Niet!

Nog één operator om te introduceren is !, ofwel Not. Je hebt al een vergelijkbare operator gezien, !=, dus je kunt vast raden wat deze doet. Zet ! voor een logische expressie en hij keert de waarde om: van TRUE naar FALSE (en andersom).

!TRUE
[1] FALSE

apple <- c(120.00, 120.08, 119.97, 121.88)

!(apple < 121)
[1] FALSE FALSE FALSE  TRUE

Het stocks data frame is beschikbaar voor je om te gebruiken.

Deze oefening maakt deel uit van de cursus

R voor finance voor gevorderden

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Oefeninstructies

Gebruik ! en een relationele operator om te bepalen wanneer ibm niet boven 176 is.
Er is een nieuwe vector missing gemaakt met ontbrekende waarden.
De functie is.na() controleert op ontbrekende data. Gebruik is.na() op missing.
Stel dat je vooral wilt weten waar je geen ontbrekende data hebt. ! kan je dit laten zien. Zet ! voor is.na() om de posities te tonen waar je wél data hebt.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# IBM range
___

# Missing data
missing <- c(24.5, 25.7, NA, 28, 28.6, NA)

# Is missing?
___

# Not missing?
___

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

R voor finance voor gevorderden

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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!

Huidige oefening

Exercise 7: Logicals en subset()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