monitoring and evaluation officers, researchers, biostatisticians, and health information managers with practical knowledge and advanced skills in using R and RStudio for healthcare data management, statistical analysis, epidemiological modeling, data visualization, and evidence-based decision-making. As healthcare organizations increasingly adopt digital health technologies, Health Management Information Systems (HMIS), Results-Based Management (RBM), Monitoring and Evaluation (M&E), Universal Health Coverage (UHC), Sustainable Development Goals (SDGs), clinical research, disease surveillance, and healthcare analytics, the R programming language has become one of the most powerful open-source platforms for statistical computing and health data science. This comprehensive training enables participants to efficiently manage healthcare datasets, automate analytical workflows, and produce reproducible research that supports healthcare planning, policy development, and organizational performance improvement.

The course provides participants with practical experience in R programming fundamentals, data import and management, data cleaning, descriptive and inferential statistics, epidemiological analysis, predictive modeling, data visualization using ggplot2, report automation with R Markdown, dashboard development, and healthcare analytics. Participants will learn how to manage data from DHIS2, Electronic Medical Records (EMRs), Health Management Information Systems (HMIS), KoboToolbox, SurveyCTO, ODK, Microsoft Excel, SQL databases, GIS platforms, and cloud-based health information systems. The training emphasizes reproducible research practices, efficient coding techniques, automation, and integration of R into healthcare monitoring, evaluation, operational research, and policy analysis.

Participants will further explore advanced topics including regression modeling, survival analysis, time series forecasting, machine learning, geospatial health analysis, outbreak investigation, health economics, healthcare quality improvement, and predictive analytics using widely adopted R packages such as tidyverse, dplyr, tidyr, ggplot2, readr, lubridate, sf, survival, forecast, caret, shiny, and R Markdown. The course demonstrates how R supports evidence generation for maternal and child health, communicable and non-communicable disease programs, nutrition, immunization, health financing, hospital performance, and humanitarian health interventions. Practical exercises focus on transforming raw health data into meaningful insights that improve healthcare delivery and organizational performance.

Through instructor-led practical sessions, interactive workshops, coding laboratories, collaborative assignments, and comprehensive healthcare case studies, participants will develop hands-on experience in implementing R-based analytical workflows from data collection to advanced reporting and visualization. Upon successful completion of the course, participants will possess the technical expertise to perform high-quality statistical analyses, automate health reporting systems, develop interactive dashboards, strengthen monitoring and evaluation systems, support scientific research, and promote data-driven healthcare management across ministries of health, hospitals, research institutions, NGOs, humanitarian organizations, and donor-funded health programs.

Course Objectives

1. Understand the fundamentals of R programming and RStudio for healthcare analytics.
2. Import, clean, manage, and transform healthcare datasets using R.
3. Perform descriptive, inferential, and epidemiological statistical analyses.
4. Develop advanced healthcare data visualizations using R packages.
5. Apply regression, predictive modeling, and machine learning techniques in health research.
6. Automate healthcare reports using R Markdown and reproducible research workflows.
7. Integrate R with healthcare databases, GIS, and digital health platforms.
8. Develop interactive dashboards and health analytics applications using Shiny.
9. Apply international best practices in healthcare data management and statistical analysis.
10. Strengthen evidence-based healthcare planning, research, and policy development.

Organizational Benefits

1. Strengthens evidence-based healthcare planning and strategic decision-making.
2. Improves organizational capacity for healthcare analytics and research.
3. Enhances monitoring, evaluation, and performance measurement systems.
4. Reduces dependence on proprietary statistical software through open-source solutions.
5. Improves healthcare reporting accuracy and automation.
6. Supports disease surveillance and epidemiological investigations.
7. Enhances institutional research and innovation capabilities.
8. Improves healthcare resource allocation through predictive analytics.
9. Supports donor reporting and regulatory compliance.
10. Promotes digital transformation and sustainable health information management.

Target Participants

This course is suitable for:

• Public Health Specialists
• Epidemiologists
• Biostatisticians
• Monitoring and Evaluation Officers
• Health Researchers
• Data Analysts
• Health Information Managers
• Clinical Researchers
• Hospital Administrators
• Ministry of Health Officials
• Health Program Managers
• NGO and Humanitarian Project Staff
• Disease Surveillance Officers
• Digital Health Specialists
• Health Informatics Professionals
• Academic Researchers
• Policy Analysts
• Healthcare Consultants
• Development Partners
• Professionals involved in healthcare analytics, research, monitoring, evaluation, and statistical programming.

Course Outline

Module 1: Introduction to R Programming for Health

• Introduction to R and RStudio
• R programming environment
• Healthcare applications of R
• Installing packages
• Data types and objects
• Reproducible research principles

Case Study: Setting up an R analytical environment for national health survey data analysis.

Module 2: Healthcare Data Import and Management

• Importing Excel and CSV files
• Connecting to SQL databases
• Importing DHIS2 data
• Data structures in R
• Data transformation
• Data management workflows

Case Study: Managing hospital patient records using R.

Module 3: Data Cleaning and Preparation

• Missing value treatment
• Data validation
• Outlier detection
• Variable transformation
• Data reshaping
• Quality assurance

Case Study: Cleaning maternal health datasets before national reporting.

Module 4: Descriptive Statistics and Data Exploration

• Summary statistics
• Frequency distributions
• Cross-tabulations
• Exploratory data analysis
• Health indicator calculations
• Statistical summaries

Case Study: Exploring immunization coverage trends across healthcare facilities.

Module 5: Data Visualization Using ggplot2

• Graph design principles
• Bar charts
• Line charts
• Scatter plots
• Heat maps
• Publication-quality graphics

Case Study: Visualizing malaria incidence trends across multiple regions.

Module 6: Inferential Statistics and Regression Analysis

• Hypothesis testing
• Correlation analysis
• Linear regression
• Logistic regression
• Generalized linear models
• Model diagnostics

Case Study: Identifying determinants of childhood malnutrition using regression analysis.

Module 7: Epidemiological Analysis in R

• Disease prevalence estimation
• Incidence calculations
• Survival analysis
• Cohort studies
• Case-control analysis
• Outbreak investigation

Case Study: Analyzing disease outbreak data to support emergency response planning.

Module 8: Predictive Analytics and Machine Learning

• Predictive modeling
• Classification algorithms
• Decision trees
• Random forests
• Model evaluation
• Forecasting healthcare trends

Case Study: Predicting hospital admission rates using machine learning techniques.

Module 9: Geospatial Health Analysis

• GIS integration
• Spatial data analysis
• Disease mapping
• Hotspot analysis
• Healthcare accessibility analysis
• Geographic visualization

Case Study: Mapping healthcare service coverage for rural communities.

Module 10: Report Automation and Dashboard Development

• R Markdown
• Automated report generation
• Interactive dashboards
• Shiny applications
• Executive reporting
• Data storytelling

Case Study: Developing automated dashboards for Ministry of Health performance reporting.

Module 11: Integrating R with Digital Health Systems

• DHIS2 integration
• KoboToolbox integration
• SurveyCTO integration
• API connectivity
• SQL database integration
• Power BI interoperability

Case Study: Integrating multiple healthcare data sources for national health system reporting.

Module 12: Emerging Trends in Healthcare Data Science

• Artificial Intelligence in healthcare
• Big data analytics
• Cloud computing with R
• Real-time healthcare analytics
• Digital health innovation
• Future trends in health data science

Case Study: Developing predictive analytics models to strengthen disease surveillance and healthcare resource planning.

General Information

1. Customized Training: All our courses can be tailored to meet the specific needs of participants.
2. Language Proficiency: Participants should have a good command of the English language.
3. Comprehensive Learning: Our training includes well-structured presentations, practical exercises, web-based tutorials, coding laboratories, and collaborative group work. Our facilitators are seasoned experts with over a decade of experience in healthcare analytics, biostatistics, epidemiology, and data science.
4. Certification: Upon successful completion of the training, participants will receive a certificate from Foscore Development Center (FDC-K).
5. Training Locations: Training sessions are conducted at Foscore Development Center (FDC-K) centers. We also offer options for in-house and online training customized to the client's schedule.
6. Flexible Duration: Course durations are adaptable, and content can be adjusted to fit the required number of training days.
7. Onsite Training Inclusions: The course fee for onsite training covers facilitation, training materials, two coffee breaks, a buffet lunch, and a Certificate of Successful Completion. Participants are responsible for their travel expenses, airport transfers, visa applications, dinners, health/accident insurance, and personal expenses.
8. Additional Services: Accommodation, pickup services, flight booking, and visa processing arrangements are available upon request at discounted rates.
9. Equipment: Tablets and laptops can be provided to participants at an additional cost.
10. Post-Training Support: We offer one year of free consultation and coaching after the course.
11. Group Discounts: Register as a group of more than two participants and enjoy a discount ranging from 10% to 50%.
12. Payment Terms: Payment should be made before the commencement of the training or as mutually agreed upon, to the Foscore Development Center account. This ensures better preparation for your training.
13. Contact Us: For any inquiries, please reach out to us at training@fdc-k.org or call +254712260031.
14. Website: Visit www.fdc-k.org for more information.