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Intermediate R for Finance

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

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Descripción del curso

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!
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En las siguientes pistas

Fundamentos de Finanzas en R

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  1. 1

    Dates

    Gratuito

    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.

    Reproducir Capítulo Ahora
    An introduction to dates in R
    50 xp
    What day is it?
    100 xp
    From char to date
    100 xp
    Many dates
    100 xp
    Date formats and extractor functions
    50 xp
    Date formats (1)
    100 xp
    Date formats (2)
    100 xp
    Subtraction of dates
    100 xp
    months() and weekdays() and quarters(), oh my!
    100 xp
  2. 2

    If Statements and Operators

    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.

    Reproducir Capítulo Ahora
    Relational operators
    50 xp
    Relational practice
    100 xp
    Vectorized operations
    100 xp
    Logical operators
    50 xp
    And / Or
    100 xp
    Not!
    100 xp
    Logicals and subset()
    100 xp
    All together now!
    100 xp
    If statements
    50 xp
    If this
    100 xp
    If this, Else that
    100 xp
    If this, Else If that, Else that other thing
    100 xp
    Can you If inside an If?
    100 xp
    ifelse()
    100 xp
  3. 3

    Loops

    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!

    Reproducir Capítulo Ahora
    Repeat loops
    50 xp
    Repeat, repeat, repeat
    100 xp
    When to break?
    100 xp
    While loops
    50 xp
    While with a print
    100 xp
    While with a plot
    100 xp
    Break it
    100 xp
    For loops
    50 xp
    Loop over a vector
    100 xp
    Loop over data frame rows
    100 xp
    Loop over matrix elements
    100 xp
    Break and next
    100 xp
  4. 4

    Functions

    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.

    Reproducir Capítulo Ahora
    What are functions?
    50 xp
    Function help and documentation
    100 xp
    Optional arguments
    100 xp
    Functions in functions
    100 xp
    Writing functions
    50 xp
    Your first function
    100 xp
    Multiple arguments (1)
    100 xp
    Multiple arguments (2)
    100 xp
    Function scope (1)
    50 xp
    Function scope (2)
    50 xp
    Packages
    50 xp
    tidyquant package
    100 xp
  5. 5

    Apply

    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!

    Reproducir Capítulo Ahora
    Why use apply?
    50 xp
    lapply() on a list
    100 xp
    lapply() on a data frame
    100 xp
    FUN arguments
    100 xp
    sapply() - simplify it!
    50 xp
    sapply() vs. lapply()
    100 xp
    Failing to simplify
    100 xp
    vapply() - specify your output!
    50 xp
    vapply() vs. sapply()
    100 xp
    More vapply()
    100 xp
    Anonymous functions
    100 xp
    Congratulations
    50 xp
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Obtenga acceso de su equipo a la biblioteca completa de DataCamp, con informes centralizados, tareas, proyectos y más

En las siguientes pistas

Fundamentos de Finanzas en R

Ir a la pista

Analista cuantitativo con R

Ir a la pista

Colaboradores

Collaborator's avatar
Davis Vaughan

Requisitos Previos

Introduction to R for Finance
Lore Dirick HeadshotLore Dirick

Director of Data Science Education at Flatiron School

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