Packages

Topics: package, library, source, script

Understanding packages is essential to organizing your code and taking full advantage of R’s extensive ecosystem. In healthcare and biomedical research, packages allow you to separate reusable logic — such as calculations, data cleaning, or patient analytics — into organized files and shareable bundles.

What Are Packages?

A package is a collection of functions, datasets, and documentation stored in a standardized structure.

Any .R file can be sourced into another R script using source(). Formal packages are built with tools like devtools and submitted to CRAN or GitHub.

Packages help you:

Reuse code across multiple projects
Keep your code organized and maintainable
Share your work with colleagues or research collaborators

Creating Your Own Script of Functions

Let’s create a file named medical_calculator.R — a simple script that performs basic medical computations.

Example: medical_calculator.R

This script provides basic biomedical calculations that could be used in clinical or research contexts.

# medical_calculator.R

bmi <- function(weight, height) {
  # Calculate Body Mass Index.
  weight / (height ^ 2)
}

bsa <- function(weight, height) {
  # Calculate Body Surface Area (Mosteller formula).
  # weight in kg, height in cm
  sqrt((height * weight) / 3600)
}

mean_arterial_pressure <- function(systolic, diastolic) {
  # Calculate Mean Arterial Pressure (MAP).
  (2 * diastolic + systolic) / 3
}

temperature_c_to_f <- function(celsius) {
  # Convert Celsius to Fahrenheit.
  (celsius * 9 / 5) + 32
}

temperature_f_to_c <- function(fahrenheit) {
  # Convert Fahrenheit to Celsius.
  (fahrenheit - 32) * 5 / 9
}

# Module-level constants
NORMAL_BMI_RANGE   <- c(18.5, 24.9)
BODY_WATER_PERCENT <- 0.6

cat("Medical Calculator script loaded successfully.\n")

Self-test block (runs only when the file itself is executed directly):

if (sys.nframe() == 0) {
  cat("Running tests for Medical Calculator...\n")
  cat(sprintf("BMI (70 kg, 1.75 m): %.2f\n", bmi(70, 1.75)))
  cat(sprintf("BSA (70 kg, 175 cm): %.2f\n", bsa(70, 175)))
  cat(sprintf("MAP (120/80 mmHg):   %.2f\n", mean_arterial_pressure(120, 80)))
}

Using Your Script

Now create another file, main.R, where we source and use our script.

source("medical_calculator.R")

Use functions from the script:

bmi_value <- bmi(65, 1.68)
map_value <- mean_arterial_pressure(118, 76)

cat(sprintf("BMI: %.2f\n",                         bmi_value))
cat(sprintf("Mean Arterial Pressure: %.1f mmHg\n", map_value))

Using specific functions after sourcing

Because source() loads everything in the file, all functions are immediately available in your environment. You can then call any of them directly:

cat(sprintf("BMI: %.2f\n",           bmi(58, 1.60)))
cat(sprintf("BSA: %.2f\n",           bsa(58, 160)))
cat(sprintf("Temperature: %.1f°F\n", temperature_c_to_f(37)))

🩺 Why This Matters?

Using scripts and packages helps healthcare professionals, researchers, and data analysts reuse and standardize computations across projects.

For example:

Standardize BMI formulas for all datasets
Maintain consistency in how blood pressure is interpreted
Reduce errors when analyzing patient data

Scripts and packages make your code modular, reusable, and easier to maintain — crucial for building reliable healthcare tools and analysis pipelines.

Whether you’re creating a clinical calculator, automating data cleaning, or building a patient outcome model, modular programming ensures accuracy, collaboration, and scalability.

Importing Functions from Packages Errors and Exception Handling