Table of Contents

1. Challenge

   Understanding Functional Programming and Apply

   RStudio Guide

   To get started, click on the 'workspace' folder in the bottom right pane of RStudio. Click on the file entitled "Step 1...". You may want to drag the console pane to be smaller so that you have more room to work. You'll complete each task for Step 1 in that R Markdown file. Remember, you must run the cells with the play button at the top right of each cell for a task before moving onto the next task in the R Markdown file. Continue until you have completed all tasks in this step. Then when you are ready to move onto the next step, you'll come back and click on the file for the next step until you have completed all tasks in all steps of the lab.

   ---

   Understanding Functional Programming and Apply

   To review the concepts covered in this step, please refer to the Understanding Functional Programming and Apply module of the Applying Functions in R course.

   Understanding Functional Programming and the use of Apply function is important because it helps you write efficient and readable code in R. It introduces you to the concept of vectorization and why it is preferred over for loops in R.

   In this step, we will practice the functional approach to programming in R. We will create a function and use the sapply function to call it across a vector. This will help us understand how vectorizing operations can be faster and more efficient than using for loops in R. The goal here is to get comfortable with the concept of apply in a simple use case using the sapply function.

   ---

   Task 1.1: Create a Simple Function

   Create a function named square that takes an argument x and returns the square of x.

   🔍 Hint

   Use the ^ operator to calculate the square of x.

   🔑 Solution

   square <- function(x) {
     return(x^2)
   }
   

   ---

   Task 1.2: Create a Vector

   Create a numeric vector numbers with values from 1 to 10.

   🔍 Hint

   Use the : operator to create a sequence of numbers.

   🔑 Solution

   numbers <- 1:10
   

   ---

   Task 1.3: Apply the Function to the Vector

   Use the sapply function to apply the square function to each element in the numbers vector.

   🔍 Hint

   Call the sapply() function on the vector. The first argument should be the vector and the second argument should be the function.

   🔑 Solution

   squared_numbers <- sapply(numbers, square)
   

   ---

   Task 1.4: Print the Result

   Print the squared_numbers vector to the console.

   🔍 Hint

   Use the print function to print the squared_numbers vector.

   🔑 Solution

   print(squared_numbers)
   

2. Challenge

   Working with Anonymous Functions in Apply

   Working with Anonymous Functions in Apply

   To review the concepts covered in this step, please refer to the Adding Anonymous Functions to Apply module of the Applying Functions in R course.

   Understanding how to use Anonymous Functions with Apply is important because it can make your code more clear and concise. It also helps you avoid some common pitfalls associated with anonymous functions.

   In this step, we will practice creating and using anonymous functions in R. We will use these anonymous functions with the apply function to perform operations on a data frame. The goal here is to understand the basics of anonymous functions, how to pass them into apply, and when to use a named function instead.

   ---

   Task 2.1: Using an Anonymous Function

   Create a vector named numbers that contains the values 1 through 10. Using sapply(), apply an anonymous function to numbers that multipies each element by 3.

   🔍 Hint

   Use the : operator to create a sequence of numbers, and assign this to a variable called numbers.
   Then call the sapply() function. The first argument should be numbers, and the second argument should be a function.
   Note: If a function is contained on a single line, curly braces {} are unnecessary.

   🔑 Solution

   numbers <- 1:10
   sapply(numbers, function(x) x*3)
   

   ---

   Task 2.2: Applying an Anonymous Function to a Data Frame

   Load the datasets package and use the data() function to load the mtcars dataset into the environment.

   Using the apply() function, apply an anonymous function to the mtcars data frame that computes the mean of each column.

   🔍 Hint

   Use the apply function with mtcars as the first argument, 2 as the second argument to indicate that you want to apply the function to each column, and an anonymous function as the third argument.

   🔑 Solution

   library('datasets')
   data('mtcars')
   apply(mtcars, 2, function(x) mean(x))
   
   # because `mean` is itself a function, this solution is also valid
   apply(mtcars, 2, mean)
   

   ---

   Task 2.3: Creating a Multi-Line Anonymous Function

   Using the apply() function, apply a multi-line anonymous function to the mtcars data frame that prints the mean and median of each column and then returns the sum.

   🔍 Hint

   Use curly braces {} to make the anonymous function span several lines. Use the mean, median, and sum functions to compute the values. Use print() for the mean and median, and return() for the sum.

   🔑 Solution

   library('datasets')
   
   apply(mtcars, 2, function(x) {
     print(mean(x))
     print(median(x))
     return(sum(x))
   })
   

   ---

   Task 2.4: When to Use a Named Function Instead of an Anonymous Function

   For each of the three functions below, decide whether it would be more appropriate to use a named or anonymous function.

   # Function 1:
   function(n) matrix(c(1,1,1,0),2,2)^n[1,2]
   
   # Function 2:
   function(x) x - mean(x)
   
   # Function 3:
   function(x) {
     mean_val <- mean(x)
     sd_val <- sd(x)
     z_score <- (x - mean_val) / sd_val
     return(z_score)
   }
   

   🔍 Hint

   It is more appropriate to use a named function if the function spans multiple lines, takes more than a few seconds to understand, or if you intend to use the function many times.

   🔑 Solution

   # Function 1: Named
   # This function, which computes the Fibonacci sequence up 
   # to the nth term, is short but difficult to understand quickly
   
   # Function 2: Anonymous
   # This function is short and easy to understand
   
   # Function 3: Named
   # This function spans multiple lines and would 
   # be more appropriate as a named function called `z_score`  
   

3. Challenge

   Exploring Common Apply Functions: lapply, sapply, and vapply

   Exploring Common Apply Functions: lapply, sapply, and vapply

   To review the concepts covered in this step, please refer to the Using Common Apply Functions module of the Applying Functions in R course.

   Understanding the differences and use cases of lapply, sapply, and vapply is important because these functions offer different ways to apply a function over a list or vector in R. They each have their own benefits and specific use cases.

   In this step, we will practice using the lapply, sapply, and vapply functions in R. We will understand how these functions iterate over a list or vector and how their outputs differ. The goal here is to get comfortable with these functions and understand when to use each one.

   ---

   Task 3.1: Using the lapply Function

   Use the lapply function to apply the mean function to each vector in the provided list.

   🔍 Hint

   Use lapply function with my_list as the first argument and mean as the second argument.

   🔑 Solution

   my_list <- list(a = c(1, 2, 3, 4, 5), b = c(6, 7, 8, 9, 10))
   lapply(my_list, mean)
   

   ---

   Task 3.2: Using the sapply Function

   Now, use the sapply function to apply the mean function to each vector in the list.

   🔍 Hint

   Use sapply function with my_list and mean as arguments.

   🔑 Solution

   sapply_result <- sapply(my_list, mean)
   

   ---

   Task 3.3: Using the vapply Function

   Finally, use the vapply function to apply the mean function to each vector in the list. Remember that vapply takes a third argument to specify the expected output type.

   First, use vapply while specifying the correct output type.

   Then, specify an incorrect output type and verify that vapply raises an error message.

   🔍 Hint

   Use the vapply function with my_list and mean as the first two arguments. The third argument is a function that specifies the type of output you expect (e.g., numeric(), logical(), or character()), and an argument given to that function specifies the length of output you expect.

   🔑 Solution

   vapply(my_list, mean, numeric(1))
   
   # examples of an incorrect type
   vapply(my_list, mean, numeric(5))
   vapply(my_list, mean, character(1))
   

4. Challenge

   Using and Understanding Which Function to Use: tapply and replicate

   Using and Understanding Which Function to Use: tapply and replicate

   To review the concepts covered in this step, please refer to the Using and Understanding Which Function to Use module of the Applying Functions in R course.

   Understanding when to use tapply and replicate is important because these functions offer unique functionalities in R. tapply allows you to group and apply a function over a specific group, while replicate helps in creating repeatable experiments.

   In this step, we will practice using the tapply and replicate functions in R. We will understand how tapply applies a function over groups and how replicate can be used to iterate over a function. The goal here is to understand the use cases of these functions and when to use each one.

   ---

   Task 4.1: Load and Examine the Dataset

   Load the pre-installed datasets library and load the iris dataset into your environment.

   Inspect the structure of the iris dataset and read through the iris help page.

   🔍 Hint

   Use the library() function to load the datasets package. Then use the data() function to load the iris dataset into your environment.
   You can inspect the structure of a data frame using str(), and can use the ? operator to bring up the help page for a function or dataset.

   🔑 Solution

   library('datasets')
   data(iris)
   
   str(iris)
   ?iris
   

   ---

   Task 4.2: Using the tapply function

   Use the tapply function to calculate the mean of Sepal.Length for each Species in the iris dataset.

   🔍 Hint

   The first argument should specify the Sepal.Length column to indicate the data you wish to analyze. For the second argument, use the Species column to define the grouping categories. Lastly, the third argument should be the function you intend to apply to each group.

   🔑 Solution

   tapply(iris$Sepal.Length, iris$Species, mean)
   

   ---

   Task 4.2: Using the replicate function

   Use the replicate function to generate 5 random uniform distributions. Each distribution should have a minimum of 0, a maximum of 1, and should contain 10 values.

   🔍 Hint

   Use the replicate() function with the number of replications as the first argument, and the function to replicate as the second argument. The function to replicate in this case is runif() with arguments 10, 0, and 1.

   🔑 Solution

   replicate(5, runif(10, 0, 1))