Built-in Functions

Why Learn Built-in Functions in Data Analytics?

Imagine you are analyzing product reviews for an e-commerce website. You have a customer rating stored as a text string: "4.8". Additionally, another review has a typo storing the rating as a negative number: -3.5 (which should be positive 3.5).

To use these ratings in your analytics model, you need to:

  1. Convert the text "4.8" into a real decimal number.
  2. Convert the negative -3.5 to its absolute value 3.5.
  3. Round a long average rating like 4.166667 to a clean 4.2 for display.

Instead of writing complex math formulas to achieve this, R provides pre-built solutions called built-in functions. Knowing these functions allows you to clean and format data in seconds.

1. What is a Function?

A function is a reusable block of code that takes one or more inputs (arguments), processes them, and returns an output.

In the previous chapter, you used print() and class(). These are built-in functions:

  • class(4.5): The argument is 4.5, and the function returns the character string "numeric".

2. R Built-in Data Type Conversions

Since data importing often reads values as text (character class), you must frequently convert them to numbers or logials. In R, these functions are prefixed with as.:

  • as.numeric(): Converts to decimal/numeric.
  • as.integer(): Converts to integer.
  • as.character(): Converts to character text.
  • as.logical(): Converts to logical (TRUE/FALSE).
# Data cleaning example
raw_rating <- "4.8"
clean_rating <- as.numeric(raw_rating)

print(class(raw_rating))     # "character"
print(class(clean_rating))   # "numeric"
print(clean_rating * 2)      # 9.6 (Now mathematical calculations work!)

3. Handy Mathematical Built-in Functions

R is built for statistics, so it includes outstanding math functions out-of-the-box:

  • abs(x): Returns the absolute value of x.
  • sqrt(x): Returns the square root of x.
  • round(x, digits): Rounds x to a specified number of decimal places.
  • ceiling(x): Rounds x up to the nearest integer.
  • floor(x): Rounds x down to the nearest integer.
# Cleaning a negative rating and rounding
bad_entry <- -3.567
clean_entry <- abs(bad_entry)
rounded_entry <- round(clean_entry, digits = 1)

print(clean_entry)    # 3.567
print(rounded_entry)  # 3.6

4. Getting User Input in R: readline()

To pause a script and receive input from the user, R uses the readline() function. You can pass a prompt message inside the function.

# Get user input
user_name <- readline(prompt = "Enter your name: ")
print(paste("Hello", user_name))

Important: Just like Python's input(), R's readline() always returns the input as a character string, even if the user types a number. If you need a number, you must wrap it in as.numeric() or as.integer():

# Chaining readline and conversion
user_age <- as.integer(readline(prompt = "Enter your age: "))
print(user_age + 1) # Will successfully add 1

Hands-on Exercises

Exercise 1: Cleanup and Compute

You receive a sensor temperature value as a text string: "102.73". Write R code to:

  1. Store "102.73" in a variable.
  2. Convert it to a numeric data type.
  3. Calculate the square root of the temperature.
  4. Round the square root to 2 decimal places and print it.
# Write your code below and click Run Code
Click to view Answer
temp_str <- "102.73"
temp_num <- as.numeric(temp_str)
temp_sqrt <- sqrt(temp_num)
temp_rounded <- round(temp_sqrt, digits = 2)

print(temp_rounded)

Exercise 2: Absolute Growth Target

A company had a sales change metric of -12.4%. Write R code to:

  1. Store -12.4 in a variable.
  2. Convert it to its absolute value to represent the absolute magnitude of change.
  3. Ceiling the result to the next whole percentage integer.
  4. Print the final absolute target change.
# Write your code below and click Run Code
Click to view Answer
sales_change <- -12.4
absolute_magnitude <- abs(sales_change)
target_change <- ceiling(absolute_magnitude)

print(target_change) # Output: 13