Introduction to Functions
Function documentation
# Consult the documentation on the mean() function
?mean
## mean package:base R Documentation
## ...
# Inspect the arguments of the mean() function
args(mean)
## function (x, ...)
## NULL
Use a function
# The linkedin and facebook vectors have already been created for you
linkedin <- c(16, 9, 13, 5, 2, 17, 14)
facebook <- c(17, 7, 5, 16, 8, 13, 14)
# Calculate average number of views
avg_li <- mean(linkedin)
avg_fb <- mean(facebook)
# Inspect avg_li and avg_fb
avg_li
## [1] 10.85714
avg_fb
## [1] 11.42857
Use a function (2)
# The linkedin and facebook vectors have already been created for you
linkedin <- c(16, 9, 13, 5, 2, 17, 14)
facebook <- c(17, 7, 5, 16, 8, 13, 14)
# Calculate the mean of the sum
avg_sum <- mean(linkedin + facebook)
# Calculate the trimmed mean of the sum
avg_sum_trimmed <- mean(linkedin +facebook, trim = 0.2)
# Inspect both new variables
avg_sum
## [1] 22.28571
avg_sum_trimmed
## [1] 22.6
Use a function (3)
# The linkedin and facebook vectors have already been created for you
linkedin <- c(16, 9, 13, 5, NA, 17, 14)
facebook <- c(17, NA, 5, 16, 8, 13, 14)
# Basic average of linkedin
mean(linkedin)
## [1] NA
# Advanced average of linkedin
mean(linkedin, na.rm = TRUE)
## [1] 12.33333
Functions inside functions
# The linkedin and facebook vectors have already been created for you
linkedin <- c(16, 9, 13, 5, NA, 17, 14)
facebook <- c(17, NA, 5, 16, 8, 13, 14)
# Calculate the mean absolute deviation
mean(abs(linkedin - facebook), na.rm = TRUE)
## [1] 4.8
Required, or optional?
Writing Functions
Write your own function
# Create a function pow_two()
pow_two <- function(x) {
x^2
}
# Use the function
pow_two(12)
## [1] 144
# Create a function sum_abs()
sum_abs <- function(x, y) {
abs(x) + abs(y)
}
# Use the function
sum_abs(-2, 3)
## [1] 5
Write your own function (2)
# Define the function hello()
hello <- function() {
print("Hi there!")
TRUE
}
# Call the function hello()
hello()
## [1] "Hi there!"
## [1] TRUE
Write your own function (3)
# Finish the pow_two() function
pow_two <- function(x, print_info = TRUE) {
y <- x ^ 2
if(print_info == TRUE) {
print(paste(x, "to the power two equals", y))
}
return(y)
}
Function scoping
two_dice <- function() {
possibilities <- 1:6
dice1 <- sample(possibilities, size = 1)
dice2 <- sample(possibilities, size = 1)
dice1 + dice2
}
# Whatever the way of calling the two_dice() function, R won't have access to dice1 and dice2 outside the function.
R passes arguments by value
increment <- function(x, inc = 1) {
x <- x + inc
x
}
count <- 5
a <- increment(count, 2)
b <- increment(count)
count <- increment(count, 2)
#a and b equal 7 and 6 respectively after executing this code block.
#After the first call of increment(), where a is defined, a equals 7 and count equals 5.
#In the last expression, the value of count was actually changed because of the explicit assignment.
R you functional?
# The linkedin and facebook vectors have already been created for you
linkedin <- c(16, 9, 13, 5, 2, 17, 14)
facebook <- c(17, 7, 5, 16, 8, 13, 14)
# Define the interpret function
interpret <- function(num_views) {
if (num_views > 15) {
print("You're popular!")
return(num_views)
} else {
print("Try to be more visible!")
return(0)
}
}
# Call the interpret function twice
interpret(linkedin[1])
## [1] "You're popular!"
## [1] 16
interpret(facebook[2])
## [1] "Try to be more visible!"
## [1] 0
R you functional? (2)
# The linkedin and facebook vectors have already been created for you
linkedin <- c(16, 9, 13, 5, 2, 17, 14)
facebook <- c(17, 7, 5, 16, 8, 13, 14)
# The interpret() can be used inside interpret_all()
interpret <- function(num_views) {
if (num_views > 15) {
print("You're popular!")
return(num_views)
} else {
print("Try to be more visible!")
return(0)
}
}
# Define the interpret_all() function
# views: vector with data to interpret
# return_sum: return total number of views on popular days?
interpret_all <- function(views, return_sum = TRUE) {
count <- 0
for (v in views) {
count <- count + interpret(v)
}
if (return_sum == TRUE) {
return(count)
} else {
return(NULL)
}
}
# Call the interpret_all() function on both linkedin and facebook
interpret_all(linkedin)
## [1] "You're popular!"
## [1] "Try to be more visible!"
## [1] "Try to be more visible!"
## [1] "Try to be more visible!"
## [1] "Try to be more visible!"
## [1] "You're popular!"
## [1] "Try to be more visible!"
## [1] 33
interpret_all(facebook)
## [1] "You're popular!"
## [1] "Try to be more visible!"
## [1] "Try to be more visible!"
## [1] "You're popular!"
## [1] "Try to be more visible!"
## [1] "Try to be more visible!"
## [1] "Try to be more visible!"
## [1] 33
R Packages
Load an R Package
# Load the ggplot2 package
library(ggplot2)
# Retry the qplot() function
qplot(mtcars$wt, mtcars$hp)
# Check out the currently attached packages again
search()
## [1] ".GlobalEnv" "package:ggplot2" "package:stats"
## [4] "package:graphics" "package:grDevices" "package:utils"
## [7] "package:datasets" "package:methods" "Autoloads"
## [10] "package:base"
Different ways to load a package
