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.

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

Task 1.2: Create a Vector

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

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.

squared_numbers <- sapply(numbers, square)

Task 1.4: Print the Result

Print the squared_numbers vector to the console.

print(squared_numbers)

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.

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.

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.

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)
}

# 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`  

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.

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.

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.

vapply(my_list, mean, numeric(1))

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

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.

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.

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.

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