Chapter_03_-_Practice_-_Functions.md

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DataCamp - Intermediate R	[Luka Ignjatović](https://github.com/LukaIgnjatovic)	html_document md_document highlight theme toc toc_depth keep_md tango united true 4 true toc toc_depth true 4

Functions

Functions are an extremely important concept in almost every programming language; R is not different. After learning what a function is and how you can use one, you'll take full control by writing your own functions.

Document: ["Slides - Functions"](./Slides/Chapter 03 - Functions.pdf)

Function documentation

Before even thinking of using an R function, you should clarify which arguments it expects. All the relevant details such as a description, usage, and arguments can be found in the documentation. To consult the documentation on the sample() function, for example, you can use one of following R commands:

help(sample)
?sample

If you execute these commands in the console of the DataCamp interface, you'll be redirected to www.rdocumentation.org.

A quick hack to see the arguments of the sample() function is the args() function. Try it out in the console:

args(sample)

In the next exercises, you'll be learning how to use the mean() function with increasing complexity. The first thing you'll have to do is get acquainted with the mean() function.

Instructions

• Consult the documentation on the mean() function: ?mean or help(mean).
• Inspect the arguments of the mean() function using the args() function.

# Consult the documentation on the mean() function
?mean

# Inspect the arguments of the mean() function
args(mean)

Great! That wasn't too hard, was it? Take a look at the documentation and head over to the next exercise.

Use a function

The documentation on the mean() function gives us quite some information:

• The mean() function computes the arithmetic mean.
• The most general method takes multiple arguments: x and ....
• The x argument should be a vector containing numeric, logical or time-related information.

Remember that R can match arguments both by position and by name. Can you still remember the difference? You'll find out in this exercise!

Once more, you'll be working with the view counts of your social network profiles for the past 7 days. These are stored in the linkedin and facebook vectors and have already been defined in the editor on the right.

Instructions

• Calculate the average number of views for both linkedin and facebook and assign the result to avg_li and avg_fb, respectively. Experiment with different types of argument matching!
• Print out both avg_li and avg_fb.

# 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

Nice! I'm sure you've already called more advanced R functions in your history as a programmer. Now you also know what actually happens under the hood.

Use a function (2)

Check the documentation on the mean() function again:

?mean

The Usage section of the documentation includes two versions of the mean() function. The first usage,

mean(x, ...)

is the most general usage of the mean function. The 'Default S3 method', however, is:

mean(x, trim = 0, na.rm = FALSE, ...)

The ... is called the ellipsis. It is a way for R to pass arguments along without the function having to name them explicitly. The ellipsis will be treated in more detail in future courses.

For the remainder of this exercise, just work with the second usage of the mean function. Notice that both trim and na.rm have default values. This makes them optional arguments.

Instructions

• Calculate the mean of the element-wise sum of linkedin and facebook and store the result in a variable avg_sum.
• Calculate the mean once more, but this time set the trim argument equal to 0.2 and assign the result to avg_sum_trimmed.
• Print out both avg_sum and avg_sum_trimmed; can you spot the difference?

# 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

Nice! When the trim argument is not zero, it chops off a fraction (equal to trim) of the vector you pass as argument x.

Use a function (3)

In the video, Filip guided you through the example of specifying arguments of the sd() function. The sd() function has an optional argument, na.rm that specified whether or not to remove missing values from the input vector before calculating the standard deviation.

If you've had a good look at the documentation, you'll know by now that the mean() function also has this argument, na.rm, and it does the exact same thing. By default, it is set to FALSE, as the Usage of the Default S3 method shows:

mean(x, trim = 0, na.rm = FALSE, ...)

Let's see what happens if your vectors linkedin and facebook contain missing values (NA).

Instructions

• Calculate the average number of LinkedIn profile views, without specifying any optional arguments. Simply print the result to the console.
• Calculate the average number of LinkedIn profile views, but this time tell R to strip missing values from the input vector.

# 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

Awesome! Up to the next exercise!

Functions inside functions

You already know that R functions return objects that you can then use somewhere else. This makes it easy to use functions inside functions, as you've seen before:

speed <- 31
print(paste("Your speed is", speed))

Notice that both the print() and paste() functions use the ellipsis - ... - as an argument. Can you figure out how they're used?

Instructions

Use abs() on linkedin - facebook to get the absolute differences between the daily Linkedin and Facebook profile views. Next, use this function call inside mean() to calculate the Mean Absolute Deviation. In the mean() call, make sure to specify na.rm to treat missing values correctly!

# 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

Excellent! Proceed to the next exercise.

Required, or optional?

By now, you will probably have a good understanding of the difference between required and optional arguments. Let's refresh this difference by having one last look at the mean() function:

mean(x, trim = 0, na.rm = FALSE, ...)

x is required; if you do not specify it, R will throw an error. trim and na.rm are optional arguments: they have a default value which is used if the arguments are not explicitly specified.

Which of the following statements about the read.table() function are true?

1. header, sep and quote are all optional arguments.
2. row.names and fileEncoding don't have default values.
3. read.table("myfile.txt", "-", TRUE) will throw an error.
4. read.table("myfile.txt", sep = "-", header = TRUE) will throw an error.

Possible answers:

• (1) and (3)
• (2) and (4)
• (1), (2), and (3)
• (1), (2), and (4)

Great! Using functions that are already available in R is pretty straightforward, but how about writing your own functions to supercharge your R programs? The next video will tell you how.

Write your own function

Wow, things are getting serious... You're about to write your own function! Before you have a go at it, have a look at the following function template:

my_fun <- function(arg1, arg2) {
  body
}

Notice that this recipe uses the assignment operator (<-) just as if you were assigning a vector to a variable for example. This is not a coincidence. Creating a function in R basically is the assignment of a function object to a variable! In the recipe above, you're creating a new R variable my_fun, that becomes available in the workspace as soon as you execute the definition. From then on, you can use the my_fun as a function.

Instructions

• Create a function pow_two(): it takes one argument and returns that number squared (that number times itself).
• Call this newly defined function with 12 as input.
• Next, create a function sum_abs(), that takes two arguments and returns the sum of the absolute values of both arguments.
• Finally, call the function sum_abs() with arguments -2 and 3 afterwards.

# 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(a, b) {
  abs(a) + abs(b)  
}

# Use the function
sum_abs(-2, 3)

## [1] 5

Great! Step it up a notch in the next exercise!

Write your own function (2)

There are situations in which your function does not require an input. Let's say you want to write a function that gives us the random outcome of throwing a fair die:

throw_die <- function() {
  number <- sample(1:6, size = 1)
  number
}

throw_die()

Up to you to code a function that doesn't take any arguments!

Instructions

• Define a function, hello(). It prints out "Hi there!" and returns TRUE. It has no arguments.
• Call the function hello(), without specifying arguments of course.

# Define the function hello()
hello <- function() {
  print("Hi there!")
  return(TRUE)
}

# Call the function hello()
hello()

## [1] "Hi there!"

## [1] TRUE

Truly impressive! Head over to the next exercise.

Write your own function (3)

Do you still remember the difference between an argument with and without default values? Have another look at the sd() function by typing ?sd in the console. The usage section shows the following information:

sd(x, na.rm = FALSE)

This tells us that x has to be defined for the sd() function to be called correctly, however, na.rm already has a default value. Not specifying this argument won't cause an error.

You can define default argument values in your own R functions as well. You can use the following recipe to do so:

my_fun <- function(arg1, arg2 = val2) {
  body
}

The editor on the right already includes an extended version of the pow_two() function from before. Can you finish it?

Instructions

• Add an optional argument, named print_info, that is TRUE by default.
• Wrap an if construct around the print() function: this function should only be executed if print_info is TRUE.
• Feel free to experiment with the pow_two() function you've just coded.

# 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)
}

Wonderful! Have you tried calling this pow_two() function? Try pow_two(5), pow_two(5, TRUE) and pow_two(5, FALSE). Which ones give different results?

Function scoping

An issue that Filip did not discuss in the video is function scoping. It implies that variables that are defined inside a function are not accessible outside that function. Try running the following code and see if you understand the results:

pow_two <- function(x) {
  y <- x ** 2
  return(y)
}

pow_two(4)
y
x

y was defined inside the pow_two() function and therefore it is not accessible outside of that function. This is also true for the function's arguments of course - x in this case.

Which statement is correct about the following chunk of code? The function two_dice() is already available in the workspace.

two_dice <- function() {
  possibilities <- 1:6
  dice1 <- sample(possibilities, size = 1)
  dice2 <- sample(possibilities, size = 1)
  dice1 + dice2
}

Possible answers:

• Executing two_dice() causes an error.
• Executing res <- two_dice() makes the contents of dice1 and dice2 available outside the function.
• Whatever the way of calling the two_dice() function, R won't have access to dice1 and dice2 outside the function.

Great! If you're familiar with other programming languages, you might wonder whether R passes arguments by value or by reference. Find out in the next exercise!

R passes arguments by value

The title gives it away already: R passes arguments by value. What does this mean? Simply put, it means that an R function cannot change the variable that you input to that function. Let's look at a simple example (try it in the console):

triple <- function(x) {
  x <- 3*x
  x
}

a <- 5
triple(a)
a

Inside the triple() function, the argument x gets overwritten with its value times three. Afterwards this new x is returned. If you call this function with a variable a set equal to 5, you obtain 15. But did the value of a change? If R were to pass a to triple() by reference, the override of the x inside the function would ripple through to the variable a, outside the function. However, R passes by value, so the R objects you pass to a function can never change unless you do an explicit assignment. a remains equal to 5, even after calling triple(a).

Can you tell which one of the following statements is false about the following piece of code?

increment <- function(x, inc = 1) {
  x <- x + inc
  x
}

count <- 5
a <- increment(count, 2)
b <- increment(count)
count <- increment(count, 2)

Possible answers:

• 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 end, count will equal 10.
• In the last expression, the value of count was actually changed because of the explicit assignment.

Well done! Given that R passes arguments by value and not by reference, the value of count is not changed after the first two calls of increment(). Only in the final expression, where count is re-assigned explicitly, does the value of count change.

R you functional?

Now that you've acquired some skills in defining functions with different types of arguments and return values, you should try to create more advanced functions. As you've noticed in the previous exercises, it's perfectly possible to add control-flow constructs, loops and even other functions to your function body.

Remember our social media example? The vectors linkedin and facebook are already defined in the workspace so you can get your hands dirty straight away. As a first step, you will be writing a function that can interpret a single value of this vector. In the next exercise, you will write another function that can handle an entire vector at once.

Instructions

• Finish the function definition for interpret(), that interprets the number of profile views on a single day:
• The function takes one argument, num_views.
• If num_views is greater than 15, the function prints out "You're popular!" to the console and returns num_views.
• Else, the function prints out "Try to be more visible!" and returns 0.
• Finally, call the interpret() function twice: on the first value of the linkedin vector and on the second element of the facebook vector.

# 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

Funkadelic! The annoying thing here is that interpret() only takes one argument. Proceed to the next exercise to implement something more useful.

R you functional? (2)

A possible implementation of the interpret() function is already available in the editor. In this exercise you'll be writing another function that will use the interpret() function to interpret all the data from your daily profile views inside a vector. Furthermore, your function will return the sum of views on popular days, if asked for. A for loop is ideal for iterating over all the vector elements. The ability to return the sum of views on popular days is something you can code through a function argument with a default value.

Instructions

Finish the template for the interpret_all() function:

• Make return_sum an optional argument, that is TRUE by default.
• Inside the for loop, iterate over all views: on every iteration, add the result of interpret(v) to count. Remember that interpret(v) returns v for popular days, and 0 otherwise. At the same time, interpret(v) will also do some printouts.
• Finish the if construct so that if return_sum is TRUE, return count and else, return NULL.

Call this newly defined function on both linkedin and facebook.

# 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

Perfect! Have a look at the results; it appears that the sum of views on popular days are the same for Facebook and LinkedIn, what a coincidence! Your different social profiles must be fairly balanced. Head over to the next video!

Load an R Package

There are basically two extremely important functions when it comes down to R packages:

• install.packages(), which as you can expect, installs a given package.
• library() which loads packages, i.e. attaches them to the search list on your R workspace.

To install packages, you need administrator privileges. This means that install.packages() will thus not work in the DataCamp interface. However, almost all CRAN packages are installed on our servers. You can load them with library().

In this exercise, you'll be learning how to load the ggplot2 package, a powerful package for data visualization. You'll use it to create a plot of two variables of the mtcars data frame. The data has already been prepared for you in the workspace.

Before starting, execute the following commands in the console:

• search(), to look at the currently attached packages and
• qplot(mtcars$wt, mtcars$hp), to build a plot of two variables of the mtcars data frame.

An error should occur, because you haven't loaded the ggplot2 package yet!

Instructions

• To fix the error you saw in the console, load the ggplot2 package.
• Now, retry calling the qplot() function with the same arguments.
• Finally, check out the currently attached packages again.

# 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"

Awesome! Notice how search() and library() are closely interconnected functions. Head over to the next exercise.

Different ways to load a package

The library() and require() functions are not very picky when it comes down to argument types: both library(rjson) and library("rjson") work perfectly fine for loading a package.

Have a look at some more code chunks that (attempt to) load one or more packages:

# Chunk 1
library(data.table)
require(rjson)

# Chunk 2
library("data.table")
require(rjson)

# Chunk 3
library(data.table)
require(rjson, character.only = TRUE)

# Chunk 4
library(c("data.table", "rjson"))

Select the option that lists all of the chunks that do not generate an error. The console on the right is yours to experiment in.

Possible answers:

• Only (1)
• (1) and (2)
• (1), (2) and (3)
• All of them are valid

Great! Indeed, only chunk 1 and chunk 2 are correct. Can you figure out why the last two aren't valid? This exercise concludes the chapter on functions. Well done!
You have finished the chapter "Functions"!