Failing to simplify

For interactive use, sapply() is great. It guesses the output type so that it can simplify, and normally that is fine. However, sapply() is not a safe option to be used when writing functions. If sapply() cannot simplify your output, then it will default to returning a list just like lapply(). This can be dangerous and break custom functions if you wrote them expecting sapply() to return a simplified vector.

Let's look at an exercise using a list containing information about the stock market crash of 2008.

This exercise is part of the course

Intermediate R for Finance

Exercise instructions

The list market_crash has been created for you.

Use sapply() to get the class() of each element in market_crash.

A new list, market_crash2 has been created. The difference is in the creation of the date!

Use lapply() to get the class() of each element in market_crash2.
Use sapply() to get the class() of each element in market_crash2.

date in market_crash2 has multiple classes. Why couldn't sapply() simplify this?

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Market crash with as.Date()
market_crash <- list(dow_jones_drop = 777.68, 
                     date = as.Date("2008-09-28"))
                     
# Find the classes with sapply()
___

# Market crash with as.POSIXct()
market_crash2 <- list(dow_jones_drop = 777.68, 
                      date = as.POSIXct("2008-09-28"))

# Find the classes with lapply()
___

# Find the classes with sapply()
___

Edit and Run Code

This exercise is part of the course

Intermediate R for Finance

BeginnerSkill Level

5.0+

Start Course for Free

Welcome! Before we go deeper into the world of R, it will be nice to have an understanding of how dates and times are created. This chapter will teach you enough to begin working with dates, but only scratches the surface of what you can do with them.

Exercise 1: An introduction to dates in R Exercise 2: What day is it?Exercise 3: From char to date Exercise 4: Many dates Exercise 5: Date formats and extractor functions Exercise 6: Date formats (1)Exercise 7: Date formats (2)Exercise 8: Subtraction of dates Exercise 9: months() and weekdays() and quarters(), oh my!

Imagine you own stock in a company. If the stock goes above a certain price, you might want to sell. If the stock drops below a certain price, you might want to buy it while it's cheap! This kind of thinking can be implemented using operators and if statements. In this chapter, you will learn all about them, and create a program that tells you to buy or sell a stock.

Exercise 1: Relational operators Exercise 2: Relational practice Exercise 3: Vectorized operations Exercise 4: Logical operators Exercise 5: And / Or Exercise 6: Not!Exercise 7: Logicals and subset()Exercise 8: All together now!Exercise 9: If statements Exercise 10: If this Exercise 11: If this, Else that Exercise 12: If this, Else If that, Else that other thing Exercise 13: Can you If inside an If?Exercise 14: ifelse()

Loops can be useful for doing the same operation to each element of your data structure. In this chapter you will learn all about repeat, while, and for loops!

Exercise 1: Repeat loops Exercise 2: Repeat, repeat, repeat Exercise 3: When to break?Exercise 4: While loops Exercise 5: While with a print Exercise 6: While with a plot Exercise 7: Break it Exercise 8: For loops Exercise 9: Loop over a vector Exercise 10: Loop over data frame rows Exercise 11: Loop over matrix elements Exercise 12: Break and next

If data structures like data frames and vectors are how you hold your data, functions are how you tell R what to do with your data. In this chapter, you will learn about using built-in functions, creating your own unique functions, and you will finish off with a brief introduction to packages.

Exercise 1: What are functions?Exercise 2: Function help and documentation Exercise 3: Optional arguments Exercise 4: Functions in functions Exercise 5: Writing functions Exercise 6: Your first function Exercise 7: Multiple arguments (1)Exercise 8: Multiple arguments (2)Exercise 9: Function scope (1)Exercise 10: Function scope (2)Exercise 11: Packages Exercise 12: tidyquant package

A popular alternative to loops in R are the apply functions. These are often more readable than loops, and are incredibly useful for scaling the data science workflow to perform a complicated calculation on any number of observations. Learn about them here!

Exercise 1: Why use apply?Exercise 2: lapply() on a list Exercise 3: lapply() on a data frame Exercise 4: FUN arguments Exercise 5: sapply() - simplify it!Exercise 6: sapply() vs. lapply()Exercise 7: Failing to simplify

Current Exercise

Exercise 8: vapply() - specify your output!Exercise 9: vapply() vs. sapply()Exercise 10: More vapply()Exercise 11: Anonymous functions Exercise 12: Congratulations