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.

An introduction to dates in R

What day is it?

From char to date

Many dates

Date formats and extractor functions

Date formats (1)

Date formats (2)

Subtraction of dates

months() and weekdays() and quarters(), oh my!

Dates

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.

Relational operators

Relational practice

Vectorized operations

Logical operators

And / Or

Not!

Logicals and subset()

All together now!

If statements

If this

If this, Else that

If this, Else If that, Else that other thing

Can you If inside an If?

ifelse()

If Statements and Operators

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!

Repeat loops

Repeat, repeat, repeat

When to break?

While loops

While with a print

While with a plot

Break it

For loops

Loop over a vector

Loop over data frame rows

Loop over matrix elements

Break and next

Loops

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.

What are functions?

Function help and documentation

Optional arguments

Functions in functions

Writing functions

Your first function

Multiple arguments (1)

Multiple arguments (2)

Function scope (1)

Function scope (2)

Packages

tidyquant package

Functions

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!

Why use apply?

lapply() on a list

lapply() on a data frame

FUN arguments

sapply() - simplify it!

sapply() vs. lapply()

Failing to simplify

vapply() - specify your output!

vapply() vs. sapply()

More vapply()

Anonymous functions

Congratulations

Apply

If you enjoyed the Introduction to R for Finance course, then you will love Intermediate R for Finance. Here, you will first learn the basics about how dates work in R, an important skill for the rest of the course. Your next step will be to explore the world of if statements, loops, and functions. These are powerful ideas that are essential to any financial data scientist's toolkit. Finally, we will spend some time working with the family of apply functions as a vectorized alternative to loops. And of course, all examples will be finance related! Enjoy!

Introduction to R for Finance

Learn about how dates work in R, and explore the world of if statements, loops, and functions. You'll practice this knowledge using financial examples.

Intermediate R for Finance

Learn about how dates work in R, and explore the world of if statements, loops, and functions using financial examples.

Finans Temelleri in R

Kantitatif Analist  in R

Function scope (2)

Intermediate R for Finance

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