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Introduction to R for Finance

Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.

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4 Hours14 Videos62 Exercises64,209 Learners5300 XPFinance Fundamentals TrackQuantitative Analyst Track

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Course Description

In this finance-oriented introduction to R, you will learn essential data structures such as lists and data frames and have the chance to apply that knowledge to real-world financial examples. By the end of the course, you will be comfortable with the basics of manipulating your data to perform financial analysis in R.

  1. 1

    The Basics

    Free

    Get comfortable with the very basics of R and learn how to use it as a calculator. Also, create your first variables in R and explore some of the base data types such as numerics and characters.

    Play Chapter Now
    Welcome to Introduction to R for Finance!
    50 xp
    Your first R script
    100 xp
    Arithmetic in R (1)
    100 xp
    Arithmetic in R (2)
    50 xp
    Assignment and variables (1)
    100 xp
    Assignment and variables (2)
    100 xp
    Financial returns
    50 xp
    Financial returns (1)
    100 xp
    Financial returns (2)
    100 xp
    Basic data types
    50 xp
    Data type exploration
    100 xp
    What's that data type?
    50 xp
  2. 2

    Vectors and Matrices

    In this chapter, you will learn all about vectors and matrices using historical stock prices for companies like Apple and IBM. You will then be able to feel confident creating, naming, manipulating, and selecting from vectors and matrices.

    Play Chapter Now
    What is a vector?
    50 xp
    c()ombine
    100 xp
    Coerce it
    50 xp
    Vector names()
    100 xp
    Visualize your vector
    100 xp
    Vector manipulation
    50 xp
    Weighted average (1)
    100 xp
    Weighted average (2)
    100 xp
    Weighted average (3)
    100 xp
    Vector subsetting
    100 xp
    Matrix - a 2D vector
    50 xp
    Create a matrix!
    100 xp
    Matrix <- bind vectors
    100 xp
    Visualize your matrix
    100 xp
    cor()relation
    100 xp
    Matrix subsetting
    100 xp
  3. 3

    Data Frames

    Arguably the most important data structure in R, the data frame is what most of your data will take the form of. It combines the structure of a matrix with the flexibility of having different types of data in each column.

    Play Chapter Now
    What is a data frame?
    50 xp
    Create your first data.frame()
    100 xp
    What goes in a data frame?
    50 xp
    Making head()s and tail()s of your data with some str()ucture
    100 xp
    Naming your columns / rows
    100 xp
    Data frame manipulation
    50 xp
    Accessing and subsetting data frames (1)
    100 xp
    Accessing and subsetting data frames (2)
    100 xp
    Accessing and subsetting data frames (3)
    100 xp
    Adding new columns
    100 xp
    Present value
    50 xp
    Present value of projected cash flows (1)
    100 xp
    Present value of projected cash flows (2)
    100 xp
  4. 4

    Factors

    Questions with answers that fall into a limited number of categories can be classified as factors. In this chapter, you will use bond credit ratings to learn all about creating, ordering, and subsetting factors.

    Play Chapter Now
    What is a factor?
    50 xp
    Create a factor
    100 xp
    Factor levels
    100 xp
    Factor summary
    100 xp
    Visualize your factor
    100 xp
    Bucketing a numeric variable into a factor
    100 xp
    Ordering and subsetting factors
    50 xp
    Create an ordered factor
    100 xp
    Subsetting a factor
    100 xp
    stringsAsFactors
    100 xp
  5. 5

    Lists

    Wouldn't it be nice if there was a way to hold related vectors, matrices, or data frames together in R? In this final chapter, you will explore lists and many of their interesting features by building a small portfolio of stocks.

    Play Chapter Now
    What is a list?
    50 xp
    Create a list
    100 xp
    Named lists
    100 xp
    Access elements in a list
    100 xp
    Adding to a list
    100 xp
    Removing from a list
    100 xp
    A few list creating functions
    50 xp
    Split it
    100 xp
    Split-Apply-Combine
    100 xp
    Attributes
    100 xp
    Congratulations!
    50 xp

In the following tracks

Finance FundamentalsQuantitative Analyst

Collaborators

dvaughan1130Davis Vaughan
Lore Dirick Headshot

Lore Dirick

Director of Data Science Education at Flatiron School

Lore is a data scientist with expertise in applied finance. She obtained her PhD in Business Economics and Statistics at KU Leuven, Belgium. During her PhD, she collaborated with several banks working on advanced methods for the analysis of credit risk data. Lore formerly worked as a Data Science Curriculum Lead at DataCamp, and is and is now Director of Data Science Education at Flatiron School, a coding school with branches in 8 cities and online programs.
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