Course Introduction
The R for Health and Epidemiological Analytics Training Course provides a practical and comprehensive learning experience for health professionals, epidemiologists, researchers, public health analysts, and data specialists who need to transform health data into reliable analytical evidence. The course develops practical R skills for managing, exploring, analyzing, visualizing, and reporting health and epidemiological datasets across research, surveillance, monitoring, and program evaluation contexts.
This R health analytics training course introduces participants to R programming, data structures, statistical workflows, and reproducible analytical practices relevant to public health and clinical research. Learners work with datasets representing patient records, disease surveillance, health service utilization, population characteristics, risk factors, mortality, morbidity, laboratory observations, and program indicators. R data frames provide a fundamental structure for organizing variables and observations and are extensively used by R's statistical modeling tools.
A major focus of the training is health data preparation and quality management. Participants learn how to import datasets, inspect variable definitions, identify missing observations, detect duplicate records, standardize categorical variables, transform dates, manage inconsistent coding, and construct analysis-ready datasets. These capabilities are essential when working with health information collected from different facilities, surveys, surveillance systems, registries, laboratories, or reporting periods, where data quality and consistent definitions directly affect analytical interpretation.
The course develops practical epidemiological analysis skills, including measures of disease frequency, risk, association, and population characteristics. Participants learn to calculate and interpret prevalence, incidence, proportions, rates, ratios, risk measures, and comparative indicators while examining patterns across demographic groups, geographic areas, exposure categories, and time periods. Statistical concepts are presented through practical health examples so learners can connect R outputs with epidemiological questions and research objectives.
Data visualization is an important component of the course, with participants using R and ggplot2 to communicate health patterns through clear and informative graphics. ggplot2 uses a grammar-of-graphics framework in which data, aesthetic mappings, layers, scales, facets, coordinates, and themes can be combined to construct visualizations. Learners apply these capabilities to disease trends, surveillance indicators, population distributions, treatment outcomes, risk factors, geographic comparisons, and health-service performance measures.
By the end of this R epidemiological analytics workshop, participants will be able to develop reproducible R workflows for health research, surveillance, epidemiological investigation, monitoring, and program evaluation. They will be able to prepare health datasets, perform descriptive and inferential analyses, develop regression models, visualize epidemiological findings, evaluate analytical assumptions, and communicate results appropriately. The course culminates in an end-to-end health analytics project that integrates data preparation, epidemiological analysis, visualization, interpretation, and professional reporting.
Duration
5 days
Who Should Attend
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Epidemiologists responsible for quantitative analysis of disease, population, and health research data.
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Public health professionals working with surveillance, monitoring, evaluation, and health program datasets.
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Health data analysts responsible for preparing and analyzing clinical, administrative, or population health data.
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Biostatisticians seeking practical R skills for health and epidemiological analysis workflows.
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Medical and clinical researchers conducting observational or quantitative health research.
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Public health researchers analyzing risk factors, disease patterns, interventions, and health outcomes.
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Monitoring and evaluation specialists assessing health programs, services, indicators, and intervention outcomes.
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Health information system professionals working with routine health information and reporting datasets.
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Disease surveillance officers analyzing cases, trends, outbreaks, and population-level indicators.
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Healthcare quality and performance analysts examining service utilization and health outcomes.
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Research assistants and data managers supporting epidemiological studies and health research projects.
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Health policy and program professionals who need to interpret quantitative epidemiological evidence.
Course Objectives
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Develop practical proficiency in R programming and apply R to complete health data management, epidemiological analysis, visualization, and reporting workflows.
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Import, inspect, clean, transform, validate, and document health datasets from surveys, surveillance systems, clinical records, registries, and administrative sources.
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Calculate and interpret epidemiological measures including prevalence, incidence, rates, ratios, risks, odds, and other indicators relevant to health research.
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Conduct descriptive analyses of health populations by person, place, time, exposure, outcome, demographic characteristics, and other epidemiologically relevant dimensions.
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Apply statistical methods including confidence intervals, hypothesis testing, association analysis, contingency tables, correlation, and regression to appropriate health research questions.
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Build and interpret regression models for epidemiological analysis while considering confounding, interaction, model assumptions, diagnostics, uncertainty, and limitations.
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Create clear health and epidemiological visualizations using R and ggplot2 to communicate disease trends, risk patterns, population differences, and research findings.
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Analyze longitudinal and time-series health data to identify temporal patterns, changes in disease indicators, seasonal behavior, and trends requiring further investigation.
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Develop reproducible R workflows that document data preparation, analytical decisions, statistical methods, visualizations, results, assumptions, and limitations.
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Complete an end-to-end epidemiological analytics project that transforms a realistic health dataset into validated analytical findings and professionally communicated results.
Comprehensive Course Outline
Module 1: R Foundations for Health and Epidemiological Analytics
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Understanding R, RStudio or compatible development environments, projects, scripts, packages, objects, functions, and programming concepts used in health analytics.
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Working with vectors, factors, data frames, lists, dates, strings, logical values, and other R structures commonly encountered in epidemiological datasets.
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Using variables, operators, conditional logic, functions, indexing, filtering, and reusable programming techniques to automate recurring health-data analysis tasks.
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Establishing organized R projects with data directories, scripts, analytical outputs, documentation, package management, and reproducibility controls for health research.
Module 2: Health Data Import, Cleaning, and Quality Management
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Importing clinical, surveillance, survey, registry, laboratory, demographic, and administrative health datasets into R for systematic analysis.
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Inspecting variable definitions, coding structures, data types, identifiers, dates, measurement units, and metadata before beginning substantive epidemiological analysis.
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Identifying missing observations, duplicate records, inconsistent classifications, invalid values, implausible measurements, and other common health-data quality problems.
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Creating transparent data-cleaning workflows that preserve source information while producing validated and analysis-ready datasets for health research.
Module 3: Epidemiological Data Transformation and Indicators
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Filtering, selecting, arranging, grouping, and summarizing health records to construct analytical datasets and epidemiological summary tables.
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Calculating prevalence, incidence, mortality, case-fatality, attack rates, standardized indicators, proportions, ratios, and other measures used in health analytics.
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Creating age groups, exposure categories, outcome classifications, follow-up periods, geographic groups, and other analytical variables required for epidemiological research.
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Validating calculated indicators through denominator checks, reconciliation, missing-data assessment, consistency testing, and comparison with established health reporting definitions.
Module 4: Descriptive Epidemiology and Exploratory Health Analysis
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Applying descriptive statistics to understand distributions, central tendency, variation, percentiles, missingness, and unusual observations in health datasets.
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Analyzing disease and health outcomes by person, place, and time while examining demographic, geographic, clinical, behavioral, and socioeconomic characteristics.
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Comparing health indicators across population groups, exposure categories, facilities, geographic areas, and reporting periods to identify meaningful patterns.
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Developing epidemiological questions from exploratory findings while distinguishing descriptive associations from conclusions that require formal statistical or study-design analysis.
Module 5: Health Data Visualization and Epidemiological Reporting
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Building health visualizations with ggplot2 using data, aesthetic mappings, layers, scales, facets, coordinates, and themes to communicate epidemiological findings effectively.
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Creating line charts, bar charts, histograms, box plots, density plots, scatter plots, heatmaps, and other visualizations for health and surveillance datasets.
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Designing disease trend charts, population comparisons, risk-factor visualizations, clinical outcome summaries, and health-service performance graphics for professional reporting.
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Applying appropriate titles, labels, annotations, scales, legends, and visual encodings to ensure health findings are communicated accurately to technical and non-technical audiences.
Module 6: Statistical Methods for Epidemiological Analysis
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Applying descriptive statistics, probability concepts, sampling principles, confidence intervals, and measures of uncertainty to epidemiological research questions.
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Constructing and interpreting contingency tables, proportions, comparisons, association measures, and statistical tests for categorical health outcomes.
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Applying appropriate hypothesis-testing approaches while considering sample characteristics, assumptions, statistical significance, confidence intervals, and practical health relevance.
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Interpreting statistical findings responsibly by distinguishing statistical association from causation and documenting uncertainty, limitations, and potential sources of bias.
Module 7: Regression and Multivariable Epidemiological Analysis
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Building and interpreting linear regression models for continuous health outcomes while evaluating coefficients, model fit, residuals, assumptions, and influential observations.
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Applying logistic regression to binary health outcomes and interpreting odds ratios, confidence intervals, model diagnostics, and covariate effects in epidemiological contexts.
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Introducing count and rate modeling concepts for health outcomes involving events, populations at risk, follow-up periods, and exposure time.
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Examining confounding, effect modification, interaction, variable selection, model specification, and other considerations that influence interpretation of multivariable epidemiological models.
Module 8: Surveillance, Longitudinal, and Time-Series Health Analytics
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Preparing surveillance datasets for analysis by organizing cases, reporting dates, geographic identifiers, disease classifications, demographic variables, and relevant epidemiological measures.
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Analyzing disease and health indicators over time using R time-series structures, trends, seasonal patterns, rolling summaries, and temporal comparisons. R provides dedicated time-series structures through its
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Investigating changes in disease occurrence, service utilization, mortality, treatment outcomes, and other health indicators across reporting periods.
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Developing analytical outputs for surveillance monitoring while distinguishing observed temporal patterns from evidence requiring further epidemiological investigation.
Module 9: Epidemiological Research, Reproducibility, and Health Reporting
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Structuring reproducible R research projects so that data preparation, statistical analysis, visualization, and reporting can be repeated consistently as datasets are updated.
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Documenting study populations, variable definitions, inclusion criteria, exclusions, analytical methods, assumptions, missing-data procedures, and statistical decisions.
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Creating professional health analytics reports containing tables, figures, statistical results, methodological explanations, limitations, and clearly stated interpretations.
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Communicating epidemiological findings to researchers, clinicians, public health officials, program managers, and other stakeholders using precise and accessible analytical language.
Module 10: End-to-End Health and Epidemiological Analytics Project
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Designing a complete R health analytics project using a realistic surveillance, clinical, survey, population, or program dataset from initial preparation through final reporting.
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Developing an analytical workflow that defines the research question, prepares the data, calculates epidemiological measures, performs statistical analysis, and validates analytical outputs.
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Producing publication-style tables, epidemiological visualizations, statistical results, and narrative findings that clearly communicate the evidence generated from the dataset.
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Presenting the completed project with reproducible R code, documented methodology, analytical assumptions, limitations, visual outputs, and a professional interpretation of the findings.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
