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DC Intermediate R - Functions

# DC Intermediate R
DC Intermediate R - Conditionals and Control Flow
DC Intermediate R - Loops
DC Intermediate R - Functions
DC Intermediate R - The apply family
DC Intermediate R - Utilities

1 Introduction to Functions[ | ]

2 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

3 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

4 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

5 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

6 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

7 Required, or optional?[ | ]

8 Writing Functions[ | ]

9 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

10 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

11 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)
}

12 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.

13 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.

14 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

15 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

16 R Packages[ | ]

17 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"

18 Different ways to load a package[ | ]

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