Week 2 Overview

This wraps up the content for week 1. Now we require some practice! You should head back to our Moodle site to check out your homework assignment for this week.

This second week we’ll go through a systematic view of the most common data objects you’ll see in R. We’ll see how to access these objects using Base R commands. 

We learned, and will continue investigating an R object via

• class()
• typeof()
• str()

We see that R commonly uses the attributes of the object to determine how to apply (or dispatch) a function. We will look at common R objects and how to access their elements:

• Atomic vectors - x[ ]
• Matrices - x[ , ]
• Data Frames - x[ , ] or x$name
• Lists - x[[ ]] or x$name

and continue to practice documenting our code by using a notebook environment such as Quarto (along with git/github for version control and sharing/collaborating).

Now we want to look at how to control the execution of our code. The three main things we’ll look at here are

• if/then/else logic and syntax
• looping to repeatedly execute code
• vectorized functions for improved efficiency

Then we’ll see how to write our own functions! We are already learning a lot of necessary material to be a data scientist. Let’s go!!

Week 2 Additional Readings/Learning Materials

Common Data Objects in R

• Chapter 12 of R 4 Data Science
• Chapter 27 of R 4 Data Science (27.1-27.3)

Writing Functions

• Chapter 25 of R 4 Data Science
• Chapter 5 of Advanced R
• Consistent naming conventions in R> and R’s style guide
• Chapter 7 of Modern R with the tidyverse

Loops and Vectorized Functions

• Chapter 27 of R 4 Data Science (27.4-27.5)

Week 2 Learning Objectives

Upon completion of this week, students will be able to: (CO is the corresponding course learning objective this helps build toward)

R Basics

1.   create common data objects such as data frames, matrices, vectors, and lists and subset each of them (CO 1, 2, 3)

   1. recognize the most appropriate method for accessing an R object b. determine the appropriate type of object in which to save differing types of data c. contrast matrices and data frames d. determine the type of R object you are working with e. compare and contrast the attributes of the common data types

2.   utilize R help and other resources to appropriately use and/or modify an R function or object (CO 3)

Upon completion of this week, students will be able to: (CO is the corresponding course learning objective this helps build toward)

Logicals & if/then/else

1. describe the idea of coercion and compare and contrast the use of implicit and explicit coercion (CO 1, 4)

   a.    outline the rules for implicit coercion b.    use common explicit coercion functions c.    hypothesize the type of coercion implicitly done by a section of R code

For Loops and Vectorized Functions

1. describe the process of a for loop, while loop, and repeat statement in R (CO 1, 3, 4)

   a.    program R to break from a loop when conditions are met b.    program R to skip an iteration of a loop c.    utilize different “counters” or “iterators” in an R loop d.    explain the use and process of nested for loops e.    program a nested R loop in R f.    explain why loops are not always ideal in R

2.    program using the “apply family” of functions in R (CO 1, 3, 4)

   a.    differentiate between the apply family of functions in R including, but not limited to, mapply, sapply, lapply, apply, and replicate b.    manipulate R code that uses loops to instead use the proper apply function c.    utilize anonymous functions in calls to the apply family of functions in R

3.    recall common “vectorized” functions in R such as colMeans, rowMeans, ifelse, etc. (CO 1, 4)

4.    write their own vectorized version of a function in R (CO 1, 4)

5.    describe what an anonymous function is and how it differs from other functions (CO 1, 4)

Writing Functions

Note: Not all of these are covered in this week’s function writing videos - we’ll revisit the topic later!

1. display code from previously written functions in R (CO 1, 3)

2. describe the parts of an R function (CO 1, 4)

3. describe the difference between a prefix and an infix function (CO 1, 4)

   1. understand the order of evaluation for user created and built-in infix functions

4. write an R function that has default arguments, allows for additional arguments, and returns a well-structured named object (CO 1, 3, 4)

   1. determine appropriate uses for the stop function
   2. develop a pipeable function that acts on a data frame and one that produces side effects
   3. create a custom infix function

5. describe the way that inputs can be specified to a function (CO 1, 3)

   1. describe positional matching and predict what input values will be assigned to which function values
   2. explain lazy evaluation and give examples of where it may be important

6. use if/then/else logic to conditionally execute code

7. utilize the switch function and compare and contrast its use against if/then/else logic (CO 1, 3, 4)

8. define environments including temporary environments (CO 1)

   1. summarize the order in which R attempts to find a requested object
   2. sketch out the R environment framework and explain the idea of lexical scoping
   3. discuss the concept of local variables and local environments as compared to global variables and environments
   4. state the naming conventions for R objects

Use the table of contents on the left or the arrows at the bottom of this page to navigate to the next learning material!