Logicals en subset()

Hier is een leuke uitdaging. Je weet hoe je logische vectoren maakt die aangeven wanneer een bepaalde voorwaarde waar is, maar kun je een data frame subsetten zodat het alleen rijen bevat waarvoor die voorwaarde waar is?

Als je Introduction to R for Finance hebt gevolgd, herinner je je misschien de functie subset(). subset() neemt als argumenten een data frame (of vector/matrix) en een logische vector die aangeeft welke rijen je wilt terugkrijgen:

stocks
        date    ibm panera
1 2017-01-20 170.55 216.65
2 2017-01-23 171.03 216.06
3 2017-01-24 175.90 213.55
4 2017-01-25 178.29 212.22

subset(stocks, ibm < 175)
        date    ibm panera
1 2017-01-20 170.55 216.65
2 2017-01-23 171.03 216.06

Handig, toch? Het data frame stocks is voor je beschikbaar.

Deze oefening maakt deel uit van de cursus

R voor finance voor gevorderden

Cursus bekijken

Oefeninstructies

Subset stocks zodat je rijen opneemt waar panera groter is dan 216.
Subset stocks om de rij op te halen waar date gelijk is aan "2017-01-23". Vergeet as.Date() niet!
Subset stocks om rijen op te halen waar ibm kleiner is dan 175 en panera kleiner is dan 216.50.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Panera range
___

# Specific date
___

# IBM and Panera joint range
___

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

R voor finance voor gevorderden

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Begin de cursus gratis

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: Relationele operatoren Exercise 2: Oefenen met relationele operatoren Exercise 3: Gevectoriseerde bewerkingen Exercise 4: Logische operatoren Exercise 5: And / Or Exercise 6: Niet!Exercise 7: Logicals en subset()

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Exercise 8: Alles komt samen!Exercise 9: If-statements Exercise 10: Als dit Exercise 11: Als dit, anders dat Exercise 12: If dit, Else If dat, Else iets anders Exercise 13: Kun je een if in een if zetten?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 Exercise 8: vapply() - specify your output!Exercise 9: vapply() vs. sapply()Exercise 10: More vapply()Exercise 11: Anonymous functions Exercise 12: Congratulations