And / Or

You might want to check multiple relational conditions at once. What if you wanted to know if Apple stock was above 120, but below 121? Simple relational operators are not enough! For multiple conditions, you need the And operator &, and the Or operator |.

& (And): An intersection. a & b is true only if both a and b are true.
| (Or): A union. a | b is true if either a or b is true.

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

# Both conditions must hold
(apple > 120) & (apple < 121)
[1] FALSE  TRUE FALSE FALSE

# Only one condition has to hold
(apple <= 120) | (apple > 121)
[1]  TRUE FALSE  TRUE  TRUE

The stocks data frame is available for you to use.

Deze oefening maakt deel uit van de cursus

Intermediate R for Finance

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Oefeninstructies

When is ibm between 171 and 176? Add the logical vector to stocks as ibm_buy_range.
Check if panera drops below 213.20 or rises above 216.50, then add it to stocks as the column panera_spike.
Suppose you are interested in dates after 2017-01-21 but before 2017-01-25, exclusive. Use as.Date() and & for this. Add the result to stocks as good_dates.
Print stocks.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# IBM buy range	
___

# Panera spikes	
___

# Date range
___

# Print stocks	
___

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Intermediate R for Finance

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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: Relational operators Exercise 2: Relational practice Exercise 3: Vectorized operations Exercise 4: Logical operators Exercise 5: And / Or

Huidige oefening

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