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:

Convert the text "4.8" into a real decimal number.
Convert the negative -3.5 to its absolute value 3.5.
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. Built-in Statistical Summary Functions

Data analysts frequently need to summarize entire lists (vectors) of numbers to calculate key metrics. R includes highly optimized base statistical functions out-of-the-box:

sum(x): Adds all elements in a vector.
mean(x): Calculates the arithmetic average.
median(x): Finds the middle value (50th percentile).
sd(x): Computes the standard deviation (how spread out the values are).
min(x) and max(x): Finds the minimum and maximum values.

Handling Missing Data: The na.rm = TRUE Argument

In real-world data science, missing values are extremely common and are represented by the reserved value NA (Not Available). If a vector contains even a single NA value, R's statistical summary functions will return NA by default!

To bypass this, base R statistical functions include a special argument: na.rm = TRUE (which stands for "NA remove = TRUE").

Let's look at a practical weekly analytics summary:

# Daily store customer count (with one missing day due to sensor error)
daily_customers <- c(120, 150, NA, 190, 110, 140, 175)

# Summary functions fail by default if NA is present:
print(mean(daily_customers)) # Output: NA

# Clean calculation using the na.rm = TRUE argument
total_customers <- sum(daily_customers, na.rm = TRUE)
avg_customers   <- mean(daily_customers, na.rm = TRUE)
variation       <- sd(daily_customers, na.rm = TRUE)

print(paste("Total Weekly Customers:", total_customers))
print(paste("Average Daily Customers:", round(avg_customers, digits = 1)))
print(paste("Standard Deviation:", round(variation, digits = 2)))

5. Simple Text Manipulation Built-in Functions

Text data (character strings) is often messy, containing mixed capitalization or accidental leading and trailing whitespace. Base R provides extremely handy built-in functions to clean text:

nchar(x): Counts the number of characters (letters, numbers, punctuation, spaces) in a string.
tolower(x) / toupper(x): Standardizes text casing.
trimws(x): Trims off any accidental leading or trailing blank spaces.
paste(..., sep = " "): Combines multiple strings or vectors together with a custom separator.
paste0(...): A faster version of paste() that joins strings together with no space/separator.

Let's look at a text-standardization example:

# Messy customer submissions
raw_location <- "   Ann Arbor  "
promo_code   <- "SAVE_20_Percent"

# 1. Trim blanks and convert location to standard uppercase (nested functions!)
clean_location <- toupper(trimws(raw_location))

# 2. Check characters of promo code to ensure it meets system limits
code_length <- nchar(promo_code)

# 3. Join them into a clean transaction receipt entry
receipt_entry <- paste0("LOCATION: ", clean_location, " | PROMO_CODE: ", promo_code, " (Length: ", code_length, ")")
print(receipt_entry)

Hands-on Exercises

Exercise 1: Cleanup and Compute

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

Store "102.73" in a variable.
Convert it to a numeric data type.
Calculate the square root of the temperature.
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:

Store -12.4 in a variable.
Convert it to its absolute value to represent the absolute magnitude of change.
Ceiling the result to the next whole percentage integer.
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