Importing Functions from Packages

Topics: import, function, module

R’s power comes from its vast ecosystem of packages. Learning to load and use functions from packages effectively is crucial for productive programming.

Basic Library Statements

Below are the common ways to load packages that are already built by other developers.

Load an entire package

library(dplyr)

# Create a sample data
sample_df <- data.frame(
  Patient_ID  = 101:105,
  Name        = c("Ana", "Luis", "Maria", "Jose", "Carmen"),
  Age         = c(34, 45, 29, 50, 41)
)

# This is what sample_df looks like:
print(sample_df)
#   Patient_ID   Name Age
# 1        101    Ana  34
# 2        102   Luis  45
# 3        103  Maria  29
# 4        104   Jose  50
# 5        105 Carmen  41

result <- filter(sample_df, Age > 40)   # Use package functions directly

# This is what the filtered data looks like:
print(result)
#   Patient_ID   Name Age
# 1        102   Luis  45
# 2        104   Jose  50
# 3        105 Carmen  41

Install a package first (if not already installed)

install.packages("dplyr")
library(dplyr)

Use a function from a package without loading it

You can call a single function from a package using :: without loading the whole package.

result <- dplyr::filter(sample_df, Age > 40)

This is useful when you only need one function, or when two packages have functions with the same name.

Why not load everything?

  • Packages can contain many functions; loading only what you need keeps your workspace clean.
  • Name conflicts between packages can cause unexpected errors.
  • Use package::function() notation when conflicts arise.

Common Built-in and Base R Functions

Math Functions (base R)

R has a rich set of built-in mathematical functions — no package needed.

# Square root and power
sqrt(16)         # 4
2 ^ 3            # 8

# Factorial and logarithms
factorial(5)     # 120
log(10)          # Natural log:    2.302585
log10(100)       # Base-10 log:    2
log2(8)          # Base-2 log:     3

# Trigonometric
sin(pi / 2)      # 1
cos(0)           # 1

# Rounding
ceiling(4.2)     # Round up:   5
floor(4.8)       # Round down: 4
round(4.567, 2)  # Round to 2 decimal places: 4.57

# Constants
pi               # 3.141593
exp(1)           # e = 2.718282
Random Number Functions (base R)

R has built-in random number generation — useful for randomization in research.

# Generate random numbers
runif(1)                   # One random float between 0 and 1
sample(1:10, 1)            # Random integer between 1 and 10
runif(1, min = 1.5, max = 10.5)   # Random float in a range

Work with vectors

fruits <- c("apple", "banana", "orange", "grape")
sample(fruits, 1)    # Random choice (1 item)
sample(fruits, 2)    # Random sample of 2 items (without replacement)

Shuffle a vector

numbers <- 1:5
shuffled <- sample(numbers)
print(shuffled)   # Vector is now shuffled

Set seed for reproducible results

set.seed(42)
sample(1:100, 1)   # Will always return the same value with seed 42

# The result will always be 49
Date and Time Functions (base R)
# Current date and time
now   <- Sys.time()
today <- Sys.Date()

cat("Current datetime:", format(now), "\n")
cat("Today's date:",    format(today), "\n")

Formatting dates

formatted_date <- format(now, "%Y-%m-%d %H:%M:%S")
cat("Formatted:", formatted_date, "\n")

Date arithmetic

tomorrow  <- today + 1
next_week <- today + 7
past_date <- today - 30

cat("Tomorrow:",  format(tomorrow),  "\n")
cat("Next week:", format(next_week), "\n")
cat("30 days ago:", format(past_date), "\n")

Parse date strings

date_string <- "2025-12-25"
christmas   <- as.Date(date_string, format = "%Y-%m-%d")
cat("Christmas:", format(christmas), "\n")
File and Path Utilities (base R)
# Get current working directory
current_dir <- getwd()
cat("Current directory:", current_dir, "\n")

# List files in directory
files <- list.files(".")
cat("Files in current directory:", paste(files, collapse = ", "), "\n")

# Path construction
file_path <- file.path("data", "files", "example.txt")
cat("File path:", file_path, "\n")

# Check if file/directory exists
cat("Path exists:", file.exists(file_path), "\n")
Creating Reusable Functions in a Script

You can organize your own reusable functions in a separate .R file and load them with source().

# utils.R — A script you might create
format_currency <- function(amount, currency = "PHP") {
  # Format a number as currency.
  formatC(amount, format = "f", digits = 2, big.mark = ",") |>
    paste(currency, x = currency, sep = " ")
}

validate_email <- function(email) {
  # Basic email validation.
  grepl("@", email) & grepl("\\\\.", sub(".*@", "", email))
}

calculate_tip <- function(bill_amount, tip_percentage = 15) {
  # Calculate tip amount.
  bill_amount * (tip_percentage / 100)
}

# main.R — Using your script
source("utils.R")

bill  <- 85.50
tip   <- calculate_tip(bill, 18)
total <- bill + tip

cat(sprintf("Bill:  PHP %.2f\n", bill))    # Bill:  PHP 85.50
cat(sprintf("Tip:   PHP %.2f\n", tip))     # Tip:   PHP 15.39
cat(sprintf("Total: PHP %.2f\n", total))   # Total: PHP 100.89
Writing Functions Packages