Introduction

R has become one of the most widely used languages in healthcare research and medical informatics. From analyzing patient data and visualizing disease trends to supporting AI-based diagnostics, R is transforming how health professionals handle data.

This workshop provides a hands-on, structured approach to learning R fundamentals and applying them to real-world medical and research datasets.

R has become one of the most widely used languages in healthcare research and medical informatics. From analyzing patient data and visualizing disease trends to supporting AI-based diagnostics, R is transforming how health professionals handle data.

This workshop provides a hands-on, structured approach to learning R fundamentals and applying them to real-world medical and research datasets.

• Duration: 3-day intensive workshop
• Format: Hands-on learning with practical exercises
• Schedule: Sometime in 2026
• Delivery: Interactive sessions with live coding demonstrations

• Handle Data Effectively: Organize and manage patient or laboratory data using R’s built-in structures
• Perform Data Analysis: Use dplyr to explore health-related datasets and compute descriptive statistics
• Create Visualizations: Generate charts for patient outcomes, clinical trials, or epidemiological trends
• Perform Common statistical tests: Apply tests such as t-test, ANOVA, chi-square, and logistic regression tests to biomedical datasets
• Build a Foundation: Prepare for advanced applications like biostatistics, machine learning, and clinical decision support

• Read and process data from various file formats
• Filter and query datasets to extract meaningful insights
• Perform statistical tests (t-tests, ANOVA, chi-square, logistic regression)
• Create professional data visualizations
• Build a foundation for advanced R applications

• Students in the Davao Medical School Foundation, Inc.
• Complete beginners with no prior programming experience
• Professionals looking to add R skills to their toolkit
• Researchers who need to analyze data programmatically

• No programming experience required - we start from the very basics
• Basic computer literacy (file management, using applications)
• Willingness to learn and practice hands-on coding
• A laptop with internet connection for installation and practice

• Medical professionals aiming to integrate data science into clinical practice or research
• Medical students preparing for coursework or electives involving biostatistics, informatics, or computational tools
• Clinicians and researchers seeking to enhance decision-making through data-driven approaches
• Healthcare professionals curious about programming, data analysis, and their applications in medicine

• Confidently write basic to intermediate R programs and functions
• Understand and apply fundamental programming concepts
• Perform basic data manipulation and analysis tasks
• Create simple visualizations from datasets

• Data-Driven Insight: Confidently explore and interpret clinical or survey data
• Research Independence: Perform your own data cleaning and analysis for publications
• Problem-Solving: Develop computational thinking skills
• Foundation Building: Prepared for advanced topics like machine learning
• Automation in Practice: Streamline repetitive workflows, such as summarizing lab data or generating reports

By workshop completion, you’ll have created:

• A personal library of useful R functions
• Mini-projects analyzing sample clinical datasets (e.g., patient demographics, vital signs, or lab results)
• Solutions to data-driven questions often encountered in healthcare and research
• A foundation for continued learning and development

• R basics and development environment setup
• Variables, data types, and basic operations
• Introduction to programming logic

• Control structures and functions
• Working with packages and handling errors
• Hands-on practice with increasingly complex problems

• Data analysis with dplyr
• Statistical analysis and visualization
• Capstone project and wrap-up

• Comprehensive understanding of R fundamentals
• Experience with industry-standard data analysis tools
• Portfolio of completed programming projects
• Confidence to tackle new programming challenges

• Complete workshop materials and code examples
• Recommended resources for further study
• Clear pathways for advancing your R skills

Participants who complete all workshop activities will receive a Certificate of Completion in “Data Analysis in R for Complete Beginners,” issued by the Center for Research and Development, DMSFI.

• Variable – A named container used to store data values in a program.
• Data Type – The classification of data that tells R what kind of value is being stored (e.g., numeric, character, logical).
• Vector – An ordered collection of values of the same type (e.g., c(10, 20, 30)).
• List – A flexible collection that can hold elements of different types.
• Function – A reusable block of code that performs a specific task.
• Package – A collection of pre-written R functions and tools (e.g., dplyr, gtsummary, ggplot2).
• Data Frame – R’s version of a spreadsheet or database table. It organizes data into rows and columns, making it easy to store, manipulate, analyze, and visualize datasets.
• Control Structures – Logical structures that determine the order in which code executes, such as loops (for, while) and conditionals (if, else).

• Querying – Extracting specific subsets of data based on defined criteria or conditions.
• Filtering – Selecting rows that meet one or more conditions (e.g., heart rate > 100 BPM).
• Subsetting – Accessing specific rows, columns, or observations from a dataset.
• Indexing – Accessing elements using row and column positions or names.
• Bracket Notation [ ] – Base R method for selecting rows and columns.
• Logical Indexing – Using logical conditions (TRUE/FALSE) to subset data.
• dplyr::filter() – A convenient function for selecting rows based on conditions.
• dplyr::select() – Used to choose specific columns from a dataset.
• Membership Testing (%in%) – Filters observations whose values match one or more values in a specified list.
• String Operations – Text-based filtering and transformation using functions such as grepl(), gsub(), stringr::str_detect(), and stringr::str_replace().
• Conditional Transformation – Creating new variables based on conditions using ifelse() or dplyr::case_when().

• Grouping (group_by()) – Organizing observations into groups based on one or more variables.
• Aggregation (summarise()) – Computing summary statistics such as mean, median, count, standard deviation, or percentages for each group.
• Custom Aggregation – Applying user-defined calculations to summarize grouped data.
• Mutating (mutate()) – Creating new variables or transforming existing variables.
• Sorting (arrange()) – Ordering rows in ascending or descending order based on one or more variables.
• Pipelining (|>, %>%) – Combining multiple data manipulation steps into a streamlined workflow.
• Chained Operations – Performing filtering, transformation, aggregation, and sorting in a single sequence of commands.

• ggplot2 – The most widely used R package for creating elegant and customizable data visualizations.
• Base R Graphics – Built-in plotting functions such as plot(), hist(), and boxplot().
• Plot – A graphical representation of data used to explore patterns and communicate results.
• Aesthetics (aes()) – Visual properties such as position, color, shape, and size mapped to variables.
• Histogram – Displays the frequency distribution of a numeric variable.
• Box Plot – Visualizes the distribution of data and highlights potential outliers.
• Scatter Plot – Displays the relationship between two numeric variables.
• Bar Plot – Represents categorical data using bars.
• Violin Plot – Combines box plots and density plots to show the distribution of data.
• Density Plot – Visualizes the distribution of a continuous variable.
• Heatmap – Displays values using colors, often for correlation matrices or expression data.
• Pie Chart – Represents category proportions in a circular chart.
• Legend – Identifies the meaning of colors, shapes, or other visual elements.
• Regression Line – A fitted line showing the trend or relationship between variables.
• Faceting – Creating multiple plots based on levels of a categorical variable.

• Statistics – The science of collecting, analyzing, interpreting, and presenting data.
• Descriptive Statistics – Methods used to summarize and describe the characteristics of a dataset.
• Inferential Statistics – Methods used to draw conclusions about a population based on sample data.

🔹 Descriptive Measures

• Mean – The arithmetic average.
• Median – The middle value when data are sorted.
• Mode – The most frequently occurring value.
• Range – Difference between the maximum and minimum values.
• Standard Deviation (SD) – Measures how spread out the data are from the mean.
• Variance – Square of the standard deviation.
• Interquartile Range (IQR) – Difference between the 75th and 25th percentiles.
• Quartiles – Values that divide data into four equal parts.
• Summary Statistics – A collection of descriptive measures used to characterize a dataset.

• Null Hypothesis (H₀) – The default assumption that there is no effect or difference.
• Alternative Hypothesis (H₁) – States that there is an effect or difference.
• p-value – Probability of observing the data (or more extreme) assuming H₀ is true.
• Significance Level (α) – Threshold (commonly 0.05) used to decide whether to reject H₀.
• Type I Error – Incorrectly rejecting a true null hypothesis (false positive).
• Type II Error – Failing to reject a false null hypothesis (false negative).
• Statistical Significance – Evidence that an observed result is unlikely to have occurred by chance alone.

• t-test – Compares means between groups.

  • One-sample t-test: compares sample mean to a known population mean.
  • Independent (two-sample) t-test: compares means of two independent groups.
  • Paired t-test: compares means of the same group before and after a treatment.

• ANOVA (Analysis of Variance) – Tests differences among means of three or more groups.

  • One-way ANOVA: compares one factor across multiple groups.
  • Two-way ANOVA: examines the effect of two factors simultaneously.
  • Post-hoc tests: pairwise comparisons following a significant ANOVA result.

• Chi-Square Test – Tests relationships between categorical variables.

  • Test of Independence: determines if two categorical variables are related.
  • Goodness of Fit: checks if observed data match expected distributions.

• Correlation – Measures the strength and direction of a linear relationship between two variables (range: -1 to +1).

  • Positive correlation: variables increase together.

  • Negative correlation: one increases while the other decreases.

  • Pearson’s r – The most common correlation coefficient for continuous variables.

• Regression Analysis – Predicts the value of a dependent variable based on one or more independent variables.

  • Simple Linear Regression – Uses one predictor variable to predict an outcome.

    • Slope: change in outcome for every one-unit increase in predictor.
    • Intercept: predicted outcome when the predictor is zero.
    • R-squared: proportion of variance in the dependent variable explained by the independent variable.

  • Logistic Regression – Predicts binary outcomes (e.g., yes/no, 0/1, Disease/Healthy) using continuous or categorical predictors.

    • Odds Ratio: how much the odds of the outcome change with a one-unit increase in the predictor.
    • onfusion Matrix: table showing correct and incorrect predictions.
    • Accuracy: proportion of correct predictions.

• Train-Test Split – Dividing data into training (for model building) and testing (for evaluation).
• Random Seed (set.seed()) – Ensures reproducibility of analyses involving randomness.
• Feature (Predictor Variable) – An independent variable or predictor in a dataset.
• Target Variable (Response Variable) – The dependent variable or outcome being predicted.
• Normalization / Scaling – Adjusting data values to a common scale without distorting differences.
• Standardization – Transforming data to have a mean of 0 and a standard deviation of 1.
• Missing Data Handling – Identifying, removing, or imputing missing values.
• Reproducibility – Ensuring analyses can be consistently repeated and verified by others.

1. To download R, go to https://cran.r-project.org/. Select your operating system (Windows, macOS, or Linux), download the installer, and follow the default installation steps.
2. To download Rstudio, go to https://posit.co/download/rstudio-desktop/. RStudio provides an integrated development environment (IDE) that makes writing, running, and managing R code much easier.